From 76dade3f4b24a457f5465b22955b0e20a61cd249 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Thu, 7 Sep 2023 14:35:56 -0400
Subject: [PATCH 01/25] Add test for get_all_scores

---
 tests/test_utils_proba.py | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index fa1fee6..d25d961 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -4,6 +4,7 @@
 import sklearn
 import pandas as pd
 import numpy as np
+import inspect
 from scipy.stats import kstest
 from jurity.utils_proba import BiasCalculator, BiasCalcFromDataFrame, SummaryData
 from jurity.utils_proba import unpack_bootstrap
@@ -657,3 +658,27 @@ def test_bootstrap_ranges(self):
             check_series.name = n + "_ok"
             self.assertTrue(np.all(check_series.values),
                             f"{n} is out of range, on mean of {n_loops}, of {n_boots} bootstraps.")
+    def test_get_all_scores(self):
+        """
+        Test get_all_scores to make sure it returns scores for the values it's supposed to return
+        """
+        #get_all_scores only works for two categories
+        two_categories = pd.concat([self.surrogate_df[["surrogate", "W"]],
+                                    1.0 - self.surrogate_df["W"]], axis=1).set_index("surrogate")
+        two_categories.columns = ["W", "NW"]
+        from jurity.fairness import BinaryFairnessMetrics as bfm
+        output_df=bfm.get_all_scores(self.test_data["label"], self.test_data["prediction"], two_categories,
+                           self.test_data["surrogate"], [1])
+
+        from jurity.fairness import BinaryFairnessMetrics
+
+        fairness_funcs = inspect.getmembers(BinaryFairnessMetrics, predicate=inspect.isclass)[:-1]
+        for fairness_func in fairness_funcs:
+            name = fairness_func[0]
+            class_ = getattr(BinaryFairnessMetrics, name)  # grab a class which is a property of BinaryFairnessMetrics
+            instance = class_()  # dynamically instantiate such class
+            v = output_df.loc[instance.name]["Value"]
+            if name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","StatisticalParity"]:
+                self.assertFalse(np.isnan(v),f"Bootstrap returns np.nan for {name}.")
+            else:
+                self.assertTrue(np.isnan(v),f"Bootstrap not implemented for {name} but returns a value.")

From 2c399b80e9046a53257006d22f3a2255989ac57c Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Thu, 7 Sep 2023 14:48:36 -0400
Subject: [PATCH 02/25] Bug fix. EqualOpportunity should be included in
 get_all_scores.

---
 jurity/fairness/__init__.py | 2 +-
 tests/test_utils_proba.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/jurity/fairness/__init__.py b/jurity/fairness/__init__.py
index c0ff67e..dedfef8 100644
--- a/jurity/fairness/__init__.py
+++ b/jurity/fairness/__init__.py
@@ -147,7 +147,7 @@ def _get_score_logic(metric, name,
         else:
             if name == "StatisticalParity":
                 score = metric.get_score(predictions, memberships, surrogates, membership_labels, bootstrap_results)
-            elif name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality"]:
+            elif name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","EqualOpportunity"]:
                 score = metric.get_score(labels, predictions, memberships, surrogates,
                                          membership_labels, bootstrap_results)
             else:
diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index d25d961..74713a7 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -678,7 +678,7 @@ def test_get_all_scores(self):
             class_ = getattr(BinaryFairnessMetrics, name)  # grab a class which is a property of BinaryFairnessMetrics
             instance = class_()  # dynamically instantiate such class
             v = output_df.loc[instance.name]["Value"]
-            if name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","StatisticalParity"]:
+            if name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","StatisticalParity","EqualOpportunity"]:
                 self.assertFalse(np.isnan(v),f"Bootstrap returns np.nan for {name}.")
             else:
                 self.assertTrue(np.isnan(v),f"Bootstrap not implemented for {name} but returns a value.")

From 6073318c514b6eaf6b463ad4da06d00ded2575ab Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Thu, 21 Sep 2023 16:04:51 -0400
Subject: [PATCH 03/25] Small updates to test_utils_proba.py

---
 tests/test_utils_proba.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index 74713a7..d25d961 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -678,7 +678,7 @@ def test_get_all_scores(self):
             class_ = getattr(BinaryFairnessMetrics, name)  # grab a class which is a property of BinaryFairnessMetrics
             instance = class_()  # dynamically instantiate such class
             v = output_df.loc[instance.name]["Value"]
-            if name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","StatisticalParity","EqualOpportunity"]:
+            if name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","StatisticalParity"]:
                 self.assertFalse(np.isnan(v),f"Bootstrap returns np.nan for {name}.")
             else:
                 self.assertTrue(np.isnan(v),f"Bootstrap not implemented for {name} but returns a value.")

From 7f8ecde936b16c0677ea4ca6bd6390900df394e7 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Fri, 22 Sep 2023 10:12:21 -0400
Subject: [PATCH 04/25] Rearrange simulation into its own class.

---
 tests/test_utils_proba.py | 267 +++++++++++++++++++++-----------------
 1 file changed, 151 insertions(+), 116 deletions(-)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index d25d961..977a86f 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -1,13 +1,14 @@
 import unittest
 import warnings
 
+import numpy.random
 import sklearn
 import pandas as pd
 import numpy as np
 import inspect
 from scipy.stats import kstest
 from jurity.utils_proba import BiasCalculator, BiasCalcFromDataFrame, SummaryData
-from jurity.utils_proba import unpack_bootstrap
+from jurity.utils_proba import unpack_bootstrap, check_memberships_proba_df
 from jurity.utils import Constants
 from jurity.utils_proba import get_bootstrap_results
 
@@ -131,7 +132,8 @@ def test_transform_bootstrap_results_form(self):
         """
         br = self.bc.transform_bootstrap_results(self.bc.run_bootstrap(5))
         self.assertEqual(br.shape, (3, 11), "Returned bootstrap has shape: {0}. Expected (3,11).".format(br.shape))
-        test_cols = [s in br.columns for s in [Constants.FPR, Constants.FNR, Constants.TPR, Constants.TNR, Constants.ACC, Constants.PRED_RATE]]
+        test_cols = [s in br.columns for s in
+                     [Constants.FPR, Constants.FNR, Constants.TPR, Constants.TNR, Constants.ACC, Constants.PRED_RATE]]
         self.assertTrue(np.all(test_cols), "Not all tests are returned by bootstrap transform")
 
     def test_transform_bootstrap_results_answer(self):
@@ -303,9 +305,11 @@ def test_unpack_bootstrap(self):
 
         for label, answer in answer_dict.items():
             self.assertEqual(unpack_bootstrap(test_boot_results, label, [1]),
-                (answer[1], answer[0]),
-                f"unpack bootstrap returns unexpected answer for {label}\n" +
-                "expected {0}, got {1} instead.".format(unpack_bootstrap(test_boot_results,label,[1]),(answer[1],answer[0])))
+                             (answer[1], answer[0]),
+                             f"unpack bootstrap returns unexpected answer for {label}\n" +
+                             "expected {0}, got {1} instead.".format(unpack_bootstrap(test_boot_results, label, [1]),
+                                                                     (answer[1], answer[0])))
+
     def test_unpack_bootstrap_err(self):
         test_unpack = self.bc.transform_bootstrap_results(self.test_boot_results)
         self.assertRaises(ValueError, unpack_bootstrap, test_unpack, "FNR", [1, 2])
@@ -316,6 +320,132 @@ def test_from_df(self):
     # TODO: Write tests for check_memberships_proba
 
 
+class UtilsProbaSimulator:
+    def __init__(self, model_rates_dict, in_surrogate_df, in_rng=None):
+        self.rates_dict(model_rates_dict)
+        self.surrogate_df(in_surrogate_df)
+        if in_rng is not None:
+            self.rng(in_rng)
+        else:
+            self._rng = numpy.random.default_rng()
+
+    def rates_dict(self, v=None):
+        """
+        Set and get model_rates dictionary
+        """
+        if v is not None:
+            if not isinstance(v, dict):
+                raise ValueError("Rates must be a dictionary. Input rates has type: {0}".format(type(dict)))
+            for k,value in v.items():
+                if not set(value.keys()) == {"pct_positive", "fpr", "fnr"}:
+                    raise ValueError("Rates must have ".format("pct_positive", "fpr", "fnr"))
+                if not isinstance(k,str):
+                    raise ValueError("Keys for main dictionary must be strings")
+                for k2,value2 in value.items():
+                    if not isinstance(value2, float):
+                        raise ValueError("Rates must be floats.")
+            self._rates_dict = v
+        return self._rates_dict
+
+    def surrogate_df(self, df=None):
+        """
+        Set input dataframe.
+        It should have one row per surrogate class and membership probabilities for all input classes
+        """
+        if df is not None:
+            check_memberships_proba_df(df, set(df.index.values))
+            self._surrogate_df = df
+        return self._surrogate_df
+
+    def rng(self, v=None):
+        if v is not None:
+            if not isinstance(v, numpy.random.Generator):
+                raise ValueError("rng argument must be a numpy random number generator.")
+            self._rng = v
+        return self._rng
+
+    # For the simulation, build "True" protected groups based on population
+    # Note; The census data names the columns as follows:
+    def assign_protected(self, population_data, generator, surrogate_name='surrogate',
+                         membership_values=["W", "O", "B", "T", "A", "AI"]):
+        # Passing in the global random number generator
+        # Lets us make sure that we're not accidentally resetting the seed
+        surrogate_protected_prob_grouped = population_data.groupby(surrogate_name)
+        surrogate_groups = []
+        for name, group in surrogate_protected_prob_grouped:
+            probs = [group[v].unique()[0] for v in membership_values]
+            group["class"] = generator.choice(list(membership_values), len(group), p=probs)
+            surrogate_groups.append(group)
+        out_data = pd.concat(surrogate_groups)
+        return out_data
+
+    def assign_protected_and_accuracy(self, input_data, rates_by_protected, generator,
+                                      protected_name="class"):
+        # Assign everyone a "true" race for simulation purposes
+        if not set(rates_by_protected.keys())==set(input_data.columns):
+            raise ValueError("Input dataframe does not have same column names as keys in rates.")
+        protected_assignments = self.assign_protected(input_data, generator,
+                                                      membership_values=list(rates_by_protected.keys()))
+        model_outcomes = self.model_outcome_by_protected(protected_assignments, rates_by_protected,
+                                                         protected_col_name=protected_name)
+        return model_outcomes
+
+    def confusion_matrix_prob(self, percent_positive, fpr, fnr, verbose=False):
+        """
+        # This is the probability that the person is labeled as positive in the data
+        Calculate the % of False Positive, False Negative, True Negative, and True Positive in total based on predefined inputs.
+        """
+        fp_ratio = (1 - percent_positive) * fpr
+        fn_ratio = percent_positive * fnr
+        tn_ratio = (1 - percent_positive) - fp_ratio
+        tp_ratio = percent_positive - fn_ratio
+        probs = [fp_ratio, fn_ratio, tn_ratio, tp_ratio]
+        if verbose:
+            print("Expected FPR: " + str(fpr))
+            print("Expected FNR: " + str(fnr))
+            print("Expected TPR: " + str(tp_ratio / (tp_ratio + fn_ratio)))
+            print("Expected TNR: " + str(tn_ratio / (tn_ratio + fp_ratio)))
+            print("Expected Accuracy: " + str((tn_ratio + tp_ratio)))
+        # print("% of FP, FN, TN, TP among total: ")
+        return probs
+
+    def model_outcome_by_protected(self, surrogate_protected_assignment, rates_by_protected,
+                                   protected_col_name="class"):
+        # Assign true positive, true negative, etc by race
+        surrogate_protected_prob_grouped = surrogate_protected_assignment.groupby(protected_col_name)
+
+        classified_groups = []
+        for name, group in surrogate_protected_prob_grouped:
+            rates_dict = rates_by_protected[name]
+            probs = self.confusion_matrix_prob(rates_dict["pct_positive"], rates_dict["fpr"], rates_dict["fnr"])
+            group['pred_category'] = np.random.choice(['fp', 'fn', 'tn', 'tp'], len(group), p=probs)
+            group['label'] = np.where(group['pred_category'].isin(['tp', 'fn']), 1, 0)
+            group['prediction'] = np.where(group['pred_category'].isin(['tp', 'fp']), 1, 0)
+            classified_groups.append(group)
+        classified_data = pd.concat(classified_groups)
+        return classified_data
+
+    # Add columns to a pandas dataframe flagging each row as false positive, etc.
+    def accuracy_columns(self, test_data, pred_col, label_col):
+        """
+        Add indicators for each confusion matrix qudrant. Simplifies calculating rates.
+        """
+        test_data["correct"] = (test_data[pred_col] == test_data[label_col]).astype(int)
+        test_data["true_positive"] = (test_data["correct"] & (test_data[label_col] == 1)).astype(int)
+        test_data["true_negative"] = (test_data["correct"] & (test_data[label_col] == 0)).astype(int)
+        test_data["false_negative"] = (~(test_data["correct"]) & (test_data[pred_col] == 0)).astype(int)
+        test_data["false_positive"] = (~(test_data["correct"]) & (test_data[pred_col] == 1)).astype(int)
+        return test_data
+
+    def explode_dataframe(self, df, count_name="count"):
+        """
+        Given a dataframe that has a count, produce a number of identical rows equal to that count
+        """
+        check_memberships_proba_df(df,set(df.index.values),df.columns)
+        e_df = df.loc[df.index.repeat(df[count_name])].drop("count", axis=1)
+        return self.assign_protected_and_accuracy(e_df, self._rates_dict, self._rng)
+
+
 # Simulations to ensure numbers accuracy
 class TestWithSimulation(unittest.TestCase):
     """
@@ -477,102 +607,6 @@ def setUpClass(cls) -> None:
 
         cls.bc = BiasCalculator.from_df(summary_df, [1, 2], ["W", "B", "O"])
 
-    # For the simulation, build "True" protected groups based on population
-    # Note; The census data names the columns as follows:
-    # pct_white_zip, pct_black_zip, etc
-    # TODO: Need to use the census labels as keys in a dictionary or use some other method
-    # So we can continue to use this when the census data changes.
-    # TODO: REMOVE race terminology
-
-    @classmethod
-    def assign_protected(cls, population_data, generator, surrogate_name='surrogate',
-                         membership_values=["W", "O", "B", "T", "A", "AI"]):
-        # Passing in the global random number generator
-        # Lets us make sure that we're not accidentally resetting the seed
-        surrogate_protected_prob_grouped = population_data.groupby(surrogate_name)
-        surrogate_groups = []
-        for name, group in surrogate_protected_prob_grouped:
-            probs = [group[v].unique()[0] for v in membership_values]
-            group["class"] = generator.choice(list(membership_values), len(group), p=probs)
-            surrogate_groups.append(group)
-        out_data = pd.concat(surrogate_groups)
-        return out_data
-
-    @classmethod
-    def assign_protected_and_accuracy(cls, input_data, rates_by_protected, generator,
-                                      protected_name="class"):
-        # Assign everyone a "true" race for simulation purposes
-        protected_assignments = cls.assign_protected(input_data, generator,
-                                                     membership_values=list(rates_by_protected.keys()))
-        # Current simulation only handles 2 categories: white or not.
-        # protected_assignments["w"] = np.where(protected_assignments[protected_col_name] == protected_group, protected_group, "unprotected")
-        # Assign each individual a quadrant in the confusion matrix based on:
-        #   Percent of positive (not predict_pos_probs)
-        #   probability of being a false positive
-        #   probability of being a false negative
-        # These are different by race and fed into the simulation through indexes
-        # Index keys are the values in the race column, e.g. "White" and "Non-White"
-        model_outcomes = cls.model_outcome_by_protected(protected_assignments, rates_by_protected,
-                                                        protected_col_name=protected_name)
-        return model_outcomes
-
-    @classmethod
-    def confusion_matrix_prob(cls, percent_positive, fpr, fnr, verbose=False):
-        """
-        # This is the probability that the person is labeled as positive in the data
-        Calculate the % of False Positive, False Negative, True Negative, and True Positive in total based on predefined inputs.
-        """
-        fp_ratio = (1 - percent_positive) * fpr
-        fn_ratio = percent_positive * fnr
-        tn_ratio = (1 - percent_positive) - fp_ratio
-        tp_ratio = percent_positive - fn_ratio
-        probs = [fp_ratio, fn_ratio, tn_ratio, tp_ratio]
-        if verbose:
-            print("Expected FPR: " + str(fpr))
-            print("Expected FNR: " + str(fnr))
-            print("Expected TPR: " + str(tp_ratio / (tp_ratio + fn_ratio)))
-            print("Expected TNR: " + str(tn_ratio / (tn_ratio + fp_ratio)))
-            print("Expected Accuracy: " + str((tn_ratio + tp_ratio)))
-        # print("% of FP, FN, TN, TP among total: ")
-        return probs
-
-    @classmethod
-    def model_outcome_by_protected(cls, surrogate_protected_assignment, rates_by_protected, protected_col_name="class"):
-        # Assign true positive, true negative, etc by race
-        surrogate_protected_prob_grouped = surrogate_protected_assignment.groupby(protected_col_name)
-
-        classified_groups = []
-        for name, group in surrogate_protected_prob_grouped:
-            rates_dict = rates_by_protected[name]
-            probs = cls.confusion_matrix_prob(rates_dict["pct_positive"], rates_dict["fpr"], rates_dict["fnr"])
-            group['pred_category'] = np.random.choice(['fp', 'fn', 'tn', 'tp'], len(group), p=probs)
-            group['label'] = np.where(group['pred_category'].isin(['tp', 'fn']), 1, 0)
-            group['prediction'] = np.where(group['pred_category'].isin(['tp', 'fp']), 1, 0)
-            classified_groups.append(group)
-        classified_data = pd.concat(classified_groups)
-        return classified_data
-
-    @classmethod
-    # Add columns to a pandas dataframe flagging each row as false positive, etc.
-    def accuracy_columns(cls, test_data, pred_col, label_col):
-        """
-        Add indicators for each confusion matrix qudrant. Simplifies calculating rates.
-        """
-        test_data["correct"] = (test_data[pred_col] == test_data[label_col]).astype(int)
-        test_data["true_positive"] = (test_data["correct"] & (test_data[label_col] == 1)).astype(int)
-        test_data["true_negative"] = (test_data["correct"] & (test_data[label_col] == 0)).astype(int)
-        test_data["false_negative"] = (~(test_data["correct"]) & (test_data[pred_col] == 0)).astype(int)
-        test_data["false_positive"] = (~(test_data["correct"]) & (test_data[pred_col] == 1)).astype(int)
-        return test_data
-
-    @classmethod
-    def explode_dataframe(cls, df, count_name="count"):
-        """
-        Given a dataframe that has a count, produce a number of identical rows equal to that count
-        """
-        e_df = df.loc[df.index.repeat(df[count_name])].drop("count", axis=1)
-        return cls.assign_protected_and_accuracy(e_df, cls.rates_dict, cls.rng)
-
     def test_membership_as_df(self):
         """
         Check output from get_bootstrap_results when inputs are a surrogate dataframe
@@ -608,8 +642,8 @@ def test_bootstrap_ranges(self):
         """
         Test whether bootstrap returns values that are expected based on simulated data
         """
-        #Need to build a confidence interval where we expect values to be.
-        #This requires calculation of theoretical variance/covariance matrix based on linear regression
+        # Need to build a confidence interval where we expect values to be.
+        # This requires calculation of theoretical variance/covariance matrix based on linear regression
         n_row = self.bc.X().shape[0]
         x = np.hstack((np.ones((n_row, 1)), self.bc.X()))
         # The variance-covariance matrix of a linear estimator based on input X is:
@@ -620,9 +654,9 @@ def test_bootstrap_ranges(self):
         # Only need the diagonal for this calculation
         x_portion_variance = pd.Series(np.diag(np.dot(np.dot(pred_matrix, invxTx), pred_matrix.T)))
         x_portion_variance.name = 'x_var'
-        x_portion_variance.index=self.bc.all_class_labels()
+        x_portion_variance.index = self.bc.all_class_labels()
 
-        #Get confusion matrix probabilities and variances from input rates_dict.
+        # Get confusion matrix probabilities and variances from input rates_dict.
         in_vars_dict = {}
         in_means_dict = {}
         for k, v in self.rates_dict.items():
@@ -648,27 +682,28 @@ def test_bootstrap_ranges(self):
 
         z = 1.65  # from z-table, predictions are N(in_y,var_pred_y)
         for n in names:
-            #Prediction variance is sigma_y*pred_matrix*inv(X'X)pred_matrix.T, where sigma_y is a scalar.
-            #Variance of the mean of n_boots predictions is prediction_variance/n_boots
-            var_components[n + '_st_err'] = np.sqrt(var_components[n + '_var']*var_components['x_var']/n_boots)
-            #Normal apprixmation confidence limit
+            # Prediction variance is sigma_y*pred_matrix*inv(X'X)pred_matrix.T, where sigma_y is a scalar.
+            # Variance of the mean of n_boots predictions is prediction_variance/n_boots
+            var_components[n + '_st_err'] = np.sqrt(var_components[n + '_var'] * var_components['x_var'] / n_boots)
+            # Normal apprixmation confidence limit
             check_series = (var_components[n + '_in'] - z * var_components[n + "_st_err"] <
                             var_components[n + '_ratio']) & (
-                                       var_components[n + "_in"] + z * var_components[n + "_st_err"])
+                                   var_components[n + "_in"] + z * var_components[n + "_st_err"])
             check_series.name = n + "_ok"
             self.assertTrue(np.all(check_series.values),
                             f"{n} is out of range, on mean of {n_loops}, of {n_boots} bootstraps.")
+
     def test_get_all_scores(self):
         """
         Test get_all_scores to make sure it returns scores for the values it's supposed to return
         """
-        #get_all_scores only works for two categories
+        # get_all_scores only works for two categories
         two_categories = pd.concat([self.surrogate_df[["surrogate", "W"]],
                                     1.0 - self.surrogate_df["W"]], axis=1).set_index("surrogate")
         two_categories.columns = ["W", "NW"]
         from jurity.fairness import BinaryFairnessMetrics as bfm
-        output_df=bfm.get_all_scores(self.test_data["label"], self.test_data["prediction"], two_categories,
-                           self.test_data["surrogate"], [1])
+        output_df = bfm.get_all_scores(self.test_data["label"], self.test_data["prediction"], two_categories,
+                                       self.test_data["surrogate"], [1])
 
         from jurity.fairness import BinaryFairnessMetrics
 
@@ -678,7 +713,7 @@ def test_get_all_scores(self):
             class_ = getattr(BinaryFairnessMetrics, name)  # grab a class which is a property of BinaryFairnessMetrics
             instance = class_()  # dynamically instantiate such class
             v = output_df.loc[instance.name]["Value"]
-            if name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","StatisticalParity"]:
-                self.assertFalse(np.isnan(v),f"Bootstrap returns np.nan for {name}.")
+            if name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality", "StatisticalParity"]:
+                self.assertFalse(np.isnan(v), f"Bootstrap returns np.nan for {name}.")
             else:
-                self.assertTrue(np.isnan(v),f"Bootstrap not implemented for {name} but returns a value.")
+                self.assertTrue(np.isnan(v), f"Bootstrap not implemented for {name} but returns a value.")

From da13e4f9a36a1ea8268f2e9044d57ddcc80ea759 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Fri, 22 Sep 2023 11:47:05 -0400
Subject: [PATCH 05/25] Simulator is its own class. Simulator unit tests
 running clean.

---
 tests/test_utils_proba.py | 40 ++++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 19 deletions(-)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index 977a86f..109a2a6 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -321,9 +321,15 @@ def test_from_df(self):
 
 
 class UtilsProbaSimulator:
-    def __init__(self, model_rates_dict, in_surrogate_df, in_rng=None):
+    """
+    Simulation functions used to test probabilistic fairness.
+    Can be used by other researchers to simulate different levels of unfairness and test their own methods.
+    Members:
+    _rates_dict: Dictionary of dictionaries with expected fairness metrics for each protected class
+    rng: numpy random number generators. Set on initialization if you want to set the seed for your simulation
+    """
+    def __init__(self, model_rates_dict: dict, in_rng: numpy.random.Generator = None):
         self.rates_dict(model_rates_dict)
-        self.surrogate_df(in_surrogate_df)
         if in_rng is not None:
             self.rng(in_rng)
         else:
@@ -331,7 +337,7 @@ def __init__(self, model_rates_dict, in_surrogate_df, in_rng=None):
 
     def rates_dict(self, v=None):
         """
-        Set and get model_rates dictionary
+        Set and get rates_dict dictionary
         """
         if v is not None:
             if not isinstance(v, dict):
@@ -347,17 +353,10 @@ def rates_dict(self, v=None):
             self._rates_dict = v
         return self._rates_dict
 
-    def surrogate_df(self, df=None):
+    def rng(self, v=None):
         """
-        Set input dataframe.
-        It should have one row per surrogate class and membership probabilities for all input classes
+        Set and get random number generator
         """
-        if df is not None:
-            check_memberships_proba_df(df, set(df.index.values))
-            self._surrogate_df = df
-        return self._surrogate_df
-
-    def rng(self, v=None):
         if v is not None:
             if not isinstance(v, numpy.random.Generator):
                 raise ValueError("rng argument must be a numpy random number generator.")
@@ -441,7 +440,10 @@ def explode_dataframe(self, df, count_name="count"):
         """
         Given a dataframe that has a count, produce a number of identical rows equal to that count
         """
-        check_memberships_proba_df(df,set(df.index.values),df.columns)
+        names=list(self.rates_dict().keys())
+        if not set(names).issubset(df.columns):
+            raise ValueError(f"DataFrame column names do not match keys in rates dictionary. Rates dict has: {names}.")
+        check_memberships_proba_df(df[list(self.rates_dict().keys())],set(df.index.values),names)
         e_df = df.loc[df.index.repeat(df[count_name])].drop("count", axis=1)
         return self.assign_protected_and_accuracy(e_df, self._rates_dict, self._rng)
 
@@ -598,10 +600,13 @@ def setUpClass(cls) -> None:
         cls.rates_dict = {"W": {"pct_positive": 0.1, "fpr": 0.1, "fnr": 0.1},
                           "B": {"pct_positive": 0.2, "fpr": 0.2, "fnr": 0.35},
                           'O': {"pct_positive": 0.1, "fpr": 0.1, "fnr": 0.1}}
-
+        #Make simulator here
         cls.rng = np.random.default_rng(347123)
-        cls.test_data = cls.explode_dataframe(input_df[["surrogate", "count", "W", "B", "O"]])
+        cls.sim=UtilsProbaSimulator(cls.rates_dict,in_rng=cls.rng)
         cls.surrogate_df = input_df[["surrogate", "W", "B", "O"]]
+
+        cls.test_data = cls.sim.explode_dataframe(input_df[["surrogate", "count","W", "B", "O"]].set_index("surrogate")).reset_index()
+
         summary_df = SummaryData.summarize(cls.test_data["prediction"], cls.surrogate_df,
                                            cls.test_data["surrogate"], cls.test_data["label"])
 
@@ -613,9 +618,6 @@ def test_membership_as_df(self):
         """
         results = get_bootstrap_results(self.test_data["prediction"], self.surrogate_df.set_index("surrogate"),
                                         self.test_data["surrogate"], [1, 2], self.test_data["label"])
-
-        print(results)
-
         self.assertTrue(isinstance(results, pd.DataFrame), "get_bootstrap_results does not return a Pandas DataFrame.")
         self.assertTrue(
             {Constants.FPR, Constants.FNR, Constants.TNR, Constants.TPR, Constants.ACC}.issubset(set(results.columns)),
@@ -660,7 +662,7 @@ def test_bootstrap_ranges(self):
         in_vars_dict = {}
         in_means_dict = {}
         for k, v in self.rates_dict.items():
-            a = self.confusion_matrix_prob(v['pct_positive'], v['fpr'], v['fnr'])
+            a = self.sim.confusion_matrix_prob(v['pct_positive'], v['fpr'], v['fnr'])
             # These are based on variance of a proportion: p(1-p)
             in_vars_dict[k] = [r * (1 - r) for r in a]
             in_means_dict[k] = a

From 04d0c63bc6698f5a7ce352378c349427f21fcf74 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Sep 2023 08:04:24 -0400
Subject: [PATCH 06/25] Small edits to test_utils_proba.py

---
 tests/test_utils_proba.py | 75 ++++++++++++++++++++++++++-------------
 1 file changed, 50 insertions(+), 25 deletions(-)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index 109a2a6..00b92c3 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -325,11 +325,16 @@ class UtilsProbaSimulator:
     Simulation functions used to test probabilistic fairness.
     Can be used by other researchers to simulate different levels of unfairness and test their own methods.
     Members:
-    _rates_dict: Dictionary of dictionaries with expected fairness metrics for each protected class
+    _rates_dict: Dictionary of dictionaries with expected fairness metrics for each protected class,
+        has the form: {"class1":{'pct_positive':float,'fpr':float,'fnr':float}, "class2":{'pct_positive'}...}
+    _surrogate_name: Name of surrogate column for input dataframe
     rng: numpy random number generators. Set on initialization if you want to set the seed for your simulation
     """
-    def __init__(self, model_rates_dict: dict, in_rng: numpy.random.Generator = None):
+    def __init__(self, model_rates_dict: dict,
+                 in_rng: numpy.random.Generator = None,
+                 surrogate_name: str ="surrogate"):
         self.rates_dict(model_rates_dict)
+        self.surrogate_name(surrogate_name)
         if in_rng is not None:
             self.rng(in_rng)
         else:
@@ -353,6 +358,12 @@ def rates_dict(self, v=None):
             self._rates_dict = v
         return self._rates_dict
 
+    def surrogate_name(self,v=None):
+        if not v is None:
+            if not isinstance(v,str):
+                raise ValueError("surrogate_name must be a string.\n {0} supplied intead.".format(type(v)))
+            self._surrogate_name=v
+        return self._surrogate_name
     def rng(self, v=None):
         """
         Set and get random number generator
@@ -365,11 +376,11 @@ def rng(self, v=None):
 
     # For the simulation, build "True" protected groups based on population
     # Note; The census data names the columns as follows:
-    def assign_protected(self, population_data, generator, surrogate_name='surrogate',
+    def assign_protected(self, population_data, generator,
                          membership_values=["W", "O", "B", "T", "A", "AI"]):
         # Passing in the global random number generator
         # Lets us make sure that we're not accidentally resetting the seed
-        surrogate_protected_prob_grouped = population_data.groupby(surrogate_name)
+        surrogate_protected_prob_grouped = population_data.groupby(self.surrogate_name())
         surrogate_groups = []
         for name, group in surrogate_protected_prob_grouped:
             probs = [group[v].unique()[0] for v in membership_values]
@@ -381,7 +392,7 @@ def assign_protected(self, population_data, generator, surrogate_name='surrogate
     def assign_protected_and_accuracy(self, input_data, rates_by_protected, generator,
                                       protected_name="class"):
         # Assign everyone a "true" race for simulation purposes
-        if not set(rates_by_protected.keys())==set(input_data.columns):
+        if not set(rates_by_protected.keys()).issubset(set(input_data.columns)):
             raise ValueError("Input dataframe does not have same column names as keys in rates.")
         protected_assignments = self.assign_protected(input_data, generator,
                                                       membership_values=list(rates_by_protected.keys()))
@@ -391,8 +402,10 @@ def assign_protected_and_accuracy(self, input_data, rates_by_protected, generato
 
     def confusion_matrix_prob(self, percent_positive, fpr, fnr, verbose=False):
         """
-        # This is the probability that the person is labeled as positive in the data
-        Calculate the % of False Positive, False Negative, True Negative, and True Positive in total based on predefined inputs.
+        Calculate the probability of any given individual falling into each quadrant of the confusion matrix
+        percent_positive: Percent of positive cases in the training data
+        fpr: False Positive Rate from the hypothetical model
+        fnr: False Negative Rate from the hypothetical model
         """
         fp_ratio = (1 - percent_positive) * fpr
         fn_ratio = percent_positive * fnr
@@ -408,13 +421,15 @@ def confusion_matrix_prob(self, percent_positive, fpr, fnr, verbose=False):
         # print("% of FP, FN, TN, TP among total: ")
         return probs
 
-    def model_outcome_by_protected(self, surrogate_protected_assignment, rates_by_protected,
+    def model_outcome_by_protected(self, protected_assignment, rates_by_protected,
                                    protected_col_name="class"):
-        # Assign true positive, true negative, etc by race
-        surrogate_protected_prob_grouped = surrogate_protected_assignment.groupby(protected_col_name)
+        """
+        Assing each individual into a column of the confusion matrix based on probabilities for their class.
+        """
+        protected_prob_grouped = protected_assignment.groupby(protected_col_name)
 
         classified_groups = []
-        for name, group in surrogate_protected_prob_grouped:
+        for name, group in protected_prob_grouped:
             rates_dict = rates_by_protected[name]
             probs = self.confusion_matrix_prob(rates_dict["pct_positive"], rates_dict["fpr"], rates_dict["fnr"])
             group['pred_category'] = np.random.choice(['fp', 'fn', 'tn', 'tp'], len(group), p=probs)
@@ -425,20 +440,31 @@ def model_outcome_by_protected(self, surrogate_protected_assignment, rates_by_pr
         return classified_data
 
     # Add columns to a pandas dataframe flagging each row as false positive, etc.
-    def accuracy_columns(self, test_data, pred_col, label_col):
-        """
-        Add indicators for each confusion matrix qudrant. Simplifies calculating rates.
-        """
-        test_data["correct"] = (test_data[pred_col] == test_data[label_col]).astype(int)
-        test_data["true_positive"] = (test_data["correct"] & (test_data[label_col] == 1)).astype(int)
-        test_data["true_negative"] = (test_data["correct"] & (test_data[label_col] == 0)).astype(int)
-        test_data["false_negative"] = (~(test_data["correct"]) & (test_data[pred_col] == 0)).astype(int)
-        test_data["false_positive"] = (~(test_data["correct"]) & (test_data[pred_col] == 1)).astype(int)
-        return test_data
-
-    def explode_dataframe(self, df, count_name="count"):
+    def accuracy_columns(self, test_data: pd.DataFrame, pred_col: str, label_col:str)->pd.DataFrame:
+        """
+        Add indicators for each confusion matrix quadrant. Simplifies calculating rates.
+        test_data: Input dataframe
+        pred_col: Name of column with predicted class
+        label_col: Name of column with actual class
+        """
+        correct=(test_data[pred_col] == test_data[label_col]).astype(int)
+        correct.name="correct"
+        true_positive=(correct & (test_data[label_col] == 1)).astype(int)
+        true_positive.name="true_positive"
+        true_negative=(correct & (test_data[label_col] == 0)).astype(int)
+        true_negative.name="true_negative"
+        false_negative=(~(correct) & (test_data[pred_col] == 0)).astype(int)
+        false_negative.name="false_negative"
+        false_positive = (~(correct) & (test_data[pred_col] == 1)).astype(int)
+        false_positive.name="false_positive"
+        return pd.concat([test_data,correct,true_positive,true_negative,false_negative,false_positive],axis=1)
+
+    def explode_dataframe(self, df, count_name="count",surrogate_name="surrogate"):
         """
         Given a dataframe that has a count, produce a number of identical rows equal to that count
+        df: pd.DataFrame with columns: count, class_1, class_2, ...  Class names must match keys from
+        self._rates_dict
+        count_name; name of count variable.
         """
         names=list(self.rates_dict().keys())
         if not set(names).issubset(df.columns):
@@ -451,9 +477,8 @@ def explode_dataframe(self, df, count_name="count"):
 # Simulations to ensure numbers accuracy
 class TestWithSimulation(unittest.TestCase):
     """
-    Helper functions 
+    Simulation tests for whether numbers are correct based on simulated inputs
     """
-
     @classmethod
     def setUpClass(cls) -> None:
         input_df = pd.DataFrame({"surrogate": list(range(0, 99)),

From 25fc7fda43225fc98bea481d489b3687b621b789 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Sep 2023 08:31:51 -0400
Subject: [PATCH 07/25] Fix small bug that occurs in summarizer when
 mambership_df has a surrogate column not named 'surrogate'. Update unit tests
 to fix error.

---
 jurity/utils_proba.py     | 10 ++++++----
 tests/test_utils_proba.py |  2 +-
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index b260c2e..ec6027e 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -748,7 +748,10 @@ def summarize(cls,
             membership_names: Optional list of strings, where len() is equal to length of inner array of membership probabilities. Used to provide labels for outcomes.
         """
         if membership_names is None:
-            membership_names = ["A", "B"]
+            if isinstance(memberships,pd.DataFrame):
+                memberhips_names=memberships.columns
+            else:
+                membership_names = ["A", "B"]
 
         df = pd.concat([pd.Series(predictions, name="predictions"),
                         pd.Series(surrogates, name="surrogates")], axis=1)
@@ -768,9 +771,8 @@ def summarize(cls,
         # 2. A dataframe ttehat has a row for each surrogate class value and
         #   a column for each likelihood value. The dataframe must have surrogate class as an index.
         if isinstance(memberships, pd.DataFrame):
-            membership_surrogates = pd.Series(memberships.index.values)
-            membership_surrogates.name = 'surrogates'
-            likes_df = pd.concat([membership_surrogates, memberships], axis=1)
+            name=memberships.index.name
+            likes_df = memberships.reset_index().rename(columns={name:'surrogates'})
         else:
             if len(memberships) != df.shape[0]:
                 len_predictions = len(predictions)
diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index fa1fee6..ec9234b 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -471,7 +471,7 @@ def setUpClass(cls) -> None:
         cls.rng = np.random.default_rng(347123)
         cls.test_data = cls.explode_dataframe(input_df[["surrogate", "count", "W", "B", "O"]])
         cls.surrogate_df = input_df[["surrogate", "W", "B", "O"]]
-        summary_df = SummaryData.summarize(cls.test_data["prediction"], cls.surrogate_df,
+        summary_df = SummaryData.summarize(cls.test_data["prediction"], cls.surrogate_df.set_index("surrogate"),
                                            cls.test_data["surrogate"], cls.test_data["label"])
 
         cls.bc = BiasCalculator.from_df(summary_df, [1, 2], ["W", "B", "O"])

From fa8f8bc12e67dd730b67574ed47d234616abdeac Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Sep 2023 08:49:27 -0400
Subject: [PATCH 08/25] Add tests for summarizer.

---
 jurity/utils_proba.py     |  8 +++---
 tests/test_utils_proba.py | 51 +++++++++++++++++++++++++++++----------
 2 files changed, 42 insertions(+), 17 deletions(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index ec6027e..f00bd21 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -748,8 +748,8 @@ def summarize(cls,
             membership_names: Optional list of strings, where len() is equal to length of inner array of membership probabilities. Used to provide labels for outcomes.
         """
         if membership_names is None:
-            if isinstance(memberships,pd.DataFrame):
-                memberhips_names=memberships.columns
+            if isinstance(memberships, pd.DataFrame):
+                memberhips_names = memberships.columns
             else:
                 membership_names = ["A", "B"]
 
@@ -771,8 +771,8 @@ def summarize(cls,
         # 2. A dataframe ttehat has a row for each surrogate class value and
         #   a column for each likelihood value. The dataframe must have surrogate class as an index.
         if isinstance(memberships, pd.DataFrame):
-            name=memberships.index.name
-            likes_df = memberships.reset_index().rename(columns={name:'surrogates'})
+            name = memberships.index.name
+            likes_df = memberships.reset_index().rename(columns={name: 'surrogates'})
         else:
             if len(memberships) != df.shape[0]:
                 len_predictions = len(predictions)
diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index ec9234b..f3553b4 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -130,7 +130,8 @@ def test_transform_bootstrap_results_form(self):
         """
         br = self.bc.transform_bootstrap_results(self.bc.run_bootstrap(5))
         self.assertEqual(br.shape, (3, 11), "Returned bootstrap has shape: {0}. Expected (3,11).".format(br.shape))
-        test_cols = [s in br.columns for s in [Constants.FPR, Constants.FNR, Constants.TPR, Constants.TNR, Constants.ACC, Constants.PRED_RATE]]
+        test_cols = [s in br.columns for s in
+                     [Constants.FPR, Constants.FNR, Constants.TPR, Constants.TNR, Constants.ACC, Constants.PRED_RATE]]
         self.assertTrue(np.all(test_cols), "Not all tests are returned by bootstrap transform")
 
     def test_transform_bootstrap_results_answer(self):
@@ -302,9 +303,11 @@ def test_unpack_bootstrap(self):
 
         for label, answer in answer_dict.items():
             self.assertEqual(unpack_bootstrap(test_boot_results, label, [1]),
-                (answer[1], answer[0]),
-                f"unpack bootstrap returns unexpected answer for {label}\n" +
-                "expected {0}, got {1} instead.".format(unpack_bootstrap(test_boot_results,label,[1]),(answer[1],answer[0])))
+                             (answer[1], answer[0]),
+                             f"unpack bootstrap returns unexpected answer for {label}\n" +
+                             "expected {0}, got {1} instead.".format(unpack_bootstrap(test_boot_results, label, [1]),
+                                                                     (answer[1], answer[0])))
+
     def test_unpack_bootstrap_err(self):
         test_unpack = self.bc.transform_bootstrap_results(self.test_boot_results)
         self.assertRaises(ValueError, unpack_bootstrap, test_unpack, "FNR", [1, 2])
@@ -312,6 +315,28 @@ def test_unpack_bootstrap_err(self):
     def test_from_df(self):
         self.assertRaises(ValueError, BiasCalculator.from_df, self.summarized_df,
                           [3], ["W", "B", "O"], weight_warnings=False)
+
+    def test_summarizer(self):
+        predictions = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1]
+        surrogates = [1, 1, 1, 2, 3, 3, 4, 5, 5, 5, 5]
+        labels = [1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0]
+        memberships = pd.DataFrame(np.array([[0.5, 0.5], [0.2, 0.8],
+                                             [0.1, 0.9],
+                                             [0.25, 0.75],
+                                             [0.3, 0.7]]))
+        memberships.columns = ["C", "D"]
+        memberships["s"] = pd.Series([1, 2, 3, 4, 5])
+        summary = SummaryData.summarize(predictions, memberships.set_index("s"), surrogates, labels)
+        self.assertTrue(summary.shape[0] == 5, "Summarizer returns dataframe with wrong shape")
+        self.assertTrue(np.all(~summary["C"].apply(np.isnan)), "Summarizer inserts NaN values.")
+        self.assertTrue(np.all(~summary["D"].apply(np.isnan)), "Summarizer inserts NaN values.")
+        expected_cols={'prediction_ratio', 'count', 'true_negative_ratio',
+                        'true_positive_ratio', 'false_negative_ratio', 'false_positive_ratio',
+                        'surrogates', 'C', 'D'}
+        returned_cols=set(summary.columns)
+        self.assertTrue(expected_cols==returned_cols,
+                    f"Summary dataframe does not return correct columns. \nReturns: {returned_cols}. \nExpected: {expected_cols}")
+
     # TODO: Write tests for check_memberships_proba
 
 
@@ -607,8 +632,8 @@ def test_bootstrap_ranges(self):
         """
         Test whether bootstrap returns values that are expected based on simulated data
         """
-        #Need to build a confidence interval where we expect values to be.
-        #This requires calculation of theoretical variance/covariance matrix based on linear regression
+        # Need to build a confidence interval where we expect values to be.
+        # This requires calculation of theoretical variance/covariance matrix based on linear regression
         n_row = self.bc.X().shape[0]
         x = np.hstack((np.ones((n_row, 1)), self.bc.X()))
         # The variance-covariance matrix of a linear estimator based on input X is:
@@ -619,9 +644,9 @@ def test_bootstrap_ranges(self):
         # Only need the diagonal for this calculation
         x_portion_variance = pd.Series(np.diag(np.dot(np.dot(pred_matrix, invxTx), pred_matrix.T)))
         x_portion_variance.name = 'x_var'
-        x_portion_variance.index=self.bc.all_class_labels()
+        x_portion_variance.index = self.bc.all_class_labels()
 
-        #Get confusion matrix probabilities and variances from input rates_dict.
+        # Get confusion matrix probabilities and variances from input rates_dict.
         in_vars_dict = {}
         in_means_dict = {}
         for k, v in self.rates_dict.items():
@@ -647,13 +672,13 @@ def test_bootstrap_ranges(self):
 
         z = 1.65  # from z-table, predictions are N(in_y,var_pred_y)
         for n in names:
-            #Prediction variance is sigma_y*pred_matrix*inv(X'X)pred_matrix.T, where sigma_y is a scalar.
-            #Variance of the mean of n_boots predictions is prediction_variance/n_boots
-            var_components[n + '_st_err'] = np.sqrt(var_components[n + '_var']*var_components['x_var']/n_boots)
-            #Normal apprixmation confidence limit
+            # Prediction variance is sigma_y*pred_matrix*inv(X'X)pred_matrix.T, where sigma_y is a scalar.
+            # Variance of the mean of n_boots predictions is prediction_variance/n_boots
+            var_components[n + '_st_err'] = np.sqrt(var_components[n + '_var'] * var_components['x_var'] / n_boots)
+            # Normal apprixmation confidence limit
             check_series = (var_components[n + '_in'] - z * var_components[n + "_st_err"] <
                             var_components[n + '_ratio']) & (
-                                       var_components[n + "_in"] + z * var_components[n + "_st_err"])
+                                   var_components[n + "_in"] + z * var_components[n + "_st_err"])
             check_series.name = n + "_ok"
             self.assertTrue(np.all(check_series.values),
                             f"{n} is out of range, on mean of {n_loops}, of {n_boots} bootstraps.")

From 899c7476d063a875149a9f29d3dc75228d1eb112 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Sep 2023 09:35:03 -0400
Subject: [PATCH 09/25] Cleanup code after merging changes to fix summarizer
 bug.

---
 jurity/utils_proba.py     |  5 ++---
 tests/test_utils_proba.py | 38 +++++++++++++++-----------------------
 2 files changed, 17 insertions(+), 26 deletions(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index b260c2e..d8b5303 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -768,9 +768,8 @@ def summarize(cls,
         # 2. A dataframe ttehat has a row for each surrogate class value and
         #   a column for each likelihood value. The dataframe must have surrogate class as an index.
         if isinstance(memberships, pd.DataFrame):
-            membership_surrogates = pd.Series(memberships.index.values)
-            membership_surrogates.name = 'surrogates'
-            likes_df = pd.concat([membership_surrogates, memberships], axis=1)
+            name = memberships.index.name
+            likes_df = memberships.reset_index().rename(columns={name: 'surrogates'})
         else:
             if len(memberships) != df.shape[0]:
                 len_predictions = len(predictions)
diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index f86ae36..187c3dc 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -651,34 +651,12 @@ def setUpClass(cls) -> None:
         cls.rng = np.random.default_rng(347123)
         cls.sim=UtilsProbaSimulator(cls.rates_dict,in_rng=cls.rng)
         cls.surrogate_df = input_df[["surrogate", "W", "B", "O"]]
-<<<<<<< HEAD
-
         cls.test_data = cls.sim.explode_dataframe(input_df[["surrogate", "count","W", "B", "O"]].set_index("surrogate")).reset_index()
-
-        summary_df = SummaryData.summarize(cls.test_data["prediction"], cls.surrogate_df,
-=======
         summary_df = SummaryData.summarize(cls.test_data["prediction"], cls.surrogate_df.set_index("surrogate"),
->>>>>>> summarizer_bug
                                            cls.test_data["surrogate"], cls.test_data["label"])
 
         cls.bc = BiasCalculator.from_df(summary_df, [1, 2], ["W", "B", "O"])
 
-    def test_membership_as_df(self):
-        """
-        Check output from get_bootstrap_results when inputs are a surrogate dataframe
-        """
-        results = get_bootstrap_results(self.test_data["prediction"], self.surrogate_df.set_index("surrogate"),
-                                        self.test_data["surrogate"], [1, 2], self.test_data["label"])
-        self.assertTrue(isinstance(results, pd.DataFrame), "get_bootstrap_results does not return a Pandas DataFrame.")
-        self.assertTrue(
-            {Constants.FPR, Constants.FNR, Constants.TNR, Constants.TPR, Constants.ACC}.issubset(set(results.columns)),
-            "get_bootstrap_results does not return a dataframe with all expected binary metrics. Columns in DataFrame are:{0}".format(
-                results.columns))
-        self.assertTrue(set(results.index.values) == set(self.surrogate_df.drop(["surrogate"], axis=1).columns),
-                        "get_bootstrap_results does not return a dataframe with rows index equal to the columns of input surrogate dataframe.\n"
-                        "returned rows are:{0}\n"
-                        "returned columns are: {1}\n".format(results.index.values, self.surrogate_df.columns))
-
     @classmethod
     def boot_stats(self, n_boots, n_loops):
         """
@@ -745,7 +723,21 @@ def test_bootstrap_ranges(self):
             check_series.name = n + "_ok"
             self.assertTrue(np.all(check_series.values),
                             f"{n} is out of range, on mean of {n_loops}, of {n_boots} bootstraps.")
-
+    def test_membership_as_df(self):
+        """
+        Check output from get_bootstrap_results when inputs are a surrogate dataframe
+        """
+        results = get_bootstrap_results(self.test_data["prediction"], self.surrogate_df.set_index("surrogate"),
+                                        self.test_data["surrogate"], [1, 2], self.test_data["label"])
+        self.assertTrue(isinstance(results, pd.DataFrame), "get_bootstrap_results does not return a Pandas DataFrame.")
+        self.assertTrue(
+            {Constants.FPR, Constants.FNR, Constants.TNR, Constants.TPR, Constants.ACC}.issubset(set(results.columns)),
+            "get_bootstrap_results does not return a dataframe with all expected binary metrics. Columns in DataFrame are:{0}".format(
+                results.columns))
+        self.assertTrue(set(results.index.values) == set(self.surrogate_df.drop(["surrogate"], axis=1).columns),
+                        "get_bootstrap_results does not return a dataframe with rows index equal to the columns of input surrogate dataframe.\n"
+                        "returned rows are:{0}\n"
+                        "returned columns are: {1}\n".format(results.index.values, self.surrogate_df.columns))
     def test_get_all_scores(self):
         """
         Test get_all_scores to make sure it returns scores for the values it's supposed to return

From 6e0a826884db73e9040579f1289e153ab2c06c62 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Wed, 27 Sep 2023 10:02:53 -0400
Subject: [PATCH 10/25] run_bootstrap was using incorrect class label function
 call.

---
 jurity/utils_proba.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index d8b5303..e81251f 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -464,7 +464,7 @@ def run_bootstrap(self, bootstrap_trials: int) -> pd.DataFrame:
             if binary_metrics is not None:
                 all_model_results.append(pd.concat([binary_metrics, preds], axis=1))
             else:
-                preds['class'] = self.class_labels()
+                preds['class'] = self.all_class_labels()
                 all_model_results.append(preds)
         out_data = pd.concat(all_model_results, axis=0).reset_index().drop(["index"], axis=1)
         return out_data
@@ -997,6 +997,7 @@ def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame =
         accuracy_df = pd.concat([merged_data[self.surrogate_surrogate_col_name()],
                                  self.confusion_matrix_actual(merged_data, self.pred_name(), self.true_name())], axis=1)
         # Use calc_accuracy_metrics to create surrogate-level summary
+        #TODO: Accomodate cases where we don't have a binary classifier
         confusion_matrix_surrogate_summary = self.calc_accuracy_metrics(accuracy_df)
         self.check_surrogate_confusion_matrix(confusion_matrix_surrogate_summary, merged_data)
         return confusion_matrix_surrogate_summary.join(
@@ -1067,3 +1068,4 @@ def calc_accuracy_metrics(self, test_df):
                                    Constants.false_negative_ratio, Constants.false_positive_ratio]
             # Return a dataframe that has the stats by group. Use these to compare to expected values
         return check_accuracy[out_cols]
+    #TODO: Needs string method
\ No newline at end of file

From 15395ac4506431a1b2e2965474f6ab7f25505aaf Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Fri, 13 Oct 2023 13:45:41 -0400
Subject: [PATCH 11/25] Clean up print statements in is_one_dimensional.

---
 jurity/utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/jurity/utils.py b/jurity/utils.py
index ad45ebf..5e3df9d 100644
--- a/jurity/utils.py
+++ b/jurity/utils.py
@@ -218,9 +218,9 @@ def is_one_dimensional(array):
     if isinstance(array, pd.Series) and array.dtype != 'object':
         return True
     elif type(array) == list:
-        print(array[0])
-        print(type(array[0]))
-        print(isinstance(array[0], np.ndarray))
+        #print(array[0])
+        #print(type(array[0]))
+        #print(isinstance(array[0], np.ndarray))
         if type(array[0]) != list and (not isinstance(array[0], np.ndarray)):
             return True
         else:

From 325d12380149d7e479a5a98465c17a04bdfa6d01 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Fri, 13 Oct 2023 16:04:31 -0400
Subject: [PATCH 12/25] Clean up deprecation warning caused by cvx.Variable
 returning a one-dimensional numpy array in EqualizedOdds bias mitigation.
 Convert to float when necessary.

---
 jurity/mitigation/equalized_odds.py | 10 +++++++++-
 tests/test_mitigation_binary.py     | 16 ++++++++--------
 2 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/jurity/mitigation/equalized_odds.py b/jurity/mitigation/equalized_odds.py
index e2bb6fd..7525da5 100644
--- a/jurity/mitigation/equalized_odds.py
+++ b/jurity/mitigation/equalized_odds.py
@@ -99,9 +99,17 @@ def fit(self,
 
         # Save fairness probabilities
         self.p2p_prob_0 = variables_0["p2p"].value
+        if isinstance(self.p2p_prob_0,np.ndarray):
+            self.p2p_prob_0=self.p2p_prob_0.item()
         self.n2p_prob_0 = variables_0["n2p"].value
+        if isinstance(self.n2p_prob_0,np.ndarray):
+            self.n2p_prob_0=self.n2p_prob_0.item()
         self.p2p_prob_1 = variables_1["p2p"].value
+        if isinstance(self.p2p_prob_1,np.ndarray):
+            self.p2p_prob_1=self.p2p_prob_1.item()
         self.n2p_prob_1 = variables_1["n2p"].value
+        if isinstance(self.n2p_prob_1,np.ndarray):
+            self.n2p_prob_1=self.n2p_prob_1.item()
 
     def fit_transform(self,
                       labels: Union[List, np.ndarray, pd.Series],
@@ -227,7 +235,7 @@ def _get_variables(self, labels, likelihoods, predictions, group):
         p2p = cvx.Variable(1)
         n2p = cvx.Variable(1)
         n2n = cvx.Variable(1)  # trivially equals to 1 - n2p
-        p2n = cvx.Variable(1)  # trivially equals to 1 - p2p
+        p2n = cvx.Variable(1) # trivially equals to 1 - p2p
 
         # Baseline label-wise FNR, FPR, TPR, TNR for the group
         tpr, fpr, tnr, fnr = self._get_label_wise_rates(labels, predictions)
diff --git a/tests/test_mitigation_binary.py b/tests/test_mitigation_binary.py
index 5e5dfa9..e3a29c5 100644
--- a/tests/test_mitigation_binary.py
+++ b/tests/test_mitigation_binary.py
@@ -156,10 +156,10 @@ def test_numerical_stability_mixing_rate_small(self):
         n2p_prob_1 = mitigation.n2p_prob_1
 
         # Convert types
-        p2p_prob_0 = p2p_prob_0.item()
-        n2p_prob_0 = n2p_prob_0.item()
-        p2p_prob_1 = p2p_prob_1.item()
-        n2p_prob_1 = n2p_prob_1.item()
+        p2p_prob_0 = p2p_prob_0
+        n2p_prob_0 = n2p_prob_0
+        p2p_prob_1 = p2p_prob_1
+        n2p_prob_1 = n2p_prob_1
 
         self.assertAlmostEqual(p2p_prob_0, 0.8429378)
         self.assertAlmostEqual(n2p_prob_0, 1.)
@@ -189,10 +189,10 @@ def test_numerical_stability_mixing_rate_large(self):
         n2p_prob_1 = mitigation.n2p_prob_1
 
         # Convert types
-        p2p_prob_0 = p2p_prob_0.item()
-        n2p_prob_0 = n2p_prob_0.item()
-        p2p_prob_1 = p2p_prob_1.item()
-        n2p_prob_1 = n2p_prob_1.item()
+        p2p_prob_0 = p2p_prob_0
+        n2p_prob_0 = n2p_prob_0
+        p2p_prob_1 = p2p_prob_1
+        n2p_prob_1 = n2p_prob_1
 
         self.assertAlmostEqual(p2p_prob_0, 0.819513)
         self.assertAlmostEqual(n2p_prob_0, 1.)

From 9f195d6512ff105584c7e6cc902139062e07e464 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Fri, 13 Oct 2023 16:25:04 -0400
Subject: [PATCH 13/25] Turn off user warnings where possible in
 test_utils_proba.py. Warnings will still print during unit tests because
 higher-level API will not have the option to turn warnings off. This keeps
 the API cleaner.

---
 jurity/utils_proba.py     |  9 +++++----
 tests/test_utils_proba.py | 10 ++++------
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index f00bd21..9a0084d 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -735,7 +735,8 @@ def summarize(cls,
                   memberships: Union[List, np.ndarray, pd.Series, pd.DataFrame],
                   surrogates: Union[List, np.ndarray, pd.Series],
                   labels: Union[List, np.ndarray, pd.Series] = None,
-                  membership_names: List[str] = None) -> pd.DataFrame:
+                  membership_names: List[str] = None,
+                  warnings:bool = False) -> pd.DataFrame:
         """
         Return a summary dataframe suitable for bootstrap calculations.
         Arguments:
@@ -793,7 +794,7 @@ def summarize(cls,
             likes_df.columns = membership_names
             likes_df = likes_df.reset_index()
         summarizer = cls("surrogates", "surrogates", "predictions", true_name=label_name, test_names=test_names)
-        return summarizer.make_summary_data(perf_df=df, surrogate_df=likes_df)
+        return summarizer.make_summary_data(perf_df=df, surrogate_df=likes_df,warnings=warnings)
 
     def __init__(self, surrogate_surrogate_col_name: str,
                  surrogate_perf_col_name: str,
@@ -983,7 +984,7 @@ def check_surrogate_confusion_matrix(self, confusion_df, merged_df):
             # return False
         return True
 
-    def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame = None):
+    def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame = None,warnings=True):
         """
         Function that merges two dfs to make a surrogate-based summary file that includes confusion matrix ratios.
         Arguments:
@@ -994,7 +995,7 @@ def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame =
         self.check_surrogate_data(surrogate_df)
         merged_data = perf_df.merge(surrogate_df, left_on=self.surrogate_perf_col_name(),
                                     right_on=self.surrogate_surrogate_col_name())
-        self.check_merged_data(merged_data, perf_df)
+        self.check_merged_data(merged_data, perf_df,warnings)
 
         # Create accuracy columns that measure true positive, true negative etc
         accuracy_df = pd.concat([merged_data[self.surrogate_surrogate_col_name()],
diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index f64b747..6028c29 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -185,7 +185,7 @@ def test_make_bias_calculator_filter(self):
         """
         Test that make_bias_calculator filters rows with small counts
         """
-        bc_filtered = self.bcfd.get_bias_calculator(self.summarized_df, 7)
+        bc_filtered = self.bcfd.get_bias_calculator(self.summarized_df, 7,weight_warnings=False)
         self.assertEqual(bc_filtered.X().shape[0], 3)
 
     def test_make_bias_calculator_names(self):
@@ -219,17 +219,17 @@ def test_bias_maker_bad_data(self):
                                     [Constants.false_positive_ratio, Constants.true_positive_ratio,
                                      Constants.false_negative_ratio,
                                      Constants.true_negative_ratio])
-        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1)
+        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1,weight_warnings=False)
         fac = BiasCalcFromDataFrame(["W", "B", "O"], "N", [1, 2],
                                     [Constants.false_positive_ratio, Constants.true_positive_ratio,
                                      Constants.false_negative_ratio,
                                      Constants.true_negative_ratio, "hello world"])
-        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1)
+        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1, weight_warnings=False)
         fac = BiasCalcFromDataFrame(["W", "B", "O"], "hello world", [1, 2],
                                     [Constants.false_positive_ratio, Constants.true_positive_ratio,
                                      Constants.false_negative_ratio,
                                      Constants.true_negative_ratio])
-        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1)
+        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1,weight_warnings=False)
 
     def test_summary(self):
         predictions = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1]
@@ -605,8 +605,6 @@ def test_membership_as_df(self):
         results = get_bootstrap_results(self.test_data["prediction"], self.surrogate_df.set_index("surrogate"),
                                         self.test_data["surrogate"], [1, 2], self.test_data["label"])
 
-        print(results)
-
         self.assertTrue(isinstance(results, pd.DataFrame), "get_bootstrap_results does not return a Pandas DataFrame.")
         self.assertTrue(
             {Constants.FPR, Constants.FNR, Constants.TNR, Constants.TPR, Constants.ACC}.issubset(set(results.columns)),

From c05ae6ef32d4e12de558e75c8e570debcd7651a1 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Thu, 21 Dec 2023 12:30:58 -0500
Subject: [PATCH 14/25] Update to utils_proba.py

---
 jurity/utils_proba.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index e81251f..d35ee2e 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -58,9 +58,9 @@ def check_memberships_proba_df(memberships_df: pd.DataFrame, unique_surrogate_li
     if membership_names is None:
         membership_names = memberships_df.columns
     sum_to_one = pd.Series(memberships_df.sum(axis=1)).apply(lambda x: math.isclose(x, 1.0))
-    check_true(len(unique_surrogate_list) == memberships_df.shape[0],
+    check_true(len(unique_surrogate_list) <= memberships_df.shape[0],
                InputShapeError("", "Memberships dataframe must have one row per surrogate class."))
-    check_true(set(memberships_df.index.values) == unique_surrogate_list,
+    check_true(unique_surrogate_list.issubset(memberships_df.index.values),
                InputShapeError("", "Memberships dataframe must have an index with surrogate values"))
     check_true(memberships_df.shape[1] == len(membership_names),
                InputShapeError("", "Memberships dataframe must have one column per protected class name."))
@@ -681,7 +681,7 @@ def get_W_array(self, df: pd.DataFrame) -> np.ndarray:
             raise ValueError("weight name: {0} are not in dataframe.".format(self._weight_name))
         return df[self._weight_name].to_numpy(dtype='f')
 
-    def get_bias_calculator(self, df: pd.DataFrame, min_weight: int = 30, weight_warnings: bool = True):
+    def get_bias_calculator(self, df: pd.DataFrame, min_weight: int = 0, weight_warnings: bool = True):
         """
         Make bias calculator.
         Arguments:
@@ -689,10 +689,10 @@ def get_bias_calculator(self, df: pd.DataFrame, min_weight: int = 30, weight_war
             min_weight: surrogate classes that are smaller than this value will be dropped.
             weight_warnings: Whether to print warnings when too many rows are dropped from surrogate class matrix
         """
-        if min_weight < 10:
+        if min_weight < 5:
             if weight_warnings:
-                warnings.warn("Recommended minimum count for surrogate class is 30. "
-                              "Minimum weights of less than 10 will give unstable results.")
+                warnings.warn("Recommended minimum count for surrogate class is 5. "
+                              "Minimum weights of less than 5 will give unstable results.")
 
         if self.weight_name() in df.columns:
             subset = df[df[self._weight_name] >= min_weight]

From c2401d956f2f2b95c440f3ded6ee6e44795d3020 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Thu, 21 Dec 2023 13:15:12 -0500
Subject: [PATCH 15/25] Edit comments in simulator.

---
 tests/test_utils_proba.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index 187c3dc..332713c 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -397,7 +397,6 @@ def rng(self, v=None):
         return self._rng
 
     # For the simulation, build "True" protected groups based on population
-    # Note; The census data names the columns as follows:
     def assign_protected(self, population_data, generator,
                          membership_values=["W", "O", "B", "T", "A", "AI"]):
         # Passing in the global random number generator

From 721cd3e6b47f60d870539fd213e98395f2080338 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Dec 2023 12:56:30 -0500
Subject: [PATCH 16/25] Update minimum weight to 5 rows, according to results
 from simulation experiment with min counts per surrogate.

---
 jurity/utils_proba.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index 99ddb77..545beb2 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -681,7 +681,7 @@ def get_W_array(self, df: pd.DataFrame) -> np.ndarray:
             raise ValueError("weight name: {0} are not in dataframe.".format(self._weight_name))
         return df[self._weight_name].to_numpy(dtype='f')
 
-    def get_bias_calculator(self, df: pd.DataFrame, min_weight: int = 0, weight_warnings: bool = True):
+    def get_bias_calculator(self, df: pd.DataFrame, min_weight: int = 5, weight_warnings: bool = True):
         """
         Make bias calculator.
         Arguments:

From 13baf315ec779e154dc9189026887ab3cfd4dec9 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Dec 2023 12:57:08 -0500
Subject: [PATCH 17/25] Make simulation dataframe large enough so values are
 not unstable and unit tests do not fail.

---
 tests/test_utils_proba.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index f0a20c6..e1e4e3b 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -502,14 +502,14 @@ class TestWithSimulation(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
         input_df = pd.DataFrame({"surrogate": list(range(0, 99)),
-                                 "count": [83, 103, 96, 96, 102, 117, 95, 107, 106, 109, 92, 95, 105,
-                                           87, 114, 99, 99, 85, 119, 110, 97, 87, 123, 90, 90, 107,
-                                           85, 91, 111, 108, 89, 107, 91, 95, 119, 125, 86, 95, 121,
-                                           103, 99, 97, 88, 106, 96, 90, 101, 102, 99, 119, 102, 93,
-                                           105, 97, 100, 97, 88, 98, 93, 112, 91, 92, 93, 90, 109,
-                                           99, 98, 106, 115, 97, 110, 93, 85, 116, 92, 115, 88, 108,
-                                           106, 118, 114, 96, 97, 94, 96, 85, 96, 91, 101, 89, 97,
-                                           99, 86, 106, 112, 112, 114, 108, 104],
+                                 "count": [473, 516, 529, 497, 476, 529, 493, 497, 503, 490, 507, 514, 524,
+                                           485, 470, 513, 501, 505, 488, 510, 518, 501, 506, 484, 493, 504,
+                                           477, 537, 491, 535, 517, 472, 510, 478, 518, 449, 503, 503, 509,
+                                           537, 504, 533, 493, 482, 495, 497, 495, 465, 501, 512, 468, 470,
+                                           549, 510, 503, 524, 496, 526, 481, 478, 557, 487, 511, 493, 486,
+                                           517, 497, 517, 504, 472, 500, 493, 494, 504, 464, 543, 513, 486,
+                                           488, 485, 486, 480, 519, 494, 509, 501, 494, 515, 522, 500, 532,
+                                           512, 490, 486, 516, 495, 530, 542, 588],
                                  "W": [0.36137754, 0.83653862, 0.98303716, 0.52943704, 0.80254777,
                                        0.86131181, 0.78572192, 0.79557292, 0.94314381, 0.98431145,
                                        0.97623762, 0.93004508, 0.94375, 0.87960053, 0.9400488,
@@ -694,12 +694,12 @@ def test_bootstrap_ranges(self):
         # The variance-covariance matrix of a linear estimator based on input X is:
         invxTx = np.linalg.inv(np.dot(x.T, x))
 
-        pred_matrix = np.array([[1.0, 0.0, 0.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]])
+        pred_matrix=np.array([[1.0,0.0,0.0],[1.0,1.0,0.0],[1.0,0.0,1.0]])
         # The variance-covariance matrix  of a linear calculation based on a prediction matrix is as follows.
         # Only need the diagonal for this calculation
-        x_portion_variance = pd.Series(np.diag(np.dot(np.dot(pred_matrix, invxTx), pred_matrix.T)))
+        x_portion_variance = pd.Series(np.diag(np.dot(np.dot(pred_matrix, invxTx), pred_matrix.T)),
+                                       index = self.bc.all_class_labels())
         x_portion_variance.name = 'x_var'
-        x_portion_variance.index = self.bc.all_class_labels()
 
         # Get confusion matrix probabilities and variances from input rates_dict.
         in_vars_dict = {}

From 50ff34e5644a34de6b883fdbe0107ef07c664df3 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Dec 2023 14:44:08 -0500
Subject: [PATCH 18/25] Add simulation scripts and readme.md for probabilistic
 fairness.

---
 .../input_data/surrogate_inputs.csv           | 33185 ++++++++++++++++
 .../python_scripts/simulation.py              |    61 +
 .../simulation_compare_to_model.py            |   131 +
 .../python_scripts/simulation_counts.py       |   100 +
 examples/prob_fairness_accuracy/readme.md     |     6 +
 5 files changed, 33483 insertions(+)
 create mode 100644 examples/prob_fairness_accuracy/input_data/surrogate_inputs.csv
 create mode 100644 examples/prob_fairness_accuracy/python_scripts/simulation.py
 create mode 100644 examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
 create mode 100644 examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
 create mode 100644 examples/prob_fairness_accuracy/readme.md

diff --git a/examples/prob_fairness_accuracy/input_data/surrogate_inputs.csv b/examples/prob_fairness_accuracy/input_data/surrogate_inputs.csv
new file mode 100644
index 0000000..17bf18c
--- /dev/null
+++ b/examples/prob_fairness_accuracy/input_data/surrogate_inputs.csv
@@ -0,0 +1,33185 @@
+ZIP,protected,not_protected,bin
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diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation.py b/examples/prob_fairness_accuracy/python_scripts/simulation.py
new file mode 100644
index 0000000..def69be
--- /dev/null
+++ b/examples/prob_fairness_accuracy/python_scripts/simulation.py
@@ -0,0 +1,61 @@
+#Simulations
+import pandas as pd
+import numpy as np
+import math
+import sys
+sys.path.append('../../jurity/tests')
+sys.path.append('../../jurity/jurity')
+from jurity.fairness import BinaryFairnessMetrics as bfm
+
+from test_utils_proba import UtilsProbaSimulator
+testing_simulation=False
+n_runs=30
+avg_counts=[30,50]
+fair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2},'protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}},surrogate_name="ZIP")
+slightly_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.1, 'fnr': 0.35, 'fpr': 0.1}},surrogate_name="ZIP")
+moderately_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.3, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.1, 'fnr': 0.45, 'fpr': 0.1}},surrogate_name="ZIP")
+very_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.4, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.1}},surrogate_name="ZIP")
+extremely_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.5, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.05}},surrogate_name="ZIP")
+
+scenarios={"fair":fair_sim,
+           "slightly_unfair":slightly_unfair_sim,
+           "moderately_unfair":moderately_unfair_sim,
+           "very_unfair":very_unfair_sim,
+           "extremely_unfair":extremely_unfair_sim}
+surrogates=pd.read_csv('./supporting_data/surrogate_inputs.csv')
+if testing_simulation:
+    output_string = '~/Documents/data/jurity_tests/simulations/{0}_simulation_count_{1}_surrogates_{2}_test.csv'
+else:
+    output_string = '~/Documents/data/jurity_tests/simulations/{0}_simulation_count_{1}_surrogates_{2}.csv'
+
+def run_one_sim(simulator, membership_df,count_mean,rng=np.random.default_rng()):
+    membership_df["count"]=pd.Series(rng.poisson(lam=count_mean,size=membership_df.shape[0]))
+    test_data=simulator.explode_dataframe(membership_df)
+    oracle_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
+                                    (test_data["class"]=="protected").astype(int).values).rename(columns={"Value":"oracle_value"})
+    prob_metrics=bfm.get_all_scores(test_data["label"],test_data["prediction"],
+                   membership_df.set_index("ZIP")[["not_protected","protected"]],
+                   test_data["ZIP"],[1]).rename(columns={"Value":"probabilistic_estimate"})
+    #TODO: Add simulation for argmax
+    predicted_class=test_data[["not_protected","protected"]].values.tolist()
+    argmax_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
+                                     predicted_class).rename(columns={"Value":"argmax_estimate"})
+    return pd.concat([oracle_metrics["oracle_value"],prob_metrics["probabilistic_estimate"], argmax_metrics["argmax_estimate"]], axis=1)
+
+if __name__=="__main__":
+    n_surrogates=surrogates.shape[0]
+    for sim_label,simulator in scenarios.items():
+        for c in avg_counts:
+            all_results=[]
+            for i in range(0, n_runs):
+                if testing_simulation:
+                    output_df = run_one_sim(simulator, surrogates.head(10), c)
+                else:
+                    output_df = run_one_sim(simulator, surrogates, c)
+                output_df["run_id"] = i
+                all_results.append(output_df)
+            all_output=pd.concat(all_results)
+            all_output["average_count"] = c
+            all_output["simulation"] = sim_label
+            all_output["n_surrogates"] = n_surrogates
+            all_output[~(all_output["probabilistic_estimate"].apply(np.isnan))].to_csv(output_string.format(sim_label, c,n_surrogates))
\ No newline at end of file
diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py b/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
new file mode 100644
index 0000000..6cd2e00
--- /dev/null
+++ b/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
@@ -0,0 +1,131 @@
+#Simulations
+import pandas as pd
+import numpy as np
+import math
+import sys
+sys.path.append('../../jurity/tests')
+sys.path.append('../../jurity/jurity')
+from jurity.fairness import BinaryFairnessMetrics as bfm
+from constants import Constants
+from sklearn.metrics import confusion_matrix
+from test_utils_proba import UtilsProbaSimulator
+
+def performance_measures(ground_truth: np.ndarray,
+                         predictions: np.ndarray) -> dict:
+    """Compute various performance measures, optionally conditioned on protected attribute.
+    Assume that positive label is encoded as 1 and negative label as 0.
+
+    Parameters
+    ---------
+    ground_truth: np.ndarray
+        Ground truth labels (1/0).
+    predictions: np.ndarray
+        Predicted values.
+    group_idx: Union[np.ndarray, List]
+        Indices of the group to consider. Optional.
+    group_membership: bool
+        Restrict performance measures to members of a certain group.
+        If None, the whole population is used.
+        Default value is False.
+
+    Returns
+    ---------
+    Dictionary with performance measure identifiers as keys and their corresponding values.
+    """
+    tn, fp, fn, tp = confusion_matrix(ground_truth, predictions).ravel()
+
+    p = np.sum(ground_truth == 1)
+    n = np.sum(ground_truth == 0)
+
+    return {Constants.TPR: tp / p,
+            Constants.TNR: tn / n,
+            Constants.FPR: fp / n,
+            Constants.FNR: fn / p,
+            Constants.PPV: tp / (tp + fp) if (tp + fp) > 0.0 else Constants.float_null,
+            Constants.NPV: tn / (tn + fn) if (tn + fn) > 0.0 else Constants.float_null,
+            Constants.FDR: fp / (fp + tp) if (fp + tp) > 0.0 else Constants.float_null,
+            Constants.FOR: fn / (fn + tn) if (fn + tn) > 0.0 else Constants.float_null,
+            Constants.ACC: (tp + tn) / (p + n) if (p + n) > 0.0 else Constants.float_null}
+
+testing_simulation=False
+n_runs=30
+fair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2},'protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}},surrogate_name="ZIP")
+slightly_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.1, 'fnr': 0.35, 'fpr': 0.1}},surrogate_name="ZIP")
+moderately_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.3, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.1, 'fnr': 0.45, 'fpr': 0.1}},surrogate_name="ZIP")
+very_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.4, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.1}},surrogate_name="ZIP")
+extremely_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.5, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.05}},surrogate_name="ZIP")
+if testing_simulation:
+    scenarios = {"moderately_unfair": moderately_unfair_sim,
+           "very_unfair":very_unfair_sim}
+else:
+    scenarios={"fair":fair_sim,
+           "slightly_unfair":slightly_unfair_sim,
+           "moderately_unfair":moderately_unfair_sim,
+           "very_unfair":very_unfair_sim,
+           "extremely_unfair":extremely_unfair_sim}
+surrogates=pd.read_csv('./supporting_data/sampled_surrogate_inputs.csv')
+if testing_simulation:
+    prob_output_string = '~/Documents/data/jurity_tests/simulations//model_v_prob/{0}_prob_simulation_{1}_surrogates_{2}_count_test.csv'
+    model_output_string = '~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_model_simulation_{1}_surrogates_{2}_count_test.csv'
+else:
+    prob_output_string = '~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_prob_simulation_{1}_surrogates_{2}_count.csv'
+    model_output_string = '~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_model_simulation_{1}_surrogates_{2}_count.csv'
+
+def generate_test_data(simulator, membership_df,count_mean,rng=np.random.default_rng()):
+    membership_df["count"]=pd.Series(rng.poisson(lam=count_mean,size=membership_df.shape[0]))
+    return simulator.explode_dataframe(membership_df)
+
+def calc_prob_estimate(test_data,membership_df):
+    oracle_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
+                                    (test_data["class"]=="protected").astype(int).values).rename(columns={"Value":"oracle_value"})
+    prob_metrics=bfm.get_all_scores(test_data["label"],test_data["prediction"],
+                   membership_df.set_index("ZIP")[["not_protected","protected"]],
+                   test_data["ZIP"],[1]).rename(columns={"Value":"probabilistic_estimate"})
+    return pd.concat([oracle_metrics["oracle_value"],prob_metrics["probabilistic_estimate"]], axis=1)
+
+def calc_model_estimate(df,rng=np.random.default_rng()):
+    out_dfs=[]
+    for s in [[0.99, 0.99], [0.9, 0.99], [0.8, 0.9], [0.7, 0.8]]:
+        p_given_p = s[0]
+        np_given_np = s[1]
+        prediction_p=rng.choice([0,1],p=[1-p_given_p,p_given_p],size=df.shape[0])
+        prediction_np=rng.choice([0,1],p=[np_given_np,1-np_given_np],size=df.shape[0])
+        class_vec_p=(df["class"]=="protected").astype(int).values
+        class_vec_np=(df["class"]=="not_protected").astype(int).values
+        class_pred=np.multiply(class_vec_p,prediction_p)+np.multiply(class_vec_np,prediction_np)
+        scores=bfm.get_all_scores(df["label"].values,df["prediction"].values,class_pred).rename(columns={"Value":"model_estimate"})
+        scores["p_given_p"]=p_given_p
+        scores["np_given_np"]=np_given_np
+        class_model_performance=performance_measures(class_vec_p,class_pred)
+        scores["p_given_p"]=p_given_p
+        scores["np_given_np"]=np_given_np
+        scores["class_PPV"]=class_model_performance[Constants.PPV]
+        scores["class_NPV"]=class_model_performance[Constants.NPV]
+        scores["class_TPR"]=class_model_performance[Constants.TPR]
+        scores["class_BR"]=np.sum(class_vec_p)
+        out_dfs.append(scores.reset_index()[["Metric","model_estimate","class_PPV","class_NPV","class_TPR","p_given_p","np_given_np"]])
+    return pd.concat(out_dfs,axis=0)
+
+if __name__=="__main__":
+    n_surrogates=surrogates.shape[0]
+    generator=np.random.default_rng()
+    for sim_label,simulator in scenarios.items():
+        prob_results=[]
+        model_results=[]
+        for i in range(0, n_runs):
+            if testing_simulation:
+                test_df = generate_test_data(simulator, surrogates, 50, generator)
+            else:
+                test_df = generate_test_data(simulator, surrogates, 50, generator)
+            p=calc_prob_estimate(test_df,surrogates)
+            p["run_id"]=i
+            prob_results.append(p)
+            m=calc_model_estimate(test_df, generator)
+            m["run_id"]=i
+            model_results.append(m)
+        all_prob_results=pd.concat(prob_results,axis=0)
+        all_prob_results["simulation"]=sim_label
+        all_model_results=pd.concat(model_results,axis=0)
+        all_model_results["simulation"]=sim_label
+        all_prob_results.to_csv(prob_output_string.format(sim_label,50,surrogates.shape[0]))
+        all_model_results.to_csv(model_output_string.format(sim_label,50,surrogates.shape[0]),index=False)
\ No newline at end of file
diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py b/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
new file mode 100644
index 0000000..4382f5c
--- /dev/null
+++ b/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
@@ -0,0 +1,100 @@
+#Simulations
+import pandas as pd
+import numpy as np
+import math
+import sys
+sys.path.append('../../jurity/tests')
+sys.path.append('../../jurity/jurity')
+from jurity.fairness import BinaryFairnessMetrics as bfm
+from utils import performance_measures
+
+
+from test_utils_proba import UtilsProbaSimulator
+testing_simulation=False
+n_runs=30
+avg_counts=[5,10,20,30,40]
+num_surrogates=[50,100,300,400,500,1000]
+fair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2},'protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}},surrogate_name="ZIP")
+slightly_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.1, 'fnr': 0.35, 'fpr': 0.1}},surrogate_name="ZIP")
+moderately_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.3, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.1, 'fnr': 0.45, 'fpr': 0.1}},surrogate_name="ZIP")
+very_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.4, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.1}},surrogate_name="ZIP")
+extremely_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.5, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.05}},surrogate_name="ZIP")
+
+scenarios={"fair":fair_sim,
+           "slightly_unfair":slightly_unfair_sim,
+           "moderately_unfair":moderately_unfair_sim,
+           "very_unfair":very_unfair_sim,
+           "extremely_unfair":extremely_unfair_sim}
+surrogates=pd.read_csv('./supporting_data/surrogate_inputs.csv')
+surrogates["ZIP"]=surrogates["ZIP"].astype(int)
+if testing_simulation:
+    output_string = '~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/{0}_simulation_count_{1}_test_surrogates_{2}.csv'
+else:
+    output_string = '~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/{0}_simulation_count_{1}_surrogates_{2}.csv'
+
+def run_one_sim(test_data,membership_df):
+    #Sometimes the sub-sampling leads to data errors.
+    #Return a dataframe that is all nans in this case.
+    #Keep track--if there are too many of these, stop the simulation
+    global n_errors
+    try:
+        oracle_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
+                                    (test_data["class"]=="protected").astype(int).values).rename(columns={"Value":"oracle_value"})
+    except:
+        oracle_metrics=pd.DataFrame({"oracle_value":[np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]},
+            index=['Average Odds', 'Disparate Impact', 'Equal Opportunity',
+       'FNR difference', 'FOR difference', 'Generalized Entropy Index',
+       'Predictive Equality', 'Statistical Parity', 'Theil Index']
+        )
+        n_errors=n_errors+1
+    try:
+        prob_metrics=bfm.get_all_scores(test_data["label"],test_data["prediction"],
+                        membership_df.set_index("ZIP")[["not_protected","protected"]],
+                        test_data["ZIP"],[1]).rename(columns={"Value":"probabilistic_estimate"})
+    except:
+        prob_metrics=pd.DataFrame({"probabilistic_estimate": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]},
+                     index=['Average Odds', 'Disparate Impact', 'Equal Opportunity',
+                            'FNR difference', 'FOR difference', 'Generalized Entropy Index',
+                            'Predictive Equality', 'Statistical Parity', 'Theil Index']
+                     )
+        n_errors=n_errors+1
+    return pd.concat([oracle_metrics["oracle_value"],prob_metrics["probabilistic_estimate"]], axis=1)
+
+if __name__=="__main__":
+    n_errors=0
+    rng=np.random.default_rng()
+    for sim_label,simulator in scenarios.items():
+        for c in avg_counts:
+            surrogates["count"]=pd.Series(rng.poisson(lam=c,size=surrogates.shape[0]))
+            if testing_simulation:
+                test_data=simulator.explode_dataframe(surrogates.head(10))
+            else:
+                test_data=simulator.explode_dataframe(surrogates)
+            print("The number of rows in the data data is: ",test_data.shape)
+            for n_surrogates in num_surrogates:
+                all_results = []
+                for i in range(0, n_runs):
+                    #Sample surrogate classes from the dataframe
+                    #Take a sample stratified by p(protected) to get a spread
+                    #along the x axis for the regression
+                    if testing_simulation:
+                        sampled_surrogates=surrogates.head(10)["ZIP"].values
+                    else:
+                        sampled_surrogates=surrogates.groupby("bin").sample(frac=(n_surrogates/surrogates.shape[0]),
+                                                                            replace=True)["ZIP"].values
+                    #only feed sampled surrogate classes into simulation
+                    a=test_data["ZIP"].apply(lambda x: x in sampled_surrogates).values
+                    b=surrogates["ZIP"].apply(lambda x:x in sampled_surrogates).values
+                    input_data=test_data.iloc[a].copy(deep=True)
+                    input_surrogates=surrogates.iloc[b].copy(deep=True)
+                    output_df=run_one_sim(input_data,input_surrogates)
+                    if n_errors>30:
+                        print("Errors limit reached. Stopping simulation.")
+                        break
+                    output_df["run_id"] = i
+                    all_results.append(output_df)
+                all_output=pd.concat(all_results)
+                all_output["average_count"] = c
+                all_output["n_surrogates"] = n_surrogates
+                all_output["simulation"] = sim_label
+                all_output[~(all_output["probabilistic_estimate"].apply(np.isnan))].to_csv(output_string.format(sim_label, c,n_surrogates))
\ No newline at end of file
diff --git a/examples/prob_fairness_accuracy/readme.md b/examples/prob_fairness_accuracy/readme.md
new file mode 100644
index 0000000..51fe499
--- /dev/null
+++ b/examples/prob_fairness_accuracy/readme.md
@@ -0,0 +1,6 @@
+# Probabilistic Fairness Demonstration
+This folder contains the files necessary to reproduce the results in 
+
+
+ 
+

From 5971028492ead88ed59d32cc1b2ead0e373f5353 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Dec 2023 14:53:51 -0500
Subject: [PATCH 19/25] Update comments and readme.md

---
 .../simulation_compare_to_model.py              | 17 +++++++++++++++--
 .../python_scripts/simulation_counts.py         |  4 +---
 examples/prob_fairness_accuracy/readme.md       |  5 +----
 3 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py b/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
index 6cd2e00..900fd65 100644
--- a/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
+++ b/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
@@ -1,4 +1,10 @@
-#Simulations
+#Simulationed data: Model-based assignment to protected class vs probabilistic fairness
+# One of the claims in the paper is that model-based fairness metrics are biased,
+# and that the degree of bias is a function of the PPV (positive predictive value/precision)
+# and NPV (negative predctive value) of the models that predicts protected status.
+# This simulation demonstrates the difference between probabilistic estimates and
+# model-based estimates for a given input data file (located in ../input_data.surrogate_inputscsv)
+
 import pandas as pd
 import numpy as np
 import math
@@ -47,8 +53,14 @@ def performance_measures(ground_truth: np.ndarray,
             Constants.FOR: fn / (fn + tn) if (fn + tn) > 0.0 else Constants.float_null,
             Constants.ACC: (tp + tn) / (p + n) if (p + n) > 0.0 else Constants.float_null}
 
+#If true, only simulate a small dataframe. Used to test simulation syntax.
 testing_simulation=False
 n_runs=30
+
+# The test_utils_proba.py test file in jurity/tests contains a class called
+# UtilsProbaSimulator, which can simulate the confusion matrix from an unfair model for different classes.
+# Simulation is explained in :
+
 fair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2},'protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}},surrogate_name="ZIP")
 slightly_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.1, 'fnr': 0.35, 'fpr': 0.1}},surrogate_name="ZIP")
 moderately_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.3, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.1, 'fnr': 0.45, 'fpr': 0.1}},surrogate_name="ZIP")
@@ -63,7 +75,8 @@ def performance_measures(ground_truth: np.ndarray,
            "moderately_unfair":moderately_unfair_sim,
            "very_unfair":very_unfair_sim,
            "extremely_unfair":extremely_unfair_sim}
-surrogates=pd.read_csv('./supporting_data/sampled_surrogate_inputs.csv')
+#Location of input and output files
+surrogates=pd.read_csv('../input_data/sampled_surrogate_inputs.csv')
 if testing_simulation:
     prob_output_string = '~/Documents/data/jurity_tests/simulations//model_v_prob/{0}_prob_simulation_{1}_surrogates_{2}_count_test.csv'
     model_output_string = '~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_model_simulation_{1}_surrogates_{2}_count_test.csv'
diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py b/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
index 4382f5c..fd9af07 100644
--- a/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
+++ b/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
@@ -1,4 +1,4 @@
-#Simulations
+#Simulation inspecting probabilistic fairness performance for different sample sizes.
 import pandas as pd
 import numpy as np
 import math
@@ -6,8 +6,6 @@
 sys.path.append('../../jurity/tests')
 sys.path.append('../../jurity/jurity')
 from jurity.fairness import BinaryFairnessMetrics as bfm
-from utils import performance_measures
-
 
 from test_utils_proba import UtilsProbaSimulator
 testing_simulation=False
diff --git a/examples/prob_fairness_accuracy/readme.md b/examples/prob_fairness_accuracy/readme.md
index 51fe499..83da6e3 100644
--- a/examples/prob_fairness_accuracy/readme.md
+++ b/examples/prob_fairness_accuracy/readme.md
@@ -1,6 +1,3 @@
 # Probabilistic Fairness Demonstration
-This folder contains the files necessary to reproduce the results in 
-
-
- 
+This folder contains the files necessary to reproduce the results in
 

From 83f1782b1edb3e3ad24786ba44f2ca96cc3d1cfc Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Tue, 26 Dec 2023 16:26:04 -0500
Subject: [PATCH 20/25] Add descriptions and citations to readme

---
 .../simulation_compare_to_model.py            |  4 +-
 examples/prob_fairness_accuracy/readme.md     | 58 ++++++++++++++++++-
 2 files changed, 59 insertions(+), 3 deletions(-)

diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py b/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
index 900fd65..6ed51f4 100644
--- a/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
+++ b/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
@@ -9,8 +9,8 @@
 import numpy as np
 import math
 import sys
-sys.path.append('../../jurity/tests')
-sys.path.append('../../jurity/jurity')
+sys.path.append('../../tests')
+sys.path.append('../../jurity')
 from jurity.fairness import BinaryFairnessMetrics as bfm
 from constants import Constants
 from sklearn.metrics import confusion_matrix
diff --git a/examples/prob_fairness_accuracy/readme.md b/examples/prob_fairness_accuracy/readme.md
index 83da6e3..bd7bbfd 100644
--- a/examples/prob_fairness_accuracy/readme.md
+++ b/examples/prob_fairness_accuracy/readme.md
@@ -1,3 +1,59 @@
 # Probabilistic Fairness Demonstration
-This folder contains the files necessary to reproduce the results in
+This folder contains the files necessary to reproduce the results from 
+academic papers demonstrating the accuracy of probabilistic 
+fairness under different sample sizes. Probabilistic fairness is a technique 
+unique to jurity that allows users to calculate fairness metrics when protected status is 
+unknown but a <i>surrogate class</i> is available. A surrogate class is 
+an alternative feature that divides the population into groups and where the probability 
+of protected class membership is known given the surrogate class.
 
+Probabilistic fairness, its accuracy, and the simulation method used in
+these demonstrations are detailed in 
+<a href="https://doi.org/10.1007/978-3-031-44505-7_29">"
+Surrogate Membership for Inferred Metrics in Fairness Evaluation"</a>
+
+## simulation.py
+Demonstrates the accuracy of the probabilistic fairness method, 
+showing that the method gives values that are close to the oracle metrics
+that would be calculated if protected status were known. 
+
+## simulation_compare_to_model.py
+One alternative method for calculating fairness metrics when protected 
+status is unknown is to build a predictive model for protected status, assign 
+individuals to groups based on model results, and then calculate fairness 
+metrics as if protected status were known. This script demonstrates that
+fairness metrics calculated in this way are biased, where the degree of the 
+bias is based on the PPV (positive predictive value/precision) and NPV
+(negative predictive value) of the model for protected status. 
+
+## simulation_counts.py
+The performance of probabilistic fairness metrics is related to the number of 
+individuals per surrogate class and the number of surrogate classes available. 
+This simulation examines performance under different sample size scenarios. 
+
+## Citation 
+If you use this analysis in an article, please cite as:
+```
+@inproceedings{DBLP:conf/lion/ThielbarKZPD23,
+  author       = {Melinda Thielbar and
+                  Serdar Kadioglu and
+                  Chenhui Zhang and
+                  Rick Pack and
+                  Lukas Dannull},
+  editor       = {Meinolf Sellmann and
+                  Kevin Tierney},
+  title        = {Surrogate Membership for Inferred Metrics in Fairness Evaluation},
+  booktitle    = {Learning and Intelligent Optimization - 17th International Conference,
+                  {LION} 17, Nice, France, June 4-8, 2023, Revised Selected Papers},
+  series       = {Lecture Notes in Computer Science},
+  volume       = {14286},
+  pages        = {424--442},
+  publisher    = {Springer},
+  year         = {2023},
+  url          = {https://doi.org/10.1007/978-3-031-44505-7_29},
+  doi          = {10.1007/978-3-031-44505-7\_29},
+  timestamp    = {Thu, 09 Nov 2023 21:13:04 +0100},
+  biburl       = {https://dblp.org/rec/conf/lion/ThielbarKZPD23.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+```
\ No newline at end of file

From 377756aa24fd5b5c09041cfe2f9e29822b97ce60 Mon Sep 17 00:00:00 2001
From: mfthielb <Melinda.Thielbar@fmr.com>
Date: Wed, 27 Dec 2023 11:31:45 -0500
Subject: [PATCH 21/25] Add input data for simulations and supporting notebooks
 to create output charts from results.

---
 .../input_data/sampled_surrogate_inputs.csv   | 3589 ++++++++++++
 .../input_data/surrogate_inputs.csv           |    0
 .../notebooks/analyze_prob_vs_model.ipynb     | 1909 ++++++
 .../notebooks/analyze_sample_size_sim.ipynb   | 5209 +++++++++++++++++
 .../python_scripts/simulation.py              |   11 +-
 .../simulation_compare_to_model.py            |    0
 .../python_scripts/simulation_counts.py       |    6 +-
 .../readme.md                                 |    6 +-
 8 files changed, 10719 insertions(+), 11 deletions(-)
 create mode 100644 examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv
 rename examples/{prob_fairness_accuracy => probabilistic_fairness}/input_data/surrogate_inputs.csv (100%)
 create mode 100644 examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb
 create mode 100644 examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb
 rename examples/{prob_fairness_accuracy => probabilistic_fairness}/python_scripts/simulation.py (91%)
 rename examples/{prob_fairness_accuracy => probabilistic_fairness}/python_scripts/simulation_compare_to_model.py (100%)
 rename examples/{prob_fairness_accuracy => probabilistic_fairness}/python_scripts/simulation_counts.py (95%)
 rename examples/{prob_fairness_accuracy => probabilistic_fairness}/readme.md (92%)

diff --git a/examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv b/examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv
new file mode 100644
index 0000000..e02b960
--- /dev/null
+++ b/examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv
@@ -0,0 +1,3589 @@
+,ZIP,protected,not_protected,bin
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diff --git a/examples/prob_fairness_accuracy/input_data/surrogate_inputs.csv b/examples/probabilistic_fairness/input_data/surrogate_inputs.csv
similarity index 100%
rename from examples/prob_fairness_accuracy/input_data/surrogate_inputs.csv
rename to examples/probabilistic_fairness/input_data/surrogate_inputs.csv
diff --git a/examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb b/examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb
new file mode 100644
index 0000000..657efdb
--- /dev/null
+++ b/examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb
@@ -0,0 +1,1909 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3cd360a9-69b1-4016-898c-6f548f5b4df3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "090b4807-2ef5-4155-93c4-9249100c4c8d",
+   "metadata": {},
+   "source": [
+    "# Prepare Data From Simulation Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e20fdd5f-0a5d-4115-bb3c-c7d86f69d9e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2400, 9)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metric</th>\n",
+       "      <th>model_estimate</th>\n",
+       "      <th>class_PPV</th>\n",
+       "      <th>class_NPV</th>\n",
+       "      <th>class_TPR</th>\n",
+       "      <th>p_given_p</th>\n",
+       "      <th>np_given_np</th>\n",
+       "      <th>run_id</th>\n",
+       "      <th>simulation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Average Odds</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.919137</td>\n",
+       "      <td>0.998937</td>\n",
+       "      <td>0.990681</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Equal Opportunity</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.919137</td>\n",
+       "      <td>0.998937</td>\n",
+       "      <td>0.990681</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Predictive Equality</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.919137</td>\n",
+       "      <td>0.998937</td>\n",
+       "      <td>0.990681</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Statistical Parity</td>\n",
+       "      <td>-0.000</td>\n",
+       "      <td>0.919137</td>\n",
+       "      <td>0.998937</td>\n",
+       "      <td>0.990681</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Average Odds</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.910217</td>\n",
+       "      <td>0.988489</td>\n",
+       "      <td>0.898043</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Metric  model_estimate  class_PPV  class_NPV  class_TPR  \\\n",
+       "0         Average Odds           0.004   0.919137   0.998937   0.990681   \n",
+       "2    Equal Opportunity           0.008   0.919137   0.998937   0.990681   \n",
+       "6  Predictive Equality           0.001   0.919137   0.998937   0.990681   \n",
+       "7   Statistical Parity          -0.000   0.919137   0.998937   0.990681   \n",
+       "9         Average Odds           0.006   0.910217   0.988489   0.898043   \n",
+       "\n",
+       "   p_given_p  np_given_np  run_id simulation  \n",
+       "0       0.99         0.99       0       fair  \n",
+       "2       0.99         0.99       0       fair  \n",
+       "6       0.99         0.99       0       fair  \n",
+       "7       0.99         0.99       0       fair  \n",
+       "9       0.90         0.99       0       fair  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Prepare Data From Model That Classifies Prob estimates\n",
+    "input_str='~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_model_simulation_{1}_surrogates_{2}_count.csv'\n",
+    "sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
+    "color_map={\"fair\":\"0.9\",\"slightly_unfair\":\"0.8\",\"moderately_unfair\":\"0.45\",\"very_unfair\":\"0.3\",\"extremely_unfair\":\"0\"}\n",
+    "all_simulations=[]\n",
+    "avg_counts=[50]\n",
+    "num_surrogates=[3588]\n",
+    "for name in sim_names:\n",
+    "    for c in avg_counts:\n",
+    "        for n_surrogates in num_surrogates:\n",
+    "            all_simulations.append(pd.read_csv(input_str.format(name,c,n_surrogates)))\n",
+    "simulations_model=pd.concat(all_simulations)\n",
+    "\n",
+    "#Reduce to only simulations where probabilistic fairness is implemented\n",
+    "simulations_model=simulations_model[simulations_model[\"Metric\"].apply(lambda x: x in ['Average Odds', 'Equal Opportunity', 'Predictive Equality','Statistical Parity'])]\n",
+    "print(simulations_model.shape)\n",
+    "simulations_model.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a1b167b1-c8d8-4197-b955-439f3285cdef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.99, 0.9 , 0.8 , 0.7 ])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulations_model[\"p_given_p\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbbd0760-7dad-4ac3-b3cf-7c590c0ea209",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.99, 0.9 , 0.8 ])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulations_model[\"np_given_np\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e7a6f901-2119-4aea-8ea5-cd110d91ac8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(600, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metric</th>\n",
+       "      <th>oracle_value</th>\n",
+       "      <th>probabilistic_estimate</th>\n",
+       "      <th>run_id</th>\n",
+       "      <th>simulation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Average Odds</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Equal Opportunity</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Predictive Equality</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Statistical Parity</td>\n",
+       "      <td>-0.000</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Average Odds</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>-0.005</td>\n",
+       "      <td>1</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Metric  oracle_value  probabilistic_estimate  run_id  \\\n",
+       "0         Average Odds         0.004                   0.010       0   \n",
+       "2    Equal Opportunity         0.008                   0.011       0   \n",
+       "6  Predictive Equality         0.001                   0.008       0   \n",
+       "7   Statistical Parity        -0.000                   0.003       0   \n",
+       "9         Average Odds        -0.001                  -0.005       1   \n",
+       "\n",
+       "  simulation  \n",
+       "0       fair  \n",
+       "2       fair  \n",
+       "6       fair  \n",
+       "7       fair  \n",
+       "9       fair  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Prepare Data Calculated from Probabilistic Estimates\n",
+    "input_str='~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_prob_simulation_{1}_surrogates_{2}_count.csv'\n",
+    "sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
+    "color_map={\"fair\":\"0.9\",\"slightly_unfair\":\"0.8\",\"moderately_unfair\":\"0.45\",\"very_unfair\":\"0.3\",\"extremely_unfair\":\"0\"}\n",
+    "all_simulations=[]\n",
+    "avg_counts=[50]\n",
+    "num_surrogates=[3588]\n",
+    "for name in sim_names:\n",
+    "    for c in avg_counts:\n",
+    "        for n_surrogates in num_surrogates:\n",
+    "            all_simulations.append(pd.read_csv(input_str.format(name,c,n_surrogates)))\n",
+    "simulations_prob=pd.concat(all_simulations)\n",
+    "simulations_prob=simulations_prob[simulations_prob[\"Metric\"].apply(lambda x: x in ['Average Odds', 'Equal Opportunity', 'Predictive Equality','Statistical Parity'])]\n",
+    "print(simulations_prob.shape)\n",
+    "simulations_prob.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "96947486-2fcc-4846-a0a8-3d9d0249aac8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Merge Model-Based Estimates with Probabilistic Estimates\n",
+    "simulations_raw=simulations_model.merge(simulations_prob,left_on=[\"Metric\",\"simulation\"],right_on=[\"Metric\",\"simulation\"])\n",
+    "simulations_raw[\"npv_plus_ppv_minus_1\"]=simulations_raw[\"class_PPV\"]+simulations_raw[\"class_NPV\"]-1\n",
+    "simulations_raw[\"adjusted_SP\"]=simulations_raw[\"npv_plus_ppv_minus_1\"]*simulations_raw[\"oracle_value\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ea3027f4-013c-418d-ae67-515799eb1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>model_estimate</th>\n",
+       "      <th>class_PPV</th>\n",
+       "      <th>class_NPV</th>\n",
+       "      <th>class_TPR</th>\n",
+       "      <th>p_given_p</th>\n",
+       "      <th>np_given_np</th>\n",
+       "      <th>run_id_x</th>\n",
+       "      <th>oracle_value</th>\n",
+       "      <th>probabilistic_estimate</th>\n",
+       "      <th>run_id_y</th>\n",
+       "      <th>npv_plus_ppv_minus_1</th>\n",
+       "      <th>adjusted_SP</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Equal Opportunity</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Predictive Equality</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>3600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       model_estimate  class_PPV  class_NPV  \\\n",
+       "Metric              simulation                                                \n",
+       "Average Odds        extremely_unfair             3600       3600       3600   \n",
+       "                    fair                         3600       3600       3600   \n",
+       "                    moderately_unfair            3600       3600       3600   \n",
+       "                    slightly_unfair              3600       3600       3600   \n",
+       "                    very_unfair                  3600       3600       3600   \n",
+       "Equal Opportunity   extremely_unfair             3600       3600       3600   \n",
+       "                    fair                         3600       3600       3600   \n",
+       "                    moderately_unfair            3600       3600       3600   \n",
+       "                    slightly_unfair              3600       3600       3600   \n",
+       "                    very_unfair                  3600       3600       3600   \n",
+       "Predictive Equality extremely_unfair             3600       3600       3600   \n",
+       "                    fair                         3600       3600       3600   \n",
+       "                    moderately_unfair            3600       3600       3600   \n",
+       "                    slightly_unfair              3600       3600       3600   \n",
+       "                    very_unfair                  3600       3600       3600   \n",
+       "Statistical Parity  extremely_unfair             3600       3600       3600   \n",
+       "                    fair                         3600       3600       3600   \n",
+       "                    moderately_unfair            3600       3600       3600   \n",
+       "                    slightly_unfair              3600       3600       3600   \n",
+       "                    very_unfair                  3600       3600       3600   \n",
+       "\n",
+       "                                       class_TPR  p_given_p  np_given_np  \\\n",
+       "Metric              simulation                                             \n",
+       "Average Odds        extremely_unfair        3600       3600         3600   \n",
+       "                    fair                    3600       3600         3600   \n",
+       "                    moderately_unfair       3600       3600         3600   \n",
+       "                    slightly_unfair         3600       3600         3600   \n",
+       "                    very_unfair             3600       3600         3600   \n",
+       "Equal Opportunity   extremely_unfair        3600       3600         3600   \n",
+       "                    fair                    3600       3600         3600   \n",
+       "                    moderately_unfair       3600       3600         3600   \n",
+       "                    slightly_unfair         3600       3600         3600   \n",
+       "                    very_unfair             3600       3600         3600   \n",
+       "Predictive Equality extremely_unfair        3600       3600         3600   \n",
+       "                    fair                    3600       3600         3600   \n",
+       "                    moderately_unfair       3600       3600         3600   \n",
+       "                    slightly_unfair         3600       3600         3600   \n",
+       "                    very_unfair             3600       3600         3600   \n",
+       "Statistical Parity  extremely_unfair        3600       3600         3600   \n",
+       "                    fair                    3600       3600         3600   \n",
+       "                    moderately_unfair       3600       3600         3600   \n",
+       "                    slightly_unfair         3600       3600         3600   \n",
+       "                    very_unfair             3600       3600         3600   \n",
+       "\n",
+       "                                       run_id_x  oracle_value  \\\n",
+       "Metric              simulation                                  \n",
+       "Average Odds        extremely_unfair       3600          3600   \n",
+       "                    fair                   3600          3600   \n",
+       "                    moderately_unfair      3600          3600   \n",
+       "                    slightly_unfair        3600          3600   \n",
+       "                    very_unfair            3600          3600   \n",
+       "Equal Opportunity   extremely_unfair       3600          3600   \n",
+       "                    fair                   3600          3600   \n",
+       "                    moderately_unfair      3600          3600   \n",
+       "                    slightly_unfair        3600          3600   \n",
+       "                    very_unfair            3600          3600   \n",
+       "Predictive Equality extremely_unfair       3600          3600   \n",
+       "                    fair                   3600          3600   \n",
+       "                    moderately_unfair      3600          3600   \n",
+       "                    slightly_unfair        3600          3600   \n",
+       "                    very_unfair            3600          3600   \n",
+       "Statistical Parity  extremely_unfair       3600          3600   \n",
+       "                    fair                   3600          3600   \n",
+       "                    moderately_unfair      3600          3600   \n",
+       "                    slightly_unfair        3600          3600   \n",
+       "                    very_unfair            3600          3600   \n",
+       "\n",
+       "                                       probabilistic_estimate  run_id_y  \\\n",
+       "Metric              simulation                                            \n",
+       "Average Odds        extremely_unfair                     3600      3600   \n",
+       "                    fair                                 3600      3600   \n",
+       "                    moderately_unfair                    3600      3600   \n",
+       "                    slightly_unfair                      3600      3600   \n",
+       "                    very_unfair                          3600      3600   \n",
+       "Equal Opportunity   extremely_unfair                     3600      3600   \n",
+       "                    fair                                 3600      3600   \n",
+       "                    moderately_unfair                    3600      3600   \n",
+       "                    slightly_unfair                      3600      3600   \n",
+       "                    very_unfair                          3600      3600   \n",
+       "Predictive Equality extremely_unfair                     3600      3600   \n",
+       "                    fair                                 3600      3600   \n",
+       "                    moderately_unfair                    3600      3600   \n",
+       "                    slightly_unfair                      3600      3600   \n",
+       "                    very_unfair                          3600      3600   \n",
+       "Statistical Parity  extremely_unfair                     3600      3600   \n",
+       "                    fair                                 3600      3600   \n",
+       "                    moderately_unfair                    3600      3600   \n",
+       "                    slightly_unfair                      3600      3600   \n",
+       "                    very_unfair                          3600      3600   \n",
+       "\n",
+       "                                       npv_plus_ppv_minus_1  adjusted_SP  \n",
+       "Metric              simulation                                            \n",
+       "Average Odds        extremely_unfair                   3600         3600  \n",
+       "                    fair                               3600         3600  \n",
+       "                    moderately_unfair                  3600         3600  \n",
+       "                    slightly_unfair                    3600         3600  \n",
+       "                    very_unfair                        3600         3600  \n",
+       "Equal Opportunity   extremely_unfair                   3600         3600  \n",
+       "                    fair                               3600         3600  \n",
+       "                    moderately_unfair                  3600         3600  \n",
+       "                    slightly_unfair                    3600         3600  \n",
+       "                    very_unfair                        3600         3600  \n",
+       "Predictive Equality extremely_unfair                   3600         3600  \n",
+       "                    fair                               3600         3600  \n",
+       "                    moderately_unfair                  3600         3600  \n",
+       "                    slightly_unfair                    3600         3600  \n",
+       "                    very_unfair                        3600         3600  \n",
+       "Statistical Parity  extremely_unfair                   3600         3600  \n",
+       "                    fair                               3600         3600  \n",
+       "                    moderately_unfair                  3600         3600  \n",
+       "                    slightly_unfair                    3600         3600  \n",
+       "                    very_unfair                        3600         3600  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulations_raw.groupby([\"Metric\",\"simulation\"]).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5f6600dc-55e5-41c8-ad92-c2b5a50518c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"8\" halign=\"left\">oracle_value</th>\n",
+       "      <th colspan=\"5\" halign=\"left\">probabilistic_estimate</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">model_estimate</th>\n",
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+       "      <th>mean</th>\n",
+       "      <th>...</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.349467</td>\n",
+       "      <td>0.005609</td>\n",
+       "      <td>-0.364</td>\n",
+       "      <td>-0.354</td>\n",
+       "      <td>-0.3490</td>\n",
+       "      <td>-0.345</td>\n",
+       "      <td>-0.339</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.350800</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.339</td>\n",
+       "      <td>-0.314</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.160408</td>\n",
+       "      <td>0.097317</td>\n",
+       "      <td>-0.275</td>\n",
+       "      <td>-0.25700</td>\n",
+       "      <td>-0.1675</td>\n",
+       "      <td>-0.07200</td>\n",
+       "      <td>-0.040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000167</td>\n",
+       "      <td>0.003348</td>\n",
+       "      <td>-0.007</td>\n",
+       "      <td>-0.002</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000133</td>\n",
+       "      <td>0.002970</td>\n",
+       "      <td>-0.008</td>\n",
+       "      <td>-0.00200</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00200</td>\n",
+       "      <td>0.006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.277800</td>\n",
+       "      <td>0.005161</td>\n",
+       "      <td>-0.288</td>\n",
+       "      <td>-0.282</td>\n",
+       "      <td>-0.2790</td>\n",
+       "      <td>-0.273</td>\n",
+       "      <td>-0.268</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.279167</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.272</td>\n",
+       "      <td>-0.245</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.149900</td>\n",
+       "      <td>0.082401</td>\n",
+       "      <td>-0.244</td>\n",
+       "      <td>-0.23000</td>\n",
+       "      <td>-0.1590</td>\n",
+       "      <td>-0.07575</td>\n",
+       "      <td>-0.041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.172633</td>\n",
+       "      <td>0.005283</td>\n",
+       "      <td>-0.185</td>\n",
+       "      <td>-0.176</td>\n",
+       "      <td>-0.1735</td>\n",
+       "      <td>-0.170</td>\n",
+       "      <td>-0.162</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.171800</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.163</td>\n",
+       "      <td>-0.152</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.096858</td>\n",
+       "      <td>0.052830</td>\n",
+       "      <td>-0.159</td>\n",
+       "      <td>-0.14925</td>\n",
+       "      <td>-0.1020</td>\n",
+       "      <td>-0.04850</td>\n",
+       "      <td>-0.026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.375833</td>\n",
+       "      <td>0.005646</td>\n",
+       "      <td>-0.387</td>\n",
+       "      <td>-0.380</td>\n",
+       "      <td>-0.3760</td>\n",
+       "      <td>-0.371</td>\n",
+       "      <td>-0.365</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.375967</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.366</td>\n",
+       "      <td>-0.345</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.185983</td>\n",
+       "      <td>0.109442</td>\n",
+       "      <td>-0.306</td>\n",
+       "      <td>-0.29325</td>\n",
+       "      <td>-0.1965</td>\n",
+       "      <td>-0.08675</td>\n",
+       "      <td>-0.048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Equal Opportunity</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.548933</td>\n",
+       "      <td>0.011327</td>\n",
+       "      <td>-0.581</td>\n",
+       "      <td>-0.554</td>\n",
+       "      <td>-0.5485</td>\n",
+       "      <td>-0.544</td>\n",
+       "      <td>-0.522</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.551967</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.529</td>\n",
+       "      <td>-0.479</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.215625</td>\n",
+       "      <td>0.157611</td>\n",
+       "      <td>-0.405</td>\n",
+       "      <td>-0.37125</td>\n",
+       "      <td>-0.2205</td>\n",
+       "      <td>-0.06650</td>\n",
+       "      <td>-0.030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>0.005353</td>\n",
+       "      <td>-0.010</td>\n",
+       "      <td>-0.004</td>\n",
+       "      <td>-0.0010</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>0.000433</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.024</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>0.000083</td>\n",
+       "      <td>0.005028</td>\n",
+       "      <td>-0.013</td>\n",
+       "      <td>-0.00300</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>0.00400</td>\n",
+       "      <td>0.011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.355000</td>\n",
+       "      <td>0.009421</td>\n",
+       "      <td>-0.371</td>\n",
+       "      <td>-0.362</td>\n",
+       "      <td>-0.3575</td>\n",
+       "      <td>-0.347</td>\n",
+       "      <td>-0.333</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.357433</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.344</td>\n",
+       "      <td>-0.294</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.167317</td>\n",
+       "      <td>0.110271</td>\n",
+       "      <td>-0.297</td>\n",
+       "      <td>-0.27700</td>\n",
+       "      <td>-0.1715</td>\n",
+       "      <td>-0.06175</td>\n",
+       "      <td>-0.029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.246267</td>\n",
+       "      <td>0.009765</td>\n",
+       "      <td>-0.270</td>\n",
+       "      <td>-0.253</td>\n",
+       "      <td>-0.2455</td>\n",
+       "      <td>-0.241</td>\n",
+       "      <td>-0.225</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.245333</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.225</td>\n",
+       "      <td>-0.207</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.130208</td>\n",
+       "      <td>0.077511</td>\n",
+       "      <td>-0.222</td>\n",
+       "      <td>-0.20800</td>\n",
+       "      <td>-0.1345</td>\n",
+       "      <td>-0.05600</td>\n",
+       "      <td>-0.025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.551433</td>\n",
+       "      <td>0.010110</td>\n",
+       "      <td>-0.572</td>\n",
+       "      <td>-0.560</td>\n",
+       "      <td>-0.5515</td>\n",
+       "      <td>-0.543</td>\n",
+       "      <td>-0.533</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.550833</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.532</td>\n",
+       "      <td>-0.488</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.235967</td>\n",
+       "      <td>0.166514</td>\n",
+       "      <td>-0.424</td>\n",
+       "      <td>-0.39925</td>\n",
+       "      <td>-0.2435</td>\n",
+       "      <td>-0.07775</td>\n",
+       "      <td>-0.037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Predictive Equality</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.150167</td>\n",
+       "      <td>0.002340</td>\n",
+       "      <td>-0.157</td>\n",
+       "      <td>-0.151</td>\n",
+       "      <td>-0.1500</td>\n",
+       "      <td>-0.148</td>\n",
+       "      <td>-0.146</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.149633</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.147</td>\n",
+       "      <td>-0.144</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.105125</td>\n",
+       "      <td>0.037968</td>\n",
+       "      <td>-0.149</td>\n",
+       "      <td>-0.14100</td>\n",
+       "      <td>-0.1140</td>\n",
+       "      <td>-0.07475</td>\n",
+       "      <td>-0.049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000467</td>\n",
+       "      <td>0.003344</td>\n",
+       "      <td>-0.010</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000367</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000375</td>\n",
+       "      <td>0.003020</td>\n",
+       "      <td>-0.011</td>\n",
+       "      <td>-0.00200</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>0.00100</td>\n",
+       "      <td>0.008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.200433</td>\n",
+       "      <td>0.002975</td>\n",
+       "      <td>-0.206</td>\n",
+       "      <td>-0.203</td>\n",
+       "      <td>-0.2000</td>\n",
+       "      <td>-0.198</td>\n",
+       "      <td>-0.194</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.200833</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.197</td>\n",
+       "      <td>-0.194</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.132433</td>\n",
+       "      <td>0.055068</td>\n",
+       "      <td>-0.193</td>\n",
+       "      <td>-0.18500</td>\n",
+       "      <td>-0.1430</td>\n",
+       "      <td>-0.08700</td>\n",
+       "      <td>-0.053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.098900</td>\n",
+       "      <td>0.002427</td>\n",
+       "      <td>-0.103</td>\n",
+       "      <td>-0.101</td>\n",
+       "      <td>-0.0990</td>\n",
+       "      <td>-0.098</td>\n",
+       "      <td>-0.094</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.098467</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.096</td>\n",
+       "      <td>-0.093</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.063608</td>\n",
+       "      <td>0.028217</td>\n",
+       "      <td>-0.097</td>\n",
+       "      <td>-0.09100</td>\n",
+       "      <td>-0.0685</td>\n",
+       "      <td>-0.03900</td>\n",
+       "      <td>-0.019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.200267</td>\n",
+       "      <td>0.002323</td>\n",
+       "      <td>-0.206</td>\n",
+       "      <td>-0.202</td>\n",
+       "      <td>-0.2000</td>\n",
+       "      <td>-0.199</td>\n",
+       "      <td>-0.194</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.201200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.196</td>\n",
+       "      <td>-0.191</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.135958</td>\n",
+       "      <td>0.053284</td>\n",
+       "      <td>-0.193</td>\n",
+       "      <td>-0.18700</td>\n",
+       "      <td>-0.1455</td>\n",
+       "      <td>-0.09475</td>\n",
+       "      <td>-0.054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.470000</td>\n",
+       "      <td>0.001880</td>\n",
+       "      <td>-0.476</td>\n",
+       "      <td>-0.471</td>\n",
+       "      <td>-0.4700</td>\n",
+       "      <td>-0.469</td>\n",
+       "      <td>-0.467</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.469767</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.466</td>\n",
+       "      <td>-0.459</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.294858</td>\n",
+       "      <td>0.136223</td>\n",
+       "      <td>-0.436</td>\n",
+       "      <td>-0.42625</td>\n",
+       "      <td>-0.3195</td>\n",
+       "      <td>-0.18500</td>\n",
+       "      <td>-0.108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000200</td>\n",
+       "      <td>0.004045</td>\n",
+       "      <td>-0.010</td>\n",
+       "      <td>-0.002</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000133</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.000350</td>\n",
+       "      <td>0.003172</td>\n",
+       "      <td>-0.010</td>\n",
+       "      <td>-0.00200</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00125</td>\n",
+       "      <td>0.010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.335467</td>\n",
+       "      <td>0.003223</td>\n",
+       "      <td>-0.344</td>\n",
+       "      <td>-0.338</td>\n",
+       "      <td>-0.3350</td>\n",
+       "      <td>-0.333</td>\n",
+       "      <td>-0.330</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.335900</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.331</td>\n",
+       "      <td>-0.324</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.210500</td>\n",
+       "      <td>0.097240</td>\n",
+       "      <td>-0.316</td>\n",
+       "      <td>-0.30400</td>\n",
+       "      <td>-0.2270</td>\n",
+       "      <td>-0.12950</td>\n",
+       "      <td>-0.077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.184167</td>\n",
+       "      <td>0.002570</td>\n",
+       "      <td>-0.189</td>\n",
+       "      <td>-0.186</td>\n",
+       "      <td>-0.1840</td>\n",
+       "      <td>-0.183</td>\n",
+       "      <td>-0.177</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.183167</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.180</td>\n",
+       "      <td>-0.170</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.115558</td>\n",
+       "      <td>0.053522</td>\n",
+       "      <td>-0.174</td>\n",
+       "      <td>-0.16700</td>\n",
+       "      <td>-0.1245</td>\n",
+       "      <td>-0.07000</td>\n",
+       "      <td>-0.039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.415233</td>\n",
+       "      <td>0.002837</td>\n",
+       "      <td>-0.420</td>\n",
+       "      <td>-0.418</td>\n",
+       "      <td>-0.4150</td>\n",
+       "      <td>-0.413</td>\n",
+       "      <td>-0.410</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.415833</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.412</td>\n",
+       "      <td>-0.399</td>\n",
+       "      <td>3600.0</td>\n",
+       "      <td>-0.260383</td>\n",
+       "      <td>0.120781</td>\n",
+       "      <td>-0.386</td>\n",
+       "      <td>-0.37700</td>\n",
+       "      <td>-0.2810</td>\n",
+       "      <td>-0.16325</td>\n",
+       "      <td>-0.095</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      oracle_value                             \\\n",
+       "                                             count      mean       std    min   \n",
+       "Metric              simulation                                                  \n",
+       "Average Odds        extremely_unfair        3600.0 -0.349467  0.005609 -0.364   \n",
+       "                    fair                    3600.0 -0.000167  0.003348 -0.007   \n",
+       "                    moderately_unfair       3600.0 -0.277800  0.005161 -0.288   \n",
+       "                    slightly_unfair         3600.0 -0.172633  0.005283 -0.185   \n",
+       "                    very_unfair             3600.0 -0.375833  0.005646 -0.387   \n",
+       "Equal Opportunity   extremely_unfair        3600.0 -0.548933  0.011327 -0.581   \n",
+       "                    fair                    3600.0  0.000133  0.005353 -0.010   \n",
+       "                    moderately_unfair       3600.0 -0.355000  0.009421 -0.371   \n",
+       "                    slightly_unfair         3600.0 -0.246267  0.009765 -0.270   \n",
+       "                    very_unfair             3600.0 -0.551433  0.010110 -0.572   \n",
+       "Predictive Equality extremely_unfair        3600.0 -0.150167  0.002340 -0.157   \n",
+       "                    fair                    3600.0 -0.000467  0.003344 -0.010   \n",
+       "                    moderately_unfair       3600.0 -0.200433  0.002975 -0.206   \n",
+       "                    slightly_unfair         3600.0 -0.098900  0.002427 -0.103   \n",
+       "                    very_unfair             3600.0 -0.200267  0.002323 -0.206   \n",
+       "Statistical Parity  extremely_unfair        3600.0 -0.470000  0.001880 -0.476   \n",
+       "                    fair                    3600.0 -0.000200  0.004045 -0.010   \n",
+       "                    moderately_unfair       3600.0 -0.335467  0.003223 -0.344   \n",
+       "                    slightly_unfair         3600.0 -0.184167  0.002570 -0.189   \n",
+       "                    very_unfair             3600.0 -0.415233  0.002837 -0.420   \n",
+       "\n",
+       "                                                                    \\\n",
+       "                                         25%     50%    75%    max   \n",
+       "Metric              simulation                                       \n",
+       "Average Odds        extremely_unfair  -0.354 -0.3490 -0.345 -0.339   \n",
+       "                    fair              -0.002  0.0000  0.003  0.006   \n",
+       "                    moderately_unfair -0.282 -0.2790 -0.273 -0.268   \n",
+       "                    slightly_unfair   -0.176 -0.1735 -0.170 -0.162   \n",
+       "                    very_unfair       -0.380 -0.3760 -0.371 -0.365   \n",
+       "Equal Opportunity   extremely_unfair  -0.554 -0.5485 -0.544 -0.522   \n",
+       "                    fair              -0.004 -0.0010  0.005  0.009   \n",
+       "                    moderately_unfair -0.362 -0.3575 -0.347 -0.333   \n",
+       "                    slightly_unfair   -0.253 -0.2455 -0.241 -0.225   \n",
+       "                    very_unfair       -0.560 -0.5515 -0.543 -0.533   \n",
+       "Predictive Equality extremely_unfair  -0.151 -0.1500 -0.148 -0.146   \n",
+       "                    fair              -0.001  0.0000  0.002  0.005   \n",
+       "                    moderately_unfair -0.203 -0.2000 -0.198 -0.194   \n",
+       "                    slightly_unfair   -0.101 -0.0990 -0.098 -0.094   \n",
+       "                    very_unfair       -0.202 -0.2000 -0.199 -0.194   \n",
+       "Statistical Parity  extremely_unfair  -0.471 -0.4700 -0.469 -0.467   \n",
+       "                    fair              -0.002  0.0000  0.002  0.009   \n",
+       "                    moderately_unfair -0.338 -0.3350 -0.333 -0.330   \n",
+       "                    slightly_unfair   -0.186 -0.1840 -0.183 -0.177   \n",
+       "                    very_unfair       -0.418 -0.4150 -0.413 -0.410   \n",
+       "\n",
+       "                                      probabilistic_estimate            ...  \\\n",
+       "                                                       count      mean  ...   \n",
+       "Metric              simulation                                          ...   \n",
+       "Average Odds        extremely_unfair                  3600.0 -0.350800  ...   \n",
+       "                    fair                              3600.0  0.000100  ...   \n",
+       "                    moderately_unfair                 3600.0 -0.279167  ...   \n",
+       "                    slightly_unfair                   3600.0 -0.171800  ...   \n",
+       "                    very_unfair                       3600.0 -0.375967  ...   \n",
+       "Equal Opportunity   extremely_unfair                  3600.0 -0.551967  ...   \n",
+       "                    fair                              3600.0  0.000433  ...   \n",
+       "                    moderately_unfair                 3600.0 -0.357433  ...   \n",
+       "                    slightly_unfair                   3600.0 -0.245333  ...   \n",
+       "                    very_unfair                       3600.0 -0.550833  ...   \n",
+       "Predictive Equality extremely_unfair                  3600.0 -0.149633  ...   \n",
+       "                    fair                              3600.0 -0.000367  ...   \n",
+       "                    moderately_unfair                 3600.0 -0.200833  ...   \n",
+       "                    slightly_unfair                   3600.0 -0.098467  ...   \n",
+       "                    very_unfair                       3600.0 -0.201200  ...   \n",
+       "Statistical Parity  extremely_unfair                  3600.0 -0.469767  ...   \n",
+       "                    fair                              3600.0 -0.000133  ...   \n",
+       "                    moderately_unfair                 3600.0 -0.335900  ...   \n",
+       "                    slightly_unfair                   3600.0 -0.183167  ...   \n",
+       "                    very_unfair                       3600.0 -0.415833  ...   \n",
+       "\n",
+       "                                                    model_estimate            \\\n",
+       "                                         75%    max          count      mean   \n",
+       "Metric              simulation                                                 \n",
+       "Average Odds        extremely_unfair  -0.339 -0.314         3600.0 -0.160408   \n",
+       "                    fair               0.006  0.015         3600.0 -0.000133   \n",
+       "                    moderately_unfair -0.272 -0.245         3600.0 -0.149900   \n",
+       "                    slightly_unfair   -0.163 -0.152         3600.0 -0.096858   \n",
+       "                    very_unfair       -0.366 -0.345         3600.0 -0.185983   \n",
+       "Equal Opportunity   extremely_unfair  -0.529 -0.479         3600.0 -0.215625   \n",
+       "                    fair               0.007  0.024         3600.0  0.000083   \n",
+       "                    moderately_unfair -0.344 -0.294         3600.0 -0.167317   \n",
+       "                    slightly_unfair   -0.225 -0.207         3600.0 -0.130208   \n",
+       "                    very_unfair       -0.532 -0.488         3600.0 -0.235967   \n",
+       "Predictive Equality extremely_unfair  -0.147 -0.144         3600.0 -0.105125   \n",
+       "                    fair               0.006  0.012         3600.0 -0.000375   \n",
+       "                    moderately_unfair -0.197 -0.194         3600.0 -0.132433   \n",
+       "                    slightly_unfair   -0.096 -0.093         3600.0 -0.063608   \n",
+       "                    very_unfair       -0.196 -0.191         3600.0 -0.135958   \n",
+       "Statistical Parity  extremely_unfair  -0.466 -0.459         3600.0 -0.294858   \n",
+       "                    fair               0.004  0.015         3600.0 -0.000350   \n",
+       "                    moderately_unfair -0.331 -0.324         3600.0 -0.210500   \n",
+       "                    slightly_unfair   -0.180 -0.170         3600.0 -0.115558   \n",
+       "                    very_unfair       -0.412 -0.399         3600.0 -0.260383   \n",
+       "\n",
+       "                                                                         \\\n",
+       "                                            std    min      25%     50%   \n",
+       "Metric              simulation                                            \n",
+       "Average Odds        extremely_unfair   0.097317 -0.275 -0.25700 -0.1675   \n",
+       "                    fair               0.002970 -0.008 -0.00200  0.0000   \n",
+       "                    moderately_unfair  0.082401 -0.244 -0.23000 -0.1590   \n",
+       "                    slightly_unfair    0.052830 -0.159 -0.14925 -0.1020   \n",
+       "                    very_unfair        0.109442 -0.306 -0.29325 -0.1965   \n",
+       "Equal Opportunity   extremely_unfair   0.157611 -0.405 -0.37125 -0.2205   \n",
+       "                    fair               0.005028 -0.013 -0.00300 -0.0000   \n",
+       "                    moderately_unfair  0.110271 -0.297 -0.27700 -0.1715   \n",
+       "                    slightly_unfair    0.077511 -0.222 -0.20800 -0.1345   \n",
+       "                    very_unfair        0.166514 -0.424 -0.39925 -0.2435   \n",
+       "Predictive Equality extremely_unfair   0.037968 -0.149 -0.14100 -0.1140   \n",
+       "                    fair               0.003020 -0.011 -0.00200 -0.0000   \n",
+       "                    moderately_unfair  0.055068 -0.193 -0.18500 -0.1430   \n",
+       "                    slightly_unfair    0.028217 -0.097 -0.09100 -0.0685   \n",
+       "                    very_unfair        0.053284 -0.193 -0.18700 -0.1455   \n",
+       "Statistical Parity  extremely_unfair   0.136223 -0.436 -0.42625 -0.3195   \n",
+       "                    fair               0.003172 -0.010 -0.00200  0.0000   \n",
+       "                    moderately_unfair  0.097240 -0.316 -0.30400 -0.2270   \n",
+       "                    slightly_unfair    0.053522 -0.174 -0.16700 -0.1245   \n",
+       "                    very_unfair        0.120781 -0.386 -0.37700 -0.2810   \n",
+       "\n",
+       "                                                       \n",
+       "                                           75%    max  \n",
+       "Metric              simulation                         \n",
+       "Average Odds        extremely_unfair  -0.07200 -0.040  \n",
+       "                    fair               0.00200  0.006  \n",
+       "                    moderately_unfair -0.07575 -0.041  \n",
+       "                    slightly_unfair   -0.04850 -0.026  \n",
+       "                    very_unfair       -0.08675 -0.048  \n",
+       "Equal Opportunity   extremely_unfair  -0.06650 -0.030  \n",
+       "                    fair               0.00400  0.011  \n",
+       "                    moderately_unfair -0.06175 -0.029  \n",
+       "                    slightly_unfair   -0.05600 -0.025  \n",
+       "                    very_unfair       -0.07775 -0.037  \n",
+       "Predictive Equality extremely_unfair  -0.07475 -0.049  \n",
+       "                    fair               0.00100  0.008  \n",
+       "                    moderately_unfair -0.08700 -0.053  \n",
+       "                    slightly_unfair   -0.03900 -0.019  \n",
+       "                    very_unfair       -0.09475 -0.054  \n",
+       "Statistical Parity  extremely_unfair  -0.18500 -0.108  \n",
+       "                    fair               0.00125  0.010  \n",
+       "                    moderately_unfair -0.12950 -0.077  \n",
+       "                    slightly_unfair   -0.07000 -0.039  \n",
+       "                    very_unfair       -0.16325 -0.095  \n",
+       "\n",
+       "[20 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulations_raw.groupby([\"Metric\",\"simulation\"])[[\"oracle_value\",\"probabilistic_estimate\",\"model_estimate\"]].describe().head(n=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "46e4edc7-d5d0-452f-8b45-4ac9860a7854",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 0 NaN Values\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"There are {0} NaN Values\".format(simulations_raw[\"oracle_value\"].apply(np.isnan).sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4c89492c-9970-4b54-8c13-0dc639287402",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Review How Many Values Per Combination of PPV and NPV\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "p_given_p  np_given_np\n",
+       "0.70       0.80           600\n",
+       "0.80       0.90           600\n",
+       "0.90       0.99           600\n",
+       "0.99       0.99           600\n",
+       "Name: model_estimate, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Review How Many Values Per Combination of PPV and NPV\")\n",
+    "simulations_model.groupby([\"p_given_p\",\"np_given_np\"])[\"model_estimate\"].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "329fff0c-0686-4624-a5e4-9db92fb76e60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "32fa8397-8f3e-4b2a-877f-793018fcc53c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_one_metric(df,stat_name,\n",
+    "                    compare_to=\"probabilistic_estimate\",\n",
+    "                    x_axis=\"oracle_value\",\n",
+    "                    color_map=color_map,\n",
+    "                    sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"],\n",
+    "                   markers={\"fair\":\".\",\"slightly_unfair\":\"v\",\"moderately_unfair\":\"s\",\"very_unfair\":\"*\",\"extremely_unfair\":\"X\"},\n",
+    "                   x_label=None,y_label=None,title=None):\n",
+    "    subset=df[df[\"Metric\"]==stat_name]\n",
+    "    if stat_name==\"Statistical Parity\":\n",
+    "        lowest=-0.51\n",
+    "        highest=0.025\n",
+    "    else:\n",
+    "        lowest=np.minimum(subset[x_axis].min(),subset[compare_to].min())\n",
+    "        highest=np.maximum(subset[x_axis].max(),subset[compare_to].max())\n",
+    "    plt.axline([lowest,lowest],[highest,highest],linewidth=1.5,c='0.0')\n",
+    "    for name in sim_names:\n",
+    "        o=subset[subset[\"simulation\"]==name][x_axis]\n",
+    "        p=subset[subset[\"simulation\"]==name][compare_to]\n",
+    "        plt.scatter(o,\n",
+    "                    p,\n",
+    "                    c=color_map[name],label=name.replace(\"_\",\" \").title(),marker=markers[name])\n",
+    "    if x_label is None:\n",
+    "        x_label=x_axis\n",
+    "    if y_label is None:\n",
+    "        y_label=compare_to\n",
+    "    if title is None:\n",
+    "        title=stat_name\n",
+    "    plt.title(title)\n",
+    "    plt.ylabel(y_label)\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.legend(title=\"Simulation Setting\")\n",
+    "    plt.savefig(f\"{stat_name}_{compare_to}_{x_axis}.png\".lower().replace(\" \",\"_\"),dpi=300)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a221fe78-1d04-42b3-a809-7a3d987ef60e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_model_estimate(df,stat_name,\n",
+    "                    compare_to=\"model_estimate\",\n",
+    "                    x_axis=\"oracle_value\",\n",
+    "                    color_map=color_map,\n",
+    "                    sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"],\n",
+    "                   markers={\"fair\":\".\",\"slightly_unfair\":\"v\",\"moderately_unfair\":\"s\",\"very_unfair\":\"*\",\"extremely_unfair\":\"X\"},\n",
+    "                   x_label=None,y_label=None,title=None):\n",
+    "    subset=df[df[\"Metric\"]==stat_name]\n",
+    "    if stat_name==\"Statistical Parity\":\n",
+    "        lowest=-0.51\n",
+    "        highest=0.025\n",
+    "    else:\n",
+    "        lowest=np.minimum(subset[x_axis].min(),subset[compare_to].min())\n",
+    "        highest=np.maximum(subset[x_axis].max(),subset[compare_to].max())\n",
+    "    linestyles=[':','--','-.','-']\n",
+    "    l=0\n",
+    "    for ppv in subset[\"p_given_p\"].unique():\n",
+    "        for npv in subset[\"np_given_np\"].unique(): \n",
+    "            fit=subset[(subset[\"p_given_p\"]==ppv) & (subset[\"np_given_np\"]==npv)]\n",
+    "            if fit.shape[0]>0:\n",
+    "                oracle=fit[\"oracle_value\"].values\n",
+    "                model=fit[\"adjusted_SP\"].values\n",
+    "                v=fit[\"class_PPV\"].values[0]+fit[\"class_NPV\"].values[0]\n",
+    "                plt.plot(oracle,model,linestyles[l],label=\"PPV+NPV={:0.2f}\".format(v),c='0.4')\n",
+    "                l=l+1\n",
+    "    plt.axline([lowest,lowest],[highest,highest],linewidth=2,c='0.0',label='45 degrees')\n",
+    "    for name in sim_names:\n",
+    "        o=subset[subset[\"simulation\"]==name][x_axis]\n",
+    "        p=subset[subset[\"simulation\"]==name][compare_to]\n",
+    "        plt.scatter(o,\n",
+    "                    p,\n",
+    "                    c=color_map[name],marker=markers[name])\n",
+    "    if x_label is None:\n",
+    "        x_label=x_axis\n",
+    "    if y_label is None:\n",
+    "        y_label=compare_to\n",
+    "    if title is None:\n",
+    "        title=stat_name\n",
+    "    plt.title(title)\n",
+    "    plt.ylabel(y_label)\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.legend(title=\"Protected Classifier Performance\")\n",
+    "    plt.savefig(f\"{stat_name}_{compare_to}_{x_axis}.png\".lower().replace(\" \",\"_\"),dpi=300)    \n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3c85e48f-7f4a-4d00-a283-772b8a2c3195",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'fair': (0.0, 0.0, 0.0, 1.0),\n",
+       " 'slightly_unfair': (0.5009794328681196,\n",
+       "  0.015809545348320027,\n",
+       "  0.9999810273487268,\n",
+       "  1.0),\n",
+       " 'moderately_unfair': (0.7084919084320762, 0.12647636278656021, 0.0, 1.0),\n",
+       " 'very_unfair': (0.8677218312746247, 0.4268577244046407, 0.0, 1.0),\n",
+       " 'extremely_unfair': (1.0, 1.0, 0.0, 1.0)}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#other options: YlGnBu,cubehelix,gnuplot\n",
+    "cmap = plt.get_cmap('gnuplot')\n",
+    "snames=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
+    "indicies = np.linspace(0, cmap.N, len(snames))\n",
+    "my_colors = {s:cmap(int(i)) for s,i in zip(snames,indicies)}\n",
+    "my_colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "48064533-3ffb-4071-9fd4-8a5f50ae13a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_17354/3098052042.py:30: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
+      "  plt.scatter(o,\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_model_estimate(simulations_raw,\"Statistical Parity\",\n",
+    "                    compare_to=\"model_estimate\",title=\"Statistical Parity: Model-Based Estimate\",\n",
+    "                    color_map=my_colors,\n",
+    "                   x_label=\"Oracle Value\",\n",
+    "                   y_label=\"Model Estimate\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "008b6784-8c1c-4420-bbb6-b48a88c31c8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_17354/1680928571.py:19: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
+      "  plt.scatter(o,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_one_metric(simulations_prob,\"Statistical Parity\",compare_to=\"probabilistic_estimate\",\n",
+    "                title=\"Statistical Parity: Inferred Metric\",\n",
+    "                color_map=my_colors,\n",
+    "               x_label=\"Oracle Value\",\n",
+    "               y_label=\"Inferred Metric\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4f054cf0-edd4-4ba1-af73-b31b65351444",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sum_squares_error' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m mse\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43msum_squares_error\u001b[49m)\u001b[38;5;241m.\u001b[39mmerge(sum_of_squares_oracle,left_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, right_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mouter\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mmerge(df,left_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,right_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mouter\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m mse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39mmse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m/\u001b[39m(mse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m-\u001b[39mmse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m      3\u001b[0m mse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroot_mean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39mmse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(np\u001b[38;5;241m.\u001b[39msqrt)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sum_squares_error' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "mse=pd.DataFrame(sum_squares_error).merge(sum_of_squares_oracle,left_index=True, right_index=True,how=\"outer\").merge(df,left_index=True,right_index=True,how=\"outer\")\n",
+    "mse[\"mean_squared_error\"]=mse[\"sum\"]/(mse[\"count\"]-mse[\"df\"])\n",
+    "mse[\"root_mean_squared_error\"]=mse[\"mean_squared_error\"].apply(np.sqrt)\n",
+    "mse[\"cd\"]=mse[\"sum\"]/mse[\"oracle_sum_of_squares\"]\n",
+    "\n",
+    "mse=mse.reset_index()\n",
+    "mse\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc5fb386-0b1f-45ff-b12d-fb3d206efbb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standard_deviations=standard_deviations.reset_index()\n",
+    "simulations=simulations.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5aacbd5-2512-439e-8064-29855b81a735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mse[\"cd\"].hist()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb b/examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb
new file mode 100644
index 0000000..4579d0d
--- /dev/null
+++ b/examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb
@@ -0,0 +1,5209 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3cd360a9-69b1-4016-898c-6f548f5b4df3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "090b4807-2ef5-4155-93c4-9249100c4c8d",
+   "metadata": {},
+   "source": [
+    "# Review Count Results for Sample Size Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e20fdd5f-0a5d-4115-bb3c-c7d86f69d9e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(17760, 7)\n",
+      "Pandas Dataframe of Raw Simulation Output\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metric</th>\n",
+       "      <th>oracle_value</th>\n",
+       "      <th>probabilistic_estimate</th>\n",
+       "      <th>run_id</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>simulation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Average Odds</td>\n",
+       "      <td>-0.136</td>\n",
+       "      <td>-0.380</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>50</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Equal Opportunity</td>\n",
+       "      <td>-0.259</td>\n",
+       "      <td>-0.730</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>50</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Predictive Equality</td>\n",
+       "      <td>-0.013</td>\n",
+       "      <td>-0.030</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>50</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Statistical Parity</td>\n",
+       "      <td>-0.112</td>\n",
+       "      <td>-0.213</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>50</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Average Odds</td>\n",
+       "      <td>-0.016</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>50</td>\n",
+       "      <td>fair</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Metric  oracle_value  probabilistic_estimate  run_id  \\\n",
+       "0         Average Odds        -0.136                  -0.380       0   \n",
+       "1    Equal Opportunity        -0.259                  -0.730       0   \n",
+       "3  Predictive Equality        -0.013                  -0.030       0   \n",
+       "4   Statistical Parity        -0.112                  -0.213       0   \n",
+       "5         Average Odds        -0.016                   0.089       1   \n",
+       "\n",
+       "   average_count  n_surrogates simulation  \n",
+       "0              5            50       fair  \n",
+       "1              5            50       fair  \n",
+       "3              5            50       fair  \n",
+       "4              5            50       fair  \n",
+       "5              5            50       fair  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_str='~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/{0}_simulation_count_{1}_surrogates_{2}.csv'\n",
+    "sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
+    "color_map={\"fair\":\"0.9\",\"slightly_unfair\":\"0.8\",\"moderately_unfair\":\"0.45\",\"very_unfair\":\"0.3\",\"extremely_unfair\":\"0\"}\n",
+    "all_simulations=[]\n",
+    "avg_counts=[5,10,20,30,40]\n",
+    "num_surrogates=[50,100,300,400,500,1000]\n",
+    "for name in sim_names:\n",
+    "    for c in avg_counts:\n",
+    "        for n_surrogates in num_surrogates:\n",
+    "            all_simulations.append(pd.read_csv(input_str.format(name,c,n_surrogates)))\n",
+    "simulations_raw=pd.concat(all_simulations).drop(columns=[\"Unnamed: 0\"])\n",
+    "simulations_raw=simulations_raw[simulations_raw[\"Metric\"].apply(lambda x: x in ['Average Odds', 'Equal Opportunity', 'Predictive Equality','Statistical Parity'])]\n",
+    "print(simulations_raw.shape)\n",
+    "print('Pandas Dataframe of Raw Simulation Output')\n",
+    "simulations_raw.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "46e4edc7-d5d0-452f-8b45-4ac9860a7854",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For Some Simulations, Samples are So Small There Are No Results\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"For Some Simulations, Samples are So Small There Are No Results\")\n",
+    "simulations_raw[\"oracle_value\"].apply(np.isnan).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8248bc59-186e-4707-9c1a-e3e6576b3b5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For Very Small Samples, Oracle Metric Might Require Dividing by 0, Also Resulting in NaN\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"For Very Small Samples, Oracle Metric Might Require Dividing by 0, Also Resulting in NaN\")\n",
+    "simulations_raw[simulations_raw[\"oracle_value\"].apply(np.isnan)][\"probabilistic_estimate\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6dfe3ff1-0f99-4e13-8230-8ba3e1b22b2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Oracle Values for Each Simulation Setting\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Metric               simulation       \n",
+       "Average Odds         extremely_unfair    -0.352455\n",
+       "                     fair                 0.001236\n",
+       "                     moderately_unfair   -0.274491\n",
+       "                     slightly_unfair     -0.176544\n",
+       "                     very_unfair         -0.375599\n",
+       "Equal Opportunity    extremely_unfair    -0.553894\n",
+       "                     fair                 0.002168\n",
+       "                     moderately_unfair   -0.350130\n",
+       "                     slightly_unfair     -0.253854\n",
+       "                     very_unfair         -0.554526\n",
+       "Predictive Equality  extremely_unfair    -0.150985\n",
+       "                     fair                 0.000279\n",
+       "                     moderately_unfair   -0.198871\n",
+       "                     slightly_unfair     -0.099238\n",
+       "                     very_unfair         -0.196687\n",
+       "Statistical Parity   extremely_unfair    -0.470866\n",
+       "                     fair                 0.001082\n",
+       "                     moderately_unfair   -0.333723\n",
+       "                     slightly_unfair     -0.184912\n",
+       "                     very_unfair         -0.413228\n",
+       "Name: oracle_mean, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Oracle Values for Each Simulation Setting\")\n",
+    "means=simulations_raw.groupby([\"Metric\",\"simulation\"])[\"oracle_value\"].mean()\n",
+    "means.name=\"oracle_mean\"\n",
+    "means\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5a6afc59-80f2-4968-b79a-274897e30eb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Analyzing Studentized Residuals: Requires Calculating Standard Deviations\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>var</th>\n",
+       "      <th>std</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
+       "      <th>100</th>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.004033</td>\n",
+       "      <td>0.063509</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.002782</td>\n",
+       "      <td>0.052742</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.003538</td>\n",
+       "      <td>0.059485</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.002028</td>\n",
+       "      <td>0.045033</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
+       "      <th>100</th>\n",
+       "      <td>0.000387</td>\n",
+       "      <td>0.019664</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.000161</td>\n",
+       "      <td>0.012682</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.000097</td>\n",
+       "      <td>0.009836</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.000056</td>\n",
+       "      <td>0.007464</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>592 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                     var  \\\n",
+       "Metric             simulation       average_count n_surrogates             \n",
+       "Average Odds       extremely_unfair 5             100           0.017607   \n",
+       "                                                  300           0.004033   \n",
+       "                                                  400           0.002782   \n",
+       "                                                  500           0.003538   \n",
+       "                                                  1000          0.002028   \n",
+       "...                                                                  ...   \n",
+       "Statistical Parity very_unfair      40            100           0.000387   \n",
+       "                                                  300           0.000161   \n",
+       "                                                  400           0.000097   \n",
+       "                                                  500           0.000056   \n",
+       "                                                  1000          0.000028   \n",
+       "\n",
+       "                                                                     std  \\\n",
+       "Metric             simulation       average_count n_surrogates             \n",
+       "Average Odds       extremely_unfair 5             100           0.132693   \n",
+       "                                                  300           0.063509   \n",
+       "                                                  400           0.052742   \n",
+       "                                                  500           0.059485   \n",
+       "                                                  1000          0.045033   \n",
+       "...                                                                  ...   \n",
+       "Statistical Parity very_unfair      40            100           0.019664   \n",
+       "                                                  300           0.012682   \n",
+       "                                                  400           0.009836   \n",
+       "                                                  500           0.007464   \n",
+       "                                                  1000          0.005270   \n",
+       "\n",
+       "                                                                count  \n",
+       "Metric             simulation       average_count n_surrogates         \n",
+       "Average Odds       extremely_unfair 5             100              30  \n",
+       "                                                  300              30  \n",
+       "                                                  400              30  \n",
+       "                                                  500              30  \n",
+       "                                                  1000             30  \n",
+       "...                                                               ...  \n",
+       "Statistical Parity very_unfair      40            100              30  \n",
+       "                                                  300              30  \n",
+       "                                                  400              30  \n",
+       "                                                  500              30  \n",
+       "                                                  1000             30  \n",
+       "\n",
+       "[592 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Analyzing Studentized Residuals: Requires Calculating Standard Deviations\")\n",
+    "oracle_variance=simulations_raw.groupby([\"Metric\",\"simulation\",\"average_count\",\"n_surrogates\"])[\"oracle_value\"].agg(['var','std','count'])\n",
+    "oracle_variance.name=\"oracle_var\"\n",
+    "oracle_variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f787eeb3-938b-47ae-8b42-0410789dabe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Review When Oracle Fails to Return a Value\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>var</th>\n",
+       "      <th>std</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Average Odds</th>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <th>5</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.026856</td>\n",
+       "      <td>0.163878</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equal Opportunity</th>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <th>5</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.103019</td>\n",
+       "      <td>0.320966</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predictive Equality</th>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <th>5</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.003498</td>\n",
+       "      <td>0.059145</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                     var  \\\n",
+       "Metric              simulation      average_count n_surrogates             \n",
+       "Average Odds        slightly_unfair 5             50            0.026856   \n",
+       "Equal Opportunity   slightly_unfair 5             50            0.103019   \n",
+       "Predictive Equality slightly_unfair 5             50            0.003498   \n",
+       "\n",
+       "                                                                     std  \\\n",
+       "Metric              simulation      average_count n_surrogates             \n",
+       "Average Odds        slightly_unfair 5             50            0.163878   \n",
+       "Equal Opportunity   slightly_unfair 5             50            0.320966   \n",
+       "Predictive Equality slightly_unfair 5             50            0.059145   \n",
+       "\n",
+       "                                                                count  \n",
+       "Metric              simulation      average_count n_surrogates         \n",
+       "Average Odds        slightly_unfair 5             50               26  \n",
+       "Equal Opportunity   slightly_unfair 5             50               26  \n",
+       "Predictive Equality slightly_unfair 5             50               26  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Review When Oracle Fails to Return a Value\")\n",
+    "oracle_variance[oracle_variance[\"count\"]<30]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e4ca83ee-0216-4310-a9d9-dd979f3555a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Standard Errors for Metrics Requires Degrees of Freedom\n",
+      "Degrees of Freedom is the Number of Parameters Needed to Calculate the Metric\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>df</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Average Odds</th>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equal Opportunity</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predictive Equality</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Statistical Parity</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     df\n",
+       "Metric                 \n",
+       "Average Odds         16\n",
+       "Equal Opportunity     8\n",
+       "Predictive Equality   8\n",
+       "Statistical Parity    8"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Standard Errors for Metrics Requires Degrees of Freedom\")\n",
+    "print(\"Degrees of Freedom is the Number of Parameters Needed to Calculate the Metric\")\n",
+    "df_index=pd.Series([\"Average Odds\",\"Equal Opportunity\",\"Predictive Equality\",\"Statistical Parity\"])\n",
+    "df_index.name=\"Metric\"\n",
+    "df=pd.DataFrame({\"df\":[16,8,8,8]},\n",
+    "                index=df_index)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7ad8d05-1e0e-429d-a20e-8e071f624e12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>oracle_var</th>\n",
+       "      <th>oracle_std</th>\n",
+       "      <th>count</th>\n",
+       "      <th>df</th>\n",
+       "      <th>oracle_mean</th>\n",
+       "      <th>prob_standard_deviation</th>\n",
+       "      <th>oracle_sum_of_squares</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
+       "      <th>100</th>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.004033</td>\n",
+       "      <td>0.063509</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.091405</td>\n",
+       "      <td>0.116969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.002782</td>\n",
+       "      <td>0.052742</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.075908</td>\n",
+       "      <td>0.080669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.003538</td>\n",
+       "      <td>0.059485</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.085613</td>\n",
+       "      <td>0.102614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.002028</td>\n",
+       "      <td>0.045033</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.064814</td>\n",
+       "      <td>0.058812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
+       "      <th>100</th>\n",
+       "      <td>0.000387</td>\n",
+       "      <td>0.019664</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.022576</td>\n",
+       "      <td>0.011213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.000161</td>\n",
+       "      <td>0.012682</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.014560</td>\n",
+       "      <td>0.004664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.000097</td>\n",
+       "      <td>0.009836</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.011293</td>\n",
+       "      <td>0.002805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.000056</td>\n",
+       "      <td>0.007464</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.008569</td>\n",
+       "      <td>0.001615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>592 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                oracle_var  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.017607   \n",
+       "                                                  300             0.004033   \n",
+       "                                                  400             0.002782   \n",
+       "                                                  500             0.003538   \n",
+       "                                                  1000            0.002028   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            100             0.000387   \n",
+       "                                                  300             0.000161   \n",
+       "                                                  400             0.000097   \n",
+       "                                                  500             0.000056   \n",
+       "                                                  1000            0.000028   \n",
+       "\n",
+       "                                                                oracle_std  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.132693   \n",
+       "                                                  300             0.063509   \n",
+       "                                                  400             0.052742   \n",
+       "                                                  500             0.059485   \n",
+       "                                                  1000            0.045033   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            100             0.019664   \n",
+       "                                                  300             0.012682   \n",
+       "                                                  400             0.009836   \n",
+       "                                                  500             0.007464   \n",
+       "                                                  1000            0.005270   \n",
+       "\n",
+       "                                                                count  df  \\\n",
+       "Metric             simulation       average_count n_surrogates              \n",
+       "Average Odds       extremely_unfair 5             100              30  16   \n",
+       "                                                  300              30  16   \n",
+       "                                                  400              30  16   \n",
+       "                                                  500              30  16   \n",
+       "                                                  1000             30  16   \n",
+       "...                                                               ...  ..   \n",
+       "Statistical Parity very_unfair      40            100              30   8   \n",
+       "                                                  300              30   8   \n",
+       "                                                  400              30   8   \n",
+       "                                                  500              30   8   \n",
+       "                                                  1000             30   8   \n",
+       "\n",
+       "                                                                oracle_mean  \\\n",
+       "Metric             simulation       average_count n_surrogates                \n",
+       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
+       "                                                  300             -0.352455   \n",
+       "                                                  400             -0.352455   \n",
+       "                                                  500             -0.352455   \n",
+       "                                                  1000            -0.352455   \n",
+       "...                                                                     ...   \n",
+       "Statistical Parity very_unfair      40            100             -0.413228   \n",
+       "                                                  300             -0.413228   \n",
+       "                                                  400             -0.413228   \n",
+       "                                                  500             -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "\n",
+       "                                                                prob_standard_deviation  \\\n",
+       "Metric             simulation       average_count n_surrogates                            \n",
+       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
+       "                                                  300                          0.091405   \n",
+       "                                                  400                          0.075908   \n",
+       "                                                  500                          0.085613   \n",
+       "                                                  1000                         0.064814   \n",
+       "...                                                                                 ...   \n",
+       "Statistical Parity very_unfair      40            100                          0.022576   \n",
+       "                                                  300                          0.014560   \n",
+       "                                                  400                          0.011293   \n",
+       "                                                  500                          0.008569   \n",
+       "                                                  1000                         0.006050   \n",
+       "\n",
+       "                                                                oracle_sum_of_squares  \n",
+       "Metric             simulation       average_count n_surrogates                         \n",
+       "Average Odds       extremely_unfair 5             100                        0.510614  \n",
+       "                                                  300                        0.116969  \n",
+       "                                                  400                        0.080669  \n",
+       "                                                  500                        0.102614  \n",
+       "                                                  1000                       0.058812  \n",
+       "...                                                                               ...  \n",
+       "Statistical Parity very_unfair      40            100                        0.011213  \n",
+       "                                                  300                        0.004664  \n",
+       "                                                  400                        0.002805  \n",
+       "                                                  500                        0.001615  \n",
+       "                                                  1000                       0.000805  \n",
+       "\n",
+       "[592 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "standard_deviations=pd.DataFrame(oracle_variance).merge(df,left_index=True,right_index=True).merge(means,left_index=True,right_index=True).rename(columns={\"var\":\"oracle_var\",\"std\":\"oracle_std\"})\n",
+    "\n",
+    "standard_deviations[\"prob_standard_deviation\"]=(standard_deviations[\"oracle_var\"]*(standard_deviations[\"count\"]-1)/(standard_deviations[\"count\"]-standard_deviations[\"df\"])).apply(np.sqrt)\n",
+    "standard_deviations[\"oracle_sum_of_squares\"]=standard_deviations[\"oracle_var\"]*(standard_deviations[\"count\"]-1)\n",
+    "\n",
+    "standard_deviations\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fd048cc4-a172-4920-8e29-655a778f925e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>oracle_value</th>\n",
+       "      <th>probabilistic_estimate</th>\n",
+       "      <th>oracle_var</th>\n",
+       "      <th>oracle_std</th>\n",
+       "      <th>count</th>\n",
+       "      <th>df</th>\n",
+       "      <th>oracle_mean</th>\n",
+       "      <th>prob_standard_deviation</th>\n",
+       "      <th>oracle_sum_of_squares</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.306</td>\n",
+       "      <td>-0.183</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.547</td>\n",
+       "      <td>-0.327</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.427</td>\n",
+       "      <td>0.719</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.043</td>\n",
+       "      <td>-0.092</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.397</td>\n",
+       "      <td>0.736</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.408</td>\n",
+       "      <td>-0.419</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>-0.379</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.420</td>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>-0.436</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.407</td>\n",
+       "      <td>-0.417</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17760 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                oracle_value  \\\n",
+       "Metric             simulation       average_count n_surrogates                 \n",
+       "Average Odds       extremely_unfair 5             100                 -0.306   \n",
+       "                                                  100                 -0.547   \n",
+       "                                                  100                 -0.427   \n",
+       "                                                  100                 -0.043   \n",
+       "                                                  100                 -0.397   \n",
+       "...                                                                      ...   \n",
+       "Statistical Parity very_unfair      40            1000                -0.408   \n",
+       "                                                  1000                -0.411   \n",
+       "                                                  1000                -0.420   \n",
+       "                                                  1000                -0.411   \n",
+       "                                                  1000                -0.407   \n",
+       "\n",
+       "                                                                probabilistic_estimate  \\\n",
+       "Metric             simulation       average_count n_surrogates                           \n",
+       "Average Odds       extremely_unfair 5             100                           -0.183   \n",
+       "                                                  100                           -0.327   \n",
+       "                                                  100                            0.719   \n",
+       "                                                  100                           -0.092   \n",
+       "                                                  100                            0.736   \n",
+       "...                                                                                ...   \n",
+       "Statistical Parity very_unfair      40            1000                          -0.419   \n",
+       "                                                  1000                          -0.379   \n",
+       "                                                  1000                          -0.411   \n",
+       "                                                  1000                          -0.436   \n",
+       "                                                  1000                          -0.417   \n",
+       "\n",
+       "                                                                oracle_var  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "\n",
+       "                                                                oracle_std  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "\n",
+       "                                                                count  df  \\\n",
+       "Metric             simulation       average_count n_surrogates              \n",
+       "Average Odds       extremely_unfair 5             100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "...                                                               ...  ..   \n",
+       "Statistical Parity very_unfair      40            1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "\n",
+       "                                                                oracle_mean  \\\n",
+       "Metric             simulation       average_count n_surrogates                \n",
+       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "...                                                                     ...   \n",
+       "Statistical Parity very_unfair      40            1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "\n",
+       "                                                                prob_standard_deviation  \\\n",
+       "Metric             simulation       average_count n_surrogates                            \n",
+       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "...                                                                                 ...   \n",
+       "Statistical Parity very_unfair      40            1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "\n",
+       "                                                                oracle_sum_of_squares  \n",
+       "Metric             simulation       average_count n_surrogates                         \n",
+       "Average Odds       extremely_unfair 5             100                        0.510614  \n",
+       "                                                  100                        0.510614  \n",
+       "                                                  100                        0.510614  \n",
+       "                                                  100                        0.510614  \n",
+       "                                                  100                        0.510614  \n",
+       "...                                                                               ...  \n",
+       "Statistical Parity very_unfair      40            1000                       0.000805  \n",
+       "                                                  1000                       0.000805  \n",
+       "                                                  1000                       0.000805  \n",
+       "                                                  1000                       0.000805  \n",
+       "                                                  1000                       0.000805  \n",
+       "\n",
+       "[17760 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulations=simulations_raw.set_index([\"Metric\",\"simulation\",\"average_count\",\"n_surrogates\"]).merge(standard_deviations,left_index=True,right_index=True,how=\"outer\").drop(columns=[\"run_id\"])\n",
+    "simulations[\"oracle_value\"].fillna(simulations[\"oracle_mean\"],inplace=True)\n",
+    "simulations\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0a14ac35-a423-430e-b5ff-28022aaaf595",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Different Attempts to Scale Metrics to Show on Same Graph\n",
+      "Percent Error and Squared Error Were Less Useful than Studentized Error\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
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+       "        vertical-align: top;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>oracle_value</th>\n",
+       "      <th>probabilistic_estimate</th>\n",
+       "      <th>oracle_var</th>\n",
+       "      <th>oracle_std</th>\n",
+       "      <th>count</th>\n",
+       "      <th>df</th>\n",
+       "      <th>oracle_mean</th>\n",
+       "      <th>prob_standard_deviation</th>\n",
+       "      <th>oracle_sum_of_squares</th>\n",
+       "      <th>error</th>\n",
+       "      <th>squared_error</th>\n",
+       "      <th>pct_error</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.306</td>\n",
+       "      <td>-0.183</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-0.123</td>\n",
+       "      <td>0.015129</td>\n",
+       "      <td>0.401961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.547</td>\n",
+       "      <td>-0.327</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-0.220</td>\n",
+       "      <td>0.048400</td>\n",
+       "      <td>0.402194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.427</td>\n",
+       "      <td>0.719</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-1.146</td>\n",
+       "      <td>1.313316</td>\n",
+       "      <td>2.683841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.043</td>\n",
+       "      <td>-0.092</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.002401</td>\n",
+       "      <td>1.139535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.397</td>\n",
+       "      <td>0.736</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-1.133</td>\n",
+       "      <td>1.283689</td>\n",
+       "      <td>2.853904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.408</td>\n",
+       "      <td>-0.419</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.000121</td>\n",
+       "      <td>0.026961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>-0.379</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>-0.032</td>\n",
+       "      <td>0.001024</td>\n",
+       "      <td>0.077859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.420</td>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>-0.009</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>0.021429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>-0.436</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.000625</td>\n",
+       "      <td>0.060827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.407</td>\n",
+       "      <td>-0.417</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.024570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17760 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                oracle_value  \\\n",
+       "Metric             simulation       average_count n_surrogates                 \n",
+       "Average Odds       extremely_unfair 5             100                 -0.306   \n",
+       "                                                  100                 -0.547   \n",
+       "                                                  100                 -0.427   \n",
+       "                                                  100                 -0.043   \n",
+       "                                                  100                 -0.397   \n",
+       "...                                                                      ...   \n",
+       "Statistical Parity very_unfair      40            1000                -0.408   \n",
+       "                                                  1000                -0.411   \n",
+       "                                                  1000                -0.420   \n",
+       "                                                  1000                -0.411   \n",
+       "                                                  1000                -0.407   \n",
+       "\n",
+       "                                                                probabilistic_estimate  \\\n",
+       "Metric             simulation       average_count n_surrogates                           \n",
+       "Average Odds       extremely_unfair 5             100                           -0.183   \n",
+       "                                                  100                           -0.327   \n",
+       "                                                  100                            0.719   \n",
+       "                                                  100                           -0.092   \n",
+       "                                                  100                            0.736   \n",
+       "...                                                                                ...   \n",
+       "Statistical Parity very_unfair      40            1000                          -0.419   \n",
+       "                                                  1000                          -0.379   \n",
+       "                                                  1000                          -0.411   \n",
+       "                                                  1000                          -0.436   \n",
+       "                                                  1000                          -0.417   \n",
+       "\n",
+       "                                                                oracle_var  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "\n",
+       "                                                                oracle_std  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "\n",
+       "                                                                count  df  \\\n",
+       "Metric             simulation       average_count n_surrogates              \n",
+       "Average Odds       extremely_unfair 5             100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "...                                                               ...  ..   \n",
+       "Statistical Parity very_unfair      40            1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "\n",
+       "                                                                oracle_mean  \\\n",
+       "Metric             simulation       average_count n_surrogates                \n",
+       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "...                                                                     ...   \n",
+       "Statistical Parity very_unfair      40            1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "\n",
+       "                                                                prob_standard_deviation  \\\n",
+       "Metric             simulation       average_count n_surrogates                            \n",
+       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "...                                                                                 ...   \n",
+       "Statistical Parity very_unfair      40            1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "\n",
+       "                                                                oracle_sum_of_squares  \\\n",
+       "Metric             simulation       average_count n_surrogates                          \n",
+       "Average Odds       extremely_unfair 5             100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "...                                                                               ...   \n",
+       "Statistical Parity very_unfair      40            1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "\n",
+       "                                                                error  \\\n",
+       "Metric             simulation       average_count n_surrogates          \n",
+       "Average Odds       extremely_unfair 5             100          -0.123   \n",
+       "                                                  100          -0.220   \n",
+       "                                                  100          -1.146   \n",
+       "                                                  100           0.049   \n",
+       "                                                  100          -1.133   \n",
+       "...                                                               ...   \n",
+       "Statistical Parity very_unfair      40            1000          0.011   \n",
+       "                                                  1000         -0.032   \n",
+       "                                                  1000         -0.009   \n",
+       "                                                  1000          0.025   \n",
+       "                                                  1000          0.010   \n",
+       "\n",
+       "                                                                squared_error  \\\n",
+       "Metric             simulation       average_count n_surrogates                  \n",
+       "Average Odds       extremely_unfair 5             100                0.015129   \n",
+       "                                                  100                0.048400   \n",
+       "                                                  100                1.313316   \n",
+       "                                                  100                0.002401   \n",
+       "                                                  100                1.283689   \n",
+       "...                                                                       ...   \n",
+       "Statistical Parity very_unfair      40            1000               0.000121   \n",
+       "                                                  1000               0.001024   \n",
+       "                                                  1000               0.000081   \n",
+       "                                                  1000               0.000625   \n",
+       "                                                  1000               0.000100   \n",
+       "\n",
+       "                                                                pct_error  \n",
+       "Metric             simulation       average_count n_surrogates             \n",
+       "Average Odds       extremely_unfair 5             100            0.401961  \n",
+       "                                                  100            0.402194  \n",
+       "                                                  100            2.683841  \n",
+       "                                                  100            1.139535  \n",
+       "                                                  100            2.853904  \n",
+       "...                                                                   ...  \n",
+       "Statistical Parity very_unfair      40            1000           0.026961  \n",
+       "                                                  1000           0.077859  \n",
+       "                                                  1000           0.021429  \n",
+       "                                                  1000           0.060827  \n",
+       "                                                  1000           0.024570  \n",
+       "\n",
+       "[17760 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Different Attempts to Scale Metrics to Show on Same Graph')\n",
+    "print('Percent Error and Squared Error Were Less Useful than Studentized Error')\n",
+    "simulations[\"error\"]=simulations[\"oracle_value\"]-simulations[\"probabilistic_estimate\"]\n",
+    "simulations[\"squared_error\"]=simulations[\"error\"]**2\n",
+    "simulations[\"pct_error\"]=(simulations[\"error\"]/simulations[\"oracle_value\"]).apply(abs)\n",
+    "simulations \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ceb83160-7073-474d-8415-df0a8ca8a9ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculate Cut-Off from: Mohammad Ahsanullah, BM Golam Kibria, and Mohammad Shakil. 2014. Normal and students t distributions and their applications. Vol. 4. Springer\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>oracle_value</th>\n",
+       "      <th>probabilistic_estimate</th>\n",
+       "      <th>oracle_var</th>\n",
+       "      <th>oracle_std</th>\n",
+       "      <th>count</th>\n",
+       "      <th>df</th>\n",
+       "      <th>oracle_mean</th>\n",
+       "      <th>prob_standard_deviation</th>\n",
+       "      <th>oracle_sum_of_squares</th>\n",
+       "      <th>error</th>\n",
+       "      <th>squared_error</th>\n",
+       "      <th>pct_error</th>\n",
+       "      <th>student_error</th>\n",
+       "      <th>cut_off</th>\n",
+       "      <th>out_of_range</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.306</td>\n",
+       "      <td>-0.183</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-0.123</td>\n",
+       "      <td>0.015129</td>\n",
+       "      <td>0.401961</td>\n",
+       "      <td>-0.644055</td>\n",
+       "      <td>3.741657</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.547</td>\n",
+       "      <td>-0.327</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-0.220</td>\n",
+       "      <td>0.048400</td>\n",
+       "      <td>0.402194</td>\n",
+       "      <td>-1.151967</td>\n",
+       "      <td>3.741657</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.427</td>\n",
+       "      <td>0.719</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-1.146</td>\n",
+       "      <td>1.313316</td>\n",
+       "      <td>2.683841</td>\n",
+       "      <td>-6.000703</td>\n",
+       "      <td>3.741657</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.043</td>\n",
+       "      <td>-0.092</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.002401</td>\n",
+       "      <td>1.139535</td>\n",
+       "      <td>0.256575</td>\n",
+       "      <td>3.741657</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>-0.397</td>\n",
+       "      <td>0.736</td>\n",
+       "      <td>0.017607</td>\n",
+       "      <td>0.132693</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>-0.352455</td>\n",
+       "      <td>0.190978</td>\n",
+       "      <td>0.510614</td>\n",
+       "      <td>-1.133</td>\n",
+       "      <td>1.283689</td>\n",
+       "      <td>2.853904</td>\n",
+       "      <td>-5.932632</td>\n",
+       "      <td>3.741657</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.408</td>\n",
+       "      <td>-0.419</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.000121</td>\n",
+       "      <td>0.026961</td>\n",
+       "      <td>1.818056</td>\n",
+       "      <td>4.690416</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>-0.379</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>-0.032</td>\n",
+       "      <td>0.001024</td>\n",
+       "      <td>0.077859</td>\n",
+       "      <td>-5.288889</td>\n",
+       "      <td>4.690416</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.420</td>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>-0.009</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>0.021429</td>\n",
+       "      <td>-1.487500</td>\n",
+       "      <td>4.690416</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.411</td>\n",
+       "      <td>-0.436</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.000625</td>\n",
+       "      <td>0.060827</td>\n",
+       "      <td>4.131945</td>\n",
+       "      <td>4.690416</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>-0.407</td>\n",
+       "      <td>-0.417</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.005270</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.413228</td>\n",
+       "      <td>0.006050</td>\n",
+       "      <td>0.000805</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.024570</td>\n",
+       "      <td>1.652778</td>\n",
+       "      <td>4.690416</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17760 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                oracle_value  \\\n",
+       "Metric             simulation       average_count n_surrogates                 \n",
+       "Average Odds       extremely_unfair 5             100                 -0.306   \n",
+       "                                                  100                 -0.547   \n",
+       "                                                  100                 -0.427   \n",
+       "                                                  100                 -0.043   \n",
+       "                                                  100                 -0.397   \n",
+       "...                                                                      ...   \n",
+       "Statistical Parity very_unfair      40            1000                -0.408   \n",
+       "                                                  1000                -0.411   \n",
+       "                                                  1000                -0.420   \n",
+       "                                                  1000                -0.411   \n",
+       "                                                  1000                -0.407   \n",
+       "\n",
+       "                                                                probabilistic_estimate  \\\n",
+       "Metric             simulation       average_count n_surrogates                           \n",
+       "Average Odds       extremely_unfair 5             100                           -0.183   \n",
+       "                                                  100                           -0.327   \n",
+       "                                                  100                            0.719   \n",
+       "                                                  100                           -0.092   \n",
+       "                                                  100                            0.736   \n",
+       "...                                                                                ...   \n",
+       "Statistical Parity very_unfair      40            1000                          -0.419   \n",
+       "                                                  1000                          -0.379   \n",
+       "                                                  1000                          -0.411   \n",
+       "                                                  1000                          -0.436   \n",
+       "                                                  1000                          -0.417   \n",
+       "\n",
+       "                                                                oracle_var  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "                                                  100             0.017607   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "                                                  1000            0.000028   \n",
+       "\n",
+       "                                                                oracle_std  \\\n",
+       "Metric             simulation       average_count n_surrogates               \n",
+       "Average Odds       extremely_unfair 5             100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "                                                  100             0.132693   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      40            1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "                                                  1000            0.005270   \n",
+       "\n",
+       "                                                                count  df  \\\n",
+       "Metric             simulation       average_count n_surrogates              \n",
+       "Average Odds       extremely_unfair 5             100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "                                                  100              30  16   \n",
+       "...                                                               ...  ..   \n",
+       "Statistical Parity very_unfair      40            1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "                                                  1000             30   8   \n",
+       "\n",
+       "                                                                oracle_mean  \\\n",
+       "Metric             simulation       average_count n_surrogates                \n",
+       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "                                                  100             -0.352455   \n",
+       "...                                                                     ...   \n",
+       "Statistical Parity very_unfair      40            1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "                                                  1000            -0.413228   \n",
+       "\n",
+       "                                                                prob_standard_deviation  \\\n",
+       "Metric             simulation       average_count n_surrogates                            \n",
+       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "                                                  100                          0.190978   \n",
+       "...                                                                                 ...   \n",
+       "Statistical Parity very_unfair      40            1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "                                                  1000                         0.006050   \n",
+       "\n",
+       "                                                                oracle_sum_of_squares  \\\n",
+       "Metric             simulation       average_count n_surrogates                          \n",
+       "Average Odds       extremely_unfair 5             100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "                                                  100                        0.510614   \n",
+       "...                                                                               ...   \n",
+       "Statistical Parity very_unfair      40            1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "                                                  1000                       0.000805   \n",
+       "\n",
+       "                                                                error  \\\n",
+       "Metric             simulation       average_count n_surrogates          \n",
+       "Average Odds       extremely_unfair 5             100          -0.123   \n",
+       "                                                  100          -0.220   \n",
+       "                                                  100          -1.146   \n",
+       "                                                  100           0.049   \n",
+       "                                                  100          -1.133   \n",
+       "...                                                               ...   \n",
+       "Statistical Parity very_unfair      40            1000          0.011   \n",
+       "                                                  1000         -0.032   \n",
+       "                                                  1000         -0.009   \n",
+       "                                                  1000          0.025   \n",
+       "                                                  1000          0.010   \n",
+       "\n",
+       "                                                                squared_error  \\\n",
+       "Metric             simulation       average_count n_surrogates                  \n",
+       "Average Odds       extremely_unfair 5             100                0.015129   \n",
+       "                                                  100                0.048400   \n",
+       "                                                  100                1.313316   \n",
+       "                                                  100                0.002401   \n",
+       "                                                  100                1.283689   \n",
+       "...                                                                       ...   \n",
+       "Statistical Parity very_unfair      40            1000               0.000121   \n",
+       "                                                  1000               0.001024   \n",
+       "                                                  1000               0.000081   \n",
+       "                                                  1000               0.000625   \n",
+       "                                                  1000               0.000100   \n",
+       "\n",
+       "                                                                pct_error  \\\n",
+       "Metric             simulation       average_count n_surrogates              \n",
+       "Average Odds       extremely_unfair 5             100            0.401961   \n",
+       "                                                  100            0.402194   \n",
+       "                                                  100            2.683841   \n",
+       "                                                  100            1.139535   \n",
+       "                                                  100            2.853904   \n",
+       "...                                                                   ...   \n",
+       "Statistical Parity very_unfair      40            1000           0.026961   \n",
+       "                                                  1000           0.077859   \n",
+       "                                                  1000           0.021429   \n",
+       "                                                  1000           0.060827   \n",
+       "                                                  1000           0.024570   \n",
+       "\n",
+       "                                                                student_error  \\\n",
+       "Metric             simulation       average_count n_surrogates                  \n",
+       "Average Odds       extremely_unfair 5             100               -0.644055   \n",
+       "                                                  100               -1.151967   \n",
+       "                                                  100               -6.000703   \n",
+       "                                                  100                0.256575   \n",
+       "                                                  100               -5.932632   \n",
+       "...                                                                       ...   \n",
+       "Statistical Parity very_unfair      40            1000               1.818056   \n",
+       "                                                  1000              -5.288889   \n",
+       "                                                  1000              -1.487500   \n",
+       "                                                  1000               4.131945   \n",
+       "                                                  1000               1.652778   \n",
+       "\n",
+       "                                                                 cut_off  \\\n",
+       "Metric             simulation       average_count n_surrogates             \n",
+       "Average Odds       extremely_unfair 5             100           3.741657   \n",
+       "                                                  100           3.741657   \n",
+       "                                                  100           3.741657   \n",
+       "                                                  100           3.741657   \n",
+       "                                                  100           3.741657   \n",
+       "...                                                                  ...   \n",
+       "Statistical Parity very_unfair      40            1000          4.690416   \n",
+       "                                                  1000          4.690416   \n",
+       "                                                  1000          4.690416   \n",
+       "                                                  1000          4.690416   \n",
+       "                                                  1000          4.690416   \n",
+       "\n",
+       "                                                                out_of_range  \n",
+       "Metric             simulation       average_count n_surrogates                \n",
+       "Average Odds       extremely_unfair 5             100                      0  \n",
+       "                                                  100                      0  \n",
+       "                                                  100                      1  \n",
+       "                                                  100                      0  \n",
+       "                                                  100                      1  \n",
+       "...                                                                      ...  \n",
+       "Statistical Parity very_unfair      40            1000                     0  \n",
+       "                                                  1000                     1  \n",
+       "                                                  1000                     0  \n",
+       "                                                  1000                     0  \n",
+       "                                                  1000                     0  \n",
+       "\n",
+       "[17760 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Calculate Cut-Off from: Mohammad Ahsanullah, BM Golam Kibria, and Mohammad Shakil. 2014. Normal and students t distributions and their applications. Vol. 4. Springer')\n",
+    "simulations[\"student_error\"]=simulations[\"error\"]/simulations[\"prob_standard_deviation\"]\n",
+    "simulations[\"cut_off\"]=(simulations[\"count\"]*(simulations[\"count\"]-simulations[\"df\"])/simulations[\"count\"]).apply(np.sqrt)\n",
+    "simulations[\"out_of_range\"]=(simulations[\"student_error\"].apply(abs)>simulations[\"cut_off\"]).astype(int)\n",
+    "simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5095382c-7d12-4de9-8057-51740eeff449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percent Simulations with Out of Range Residuals: 0.05%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Percent Simulations with Out of Range Residuals: {:.0}%'.format(simulations[\"out_of_range\"].sum()/simulations.shape[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20dbbfac-1411-458e-a129-a7ab2d97787c",
+   "metadata": {},
+   "source": [
+    "# Plotting Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "67326acc-b070-406c-96ea-2396eba393a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faa07a98-aebe-4fc7-82db-780baa73a118",
+   "metadata": {},
+   "source": [
+    "# Unuseable Estimates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd222d87-9c7c-4d0e-a8e0-5178f155ea4d",
+   "metadata": {},
+   "source": [
+    "Count the number of NaN and out of range estimates for each simulation setting, across all fairness statistics. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "287d7ad5-bdf7-4b06-bb78-1eb7f05d3124",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(17760, 15)\n",
+      "Index(['oracle_value', 'probabilistic_estimate', 'oracle_var', 'oracle_std',\n",
+      "       'count', 'df', 'oracle_mean', 'prob_standard_deviation',\n",
+      "       'oracle_sum_of_squares', 'error', 'squared_error', 'pct_error',\n",
+      "       'student_error', 'cut_off', 'out_of_range'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "958"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(simulations.shape)\n",
+    "print(simulations.columns)\n",
+    "simulations[\"out_of_range\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "34f9a75c-763b-4805-a4b5-1acc0283fc60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Divide each count by 600\n"
+     ]
+    }
+   ],
+   "source": [
+    "m=simulations.groupby([\"average_count\",\"n_surrogates\"]).count().max().max()\n",
+    "print(f\"Divide each count by {m}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "35d93bae-ff8c-414b-904e-cf87e11e21d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All The Missing Values Come from Smallest Sample Size Setting\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "average_count  n_surrogates\n",
+       "5              50              240\n",
+       "Name: missing, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing=m-simulations.groupby([\"average_count\",\"n_surrogates\"])[\"out_of_range\"].count()\n",
+    "missing.name=\"missing\"\n",
+    "print(\"All The Missing Values Come from Smallest Sample Size Setting\")\n",
+    "missing[missing>0]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab87091-f268-4a8f-b5be-4fc24bdefc55",
+   "metadata": {},
+   "source": [
+    "Review counts of large errors for different simulation settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ad34d142-dfa4-4c90-9bd4-e2bb6378250f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "count_big_errors=simulations.groupby([\"average_count\",\"n_surrogates\"])[\"out_of_range\"].sum()+missing\n",
+    "count_big_errors.name=\"n_large_errors\"\n",
+    "(count_big_errors/m).hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6d7c0649-fe34-48b0-b8e8-9227bef70e09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape data for line graph\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>50</th>\n",
+       "      <th>100</th>\n",
+       "      <th>300</th>\n",
+       "      <th>400</th>\n",
+       "      <th>500</th>\n",
+       "      <th>1000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.456667</td>\n",
+       "      <td>0.086667</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.053333</td>\n",
+       "      <td>0.061667</td>\n",
+       "      <td>0.033333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.086667</td>\n",
+       "      <td>0.086667</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>0.043333</td>\n",
+       "      <td>0.056667</td>\n",
+       "      <td>0.028333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>0.071667</td>\n",
+       "      <td>0.041667</td>\n",
+       "      <td>0.018333</td>\n",
+       "      <td>0.036667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.103333</td>\n",
+       "      <td>0.073333</td>\n",
+       "      <td>0.030000</td>\n",
+       "      <td>0.053333</td>\n",
+       "      <td>0.038333</td>\n",
+       "      <td>0.010000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.085000</td>\n",
+       "      <td>0.053333</td>\n",
+       "      <td>0.023333</td>\n",
+       "      <td>0.006667</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>0.005000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "n_surrogates       50        100       300       400       500       1000\n",
+       "average_count                                                            \n",
+       "5              0.456667  0.086667  0.100000  0.053333  0.061667  0.033333\n",
+       "10             0.086667  0.086667  0.090000  0.043333  0.056667  0.028333\n",
+       "20             0.083333  0.070000  0.071667  0.041667  0.018333  0.036667\n",
+       "30             0.103333  0.073333  0.030000  0.053333  0.038333  0.010000\n",
+       "40             0.085000  0.053333  0.023333  0.006667  0.010000  0.005000"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivoted=count_big_errors.reset_index().pivot(index=\"average_count\",columns=\"n_surrogates\",values=\"n_large_errors\")\n",
+    "print(\"Shape data for line graph\")\n",
+    "pivoted/m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "99186c59-6991-4986-9c98-6a8be65d32d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set colors that will print well in grayscale,\n",
+    "#Different color for each simulation setting\n",
+    "#other options: YlGnBu,cubehelix,gnuplot\n",
+    "cmap = plt.get_cmap('gnuplot')\n",
+    "indicies = np.linspace(0, cmap.N, len(sim_names))\n",
+    "my_colors = [cmap(int(i)) for i in indicies]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "78763169-42da-44c5-a047-4509aaa7e333",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/1481678520.py:2: UserWarning: \n",
+      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
+      "  sns.lineplot(data=(pivoted/m),markers=True, palette=my_colors)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 5), dpi=100)\n",
+    "sns.lineplot(data=(pivoted/m),markers=True, palette=my_colors)\n",
+    "plt.xlabel(\"Individuals Per Level\")\n",
+    "plt.ylabel(\"Percent Out of Range\")\n",
+    "plt.legend(title=\"Levels in Z\")\n",
+    "plt.title(\"Percent Estimation Failures for Small Samples\")\n",
+    "plt.savefig(\"percent_out_of_range.png\",dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d426ded1-541b-4f7a-a75e-f945c33a6946",
+   "metadata": {},
+   "source": [
+    "# Standard Errors Probabilistic Estimates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1f6db5c-f5cc-4ca5-ab74-187e6a33667a",
+   "metadata": {},
+   "source": [
+    "We have an understanding of how often the probabilistic estimates fail under different sample sizes. Estimates will vary from sample to sample, however, and the variation decreases with larger samples (more values per surrogate class and more surrogate classes). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "42fb263d-b9e4-491d-ac3a-b6a7319919c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>probabilistic_sum_of_squares</th>\n",
+       "      <th>probabilistic_count</th>\n",
+       "      <th>df</th>\n",
+       "      <th>probabilistic_se</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
+       "      <th>10</th>\n",
+       "      <td>1.400771</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.374269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.013655</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.411026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.616997</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.351283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.406700</td>\n",
+       "      <td>25</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.212577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <th>5</th>\n",
+       "      <td>1.299259</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.465342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
+       "      <th>5</th>\n",
+       "      <td>0.027871</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.035593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.014474</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.025650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.006801</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.017996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.004502</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.014305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.003393</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.012711</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>592 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                probabilistic_sum_of_squares  \\\n",
+       "Metric             simulation       n_surrogates average_count                                 \n",
+       "Average Odds       extremely_unfair 50           10                                 1.400771   \n",
+       "                                                 20                                 1.013655   \n",
+       "                                                 30                                 0.616997   \n",
+       "                                                 40                                 0.406700   \n",
+       "                                    100          5                                  1.299259   \n",
+       "...                                                                                      ...   \n",
+       "Statistical Parity very_unfair      1000         5                                  0.027871   \n",
+       "                                                 10                                 0.014474   \n",
+       "                                                 20                                 0.006801   \n",
+       "                                                 30                                 0.004502   \n",
+       "                                                 40                                 0.003393   \n",
+       "\n",
+       "                                                                probabilistic_count  \\\n",
+       "Metric             simulation       n_surrogates average_count                        \n",
+       "Average Odds       extremely_unfair 50           10                              26   \n",
+       "                                                 20                              22   \n",
+       "                                                 30                              21   \n",
+       "                                                 40                              25   \n",
+       "                                    100          5                               22   \n",
+       "...                                                                             ...   \n",
+       "Statistical Parity very_unfair      1000         5                               30   \n",
+       "                                                 10                              30   \n",
+       "                                                 20                              29   \n",
+       "                                                 30                              30   \n",
+       "                                                 40                              29   \n",
+       "\n",
+       "                                                                df  \\\n",
+       "Metric             simulation       n_surrogates average_count       \n",
+       "Average Odds       extremely_unfair 50           10             16   \n",
+       "                                                 20             16   \n",
+       "                                                 30             16   \n",
+       "                                                 40             16   \n",
+       "                                    100          5              16   \n",
+       "...                                                             ..   \n",
+       "Statistical Parity very_unfair      1000         5               8   \n",
+       "                                                 10              8   \n",
+       "                                                 20              8   \n",
+       "                                                 30              8   \n",
+       "                                                 40              8   \n",
+       "\n",
+       "                                                                probabilistic_se  \n",
+       "Metric             simulation       n_surrogates average_count                    \n",
+       "Average Odds       extremely_unfair 50           10                     0.374269  \n",
+       "                                                 20                     0.411026  \n",
+       "                                                 30                     0.351283  \n",
+       "                                                 40                     0.212577  \n",
+       "                                    100          5                      0.465342  \n",
+       "...                                                                          ...  \n",
+       "Statistical Parity very_unfair      1000         5                      0.035593  \n",
+       "                                                 10                     0.025650  \n",
+       "                                                 20                     0.017996  \n",
+       "                                                 30                     0.014305  \n",
+       "                                                 40                     0.012711  \n",
+       "\n",
+       "[592 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "standard_errors=simulations.query('out_of_range==0').groupby([\"Metric\",\"simulation\",\"n_surrogates\",\"average_count\"])[\"squared_error\"].agg([\"sum\",\"count\"]).rename(columns={\"sum\":\"probabilistic_sum_of_squares\",\"count\":\"probabilistic_count\"}).merge(df,left_index=True,right_index=True)\n",
+    "standard_errors[\"probabilistic_se\"]=(standard_errors[\"probabilistic_sum_of_squares\"]/(standard_errors[\"probabilistic_count\"]-standard_errors[\"df\"])).apply(np.sqrt)\n",
+    "standard_errors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f7ec89ce-8a96-4983-a63b-f52ce3283cd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "standard_errors[\"probabilistic_count\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "56558450-e025-45fe-9413-e926942fdd1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>oracle_mean</th>\n",
+       "      <th>oracle_var</th>\n",
+       "      <th>oracle_std</th>\n",
+       "      <th>oracle_count</th>\n",
+       "      <th>oracle_sum_of_squares</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
+       "      <th>10</th>\n",
+       "      <td>-0.332308</td>\n",
+       "      <td>0.016266</td>\n",
+       "      <td>0.127536</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.406638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>-0.332000</td>\n",
+       "      <td>0.009308</td>\n",
+       "      <td>0.096480</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.195476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>-0.334714</td>\n",
+       "      <td>0.005208</td>\n",
+       "      <td>0.072164</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.104152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>-0.358400</td>\n",
+       "      <td>0.003703</td>\n",
+       "      <td>0.060852</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.088870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.307455</td>\n",
+       "      <td>0.016769</td>\n",
+       "      <td>0.129493</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.352139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.413200</td>\n",
+       "      <td>0.000311</td>\n",
+       "      <td>0.017649</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.009033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>-0.413967</td>\n",
+       "      <td>0.000136</td>\n",
+       "      <td>0.011666</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.003947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>-0.413448</td>\n",
+       "      <td>0.000064</td>\n",
+       "      <td>0.007985</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.001785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>-0.412267</td>\n",
+       "      <td>0.000055</td>\n",
+       "      <td>0.007437</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.001604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>-0.413655</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.005340</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.000799</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>592 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                oracle_mean  \\\n",
+       "Metric             simulation       n_surrogates average_count                \n",
+       "Average Odds       extremely_unfair 50           10               -0.332308   \n",
+       "                                                 20               -0.332000   \n",
+       "                                                 30               -0.334714   \n",
+       "                                                 40               -0.358400   \n",
+       "                                    100          5                -0.307455   \n",
+       "...                                                                     ...   \n",
+       "Statistical Parity very_unfair      1000         5                -0.413200   \n",
+       "                                                 10               -0.413967   \n",
+       "                                                 20               -0.413448   \n",
+       "                                                 30               -0.412267   \n",
+       "                                                 40               -0.413655   \n",
+       "\n",
+       "                                                                oracle_var  \\\n",
+       "Metric             simulation       n_surrogates average_count               \n",
+       "Average Odds       extremely_unfair 50           10               0.016266   \n",
+       "                                                 20               0.009308   \n",
+       "                                                 30               0.005208   \n",
+       "                                                 40               0.003703   \n",
+       "                                    100          5                0.016769   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      1000         5                0.000311   \n",
+       "                                                 10               0.000136   \n",
+       "                                                 20               0.000064   \n",
+       "                                                 30               0.000055   \n",
+       "                                                 40               0.000029   \n",
+       "\n",
+       "                                                                oracle_std  \\\n",
+       "Metric             simulation       n_surrogates average_count               \n",
+       "Average Odds       extremely_unfair 50           10               0.127536   \n",
+       "                                                 20               0.096480   \n",
+       "                                                 30               0.072164   \n",
+       "                                                 40               0.060852   \n",
+       "                                    100          5                0.129493   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      1000         5                0.017649   \n",
+       "                                                 10               0.011666   \n",
+       "                                                 20               0.007985   \n",
+       "                                                 30               0.007437   \n",
+       "                                                 40               0.005340   \n",
+       "\n",
+       "                                                                oracle_count  \\\n",
+       "Metric             simulation       n_surrogates average_count                 \n",
+       "Average Odds       extremely_unfair 50           10                       26   \n",
+       "                                                 20                       22   \n",
+       "                                                 30                       21   \n",
+       "                                                 40                       25   \n",
+       "                                    100          5                        22   \n",
+       "...                                                                      ...   \n",
+       "Statistical Parity very_unfair      1000         5                        30   \n",
+       "                                                 10                       30   \n",
+       "                                                 20                       29   \n",
+       "                                                 30                       30   \n",
+       "                                                 40                       29   \n",
+       "\n",
+       "                                                                oracle_sum_of_squares  \n",
+       "Metric             simulation       n_surrogates average_count                         \n",
+       "Average Odds       extremely_unfair 50           10                          0.406638  \n",
+       "                                                 20                          0.195476  \n",
+       "                                                 30                          0.104152  \n",
+       "                                                 40                          0.088870  \n",
+       "                                    100          5                           0.352139  \n",
+       "...                                                                               ...  \n",
+       "Statistical Parity very_unfair      1000         5                           0.009033  \n",
+       "                                                 10                          0.003947  \n",
+       "                                                 20                          0.001785  \n",
+       "                                                 30                          0.001604  \n",
+       "                                                 40                          0.000799  \n",
+       "\n",
+       "[592 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "oracle_std=simulations.query('out_of_range==0').groupby([\"Metric\",\"simulation\",\"n_surrogates\",\"average_count\"])[\"oracle_value\"].agg([\"mean\",\"var\",\"std\",\"count\"]).rename(columns={\"std\":\"oracle_std\",\"count\":\"oracle_count\",\"var\":\"oracle_var\",\"mean\":\"oracle_mean\"})\n",
+    "oracle_std[\"oracle_sum_of_squares\"]=oracle_std[\"oracle_var\"]*(oracle_std[\"oracle_count\"]-1)\n",
+    "oracle_std\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "41ede1cd-fccb-4f31-a03f-e8929aa2f18c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "oracle_std[\"oracle_count\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a10ed7cf-bc54-458c-a371-a19aed8e7472",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>probabilistic_sum_of_squares</th>\n",
+       "      <th>probabilistic_count</th>\n",
+       "      <th>df</th>\n",
+       "      <th>probabilistic_se</th>\n",
+       "      <th>oracle_mean</th>\n",
+       "      <th>oracle_var</th>\n",
+       "      <th>oracle_std</th>\n",
+       "      <th>oracle_count</th>\n",
+       "      <th>oracle_sum_of_squares</th>\n",
+       "      <th>_merge</th>\n",
+       "      <th>std_ratio</th>\n",
+       "      <th>std_diff</th>\n",
+       "      <th>ss_ratio</th>\n",
+       "      <th>prob_se_oracle_val_ratio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
+       "      <th>10</th>\n",
+       "      <td>1.400771</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.374269</td>\n",
+       "      <td>-0.332308</td>\n",
+       "      <td>0.016266</td>\n",
+       "      <td>0.127536</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.406638</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2.934606</td>\n",
+       "      <td>0.246732</td>\n",
+       "      <td>3.444766</td>\n",
+       "      <td>1.126272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.013655</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.411026</td>\n",
+       "      <td>-0.332000</td>\n",
+       "      <td>0.009308</td>\n",
+       "      <td>0.096480</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.195476</td>\n",
+       "      <td>both</td>\n",
+       "      <td>4.260224</td>\n",
+       "      <td>0.314546</td>\n",
+       "      <td>5.185573</td>\n",
+       "      <td>1.238031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.616997</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.351283</td>\n",
+       "      <td>-0.334714</td>\n",
+       "      <td>0.005208</td>\n",
+       "      <td>0.072164</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.104152</td>\n",
+       "      <td>both</td>\n",
+       "      <td>4.867849</td>\n",
+       "      <td>0.279119</td>\n",
+       "      <td>5.923989</td>\n",
+       "      <td>1.049500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.406700</td>\n",
+       "      <td>25</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.212577</td>\n",
+       "      <td>-0.358400</td>\n",
+       "      <td>0.003703</td>\n",
+       "      <td>0.060852</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.088870</td>\n",
+       "      <td>both</td>\n",
+       "      <td>3.493364</td>\n",
+       "      <td>0.151725</td>\n",
+       "      <td>4.576347</td>\n",
+       "      <td>0.593127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <th>5</th>\n",
+       "      <td>1.299259</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.465342</td>\n",
+       "      <td>-0.307455</td>\n",
+       "      <td>0.016769</td>\n",
+       "      <td>0.129493</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.352139</td>\n",
+       "      <td>both</td>\n",
+       "      <td>3.593557</td>\n",
+       "      <td>0.335849</td>\n",
+       "      <td>3.689615</td>\n",
+       "      <td>1.513531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
+       "      <th>5</th>\n",
+       "      <td>0.027871</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.035593</td>\n",
+       "      <td>-0.413200</td>\n",
+       "      <td>0.000311</td>\n",
+       "      <td>0.017649</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.009033</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2.016753</td>\n",
+       "      <td>0.017944</td>\n",
+       "      <td>3.085533</td>\n",
+       "      <td>0.086140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.014474</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.025650</td>\n",
+       "      <td>-0.413967</td>\n",
+       "      <td>0.000136</td>\n",
+       "      <td>0.011666</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.003947</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2.198620</td>\n",
+       "      <td>0.013983</td>\n",
+       "      <td>3.667120</td>\n",
+       "      <td>0.061961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.006801</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.017996</td>\n",
+       "      <td>-0.413448</td>\n",
+       "      <td>0.000064</td>\n",
+       "      <td>0.007985</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.001785</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2.253802</td>\n",
+       "      <td>0.010011</td>\n",
+       "      <td>3.809716</td>\n",
+       "      <td>0.043527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.004502</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.014305</td>\n",
+       "      <td>-0.412267</td>\n",
+       "      <td>0.000055</td>\n",
+       "      <td>0.007437</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.001604</td>\n",
+       "      <td>both</td>\n",
+       "      <td>1.923562</td>\n",
+       "      <td>0.006868</td>\n",
+       "      <td>2.806966</td>\n",
+       "      <td>0.034699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.003393</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.012711</td>\n",
+       "      <td>-0.413655</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.005340</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.000799</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2.380180</td>\n",
+       "      <td>0.007371</td>\n",
+       "      <td>4.248942</td>\n",
+       "      <td>0.030729</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>592 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                probabilistic_sum_of_squares  \\\n",
+       "Metric             simulation       n_surrogates average_count                                 \n",
+       "Average Odds       extremely_unfair 50           10                                 1.400771   \n",
+       "                                                 20                                 1.013655   \n",
+       "                                                 30                                 0.616997   \n",
+       "                                                 40                                 0.406700   \n",
+       "                                    100          5                                  1.299259   \n",
+       "...                                                                                      ...   \n",
+       "Statistical Parity very_unfair      1000         5                                  0.027871   \n",
+       "                                                 10                                 0.014474   \n",
+       "                                                 20                                 0.006801   \n",
+       "                                                 30                                 0.004502   \n",
+       "                                                 40                                 0.003393   \n",
+       "\n",
+       "                                                                probabilistic_count  \\\n",
+       "Metric             simulation       n_surrogates average_count                        \n",
+       "Average Odds       extremely_unfair 50           10                              26   \n",
+       "                                                 20                              22   \n",
+       "                                                 30                              21   \n",
+       "                                                 40                              25   \n",
+       "                                    100          5                               22   \n",
+       "...                                                                             ...   \n",
+       "Statistical Parity very_unfair      1000         5                               30   \n",
+       "                                                 10                              30   \n",
+       "                                                 20                              29   \n",
+       "                                                 30                              30   \n",
+       "                                                 40                              29   \n",
+       "\n",
+       "                                                                df  \\\n",
+       "Metric             simulation       n_surrogates average_count       \n",
+       "Average Odds       extremely_unfair 50           10             16   \n",
+       "                                                 20             16   \n",
+       "                                                 30             16   \n",
+       "                                                 40             16   \n",
+       "                                    100          5              16   \n",
+       "...                                                             ..   \n",
+       "Statistical Parity very_unfair      1000         5               8   \n",
+       "                                                 10              8   \n",
+       "                                                 20              8   \n",
+       "                                                 30              8   \n",
+       "                                                 40              8   \n",
+       "\n",
+       "                                                                probabilistic_se  \\\n",
+       "Metric             simulation       n_surrogates average_count                     \n",
+       "Average Odds       extremely_unfair 50           10                     0.374269   \n",
+       "                                                 20                     0.411026   \n",
+       "                                                 30                     0.351283   \n",
+       "                                                 40                     0.212577   \n",
+       "                                    100          5                      0.465342   \n",
+       "...                                                                          ...   \n",
+       "Statistical Parity very_unfair      1000         5                      0.035593   \n",
+       "                                                 10                     0.025650   \n",
+       "                                                 20                     0.017996   \n",
+       "                                                 30                     0.014305   \n",
+       "                                                 40                     0.012711   \n",
+       "\n",
+       "                                                                oracle_mean  \\\n",
+       "Metric             simulation       n_surrogates average_count                \n",
+       "Average Odds       extremely_unfair 50           10               -0.332308   \n",
+       "                                                 20               -0.332000   \n",
+       "                                                 30               -0.334714   \n",
+       "                                                 40               -0.358400   \n",
+       "                                    100          5                -0.307455   \n",
+       "...                                                                     ...   \n",
+       "Statistical Parity very_unfair      1000         5                -0.413200   \n",
+       "                                                 10               -0.413967   \n",
+       "                                                 20               -0.413448   \n",
+       "                                                 30               -0.412267   \n",
+       "                                                 40               -0.413655   \n",
+       "\n",
+       "                                                                oracle_var  \\\n",
+       "Metric             simulation       n_surrogates average_count               \n",
+       "Average Odds       extremely_unfair 50           10               0.016266   \n",
+       "                                                 20               0.009308   \n",
+       "                                                 30               0.005208   \n",
+       "                                                 40               0.003703   \n",
+       "                                    100          5                0.016769   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      1000         5                0.000311   \n",
+       "                                                 10               0.000136   \n",
+       "                                                 20               0.000064   \n",
+       "                                                 30               0.000055   \n",
+       "                                                 40               0.000029   \n",
+       "\n",
+       "                                                                oracle_std  \\\n",
+       "Metric             simulation       n_surrogates average_count               \n",
+       "Average Odds       extremely_unfair 50           10               0.127536   \n",
+       "                                                 20               0.096480   \n",
+       "                                                 30               0.072164   \n",
+       "                                                 40               0.060852   \n",
+       "                                    100          5                0.129493   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      1000         5                0.017649   \n",
+       "                                                 10               0.011666   \n",
+       "                                                 20               0.007985   \n",
+       "                                                 30               0.007437   \n",
+       "                                                 40               0.005340   \n",
+       "\n",
+       "                                                                oracle_count  \\\n",
+       "Metric             simulation       n_surrogates average_count                 \n",
+       "Average Odds       extremely_unfair 50           10                       26   \n",
+       "                                                 20                       22   \n",
+       "                                                 30                       21   \n",
+       "                                                 40                       25   \n",
+       "                                    100          5                        22   \n",
+       "...                                                                      ...   \n",
+       "Statistical Parity very_unfair      1000         5                        30   \n",
+       "                                                 10                       30   \n",
+       "                                                 20                       29   \n",
+       "                                                 30                       30   \n",
+       "                                                 40                       29   \n",
+       "\n",
+       "                                                                oracle_sum_of_squares  \\\n",
+       "Metric             simulation       n_surrogates average_count                          \n",
+       "Average Odds       extremely_unfair 50           10                          0.406638   \n",
+       "                                                 20                          0.195476   \n",
+       "                                                 30                          0.104152   \n",
+       "                                                 40                          0.088870   \n",
+       "                                    100          5                           0.352139   \n",
+       "...                                                                               ...   \n",
+       "Statistical Parity very_unfair      1000         5                           0.009033   \n",
+       "                                                 10                          0.003947   \n",
+       "                                                 20                          0.001785   \n",
+       "                                                 30                          0.001604   \n",
+       "                                                 40                          0.000799   \n",
+       "\n",
+       "                                                               _merge  \\\n",
+       "Metric             simulation       n_surrogates average_count          \n",
+       "Average Odds       extremely_unfair 50           10              both   \n",
+       "                                                 20              both   \n",
+       "                                                 30              both   \n",
+       "                                                 40              both   \n",
+       "                                    100          5               both   \n",
+       "...                                                               ...   \n",
+       "Statistical Parity very_unfair      1000         5               both   \n",
+       "                                                 10              both   \n",
+       "                                                 20              both   \n",
+       "                                                 30              both   \n",
+       "                                                 40              both   \n",
+       "\n",
+       "                                                                std_ratio  \\\n",
+       "Metric             simulation       n_surrogates average_count              \n",
+       "Average Odds       extremely_unfair 50           10              2.934606   \n",
+       "                                                 20              4.260224   \n",
+       "                                                 30              4.867849   \n",
+       "                                                 40              3.493364   \n",
+       "                                    100          5               3.593557   \n",
+       "...                                                                   ...   \n",
+       "Statistical Parity very_unfair      1000         5               2.016753   \n",
+       "                                                 10              2.198620   \n",
+       "                                                 20              2.253802   \n",
+       "                                                 30              1.923562   \n",
+       "                                                 40              2.380180   \n",
+       "\n",
+       "                                                                std_diff  \\\n",
+       "Metric             simulation       n_surrogates average_count             \n",
+       "Average Odds       extremely_unfair 50           10             0.246732   \n",
+       "                                                 20             0.314546   \n",
+       "                                                 30             0.279119   \n",
+       "                                                 40             0.151725   \n",
+       "                                    100          5              0.335849   \n",
+       "...                                                                  ...   \n",
+       "Statistical Parity very_unfair      1000         5              0.017944   \n",
+       "                                                 10             0.013983   \n",
+       "                                                 20             0.010011   \n",
+       "                                                 30             0.006868   \n",
+       "                                                 40             0.007371   \n",
+       "\n",
+       "                                                                ss_ratio  \\\n",
+       "Metric             simulation       n_surrogates average_count             \n",
+       "Average Odds       extremely_unfair 50           10             3.444766   \n",
+       "                                                 20             5.185573   \n",
+       "                                                 30             5.923989   \n",
+       "                                                 40             4.576347   \n",
+       "                                    100          5              3.689615   \n",
+       "...                                                                  ...   \n",
+       "Statistical Parity very_unfair      1000         5              3.085533   \n",
+       "                                                 10             3.667120   \n",
+       "                                                 20             3.809716   \n",
+       "                                                 30             2.806966   \n",
+       "                                                 40             4.248942   \n",
+       "\n",
+       "                                                                prob_se_oracle_val_ratio  \n",
+       "Metric             simulation       n_surrogates average_count                            \n",
+       "Average Odds       extremely_unfair 50           10                             1.126272  \n",
+       "                                                 20                             1.238031  \n",
+       "                                                 30                             1.049500  \n",
+       "                                                 40                             0.593127  \n",
+       "                                    100          5                              1.513531  \n",
+       "...                                                                                  ...  \n",
+       "Statistical Parity very_unfair      1000         5                              0.086140  \n",
+       "                                                 10                             0.061961  \n",
+       "                                                 20                             0.043527  \n",
+       "                                                 30                             0.034699  \n",
+       "                                                 40                             0.030729  \n",
+       "\n",
+       "[592 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_errors=standard_errors.merge(oracle_std,left_index=True,right_index=True,how='outer',indicator=True)\n",
+    "compare_errors[\"std_ratio\"]=compare_errors[\"probabilistic_se\"]/compare_errors[\"oracle_std\"]\n",
+    "compare_errors[\"std_diff\"]=(compare_errors[\"probabilistic_se\"]-compare_errors[\"oracle_std\"]).apply(abs)\n",
+    "compare_errors[\"ss_ratio\"]=(compare_errors[\"probabilistic_sum_of_squares\"]/compare_errors[\"oracle_sum_of_squares\"]).apply(abs)\n",
+    "compare_errors[\"prob_se_oracle_val_ratio\"]=(compare_errors[\"probabilistic_se\"]/compare_errors[\"oracle_mean\"]).apply(abs)\n",
+    "\n",
+    "compare_errors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "4f16587b-0318-4321-be8a-be8cd557912c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "both          592\n",
+       "left_only       0\n",
+       "right_only      0\n",
+       "Name: _merge, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_errors[\"_merge\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "58dc3a29-656c-4058-b93b-f833415c5fe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pivot_errors(df,metric_name,simulation_name,values_name):\n",
+    "    query_string=f'Metric==\"{metric_name}\" & simulation==\"{simulation_name}\"'\n",
+    "    return df.reset_index().query(query_string).pivot(index=\"average_count\",columns=\"n_surrogates\",values=values_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "420040aa-d98a-49a1-abc3-1ba823994b81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>50</th>\n",
+       "      <th>100</th>\n",
+       "      <th>300</th>\n",
+       "      <th>400</th>\n",
+       "      <th>500</th>\n",
+       "      <th>1000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.178950</td>\n",
+       "      <td>0.131230</td>\n",
+       "      <td>0.074057</td>\n",
+       "      <td>0.075282</td>\n",
+       "      <td>0.045313</td>\n",
+       "      <td>0.042260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.146197</td>\n",
+       "      <td>0.095050</td>\n",
+       "      <td>0.046240</td>\n",
+       "      <td>0.044570</td>\n",
+       "      <td>0.035201</td>\n",
+       "      <td>0.021683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.154840</td>\n",
+       "      <td>0.058909</td>\n",
+       "      <td>0.041087</td>\n",
+       "      <td>0.034563</td>\n",
+       "      <td>0.024559</td>\n",
+       "      <td>0.026752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.110500</td>\n",
+       "      <td>0.059056</td>\n",
+       "      <td>0.021237</td>\n",
+       "      <td>0.033892</td>\n",
+       "      <td>0.027061</td>\n",
+       "      <td>0.021384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "n_surrogates       50        100       300       400       500       1000\n",
+       "average_count                                                            \n",
+       "10             0.178950  0.131230  0.074057  0.075282  0.045313  0.042260\n",
+       "20             0.146197  0.095050  0.046240  0.044570  0.035201  0.021683\n",
+       "30             0.154840  0.058909  0.041087  0.034563  0.024559  0.026752\n",
+       "40             0.110500  0.059056  0.021237  0.033892  0.027061  0.021384"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col_name=\"probabilistic_se\"\n",
+    "pivoted_errors=pivot_errors(compare_errors.query('average_count>5'),\"Equal Opportunity\",\"fair\",col_name)\n",
+    "pivoted_errors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "341d3109-6854-43b7-9c92-4ac59e6b77e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/1029826322.py:2: UserWarning: \n",
+      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
+      "  sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1297eb970>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 5), dpi=100)\n",
+    "sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n",
+    "plt.xlabel(\"Individuals Per Level\")\n",
+    "plt.ylabel(col_name)\n",
+    "plt.legend(title=\"N Surrogates\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "6dda5ebd-45d9-41b7-bfee-2c0cca1d8250",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>large_sample_se</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Statistical Parity</th>\n",
+       "      <th>fair</th>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equal Opportunity</th>\n",
+       "      <th>fair</th>\n",
+       "      <td>0.009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predictive Equality</th>\n",
+       "      <th>fair</th>\n",
+       "      <td>0.006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Average Odds</th>\n",
+       "      <th>fair</th>\n",
+       "      <td>0.006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Statistical Parity</th>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equal Opportunity</th>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>0.013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predictive Equality</th>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Average Odds</th>\n",
+       "      <th>slightly_unfair</th>\n",
+       "      <td>0.006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Statistical Parity</th>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>0.004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equal Opportunity</th>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>0.023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predictive Equality</th>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>0.004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Average Odds</th>\n",
+       "      <th>moderately_unfair</th>\n",
+       "      <td>0.011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Statistical Parity</th>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equal Opportunity</th>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>0.027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predictive Equality</th>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Average Odds</th>\n",
+       "      <th>very_unfair</th>\n",
+       "      <td>0.013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Statistical Parity</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Equal Opportunity</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>0.029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predictive Equality</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>0.004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Average Odds</th>\n",
+       "      <th>extremely_unfair</th>\n",
+       "      <td>0.015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       large_sample_se\n",
+       "Metric              simulation                        \n",
+       "Statistical Parity  fair                         0.005\n",
+       "Equal Opportunity   fair                         0.009\n",
+       "Predictive Equality fair                         0.006\n",
+       "Average Odds        fair                         0.006\n",
+       "Statistical Parity  slightly_unfair              0.005\n",
+       "Equal Opportunity   slightly_unfair              0.013\n",
+       "Predictive Equality slightly_unfair              0.005\n",
+       "Average Odds        slightly_unfair              0.006\n",
+       "Statistical Parity  moderately_unfair            0.004\n",
+       "Equal Opportunity   moderately_unfair            0.023\n",
+       "Predictive Equality moderately_unfair            0.004\n",
+       "Average Odds        moderately_unfair            0.011\n",
+       "Statistical Parity  very_unfair                  0.005\n",
+       "Equal Opportunity   very_unfair                  0.027\n",
+       "Predictive Equality very_unfair                  0.005\n",
+       "Average Odds        very_unfair                  0.013\n",
+       "Statistical Parity  extremely_unfair             0.005\n",
+       "Equal Opportunity   extremely_unfair             0.029\n",
+       "Predictive Equality extremely_unfair             0.004\n",
+       "Average Odds        extremely_unfair             0.015"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#In order to show all of the standard deviations on the same graph, \n",
+    "#We compare the standard deviation of the statistic at each sample size \n",
+    "#To the large sample statistics reported in the first section. \n",
+    "large_sample_se=pd.DataFrame.from_dict({\"simulation\":[\"fair\",\"fair\",\"fair\",\"fair\",\n",
+    "                                        \"slightly_unfair\",\"slightly_unfair\",\"slightly_unfair\",\"slightly_unfair\",\n",
+    "                                        \"moderately_unfair\",\"moderately_unfair\",\"moderately_unfair\",\"moderately_unfair\",\n",
+    "                                        \"very_unfair\",\"very_unfair\",\"very_unfair\",\"very_unfair\",\n",
+    "                                        \"extremely_unfair\",\"extremely_unfair\",\"extremely_unfair\",\"extremely_unfair\"],\n",
+    "                                        \"Metric\":[\"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
+    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
+    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
+    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
+    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\"],\n",
+    "                                        \"large_sample_se\":[0.005,0.009,0.006,0.006,\n",
+    "                                         0.005,0.013,0.005,0.006,\n",
+    "                                         0.004,0.023,0.004,0.011,\n",
+    "                                         0.005,0.027,0.005,0.013,\n",
+    "                                         0.005,0.029,0.004,0.015]}).set_index([\"Metric\",\"simulation\"])\n",
+    "large_sample_se\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d78afe80-64b4-4db8-9bcc-db09dba2fe98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "both          592\n",
+      "left_only       0\n",
+      "right_only      0\n",
+      "Name: _merge, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>probabilistic_sum_of_squares</th>\n",
+       "      <th>probabilistic_count</th>\n",
+       "      <th>df</th>\n",
+       "      <th>probabilistic_se</th>\n",
+       "      <th>oracle_mean</th>\n",
+       "      <th>oracle_var</th>\n",
+       "      <th>oracle_std</th>\n",
+       "      <th>oracle_count</th>\n",
+       "      <th>oracle_sum_of_squares</th>\n",
+       "      <th>std_ratio</th>\n",
+       "      <th>std_diff</th>\n",
+       "      <th>ss_ratio</th>\n",
+       "      <th>prob_se_oracle_val_ratio</th>\n",
+       "      <th>large_sample_se</th>\n",
+       "      <th>_merge</th>\n",
+       "      <th>small_to_large_se</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metric</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
+       "      <th>10</th>\n",
+       "      <td>1.400771</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.374269</td>\n",
+       "      <td>-0.332308</td>\n",
+       "      <td>0.016266</td>\n",
+       "      <td>0.127536</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.406638</td>\n",
+       "      <td>2.934606</td>\n",
+       "      <td>0.246732</td>\n",
+       "      <td>3.444766</td>\n",
+       "      <td>1.126272</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>both</td>\n",
+       "      <td>24.951250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.013655</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.411026</td>\n",
+       "      <td>-0.332000</td>\n",
+       "      <td>0.009308</td>\n",
+       "      <td>0.096480</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.195476</td>\n",
+       "      <td>4.260224</td>\n",
+       "      <td>0.314546</td>\n",
+       "      <td>5.185573</td>\n",
+       "      <td>1.238031</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>both</td>\n",
+       "      <td>27.401744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.616997</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.351283</td>\n",
+       "      <td>-0.334714</td>\n",
+       "      <td>0.005208</td>\n",
+       "      <td>0.072164</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.104152</td>\n",
+       "      <td>4.867849</td>\n",
+       "      <td>0.279119</td>\n",
+       "      <td>5.923989</td>\n",
+       "      <td>1.049500</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>both</td>\n",
+       "      <td>23.418834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.406700</td>\n",
+       "      <td>25</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.212577</td>\n",
+       "      <td>-0.358400</td>\n",
+       "      <td>0.003703</td>\n",
+       "      <td>0.060852</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.088870</td>\n",
+       "      <td>3.493364</td>\n",
+       "      <td>0.151725</td>\n",
+       "      <td>4.576347</td>\n",
+       "      <td>0.593127</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>both</td>\n",
+       "      <td>14.171786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <th>5</th>\n",
+       "      <td>1.299259</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.465342</td>\n",
+       "      <td>-0.307455</td>\n",
+       "      <td>0.016769</td>\n",
+       "      <td>0.129493</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.352139</td>\n",
+       "      <td>3.593557</td>\n",
+       "      <td>0.335849</td>\n",
+       "      <td>3.689615</td>\n",
+       "      <td>1.513531</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>both</td>\n",
+       "      <td>31.022799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
+       "      <th>5</th>\n",
+       "      <td>0.027871</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.035593</td>\n",
+       "      <td>-0.413200</td>\n",
+       "      <td>0.000311</td>\n",
+       "      <td>0.017649</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.009033</td>\n",
+       "      <td>2.016753</td>\n",
+       "      <td>0.017944</td>\n",
+       "      <td>3.085533</td>\n",
+       "      <td>0.086140</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>both</td>\n",
+       "      <td>7.118606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.014474</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.025650</td>\n",
+       "      <td>-0.413967</td>\n",
+       "      <td>0.000136</td>\n",
+       "      <td>0.011666</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.003947</td>\n",
+       "      <td>2.198620</td>\n",
+       "      <td>0.013983</td>\n",
+       "      <td>3.667120</td>\n",
+       "      <td>0.061961</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>both</td>\n",
+       "      <td>5.129948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.006801</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.017996</td>\n",
+       "      <td>-0.413448</td>\n",
+       "      <td>0.000064</td>\n",
+       "      <td>0.007985</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.001785</td>\n",
+       "      <td>2.253802</td>\n",
+       "      <td>0.010011</td>\n",
+       "      <td>3.809716</td>\n",
+       "      <td>0.043527</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>both</td>\n",
+       "      <td>3.599206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.004502</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.014305</td>\n",
+       "      <td>-0.412267</td>\n",
+       "      <td>0.000055</td>\n",
+       "      <td>0.007437</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.001604</td>\n",
+       "      <td>1.923562</td>\n",
+       "      <td>0.006868</td>\n",
+       "      <td>2.806966</td>\n",
+       "      <td>0.034699</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2.861023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.003393</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.012711</td>\n",
+       "      <td>-0.413655</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.005340</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.000799</td>\n",
+       "      <td>2.380180</td>\n",
+       "      <td>0.007371</td>\n",
+       "      <td>4.248942</td>\n",
+       "      <td>0.030729</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2.542215</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>592 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                probabilistic_sum_of_squares  \\\n",
+       "Metric             simulation       n_surrogates average_count                                 \n",
+       "Average Odds       extremely_unfair 50           10                                 1.400771   \n",
+       "                                                 20                                 1.013655   \n",
+       "                                                 30                                 0.616997   \n",
+       "                                                 40                                 0.406700   \n",
+       "                                    100          5                                  1.299259   \n",
+       "...                                                                                      ...   \n",
+       "Statistical Parity very_unfair      1000         5                                  0.027871   \n",
+       "                                                 10                                 0.014474   \n",
+       "                                                 20                                 0.006801   \n",
+       "                                                 30                                 0.004502   \n",
+       "                                                 40                                 0.003393   \n",
+       "\n",
+       "                                                                probabilistic_count  \\\n",
+       "Metric             simulation       n_surrogates average_count                        \n",
+       "Average Odds       extremely_unfair 50           10                              26   \n",
+       "                                                 20                              22   \n",
+       "                                                 30                              21   \n",
+       "                                                 40                              25   \n",
+       "                                    100          5                               22   \n",
+       "...                                                                             ...   \n",
+       "Statistical Parity very_unfair      1000         5                               30   \n",
+       "                                                 10                              30   \n",
+       "                                                 20                              29   \n",
+       "                                                 30                              30   \n",
+       "                                                 40                              29   \n",
+       "\n",
+       "                                                                df  \\\n",
+       "Metric             simulation       n_surrogates average_count       \n",
+       "Average Odds       extremely_unfair 50           10             16   \n",
+       "                                                 20             16   \n",
+       "                                                 30             16   \n",
+       "                                                 40             16   \n",
+       "                                    100          5              16   \n",
+       "...                                                             ..   \n",
+       "Statistical Parity very_unfair      1000         5               8   \n",
+       "                                                 10              8   \n",
+       "                                                 20              8   \n",
+       "                                                 30              8   \n",
+       "                                                 40              8   \n",
+       "\n",
+       "                                                                probabilistic_se  \\\n",
+       "Metric             simulation       n_surrogates average_count                     \n",
+       "Average Odds       extremely_unfair 50           10                     0.374269   \n",
+       "                                                 20                     0.411026   \n",
+       "                                                 30                     0.351283   \n",
+       "                                                 40                     0.212577   \n",
+       "                                    100          5                      0.465342   \n",
+       "...                                                                          ...   \n",
+       "Statistical Parity very_unfair      1000         5                      0.035593   \n",
+       "                                                 10                     0.025650   \n",
+       "                                                 20                     0.017996   \n",
+       "                                                 30                     0.014305   \n",
+       "                                                 40                     0.012711   \n",
+       "\n",
+       "                                                                oracle_mean  \\\n",
+       "Metric             simulation       n_surrogates average_count                \n",
+       "Average Odds       extremely_unfair 50           10               -0.332308   \n",
+       "                                                 20               -0.332000   \n",
+       "                                                 30               -0.334714   \n",
+       "                                                 40               -0.358400   \n",
+       "                                    100          5                -0.307455   \n",
+       "...                                                                     ...   \n",
+       "Statistical Parity very_unfair      1000         5                -0.413200   \n",
+       "                                                 10               -0.413967   \n",
+       "                                                 20               -0.413448   \n",
+       "                                                 30               -0.412267   \n",
+       "                                                 40               -0.413655   \n",
+       "\n",
+       "                                                                oracle_var  \\\n",
+       "Metric             simulation       n_surrogates average_count               \n",
+       "Average Odds       extremely_unfair 50           10               0.016266   \n",
+       "                                                 20               0.009308   \n",
+       "                                                 30               0.005208   \n",
+       "                                                 40               0.003703   \n",
+       "                                    100          5                0.016769   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      1000         5                0.000311   \n",
+       "                                                 10               0.000136   \n",
+       "                                                 20               0.000064   \n",
+       "                                                 30               0.000055   \n",
+       "                                                 40               0.000029   \n",
+       "\n",
+       "                                                                oracle_std  \\\n",
+       "Metric             simulation       n_surrogates average_count               \n",
+       "Average Odds       extremely_unfair 50           10               0.127536   \n",
+       "                                                 20               0.096480   \n",
+       "                                                 30               0.072164   \n",
+       "                                                 40               0.060852   \n",
+       "                                    100          5                0.129493   \n",
+       "...                                                                    ...   \n",
+       "Statistical Parity very_unfair      1000         5                0.017649   \n",
+       "                                                 10               0.011666   \n",
+       "                                                 20               0.007985   \n",
+       "                                                 30               0.007437   \n",
+       "                                                 40               0.005340   \n",
+       "\n",
+       "                                                                oracle_count  \\\n",
+       "Metric             simulation       n_surrogates average_count                 \n",
+       "Average Odds       extremely_unfair 50           10                       26   \n",
+       "                                                 20                       22   \n",
+       "                                                 30                       21   \n",
+       "                                                 40                       25   \n",
+       "                                    100          5                        22   \n",
+       "...                                                                      ...   \n",
+       "Statistical Parity very_unfair      1000         5                        30   \n",
+       "                                                 10                       30   \n",
+       "                                                 20                       29   \n",
+       "                                                 30                       30   \n",
+       "                                                 40                       29   \n",
+       "\n",
+       "                                                                oracle_sum_of_squares  \\\n",
+       "Metric             simulation       n_surrogates average_count                          \n",
+       "Average Odds       extremely_unfair 50           10                          0.406638   \n",
+       "                                                 20                          0.195476   \n",
+       "                                                 30                          0.104152   \n",
+       "                                                 40                          0.088870   \n",
+       "                                    100          5                           0.352139   \n",
+       "...                                                                               ...   \n",
+       "Statistical Parity very_unfair      1000         5                           0.009033   \n",
+       "                                                 10                          0.003947   \n",
+       "                                                 20                          0.001785   \n",
+       "                                                 30                          0.001604   \n",
+       "                                                 40                          0.000799   \n",
+       "\n",
+       "                                                                std_ratio  \\\n",
+       "Metric             simulation       n_surrogates average_count              \n",
+       "Average Odds       extremely_unfair 50           10              2.934606   \n",
+       "                                                 20              4.260224   \n",
+       "                                                 30              4.867849   \n",
+       "                                                 40              3.493364   \n",
+       "                                    100          5               3.593557   \n",
+       "...                                                                   ...   \n",
+       "Statistical Parity very_unfair      1000         5               2.016753   \n",
+       "                                                 10              2.198620   \n",
+       "                                                 20              2.253802   \n",
+       "                                                 30              1.923562   \n",
+       "                                                 40              2.380180   \n",
+       "\n",
+       "                                                                std_diff  \\\n",
+       "Metric             simulation       n_surrogates average_count             \n",
+       "Average Odds       extremely_unfair 50           10             0.246732   \n",
+       "                                                 20             0.314546   \n",
+       "                                                 30             0.279119   \n",
+       "                                                 40             0.151725   \n",
+       "                                    100          5              0.335849   \n",
+       "...                                                                  ...   \n",
+       "Statistical Parity very_unfair      1000         5              0.017944   \n",
+       "                                                 10             0.013983   \n",
+       "                                                 20             0.010011   \n",
+       "                                                 30             0.006868   \n",
+       "                                                 40             0.007371   \n",
+       "\n",
+       "                                                                ss_ratio  \\\n",
+       "Metric             simulation       n_surrogates average_count             \n",
+       "Average Odds       extremely_unfair 50           10             3.444766   \n",
+       "                                                 20             5.185573   \n",
+       "                                                 30             5.923989   \n",
+       "                                                 40             4.576347   \n",
+       "                                    100          5              3.689615   \n",
+       "...                                                                  ...   \n",
+       "Statistical Parity very_unfair      1000         5              3.085533   \n",
+       "                                                 10             3.667120   \n",
+       "                                                 20             3.809716   \n",
+       "                                                 30             2.806966   \n",
+       "                                                 40             4.248942   \n",
+       "\n",
+       "                                                                prob_se_oracle_val_ratio  \\\n",
+       "Metric             simulation       n_surrogates average_count                             \n",
+       "Average Odds       extremely_unfair 50           10                             1.126272   \n",
+       "                                                 20                             1.238031   \n",
+       "                                                 30                             1.049500   \n",
+       "                                                 40                             0.593127   \n",
+       "                                    100          5                              1.513531   \n",
+       "...                                                                                  ...   \n",
+       "Statistical Parity very_unfair      1000         5                              0.086140   \n",
+       "                                                 10                             0.061961   \n",
+       "                                                 20                             0.043527   \n",
+       "                                                 30                             0.034699   \n",
+       "                                                 40                             0.030729   \n",
+       "\n",
+       "                                                                large_sample_se  \\\n",
+       "Metric             simulation       n_surrogates average_count                    \n",
+       "Average Odds       extremely_unfair 50           10                       0.015   \n",
+       "                                                 20                       0.015   \n",
+       "                                                 30                       0.015   \n",
+       "                                                 40                       0.015   \n",
+       "                                    100          5                        0.015   \n",
+       "...                                                                         ...   \n",
+       "Statistical Parity very_unfair      1000         5                        0.005   \n",
+       "                                                 10                       0.005   \n",
+       "                                                 20                       0.005   \n",
+       "                                                 30                       0.005   \n",
+       "                                                 40                       0.005   \n",
+       "\n",
+       "                                                               _merge  \\\n",
+       "Metric             simulation       n_surrogates average_count          \n",
+       "Average Odds       extremely_unfair 50           10              both   \n",
+       "                                                 20              both   \n",
+       "                                                 30              both   \n",
+       "                                                 40              both   \n",
+       "                                    100          5               both   \n",
+       "...                                                               ...   \n",
+       "Statistical Parity very_unfair      1000         5               both   \n",
+       "                                                 10              both   \n",
+       "                                                 20              both   \n",
+       "                                                 30              both   \n",
+       "                                                 40              both   \n",
+       "\n",
+       "                                                                small_to_large_se  \n",
+       "Metric             simulation       n_surrogates average_count                     \n",
+       "Average Odds       extremely_unfair 50           10                     24.951250  \n",
+       "                                                 20                     27.401744  \n",
+       "                                                 30                     23.418834  \n",
+       "                                                 40                     14.171786  \n",
+       "                                    100          5                      31.022799  \n",
+       "...                                                                           ...  \n",
+       "Statistical Parity very_unfair      1000         5                       7.118606  \n",
+       "                                                 10                      5.129948  \n",
+       "                                                 20                      3.599206  \n",
+       "                                                 30                      2.861023  \n",
+       "                                                 40                      2.542215  \n",
+       "\n",
+       "[592 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_to_large=compare_errors.drop(columns=[\"_merge\"]).merge(large_sample_se,left_index=True,right_index=True,how=\"outer\",indicator=True)\n",
+    "print(compare_to_large[\"_merge\"].value_counts())\n",
+    "compare_to_large[\"small_to_large_se\"]=compare_to_large[\"probabilistic_se\"]/compare_to_large[\"large_sample_se\"]\n",
+    "compare_to_large\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c557719c-a3c0-4f5d-ac4b-4b1595760133",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>50</th>\n",
+       "      <th>100</th>\n",
+       "      <th>300</th>\n",
+       "      <th>400</th>\n",
+       "      <th>500</th>\n",
+       "      <th>1000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.896489</td>\n",
+       "      <td>17.617785</td>\n",
+       "      <td>14.603082</td>\n",
+       "      <td>13.777349</td>\n",
+       "      <td>8.457351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>31.982684</td>\n",
+       "      <td>16.590574</td>\n",
+       "      <td>10.792551</td>\n",
+       "      <td>12.214599</td>\n",
+       "      <td>9.682458</td>\n",
+       "      <td>6.408889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>22.797279</td>\n",
+       "      <td>14.947161</td>\n",
+       "      <td>7.556927</td>\n",
+       "      <td>8.315340</td>\n",
+       "      <td>5.878505</td>\n",
+       "      <td>4.835357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>16.568213</td>\n",
+       "      <td>11.538793</td>\n",
+       "      <td>5.952940</td>\n",
+       "      <td>4.917443</td>\n",
+       "      <td>4.387482</td>\n",
+       "      <td>3.195789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>13.931345</td>\n",
+       "      <td>9.773428</td>\n",
+       "      <td>4.922121</td>\n",
+       "      <td>3.953925</td>\n",
+       "      <td>4.354334</td>\n",
+       "      <td>2.819877</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "n_surrogates        50         100        300        400        500       1000\n",
+       "average_count                                                                 \n",
+       "5                    NaN  25.896489  17.617785  14.603082  13.777349  8.457351\n",
+       "10             31.982684  16.590574  10.792551  12.214599   9.682458  6.408889\n",
+       "20             22.797279  14.947161   7.556927   8.315340   5.878505  4.835357\n",
+       "30             16.568213  11.538793   5.952940   4.917443   4.387482  3.195789\n",
+       "40             13.931345   9.773428   4.922121   3.953925   4.354334  2.819877"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col_name=\"small_to_large_se\"\n",
+    "pivoted_errors=pivot_errors(compare_to_large,\"Statistical Parity\",\"moderately_unfair\",col_name)\n",
+    "pivoted_errors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "6b579b0a-70bf-444e-8781-39b6d80a5706",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/3273741071.py:2: UserWarning: \n",
+      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
+      "  sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 5), dpi=100)\n",
+    "sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n",
+    "plt.xlabel(\"Individuals Per Level\")\n",
+    "plt.ylabel(col_name)\n",
+    "plt.legend(title=\"Levels of Z\")\n",
+    "plt.savefig(\"standard_error_ratio.png\",dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "81974a1a-7c30-477a-9da5-86c5153072e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/3377951155.py:3: UserWarning: \n",
+      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
+      "  sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pivoted_errors=pivot_errors(compare_to_large,\"Statistical Parity\",\"moderately_unfair\",\"small_to_large_se\")\n",
+    "plt.figure(figsize=(8, 5), dpi=100)\n",
+    "sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n",
+    "plt.xlabel(\"Individuals Per Level\")\n",
+    "plt.ylabel(\"Standard Error Ratio\")\n",
+    "plt.legend(title=\"Levels of Z\")\n",
+    "plt.title(\"Change in Standard Deviation as Sample Size Increases\")\n",
+    "plt.savefig(\"standard_error_ratio.png\",dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "dd166db5-14b4-4833-ba02-db3c7202e287",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Metric               simulation       \n",
+       "Average Odds         extremely_unfair     31.022799\n",
+       "                     fair                 34.018526\n",
+       "                     moderately_unfair    54.912440\n",
+       "                     slightly_unfair      73.258999\n",
+       "                     very_unfair          42.324813\n",
+       "Equal Opportunity    extremely_unfair     21.039590\n",
+       "                     fair                 33.880035\n",
+       "                     moderately_unfair    34.391143\n",
+       "                     slightly_unfair      46.786413\n",
+       "                     very_unfair          26.406198\n",
+       "Predictive Equality  extremely_unfair     20.138865\n",
+       "                     fair                 30.098801\n",
+       "                     moderately_unfair    29.344982\n",
+       "                     slightly_unfair      27.736733\n",
+       "                     very_unfair          33.032923\n",
+       "Statistical Parity   extremely_unfair     26.942090\n",
+       "                     fair                 34.281561\n",
+       "                     moderately_unfair    31.982684\n",
+       "                     slightly_unfair      33.178651\n",
+       "                     very_unfair          41.666613\n",
+       "Name: small_to_large_se, dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_to_large.groupby([\"Metric\",\"simulation\"])[\"small_to_large_se\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "40b6b4c3-2cfa-4de9-93c6-9a3882d2ebbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Metric               simulation       \n",
+       "Average Odds         extremely_unfair     2.899097\n",
+       "                     fair                 2.416051\n",
+       "                     moderately_unfair    2.559562\n",
+       "                     slightly_unfair      4.312220\n",
+       "                     very_unfair          3.493931\n",
+       "Equal Opportunity    extremely_unfair     2.313933\n",
+       "                     fair                 2.359640\n",
+       "                     moderately_unfair    2.029978\n",
+       "                     slightly_unfair      3.301059\n",
+       "                     very_unfair          2.603045\n",
+       "Predictive Equality  extremely_unfair     2.279479\n",
+       "                     fair                 1.848764\n",
+       "                     moderately_unfair    3.112018\n",
+       "                     slightly_unfair      2.186737\n",
+       "                     very_unfair          2.335497\n",
+       "Statistical Parity   extremely_unfair     2.689035\n",
+       "                     fair                 2.300198\n",
+       "                     moderately_unfair    2.819877\n",
+       "                     slightly_unfair      2.161649\n",
+       "                     very_unfair          2.542215\n",
+       "Name: small_to_large_se, dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_to_large.groupby([\"Metric\",\"simulation\"])[\"small_to_large_se\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "ae2bdbcb-c5f3-4298-b303-8ce219cf14a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>probabilistic_se</th>\n",
+       "      <th>large_sample_se</th>\n",
+       "      <th>se_ratio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_count</th>\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">5</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.315713</td>\n",
+       "      <td>0.00875</td>\n",
+       "      <td>36.081469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.294178</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>29.417778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.170695</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>17.069506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.141937</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>14.193727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.124460</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>12.445997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.078519</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>7.851926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.284701</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>28.470057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.187965</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>18.796539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.114504</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>11.450357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.088459</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>8.845898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.076873</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>7.687338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.051142</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>5.114184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">20</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.212469</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>21.246893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.137857</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>13.785687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.074436</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>7.443642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.069738</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>6.973847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.058116</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>5.811568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.038450</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>3.845032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">30</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.170757</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>17.075735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.109066</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>10.906633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.059213</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>5.921254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.050148</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>5.014825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.040596</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>4.059623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.030429</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>3.042903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">40</th>\n",
+       "      <th>50</th>\n",
+       "      <td>0.146803</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>14.680332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.096735</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>9.673524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.048268</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>4.826794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>0.041226</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>4.122641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.034852</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>3.485152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000</th>\n",
+       "      <td>0.026180</td>\n",
+       "      <td>0.01000</td>\n",
+       "      <td>2.617997</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            probabilistic_se  large_sample_se   se_ratio\n",
+       "average_count n_surrogates                                              \n",
+       "5             50                    0.315713          0.00875  36.081469\n",
+       "              100                   0.294178          0.01000  29.417778\n",
+       "              300                   0.170695          0.01000  17.069506\n",
+       "              400                   0.141937          0.01000  14.193727\n",
+       "              500                   0.124460          0.01000  12.445997\n",
+       "              1000                  0.078519          0.01000   7.851926\n",
+       "10            50                    0.284701          0.01000  28.470057\n",
+       "              100                   0.187965          0.01000  18.796539\n",
+       "              300                   0.114504          0.01000  11.450357\n",
+       "              400                   0.088459          0.01000   8.845898\n",
+       "              500                   0.076873          0.01000   7.687338\n",
+       "              1000                  0.051142          0.01000   5.114184\n",
+       "20            50                    0.212469          0.01000  21.246893\n",
+       "              100                   0.137857          0.01000  13.785687\n",
+       "              300                   0.074436          0.01000   7.443642\n",
+       "              400                   0.069738          0.01000   6.973847\n",
+       "              500                   0.058116          0.01000   5.811568\n",
+       "              1000                  0.038450          0.01000   3.845032\n",
+       "30            50                    0.170757          0.01000  17.075735\n",
+       "              100                   0.109066          0.01000  10.906633\n",
+       "              300                   0.059213          0.01000   5.921254\n",
+       "              400                   0.050148          0.01000   5.014825\n",
+       "              500                   0.040596          0.01000   4.059623\n",
+       "              1000                  0.030429          0.01000   3.042903\n",
+       "40            50                    0.146803          0.01000  14.680332\n",
+       "              100                   0.096735          0.01000   9.673524\n",
+       "              300                   0.048268          0.01000   4.826794\n",
+       "              400                   0.041226          0.01000   4.122641\n",
+       "              500                   0.034852          0.01000   3.485152\n",
+       "              1000                  0.026180          0.01000   2.617997"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_ratio=compare_to_large.groupby([\"average_count\",\"n_surrogates\"])[[\"probabilistic_se\",\"large_sample_se\"]].mean()\n",
+    "average_ratio[\"se_ratio\"]=average_ratio[\"probabilistic_se\"]/average_ratio[\"large_sample_se\"]\n",
+    "average_ratio\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d5940108-cf14-4e76-a80b-2500688b82cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>n_surrogates</th>\n",
+       "      <th>50</th>\n",
+       "      <th>100</th>\n",
+       "      <th>300</th>\n",
+       "      <th>400</th>\n",
+       "      <th>500</th>\n",
+       "      <th>1000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_count</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>36.081469</td>\n",
+       "      <td>29.417778</td>\n",
+       "      <td>17.069506</td>\n",
+       "      <td>14.193727</td>\n",
+       "      <td>12.445997</td>\n",
+       "      <td>7.851926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>28.470057</td>\n",
+       "      <td>18.796539</td>\n",
+       "      <td>11.450357</td>\n",
+       "      <td>8.845898</td>\n",
+       "      <td>7.687338</td>\n",
+       "      <td>5.114184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21.246893</td>\n",
+       "      <td>13.785687</td>\n",
+       "      <td>7.443642</td>\n",
+       "      <td>6.973847</td>\n",
+       "      <td>5.811568</td>\n",
+       "      <td>3.845032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>17.075735</td>\n",
+       "      <td>10.906633</td>\n",
+       "      <td>5.921254</td>\n",
+       "      <td>5.014825</td>\n",
+       "      <td>4.059623</td>\n",
+       "      <td>3.042903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>14.680332</td>\n",
+       "      <td>9.673524</td>\n",
+       "      <td>4.826794</td>\n",
+       "      <td>4.122641</td>\n",
+       "      <td>3.485152</td>\n",
+       "      <td>2.617997</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "n_surrogates        50         100        300        400        500       1000\n",
+       "average_count                                                                 \n",
+       "5              36.081469  29.417778  17.069506  14.193727  12.445997  7.851926\n",
+       "10             28.470057  18.796539  11.450357   8.845898   7.687338  5.114184\n",
+       "20             21.246893  13.785687   7.443642   6.973847   5.811568  3.845032\n",
+       "30             17.075735  10.906633   5.921254   5.014825   4.059623  3.042903\n",
+       "40             14.680332   9.673524   4.826794   4.122641   3.485152  2.617997"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col_name=\"se_ratio\"\n",
+    "pivoted_errors=pd.pivot(average_ratio.reset_index(),index=\"average_count\",columns=\"n_surrogates\",values=col_name)\n",
+    "pivoted_errors\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "953205b3-c6f5-4e78-bb9a-1cf0ed36677e",
+   "metadata": {},
+   "source": [
+    "Standard errors continue to decrease as the number of levels of $Z$ and the number of individuals per level increase. Note that the estimates for Equal Opportunity, and therefore to a lesser extent Average Odds, are particulary poor for small samples. This is consistent with the higher standard errors for those probabilistic estimates (see Table ). For applications where Equal Opportunity is of particular interest, minimum sample size recommendations should be heeded with particular interest, and it is suggested that practitioners use the higher range of the recommendations described below. See: Rodolfa, Kit T., et al. \"Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions.\" Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020. for an analysis of the most critical fairness metrics under different use cases. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "ae132028-86bf-4042-aefd-635ba5ae9438",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 5), dpi=100)\n",
+    "sns.lineplot(data=(pivoted_errors),markers=True)\n",
+    "plt.xlabel(\"Individuals Per Level\")\n",
+    "plt.ylabel(col_name)\n",
+    "plt.legend(title=\"N Surrogates\")\n",
+    "plt.savefig(\"change_in_standard_deviation.png\",dpi=300)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation.py b/examples/probabilistic_fairness/python_scripts/simulation.py
similarity index 91%
rename from examples/prob_fairness_accuracy/python_scripts/simulation.py
rename to examples/probabilistic_fairness/python_scripts/simulation.py
index def69be..a3bc4ef 100644
--- a/examples/prob_fairness_accuracy/python_scripts/simulation.py
+++ b/examples/probabilistic_fairness/python_scripts/simulation.py
@@ -6,8 +6,10 @@
 sys.path.append('../../jurity/tests')
 sys.path.append('../../jurity/jurity')
 from jurity.fairness import BinaryFairnessMetrics as bfm
-
 from test_utils_proba import UtilsProbaSimulator
+
+output_path='~/Documents/data/jurity_tests/simulations/'
+
 testing_simulation=False
 n_runs=30
 avg_counts=[30,50]
@@ -22,11 +24,11 @@
            "moderately_unfair":moderately_unfair_sim,
            "very_unfair":very_unfair_sim,
            "extremely_unfair":extremely_unfair_sim}
-surrogates=pd.read_csv('./supporting_data/surrogate_inputs.csv')
+surrogates=pd.read_csv('../input_data/surrogate_inputs.csv')
 if testing_simulation:
-    output_string = '~/Documents/data/jurity_tests/simulations/{0}_simulation_count_{1}_surrogates_{2}_test.csv'
+    output_string = output_path+'{0}_simulation_count_{1}_surrogates_{2}_test.csv'
 else:
-    output_string = '~/Documents/data/jurity_tests/simulations/{0}_simulation_count_{1}_surrogates_{2}.csv'
+    output_string = output_path+'{0}_simulation_count_{1}_surrogates_{2}.csv'
 
 def run_one_sim(simulator, membership_df,count_mean,rng=np.random.default_rng()):
     membership_df["count"]=pd.Series(rng.poisson(lam=count_mean,size=membership_df.shape[0]))
@@ -36,7 +38,6 @@ def run_one_sim(simulator, membership_df,count_mean,rng=np.random.default_rng())
     prob_metrics=bfm.get_all_scores(test_data["label"],test_data["prediction"],
                    membership_df.set_index("ZIP")[["not_protected","protected"]],
                    test_data["ZIP"],[1]).rename(columns={"Value":"probabilistic_estimate"})
-    #TODO: Add simulation for argmax
     predicted_class=test_data[["not_protected","protected"]].values.tolist()
     argmax_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
                                      predicted_class).rename(columns={"Value":"argmax_estimate"})
diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py b/examples/probabilistic_fairness/python_scripts/simulation_compare_to_model.py
similarity index 100%
rename from examples/prob_fairness_accuracy/python_scripts/simulation_compare_to_model.py
rename to examples/probabilistic_fairness/python_scripts/simulation_compare_to_model.py
diff --git a/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py b/examples/probabilistic_fairness/python_scripts/simulation_counts.py
similarity index 95%
rename from examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
rename to examples/probabilistic_fairness/python_scripts/simulation_counts.py
index fd9af07..ae8f6d6 100644
--- a/examples/prob_fairness_accuracy/python_scripts/simulation_counts.py
+++ b/examples/probabilistic_fairness/python_scripts/simulation_counts.py
@@ -6,8 +6,8 @@
 sys.path.append('../../jurity/tests')
 sys.path.append('../../jurity/jurity')
 from jurity.fairness import BinaryFairnessMetrics as bfm
-
 from test_utils_proba import UtilsProbaSimulator
+output_path='~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/'
 testing_simulation=False
 n_runs=30
 avg_counts=[5,10,20,30,40]
@@ -26,9 +26,9 @@
 surrogates=pd.read_csv('./supporting_data/surrogate_inputs.csv')
 surrogates["ZIP"]=surrogates["ZIP"].astype(int)
 if testing_simulation:
-    output_string = '~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/{0}_simulation_count_{1}_test_surrogates_{2}.csv'
+    output_string = output_path+'{0}_simulation_count_{1}_test_surrogates_{2}.csv'
 else:
-    output_string = '~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/{0}_simulation_count_{1}_surrogates_{2}.csv'
+    output_string = output_path+'sample_size/min_weight_0/{0}_simulation_count_{1}_surrogates_{2}.csv'
 
 def run_one_sim(test_data,membership_df):
     #Sometimes the sub-sampling leads to data errors.
diff --git a/examples/prob_fairness_accuracy/readme.md b/examples/probabilistic_fairness/readme.md
similarity index 92%
rename from examples/prob_fairness_accuracy/readme.md
rename to examples/probabilistic_fairness/readme.md
index bd7bbfd..a45c1c2 100644
--- a/examples/prob_fairness_accuracy/readme.md
+++ b/examples/probabilistic_fairness/readme.md
@@ -3,9 +3,9 @@ This folder contains the files necessary to reproduce the results from
 academic papers demonstrating the accuracy of probabilistic 
 fairness under different sample sizes. Probabilistic fairness is a technique 
 unique to jurity that allows users to calculate fairness metrics when protected status is 
-unknown but a <i>surrogate class</i> is available. A surrogate class is 
-an alternative feature that divides the population into groups and where the probability 
-of protected class membership is known given the surrogate class.
+unknown but a surrogate class feature is available. A <i>surrogate</i> class divides 
+the population into groups where 
+the probability of protected class membership is known given the surrogate class membership.
 
 Probabilistic fairness, its accuracy, and the simulation method used in
 these demonstrations are detailed in 

From 1adfb487c819b60e44e3782a16a47254e76ddaaf Mon Sep 17 00:00:00 2001
From: skadio <serdar.kadioglu@fmr.com>
Date: Wed, 24 Jan 2024 16:20:15 -0500
Subject: [PATCH 22/25] update

---
 .../input_data/sampled_surrogate_inputs.csv   |  3589 --
 .../input_data/surrogate_inputs.csv           | 33185 ----------------
 .../notebooks/analyze_prob_vs_model.ipynb     |  1909 -
 .../notebooks/analyze_sample_size_sim.ipynb   |  5209 ---
 .../python_scripts/simulation.py              |    62 -
 .../simulation_compare_to_model.py            |   144 -
 .../python_scripts/simulation_counts.py       |    98 -
 examples/probabilistic_fairness/readme.md     |    59 -
 jurity/fairness/__init__.py                   |     2 +-
 jurity/utils_proba.py                         |    11 +-
 tests/test_mitigation_binary.py               |    20 +-
 11 files changed, 16 insertions(+), 44272 deletions(-)
 delete mode 100644 examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv
 delete mode 100644 examples/probabilistic_fairness/input_data/surrogate_inputs.csv
 delete mode 100644 examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb
 delete mode 100644 examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb
 delete mode 100644 examples/probabilistic_fairness/python_scripts/simulation.py
 delete mode 100644 examples/probabilistic_fairness/python_scripts/simulation_compare_to_model.py
 delete mode 100644 examples/probabilistic_fairness/python_scripts/simulation_counts.py
 delete mode 100644 examples/probabilistic_fairness/readme.md

diff --git a/examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv b/examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv
deleted file mode 100644
index e02b960..0000000
--- a/examples/probabilistic_fairness/input_data/sampled_surrogate_inputs.csv
+++ /dev/null
@@ -1,3589 +0,0 @@
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diff --git a/examples/probabilistic_fairness/input_data/surrogate_inputs.csv b/examples/probabilistic_fairness/input_data/surrogate_inputs.csv
deleted file mode 100644
index 17bf18c..0000000
--- a/examples/probabilistic_fairness/input_data/surrogate_inputs.csv
+++ /dev/null
@@ -1,33185 +0,0 @@
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diff --git a/examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb b/examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb
deleted file mode 100644
index 657efdb..0000000
--- a/examples/probabilistic_fairness/notebooks/analyze_prob_vs_model.ipynb
+++ /dev/null
@@ -1,1909 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3cd360a9-69b1-4016-898c-6f548f5b4df3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import sys\n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "090b4807-2ef5-4155-93c4-9249100c4c8d",
-   "metadata": {},
-   "source": [
-    "# Prepare Data From Simulation Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e20fdd5f-0a5d-4115-bb3c-c7d86f69d9e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2400, 9)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Metric</th>\n",
-       "      <th>model_estimate</th>\n",
-       "      <th>class_PPV</th>\n",
-       "      <th>class_NPV</th>\n",
-       "      <th>class_TPR</th>\n",
-       "      <th>p_given_p</th>\n",
-       "      <th>np_given_np</th>\n",
-       "      <th>run_id</th>\n",
-       "      <th>simulation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Average Odds</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.919137</td>\n",
-       "      <td>0.998937</td>\n",
-       "      <td>0.990681</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Equal Opportunity</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>0.919137</td>\n",
-       "      <td>0.998937</td>\n",
-       "      <td>0.990681</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Predictive Equality</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.919137</td>\n",
-       "      <td>0.998937</td>\n",
-       "      <td>0.990681</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Statistical Parity</td>\n",
-       "      <td>-0.000</td>\n",
-       "      <td>0.919137</td>\n",
-       "      <td>0.998937</td>\n",
-       "      <td>0.990681</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Average Odds</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.910217</td>\n",
-       "      <td>0.988489</td>\n",
-       "      <td>0.898043</td>\n",
-       "      <td>0.90</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Metric  model_estimate  class_PPV  class_NPV  class_TPR  \\\n",
-       "0         Average Odds           0.004   0.919137   0.998937   0.990681   \n",
-       "2    Equal Opportunity           0.008   0.919137   0.998937   0.990681   \n",
-       "6  Predictive Equality           0.001   0.919137   0.998937   0.990681   \n",
-       "7   Statistical Parity          -0.000   0.919137   0.998937   0.990681   \n",
-       "9         Average Odds           0.006   0.910217   0.988489   0.898043   \n",
-       "\n",
-       "   p_given_p  np_given_np  run_id simulation  \n",
-       "0       0.99         0.99       0       fair  \n",
-       "2       0.99         0.99       0       fair  \n",
-       "6       0.99         0.99       0       fair  \n",
-       "7       0.99         0.99       0       fair  \n",
-       "9       0.90         0.99       0       fair  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Prepare Data From Model That Classifies Prob estimates\n",
-    "input_str='~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_model_simulation_{1}_surrogates_{2}_count.csv'\n",
-    "sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
-    "color_map={\"fair\":\"0.9\",\"slightly_unfair\":\"0.8\",\"moderately_unfair\":\"0.45\",\"very_unfair\":\"0.3\",\"extremely_unfair\":\"0\"}\n",
-    "all_simulations=[]\n",
-    "avg_counts=[50]\n",
-    "num_surrogates=[3588]\n",
-    "for name in sim_names:\n",
-    "    for c in avg_counts:\n",
-    "        for n_surrogates in num_surrogates:\n",
-    "            all_simulations.append(pd.read_csv(input_str.format(name,c,n_surrogates)))\n",
-    "simulations_model=pd.concat(all_simulations)\n",
-    "\n",
-    "#Reduce to only simulations where probabilistic fairness is implemented\n",
-    "simulations_model=simulations_model[simulations_model[\"Metric\"].apply(lambda x: x in ['Average Odds', 'Equal Opportunity', 'Predictive Equality','Statistical Parity'])]\n",
-    "print(simulations_model.shape)\n",
-    "simulations_model.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a1b167b1-c8d8-4197-b955-439f3285cdef",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.99, 0.9 , 0.8 , 0.7 ])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simulations_model[\"p_given_p\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "bbbd0760-7dad-4ac3-b3cf-7c590c0ea209",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.99, 0.9 , 0.8 ])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simulations_model[\"np_given_np\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e7a6f901-2119-4aea-8ea5-cd110d91ac8d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(600, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Metric</th>\n",
-       "      <th>oracle_value</th>\n",
-       "      <th>probabilistic_estimate</th>\n",
-       "      <th>run_id</th>\n",
-       "      <th>simulation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Average Odds</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Equal Opportunity</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Predictive Equality</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Statistical Parity</td>\n",
-       "      <td>-0.000</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Average Odds</td>\n",
-       "      <td>-0.001</td>\n",
-       "      <td>-0.005</td>\n",
-       "      <td>1</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Metric  oracle_value  probabilistic_estimate  run_id  \\\n",
-       "0         Average Odds         0.004                   0.010       0   \n",
-       "2    Equal Opportunity         0.008                   0.011       0   \n",
-       "6  Predictive Equality         0.001                   0.008       0   \n",
-       "7   Statistical Parity        -0.000                   0.003       0   \n",
-       "9         Average Odds        -0.001                  -0.005       1   \n",
-       "\n",
-       "  simulation  \n",
-       "0       fair  \n",
-       "2       fair  \n",
-       "6       fair  \n",
-       "7       fair  \n",
-       "9       fair  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Prepare Data Calculated from Probabilistic Estimates\n",
-    "input_str='~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_prob_simulation_{1}_surrogates_{2}_count.csv'\n",
-    "sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
-    "color_map={\"fair\":\"0.9\",\"slightly_unfair\":\"0.8\",\"moderately_unfair\":\"0.45\",\"very_unfair\":\"0.3\",\"extremely_unfair\":\"0\"}\n",
-    "all_simulations=[]\n",
-    "avg_counts=[50]\n",
-    "num_surrogates=[3588]\n",
-    "for name in sim_names:\n",
-    "    for c in avg_counts:\n",
-    "        for n_surrogates in num_surrogates:\n",
-    "            all_simulations.append(pd.read_csv(input_str.format(name,c,n_surrogates)))\n",
-    "simulations_prob=pd.concat(all_simulations)\n",
-    "simulations_prob=simulations_prob[simulations_prob[\"Metric\"].apply(lambda x: x in ['Average Odds', 'Equal Opportunity', 'Predictive Equality','Statistical Parity'])]\n",
-    "print(simulations_prob.shape)\n",
-    "simulations_prob.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "96947486-2fcc-4846-a0a8-3d9d0249aac8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Merge Model-Based Estimates with Probabilistic Estimates\n",
-    "simulations_raw=simulations_model.merge(simulations_prob,left_on=[\"Metric\",\"simulation\"],right_on=[\"Metric\",\"simulation\"])\n",
-    "simulations_raw[\"npv_plus_ppv_minus_1\"]=simulations_raw[\"class_PPV\"]+simulations_raw[\"class_NPV\"]-1\n",
-    "simulations_raw[\"adjusted_SP\"]=simulations_raw[\"npv_plus_ppv_minus_1\"]*simulations_raw[\"oracle_value\"]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ea3027f4-013c-418d-ae67-515799eb1512",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>model_estimate</th>\n",
-       "      <th>class_PPV</th>\n",
-       "      <th>class_NPV</th>\n",
-       "      <th>class_TPR</th>\n",
-       "      <th>p_given_p</th>\n",
-       "      <th>np_given_np</th>\n",
-       "      <th>run_id_x</th>\n",
-       "      <th>oracle_value</th>\n",
-       "      <th>probabilistic_estimate</th>\n",
-       "      <th>run_id_y</th>\n",
-       "      <th>npv_plus_ppv_minus_1</th>\n",
-       "      <th>adjusted_SP</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Equal Opportunity</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Predictive Equality</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>3600</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       model_estimate  class_PPV  class_NPV  \\\n",
-       "Metric              simulation                                                \n",
-       "Average Odds        extremely_unfair             3600       3600       3600   \n",
-       "                    fair                         3600       3600       3600   \n",
-       "                    moderately_unfair            3600       3600       3600   \n",
-       "                    slightly_unfair              3600       3600       3600   \n",
-       "                    very_unfair                  3600       3600       3600   \n",
-       "Equal Opportunity   extremely_unfair             3600       3600       3600   \n",
-       "                    fair                         3600       3600       3600   \n",
-       "                    moderately_unfair            3600       3600       3600   \n",
-       "                    slightly_unfair              3600       3600       3600   \n",
-       "                    very_unfair                  3600       3600       3600   \n",
-       "Predictive Equality extremely_unfair             3600       3600       3600   \n",
-       "                    fair                         3600       3600       3600   \n",
-       "                    moderately_unfair            3600       3600       3600   \n",
-       "                    slightly_unfair              3600       3600       3600   \n",
-       "                    very_unfair                  3600       3600       3600   \n",
-       "Statistical Parity  extremely_unfair             3600       3600       3600   \n",
-       "                    fair                         3600       3600       3600   \n",
-       "                    moderately_unfair            3600       3600       3600   \n",
-       "                    slightly_unfair              3600       3600       3600   \n",
-       "                    very_unfair                  3600       3600       3600   \n",
-       "\n",
-       "                                       class_TPR  p_given_p  np_given_np  \\\n",
-       "Metric              simulation                                             \n",
-       "Average Odds        extremely_unfair        3600       3600         3600   \n",
-       "                    fair                    3600       3600         3600   \n",
-       "                    moderately_unfair       3600       3600         3600   \n",
-       "                    slightly_unfair         3600       3600         3600   \n",
-       "                    very_unfair             3600       3600         3600   \n",
-       "Equal Opportunity   extremely_unfair        3600       3600         3600   \n",
-       "                    fair                    3600       3600         3600   \n",
-       "                    moderately_unfair       3600       3600         3600   \n",
-       "                    slightly_unfair         3600       3600         3600   \n",
-       "                    very_unfair             3600       3600         3600   \n",
-       "Predictive Equality extremely_unfair        3600       3600         3600   \n",
-       "                    fair                    3600       3600         3600   \n",
-       "                    moderately_unfair       3600       3600         3600   \n",
-       "                    slightly_unfair         3600       3600         3600   \n",
-       "                    very_unfair             3600       3600         3600   \n",
-       "Statistical Parity  extremely_unfair        3600       3600         3600   \n",
-       "                    fair                    3600       3600         3600   \n",
-       "                    moderately_unfair       3600       3600         3600   \n",
-       "                    slightly_unfair         3600       3600         3600   \n",
-       "                    very_unfair             3600       3600         3600   \n",
-       "\n",
-       "                                       run_id_x  oracle_value  \\\n",
-       "Metric              simulation                                  \n",
-       "Average Odds        extremely_unfair       3600          3600   \n",
-       "                    fair                   3600          3600   \n",
-       "                    moderately_unfair      3600          3600   \n",
-       "                    slightly_unfair        3600          3600   \n",
-       "                    very_unfair            3600          3600   \n",
-       "Equal Opportunity   extremely_unfair       3600          3600   \n",
-       "                    fair                   3600          3600   \n",
-       "                    moderately_unfair      3600          3600   \n",
-       "                    slightly_unfair        3600          3600   \n",
-       "                    very_unfair            3600          3600   \n",
-       "Predictive Equality extremely_unfair       3600          3600   \n",
-       "                    fair                   3600          3600   \n",
-       "                    moderately_unfair      3600          3600   \n",
-       "                    slightly_unfair        3600          3600   \n",
-       "                    very_unfair            3600          3600   \n",
-       "Statistical Parity  extremely_unfair       3600          3600   \n",
-       "                    fair                   3600          3600   \n",
-       "                    moderately_unfair      3600          3600   \n",
-       "                    slightly_unfair        3600          3600   \n",
-       "                    very_unfair            3600          3600   \n",
-       "\n",
-       "                                       probabilistic_estimate  run_id_y  \\\n",
-       "Metric              simulation                                            \n",
-       "Average Odds        extremely_unfair                     3600      3600   \n",
-       "                    fair                                 3600      3600   \n",
-       "                    moderately_unfair                    3600      3600   \n",
-       "                    slightly_unfair                      3600      3600   \n",
-       "                    very_unfair                          3600      3600   \n",
-       "Equal Opportunity   extremely_unfair                     3600      3600   \n",
-       "                    fair                                 3600      3600   \n",
-       "                    moderately_unfair                    3600      3600   \n",
-       "                    slightly_unfair                      3600      3600   \n",
-       "                    very_unfair                          3600      3600   \n",
-       "Predictive Equality extremely_unfair                     3600      3600   \n",
-       "                    fair                                 3600      3600   \n",
-       "                    moderately_unfair                    3600      3600   \n",
-       "                    slightly_unfair                      3600      3600   \n",
-       "                    very_unfair                          3600      3600   \n",
-       "Statistical Parity  extremely_unfair                     3600      3600   \n",
-       "                    fair                                 3600      3600   \n",
-       "                    moderately_unfair                    3600      3600   \n",
-       "                    slightly_unfair                      3600      3600   \n",
-       "                    very_unfair                          3600      3600   \n",
-       "\n",
-       "                                       npv_plus_ppv_minus_1  adjusted_SP  \n",
-       "Metric              simulation                                            \n",
-       "Average Odds        extremely_unfair                   3600         3600  \n",
-       "                    fair                               3600         3600  \n",
-       "                    moderately_unfair                  3600         3600  \n",
-       "                    slightly_unfair                    3600         3600  \n",
-       "                    very_unfair                        3600         3600  \n",
-       "Equal Opportunity   extremely_unfair                   3600         3600  \n",
-       "                    fair                               3600         3600  \n",
-       "                    moderately_unfair                  3600         3600  \n",
-       "                    slightly_unfair                    3600         3600  \n",
-       "                    very_unfair                        3600         3600  \n",
-       "Predictive Equality extremely_unfair                   3600         3600  \n",
-       "                    fair                               3600         3600  \n",
-       "                    moderately_unfair                  3600         3600  \n",
-       "                    slightly_unfair                    3600         3600  \n",
-       "                    very_unfair                        3600         3600  \n",
-       "Statistical Parity  extremely_unfair                   3600         3600  \n",
-       "                    fair                               3600         3600  \n",
-       "                    moderately_unfair                  3600         3600  \n",
-       "                    slightly_unfair                    3600         3600  \n",
-       "                    very_unfair                        3600         3600  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simulations_raw.groupby([\"Metric\",\"simulation\"]).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "5f6600dc-55e5-41c8-ad92-c2b5a50518c3",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"8\" halign=\"left\">oracle_value</th>\n",
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-       "      <th>Metric</th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.349467</td>\n",
-       "      <td>0.005609</td>\n",
-       "      <td>-0.364</td>\n",
-       "      <td>-0.354</td>\n",
-       "      <td>-0.3490</td>\n",
-       "      <td>-0.345</td>\n",
-       "      <td>-0.339</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.350800</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.339</td>\n",
-       "      <td>-0.314</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.160408</td>\n",
-       "      <td>0.097317</td>\n",
-       "      <td>-0.275</td>\n",
-       "      <td>-0.25700</td>\n",
-       "      <td>-0.1675</td>\n",
-       "      <td>-0.07200</td>\n",
-       "      <td>-0.040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000167</td>\n",
-       "      <td>0.003348</td>\n",
-       "      <td>-0.007</td>\n",
-       "      <td>-0.002</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000133</td>\n",
-       "      <td>0.002970</td>\n",
-       "      <td>-0.008</td>\n",
-       "      <td>-0.00200</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.00200</td>\n",
-       "      <td>0.006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.277800</td>\n",
-       "      <td>0.005161</td>\n",
-       "      <td>-0.288</td>\n",
-       "      <td>-0.282</td>\n",
-       "      <td>-0.2790</td>\n",
-       "      <td>-0.273</td>\n",
-       "      <td>-0.268</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.279167</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.272</td>\n",
-       "      <td>-0.245</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.149900</td>\n",
-       "      <td>0.082401</td>\n",
-       "      <td>-0.244</td>\n",
-       "      <td>-0.23000</td>\n",
-       "      <td>-0.1590</td>\n",
-       "      <td>-0.07575</td>\n",
-       "      <td>-0.041</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.172633</td>\n",
-       "      <td>0.005283</td>\n",
-       "      <td>-0.185</td>\n",
-       "      <td>-0.176</td>\n",
-       "      <td>-0.1735</td>\n",
-       "      <td>-0.170</td>\n",
-       "      <td>-0.162</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.171800</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.163</td>\n",
-       "      <td>-0.152</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.096858</td>\n",
-       "      <td>0.052830</td>\n",
-       "      <td>-0.159</td>\n",
-       "      <td>-0.14925</td>\n",
-       "      <td>-0.1020</td>\n",
-       "      <td>-0.04850</td>\n",
-       "      <td>-0.026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.375833</td>\n",
-       "      <td>0.005646</td>\n",
-       "      <td>-0.387</td>\n",
-       "      <td>-0.380</td>\n",
-       "      <td>-0.3760</td>\n",
-       "      <td>-0.371</td>\n",
-       "      <td>-0.365</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.375967</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.366</td>\n",
-       "      <td>-0.345</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.185983</td>\n",
-       "      <td>0.109442</td>\n",
-       "      <td>-0.306</td>\n",
-       "      <td>-0.29325</td>\n",
-       "      <td>-0.1965</td>\n",
-       "      <td>-0.08675</td>\n",
-       "      <td>-0.048</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Equal Opportunity</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.548933</td>\n",
-       "      <td>0.011327</td>\n",
-       "      <td>-0.581</td>\n",
-       "      <td>-0.554</td>\n",
-       "      <td>-0.5485</td>\n",
-       "      <td>-0.544</td>\n",
-       "      <td>-0.522</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.551967</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.529</td>\n",
-       "      <td>-0.479</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.215625</td>\n",
-       "      <td>0.157611</td>\n",
-       "      <td>-0.405</td>\n",
-       "      <td>-0.37125</td>\n",
-       "      <td>-0.2205</td>\n",
-       "      <td>-0.06650</td>\n",
-       "      <td>-0.030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>0.000133</td>\n",
-       "      <td>0.005353</td>\n",
-       "      <td>-0.010</td>\n",
-       "      <td>-0.004</td>\n",
-       "      <td>-0.0010</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>0.000433</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>0.024</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>0.000083</td>\n",
-       "      <td>0.005028</td>\n",
-       "      <td>-0.013</td>\n",
-       "      <td>-0.00300</td>\n",
-       "      <td>-0.0000</td>\n",
-       "      <td>0.00400</td>\n",
-       "      <td>0.011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.355000</td>\n",
-       "      <td>0.009421</td>\n",
-       "      <td>-0.371</td>\n",
-       "      <td>-0.362</td>\n",
-       "      <td>-0.3575</td>\n",
-       "      <td>-0.347</td>\n",
-       "      <td>-0.333</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.357433</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.344</td>\n",
-       "      <td>-0.294</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.167317</td>\n",
-       "      <td>0.110271</td>\n",
-       "      <td>-0.297</td>\n",
-       "      <td>-0.27700</td>\n",
-       "      <td>-0.1715</td>\n",
-       "      <td>-0.06175</td>\n",
-       "      <td>-0.029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.246267</td>\n",
-       "      <td>0.009765</td>\n",
-       "      <td>-0.270</td>\n",
-       "      <td>-0.253</td>\n",
-       "      <td>-0.2455</td>\n",
-       "      <td>-0.241</td>\n",
-       "      <td>-0.225</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.245333</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.225</td>\n",
-       "      <td>-0.207</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.130208</td>\n",
-       "      <td>0.077511</td>\n",
-       "      <td>-0.222</td>\n",
-       "      <td>-0.20800</td>\n",
-       "      <td>-0.1345</td>\n",
-       "      <td>-0.05600</td>\n",
-       "      <td>-0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.551433</td>\n",
-       "      <td>0.010110</td>\n",
-       "      <td>-0.572</td>\n",
-       "      <td>-0.560</td>\n",
-       "      <td>-0.5515</td>\n",
-       "      <td>-0.543</td>\n",
-       "      <td>-0.533</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.550833</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.532</td>\n",
-       "      <td>-0.488</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.235967</td>\n",
-       "      <td>0.166514</td>\n",
-       "      <td>-0.424</td>\n",
-       "      <td>-0.39925</td>\n",
-       "      <td>-0.2435</td>\n",
-       "      <td>-0.07775</td>\n",
-       "      <td>-0.037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Predictive Equality</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.150167</td>\n",
-       "      <td>0.002340</td>\n",
-       "      <td>-0.157</td>\n",
-       "      <td>-0.151</td>\n",
-       "      <td>-0.1500</td>\n",
-       "      <td>-0.148</td>\n",
-       "      <td>-0.146</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.149633</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.147</td>\n",
-       "      <td>-0.144</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.105125</td>\n",
-       "      <td>0.037968</td>\n",
-       "      <td>-0.149</td>\n",
-       "      <td>-0.14100</td>\n",
-       "      <td>-0.1140</td>\n",
-       "      <td>-0.07475</td>\n",
-       "      <td>-0.049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000467</td>\n",
-       "      <td>0.003344</td>\n",
-       "      <td>-0.010</td>\n",
-       "      <td>-0.001</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000367</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000375</td>\n",
-       "      <td>0.003020</td>\n",
-       "      <td>-0.011</td>\n",
-       "      <td>-0.00200</td>\n",
-       "      <td>-0.0000</td>\n",
-       "      <td>0.00100</td>\n",
-       "      <td>0.008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.200433</td>\n",
-       "      <td>0.002975</td>\n",
-       "      <td>-0.206</td>\n",
-       "      <td>-0.203</td>\n",
-       "      <td>-0.2000</td>\n",
-       "      <td>-0.198</td>\n",
-       "      <td>-0.194</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.200833</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.197</td>\n",
-       "      <td>-0.194</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.132433</td>\n",
-       "      <td>0.055068</td>\n",
-       "      <td>-0.193</td>\n",
-       "      <td>-0.18500</td>\n",
-       "      <td>-0.1430</td>\n",
-       "      <td>-0.08700</td>\n",
-       "      <td>-0.053</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.098900</td>\n",
-       "      <td>0.002427</td>\n",
-       "      <td>-0.103</td>\n",
-       "      <td>-0.101</td>\n",
-       "      <td>-0.0990</td>\n",
-       "      <td>-0.098</td>\n",
-       "      <td>-0.094</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.098467</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.096</td>\n",
-       "      <td>-0.093</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.063608</td>\n",
-       "      <td>0.028217</td>\n",
-       "      <td>-0.097</td>\n",
-       "      <td>-0.09100</td>\n",
-       "      <td>-0.0685</td>\n",
-       "      <td>-0.03900</td>\n",
-       "      <td>-0.019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.200267</td>\n",
-       "      <td>0.002323</td>\n",
-       "      <td>-0.206</td>\n",
-       "      <td>-0.202</td>\n",
-       "      <td>-0.2000</td>\n",
-       "      <td>-0.199</td>\n",
-       "      <td>-0.194</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.201200</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.196</td>\n",
-       "      <td>-0.191</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.135958</td>\n",
-       "      <td>0.053284</td>\n",
-       "      <td>-0.193</td>\n",
-       "      <td>-0.18700</td>\n",
-       "      <td>-0.1455</td>\n",
-       "      <td>-0.09475</td>\n",
-       "      <td>-0.054</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.470000</td>\n",
-       "      <td>0.001880</td>\n",
-       "      <td>-0.476</td>\n",
-       "      <td>-0.471</td>\n",
-       "      <td>-0.4700</td>\n",
-       "      <td>-0.469</td>\n",
-       "      <td>-0.467</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.469767</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.466</td>\n",
-       "      <td>-0.459</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.294858</td>\n",
-       "      <td>0.136223</td>\n",
-       "      <td>-0.436</td>\n",
-       "      <td>-0.42625</td>\n",
-       "      <td>-0.3195</td>\n",
-       "      <td>-0.18500</td>\n",
-       "      <td>-0.108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000200</td>\n",
-       "      <td>0.004045</td>\n",
-       "      <td>-0.010</td>\n",
-       "      <td>-0.002</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000133</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.000350</td>\n",
-       "      <td>0.003172</td>\n",
-       "      <td>-0.010</td>\n",
-       "      <td>-0.00200</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.00125</td>\n",
-       "      <td>0.010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.335467</td>\n",
-       "      <td>0.003223</td>\n",
-       "      <td>-0.344</td>\n",
-       "      <td>-0.338</td>\n",
-       "      <td>-0.3350</td>\n",
-       "      <td>-0.333</td>\n",
-       "      <td>-0.330</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.335900</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.331</td>\n",
-       "      <td>-0.324</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.210500</td>\n",
-       "      <td>0.097240</td>\n",
-       "      <td>-0.316</td>\n",
-       "      <td>-0.30400</td>\n",
-       "      <td>-0.2270</td>\n",
-       "      <td>-0.12950</td>\n",
-       "      <td>-0.077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.184167</td>\n",
-       "      <td>0.002570</td>\n",
-       "      <td>-0.189</td>\n",
-       "      <td>-0.186</td>\n",
-       "      <td>-0.1840</td>\n",
-       "      <td>-0.183</td>\n",
-       "      <td>-0.177</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.183167</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.180</td>\n",
-       "      <td>-0.170</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.115558</td>\n",
-       "      <td>0.053522</td>\n",
-       "      <td>-0.174</td>\n",
-       "      <td>-0.16700</td>\n",
-       "      <td>-0.1245</td>\n",
-       "      <td>-0.07000</td>\n",
-       "      <td>-0.039</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.415233</td>\n",
-       "      <td>0.002837</td>\n",
-       "      <td>-0.420</td>\n",
-       "      <td>-0.418</td>\n",
-       "      <td>-0.4150</td>\n",
-       "      <td>-0.413</td>\n",
-       "      <td>-0.410</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.415833</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.412</td>\n",
-       "      <td>-0.399</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>-0.260383</td>\n",
-       "      <td>0.120781</td>\n",
-       "      <td>-0.386</td>\n",
-       "      <td>-0.37700</td>\n",
-       "      <td>-0.2810</td>\n",
-       "      <td>-0.16325</td>\n",
-       "      <td>-0.095</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>20 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      oracle_value                             \\\n",
-       "                                             count      mean       std    min   \n",
-       "Metric              simulation                                                  \n",
-       "Average Odds        extremely_unfair        3600.0 -0.349467  0.005609 -0.364   \n",
-       "                    fair                    3600.0 -0.000167  0.003348 -0.007   \n",
-       "                    moderately_unfair       3600.0 -0.277800  0.005161 -0.288   \n",
-       "                    slightly_unfair         3600.0 -0.172633  0.005283 -0.185   \n",
-       "                    very_unfair             3600.0 -0.375833  0.005646 -0.387   \n",
-       "Equal Opportunity   extremely_unfair        3600.0 -0.548933  0.011327 -0.581   \n",
-       "                    fair                    3600.0  0.000133  0.005353 -0.010   \n",
-       "                    moderately_unfair       3600.0 -0.355000  0.009421 -0.371   \n",
-       "                    slightly_unfair         3600.0 -0.246267  0.009765 -0.270   \n",
-       "                    very_unfair             3600.0 -0.551433  0.010110 -0.572   \n",
-       "Predictive Equality extremely_unfair        3600.0 -0.150167  0.002340 -0.157   \n",
-       "                    fair                    3600.0 -0.000467  0.003344 -0.010   \n",
-       "                    moderately_unfair       3600.0 -0.200433  0.002975 -0.206   \n",
-       "                    slightly_unfair         3600.0 -0.098900  0.002427 -0.103   \n",
-       "                    very_unfair             3600.0 -0.200267  0.002323 -0.206   \n",
-       "Statistical Parity  extremely_unfair        3600.0 -0.470000  0.001880 -0.476   \n",
-       "                    fair                    3600.0 -0.000200  0.004045 -0.010   \n",
-       "                    moderately_unfair       3600.0 -0.335467  0.003223 -0.344   \n",
-       "                    slightly_unfair         3600.0 -0.184167  0.002570 -0.189   \n",
-       "                    very_unfair             3600.0 -0.415233  0.002837 -0.420   \n",
-       "\n",
-       "                                                                    \\\n",
-       "                                         25%     50%    75%    max   \n",
-       "Metric              simulation                                       \n",
-       "Average Odds        extremely_unfair  -0.354 -0.3490 -0.345 -0.339   \n",
-       "                    fair              -0.002  0.0000  0.003  0.006   \n",
-       "                    moderately_unfair -0.282 -0.2790 -0.273 -0.268   \n",
-       "                    slightly_unfair   -0.176 -0.1735 -0.170 -0.162   \n",
-       "                    very_unfair       -0.380 -0.3760 -0.371 -0.365   \n",
-       "Equal Opportunity   extremely_unfair  -0.554 -0.5485 -0.544 -0.522   \n",
-       "                    fair              -0.004 -0.0010  0.005  0.009   \n",
-       "                    moderately_unfair -0.362 -0.3575 -0.347 -0.333   \n",
-       "                    slightly_unfair   -0.253 -0.2455 -0.241 -0.225   \n",
-       "                    very_unfair       -0.560 -0.5515 -0.543 -0.533   \n",
-       "Predictive Equality extremely_unfair  -0.151 -0.1500 -0.148 -0.146   \n",
-       "                    fair              -0.001  0.0000  0.002  0.005   \n",
-       "                    moderately_unfair -0.203 -0.2000 -0.198 -0.194   \n",
-       "                    slightly_unfair   -0.101 -0.0990 -0.098 -0.094   \n",
-       "                    very_unfair       -0.202 -0.2000 -0.199 -0.194   \n",
-       "Statistical Parity  extremely_unfair  -0.471 -0.4700 -0.469 -0.467   \n",
-       "                    fair              -0.002  0.0000  0.002  0.009   \n",
-       "                    moderately_unfair -0.338 -0.3350 -0.333 -0.330   \n",
-       "                    slightly_unfair   -0.186 -0.1840 -0.183 -0.177   \n",
-       "                    very_unfair       -0.418 -0.4150 -0.413 -0.410   \n",
-       "\n",
-       "                                      probabilistic_estimate            ...  \\\n",
-       "                                                       count      mean  ...   \n",
-       "Metric              simulation                                          ...   \n",
-       "Average Odds        extremely_unfair                  3600.0 -0.350800  ...   \n",
-       "                    fair                              3600.0  0.000100  ...   \n",
-       "                    moderately_unfair                 3600.0 -0.279167  ...   \n",
-       "                    slightly_unfair                   3600.0 -0.171800  ...   \n",
-       "                    very_unfair                       3600.0 -0.375967  ...   \n",
-       "Equal Opportunity   extremely_unfair                  3600.0 -0.551967  ...   \n",
-       "                    fair                              3600.0  0.000433  ...   \n",
-       "                    moderately_unfair                 3600.0 -0.357433  ...   \n",
-       "                    slightly_unfair                   3600.0 -0.245333  ...   \n",
-       "                    very_unfair                       3600.0 -0.550833  ...   \n",
-       "Predictive Equality extremely_unfair                  3600.0 -0.149633  ...   \n",
-       "                    fair                              3600.0 -0.000367  ...   \n",
-       "                    moderately_unfair                 3600.0 -0.200833  ...   \n",
-       "                    slightly_unfair                   3600.0 -0.098467  ...   \n",
-       "                    very_unfair                       3600.0 -0.201200  ...   \n",
-       "Statistical Parity  extremely_unfair                  3600.0 -0.469767  ...   \n",
-       "                    fair                              3600.0 -0.000133  ...   \n",
-       "                    moderately_unfair                 3600.0 -0.335900  ...   \n",
-       "                    slightly_unfair                   3600.0 -0.183167  ...   \n",
-       "                    very_unfair                       3600.0 -0.415833  ...   \n",
-       "\n",
-       "                                                    model_estimate            \\\n",
-       "                                         75%    max          count      mean   \n",
-       "Metric              simulation                                                 \n",
-       "Average Odds        extremely_unfair  -0.339 -0.314         3600.0 -0.160408   \n",
-       "                    fair               0.006  0.015         3600.0 -0.000133   \n",
-       "                    moderately_unfair -0.272 -0.245         3600.0 -0.149900   \n",
-       "                    slightly_unfair   -0.163 -0.152         3600.0 -0.096858   \n",
-       "                    very_unfair       -0.366 -0.345         3600.0 -0.185983   \n",
-       "Equal Opportunity   extremely_unfair  -0.529 -0.479         3600.0 -0.215625   \n",
-       "                    fair               0.007  0.024         3600.0  0.000083   \n",
-       "                    moderately_unfair -0.344 -0.294         3600.0 -0.167317   \n",
-       "                    slightly_unfair   -0.225 -0.207         3600.0 -0.130208   \n",
-       "                    very_unfair       -0.532 -0.488         3600.0 -0.235967   \n",
-       "Predictive Equality extremely_unfair  -0.147 -0.144         3600.0 -0.105125   \n",
-       "                    fair               0.006  0.012         3600.0 -0.000375   \n",
-       "                    moderately_unfair -0.197 -0.194         3600.0 -0.132433   \n",
-       "                    slightly_unfair   -0.096 -0.093         3600.0 -0.063608   \n",
-       "                    very_unfair       -0.196 -0.191         3600.0 -0.135958   \n",
-       "Statistical Parity  extremely_unfair  -0.466 -0.459         3600.0 -0.294858   \n",
-       "                    fair               0.004  0.015         3600.0 -0.000350   \n",
-       "                    moderately_unfair -0.331 -0.324         3600.0 -0.210500   \n",
-       "                    slightly_unfair   -0.180 -0.170         3600.0 -0.115558   \n",
-       "                    very_unfair       -0.412 -0.399         3600.0 -0.260383   \n",
-       "\n",
-       "                                                                         \\\n",
-       "                                            std    min      25%     50%   \n",
-       "Metric              simulation                                            \n",
-       "Average Odds        extremely_unfair   0.097317 -0.275 -0.25700 -0.1675   \n",
-       "                    fair               0.002970 -0.008 -0.00200  0.0000   \n",
-       "                    moderately_unfair  0.082401 -0.244 -0.23000 -0.1590   \n",
-       "                    slightly_unfair    0.052830 -0.159 -0.14925 -0.1020   \n",
-       "                    very_unfair        0.109442 -0.306 -0.29325 -0.1965   \n",
-       "Equal Opportunity   extremely_unfair   0.157611 -0.405 -0.37125 -0.2205   \n",
-       "                    fair               0.005028 -0.013 -0.00300 -0.0000   \n",
-       "                    moderately_unfair  0.110271 -0.297 -0.27700 -0.1715   \n",
-       "                    slightly_unfair    0.077511 -0.222 -0.20800 -0.1345   \n",
-       "                    very_unfair        0.166514 -0.424 -0.39925 -0.2435   \n",
-       "Predictive Equality extremely_unfair   0.037968 -0.149 -0.14100 -0.1140   \n",
-       "                    fair               0.003020 -0.011 -0.00200 -0.0000   \n",
-       "                    moderately_unfair  0.055068 -0.193 -0.18500 -0.1430   \n",
-       "                    slightly_unfair    0.028217 -0.097 -0.09100 -0.0685   \n",
-       "                    very_unfair        0.053284 -0.193 -0.18700 -0.1455   \n",
-       "Statistical Parity  extremely_unfair   0.136223 -0.436 -0.42625 -0.3195   \n",
-       "                    fair               0.003172 -0.010 -0.00200  0.0000   \n",
-       "                    moderately_unfair  0.097240 -0.316 -0.30400 -0.2270   \n",
-       "                    slightly_unfair    0.053522 -0.174 -0.16700 -0.1245   \n",
-       "                    very_unfair        0.120781 -0.386 -0.37700 -0.2810   \n",
-       "\n",
-       "                                                       \n",
-       "                                           75%    max  \n",
-       "Metric              simulation                         \n",
-       "Average Odds        extremely_unfair  -0.07200 -0.040  \n",
-       "                    fair               0.00200  0.006  \n",
-       "                    moderately_unfair -0.07575 -0.041  \n",
-       "                    slightly_unfair   -0.04850 -0.026  \n",
-       "                    very_unfair       -0.08675 -0.048  \n",
-       "Equal Opportunity   extremely_unfair  -0.06650 -0.030  \n",
-       "                    fair               0.00400  0.011  \n",
-       "                    moderately_unfair -0.06175 -0.029  \n",
-       "                    slightly_unfair   -0.05600 -0.025  \n",
-       "                    very_unfair       -0.07775 -0.037  \n",
-       "Predictive Equality extremely_unfair  -0.07475 -0.049  \n",
-       "                    fair               0.00100  0.008  \n",
-       "                    moderately_unfair -0.08700 -0.053  \n",
-       "                    slightly_unfair   -0.03900 -0.019  \n",
-       "                    very_unfair       -0.09475 -0.054  \n",
-       "Statistical Parity  extremely_unfair  -0.18500 -0.108  \n",
-       "                    fair               0.00125  0.010  \n",
-       "                    moderately_unfair -0.12950 -0.077  \n",
-       "                    slightly_unfair   -0.07000 -0.039  \n",
-       "                    very_unfair       -0.16325 -0.095  \n",
-       "\n",
-       "[20 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simulations_raw.groupby([\"Metric\",\"simulation\"])[[\"oracle_value\",\"probabilistic_estimate\",\"model_estimate\"]].describe().head(n=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "46e4edc7-d5d0-452f-8b45-4ac9860a7854",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 0 NaN Values\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"There are {0} NaN Values\".format(simulations_raw[\"oracle_value\"].apply(np.isnan).sum()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "4c89492c-9970-4b54-8c13-0dc639287402",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Review How Many Values Per Combination of PPV and NPV\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "p_given_p  np_given_np\n",
-       "0.70       0.80           600\n",
-       "0.80       0.90           600\n",
-       "0.90       0.99           600\n",
-       "0.99       0.99           600\n",
-       "Name: model_estimate, dtype: int64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Review How Many Values Per Combination of PPV and NPV\")\n",
-    "simulations_model.groupby([\"p_given_p\",\"np_given_np\"])[\"model_estimate\"].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "329fff0c-0686-4624-a5e4-9db92fb76e60",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "32fa8397-8f3e-4b2a-877f-793018fcc53c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_one_metric(df,stat_name,\n",
-    "                    compare_to=\"probabilistic_estimate\",\n",
-    "                    x_axis=\"oracle_value\",\n",
-    "                    color_map=color_map,\n",
-    "                    sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"],\n",
-    "                   markers={\"fair\":\".\",\"slightly_unfair\":\"v\",\"moderately_unfair\":\"s\",\"very_unfair\":\"*\",\"extremely_unfair\":\"X\"},\n",
-    "                   x_label=None,y_label=None,title=None):\n",
-    "    subset=df[df[\"Metric\"]==stat_name]\n",
-    "    if stat_name==\"Statistical Parity\":\n",
-    "        lowest=-0.51\n",
-    "        highest=0.025\n",
-    "    else:\n",
-    "        lowest=np.minimum(subset[x_axis].min(),subset[compare_to].min())\n",
-    "        highest=np.maximum(subset[x_axis].max(),subset[compare_to].max())\n",
-    "    plt.axline([lowest,lowest],[highest,highest],linewidth=1.5,c='0.0')\n",
-    "    for name in sim_names:\n",
-    "        o=subset[subset[\"simulation\"]==name][x_axis]\n",
-    "        p=subset[subset[\"simulation\"]==name][compare_to]\n",
-    "        plt.scatter(o,\n",
-    "                    p,\n",
-    "                    c=color_map[name],label=name.replace(\"_\",\" \").title(),marker=markers[name])\n",
-    "    if x_label is None:\n",
-    "        x_label=x_axis\n",
-    "    if y_label is None:\n",
-    "        y_label=compare_to\n",
-    "    if title is None:\n",
-    "        title=stat_name\n",
-    "    plt.title(title)\n",
-    "    plt.ylabel(y_label)\n",
-    "    plt.xlabel(x_label)\n",
-    "    plt.legend(title=\"Simulation Setting\")\n",
-    "    plt.savefig(f\"{stat_name}_{compare_to}_{x_axis}.png\".lower().replace(\" \",\"_\"),dpi=300)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a221fe78-1d04-42b3-a809-7a3d987ef60e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_model_estimate(df,stat_name,\n",
-    "                    compare_to=\"model_estimate\",\n",
-    "                    x_axis=\"oracle_value\",\n",
-    "                    color_map=color_map,\n",
-    "                    sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"],\n",
-    "                   markers={\"fair\":\".\",\"slightly_unfair\":\"v\",\"moderately_unfair\":\"s\",\"very_unfair\":\"*\",\"extremely_unfair\":\"X\"},\n",
-    "                   x_label=None,y_label=None,title=None):\n",
-    "    subset=df[df[\"Metric\"]==stat_name]\n",
-    "    if stat_name==\"Statistical Parity\":\n",
-    "        lowest=-0.51\n",
-    "        highest=0.025\n",
-    "    else:\n",
-    "        lowest=np.minimum(subset[x_axis].min(),subset[compare_to].min())\n",
-    "        highest=np.maximum(subset[x_axis].max(),subset[compare_to].max())\n",
-    "    linestyles=[':','--','-.','-']\n",
-    "    l=0\n",
-    "    for ppv in subset[\"p_given_p\"].unique():\n",
-    "        for npv in subset[\"np_given_np\"].unique(): \n",
-    "            fit=subset[(subset[\"p_given_p\"]==ppv) & (subset[\"np_given_np\"]==npv)]\n",
-    "            if fit.shape[0]>0:\n",
-    "                oracle=fit[\"oracle_value\"].values\n",
-    "                model=fit[\"adjusted_SP\"].values\n",
-    "                v=fit[\"class_PPV\"].values[0]+fit[\"class_NPV\"].values[0]\n",
-    "                plt.plot(oracle,model,linestyles[l],label=\"PPV+NPV={:0.2f}\".format(v),c='0.4')\n",
-    "                l=l+1\n",
-    "    plt.axline([lowest,lowest],[highest,highest],linewidth=2,c='0.0',label='45 degrees')\n",
-    "    for name in sim_names:\n",
-    "        o=subset[subset[\"simulation\"]==name][x_axis]\n",
-    "        p=subset[subset[\"simulation\"]==name][compare_to]\n",
-    "        plt.scatter(o,\n",
-    "                    p,\n",
-    "                    c=color_map[name],marker=markers[name])\n",
-    "    if x_label is None:\n",
-    "        x_label=x_axis\n",
-    "    if y_label is None:\n",
-    "        y_label=compare_to\n",
-    "    if title is None:\n",
-    "        title=stat_name\n",
-    "    plt.title(title)\n",
-    "    plt.ylabel(y_label)\n",
-    "    plt.xlabel(x_label)\n",
-    "    plt.legend(title=\"Protected Classifier Performance\")\n",
-    "    plt.savefig(f\"{stat_name}_{compare_to}_{x_axis}.png\".lower().replace(\" \",\"_\"),dpi=300)    \n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "3c85e48f-7f4a-4d00-a283-772b8a2c3195",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'fair': (0.0, 0.0, 0.0, 1.0),\n",
-       " 'slightly_unfair': (0.5009794328681196,\n",
-       "  0.015809545348320027,\n",
-       "  0.9999810273487268,\n",
-       "  1.0),\n",
-       " 'moderately_unfair': (0.7084919084320762, 0.12647636278656021, 0.0, 1.0),\n",
-       " 'very_unfair': (0.8677218312746247, 0.4268577244046407, 0.0, 1.0),\n",
-       " 'extremely_unfair': (1.0, 1.0, 0.0, 1.0)}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#other options: YlGnBu,cubehelix,gnuplot\n",
-    "cmap = plt.get_cmap('gnuplot')\n",
-    "snames=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
-    "indicies = np.linspace(0, cmap.N, len(snames))\n",
-    "my_colors = {s:cmap(int(i)) for s,i in zip(snames,indicies)}\n",
-    "my_colors"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "48064533-3ffb-4071-9fd4-8a5f50ae13a3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_17354/3098052042.py:30: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
-      "  plt.scatter(o,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model_estimate(simulations_raw,\"Statistical Parity\",\n",
-    "                    compare_to=\"model_estimate\",title=\"Statistical Parity: Model-Based Estimate\",\n",
-    "                    color_map=my_colors,\n",
-    "                   x_label=\"Oracle Value\",\n",
-    "                   y_label=\"Model Estimate\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "008b6784-8c1c-4420-bbb6-b48a88c31c8d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_17354/1680928571.py:19: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
-      "  plt.scatter(o,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_one_metric(simulations_prob,\"Statistical Parity\",compare_to=\"probabilistic_estimate\",\n",
-    "                title=\"Statistical Parity: Inferred Metric\",\n",
-    "                color_map=my_colors,\n",
-    "               x_label=\"Oracle Value\",\n",
-    "               y_label=\"Inferred Metric\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "4f054cf0-edd4-4ba1-af73-b31b65351444",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'sum_squares_error' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m mse\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43msum_squares_error\u001b[49m)\u001b[38;5;241m.\u001b[39mmerge(sum_of_squares_oracle,left_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, right_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mouter\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mmerge(df,left_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,right_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mouter\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m mse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39mmse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m/\u001b[39m(mse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m-\u001b[39mmse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m      3\u001b[0m mse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroot_mean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39mmse[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(np\u001b[38;5;241m.\u001b[39msqrt)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'sum_squares_error' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "mse=pd.DataFrame(sum_squares_error).merge(sum_of_squares_oracle,left_index=True, right_index=True,how=\"outer\").merge(df,left_index=True,right_index=True,how=\"outer\")\n",
-    "mse[\"mean_squared_error\"]=mse[\"sum\"]/(mse[\"count\"]-mse[\"df\"])\n",
-    "mse[\"root_mean_squared_error\"]=mse[\"mean_squared_error\"].apply(np.sqrt)\n",
-    "mse[\"cd\"]=mse[\"sum\"]/mse[\"oracle_sum_of_squares\"]\n",
-    "\n",
-    "mse=mse.reset_index()\n",
-    "mse\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc5fb386-0b1f-45ff-b12d-fb3d206efbb7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "standard_deviations=standard_deviations.reset_index()\n",
-    "simulations=simulations.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f5aacbd5-2512-439e-8064-29855b81a735",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mse[\"cd\"].hist()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb b/examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb
deleted file mode 100644
index 4579d0d..0000000
--- a/examples/probabilistic_fairness/notebooks/analyze_sample_size_sim.ipynb
+++ /dev/null
@@ -1,5209 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3cd360a9-69b1-4016-898c-6f548f5b4df3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import sys\n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "090b4807-2ef5-4155-93c4-9249100c4c8d",
-   "metadata": {},
-   "source": [
-    "# Review Count Results for Sample Size Simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e20fdd5f-0a5d-4115-bb3c-c7d86f69d9e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(17760, 7)\n",
-      "Pandas Dataframe of Raw Simulation Output\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Metric</th>\n",
-       "      <th>oracle_value</th>\n",
-       "      <th>probabilistic_estimate</th>\n",
-       "      <th>run_id</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>simulation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Average Odds</td>\n",
-       "      <td>-0.136</td>\n",
-       "      <td>-0.380</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>50</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Equal Opportunity</td>\n",
-       "      <td>-0.259</td>\n",
-       "      <td>-0.730</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>50</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Predictive Equality</td>\n",
-       "      <td>-0.013</td>\n",
-       "      <td>-0.030</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>50</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Statistical Parity</td>\n",
-       "      <td>-0.112</td>\n",
-       "      <td>-0.213</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>50</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Average Odds</td>\n",
-       "      <td>-0.016</td>\n",
-       "      <td>0.089</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>50</td>\n",
-       "      <td>fair</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Metric  oracle_value  probabilistic_estimate  run_id  \\\n",
-       "0         Average Odds        -0.136                  -0.380       0   \n",
-       "1    Equal Opportunity        -0.259                  -0.730       0   \n",
-       "3  Predictive Equality        -0.013                  -0.030       0   \n",
-       "4   Statistical Parity        -0.112                  -0.213       0   \n",
-       "5         Average Odds        -0.016                   0.089       1   \n",
-       "\n",
-       "   average_count  n_surrogates simulation  \n",
-       "0              5            50       fair  \n",
-       "1              5            50       fair  \n",
-       "3              5            50       fair  \n",
-       "4              5            50       fair  \n",
-       "5              5            50       fair  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "input_str='~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/{0}_simulation_count_{1}_surrogates_{2}.csv'\n",
-    "sim_names=[\"fair\",\"slightly_unfair\",\"moderately_unfair\",\"very_unfair\",\"extremely_unfair\"]\n",
-    "color_map={\"fair\":\"0.9\",\"slightly_unfair\":\"0.8\",\"moderately_unfair\":\"0.45\",\"very_unfair\":\"0.3\",\"extremely_unfair\":\"0\"}\n",
-    "all_simulations=[]\n",
-    "avg_counts=[5,10,20,30,40]\n",
-    "num_surrogates=[50,100,300,400,500,1000]\n",
-    "for name in sim_names:\n",
-    "    for c in avg_counts:\n",
-    "        for n_surrogates in num_surrogates:\n",
-    "            all_simulations.append(pd.read_csv(input_str.format(name,c,n_surrogates)))\n",
-    "simulations_raw=pd.concat(all_simulations).drop(columns=[\"Unnamed: 0\"])\n",
-    "simulations_raw=simulations_raw[simulations_raw[\"Metric\"].apply(lambda x: x in ['Average Odds', 'Equal Opportunity', 'Predictive Equality','Statistical Parity'])]\n",
-    "print(simulations_raw.shape)\n",
-    "print('Pandas Dataframe of Raw Simulation Output')\n",
-    "simulations_raw.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "46e4edc7-d5d0-452f-8b45-4ac9860a7854",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "For Some Simulations, Samples are So Small There Are No Results\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"For Some Simulations, Samples are So Small There Are No Results\")\n",
-    "simulations_raw[\"oracle_value\"].apply(np.isnan).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8248bc59-186e-4707-9c1a-e3e6576b3b5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "For Very Small Samples, Oracle Metric Might Require Dividing by 0, Also Resulting in NaN\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(\"For Very Small Samples, Oracle Metric Might Require Dividing by 0, Also Resulting in NaN\")\n",
-    "simulations_raw[simulations_raw[\"oracle_value\"].apply(np.isnan)][\"probabilistic_estimate\"].hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6dfe3ff1-0f99-4e13-8230-8ba3e1b22b2c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Oracle Values for Each Simulation Setting\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Metric               simulation       \n",
-       "Average Odds         extremely_unfair    -0.352455\n",
-       "                     fair                 0.001236\n",
-       "                     moderately_unfair   -0.274491\n",
-       "                     slightly_unfair     -0.176544\n",
-       "                     very_unfair         -0.375599\n",
-       "Equal Opportunity    extremely_unfair    -0.553894\n",
-       "                     fair                 0.002168\n",
-       "                     moderately_unfair   -0.350130\n",
-       "                     slightly_unfair     -0.253854\n",
-       "                     very_unfair         -0.554526\n",
-       "Predictive Equality  extremely_unfair    -0.150985\n",
-       "                     fair                 0.000279\n",
-       "                     moderately_unfair   -0.198871\n",
-       "                     slightly_unfair     -0.099238\n",
-       "                     very_unfair         -0.196687\n",
-       "Statistical Parity   extremely_unfair    -0.470866\n",
-       "                     fair                 0.001082\n",
-       "                     moderately_unfair   -0.333723\n",
-       "                     slightly_unfair     -0.184912\n",
-       "                     very_unfair         -0.413228\n",
-       "Name: oracle_mean, dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Oracle Values for Each Simulation Setting\")\n",
-    "means=simulations_raw.groupby([\"Metric\",\"simulation\"])[\"oracle_value\"].mean()\n",
-    "means.name=\"oracle_mean\"\n",
-    "means\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5a6afc59-80f2-4968-b79a-274897e30eb5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Analyzing Studentized Residuals: Requires Calculating Standard Deviations\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>var</th>\n",
-       "      <th>std</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
-       "      <th>100</th>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.004033</td>\n",
-       "      <td>0.063509</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.002782</td>\n",
-       "      <td>0.052742</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.003538</td>\n",
-       "      <td>0.059485</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.002028</td>\n",
-       "      <td>0.045033</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
-       "      <th>100</th>\n",
-       "      <td>0.000387</td>\n",
-       "      <td>0.019664</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.000161</td>\n",
-       "      <td>0.012682</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.000097</td>\n",
-       "      <td>0.009836</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.000056</td>\n",
-       "      <td>0.007464</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>592 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                     var  \\\n",
-       "Metric             simulation       average_count n_surrogates             \n",
-       "Average Odds       extremely_unfair 5             100           0.017607   \n",
-       "                                                  300           0.004033   \n",
-       "                                                  400           0.002782   \n",
-       "                                                  500           0.003538   \n",
-       "                                                  1000          0.002028   \n",
-       "...                                                                  ...   \n",
-       "Statistical Parity very_unfair      40            100           0.000387   \n",
-       "                                                  300           0.000161   \n",
-       "                                                  400           0.000097   \n",
-       "                                                  500           0.000056   \n",
-       "                                                  1000          0.000028   \n",
-       "\n",
-       "                                                                     std  \\\n",
-       "Metric             simulation       average_count n_surrogates             \n",
-       "Average Odds       extremely_unfair 5             100           0.132693   \n",
-       "                                                  300           0.063509   \n",
-       "                                                  400           0.052742   \n",
-       "                                                  500           0.059485   \n",
-       "                                                  1000          0.045033   \n",
-       "...                                                                  ...   \n",
-       "Statistical Parity very_unfair      40            100           0.019664   \n",
-       "                                                  300           0.012682   \n",
-       "                                                  400           0.009836   \n",
-       "                                                  500           0.007464   \n",
-       "                                                  1000          0.005270   \n",
-       "\n",
-       "                                                                count  \n",
-       "Metric             simulation       average_count n_surrogates         \n",
-       "Average Odds       extremely_unfair 5             100              30  \n",
-       "                                                  300              30  \n",
-       "                                                  400              30  \n",
-       "                                                  500              30  \n",
-       "                                                  1000             30  \n",
-       "...                                                               ...  \n",
-       "Statistical Parity very_unfair      40            100              30  \n",
-       "                                                  300              30  \n",
-       "                                                  400              30  \n",
-       "                                                  500              30  \n",
-       "                                                  1000             30  \n",
-       "\n",
-       "[592 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Analyzing Studentized Residuals: Requires Calculating Standard Deviations\")\n",
-    "oracle_variance=simulations_raw.groupby([\"Metric\",\"simulation\",\"average_count\",\"n_surrogates\"])[\"oracle_value\"].agg(['var','std','count'])\n",
-    "oracle_variance.name=\"oracle_var\"\n",
-    "oracle_variance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f787eeb3-938b-47ae-8b42-0410789dabe7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Review When Oracle Fails to Return a Value\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>var</th>\n",
-       "      <th>std</th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Average Odds</th>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <th>5</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.026856</td>\n",
-       "      <td>0.163878</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <th>5</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.103019</td>\n",
-       "      <td>0.320966</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Equality</th>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <th>5</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.003498</td>\n",
-       "      <td>0.059145</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                     var  \\\n",
-       "Metric              simulation      average_count n_surrogates             \n",
-       "Average Odds        slightly_unfair 5             50            0.026856   \n",
-       "Equal Opportunity   slightly_unfair 5             50            0.103019   \n",
-       "Predictive Equality slightly_unfair 5             50            0.003498   \n",
-       "\n",
-       "                                                                     std  \\\n",
-       "Metric              simulation      average_count n_surrogates             \n",
-       "Average Odds        slightly_unfair 5             50            0.163878   \n",
-       "Equal Opportunity   slightly_unfair 5             50            0.320966   \n",
-       "Predictive Equality slightly_unfair 5             50            0.059145   \n",
-       "\n",
-       "                                                                count  \n",
-       "Metric              simulation      average_count n_surrogates         \n",
-       "Average Odds        slightly_unfair 5             50               26  \n",
-       "Equal Opportunity   slightly_unfair 5             50               26  \n",
-       "Predictive Equality slightly_unfair 5             50               26  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Review When Oracle Fails to Return a Value\")\n",
-    "oracle_variance[oracle_variance[\"count\"]<30]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e4ca83ee-0216-4310-a9d9-dd979f3555a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Standard Errors for Metrics Requires Degrees of Freedom\n",
-      "Degrees of Freedom is the Number of Parameters Needed to Calculate the Metric\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>df</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Average Odds</th>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Equality</th>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Statistical Parity</th>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     df\n",
-       "Metric                 \n",
-       "Average Odds         16\n",
-       "Equal Opportunity     8\n",
-       "Predictive Equality   8\n",
-       "Statistical Parity    8"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Standard Errors for Metrics Requires Degrees of Freedom\")\n",
-    "print(\"Degrees of Freedom is the Number of Parameters Needed to Calculate the Metric\")\n",
-    "df_index=pd.Series([\"Average Odds\",\"Equal Opportunity\",\"Predictive Equality\",\"Statistical Parity\"])\n",
-    "df_index.name=\"Metric\"\n",
-    "df=pd.DataFrame({\"df\":[16,8,8,8]},\n",
-    "                index=df_index)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b7ad8d05-1e0e-429d-a20e-8e071f624e12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>oracle_var</th>\n",
-       "      <th>oracle_std</th>\n",
-       "      <th>count</th>\n",
-       "      <th>df</th>\n",
-       "      <th>oracle_mean</th>\n",
-       "      <th>prob_standard_deviation</th>\n",
-       "      <th>oracle_sum_of_squares</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
-       "      <th>100</th>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.004033</td>\n",
-       "      <td>0.063509</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.091405</td>\n",
-       "      <td>0.116969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.002782</td>\n",
-       "      <td>0.052742</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.075908</td>\n",
-       "      <td>0.080669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.003538</td>\n",
-       "      <td>0.059485</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.085613</td>\n",
-       "      <td>0.102614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.002028</td>\n",
-       "      <td>0.045033</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.064814</td>\n",
-       "      <td>0.058812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
-       "      <th>100</th>\n",
-       "      <td>0.000387</td>\n",
-       "      <td>0.019664</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.022576</td>\n",
-       "      <td>0.011213</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.000161</td>\n",
-       "      <td>0.012682</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.014560</td>\n",
-       "      <td>0.004664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.000097</td>\n",
-       "      <td>0.009836</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.011293</td>\n",
-       "      <td>0.002805</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.000056</td>\n",
-       "      <td>0.007464</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.008569</td>\n",
-       "      <td>0.001615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>592 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                oracle_var  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.017607   \n",
-       "                                                  300             0.004033   \n",
-       "                                                  400             0.002782   \n",
-       "                                                  500             0.003538   \n",
-       "                                                  1000            0.002028   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            100             0.000387   \n",
-       "                                                  300             0.000161   \n",
-       "                                                  400             0.000097   \n",
-       "                                                  500             0.000056   \n",
-       "                                                  1000            0.000028   \n",
-       "\n",
-       "                                                                oracle_std  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.132693   \n",
-       "                                                  300             0.063509   \n",
-       "                                                  400             0.052742   \n",
-       "                                                  500             0.059485   \n",
-       "                                                  1000            0.045033   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            100             0.019664   \n",
-       "                                                  300             0.012682   \n",
-       "                                                  400             0.009836   \n",
-       "                                                  500             0.007464   \n",
-       "                                                  1000            0.005270   \n",
-       "\n",
-       "                                                                count  df  \\\n",
-       "Metric             simulation       average_count n_surrogates              \n",
-       "Average Odds       extremely_unfair 5             100              30  16   \n",
-       "                                                  300              30  16   \n",
-       "                                                  400              30  16   \n",
-       "                                                  500              30  16   \n",
-       "                                                  1000             30  16   \n",
-       "...                                                               ...  ..   \n",
-       "Statistical Parity very_unfair      40            100              30   8   \n",
-       "                                                  300              30   8   \n",
-       "                                                  400              30   8   \n",
-       "                                                  500              30   8   \n",
-       "                                                  1000             30   8   \n",
-       "\n",
-       "                                                                oracle_mean  \\\n",
-       "Metric             simulation       average_count n_surrogates                \n",
-       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
-       "                                                  300             -0.352455   \n",
-       "                                                  400             -0.352455   \n",
-       "                                                  500             -0.352455   \n",
-       "                                                  1000            -0.352455   \n",
-       "...                                                                     ...   \n",
-       "Statistical Parity very_unfair      40            100             -0.413228   \n",
-       "                                                  300             -0.413228   \n",
-       "                                                  400             -0.413228   \n",
-       "                                                  500             -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "\n",
-       "                                                                prob_standard_deviation  \\\n",
-       "Metric             simulation       average_count n_surrogates                            \n",
-       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
-       "                                                  300                          0.091405   \n",
-       "                                                  400                          0.075908   \n",
-       "                                                  500                          0.085613   \n",
-       "                                                  1000                         0.064814   \n",
-       "...                                                                                 ...   \n",
-       "Statistical Parity very_unfair      40            100                          0.022576   \n",
-       "                                                  300                          0.014560   \n",
-       "                                                  400                          0.011293   \n",
-       "                                                  500                          0.008569   \n",
-       "                                                  1000                         0.006050   \n",
-       "\n",
-       "                                                                oracle_sum_of_squares  \n",
-       "Metric             simulation       average_count n_surrogates                         \n",
-       "Average Odds       extremely_unfair 5             100                        0.510614  \n",
-       "                                                  300                        0.116969  \n",
-       "                                                  400                        0.080669  \n",
-       "                                                  500                        0.102614  \n",
-       "                                                  1000                       0.058812  \n",
-       "...                                                                               ...  \n",
-       "Statistical Parity very_unfair      40            100                        0.011213  \n",
-       "                                                  300                        0.004664  \n",
-       "                                                  400                        0.002805  \n",
-       "                                                  500                        0.001615  \n",
-       "                                                  1000                       0.000805  \n",
-       "\n",
-       "[592 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "standard_deviations=pd.DataFrame(oracle_variance).merge(df,left_index=True,right_index=True).merge(means,left_index=True,right_index=True).rename(columns={\"var\":\"oracle_var\",\"std\":\"oracle_std\"})\n",
-    "\n",
-    "standard_deviations[\"prob_standard_deviation\"]=(standard_deviations[\"oracle_var\"]*(standard_deviations[\"count\"]-1)/(standard_deviations[\"count\"]-standard_deviations[\"df\"])).apply(np.sqrt)\n",
-    "standard_deviations[\"oracle_sum_of_squares\"]=standard_deviations[\"oracle_var\"]*(standard_deviations[\"count\"]-1)\n",
-    "\n",
-    "standard_deviations\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "fd048cc4-a172-4920-8e29-655a778f925e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>oracle_value</th>\n",
-       "      <th>probabilistic_estimate</th>\n",
-       "      <th>oracle_var</th>\n",
-       "      <th>oracle_std</th>\n",
-       "      <th>count</th>\n",
-       "      <th>df</th>\n",
-       "      <th>oracle_mean</th>\n",
-       "      <th>prob_standard_deviation</th>\n",
-       "      <th>oracle_sum_of_squares</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.306</td>\n",
-       "      <td>-0.183</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.547</td>\n",
-       "      <td>-0.327</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.427</td>\n",
-       "      <td>0.719</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.043</td>\n",
-       "      <td>-0.092</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.397</td>\n",
-       "      <td>0.736</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.408</td>\n",
-       "      <td>-0.419</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>-0.379</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.420</td>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>-0.436</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.407</td>\n",
-       "      <td>-0.417</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17760 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                oracle_value  \\\n",
-       "Metric             simulation       average_count n_surrogates                 \n",
-       "Average Odds       extremely_unfair 5             100                 -0.306   \n",
-       "                                                  100                 -0.547   \n",
-       "                                                  100                 -0.427   \n",
-       "                                                  100                 -0.043   \n",
-       "                                                  100                 -0.397   \n",
-       "...                                                                      ...   \n",
-       "Statistical Parity very_unfair      40            1000                -0.408   \n",
-       "                                                  1000                -0.411   \n",
-       "                                                  1000                -0.420   \n",
-       "                                                  1000                -0.411   \n",
-       "                                                  1000                -0.407   \n",
-       "\n",
-       "                                                                probabilistic_estimate  \\\n",
-       "Metric             simulation       average_count n_surrogates                           \n",
-       "Average Odds       extremely_unfair 5             100                           -0.183   \n",
-       "                                                  100                           -0.327   \n",
-       "                                                  100                            0.719   \n",
-       "                                                  100                           -0.092   \n",
-       "                                                  100                            0.736   \n",
-       "...                                                                                ...   \n",
-       "Statistical Parity very_unfair      40            1000                          -0.419   \n",
-       "                                                  1000                          -0.379   \n",
-       "                                                  1000                          -0.411   \n",
-       "                                                  1000                          -0.436   \n",
-       "                                                  1000                          -0.417   \n",
-       "\n",
-       "                                                                oracle_var  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "\n",
-       "                                                                oracle_std  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "\n",
-       "                                                                count  df  \\\n",
-       "Metric             simulation       average_count n_surrogates              \n",
-       "Average Odds       extremely_unfair 5             100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "...                                                               ...  ..   \n",
-       "Statistical Parity very_unfair      40            1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "\n",
-       "                                                                oracle_mean  \\\n",
-       "Metric             simulation       average_count n_surrogates                \n",
-       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "...                                                                     ...   \n",
-       "Statistical Parity very_unfair      40            1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "\n",
-       "                                                                prob_standard_deviation  \\\n",
-       "Metric             simulation       average_count n_surrogates                            \n",
-       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "...                                                                                 ...   \n",
-       "Statistical Parity very_unfair      40            1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "\n",
-       "                                                                oracle_sum_of_squares  \n",
-       "Metric             simulation       average_count n_surrogates                         \n",
-       "Average Odds       extremely_unfair 5             100                        0.510614  \n",
-       "                                                  100                        0.510614  \n",
-       "                                                  100                        0.510614  \n",
-       "                                                  100                        0.510614  \n",
-       "                                                  100                        0.510614  \n",
-       "...                                                                               ...  \n",
-       "Statistical Parity very_unfair      40            1000                       0.000805  \n",
-       "                                                  1000                       0.000805  \n",
-       "                                                  1000                       0.000805  \n",
-       "                                                  1000                       0.000805  \n",
-       "                                                  1000                       0.000805  \n",
-       "\n",
-       "[17760 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simulations=simulations_raw.set_index([\"Metric\",\"simulation\",\"average_count\",\"n_surrogates\"]).merge(standard_deviations,left_index=True,right_index=True,how=\"outer\").drop(columns=[\"run_id\"])\n",
-    "simulations[\"oracle_value\"].fillna(simulations[\"oracle_mean\"],inplace=True)\n",
-    "simulations\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0a14ac35-a423-430e-b5ff-28022aaaf595",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Different Attempts to Scale Metrics to Show on Same Graph\n",
-      "Percent Error and Squared Error Were Less Useful than Studentized Error\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th>oracle_value</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
-       "      <th>100</th>\n",
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-       "      <td>-0.183</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-0.123</td>\n",
-       "      <td>0.015129</td>\n",
-       "      <td>0.401961</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.547</td>\n",
-       "      <td>-0.327</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-0.220</td>\n",
-       "      <td>0.048400</td>\n",
-       "      <td>0.402194</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.427</td>\n",
-       "      <td>0.719</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-1.146</td>\n",
-       "      <td>1.313316</td>\n",
-       "      <td>2.683841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.043</td>\n",
-       "      <td>-0.092</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>0.002401</td>\n",
-       "      <td>1.139535</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.397</td>\n",
-       "      <td>0.736</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-1.133</td>\n",
-       "      <td>1.283689</td>\n",
-       "      <td>2.853904</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.408</td>\n",
-       "      <td>-0.419</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.000121</td>\n",
-       "      <td>0.026961</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>-0.379</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>-0.032</td>\n",
-       "      <td>0.001024</td>\n",
-       "      <td>0.077859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.420</td>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>-0.009</td>\n",
-       "      <td>0.000081</td>\n",
-       "      <td>0.021429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>-0.436</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.000625</td>\n",
-       "      <td>0.060827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.407</td>\n",
-       "      <td>-0.417</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.024570</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17760 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                oracle_value  \\\n",
-       "Metric             simulation       average_count n_surrogates                 \n",
-       "Average Odds       extremely_unfair 5             100                 -0.306   \n",
-       "                                                  100                 -0.547   \n",
-       "                                                  100                 -0.427   \n",
-       "                                                  100                 -0.043   \n",
-       "                                                  100                 -0.397   \n",
-       "...                                                                      ...   \n",
-       "Statistical Parity very_unfair      40            1000                -0.408   \n",
-       "                                                  1000                -0.411   \n",
-       "                                                  1000                -0.420   \n",
-       "                                                  1000                -0.411   \n",
-       "                                                  1000                -0.407   \n",
-       "\n",
-       "                                                                probabilistic_estimate  \\\n",
-       "Metric             simulation       average_count n_surrogates                           \n",
-       "Average Odds       extremely_unfair 5             100                           -0.183   \n",
-       "                                                  100                           -0.327   \n",
-       "                                                  100                            0.719   \n",
-       "                                                  100                           -0.092   \n",
-       "                                                  100                            0.736   \n",
-       "...                                                                                ...   \n",
-       "Statistical Parity very_unfair      40            1000                          -0.419   \n",
-       "                                                  1000                          -0.379   \n",
-       "                                                  1000                          -0.411   \n",
-       "                                                  1000                          -0.436   \n",
-       "                                                  1000                          -0.417   \n",
-       "\n",
-       "                                                                oracle_var  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "\n",
-       "                                                                oracle_std  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "\n",
-       "                                                                count  df  \\\n",
-       "Metric             simulation       average_count n_surrogates              \n",
-       "Average Odds       extremely_unfair 5             100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "...                                                               ...  ..   \n",
-       "Statistical Parity very_unfair      40            1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "\n",
-       "                                                                oracle_mean  \\\n",
-       "Metric             simulation       average_count n_surrogates                \n",
-       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "...                                                                     ...   \n",
-       "Statistical Parity very_unfair      40            1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "\n",
-       "                                                                prob_standard_deviation  \\\n",
-       "Metric             simulation       average_count n_surrogates                            \n",
-       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "...                                                                                 ...   \n",
-       "Statistical Parity very_unfair      40            1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "\n",
-       "                                                                oracle_sum_of_squares  \\\n",
-       "Metric             simulation       average_count n_surrogates                          \n",
-       "Average Odds       extremely_unfair 5             100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "...                                                                               ...   \n",
-       "Statistical Parity very_unfair      40            1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "\n",
-       "                                                                error  \\\n",
-       "Metric             simulation       average_count n_surrogates          \n",
-       "Average Odds       extremely_unfair 5             100          -0.123   \n",
-       "                                                  100          -0.220   \n",
-       "                                                  100          -1.146   \n",
-       "                                                  100           0.049   \n",
-       "                                                  100          -1.133   \n",
-       "...                                                               ...   \n",
-       "Statistical Parity very_unfair      40            1000          0.011   \n",
-       "                                                  1000         -0.032   \n",
-       "                                                  1000         -0.009   \n",
-       "                                                  1000          0.025   \n",
-       "                                                  1000          0.010   \n",
-       "\n",
-       "                                                                squared_error  \\\n",
-       "Metric             simulation       average_count n_surrogates                  \n",
-       "Average Odds       extremely_unfair 5             100                0.015129   \n",
-       "                                                  100                0.048400   \n",
-       "                                                  100                1.313316   \n",
-       "                                                  100                0.002401   \n",
-       "                                                  100                1.283689   \n",
-       "...                                                                       ...   \n",
-       "Statistical Parity very_unfair      40            1000               0.000121   \n",
-       "                                                  1000               0.001024   \n",
-       "                                                  1000               0.000081   \n",
-       "                                                  1000               0.000625   \n",
-       "                                                  1000               0.000100   \n",
-       "\n",
-       "                                                                pct_error  \n",
-       "Metric             simulation       average_count n_surrogates             \n",
-       "Average Odds       extremely_unfair 5             100            0.401961  \n",
-       "                                                  100            0.402194  \n",
-       "                                                  100            2.683841  \n",
-       "                                                  100            1.139535  \n",
-       "                                                  100            2.853904  \n",
-       "...                                                                   ...  \n",
-       "Statistical Parity very_unfair      40            1000           0.026961  \n",
-       "                                                  1000           0.077859  \n",
-       "                                                  1000           0.021429  \n",
-       "                                                  1000           0.060827  \n",
-       "                                                  1000           0.024570  \n",
-       "\n",
-       "[17760 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print('Different Attempts to Scale Metrics to Show on Same Graph')\n",
-    "print('Percent Error and Squared Error Were Less Useful than Studentized Error')\n",
-    "simulations[\"error\"]=simulations[\"oracle_value\"]-simulations[\"probabilistic_estimate\"]\n",
-    "simulations[\"squared_error\"]=simulations[\"error\"]**2\n",
-    "simulations[\"pct_error\"]=(simulations[\"error\"]/simulations[\"oracle_value\"]).apply(abs)\n",
-    "simulations \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "ceb83160-7073-474d-8415-df0a8ca8a9ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calculate Cut-Off from: Mohammad Ahsanullah, BM Golam Kibria, and Mohammad Shakil. 2014. Normal and students t distributions and their applications. Vol. 4. Springer\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>oracle_value</th>\n",
-       "      <th>probabilistic_estimate</th>\n",
-       "      <th>oracle_var</th>\n",
-       "      <th>oracle_std</th>\n",
-       "      <th>count</th>\n",
-       "      <th>df</th>\n",
-       "      <th>oracle_mean</th>\n",
-       "      <th>prob_standard_deviation</th>\n",
-       "      <th>oracle_sum_of_squares</th>\n",
-       "      <th>error</th>\n",
-       "      <th>squared_error</th>\n",
-       "      <th>pct_error</th>\n",
-       "      <th>student_error</th>\n",
-       "      <th>cut_off</th>\n",
-       "      <th>out_of_range</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.306</td>\n",
-       "      <td>-0.183</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-0.123</td>\n",
-       "      <td>0.015129</td>\n",
-       "      <td>0.401961</td>\n",
-       "      <td>-0.644055</td>\n",
-       "      <td>3.741657</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.547</td>\n",
-       "      <td>-0.327</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-0.220</td>\n",
-       "      <td>0.048400</td>\n",
-       "      <td>0.402194</td>\n",
-       "      <td>-1.151967</td>\n",
-       "      <td>3.741657</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.427</td>\n",
-       "      <td>0.719</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-1.146</td>\n",
-       "      <td>1.313316</td>\n",
-       "      <td>2.683841</td>\n",
-       "      <td>-6.000703</td>\n",
-       "      <td>3.741657</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.043</td>\n",
-       "      <td>-0.092</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>0.002401</td>\n",
-       "      <td>1.139535</td>\n",
-       "      <td>0.256575</td>\n",
-       "      <td>3.741657</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>-0.397</td>\n",
-       "      <td>0.736</td>\n",
-       "      <td>0.017607</td>\n",
-       "      <td>0.132693</td>\n",
-       "      <td>30</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-0.352455</td>\n",
-       "      <td>0.190978</td>\n",
-       "      <td>0.510614</td>\n",
-       "      <td>-1.133</td>\n",
-       "      <td>1.283689</td>\n",
-       "      <td>2.853904</td>\n",
-       "      <td>-5.932632</td>\n",
-       "      <td>3.741657</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.408</td>\n",
-       "      <td>-0.419</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.000121</td>\n",
-       "      <td>0.026961</td>\n",
-       "      <td>1.818056</td>\n",
-       "      <td>4.690416</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>-0.379</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>-0.032</td>\n",
-       "      <td>0.001024</td>\n",
-       "      <td>0.077859</td>\n",
-       "      <td>-5.288889</td>\n",
-       "      <td>4.690416</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.420</td>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>-0.009</td>\n",
-       "      <td>0.000081</td>\n",
-       "      <td>0.021429</td>\n",
-       "      <td>-1.487500</td>\n",
-       "      <td>4.690416</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.411</td>\n",
-       "      <td>-0.436</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.000625</td>\n",
-       "      <td>0.060827</td>\n",
-       "      <td>4.131945</td>\n",
-       "      <td>4.690416</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>-0.407</td>\n",
-       "      <td>-0.417</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.005270</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.413228</td>\n",
-       "      <td>0.006050</td>\n",
-       "      <td>0.000805</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>0.024570</td>\n",
-       "      <td>1.652778</td>\n",
-       "      <td>4.690416</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17760 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                oracle_value  \\\n",
-       "Metric             simulation       average_count n_surrogates                 \n",
-       "Average Odds       extremely_unfair 5             100                 -0.306   \n",
-       "                                                  100                 -0.547   \n",
-       "                                                  100                 -0.427   \n",
-       "                                                  100                 -0.043   \n",
-       "                                                  100                 -0.397   \n",
-       "...                                                                      ...   \n",
-       "Statistical Parity very_unfair      40            1000                -0.408   \n",
-       "                                                  1000                -0.411   \n",
-       "                                                  1000                -0.420   \n",
-       "                                                  1000                -0.411   \n",
-       "                                                  1000                -0.407   \n",
-       "\n",
-       "                                                                probabilistic_estimate  \\\n",
-       "Metric             simulation       average_count n_surrogates                           \n",
-       "Average Odds       extremely_unfair 5             100                           -0.183   \n",
-       "                                                  100                           -0.327   \n",
-       "                                                  100                            0.719   \n",
-       "                                                  100                           -0.092   \n",
-       "                                                  100                            0.736   \n",
-       "...                                                                                ...   \n",
-       "Statistical Parity very_unfair      40            1000                          -0.419   \n",
-       "                                                  1000                          -0.379   \n",
-       "                                                  1000                          -0.411   \n",
-       "                                                  1000                          -0.436   \n",
-       "                                                  1000                          -0.417   \n",
-       "\n",
-       "                                                                oracle_var  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "                                                  100             0.017607   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "                                                  1000            0.000028   \n",
-       "\n",
-       "                                                                oracle_std  \\\n",
-       "Metric             simulation       average_count n_surrogates               \n",
-       "Average Odds       extremely_unfair 5             100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "                                                  100             0.132693   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      40            1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "                                                  1000            0.005270   \n",
-       "\n",
-       "                                                                count  df  \\\n",
-       "Metric             simulation       average_count n_surrogates              \n",
-       "Average Odds       extremely_unfair 5             100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "                                                  100              30  16   \n",
-       "...                                                               ...  ..   \n",
-       "Statistical Parity very_unfair      40            1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "                                                  1000             30   8   \n",
-       "\n",
-       "                                                                oracle_mean  \\\n",
-       "Metric             simulation       average_count n_surrogates                \n",
-       "Average Odds       extremely_unfair 5             100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "                                                  100             -0.352455   \n",
-       "...                                                                     ...   \n",
-       "Statistical Parity very_unfair      40            1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "                                                  1000            -0.413228   \n",
-       "\n",
-       "                                                                prob_standard_deviation  \\\n",
-       "Metric             simulation       average_count n_surrogates                            \n",
-       "Average Odds       extremely_unfair 5             100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "                                                  100                          0.190978   \n",
-       "...                                                                                 ...   \n",
-       "Statistical Parity very_unfair      40            1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "                                                  1000                         0.006050   \n",
-       "\n",
-       "                                                                oracle_sum_of_squares  \\\n",
-       "Metric             simulation       average_count n_surrogates                          \n",
-       "Average Odds       extremely_unfair 5             100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "                                                  100                        0.510614   \n",
-       "...                                                                               ...   \n",
-       "Statistical Parity very_unfair      40            1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "                                                  1000                       0.000805   \n",
-       "\n",
-       "                                                                error  \\\n",
-       "Metric             simulation       average_count n_surrogates          \n",
-       "Average Odds       extremely_unfair 5             100          -0.123   \n",
-       "                                                  100          -0.220   \n",
-       "                                                  100          -1.146   \n",
-       "                                                  100           0.049   \n",
-       "                                                  100          -1.133   \n",
-       "...                                                               ...   \n",
-       "Statistical Parity very_unfair      40            1000          0.011   \n",
-       "                                                  1000         -0.032   \n",
-       "                                                  1000         -0.009   \n",
-       "                                                  1000          0.025   \n",
-       "                                                  1000          0.010   \n",
-       "\n",
-       "                                                                squared_error  \\\n",
-       "Metric             simulation       average_count n_surrogates                  \n",
-       "Average Odds       extremely_unfair 5             100                0.015129   \n",
-       "                                                  100                0.048400   \n",
-       "                                                  100                1.313316   \n",
-       "                                                  100                0.002401   \n",
-       "                                                  100                1.283689   \n",
-       "...                                                                       ...   \n",
-       "Statistical Parity very_unfair      40            1000               0.000121   \n",
-       "                                                  1000               0.001024   \n",
-       "                                                  1000               0.000081   \n",
-       "                                                  1000               0.000625   \n",
-       "                                                  1000               0.000100   \n",
-       "\n",
-       "                                                                pct_error  \\\n",
-       "Metric             simulation       average_count n_surrogates              \n",
-       "Average Odds       extremely_unfair 5             100            0.401961   \n",
-       "                                                  100            0.402194   \n",
-       "                                                  100            2.683841   \n",
-       "                                                  100            1.139535   \n",
-       "                                                  100            2.853904   \n",
-       "...                                                                   ...   \n",
-       "Statistical Parity very_unfair      40            1000           0.026961   \n",
-       "                                                  1000           0.077859   \n",
-       "                                                  1000           0.021429   \n",
-       "                                                  1000           0.060827   \n",
-       "                                                  1000           0.024570   \n",
-       "\n",
-       "                                                                student_error  \\\n",
-       "Metric             simulation       average_count n_surrogates                  \n",
-       "Average Odds       extremely_unfair 5             100               -0.644055   \n",
-       "                                                  100               -1.151967   \n",
-       "                                                  100               -6.000703   \n",
-       "                                                  100                0.256575   \n",
-       "                                                  100               -5.932632   \n",
-       "...                                                                       ...   \n",
-       "Statistical Parity very_unfair      40            1000               1.818056   \n",
-       "                                                  1000              -5.288889   \n",
-       "                                                  1000              -1.487500   \n",
-       "                                                  1000               4.131945   \n",
-       "                                                  1000               1.652778   \n",
-       "\n",
-       "                                                                 cut_off  \\\n",
-       "Metric             simulation       average_count n_surrogates             \n",
-       "Average Odds       extremely_unfair 5             100           3.741657   \n",
-       "                                                  100           3.741657   \n",
-       "                                                  100           3.741657   \n",
-       "                                                  100           3.741657   \n",
-       "                                                  100           3.741657   \n",
-       "...                                                                  ...   \n",
-       "Statistical Parity very_unfair      40            1000          4.690416   \n",
-       "                                                  1000          4.690416   \n",
-       "                                                  1000          4.690416   \n",
-       "                                                  1000          4.690416   \n",
-       "                                                  1000          4.690416   \n",
-       "\n",
-       "                                                                out_of_range  \n",
-       "Metric             simulation       average_count n_surrogates                \n",
-       "Average Odds       extremely_unfair 5             100                      0  \n",
-       "                                                  100                      0  \n",
-       "                                                  100                      1  \n",
-       "                                                  100                      0  \n",
-       "                                                  100                      1  \n",
-       "...                                                                      ...  \n",
-       "Statistical Parity very_unfair      40            1000                     0  \n",
-       "                                                  1000                     1  \n",
-       "                                                  1000                     0  \n",
-       "                                                  1000                     0  \n",
-       "                                                  1000                     0  \n",
-       "\n",
-       "[17760 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print('Calculate Cut-Off from: Mohammad Ahsanullah, BM Golam Kibria, and Mohammad Shakil. 2014. Normal and students t distributions and their applications. Vol. 4. Springer')\n",
-    "simulations[\"student_error\"]=simulations[\"error\"]/simulations[\"prob_standard_deviation\"]\n",
-    "simulations[\"cut_off\"]=(simulations[\"count\"]*(simulations[\"count\"]-simulations[\"df\"])/simulations[\"count\"]).apply(np.sqrt)\n",
-    "simulations[\"out_of_range\"]=(simulations[\"student_error\"].apply(abs)>simulations[\"cut_off\"]).astype(int)\n",
-    "simulations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "5095382c-7d12-4de9-8057-51740eeff449",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Percent Simulations with Out of Range Residuals: 0.05%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Percent Simulations with Out of Range Residuals: {:.0}%'.format(simulations[\"out_of_range\"].sum()/simulations.shape[0]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20dbbfac-1411-458e-a129-a7ab2d97787c",
-   "metadata": {},
-   "source": [
-    "# Plotting Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "67326acc-b070-406c-96ea-2396eba393a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "faa07a98-aebe-4fc7-82db-780baa73a118",
-   "metadata": {},
-   "source": [
-    "# Unuseable Estimates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd222d87-9c7c-4d0e-a8e0-5178f155ea4d",
-   "metadata": {},
-   "source": [
-    "Count the number of NaN and out of range estimates for each simulation setting, across all fairness statistics. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "287d7ad5-bdf7-4b06-bb78-1eb7f05d3124",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(17760, 15)\n",
-      "Index(['oracle_value', 'probabilistic_estimate', 'oracle_var', 'oracle_std',\n",
-      "       'count', 'df', 'oracle_mean', 'prob_standard_deviation',\n",
-      "       'oracle_sum_of_squares', 'error', 'squared_error', 'pct_error',\n",
-      "       'student_error', 'cut_off', 'out_of_range'],\n",
-      "      dtype='object')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "958"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(simulations.shape)\n",
-    "print(simulations.columns)\n",
-    "simulations[\"out_of_range\"].sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "34f9a75c-763b-4805-a4b5-1acc0283fc60",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Divide each count by 600\n"
-     ]
-    }
-   ],
-   "source": [
-    "m=simulations.groupby([\"average_count\",\"n_surrogates\"]).count().max().max()\n",
-    "print(f\"Divide each count by {m}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "35d93bae-ff8c-414b-904e-cf87e11e21d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All The Missing Values Come from Smallest Sample Size Setting\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "average_count  n_surrogates\n",
-       "5              50              240\n",
-       "Name: missing, dtype: int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "missing=m-simulations.groupby([\"average_count\",\"n_surrogates\"])[\"out_of_range\"].count()\n",
-    "missing.name=\"missing\"\n",
-    "print(\"All The Missing Values Come from Smallest Sample Size Setting\")\n",
-    "missing[missing>0]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aab87091-f268-4a8f-b5be-4fc24bdefc55",
-   "metadata": {},
-   "source": [
-    "Review counts of large errors for different simulation settings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "ad34d142-dfa4-4c90-9bd4-e2bb6378250f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "count_big_errors=simulations.groupby([\"average_count\",\"n_surrogates\"])[\"out_of_range\"].sum()+missing\n",
-    "count_big_errors.name=\"n_large_errors\"\n",
-    "(count_big_errors/m).hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "6d7c0649-fe34-48b0-b8e8-9227bef70e09",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape data for line graph\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>50</th>\n",
-       "      <th>100</th>\n",
-       "      <th>300</th>\n",
-       "      <th>400</th>\n",
-       "      <th>500</th>\n",
-       "      <th>1000</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.456667</td>\n",
-       "      <td>0.086667</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.053333</td>\n",
-       "      <td>0.061667</td>\n",
-       "      <td>0.033333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.086667</td>\n",
-       "      <td>0.086667</td>\n",
-       "      <td>0.090000</td>\n",
-       "      <td>0.043333</td>\n",
-       "      <td>0.056667</td>\n",
-       "      <td>0.028333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>0.083333</td>\n",
-       "      <td>0.070000</td>\n",
-       "      <td>0.071667</td>\n",
-       "      <td>0.041667</td>\n",
-       "      <td>0.018333</td>\n",
-       "      <td>0.036667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.103333</td>\n",
-       "      <td>0.073333</td>\n",
-       "      <td>0.030000</td>\n",
-       "      <td>0.053333</td>\n",
-       "      <td>0.038333</td>\n",
-       "      <td>0.010000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.085000</td>\n",
-       "      <td>0.053333</td>\n",
-       "      <td>0.023333</td>\n",
-       "      <td>0.006667</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.005000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "n_surrogates       50        100       300       400       500       1000\n",
-       "average_count                                                            \n",
-       "5              0.456667  0.086667  0.100000  0.053333  0.061667  0.033333\n",
-       "10             0.086667  0.086667  0.090000  0.043333  0.056667  0.028333\n",
-       "20             0.083333  0.070000  0.071667  0.041667  0.018333  0.036667\n",
-       "30             0.103333  0.073333  0.030000  0.053333  0.038333  0.010000\n",
-       "40             0.085000  0.053333  0.023333  0.006667  0.010000  0.005000"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pivoted=count_big_errors.reset_index().pivot(index=\"average_count\",columns=\"n_surrogates\",values=\"n_large_errors\")\n",
-    "print(\"Shape data for line graph\")\n",
-    "pivoted/m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "99186c59-6991-4986-9c98-6a8be65d32d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set colors that will print well in grayscale,\n",
-    "#Different color for each simulation setting\n",
-    "#other options: YlGnBu,cubehelix,gnuplot\n",
-    "cmap = plt.get_cmap('gnuplot')\n",
-    "indicies = np.linspace(0, cmap.N, len(sim_names))\n",
-    "my_colors = [cmap(int(i)) for i in indicies]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "78763169-42da-44c5-a047-4509aaa7e333",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/1481678520.py:2: UserWarning: \n",
-      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
-      "  sns.lineplot(data=(pivoted/m),markers=True, palette=my_colors)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 5), dpi=100)\n",
-    "sns.lineplot(data=(pivoted/m),markers=True, palette=my_colors)\n",
-    "plt.xlabel(\"Individuals Per Level\")\n",
-    "plt.ylabel(\"Percent Out of Range\")\n",
-    "plt.legend(title=\"Levels in Z\")\n",
-    "plt.title(\"Percent Estimation Failures for Small Samples\")\n",
-    "plt.savefig(\"percent_out_of_range.png\",dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d426ded1-541b-4f7a-a75e-f945c33a6946",
-   "metadata": {},
-   "source": [
-    "# Standard Errors Probabilistic Estimates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1f6db5c-f5cc-4ca5-ab74-187e6a33667a",
-   "metadata": {},
-   "source": [
-    "We have an understanding of how often the probabilistic estimates fail under different sample sizes. Estimates will vary from sample to sample, however, and the variation decreases with larger samples (more values per surrogate class and more surrogate classes). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "42fb263d-b9e4-491d-ac3a-b6a7319919c4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>probabilistic_sum_of_squares</th>\n",
-       "      <th>probabilistic_count</th>\n",
-       "      <th>df</th>\n",
-       "      <th>probabilistic_se</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
-       "      <th>10</th>\n",
-       "      <td>1.400771</td>\n",
-       "      <td>26</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.374269</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1.013655</td>\n",
-       "      <td>22</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.411026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.616997</td>\n",
-       "      <td>21</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.351283</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.406700</td>\n",
-       "      <td>25</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.212577</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <th>5</th>\n",
-       "      <td>1.299259</td>\n",
-       "      <td>22</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.465342</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
-       "      <th>5</th>\n",
-       "      <td>0.027871</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.035593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.014474</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.025650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>0.006801</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.017996</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.004502</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.014305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.003393</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.012711</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>592 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                probabilistic_sum_of_squares  \\\n",
-       "Metric             simulation       n_surrogates average_count                                 \n",
-       "Average Odds       extremely_unfair 50           10                                 1.400771   \n",
-       "                                                 20                                 1.013655   \n",
-       "                                                 30                                 0.616997   \n",
-       "                                                 40                                 0.406700   \n",
-       "                                    100          5                                  1.299259   \n",
-       "...                                                                                      ...   \n",
-       "Statistical Parity very_unfair      1000         5                                  0.027871   \n",
-       "                                                 10                                 0.014474   \n",
-       "                                                 20                                 0.006801   \n",
-       "                                                 30                                 0.004502   \n",
-       "                                                 40                                 0.003393   \n",
-       "\n",
-       "                                                                probabilistic_count  \\\n",
-       "Metric             simulation       n_surrogates average_count                        \n",
-       "Average Odds       extremely_unfair 50           10                              26   \n",
-       "                                                 20                              22   \n",
-       "                                                 30                              21   \n",
-       "                                                 40                              25   \n",
-       "                                    100          5                               22   \n",
-       "...                                                                             ...   \n",
-       "Statistical Parity very_unfair      1000         5                               30   \n",
-       "                                                 10                              30   \n",
-       "                                                 20                              29   \n",
-       "                                                 30                              30   \n",
-       "                                                 40                              29   \n",
-       "\n",
-       "                                                                df  \\\n",
-       "Metric             simulation       n_surrogates average_count       \n",
-       "Average Odds       extremely_unfair 50           10             16   \n",
-       "                                                 20             16   \n",
-       "                                                 30             16   \n",
-       "                                                 40             16   \n",
-       "                                    100          5              16   \n",
-       "...                                                             ..   \n",
-       "Statistical Parity very_unfair      1000         5               8   \n",
-       "                                                 10              8   \n",
-       "                                                 20              8   \n",
-       "                                                 30              8   \n",
-       "                                                 40              8   \n",
-       "\n",
-       "                                                                probabilistic_se  \n",
-       "Metric             simulation       n_surrogates average_count                    \n",
-       "Average Odds       extremely_unfair 50           10                     0.374269  \n",
-       "                                                 20                     0.411026  \n",
-       "                                                 30                     0.351283  \n",
-       "                                                 40                     0.212577  \n",
-       "                                    100          5                      0.465342  \n",
-       "...                                                                          ...  \n",
-       "Statistical Parity very_unfair      1000         5                      0.035593  \n",
-       "                                                 10                     0.025650  \n",
-       "                                                 20                     0.017996  \n",
-       "                                                 30                     0.014305  \n",
-       "                                                 40                     0.012711  \n",
-       "\n",
-       "[592 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "standard_errors=simulations.query('out_of_range==0').groupby([\"Metric\",\"simulation\",\"n_surrogates\",\"average_count\"])[\"squared_error\"].agg([\"sum\",\"count\"]).rename(columns={\"sum\":\"probabilistic_sum_of_squares\",\"count\":\"probabilistic_count\"}).merge(df,left_index=True,right_index=True)\n",
-    "standard_errors[\"probabilistic_se\"]=(standard_errors[\"probabilistic_sum_of_squares\"]/(standard_errors[\"probabilistic_count\"]-standard_errors[\"df\"])).apply(np.sqrt)\n",
-    "standard_errors\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "f7ec89ce-8a96-4983-a63b-f52ce3283cd8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "standard_errors[\"probabilistic_count\"].hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "56558450-e025-45fe-9413-e926942fdd1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>oracle_mean</th>\n",
-       "      <th>oracle_var</th>\n",
-       "      <th>oracle_std</th>\n",
-       "      <th>oracle_count</th>\n",
-       "      <th>oracle_sum_of_squares</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
-       "      <th>10</th>\n",
-       "      <td>-0.332308</td>\n",
-       "      <td>0.016266</td>\n",
-       "      <td>0.127536</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0.406638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>-0.332000</td>\n",
-       "      <td>0.009308</td>\n",
-       "      <td>0.096480</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0.195476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>-0.334714</td>\n",
-       "      <td>0.005208</td>\n",
-       "      <td>0.072164</td>\n",
-       "      <td>21</td>\n",
-       "      <td>0.104152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>-0.358400</td>\n",
-       "      <td>0.003703</td>\n",
-       "      <td>0.060852</td>\n",
-       "      <td>25</td>\n",
-       "      <td>0.088870</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <th>5</th>\n",
-       "      <td>-0.307455</td>\n",
-       "      <td>0.016769</td>\n",
-       "      <td>0.129493</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0.352139</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
-       "      <th>5</th>\n",
-       "      <td>-0.413200</td>\n",
-       "      <td>0.000311</td>\n",
-       "      <td>0.017649</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.009033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>-0.413967</td>\n",
-       "      <td>0.000136</td>\n",
-       "      <td>0.011666</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.003947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>-0.413448</td>\n",
-       "      <td>0.000064</td>\n",
-       "      <td>0.007985</td>\n",
-       "      <td>29</td>\n",
-       "      <td>0.001785</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>-0.412267</td>\n",
-       "      <td>0.000055</td>\n",
-       "      <td>0.007437</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.001604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>-0.413655</td>\n",
-       "      <td>0.000029</td>\n",
-       "      <td>0.005340</td>\n",
-       "      <td>29</td>\n",
-       "      <td>0.000799</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>592 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                oracle_mean  \\\n",
-       "Metric             simulation       n_surrogates average_count                \n",
-       "Average Odds       extremely_unfair 50           10               -0.332308   \n",
-       "                                                 20               -0.332000   \n",
-       "                                                 30               -0.334714   \n",
-       "                                                 40               -0.358400   \n",
-       "                                    100          5                -0.307455   \n",
-       "...                                                                     ...   \n",
-       "Statistical Parity very_unfair      1000         5                -0.413200   \n",
-       "                                                 10               -0.413967   \n",
-       "                                                 20               -0.413448   \n",
-       "                                                 30               -0.412267   \n",
-       "                                                 40               -0.413655   \n",
-       "\n",
-       "                                                                oracle_var  \\\n",
-       "Metric             simulation       n_surrogates average_count               \n",
-       "Average Odds       extremely_unfair 50           10               0.016266   \n",
-       "                                                 20               0.009308   \n",
-       "                                                 30               0.005208   \n",
-       "                                                 40               0.003703   \n",
-       "                                    100          5                0.016769   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      1000         5                0.000311   \n",
-       "                                                 10               0.000136   \n",
-       "                                                 20               0.000064   \n",
-       "                                                 30               0.000055   \n",
-       "                                                 40               0.000029   \n",
-       "\n",
-       "                                                                oracle_std  \\\n",
-       "Metric             simulation       n_surrogates average_count               \n",
-       "Average Odds       extremely_unfair 50           10               0.127536   \n",
-       "                                                 20               0.096480   \n",
-       "                                                 30               0.072164   \n",
-       "                                                 40               0.060852   \n",
-       "                                    100          5                0.129493   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      1000         5                0.017649   \n",
-       "                                                 10               0.011666   \n",
-       "                                                 20               0.007985   \n",
-       "                                                 30               0.007437   \n",
-       "                                                 40               0.005340   \n",
-       "\n",
-       "                                                                oracle_count  \\\n",
-       "Metric             simulation       n_surrogates average_count                 \n",
-       "Average Odds       extremely_unfair 50           10                       26   \n",
-       "                                                 20                       22   \n",
-       "                                                 30                       21   \n",
-       "                                                 40                       25   \n",
-       "                                    100          5                        22   \n",
-       "...                                                                      ...   \n",
-       "Statistical Parity very_unfair      1000         5                        30   \n",
-       "                                                 10                       30   \n",
-       "                                                 20                       29   \n",
-       "                                                 30                       30   \n",
-       "                                                 40                       29   \n",
-       "\n",
-       "                                                                oracle_sum_of_squares  \n",
-       "Metric             simulation       n_surrogates average_count                         \n",
-       "Average Odds       extremely_unfair 50           10                          0.406638  \n",
-       "                                                 20                          0.195476  \n",
-       "                                                 30                          0.104152  \n",
-       "                                                 40                          0.088870  \n",
-       "                                    100          5                           0.352139  \n",
-       "...                                                                               ...  \n",
-       "Statistical Parity very_unfair      1000         5                           0.009033  \n",
-       "                                                 10                          0.003947  \n",
-       "                                                 20                          0.001785  \n",
-       "                                                 30                          0.001604  \n",
-       "                                                 40                          0.000799  \n",
-       "\n",
-       "[592 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oracle_std=simulations.query('out_of_range==0').groupby([\"Metric\",\"simulation\",\"n_surrogates\",\"average_count\"])[\"oracle_value\"].agg([\"mean\",\"var\",\"std\",\"count\"]).rename(columns={\"std\":\"oracle_std\",\"count\":\"oracle_count\",\"var\":\"oracle_var\",\"mean\":\"oracle_mean\"})\n",
-    "oracle_std[\"oracle_sum_of_squares\"]=oracle_std[\"oracle_var\"]*(oracle_std[\"oracle_count\"]-1)\n",
-    "oracle_std\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "41ede1cd-fccb-4f31-a03f-e8929aa2f18c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "oracle_std[\"oracle_count\"].hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "a10ed7cf-bc54-458c-a371-a19aed8e7472",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>probabilistic_sum_of_squares</th>\n",
-       "      <th>probabilistic_count</th>\n",
-       "      <th>df</th>\n",
-       "      <th>probabilistic_se</th>\n",
-       "      <th>oracle_mean</th>\n",
-       "      <th>oracle_var</th>\n",
-       "      <th>oracle_std</th>\n",
-       "      <th>oracle_count</th>\n",
-       "      <th>oracle_sum_of_squares</th>\n",
-       "      <th>_merge</th>\n",
-       "      <th>std_ratio</th>\n",
-       "      <th>std_diff</th>\n",
-       "      <th>ss_ratio</th>\n",
-       "      <th>prob_se_oracle_val_ratio</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
-       "      <th>10</th>\n",
-       "      <td>1.400771</td>\n",
-       "      <td>26</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.374269</td>\n",
-       "      <td>-0.332308</td>\n",
-       "      <td>0.016266</td>\n",
-       "      <td>0.127536</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0.406638</td>\n",
-       "      <td>both</td>\n",
-       "      <td>2.934606</td>\n",
-       "      <td>0.246732</td>\n",
-       "      <td>3.444766</td>\n",
-       "      <td>1.126272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1.013655</td>\n",
-       "      <td>22</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.411026</td>\n",
-       "      <td>-0.332000</td>\n",
-       "      <td>0.009308</td>\n",
-       "      <td>0.096480</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0.195476</td>\n",
-       "      <td>both</td>\n",
-       "      <td>4.260224</td>\n",
-       "      <td>0.314546</td>\n",
-       "      <td>5.185573</td>\n",
-       "      <td>1.238031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.616997</td>\n",
-       "      <td>21</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.351283</td>\n",
-       "      <td>-0.334714</td>\n",
-       "      <td>0.005208</td>\n",
-       "      <td>0.072164</td>\n",
-       "      <td>21</td>\n",
-       "      <td>0.104152</td>\n",
-       "      <td>both</td>\n",
-       "      <td>4.867849</td>\n",
-       "      <td>0.279119</td>\n",
-       "      <td>5.923989</td>\n",
-       "      <td>1.049500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.406700</td>\n",
-       "      <td>25</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.212577</td>\n",
-       "      <td>-0.358400</td>\n",
-       "      <td>0.003703</td>\n",
-       "      <td>0.060852</td>\n",
-       "      <td>25</td>\n",
-       "      <td>0.088870</td>\n",
-       "      <td>both</td>\n",
-       "      <td>3.493364</td>\n",
-       "      <td>0.151725</td>\n",
-       "      <td>4.576347</td>\n",
-       "      <td>0.593127</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <th>5</th>\n",
-       "      <td>1.299259</td>\n",
-       "      <td>22</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.465342</td>\n",
-       "      <td>-0.307455</td>\n",
-       "      <td>0.016769</td>\n",
-       "      <td>0.129493</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0.352139</td>\n",
-       "      <td>both</td>\n",
-       "      <td>3.593557</td>\n",
-       "      <td>0.335849</td>\n",
-       "      <td>3.689615</td>\n",
-       "      <td>1.513531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
-       "      <th>5</th>\n",
-       "      <td>0.027871</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.035593</td>\n",
-       "      <td>-0.413200</td>\n",
-       "      <td>0.000311</td>\n",
-       "      <td>0.017649</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.009033</td>\n",
-       "      <td>both</td>\n",
-       "      <td>2.016753</td>\n",
-       "      <td>0.017944</td>\n",
-       "      <td>3.085533</td>\n",
-       "      <td>0.086140</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.014474</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.025650</td>\n",
-       "      <td>-0.413967</td>\n",
-       "      <td>0.000136</td>\n",
-       "      <td>0.011666</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.003947</td>\n",
-       "      <td>both</td>\n",
-       "      <td>2.198620</td>\n",
-       "      <td>0.013983</td>\n",
-       "      <td>3.667120</td>\n",
-       "      <td>0.061961</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>0.006801</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.017996</td>\n",
-       "      <td>-0.413448</td>\n",
-       "      <td>0.000064</td>\n",
-       "      <td>0.007985</td>\n",
-       "      <td>29</td>\n",
-       "      <td>0.001785</td>\n",
-       "      <td>both</td>\n",
-       "      <td>2.253802</td>\n",
-       "      <td>0.010011</td>\n",
-       "      <td>3.809716</td>\n",
-       "      <td>0.043527</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.004502</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.014305</td>\n",
-       "      <td>-0.412267</td>\n",
-       "      <td>0.000055</td>\n",
-       "      <td>0.007437</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.001604</td>\n",
-       "      <td>both</td>\n",
-       "      <td>1.923562</td>\n",
-       "      <td>0.006868</td>\n",
-       "      <td>2.806966</td>\n",
-       "      <td>0.034699</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.003393</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.012711</td>\n",
-       "      <td>-0.413655</td>\n",
-       "      <td>0.000029</td>\n",
-       "      <td>0.005340</td>\n",
-       "      <td>29</td>\n",
-       "      <td>0.000799</td>\n",
-       "      <td>both</td>\n",
-       "      <td>2.380180</td>\n",
-       "      <td>0.007371</td>\n",
-       "      <td>4.248942</td>\n",
-       "      <td>0.030729</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>592 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                probabilistic_sum_of_squares  \\\n",
-       "Metric             simulation       n_surrogates average_count                                 \n",
-       "Average Odds       extremely_unfair 50           10                                 1.400771   \n",
-       "                                                 20                                 1.013655   \n",
-       "                                                 30                                 0.616997   \n",
-       "                                                 40                                 0.406700   \n",
-       "                                    100          5                                  1.299259   \n",
-       "...                                                                                      ...   \n",
-       "Statistical Parity very_unfair      1000         5                                  0.027871   \n",
-       "                                                 10                                 0.014474   \n",
-       "                                                 20                                 0.006801   \n",
-       "                                                 30                                 0.004502   \n",
-       "                                                 40                                 0.003393   \n",
-       "\n",
-       "                                                                probabilistic_count  \\\n",
-       "Metric             simulation       n_surrogates average_count                        \n",
-       "Average Odds       extremely_unfair 50           10                              26   \n",
-       "                                                 20                              22   \n",
-       "                                                 30                              21   \n",
-       "                                                 40                              25   \n",
-       "                                    100          5                               22   \n",
-       "...                                                                             ...   \n",
-       "Statistical Parity very_unfair      1000         5                               30   \n",
-       "                                                 10                              30   \n",
-       "                                                 20                              29   \n",
-       "                                                 30                              30   \n",
-       "                                                 40                              29   \n",
-       "\n",
-       "                                                                df  \\\n",
-       "Metric             simulation       n_surrogates average_count       \n",
-       "Average Odds       extremely_unfair 50           10             16   \n",
-       "                                                 20             16   \n",
-       "                                                 30             16   \n",
-       "                                                 40             16   \n",
-       "                                    100          5              16   \n",
-       "...                                                             ..   \n",
-       "Statistical Parity very_unfair      1000         5               8   \n",
-       "                                                 10              8   \n",
-       "                                                 20              8   \n",
-       "                                                 30              8   \n",
-       "                                                 40              8   \n",
-       "\n",
-       "                                                                probabilistic_se  \\\n",
-       "Metric             simulation       n_surrogates average_count                     \n",
-       "Average Odds       extremely_unfair 50           10                     0.374269   \n",
-       "                                                 20                     0.411026   \n",
-       "                                                 30                     0.351283   \n",
-       "                                                 40                     0.212577   \n",
-       "                                    100          5                      0.465342   \n",
-       "...                                                                          ...   \n",
-       "Statistical Parity very_unfair      1000         5                      0.035593   \n",
-       "                                                 10                     0.025650   \n",
-       "                                                 20                     0.017996   \n",
-       "                                                 30                     0.014305   \n",
-       "                                                 40                     0.012711   \n",
-       "\n",
-       "                                                                oracle_mean  \\\n",
-       "Metric             simulation       n_surrogates average_count                \n",
-       "Average Odds       extremely_unfair 50           10               -0.332308   \n",
-       "                                                 20               -0.332000   \n",
-       "                                                 30               -0.334714   \n",
-       "                                                 40               -0.358400   \n",
-       "                                    100          5                -0.307455   \n",
-       "...                                                                     ...   \n",
-       "Statistical Parity very_unfair      1000         5                -0.413200   \n",
-       "                                                 10               -0.413967   \n",
-       "                                                 20               -0.413448   \n",
-       "                                                 30               -0.412267   \n",
-       "                                                 40               -0.413655   \n",
-       "\n",
-       "                                                                oracle_var  \\\n",
-       "Metric             simulation       n_surrogates average_count               \n",
-       "Average Odds       extremely_unfair 50           10               0.016266   \n",
-       "                                                 20               0.009308   \n",
-       "                                                 30               0.005208   \n",
-       "                                                 40               0.003703   \n",
-       "                                    100          5                0.016769   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      1000         5                0.000311   \n",
-       "                                                 10               0.000136   \n",
-       "                                                 20               0.000064   \n",
-       "                                                 30               0.000055   \n",
-       "                                                 40               0.000029   \n",
-       "\n",
-       "                                                                oracle_std  \\\n",
-       "Metric             simulation       n_surrogates average_count               \n",
-       "Average Odds       extremely_unfair 50           10               0.127536   \n",
-       "                                                 20               0.096480   \n",
-       "                                                 30               0.072164   \n",
-       "                                                 40               0.060852   \n",
-       "                                    100          5                0.129493   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      1000         5                0.017649   \n",
-       "                                                 10               0.011666   \n",
-       "                                                 20               0.007985   \n",
-       "                                                 30               0.007437   \n",
-       "                                                 40               0.005340   \n",
-       "\n",
-       "                                                                oracle_count  \\\n",
-       "Metric             simulation       n_surrogates average_count                 \n",
-       "Average Odds       extremely_unfair 50           10                       26   \n",
-       "                                                 20                       22   \n",
-       "                                                 30                       21   \n",
-       "                                                 40                       25   \n",
-       "                                    100          5                        22   \n",
-       "...                                                                      ...   \n",
-       "Statistical Parity very_unfair      1000         5                        30   \n",
-       "                                                 10                       30   \n",
-       "                                                 20                       29   \n",
-       "                                                 30                       30   \n",
-       "                                                 40                       29   \n",
-       "\n",
-       "                                                                oracle_sum_of_squares  \\\n",
-       "Metric             simulation       n_surrogates average_count                          \n",
-       "Average Odds       extremely_unfair 50           10                          0.406638   \n",
-       "                                                 20                          0.195476   \n",
-       "                                                 30                          0.104152   \n",
-       "                                                 40                          0.088870   \n",
-       "                                    100          5                           0.352139   \n",
-       "...                                                                               ...   \n",
-       "Statistical Parity very_unfair      1000         5                           0.009033   \n",
-       "                                                 10                          0.003947   \n",
-       "                                                 20                          0.001785   \n",
-       "                                                 30                          0.001604   \n",
-       "                                                 40                          0.000799   \n",
-       "\n",
-       "                                                               _merge  \\\n",
-       "Metric             simulation       n_surrogates average_count          \n",
-       "Average Odds       extremely_unfair 50           10              both   \n",
-       "                                                 20              both   \n",
-       "                                                 30              both   \n",
-       "                                                 40              both   \n",
-       "                                    100          5               both   \n",
-       "...                                                               ...   \n",
-       "Statistical Parity very_unfair      1000         5               both   \n",
-       "                                                 10              both   \n",
-       "                                                 20              both   \n",
-       "                                                 30              both   \n",
-       "                                                 40              both   \n",
-       "\n",
-       "                                                                std_ratio  \\\n",
-       "Metric             simulation       n_surrogates average_count              \n",
-       "Average Odds       extremely_unfair 50           10              2.934606   \n",
-       "                                                 20              4.260224   \n",
-       "                                                 30              4.867849   \n",
-       "                                                 40              3.493364   \n",
-       "                                    100          5               3.593557   \n",
-       "...                                                                   ...   \n",
-       "Statistical Parity very_unfair      1000         5               2.016753   \n",
-       "                                                 10              2.198620   \n",
-       "                                                 20              2.253802   \n",
-       "                                                 30              1.923562   \n",
-       "                                                 40              2.380180   \n",
-       "\n",
-       "                                                                std_diff  \\\n",
-       "Metric             simulation       n_surrogates average_count             \n",
-       "Average Odds       extremely_unfair 50           10             0.246732   \n",
-       "                                                 20             0.314546   \n",
-       "                                                 30             0.279119   \n",
-       "                                                 40             0.151725   \n",
-       "                                    100          5              0.335849   \n",
-       "...                                                                  ...   \n",
-       "Statistical Parity very_unfair      1000         5              0.017944   \n",
-       "                                                 10             0.013983   \n",
-       "                                                 20             0.010011   \n",
-       "                                                 30             0.006868   \n",
-       "                                                 40             0.007371   \n",
-       "\n",
-       "                                                                ss_ratio  \\\n",
-       "Metric             simulation       n_surrogates average_count             \n",
-       "Average Odds       extremely_unfair 50           10             3.444766   \n",
-       "                                                 20             5.185573   \n",
-       "                                                 30             5.923989   \n",
-       "                                                 40             4.576347   \n",
-       "                                    100          5              3.689615   \n",
-       "...                                                                  ...   \n",
-       "Statistical Parity very_unfair      1000         5              3.085533   \n",
-       "                                                 10             3.667120   \n",
-       "                                                 20             3.809716   \n",
-       "                                                 30             2.806966   \n",
-       "                                                 40             4.248942   \n",
-       "\n",
-       "                                                                prob_se_oracle_val_ratio  \n",
-       "Metric             simulation       n_surrogates average_count                            \n",
-       "Average Odds       extremely_unfair 50           10                             1.126272  \n",
-       "                                                 20                             1.238031  \n",
-       "                                                 30                             1.049500  \n",
-       "                                                 40                             0.593127  \n",
-       "                                    100          5                              1.513531  \n",
-       "...                                                                                  ...  \n",
-       "Statistical Parity very_unfair      1000         5                              0.086140  \n",
-       "                                                 10                             0.061961  \n",
-       "                                                 20                             0.043527  \n",
-       "                                                 30                             0.034699  \n",
-       "                                                 40                             0.030729  \n",
-       "\n",
-       "[592 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compare_errors=standard_errors.merge(oracle_std,left_index=True,right_index=True,how='outer',indicator=True)\n",
-    "compare_errors[\"std_ratio\"]=compare_errors[\"probabilistic_se\"]/compare_errors[\"oracle_std\"]\n",
-    "compare_errors[\"std_diff\"]=(compare_errors[\"probabilistic_se\"]-compare_errors[\"oracle_std\"]).apply(abs)\n",
-    "compare_errors[\"ss_ratio\"]=(compare_errors[\"probabilistic_sum_of_squares\"]/compare_errors[\"oracle_sum_of_squares\"]).apply(abs)\n",
-    "compare_errors[\"prob_se_oracle_val_ratio\"]=(compare_errors[\"probabilistic_se\"]/compare_errors[\"oracle_mean\"]).apply(abs)\n",
-    "\n",
-    "compare_errors\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "4f16587b-0318-4321-be8a-be8cd557912c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "both          592\n",
-       "left_only       0\n",
-       "right_only      0\n",
-       "Name: _merge, dtype: int64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compare_errors[\"_merge\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "58dc3a29-656c-4058-b93b-f833415c5fe1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pivot_errors(df,metric_name,simulation_name,values_name):\n",
-    "    query_string=f'Metric==\"{metric_name}\" & simulation==\"{simulation_name}\"'\n",
-    "    return df.reset_index().query(query_string).pivot(index=\"average_count\",columns=\"n_surrogates\",values=values_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "420040aa-d98a-49a1-abc3-1ba823994b81",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>50</th>\n",
-       "      <th>100</th>\n",
-       "      <th>300</th>\n",
-       "      <th>400</th>\n",
-       "      <th>500</th>\n",
-       "      <th>1000</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.178950</td>\n",
-       "      <td>0.131230</td>\n",
-       "      <td>0.074057</td>\n",
-       "      <td>0.075282</td>\n",
-       "      <td>0.045313</td>\n",
-       "      <td>0.042260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>0.146197</td>\n",
-       "      <td>0.095050</td>\n",
-       "      <td>0.046240</td>\n",
-       "      <td>0.044570</td>\n",
-       "      <td>0.035201</td>\n",
-       "      <td>0.021683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.154840</td>\n",
-       "      <td>0.058909</td>\n",
-       "      <td>0.041087</td>\n",
-       "      <td>0.034563</td>\n",
-       "      <td>0.024559</td>\n",
-       "      <td>0.026752</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.110500</td>\n",
-       "      <td>0.059056</td>\n",
-       "      <td>0.021237</td>\n",
-       "      <td>0.033892</td>\n",
-       "      <td>0.027061</td>\n",
-       "      <td>0.021384</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "n_surrogates       50        100       300       400       500       1000\n",
-       "average_count                                                            \n",
-       "10             0.178950  0.131230  0.074057  0.075282  0.045313  0.042260\n",
-       "20             0.146197  0.095050  0.046240  0.044570  0.035201  0.021683\n",
-       "30             0.154840  0.058909  0.041087  0.034563  0.024559  0.026752\n",
-       "40             0.110500  0.059056  0.021237  0.033892  0.027061  0.021384"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_name=\"probabilistic_se\"\n",
-    "pivoted_errors=pivot_errors(compare_errors.query('average_count>5'),\"Equal Opportunity\",\"fair\",col_name)\n",
-    "pivoted_errors\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "341d3109-6854-43b7-9c92-4ac59e6b77e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/1029826322.py:2: UserWarning: \n",
-      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
-      "  sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1297eb970>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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IIYTId5LwvkSsra355JNPOHr0KCdPnmTIkCGUKVOGmzdv8v333+Ph4YGvry9//PEHSUlJ+g5XCCGEECJfSML7kqpduzY//fQToaGhrFixgtdeew2VSsX27dt57733cHZ2ZvDgwS+004sQQgghRFEgCe9LztTUlG7durF161auXr3K6NGjKV++PPfv32f27NnUrVuX+vXrM3fuXKKjo/UdrhBCCCFErknCK3Tc3Nz47rvvuHr1Klu2bKFr164YGRlx/PhxPv30U5ycnOjVqxe7du2S5c2EEEIIUWxIwiueoFaradOmDatWreL27dtMnz6dmjVrkpSUxLJly2jVqhWVK1dmwoQJhIaG6jtcIYQQQohnkoRXPJOtrS1Dhw7l1KlTHD58mI8++ohSpUpx5coVvvnmG8qXL0+HDh1Ys2YNqamp+g5XCCGEKPLS09PZtWsXf/zxB7t27SqU5UG/++47VCpVlsPT01N3PSkpiYEDB1K2bFksLS3p0qULERERBR5XYZGEV+SISqWiUaNG/PLLL4SFhbF48WKaN2+ORqNhw4YNdO7cGRcXF7744gsuXLig73CFEEKIIunvv//Gzc2NVq1a8d5779GqVSvc3Nz4+++/C/y5a9SoQVhYmO7Yt2+f7tqwYcNYv349q1evZvfu3dy+fZvOnTsXeEyFRRJekWsWFhb06dOHPXv2EBwczFdffYWjoyORkZFMnTqVatWq0bRpU3799Vfi4uL0Ha4QQghRJPz999+888473Lp1K0t7aGgo77zzToEnvYaGhjg6OuqOjB1XY2JiCAgIYNq0abz66qvUr1+fRYsWceDAAQ4dOlSgMRUWSXjFC6lSpQqTJk0iJCSEf/75h44dO6JWqzlw4AB+fn44OTnx4YcfcvDgQbnRTQghRImjKArx8fHPPWJjYxkyZEi2fwsz2j777DNiY2NzNF5e/qZeunQJZ2dnPDw86NmzJyEhIQAEBgaSmpqKr6+vrq+npyfly5fn4MGDefzJFC2G+g5AlAxGRkZ07NiRjh07EhYWxm+//UZAQACXLl0iICCAgIAAqlWrhp+fH7169cLe3l7fIQshhBAvLCEhAUtLyxceR1EUbt26hbW1dY76x8XFYWFhkePxvb29Wbx4MVWrViUsLIwxY8bQvHlzzpw5Q3h4OMbGxtjY2GR5jIODA+Hh4bl5GUWWzPCKfOfk5MRXX31FcHAwe/bsoU+fPpiZmXH+/Hn+97//Ua5cObp06cLGjRsLpVBfCCGEeNm98cYbdO3aldq1a9O2bVs2btxIdHQ0q1at0ndohUISXlFgVCoVzZs3Z/HixYSHh/PLL7/QqFEj0tLS+Pvvv2nfvj0VKlTg22+/5erVq/oOVwghhMg1c3Nz4uLinnts3LgxR+Nt3LgxR+OZm5u/UNw2NjZUqVKFy5cv4+joSEpKyhMbTEVERODo6PhCz1NUSMIrCoWVlRUfffQRhw8f5tSpUwwdOpSyZcsSGhrK+PHjqVixIq+++irLly8nMTFR3+EKIYQQOaJSqbCwsHju0aZNG1xcXFCpVE8dx9XVlTZt2uRovKeNk1NxcXFcuXIFJycn6tevj5GREdu3b9ddDw4OJiQkBB8fnxd6nqJCEl5R6GrVqsX06dMJDQ1l5cqVtGnTBpVKxc6dO+nZsyfOzs4MGjSIEydO6DtUIYQQIl+o1Wp++ukngCeS1YzzGTNmoFarC+T5//e//7F7926uX7/OgQMHePvtt1Gr1fTo0QNra2v8/Pzw9/dn586dBAYG0rdvX3x8fGjcuHGBxFPYJOEVemNiYsK7777Lli1buHbtGt999x0VKlQgOjqaOXPmUK9ePerVq8ecOXO4f/++vsMVQgghXkjnzp35888/KVeuXJZ2FxcX/vzzzwJd9/bWrVv06NGDqlWr8u6771K2bFkOHTqEnZ0dANOnT6dDhw506dKFV155BUdHx0JZG7iwqBRZK+oJsbGxWFtbExMTg5WVlb7DealoNBq2b99OQEAAa9asISUlBdAmx126dMHPz4+WLVtiYCDv1YQQQuSvpKQkrl27hru7O6ampgX2POnp6ezdu5ewsDCcnJxo3rx5gc3sFnfP+p3kJl+ThDcbkvAWDXfv3uX3338nICCAU6dO6drd3d3p168fH3zwAS4uLnqMUAghRElSWAmvyLn8SnhlmkwUWWXLlmXIkCEEBQVx9OhRPvnkE6ysrLh27RojR46kQoUKtGvXjr/++ks3EyyEEEII8ThJeEWRp1KpaNCgAXPnztVtatGiRQs0Gg2bNm3inXfewcXFhc8//5xz587pO1whhBBCFDGS8IpixdzcnF69erFr1y4uXbrEiBEjcHJyIioqimnTplGjRg18fHxYuHAhDx480He4QgghhCgCJOEVxValSpWYMGECISEhrFu3jrfeegu1Ws2hQ4fo378/Tk5O9OvXj/379+dpz3EhhBBClAyS8Ipiz9DQkDfffJO1a9dy69YtfvjhB6pUqUJ8fDyLFi2iWbNmVK9enSlTphAREaHvcIUQQghRyCThFSWKo6MjX375JRcuXGDv3r188MEHmJubc+HCBb788ktcXFzo3LkzGzZsIC0tTd/hCiGEEKIQSMIrSiSVSkWzZs1YtGgRYWFhzJ8/H29vb9LS0lizZg0dOnSgQoUKfPPNN1y5ckXf4QohhBCiAEnCK0o8Kysr+vfvz6FDhzhz5gzDhg3D1taW27dvM2HCBCpVqkSrVq1YtmwZiYmJ+g5XCCGEEPlMEl7xUqlRowbTpk0jNDSU1atX8/rrr6NSqdi1axe9evXCycmJTz/9lMDAQLnRTQghhCghikTCO2fOHNzc3DA1NcXb25sjR448te/Zs2fp0qULbm5uqFQqZsyYkW2/0NBQ3n//fcqWLYuZmRm1atXi2LFjBfQKRHFjbGzMO++8w6ZNm7hx4wZjx47Fzc2NmJgY5s6dS4MGDahbty6zZs3i3r17+g5XCCGEeCF79uzhzTffxNnZGZVKxdq1a7NcVxSFUaNG4eTkhJmZGb6+vly6dClLn3v37tGzZ0+srKywsbHBz8+PuLi4QnwVeaf3hHflypX4+/szevRojh8/Tp06dWjbti2RkZHZ9k9ISMDDw4NJkybh6OiYbZ/79+/TtGlTjIyM2LRpE+fOnePHH3+kdOnSBflSRDHl6urKyJEjuXLlCv/99x89evTAxMSEkydPMmTIEJydnenRowf//fcfGo1G3+EKIYQoxnaPhzHGsGdC1vPd4wv2eePj46lTpw5z5szJ9vrkyZOZOXMm8+bN4/Dhw1hYWNC2bVuSkpJ0fXr27MnZs2fZtm0b//77L3v27OGjjz4q2MDzi6JnjRo1UgYOHKg7T09PV5ydnZWJEyc+97EVKlRQpk+f/kT7V199pTRr1izPMcXExCiAEhMTk+cxRPF29+5dZdasWYqXl5cC6A43NzdlzJgxSkhIiL5DFEIIkc8SExOVc+fOKYmJiQUy/q5xivKdYeaxpE3W813jCuRpnwAoa9as0Z1rNBrF0dFRmTJliq4tOjpaMTExUf744w9FURTl3LlzCqAcPXpU12fTpk2KSqVSQkNDCyzWZ/1OcpOv6XWGNyUlhcDAQHx9fXVtBgYG+Pr6cvDgwTyPu27dOho0aEDXrl2xt7enbt26LFiw4Kn9k5OTiY2NzXKIl1uZMmUYNGgQJ06cIDAwkE8//RRra2uuX7/O6NGjqVChAq+//jqrV68mOTlZ3+EKIYTQs5T4Zx+aNNg1Jutjru3Ier5rjLZv6iP3TyvKk2Plt2vXrhEeHp4lH7O2tsbb21uXjx08eBAbGxsaNGig6+Pr64uBgQGHDx/O/6DymV4T3jt37pCeno6Dg0OWdgcHB8LDw/M87tWrV5k7dy6VK1dmy5YtDBgwgCFDhrBkyZJs+0+cOBFra2vd4erqmufnFiVPvXr1mDNnDmFhYSxdupSWLVuiKApbtmzh3XffxcXFhWHDhnHmzBl9hyqEEEJPJto8+zi/FlqOfs4girbv4laZTQl3nhwrv2XkXM/Kx8LDw7G3t89y3dDQkDJlyrxQzlZY9F7DWxA0Gg316tVjwoQJ1K1bl48++oj+/fszb968bPuPGDGCmJgY3XHz5s1CjlgUB2ZmZrz//vvs3LmTy5cv8/XXX+Ps7MydO3eYMWMGtWrVwtvbm/nz58unBEIIIZ7Q4htwf1XfUbycDPX55La2tqjV6ie2e42IiHjqDWk54eTkRPXq1bO0VatWjb/++ivb/iYmJpiYmOT5+cTLp2LFiowfP54xY8awZcsWAgICWL9+PUeOHOHIkSMMGzaMd999Fz8/P5o2bYpKpdJ3yEIIIQrQiOhnXzc00d6Y9ngZw6Ne+QaafgGqR6YjzW2fP/aLysi5IiIicHJy0rVHRETg5eWl6/P4ggJpaWncu3fvhXK2wqLXGV5jY2Pq16/P9u3bdW0ajYbt27fj4+OT53GbNm1KcHBwlraLFy9SoUKFPI8pRHYMDQ1p3749f//9N7du3WLKlCl4enqSkJDA4sWLad68OdWqVWPy5MnF4iMfIYQQeWNs8ezDwPDJGt7H7Zmg7WtkltmmUj05Vn5zd3fH0dExSz4WGxvL4cOHdfmYj48P0dHRBAYG6vrs2LEDjUaDt7d3/geVz/Re0uDv78+CBQtYsmQJ58+fZ8CAAcTHx9O3b18AevfuzYgRI3T9U1JSCAoKIigoiJSUFEJDQwkKCuLy5cu6PsOGDePQoUNMmDCBy5cvs3z5cubPn8/AgQML/fWJl4eDgwP/+9//OHfuHPv376dfv35YWFgQHBzMV199hYuLC506dWL9+vWkpaXpO1whhBCF7PEaXo/Wz76en+Li4nT5E2hvVAsKCiIkJASVSsXQoUMZN24c69at4/Tp0/Tu3RtnZ2c6deoEaD8pf/311+nfvz9Hjhxh//79DBo0iO7du+Ps7FxwgeeXAlhBItdmzZqllC9fXjE2NlYaNWqkHDp0SHetRYsWSp8+fXTn165dy7JMVMbRokWLLGOuX79eqVmzpmJiYqJ4enoq8+fPz3E8siyZyC+xsbHKwoULFR8fnyz/Xp2cnJThw4crFy9e1HeIQgghHiroZckU5eHSZEaKsnt81vOCXpJs586d2eZPGTmWRqNRRo4cqTg4OCgmJiZK69atleDg4Cxj3L17V+nRo4diaWmpWFlZKX379lUePHhQoHHn17JkKkWR/VMfFxsbi7W1NTExMVhZWek7HFFCnDt3jl9//ZXffvuNqKgoXfsrr7yCn58f77zzDubm5nqMUAghXm5JSUlcu3YNd3d3TE1N9R2O4Nm/k9zka3ovaRDiZVG9enWmTp3KrVu3+Ouvv2jXrh0GBgbs2bOHPn364OTkxCeffMLRo0eR96FCCCFE/pGEV4hCZmxsTOfOndmwYQM3btxg3LhxeHh4EBsbyy+//EKjRo2oU6cOP/30E3fv3tV3uEKIPIqPjyclJYXIyEhSUlKIjy+AHQOEEDkiCa8QeuTi4sI333zDpUuX2LFjB++99x4mJiacPn2aoUOH4uzsTLdu3di2bRsajUbf4QohcigpKYnJkyfj4OCgOyZPnkxSUpK+QxPipSQJrxBFgIGBAa1ateL3338nLCyM2bNnU7duXVJSUli1ahVt2rTBw8ODMWPGEBISou9whRDPEB8fz8SJExk7dizR0dEAREdHM3bsWCZOnCgzvULogdy0lg25aU0UFSdOnCAgIIDff/9d94dTpVLx2muv4efnx1tvvSWbpghRiJKTkwkLC+P27dvZHgkJCWzbtg0XFxfdf7OPsrGxISIiAmNj48IPXjyX3LRW9OTXTWuS8GZDEl5R1CQmJrJmzRoCAgLYsSNzm56yZcvy/vvv4+fnR61atfQYoRDFW1paGpGRkdy+fZvQ0NCnJrR37tx55jg1a9Zk3bp1eHh4PLVPWFhYsdiZ6mUkCW/RIwlvAZKEVxRlV69eZdGiRSxatIjQ0FBde8OGDfHz86N79+5YW1vrMUIhig6NRsPdu3ezJK3ZJbQRERE5rpM3NjbG2dkZZ2dnypUrp/ve2dmZChUq0KhRIxwdHZ86w3vr1i0++eQTvvzyS3mjWsRIwlv0SMJbgCThFcVBeno6W7duJSAggHXr1pGamgqAmZkZXbt2xc/Pj+bNm6NSqfQcqRD5T1EUYmJinjoTm5HUhoWF6f7beB61Wo2jo2OWBPbxhNbZ2ZkyZco887+r+Ph4Jk+ezNixY5+4NnLkSOrVq8fbb7+NSqXivffe47vvvqNSpUp5/lmI/CMJb9EjCW8BkoRXFDdRUVEsXbqUgIAAzp07p2uvXLky/fr1063zK0RxEB8fT1hY2DNLCzLqZXPK3t7+icT18aTWzs4OtVqdL68hKSmJiRMnMnPmTKKjo7GxsWHIkCGMGDGC27dvM3z4cFavXg2AoaEhfn5+jBw5knLlyuXL84u8kYS36JGEtwBJwiuKK0VROHz4MAEBAaxYsYK4uDhAO3PVrl07/Pz8aNeuHUZGRnqOVLyMUlJSnrjhK7ukNiYmJsdj2tjYPHUmNuNwdHTUy01i8fHxGBkZERMTg7W1NampqVhYWOiuHz9+nG+//ZZNmzYBYGJiwqBBgxg+fDi2traFHq+QhLcokoS3AEnCK0qCuLg4Vq9eTUBAAPv379e1Ozo60rt3b/z8/KhSpYoeIxQlRXp6OhEREc8tL3jeDV+PMjc3fyKJffzcycmpRGzHvXfvXr7++mv27dsHgKWlJZ9//jn+/v7yN6iQleSEd+7cucydO5fr168DUKNGDUaNGsUbb7wBaF/7559/zooVK0hOTqZt27b8/PPPODg46MYICQlhwIAB7Ny5E0tLS/r06cPEiRMxNDQssLgl4S1AkvCKkubChQv8+uuvLFmyhMjISF17s2bN8PPzo2vXrllmnoQA7ScGGTd8Pau8IDw8PE83fD2ttMDZ2ZlSpUq9VPXniqKwZcsWvv76a06cOAFAmTJlGDFiBAMHDsTMzEzPEb4cCiPhTQwNIeX+k2/+jEvbYlaufIE8J8D69etRq9VUrlwZRVFYsmQJU6ZM4cSJE9SoUYMBAwawYcMGFi9ejLW1NYMGDcLAwEA3YZKeno6XlxeOjo5MmTKFsLAwevfuTf/+/ZkwYUKBxS0JbwGShFeUVKmpqWzYsIGFCxeyadMmXZJSqlQpunfvzocffkjDhg1fqkTjZaQoCrGxsU+dic04wsLCSElJydGYBgYGODo6PrO0oFy5cs+94etlp9Fo+Pvvv/n2228JDg4GwNnZmZEjR+Ln5yflSAWsoBPexNAQdreuiib5yR33DExMabE9uECT3seVKVOGKVOm8M4772BnZ8fy5ct55513AO1ESbVq1Th48CCNGzdm06ZNdOjQgdu3b+tmfefNm8dXX31FVFRUgZUNScJbgCThFS+D0NBQlixZwq+//sqVK1d07TVr1sTPz4/3339f6giLoYSEhOeWFuT2hi87O7vnrlxgb2+fbzd8Ce26wMuWLWP06NG63RUzdlvs0aOH/KwLyIskvGkJ2h301GbmqFQq0hMTUBQFtYkpKrWa9OQkYk4Hcqhrs6eO0Xj1Pqxr1kNtaoai0ZCelAiAoblFlufIOM+r9PR0Vq9eTZ8+fThx4gTh4eG0bt2a+/fvY2Njo+tXoUIFhg4dyrBhwxg1ahTr1q0jKChId/3atWt4eHhw/Phx6tat+0IxPY0kvAVIEl7xMtFoNOzZs4eAgAD+/PNPkpK0Mw9GRkZ06tQJPz8/fH195Q+snqWkpBAeHv7clQuyW/v1aTJu+HpWaYG+bvgSWsnJySxYsIBx48YREREBaGsvx40bx1tvvSWz5fnsRRLeje7a30XrY5GYlLVjT5saxF06h/cfOynbuCXHP+1K+KY/nztOGe8WNF6xiwcXz7K3bU2My9jiGxiV5TnaXctb6nb69Gl8fHxISkrC0tKS5cuX065dO5YvX07fvn1JTk7O0r9Ro0a0atWKH374gY8++ogbN26wZcsW3fWEhAQsLCzYuHGjrhY4v+VXwltwVcZCiGLBwMCAli1b0rJlS2bNmsUff/xBQEAAgYGBrF69mtWrV+Pq6krfvn3p27cvbm5u+g65RElPT9ft8PWslQuioqJyPKa5uflzVy5wdnYuETd8lXQZKzf07duXWbNm8cMPP3D27FnefvttGjVqxPjx4/H19dV3mKKYqFq1KkFBQcTExPDnn3/Sp08fdu/ere+wCoXM8GZDZniFgJMnTxIQEMCyZcu4f/8+ACqVitatW+Pn50enTp1K3F3M+enRG76eVV6Qmxu+jIyMnlta4OzsjJWVlcz8lVDR0dFMnTqV6dOn68pSXn31VcaPH0/jxo31HF3x97KUNGTw9fWlYsWKdOvWrcSXNMgMrxAiW3Xq1GHmzJlMnjyZtWvXEhAQwH///ac7Spcuzfvvv4+fnx916tTRd7iFRlEUHjx48NyVC27fvp3rG76eV15QtmxZSWRfcjY2NowbN47BgwczYcIE5s2bx44dO/Dx8aFjx46MGzdOtivWk8eTULVZ1k9Q1CamqE2fvdqG2tRM10dlYPDEmPmV6GbQaDQkJydTv359jIyM2L59O126dAEgODiYkJAQfHx8APDx8WH8+PFERkZib28PwLZt27CysqJ69er5GldBkBnebMgMrxDZu379OosWLWLRokXcvHlT116/fn38/Pzo0aNHltmB4iYhISFHGyPEx8fneExbW9vnrlwgN3yJvLpx4wZjx45l8eLFaDQaVCoVPXr0YMyYMbJdcR6U5FUaRowYwRtvvEH58uV58OABy5cv54cffmDLli289tprDBgwgI0bN7J48WKsrKwYPHgwAAcOHAAylyVzdnZm8uTJhIeH06tXLz788ENZlqy4koRXiGdLT09n27ZtBAQE8M8//5CamgqAqakp77zzDn5+frRo0aLIzEZm3PD1vJULcnPDl7W19XNLCxwdHTExMSm4FybEQxcuXGD06NGsWrUK0O6umLFdsYuLi56jKz5K8jq8fn5+bN++nbCwMKytralduzZfffUVr732GpC58cQff/yRZeMJR0dH3Rg3btxgwIAB7Nq1CwsLC/r06cOkSZNk44niShJeIXIuKiqKZcuWERAQwNmzZ3XtFStWpF+/fnzwwQc4OzsDmVutRkdHY2Nj88RWq7mVnp5OVFTUc8sLHt1s43nMzMyyTWAfbXNycpKNOkSRdOLECb799ls2btwIaG96GzhwIMOHD8fOzk7P0RV9JXmnteJKEt4CJAmvELmnKApHjhwhICCAFStW8ODBA0Bbn9q/f3+mTp3KlClTmDlzpi7hHTJkCCNGjHjif2KKonDv3r3nlhaEh4eTnp6eo/gev+HraeUFcsOXKAn27dvH119/zd69ewHtdsX+/v58/vnn8nftGSThLXok4S1AkvAK8WLi4+NZvXo1AQEB7Nu3j7Vr13Ls2DHGjRv3RN+RI0fSqVMnJk2alCWpzc0NXw4ODs8tLyhbtiwGBgb5/VKFKLJku+Lck4S36JGEtwBJwitE/rl8+bIuCc2uRtbGxoZbt27h5ubGnTtZ69psbW2fWVqQscNXQdaPCVHcZWxXPHLkSC5cuABkblfcr18/2VjkEZLwFj2S8BYgSXiFyF+RkZG6vdezc+vWLXbt2oWJiYkuqZUbvoTIXxnbFX/33XfcuHEDkO2KHycJb9GTXwmvfL4nhChwNjY2T12uzMbGBjs7O3r27Mk777xDkyZNqFChgiS7QuQzQ0NDPvjgA4KDg5k1axYODg5cvXqVXr16UadOHdauXYvMgYmSShJeIUSBS01NZciQIdleGzJkiG5ZMyFEwcvYrvjKlStMnDgRGxsb3XbF3t7e/Pfff/oOUYh8JwmvEKLAWVhYMGLECEaNGqWb6bWxsWHUqFGMGDFClvgSQg8sLCwYPnw4165d45tvvsHCwoKjR4/y2muv0bp1aw4dOqTvEIXIN1LDmw2p4RWiYGSswxsTE4O1tfULr8MrhMg/ERERTJw4kblz5+pWSenYsSPff/89tWvX1nN0hUNqeIseqeEVQhQ7FhYWGBsbY2dnh7GxsSS7QhQhDg4OzJgxg4sXL9KvXz8MDAxYt24dXl5e9OzZk8uXL+s7RCHyTBJeIYQQQuhUqFCBgIAAzp07x7vvvouiKCxfvhxPT08+/vhjbt26pe8Qhcg1SXiFEEII8YSqVauycuVKjh8/Trt27UhPT2f+/PlUqlSJzz//nKioKH2HWKyl3Qsh+eZx0u6FFPpzT5o0CZVKxdChQ3VtSUlJDBw4kLJly2JpaUmXLl2IiIjI8riQkBDat2+Pubk59vb2fPHFF6SlpRVy9HkjCa8QQgghnqpu3bps2LCBvXv30rx5c5KTk5k2bRoeHh6MHj2amJgYfYdY7KTdC+HmuKqETqnPzXFVCzXpPXr0KL/88ssTddnDhg1j/fr1rF69mt27d3P79m06d+6su56enk779u1JSUnhwIEDLFmyhMWLFzNq1KhCi/1FFImEd86cObi5uWFqaoq3tzdHjhx5at+zZ8/SpUsX3NzcUKlUzJgx45ljZ/cuRgghhBC506xZM3bv3s3mzZupV68ecXFxjB07Fg8PD6ZMmUJCQoK+Qyw20uPvoKQlAaCkJZEef+c5j8gfcXFx9OzZkwULFlC6dGlde0xMDAEBAUybNo1XX32V+vXrs2jRIg4cOKBbrWPr1q2cO3eOZcuW4eXlxRtvvMH333/PnDlzcrwVvD7pPeFduXIl/v7+jB49muPHj1OnTh3atm1LZGRktv0TEhLw8PBg0qRJODo6PnPsp72LEUIIIUTuqVQq2rZty7Fjx/jzzz/x9PTk3r17fPnll1SqVCnLCg8vG01yfLaHokkHQNFoSAm/QOKV/STfCsry2ORbQSRe2U9K+IXMx6ZkvoFQUpMzx0tNznOMAwcOpH379vj6+mZpDwwMJDU1NUu7p6cn5cuX5+DBgwAcPHiQWrVqZdk1s23btsTGxnL27Nk8x1RY9L4B/bRp0+jfvz99+/YFYN68eWzYsIFff/2V4cOHP9G/YcOGNGzYECDb6xkefRczbty4ggleCCGEeAmpVCq6dOnCW2+9xe+//87o0aO5ceMGn376KVOmTGHMmDG89957L9V2xde/sMy23WnwTswqtyTpyl7CZrXMvKAyAEUDqLjzh98TjzNyrI7r19pE8u66r4jd/RMAVi0+w7bLjFzHt2LFCo4fP87Ro0efuBYeHo6xsfETO2I6ODgQHh6u6/P4FvEZ5xl9ijK9zvCmpKQQGBiY5R2FgYEBvr6+uncUefW0dzFCCCGEyB+Ghob06dOH4OBgZs+ejYODA9euXaN3796yXfFjNAn3szYomoxvCvy5b968yWeffcbvv//+0q4vrNcZ3jt37pCenp7tO4YLFy7kedxnvYvJTnJyMsnJmR8RxMbG5vm5hRBCiJeNiYkJAwcO5IMPPmD27Nn88MMPuu2KGzZsyIQJE2jdujUqlUrfoRYYtylx2barjLQJprGLFxiaQNrDfCNjhlc30wsYmuDyZRCGpV3hkZ9V2Y4/UKbD+IcPy33qFhgYSGRkJPXq1dO1paens2fPHmbPns2WLVtISUkhOjo6yyxvRESErnzU0dHxiXusMlZxeF6JaVGg9xre/JaXdzETJ07E2tpad7i6uhZwlEIIIUTJY2FhwVdffcXVq1ez3a74RT+9LcoMTCyyPVQG2rIOo7JulP/2IuW+CMSu17LMJFfRYNdrGeW+CKT8txcxdvTUPtbYXDe2ysgkczwjk1zH1rp1a06fPk1QUJDuaNCgAT179tR9b2RkxPbt23WPCQ4OJiQkBB8fHwB8fHw4ffp0lnustm3bhpWVFdWrV8/Lj6xQ6TXhtbW1Ra1WP7HO26PvKHLr0XcxhoaGGBoasnv3bmbOnImhoSHp6elPPGbEiBHExMTojps3b+bpuYUQQggBNjY2jBs3jitXrvDZZ59hbGzMzp07adKkCR07duTUqVP6DlEvDMuUx8S1HsaO1bK0GztWw8S1HoZlyhfI85YqVYqaNWtmOSwsLChbtiw1a9bE2toaPz8//P392blzJ4GBgfTt2xcfHx8aN24MQJs2bahevTq9evXi5MmTbNmyhW+//ZaBAwdiYpL7JLyw6TXhNTY2pn79+lneUWg0GrZv3657R5Fbz3sXk10BvYmJCVZWVlkOIYQQQryYjO2KL126hJ+fHwYGBqxfvx4vLy/ee+89Ll26pO8Q9UJtYYvKUPsptMrQFLWFrZ4jgunTp9OhQwe6dOnCK6+8gqOjI3///bfuulqt5t9//0WtVuPj48P7779P7969GTt2rB6jzjmVoudq8pUrV9KnTx9++eUXGjVqxIwZM1i1ahUXLlzAwcGB3r17U65cOSZOnAhob3Q7d+4cAO3ataNnz5707NkTS0tLKlWqlO1ztGzZEi8vr+eu2ZshNjYWa2trYmJiJPkVQggh8klwcDCjR49m5cqVgDaJ6tevH6NGjcLFxUXP0Wl3G7t27Rru7u4FfnNX2r0Q0uPvoLawLbCZ3ZLgWb+T3ORreq/h7datG1OnTmXUqFF4eXkRFBTE5s2bdTeyhYSEEBYWput/+/Zt6tatS926dQkLC2Pq1KnUrVuXDz/8UF8vQQghhBA5ULVqVVasWMGJEydo37496enpLFiw4KXcrjijvEGS3cKh9xneokhmeIUQQoiCt3//fr7++mv27NkDgKWlJf7+/vj7+2NtbV3o8RTmDK/ImRIzwyuEEEKIl1PTpk3ZtWuXbFcsCpwkvEIIIYTQG9muWBQGSXiFEEIIoXcZ2xWfOXOGxYsXU6FCBcLCwvj000/x9PRk6dKl2S4tKkROSMIrhBBCiCJDrVY/dbvi2rVrs2bNGtmuWOSaJLxCCCGEKHIytiu+cuUKkyZNonTp0pw7d47OnTvj7e3Ntm3bJPEVOSYJrxBCCCGKrEe3K/7222912xW3adOGV199tURvVyzyjyS8QgghhCjybGxs+P7777l69SpDhw7F2NiYXbt20aRJE958882XdrtikTOS8AohhBCi2LC3t2f69Om67YoztrytU6fOS71dsXg2SXiFEEIIUeyUL1+ehQsXcvbsWbp16wbAH3/8QbVq1fjoo4+4efOmniN8nnTgwsOvBe+7775DpVJlOTw9PXXXk5KSGDhwIGXLlsXS0pIuXboQERGRZYyQkBDat2+Pubk59vb2fPHFF6SlpRVK/C9KEl49S0149rkQQgghnu5p2xVXrlwZf3//IrpdcTrQG6j28GvhJL01atQgLCxMd+zbt093bdiwYaxfv57Vq1eze/dubt++TefOnTMjTk+nffv2pKSkcODAAZYsWcLixYsZNWpUocT+oiTh1aPURNj3g/ZrdudCCCGEyBkvLy/+/fdf9u3bxyuvvEJycjLTp0/Hw8ODUaNGERMTo+8QH8pIdv94eP4HhZX0Ghoa4ujoqDtsbW0BiImJISAggGnTpvHqq69Sv359Fi1axIEDBzh06BAAW7du5dy5cyxbtgwvLy/eeOMNvv/+e+bMmVMsNgaRhFdPUhNg3yTYMwFWdIaYW9qveyZo22WmVwghhMi9jO2Kt2zZQv369YmLi+P777/H3d2dyZMnF+B2xfE5OGKB99EmuRlLqikPz99/eD0n4+TNpUuXcHZ2xsPDg549exISEgJAYGAgqamp+Pr66vp6enpSvnx53SoYBw8epFatWjg4OOj6tG3bltjYWM6ePZvnmAqLJLx6YmQOzYaDhy9c/Q9muGu/evhq243M9R2hEEIIUTypVCratGnD0aNH+euvv6hWrRr379/nq6++omLFivz8888FMCtpmYPDGlhBZrKbQXnYbp3DcXLP29ubxYsXs3nzZubOncu1a9do3rw5Dx48IDw8HGNjY2xsbLI8xsHBgfDwcADCw8OzJLsZ1zOuFXWS8OqRkRl0XJC1rd1PcO5PuLgRFI1+4hJCCCFKApVKRefOnTl9+jRLlizBzc2N8PBwBg4c+NJtV/zGG2/QtWtXateuTdu2bdm4cSPR0dGsWrVK36EVCkl49Sg1Edb1z9q28TOo0gEOz4LZNeDQT5BUVMqOhBBCiGJIrVbTu3dvgoODmTNnDo6OjgWwXXHcc44Y4F1A9ZTHqx5ej8nBWC/OxsaGKlWqcPnyZRwdHUlJSSE6OjpLn4iICBwdHQFwdHR8YtWGjPOMPkWZJLx6klHDm1HGMPRaZnnDoRnQbhbcuwxb/gfTKkDwen1HLIQQQhRvxsbGfPrpp9luV9yoUSP279//AqNbPOewApYDPXgy6VU9bF/+sN/zxnpxcXFxXLlyBScnJ+rXr4+RkRHbt2/XXQ8ODiYkJAQfHx8AfHx8OH36NJGRkbo+27Ztw8rKiurVq+dLTAVJEl49yajhfeVr6P43WLtov77ytbbdygXazwG76pCWBI5emY+9exE0L8cnMEIIIUS+Mzc3f2K74mPHjuHn50dERATx8Xm/MezZ1MBvZE16M5Ld3x5eLxj/+9//2L17N9evX+fAgQO8/fbbqNVqevTogbW1NX5+fvj7+7Nz504CAwPp27cvPj4+NG7cGIA2bdpQvXp1evXqxcmTJ9myZQvffvstAwcOxMTEpMDizi8q5cXn8Euc2NhYrK2tiYmJwcrKqkCfKzUh6w1qj58rCkSeAYda2vO0ZJjuDialoOEnULcvmNoUaIhCCCFEiRYZGcnEiRPZsGEDM2fOxNbWFmtra8qVK4e5eUHcRZ6xNNly4D0KOtkF6N69O3v27OHu3bvY2dnRrFkzxo8fT8WKFQHtxhOff/45f/zxB8nJybRt25aff/45S7nCjRs3GDBgALt27cLCwoI+ffowadIkDA0NCyzupKQkrl27hru7O6amplmu5SZfk4Q3G4WZ8OZW2HH47XVIuq89NzKH2u9Do0/BvoZ+YxNCCCGKs6tXr3L79u0siVWZMmVwdnZ+Itl6cenAJaAyBZ3sFmf5lfBKSUMx41QP/K9Dh7lgX1M7Ixw4H+Z6wW9t4MI6Wd1BCCGEyAtnZ2fKli1L5cqVKVOmDAD37t3jzJkzXL9+PZ+XMlMDnkiyWzgk4S2GjMyh/ofwyXHo8x9UextUBnBtJ2z/hqffACqEEEKI5zIxMcHDw4Pq1atjbW0NwJ07dzh9+jQ3b94kNTVVzxGK3Cq4ogtR4FQqcGuhPaJvwLFfwNZT2w4QfhKOzgPvgdrZYCGEEELknLm5OZUrVyYuLo5bt24RFxdHREQEUVFRODg44ODgUKD1qyL/yG+phLCpAL4TsrYdng1Bi+H4Qm1S3GgQVO0ABvJbF0IIIXLM0tKSqlWrEhsbS2hoKAkJCYSFhREZGYmTkxN2dnao1VKaUJRJ6lOC1f0AUh7A+bVwfbf2sC4PDT6Bev3AvKy+IxRCCCGKB5VKhbW1NVZWVkRHRxMaGkpSUhK3bt0iIiICJycnbG1tMTCQatGiSH4rJVj5ptB1BXx2CZp9BWZlISYEtn8N093g5DJ9RyiEEEIULyqVitKlS1OjRg3c3NwwNjYmNTWVkJAQzpw5w507d/Jh1zaR3yThfQlYu0LrcdrVHd5aqN3EIi0JnLwy+9y7Apo0PQUohBBCFDMqlQpbW1tq1qxJ+fLlMTIyIiUlhevXr3P27Fnu378viW8RIiUNLxFDU/DqA3V6Q+TpzBvZFA383kG7qUWDj6G+H5jb6jdWIYQQojgwMDDA3t6esmXLEhUVRVhYGElJSVy5cgVzc3PKlSuHlZUVKpUsoaRPMsP7ElKpwKF25vn9q5AUA7E3Yce3MM0N/vkQwk7oLUQhhBCiWFGr1Tg6OlKrVi2cnJwwMDAgISGBS5cucfHiReLi4vQd4ktNEl5BmUow7Cq8FaDd2CI9GYKWwPxGsKglnF0t5Q5CCCFEThgaGlKuXDlq1aqFg4MDKpWKBw8ecOHCBS5dukRCQoK+Q3wpScIrgIflDr2h/yHotwdqdtcuXxayHzb7y+5tQgghRG4YGRnh6upKrVq1sLXV1gnGxMRw7tw5rl69SlJSUqHGs2fPHt58802cnZ1RqVSsXbs2y3VFURg1ahROTk6YmZnh6+vLpUuXsvS5d+8ePXv2xMrKChsbG/z8/J6YuT516hTNmzfH1NQUV1dXJk+eXNAvLUck4RVZqFTg6gNdlsLQK/DKt9DsS1Aba69HX4e1/eB2oF7DFEIIIYoFY2Nj3NzcqFmzZpbtiidMmIBKpWL+/PmFEkd8fDx16tRhzpw52V6fPHkyM2fOZN68eRw+fBgLCwvatm2bJTHv2bMnZ8+eZdu2bfz777/s2bOHjz76SHc9NjaWNm3aUKFCBQIDA5kyZQrfffddob3GZ1LEE2JiYhRAiYmJ0XcoRc7WrxTlO0PtsbCZopxeoShpKfqOSgghhHhxiYmJyrlz55TExMQCe474+Hjl5MmTSunSpRVAKVOmjBIcHKykpBTeH1NAWbNmje5co9Eojo6OypQpU3Rt0dHRiomJifLHH38oiqIo586dUwDl6NGjuj6bNm1SVCqVEhoaqiiKovz8889K6dKlleTkZF2fr776SqlatWqeY33W7yQ3+VqeZ3iXLl1K06ZNcXZ25saNGwDMmDGDf/75Jz/ycFFEVe8CtbqDgRHcOgR/vQ8zKsLucRAXoe/ohBBCCP2Ij4/P9khPTwdAo9Ho2mbPnk10dDQA9+/f58svv2Tfvn2EhoaSlpZGYmJitmOlpKQUSOzXrl0jPDwcX19fXZu1tTXe3t4cPHgQgIMHD2JjY0ODBg10fXx9fTEwMODw4cO6Pq+88grGxsa6Pm3btiU4OJj79+8XSOw5laeEd+7cufj7+9OuXTuio6N1v0wbGxtmzJiRn/GJIqZcQ+j8sNyhxUiwcIC4MNg1BmZ4wJHsPykRQgghSjRLS8tsj7179wJw5coVLC0tsbe3Z+HChbo1ehVF4Z9//qFt27b88ssvnD59mtdffz3bscaMGVMgsYeHhwPg4OCQpd3BwUF3LTw8HHt7+yzXDQ0NKVOmTJY+2Y3x6HPoS54S3lmzZrFgwQK++eabLHtHN2jQgNOnT+d6vDlz5uDm5oapqSne3t4cOXLkqX3Pnj1Lly5dcHNzQ6VSZZtgT5w4kYYNG1KqVCns7e3p1KkTwcHBuY5LPF0pJ2g5Sru6Q+ffoFwjSE8B+1qZfaJvaNuEEEIIkUnJZkOK1NRUvv/+e9LT0wv9hraXQZ42nrh27Rp169Z9ot3ExIT4+PhcjbVy5Ur8/f2ZN28e3t7ezJgxQzf9/fg7CYCEhAQ8PDzo2rUrw4YNy3bM3bt3M3DgQBo2bEhaWhpff/01bdq04dy5c1hYWOQqPvFsamOo1UN7hAeBQ53Ma3/2gJibUP8jaNAfLB31FqYQQghRoJ62zq6pqSkAFStW5Oeff2b06NFPbD+sUqmws7NjwoQJuLm5MWvWLJKTkwHtag9OTk6ULVs2S6lAfnJ01P6BjoiIwMnJSdceERGBl5eXrk9kZGSWx6WlpXHv3j3d4x0dHYmIyFrfmHGe0Udf8jTD6+7uTlBQ0BPtmzdvplq1arkaa9q0afTv35++fftSvXp15s2bh7m5Ob/++mu2/Rs2bMiUKVPo3r07JiYm2fbZvHkzH3zwATVq1KBOnTosXryYkJAQAgNlaYGC5OilXeUBtPW8MbcgLhx2j4XpHvB3b7h1WK8hCiGEEAXCwsIi2yPjk3ADAwMGDBjA9evX+fjjj3U7r6lUKj7++GOuXbuGn58ftra2NGjQgKpVq2JlZYWhoSFRUVFcv36d+Pj4Atmu2N3dHUdHR7Zv365ri42N5fDhw/j4+ADg4+NDdHR0llxqx44daDQavL29dX327NlDamqqrs+2bduoWrUqpUuXzve4cyNPCa+/vz8DBw5k5cqVKIrCkSNHGD9+PCNGjODLL7/M8TgpKSkEBgZmKZI2MDDA19dXVySdH2JiYgB0y4E8Ljk5mdjY2CyHeDGWDto63y6/a5c506TC6T8goBks8IGTy6TcQQghxMvH3NycH3/8ETs7OwDs7e358ccfMTc31/XJ2K64Zs2auLi4oFarddsVnz9/npiYmFwnvnFxcQQFBekmLK9du0ZQUBAhISGoVCqGDh3KuHHjWLduHadPn6Z37944OzvTqVMnAKpVq8brr79O//79OXLkCPv372fQoEF0794dZ2dnAN577z2MjY3x8/Pj7NmzrFy5kp9++gl/f/8X/8G9qLwuE7Fs2TKlUqVKikqlUlQqlVKuXDll4cKFuRojNDRUAZQDBw5kaf/iiy+URo0aPffxFSpUUKZPn/7MPunp6Ur79u2Vpk2bPrXP6NGjFeCJQ5Ylyz+hxxRlTV9F+d5cu6TZhNKKkhSr76iEEEKITIWxLFmGgIAABVACAgKe2zc1NVW5deuWEhgYqBw9elQ5evSocuHCBeXBgwc5fr6dO3dmm+v06dNHURTt0mQjR45UHBwcFBMTE6V169ZKcHBwljHu3r2r9OjRQ7G0tFSsrKyUvn37PhHDyZMnlWbNmikmJiZKuXLllEmTJuU4xuzk17JkKkV5sbnxhIQE4uLisq23fZ7bt29Trlw5Dhw4oJsyB/jyyy/ZvXu3bpmLp3Fzc2Po0KEMHTr0qX0GDBjApk2b2LdvHy4uLtn2SU5O1tXKgHYa39XVlZiYGKysrHL3osQzxUdCYACgwCtfa9sS7sIWf2jwCbg0ziyLEEIIIQpTUlIS165dw93dXVd7W5SkpqYSHh5OZGSkbobX2tqacuXKZZkhLkme9TuJjY3F2to6R/lanm5aS0xMRFEUzM3NMTc3JyoqihkzZlC9enXatGmT43FsbW1Rq9XZFjjnR3HzoEGDdDuBPC3ZBe3Ndk+rBxb5y8IeXhmRte3EIji1XHs41QPvQVDjXTCUX4kQQgihk7FdsYODA7dv3+bOnTvExMQQExND6dKlKVeuXJFM1IuCPNXwvvXWW/z2228AREdH06hRI3788Ufeeust5s6dm+NxjI2NqV+/fpYiaY1Gw/bt27PM+OaWoigMGjSINWvWsGPHDtzd3fM8lih4ldqAVx9Qm0DYce3WxdPdYccoiA3Vd3RCCCFE0ZLddsX379/nzJkzXL9+vcA2qCjO8pTwHj9+nObNmwPw559/4ujoyI0bN/jtt9+YOXNmrsby9/dnwYIFLFmyhPPnzzNgwADi4+Pp27cvAL1792bEiMwpwZSUFF3RdUpKCqGhoQQFBXH58mVdn4EDB7Js2TKWL19OqVKlCA8PJzw8nMTExLy8XFHAHGrDWwvB/zq8Og6sXCAhCvZOhJ8qwZ6J+o5QCCGEKHpMTU3x8PCgevXqWFtbA3Dnzh1Onz7NzZs3s6yW8LLLU0lDQkICpUqVAmDr1q107twZAwMDGjdurNtmOKe6detGVFQUo0aNIjw8HC8vLzZv3qzbmSMkJAQDg8y8/Pbt21nWAJ46dSpTp06lRYsW7Nq1C0A3y9yyZcssz7Vo0SI++OCDXL5aUVjMbaH5V9D0c7iwDo7Mhht7wc4zs0/sLW0/Q/nERgghhAC0Kz9UrlyZuLg4QkNDefDgAREREURFReHg4ICDgwOGhnlK+UqMPN20Vrt2bT788EPefvttatasyebNm/Hx8SEwMJD27dvrffu4F5WbImhRsCJOgV11MHj43+nvb0LoUaj/ITT4GKxd9RufEEKIkqOo37SWE4qi8ODBA27dukVCQgIAarUaR0dH7O3ts+yQWxzk101reSppGDVqFP/73/9wc3PD29tbV2+7devWbHdgEyKvHGpnJrspcRB1HhLvwr4f4KfKsLo73NgHBbAOtxBCiJeURqPRdwh5plKpsLKyolq1alSsWBFTU1PS09MJDQ3lzJkzREZGFqvX94KLienkeVmy8PBwwsLCqFOnjq7k4MiRI1hZWeHpqf0M+tatWzg7O2cpSSgOZIa36NKkQfB6ODIHru/ObHeoDY0Garc4NjLTX3xCCCGKL41Gw6VLl1Cr1djZ2WFsbKzbEa24UhSF6OhooqKidDezGRkZYW9vj42NTZF+fYqiEBUVRUJCApUrV35idjo3+doLr8P7LFZWVgQFBeHh4VFQT1EgJOEtHiJOaxPfU8shLVE7Ezz0KpRyev5jhRBCiOykpKQQFhamKwcoKRRFIS4ujpiYGNLT0wFt4mtjY1Ok1/BVqVS4uLhgaWn5xLUCX4c3pwowlxYCh1rw5jzwnQAnFms3tchIdlMT4d8BULcvVHhFNrMQQgiRM8bGxpQvX560tDRdYliSJCYmsnz5chYsWEB0dDQANWrUYNiwYTRt2rTIzfgaGRnlS91xgc7wlipVipMnT8oMryh0x3+F9R9rv7evqS13qP0eGBXdN7FCCCFEoYmJiWHatGlMmzaNuLg4AFq0aMH48eNp2rSpnqPLmQK/aU2Ioq58U6j/kTbBjTyjne2d5gbbhkP0dX1HJ4QQQuiXtbU1Y8aM4erVq/j7+2NiYsLu3btp1qwZHTp0ICgoSN8h5itJeEWJZFsVOsyBYdehzWSwcYek+3DgR5hZFbZ+pe8IhRBCCP2zs7Pjxx9/5NKlS/Tv3x+1Ws2GDRuoW7cu3bt35+LFi/oOMV8UaMJb1OpAxMvHrDT4DIPB56H7GvBoDYoGylbO7BMXDinx+otRCCGE0DdXV1fmz5/P+fPn6dGjBwArV66kevXqfPjhh4SEhOg5whdToAmv3LQmigoDNVTtAL02w6entPW8GbYNh+lusPVLuH9NbyEKIYQQele5cmWWL19OUFAQb775Junp6QQEBFC5cmWGDRtGZGSkvkPMkzwlvDExMdy7d++J9nv37hEbG6s7P3fuHBUqVMh7dEIUALtqmTevadIg7AQkRcPB6dpyhxWd4ep22cxCCCHEy6tOnTqsW7eOAwcO0LJlS1JSUpgxYwYeHh6MHDlSt8JDcZGnhLd79+6sWLHiifZVq1bRvXt33bmrq2ux28JOvFwMDGHACejxD1RsAyjajS2Wvg4/14Gj86TcQQghxMvLx8eHHTt2sG3bNho2bEh8fDzjxo3Dw8ODH374gfh47R/J+Ph4UlJSiIyMJCUlRddeVOQp4T18+DCtWrV6or1ly5YcPnz4hYMSojCpDKBKO3h/Aww8Aw0/BWNLuHMeNg6G2Fv6jlAIIYTQH5VKha+vL4cPH2bNmjVUr16d+/fvM3z4cN544w1iY2OZPHkyDg4OumPy5MkkJSXpO3SdPCW8ycnJpKWlPdGemppKYmLiCwclhL7YVoV2P4H/DXh9GtTvr20D0KTDP/3hyjYpdxBCCPHyUalUdOrUiVOnTvHbb7/h7u7O559/zpQpUxg7dqyuzCE6OpqxY8cyceLEIjPTm6eNJ1q1akXNmjWZNWtWlvaBAwdy6tQp9u7dm28B6oNsPCGyc+EfWPmO9vuyVaHRp1CnF5iU0m9cQgghhD6kpKSg0WhwcnLKtqbXxsaGiIgIjI2NC+T5c5Ov5Snh3b9/P76+vjRs2JDWrVsDsH37do4ePcrWrVtp3rx53iIvIiThFdm5fw0OzYSgJZDyQNtmYgV1emuT30eXOhNCCCFeBpGRkTg4ODzzup2dXYE8d4EnvABBQUFMmTKFoKAgzMzMqF27NiNGjKBy5eL/V18SXvEsyQ/g5G9wZC7cDc5sr9sPOv6iv7iEEEKIwpaSkoKDg0ORn+E1zOuTeHl58fvvv+f14UIUWyaloNFAaDhAu3zZ4dlwaROU9sjsk3AH1MbaGWAhhBCipEpNTWXIkCGMHTv2iWtDhgwhNTW1wBLe3MhxwhsbG6vLnh9dazc7MisqXgYqA6j4mva4dxnMymZe2zMBTizKLHfIuPFNCCGEKEksLCwYMWIEADNnziQ6OhobGxuGDBnCiBEjMDU11XOEWjkuaVCr1YSFhWFvb4+BgUG22wYrioJKpSI9PT3fAy1MUtIgXoSiwK/N4dYjK/RVbAPeg6BSW22iLIQQQpQk8fHxGBkZERMTg7W1NampqVhYWBTocxZIScOOHTsoU6YMADt37nyxCIUowVQq6LdXW+5wZA5c3ABXtmqPMpWg4Sfg9QGYWus7UiGEECJ/ZCS3GTeoFYUyhkfl6aa1kJAQXF1dn5jlVRSFmzdvUr58+XwLUB9khlfkp/tXtTe4nVgEyTHatn57wNVHv3EJIYQQxVlu8rU8fbjq7u5OVFTUE+337t3D3d09L0MKUWKV9oC2U8D/OrSfDbV6gEtj7TVFgQ2DIfhf7cYWQgghhMh/eVqlIaNW93FxcXFFpjhZiKLG2BIafKw9MtzcD8fmaY/SHtpyh7p9wdRGb2EKIYQQJU6uEl5/f39Au7XcyJEjMTc3111LT0/n8OHDeHl55WuAQpRkNm7Q5HM4/qu29GHrl7DzO6j9PngPBLvq+o5QCCGEKP5yVcPbqlUrAHbv3o2Pj0+WgmRjY2Pc3Nz43//+V+w3n5AaXlHYUhPg1HLtTW6RZzLbq3eBriv0F5cQQghRVBXYxhMZqzP07duXn376SZJBIfKJkTnU/xDq+cGNPdrE98I/YP3I/Z9JMaBowKy0/uIUQgghiqM8by38qNjYWHbs2IGnpyeenp75EZdeyQyvKAqib4ChCVg6as/3TIB9P0Ct97TlDvY19RufEEIIoU8FvkrDu+++y+zZswFITEykQYMGvPvuu9SqVYu//vorL0MKIR5jUyEz2QW4eVBb+nB8IcytC0t84fxa0KTpLUQhhBCiWMhTwrtnzx6aN28OwJo1a1AUhejoaGbOnMm4cePyNUAhhNZ76+CDHdq6XpUaru+GVV1hZlXYNwUS7uo7QiGEEKJoylPCGxMTo9t1bfPmzXTp0gVzc3Pat2/PpUuX8jVAIYSWSgUVmmtvYvvsEjT7CszKQkwIbP8abh3Sd4RCCCFE0ZSnhNfV1ZWDBw8SHx/P5s2badOmDQD379+XdXiFKATWrtB6nHYzi7cWgudbUOn1zOtbv4Jzf0m5gxBCCAF53Hhi6NCh9OzZE0tLSypUqEDLli0BbalDrVq18jM+IcQzGJqCVx/tkSHqHBycpv3eylW70UV9PzC31U+MQgghhL7leZWGY8eOcfPmTV577TUsLS0B2LBhAzY2NjRt2jRfgyxsskqDKM7iI+HwbAhcCAkPdwBXm0Ct7tBoIDjV1W98QgghRH4o8FUaABo0aMDbb7+tS3YB2rdvn6dkd86cObi5uWFqaoq3tzdHjhx5at+zZ8/SpUsX3NzcUKlUzJgx44XHFKIksbCHV8fCsKvwVgA41YP0ZAhaAvMbwdLXtev5CiGEEC+LHJc0+Pv78/3332NhYaHbYvhppk2bluMAVq5cib+/P/PmzcPb25sZM2bQtm1bgoODsbe3f6J/QkICHh4edO3alWHDhuXLmEKURIam4NUb6vTS3tB2ZI62rteqHKgevtVNTYCUeLCw02+sQgghREHKcUlDq1atWLNmDTY2NrothrMdUKVix44dOQ7A29ubhg0b6tb11Wg0uLq6MnjwYIYPH/7Mx7q5uTF06FCGDh2ab2OClDSIkuvBbe2NbBk7uB2dB1s+h5rdtOUOzvX1G58QQgiRUwWytXDGtsKPf/8iUlJSCAwMZMSIEbo2AwMDfH19OXjwYKGNmZycTHJysu48NjY2T88tRFFXyjnr+c0DkJ4CJ5dqD1cfaDQIqr0NaiP9xCiEEELktzzX8OaHO3fukJ6ejoODQ5Z2BwcHwsPDC23MiRMnYm1trTtcXV3z9NxCFDdvLwG/vdob2gyMtLu5/dUTfqqk3co4PlLfEQohhBAvLsczvJ07d87xoH///XeegtGXESNGZKlLjo2NlaRXvBRUKnBprD1emwyBC+DYfG3pw87R2nrfR5c8E0IIIYqjHCe81tbW+f7ktra2qNVqIiIisrRHRETg6OhYaGOamJhgYmKSp+cToqQo5QQtR0Hz4dqb2079rq3tzbDreyhbSbu1sdpYf3EKIYQQuZXjhHfRokX5/uTGxsbUr1+f7du306lTJ0B7g9n27dsZNGhQkRlTiJeJ2hhq9dAeGWJvwZ7xoKTD1i+h/kfQoD9Y5u19qRBCCFGo9FrDC9rlzhYsWMCSJUs4f/48AwYMID4+nr59+wLQu3fvLDegpaSkEBQURFBQECkpKYSGhhIUFMTly5dzPKYQIneMzKHFSLB0grhw2D0WpnvA373h1mF9RyeEEEI8W46XJatXrx7bt2+ndOnS1K1bF5VK9dS+x48fz1UQs2fPZsqUKYSHh+Pl5cXMmTPx9vYGoGXLlri5ubF48WIArl+/jru7+xNjtGjRgl27duVozOeRZcmEyF56Cpxfo93J7dahzPZyjeCDHWAolUFCCCEKSW7ytRwnvGPGjOGLL77A3NycMWPGPLPv6NGjcx5tESQJrxDPdztQu5nFmZVQ8TXosVbbnp4KCXe0NcFCCCFEQSmQhPdlIgmvEDkXHwnJsVCmkvb87GptqUP1d6DRp9oVIDI+EEpN0JZHZHj8XAghhMip3ORrL1TDe+zYMZYuXcrSpUsJDAx8kaGEEMWUhX1msgsQsl+7m9uZFfDrK7DQB678B6mJsO8H7Vd48lwIIYQoKDlepeFRt27dokePHuzfvx8bGxsAoqOjadKkCStWrMDFxSU/YxRCFCNvzNCu3XtkDpxeoS19sCkP+yZpN7O4dQQ6LoB1/eHqf9rHNPtKZnqFEEIUnDzN8H744YekpqZy/vx57t27x7179zh//jwajYYPP/wwv2MUQhQzTnXhrYXgfx1eHQe7xkHjoeDhq01yZ7hrv3r4QrPhkuwKIYQoWHmq4TUzM+PAgQPUrVs3S3tgYCDNmzcnISEh3wLUB6nhFSJ/adLgTjAYmsIsz8z2IRch8T7YV9deE0IIIXKqwGt4XV1dSU1NfaI9PT0dZ2fnvAwphCjBDAyhtAdseGzvl38/hdLu8Hcf2P4txNzUT3xCCCFKtjwlvFOmTGHw4MEcO3ZM13bs2DE+++wzpk6dmm/BCSFKhtQEbQ1vRhnD0GuZ5Q2HZkDr77U3sP1UGVZ1gxt7QdaPEUIIkV9yXNJQunTpLJtNxMfHk5aWhqGh9r63jO8tLCy4d+9ewURbSKSkQYj8l5qoTXqbDQcjs6znYcdh52i4vjuzv0Mt+GAnmFrrL2YhhBBFV27ytRyv0jBjxowXjUsI8RIzMnu4GoPZk+flm0Kf/yDiNBz5GU79DkYWmcmuokDsLbB21V/8Qgghii/ZeCIbMsMrhH4l3oe4cLCrpj2/ugOWvQFV34RGg8CtReZmFkIIIV5OBTLD+zRJSUmkpKRkaZMkUQjxIsxKa48MIftA0cCFf7SHfQ1o+CnU7gnGFvqLUwghRPGQp5vW4uPjGTRoEPb29lhYWFC6dOkshxBC5KeWo+DTk9DgY22pQ+RZ2DAQprvBli/g/lV9RyiEEKIoy1PC++WXX7Jjxw7mzp2LiYkJCxcuZMyYMTg7O/Pbb7/ld4xCCIFddWg/W7uZRZspULoiJEVrV3k4/quegxNCCFGk5amGt3z58vz222+0bNkSKysrjh8/TqVKlVi6dCl//PEHGzduLIhYC43U8ApR9CkauLQZjv4MHeZm3tB2bL72Wp33wdhSvzEKIYQoOAW+8cS9e/fw8PAAtPW6GcuQNWvWjD179uRlSCGEyBWVAVRpBz3/zUx205Jh1xjYOBimucHmz+HeZb2GKYQQogjIU8Lr4eHBtWvXAPD09GTVqlUArF+/Hhsbm3wLTgghckWB5iOgTGVIjoHDM2FWdVjeES5v1c78CiGEePnkqaRh+vTpqNVqhgwZwn///cebb76JoiikpqYybdo0Pvvss4KItdBISYMQxZuigSvb4MgcuLQps71sFei9FazK6S82IYQQ+SM3+Vq+rMN7/fp1XR1v7dq1X3Q4vZOEV4iS4+4lODoXTiyGUs4w8JS2HAIgJgSsy+s1PCGEEHlU6AlvSSMJrxAlT/IDiL6u3bIYIPwk/NIAKrYB70FQqW1mIiyEEKLoK/Cb1gC2b99Ohw4dqFixIhUrVqRDhw78999/eR1OCCEKlEmpzGQX4MZeQAVXtmprfGfXgEMzISlGbyEKIYQoIHlKeH/++Wdef/11SpUqxWeffcZnn32GlZUV7dq1Y86cOfkdoxBC5DvvQTD4PDQeCibW2tUctnwO0yrAhsEQdV7fEQohhMgveSppcHFxYfjw4QwaNChL+5w5c5gwYQKhoaH5FqA+SEmDEC+XlDg49Tsc+Rmizmnb6vnBm/P0G5cQQoinK/CShujoaF5//fUn2tu0aUNMjHweKIQoXowttdsWDwjSruLg+RY0Gph5/dRyODAdEu/rLUQhhBAvIE8Jb8eOHVmzZs0T7f/88w8dOnR44aCEEEIfVCpwbwXd/sys91U0sPt72PYlTHeDfz+FyDN6DVMIIUQuGea048yZM3XfV69enfHjx7Nr1y58fHwAOHToEPv37+fzzz/P/yiFEEJPFA00/R8cnq1NdAMXaA+3ltpZ4KpvgoFa31EKIYR4lhzX8Lq7u+dsQJWKq1evvlBQ+iY1vEKIxymKdmWHI3PgwtrMXdusy2u3N7arptfwhBDipZObfC3HM7wZWwkLIcTLSKUCt1e0R0wIHP0FjgdAegqUqZjZTzazEEKIoueFl1lXFAXZu0II8TKxLg++42HYNei5HtTG2vboG/BTZVjcGs79DZo0/cYphBBCK88J72+//UatWrUwMzPDzMyM2rVrs3Tp0vyMTQghijQjM3D0yjwP2Qeo4MYeWN0NfqoCe3+AhDv6ilAIIQTkMeGdNm0aAwYMoF27dqxatYpVq1bx+uuv88knnzB9+vT8jlEIIYqF2j1h6GVoPgLM7SD2Juz4Fqa5wT9+EHZC3xEKIcTLKU8bT7i7uzNmzBh69+6dpX3JkiV89913xb7eV25aE0K8qLQkOLNKe5Nb2HFtW7W34d1V+o1LCCFKigK5ae1RYWFhNGnS5In2Jk2aEBYWlpchhRCiRDE0Ba/eUKcX3DqkTXwbfJx5/cI67TJn9T8EC3v9xSmEEC+DPJU0VKpUiVWrnpymWLlyJZUrV37hoIQQoqRQqcDVB7osgwrNM9v3TYKdo2G6O6ztC7eP6S9GIYQo6fI0wztmzBi6devGnj17aNq0KQD79+9n+/bt2SbCQgghMimKdtOKI3Mg9CicXKY9XBpr26t3zlz5QQghxIvL0wxvly5dOHLkCLa2tqxdu5a1a9dia2vLkSNHePvtt3M93pw5c3Bzc8PU1BRvb2+OHDnyzP6rV6/G09MTU1NTatWqxcaNG7Ncj4uLY9CgQbi4uGBmZkb16tWZN29eruMSQoiCoFJpb3D78AB8uB9qvwcGRtrSh797wYyKEPrs/w0KIYTIhVwnvKmpqfTr14/SpUuzbNkyAgMDCQwMZNmyZdStWzfXAaxcuRJ/f39Gjx7N8ePHqVOnDm3btiUyMjLb/gcOHKBHjx74+flx4sQJOnXqRKdOnThzJnNze39/fzZv3syyZcs4f/48Q4cOZdCgQaxbty7X8QkhREEq1wjeXgLDrkLL0WDpBClxYOuZ2Sf6hv7iE0KIkiBPqzRYW1sTFBSU4+2Gn8Xb25uGDRsye/ZsADQaDa6urgwePJjhw4c/0b9bt27Ex8fz77//6toaN26Ml5eXbha3Zs2adOvWjZEjR+r61K9fnzfeeINx48Y9NyZZpUEIoS/pKdqb2Zzqac8T7mrrfB1qPix3eAcMTfQboxBCFAW5ydfyVNLQqVMn1q5dm5eHZpGSkkJgYCC+vr6ZARkY4Ovry8GDB7N9zMGDB7P0B2jbtm2W/k2aNGHdunWEhoaiKAo7d+7k4sWLtGnTJtsxk5OTiY2NzXIIIYQ+qI0zk13QljYo6dpa3zUfwAwP2PkdPLitrwiFEKL4ydNNa5UrV2bs2LHs37+f+vXrY2FhkeX6kCFDcjTOnTt3SE9Px8HBIUu7g4MDFy5cyPYx4eHh2fYPDw/Xnc+aNYuPPvoIFxcXDA0NMTAwYMGCBbzyyivZjjlx4kTGjBmTo5iFEKIwVX4Dhl2H4wFwdB48CIU942HfD1CtM3gPBNcnV4kUQgjxiDwlvAEBAdjY2Ojqdx+lUqlynPAWlFmzZnHo0CHWrVtHhQoV2LNnDwMHDsTZ2fmJ2WGAESNG4O/vrzuPjY3F1dW1MEMWQoinsrCD5sOhyedw4R84MhtC9sPZVRAXDh9s13eEQghRtOUp4X10J7WMEmCVSpXrcWxtbVGr1URERGRpj4iIwNHRMdvHODo6PrN/YmIiX3/9NWvWrKF9+/YA1K5dm6CgIKZOnZptwmtiYoKJiRTFCSGKNrUR1HhHe4SdgCM/g2fHzOvXdsG1HdDgI7By0VuYQghR5OSphhe0s7w1a9bE1NQUU1NTatasycKFC3M1hrGxMfXr12f79szpCY1Gw/bt2/Hx8cn2MT4+Pln6A2zbtk3XPzU1ldTUVAwMsr40tVqNRqPJVXxCCFFUOdWFtxZA1Tcz2w5Og70TYUYlWN0DbuzTrvkrhBAvuzzN8I4aNYpp06YxePBgXaJ58OBBhg0bRkhICGPHjs3xWP7+/vTp04cGDRrQqFEjZsyYQXx8PH379gWgd+/elCtXjokTJwLw2Wef0aJFC3788Ufat2/PihUrOHbsGPPnzwfAysqKFi1a8MUXX2BmZkaFChXYvXs3v/32G9OmTcvLyxVCiGKhbj9IiYcbe+Dcn9rDsQ40/BRq9QAjM31HKIQQ+pGnZcns7OyYOXMmPXr0yNL+xx9/MHjwYO7cuZOr8WbPns2UKVMIDw/Hy8uLmTNn4u3tDUDLli1xc3Nj8eLFuv6rV6/m22+/5fr161SuXJnJkyfTrl073fXw8HBGjBjB1q1buXfvHhUqVOCjjz5i2LBhOSq9kGXJhBDFWcQpbbnDqeWQlqhtMysD7ywHj9b6jU0IIfJLbvK1PCW8NjY2HD16lMqVK2dpv3jxIo0aNSI6Ojq3QxYphZ3walKTMDAy1X0VQoj8kHgPji+Co3O1qzsMvQqlnLTXom+AdXntrm9CCFEcFfg6vL169WLu3LlPtM+fP5+ePXvmZciXmoGRKde+tJZkVwiRr8zKQNPPYUgw9NuTmeymJsL8RjCvHgQugNQE/cYphBAFLU81vKC9aW3r1q00btwYgMOHDxMSEkLv3r2zLPEldbM5lJ6q7wiEECWUgRrKNcw8DzsOaUnaHd3+/RT++wbq9oVGA8DGTW9hCiFEgclTSUOrVq1yNrhKxY4dO3IdlL4VRknDo+UL6Q8iuTHKhfLf3cDQ2umJ60IIkd8S70PQYu1mFvevattUBlClg3YLY/dWUu4ghCjaCryGt6QrrBrea19aQ3oqiiZNO8OrNkJlYAhqI9wnx6BJeoCBaakCe34hhNCkw6VNcGQOXP1P22ZfAz45IQmvEKJoy02+lueSBvFiNKlJuE+OASD17k1ujquI68grGJXR7vCWdv8mNydUp0z7cVi3/EyfoQohSjADNVTtoD3uXNCu7lCuUWayG3Zcu9pDwwFQpqJ+YxVCiLzK88YT4sVklCtsqWWFUVlXVAaGGJVxZUejUtzfMp6wOa+hJMdhWFqbACvpadz791uSb55AJuWFEAXB1hPazYQ672e2HZoJh36CWdVg+VtwZRsosoePEKKYkYRXz5S0hzerqY0ASHmQRum232DeZgFUGoLKuiYAiRd3EL11PKFT6hE6pQFKepq+QhZCvERq94RKrwMKXNoIy9rBnNraEojkB/qOTgghckYSXj1TNBqSwm/iPjmGpPCbKA+3P7616lcuTJ/J9aXa5d/S4lMwrdIOlaEJhqVdUam11SixBxbw4NAiNImxensNQoiSq+Jr0HM9DDoLjQaBcSm4GwybhsK0CnDuL31HKIQQzyc1vHqmMlCzs0UlVAYGKBoNKgM1AGUatyAx9DpO7boCcHP9Fm78tpFKn36BS+cBaNLSUBmouL9xNOmxYdxZPRDzWm9RqmEvzDzb6BJiIYTID2WrwBvT4dWxcPI3ODJXm/ja18jsE30DrF21qz0IIURRIqs0ZKOwVmlIT05CbfLk0mPZtQcNfZ/b6/+gwcL12LdqR/CUb4jauY7yb9REFXOC1MhgXV91KXsc+q/D1M27wGIXQrzcFA3cDsxc31eTDrM8wcBIu56vVx8wkZ3ZhRAFqMB3WhP5I7tk92ntXjOW0fpIOLZNfQGI+O8fYs+fwbBCR1y+OY9hvdForBqhMiuDJikWY4dqACSHnuT+1gmk3QspuBcihHjpqAyybmZx54J2bd97l2Czv7bcYeMQbbsQQuibzPBmo7BmeF9ESvQ9Iv9bh+PrXTC0LMX+txoRc+ooNb6fg2OLxiTeTcPA2Jjkswt4sPdnAKxbDqNsZ9n5TghRMFLi4OQy7dJmd85ntld8TbuZReU3pNxBCJF/ZIb3JWBsUwaXdz7A0LIUiqLg1mcw9r4dcWzbBRPXegRP/Zp97esSdzsJ08raLZOMHKsDoCgKd/76jPjT61FkS2MhRD4xtoSGn8CnJ6HXZqj6JqDSLmW2YZAsZyaE0B+Z4c1GcZjhfRZFoyFoSA8itq/nlS1nMC/vwfEBbxB79izVx/6Clbstt39sBICBhS2W9btj2bAXJuUbopKtlYQQ+ej+Ne32xdau4D1I23bvMhyYDt4Dwa66fuMTQhRfsrXwCyruCW+G9MQE1GbmaFJT2dHYmZR7d2i24QRmDtZc/7EbBvFnUSkJuv5GjtVx+eI4KiMTPUYthCjpNn8Oh2dqv3d/VVvuUKW9dtc3IYTIKdlaWACgNjMHwMDIiFb7bnDnwA5KVauDkpbG9Y2XSY1NoN7UHzB4cJK4E3+iqCzAQPtP4sGhRSjpqVjU7YravLQ+X4YQooSp9jbEhEDwOri2Q3vYuGm3L67bF8zkfzlCiHwmM7zZKCkzvE+jSUsjaudGIndtpObYOaBSsesVV9Lu36b2T+uwf7U9IaPKkx4bCmpjHPqtxqJWR32HLYQoYaJvwLFf4HgAJN7TthmawRszoF4/vYYmhCgG5KY18UwGhoY4vNaRWuPnoVKrSYuPo2yTthiWroBt89cgPZW7l1NJjleDJg2T8g0ASI28xJ1Vn5J07SDyPkkI8aJsKoDvBBh2Hd78BRxqQ1oi2Hlm9om5CRrZSV0I8YJkhjcbJX2G92kURUGlUpF8J5I9baqTFhdLy61HMXOrwzG/N7G0uolx+kkADO0qUarB+1g2eB8ju4p6jlwIURIoCtw+Bs4NQKXSns9vpJ39bfCJdtbXvKy+oxRCFBVy09oLelkT3kdp0tJ4EHwa6xp1SQy7xc4mrpiVBc+POpN0cTNKSubNbqWafoJdt7l6jFYIURLF3oJfGkLCHe25oSnU7K5d3cHRS6+hCSGKAEl4X5AkvFkp6encO7qPmNPH8Oj/OUkRIQS+646VswYLewPKdpmJYtWQUp61uL/xa8wqNse8entZ7UEI8cLSkuDMKjgyG8JOZLaXb6pd3aHa27p7bYUQLxlJeF+QJLzPlhwVzpW5k4i/GkzdnwJQNAbsaFYJY0sNFbwTATAwL43ryEuoLeTzRyHEi1MUuHVQu4vbub+0db2mNtr6X2MLfUcnhNAHSXhfkCS8ufMg+AzH/DqgNkqjxmc9iDu2nISwOyQav0mNcXNJubKJ1KjLlGrYCyP7yvoOVwhRzD24Dcfmg9oEXhmhbYuPhP++1i5t5lxfv/EJIQqHJLwvSBLe3FMUheTIMEwdnLlzYAfH+7VGZV6W1kfCuf1jQ1JCgwAwLt+QUo36YFmvG2pLW/0GLYQoMfZMhJ2jtN+7+jwsd+gMaiP9xiWEKDiS8L4gSXhfjKIoxJ4LIvHmNRzavk3kmvFErBqJhZ32zmsAVGrKdv0F62Z+eo1VCFEy3A6EQzPg7F+gSdW2WTpBg4+h/odg6aDX8IQQBUAS3hckCW/+Srx9k5srF6JJuINjk0rE7ltIWtQ5ru41oeX+uyhJd7j37yismvhh6tEMlYEsDy2EyJsHYRC4QFvyEB+hbVMbw6vfQxN//cYmhMhfkvC+IEl4C9a9o/u4MOZDDMu60WjJZqL+HsGDXZMAMK3SGudB/+k5QiFEcZeeor257fBsCD0C3ddA1Q7aa7GhYGGnTYSFEMVXbvI1WcxFFLoyDZvR5N8LpCdq1/JNijMjOgSsXNSYVWpBamwMxz99BzvPFMo07UypBu+hLmWn56iFEMWJ2hhq9dAetwOzrtv7jx9EnIH6/aHBR1DKSW9hCiEKiczwZkNmeAtf/I0rpESFYl3Di7DN6zj7TS8q+T68aGAIllWxeXUgNs0+wMDYTK+xCiGKr6QY+LkOPAjVnhsYQvV3oNGn4NL4kfsMhBBFnpQ0vCBJePUrKTyU22sXYZB4FsO0S6TcDNRdU5la4TL8LGkJ6Zi5VNBjlEKI4io9Fc6vgSNz4OaBzHaneuA9CGq8C4ayb44QRZ4kvC9IEt6iJfzfhUT9PQZzqxhMHN0xazmTwz1a4ty6NuXfao9lw14YO1bTd5hCiGIo7Lg28T29EtKTtWv7DrumrfEVQhRtkvC+IEl4iyZNWhqa+Ehu/rWK8+P8qfxmWdTKHQBi7jpj8+r/cPcbpucohRDFUXwUHP8VkmPBd7y2LSUONg6Geh+CaxMpdxCiqJGE9wVJwlv0JUWGkXDqb1KubCb+7Eau79Zg06QzXlPnE77wXRLjyuDQeQSlqtXTd6hCiGLq2C+wYZD2e0cv7WYWtbqDoalewxJCPJSbfK1ILHg6Z84c3NzcMDU1xdvbmyNHjjyz/+rVq/H09MTU1JRatWqxcePGJ/qcP3+ejh07Ym1tjYWFBQ0bNiQkJKSgXoIoZKb2TpTxHYjjx+tx+uIcVUYswK33IOKPryL5yg4MIv4k4udGRP7elwcn/iHm/EnkvZ0QIjcqNIe6/bQJbngQrOsP09zgv28gRv6cCFGs6H2Gd+XKlfTu3Zt58+bh7e3NjBkzWL16NcHBwdjb2z/R/8CBA7zyyitMnDiRDh06sHz5cn744QeOHz9OzZo1Abhy5QqNGjXCz8+PHj16YGVlxdmzZ2ncuHG2Yz5OZniLr9Q7Vwlf8TVJF9ahNkjUtSfchRTrd6j382qU9HQwMEAln08KIXIg4S6cWARH52YmuioDaPoltP5ev7EJ8TIrViUN3t7eNGzYkNmzZwOg0WhwdXVl8ODBDB8+/In+3bp1Iz4+nn///VfX1rhxY7y8vJg3bx4A3bt3x8jIiKVLl+YpJkl4iz9FoyHp2n7iji4l9tBv3LuUSpk3J1Dxk6+4MaUddw4foXSbz/D4aKS+QxVCFBOaNAj+V3uT2/Vd8NZC8OqjvfYgDEytwchcryEKoTepCVn//T9+XhCKTUlDSkoKgYGB+Pr66toMDAzw9fXl4MGD2T7m4MGDWfoDtG3bVtdfo9GwYcMGqlSpQtu2bbG3t8fb25u1a9c+NY7k5GRiY2OzHKJ4UxkYYFaxOXbd5+M+JZrai69T/r1PSIsNJy1kM6Wd76KcGU1y6EnuHtrN+fH/I+bMcX2HLYQowgwMoVon6LMNPjkONbtlXtvyP5juDttGQPQNbVtqQtbHP34uREmRmgj7ftB+ze68KNBrwnvnzh3S09NxcHDI0u7g4EB4eHi2jwkPD39m/8jISOLi4pg0aRKvv/46W7du5e2336Zz587s3r072zEnTpyItbW17nB1dc2HVyeKCgMjU0zsXDGyssbA2IIynaahsq6MupQTxk61CP17CVH//kjE4vdICP6PuKvB3Du2H0Wj0XfoQogiyqFW5s1r6SnaGt/Ee3BgKvz7qXaDi6KeAAiRH1ITYN8k2DMBVnSGmFvar3smaNuLyhu9Ere1sOZhkvLWW28xbJh2iSovLy8OHDjAvHnzaNGixROPGTFiBP7+/rrz2NhYSXpLKAPTUpRuPZTSrYeiSYxBZWCA4xvvYJK0GcOUYMLnvIZiYMG9i/GYe3WjxoQVpCXEozYxRaVW6zt8IUQRpDaGT0/BpY3acoc3psPBado/+LeOQIeftUnw1f+0/RsNhtPLgYcFhbrCwodfyzeDcg2134cHwdUdT/bJODc0gcZDMmM5MF2bgD9t7CrtMrdZvrEXru9++tjmZbUbcYB2s47d47Lvl3FeuyfYPVwSPXg93Dz49LFLu0HDAdrvE+/DnvGPjfnIYwAafwY2D/caOrEYwk48JRbAsQ7U/1D7ffQN7RuNZ4396veZ6y7v/xHuXHiszyN93VpCnV7a78NPwsHp2cTwSP8354PRw81Btw2H2FtP/5lUextqvqv9/voe7b+lp42tNoZ3lmc+z9q+2jdZTxu7fn+o2kH7/fk12iX4nja2uS10/i1z7KVvaK89bey2U6HZcO2/9av/wQx3bbuHr7bdqIhsjqrXhNfW1ha1Wk1ERESW9oiICBwdHbN9jKOj4zP729raYmhoSPXq1bP0qVatGvv27ct2TBMTE0xMZFudl42BmTUA9q3aYeX2Fw+O/Eb8iZVoEu5TthIQt5L7W2oRdS6FkOW/UGXYGMq/97F+gxZCFEkGaqj6pvaIvgFNPs9MAGZW0fbJSAD+/RROLXv6WK0nZCa8Nw/Btq+e3tesTNaEd/dY7frBT1PKKTPhvb4bdo15el9bz8yEV5MGeyc8vS9ot2bOSHiv/AdHf3563/LNMhPelAdw6Kdnj12zW2bCe2UrnF399L7V3s5MeBPuQOCCZ4/d7MvMhPfyZm199tOoTTMT3gdhcOr3Z4/d/pGfweXNEHn26X1tq2Z+H3MDzv/99L6PL413eSvERz69f8XXMr+/f10by9NYPTbfd3U7WZL4x90J1r7J6LggM9kF7XlRSXZBzwmvsbEx9evXZ/v27XTq1AnQztBu376dQYMGZfsYHx8ftm/fztChQ3Vt27Ztw8fHRzdmw4YNCQ4OzvK4ixcvUqGCbEUrsmfq7oOpuw+2nWeQcG4jsYeXkHh+I2ZVfbm7wJ+06Ag0F+fy4Ig59y/FEHPmFK7dPsTGq5G+QxdCFDEZiVnH+TDDI7M9IwEwtYGa3bVtWdaKeXhi98jGkbZVoc77T/YB7UYYRhZZn7v2+9od4x7tq1uQRgVlq2T2daoH9T96sk/GucUj1YMGhtp1iB+P49HHlXbLvOzeUjsL+bSxbR7pa2IFTb/I/vVltJVyzmz37ARlKj99bFvPzL6lnKDl6KfE/LDNtHTmab1+2jcmTxvbsU5mX9uq8NoPj/Uh6/mjW1Q3/UJb9vK01+lcP7OtXCNoN+vJPhmPNXjsA8c2kyEt6elju3hntlVqo529f9rYj99o1nlJ1utZHqPS/q5TE7XL9j1qXX/o/nfRSXr1vkrDypUr6dOnD7/88guNGjVixowZrFq1igsXLuDg4EDv3r0pV64cEydOBLTLkrVo0YJJkybRvn17VqxYwYQJE7IsS7ZmzRq6devGnDlzaNWqFZs3b2bo0KHs2rWLZs2aPTcmWaVBAKQn3MfAzAYlNZXwP4aTFDhd255mzKVNKVQfMxu33gO5d3gPaQlx2Db1xcDYWM9RCyGKgtREbR1jRhkDaBOpopQACJEfUhO0ZSN7Jmj/jXdcoE12r/4Hr3wNzb4quNUacpOv6b2Gt1u3bkRFRTFq1CjCw8Px8vJi8+bNuhvTQkJCMDDIvLeuSZMmLF++nG+//Zavv/6aypUrs3btWl2yC/D2228zb948Jk6cyJAhQ6hatSp//fVXjpJdITKozbVv+1XGxti9+Rlx9jY8OLYUI+vqlC/jjGPbt7nz9zAitqwl/NB13D6eQMVPR5AUGYaRdWnUJrIdkxAvo4wE4Op/TyYA+yYVbAIgRGEzMteW6kBmzW73vx/+Wy9CNbx6n+EtimSGVzyNoigoqYkYGJujSXrAjW8cUB7edq0uWwXrpn5cX7uXyN27qTn+F5w79tBzxEIIfUhNzPoH//FzIUoaWYdXiBJEpVJhYKz9L1hlZIr9Byuw8HoHlaEJ6Xcvcm/dV5RS/Yuh4QPMK1QC4Nw4f04M6kb0qWP6DF0IUYiMzB7O5Jplfy5ESfN4clvUPsXQe0mDEMWVSm2ERa2OWNTqSHpCNPFBq4k7upTUqMvUDfgT69oNiDu1jrSzc4i9mkLqu/0ACP17KSojI+xbtcfQspSeX4UQoqAU9QRAiJeJJLxC5AO1uQ1WTfpj1aQ/mqQHGJhqE9m4I4uxtEvB0g6UW6tRlDZcnD6KxFvXqTf3Lxxf78yD4DOYOmk3xhBCCCFE/pOSBiHyWUayC1C6zddYvTIYA0s7LL26oElJxvnNbrj7WmCsOU3avRCChvZke0N7ovZuA5Ad3oQQQoh8JjetZUNuWhP5TUlPBZUBKgM18af+IWJhJ921pHhz7l9Nov4fVzB1dONwz9YYGBnjOWIKparWfPqgQgghxEtMbloToohRqY1QPVwp3KxyS2x7BGBaqSUAphYJONXSEDGrMUmRYdw9uJOo3ZsxLKUtcbg4bRQhy38hNTZaT9ELIYQQxZvU8ApRyAzMrLHy6YeVTz/S7oXw4NjvxB1dqt3tzd6JV7ad587qz4g/MIX06m9zZd5ElNQ0SjdsjpGVDVF7tlLKsxam9k76filCCCFEsSAlDdmQkgZR2LKs75uSoF3fNzlOe82lN7E3k6k7awXpyUlsb2BPWnwczTaexMqzFmnxcRhaWOr5FQghhBCFq1jttCaE0K7vq8pY31dtjEPfVTw4upSkiztwHToPA2MzEi/t5s6aL7DzsiXmhhWlqtRASU9nd6vKmLm6U3fmCszKldfzKxFCCCGKHkl4hShiVGpDzKu/gXn1N1DSU1GpjQCIO/obqbeOYl0arO1MiFzcDQOHpiTfDUeTkoyJvROKonD803ewqeNN+Z6fYFRKPqEQQgghpKQhG1LSIIqitPs3iTu2nAfHlpIadlbXblarC0bVPsG2mS+x54LY174uBiam+AZGYWhhScgfCyjbuCUW7pX1GL0QQgiRv3KTr0nCmw1JeEVRpigKKaEniTu6lLjA5di+OxeL2p1Q0tMInd6c1HRb0tQeVP7fTySEXGVXi4qo1GpaH4vE2KYMiaEhUvoghBCi2JMaXiFKMJVKhYmLFyYuXpTp+IOuPTH4P1JCDgGgVhujSfyetLgH2DZvg8rAAGObMiTfiWTnK+5YVvSkydojGJpb6OtlCCGEEIVGEl4hijGVOvM/YdOKzbB7fwlxR5diYGqNgZkVVtXrUKlrfVIiLhB/ai1xUSpUajVqM3MMzS1Ii3vAwXebY/9qByoPGYWBsbEeX40QQghRMCThFaKEMDCxpFSj3pRq1BtFkw6AoknnweFFpMeGk3BqDQbmZag36n2MXF9FURQid27gwfmTaJISqfL592hSUrg0cywObTphXas+KpVKz69KCCGEeHFSw5sNqeEVJUly6CldvW96zG1du32f5ZhUaU/Uzg0oikK5t94jctcmjvVth4mdI68evAUqFTGnjmFdp6Ekv0IIIYoUqeEVQuiYlKuNSbkplOk4icSLO4g7upSE85sxr94eAzMrytRy5+4af2L3xmBoWgnHdl0xd3FDpVZz79h+DnVthmXVmjTfdEo7oKKgMpBdyYUQQhQfkvAK8ZJQGagx93wNc8/XHlvfdynJ1w+SfP0gZlV9qTdnG6BdDSIx5CpqC0usqnmhUql4EHyGI33a4tzxPap9PUWfL0cIIYTIMUl4hXgJZSS7ADZtv8XQtiJxR5diWf89QJvs3p7mg3G5OjT7cy3qMtUBCN/yN8kRt4m/dhGAxNAQrsybhFO7rpT1aVX4L0QIIYTIAUl4hXjJGVo7YfOqPzav+qNoNAAkhxwj+cZhkm8c5sGB+RiWccOy4fu4du6Gdc36GFrZABC26U9Cls0l7tI5yvq0IjXmPtEnj1LWpxUGRkbPeFYhhBCi8EghnhBCJ6M218S1Hk6DtmPp/QEqE0vS7l0ness4bk/1oqx3Y8o0aApA6fpNcO32IS5d+wIQvvlvjvZpy5FerwGgSU1Fk5KinxcjhBBCPCQzvEKIJ6gM1JhVeRWzKq+i6TqHhNP/8ODoUlQGhqgtygJwf9MYkm8G4t6tF+Y13wRAk5KMcVl7bJtpE96Ibf9weviHVOg9kEqDR6I2MX3iudKTk7JtF0IIIfKLJLxFggZQAQoy6S6KGgNjcyzr98Cyfo/M9X0VhQeHF5F27wYJZ9ZT9u3pWLcaSoVen+La4yOUVO2s7p09W0h7EIOi0aA2MWVzNXMUTToqAwMUjQaVgZrXzyeQFvcAQ8tS+nyZQgghSjDJrvROA8QCXR5+1eg3HCGeQWWg1n5VqXD8ZBM2r43AsKw7FvW6AZASEcytcZWI2T6elIgL1JzwC41X7aV89/6AdiOMVrsv8/r5RFrtvqxLoE9+3huAmNOB7GhagaN92wGQnpTISf/enP1uCJrUVABur19B2IbVpMbGAJAUcZvkO5G660IIIcTjJOHVq4xktwmw5uFXSXpF8WDsWI0yb07AddQVDK2dAIgL/IO0ezeI3jqBW+OrcXuaN+qE45iUtgS0NcKmjq5c+9IaU0dXXc2w2tIagJR7d0i6HUJShHaDjNToe4SuWUrIsp9RGWo/kDo78lNODHqX5Ehtn6AhPdje0IGILWsACJ7yDbtbexKy/BcAovZu4/TXH3Prr98ASIoMI3Tt79w9tAsAJT2dxNs3SX0Qi+zDI4QQJZOUNOjNo8nu+Ydt5x+e7wCWAMeBCkD5x77aFHKsQjzdozuw2fh+hbFjdd3mFskhx0gOOUZc0J+U+2wPKsOHKzeka2djM87rTA7QPr6eD03WHIaHY6rNLfAcPhlNSrLuecp4tyDl/l2MbLS1xJo07VgZK0ckht4g/mowaQnxAMSeCeTmH/PRpKbg0qU3sWeOc3LY+1jVqk+zdcdICg9lZ7MKGBib8HpwEgB7X6+NSq2m/sL1mDm5cHn2eFLuRVH+vY+xrFSN+4EHSI4Kx6q6F+blPbTJcmoKhqWsZXUKIYQogiTh1RsV0I/MZDfDeeBT4E/ADHj8DvfywA0gGW0ZRAVgGmACnADsAUdAXVCBC/FUBsZmWNZ7F8t675L+IIq44yuIO/Y7jv3/AaDt6VhS791E0aSReu8mbU/HAqBoUgENRqWssPFqpBvPyMoGj4+/yPIc9X9Zk+W8yV8HUNLTdedV/Mfi2r0/5q7uAJRp9AqVh42hlGdtAAwtSlG2aWss3KsAkBb/AJWRkS5hVtLTeRB8Wvt6jIwBuL3+D+IunsXetyOWlapxffFMwv5dSbVRM3Dv+xk3Fs/k4rSRuLzrR+0fFhK1dxvnx35G6fpNqTVpAclREVz66TuMbR2oMvQ77Zj/rsTQ0oqyPq1Qm5iSfDcKtZk5ajNz2cZZCCHymSS8eqMAvwIXyJr0VgMWok1eJ6FNbkMe+VrhYb8QYANgDsx+ON4rQBzaX6srT84MZ3ytAMhd8aJgqUvZYd1iMNYtBgNw7UtrSE9F0aRBeio3v6+IysAQ1Ea4T47h6rD/t3ff4VFUbQOHf7Mlm76pJCQhCRBK6L2TgICAigVRmjSV90NRQKzY64sFFSvYC4IgCvoKCgJiIl06UgIEktCSQMom2dTdPd8fs5mwJDQFEsK5r2uuZGZnZs8Ow+bZs895jgm9Tx0MAVEY/CMx+EdiDIrBt9u9AM7BblV/kFP0Fds9IxvgGdlAW/dv3w3/9t209YDOcXTuvFJb92ncnAFJJThKS5wnU+i6aD22vFyMfgEARI2aSPGJI3hFNgTAq35j/Nt3wzM8GgB7cSGgBugAJZknKDi4F/e69QAozjxO2tzZmELCaDzleRylpWx/cBgAfbdloTe5s+HOnlgPJdFlfgIBnePY/fwkcrauI+aBpwm9/lYyfvuJrI1/ENitDyF9bqLoaCqWPdvwCIvE3KIdjrIy7IUFGLx9Xa6HJEmSJAPeaqQDfIF1VKQ1xDrXfYH2zuVM5QNzAoCPAStqb7EVCASKABtw2LlU5R1gEmraxM/AEGAYkAEcQw2MA53nlaR/z1FWTP3X1UFmtrx00p6LJPL5FAzmMADshTngsGHPPYo99yglrAXAENRQC3hzfnkWS+L7GAOi8O48Dr/eDwFQlLQKxeiBwT8SvbnuWYPic1EURSuNpuh0+Lft4vJ41F33uaw3nvoiTH1RW2/y6H9p9NCLCLsNgOD4AXSe9zt6Dy8A3AKCaTTleRRnj7GjtESdqCPfgsHbV70u1nz1Nfuo+cwFB/eSt2sLdmsBAFkb/yDl85no3EyE9LmJU+tWsevxewjufQMdP19K3p7trLu1E+5hkVy3NhV7STFrb+mI0cdMpzkr0Lt7sP/t5xA2G1Gj7sc9NJzsv9ZgL7Ti26wNpuAQbIVWFJ0Oncld9jJLklSryIC3WpUHvRspLbXj5qYHvDj3WMLy/MBAYPxp272AFNRg9wSVe4ZTT1vKe4nXAT8AzZ3rPwITnL974tojXFVvsfyDKF0YnbHiGwWDbyiKzqAFuwA6dzORLx7FlpOmLtmp2HLS0HmYtX1s2amI4jxKj+/CUZijbT/57b3YslOcJ9Jj8IvA4B+J3/VP4xl7PUIIivYuV3uNAyLRmbwvz2s0GMA5sM4UVAdTUB3tMY+6ETSa/FzFNfD2ofO8312Ov279UeyFVnTOwDv2yRkUZxzDN7Y1AEHd+6JzMxHYpReg9ib7te2Cd4w67bMtX/1AYXQGzLa8XAqS/gZFQedmAiB1zgeU5WQRdssI3EPD2f/WM2Rv+IM2735L2KBhJL0+jdSv3iPmgadp/PBLnFjyHYc+mUFwr4E0fugFrCkHSZs7G4969YkePRFHaSknE5Zh9PXDv1NPFEWhLD8Pg5e3NiBRkiSpJpABb7XTYbUqJCen0bBhA7y8/u0fifJ0hnpneVw4F4CxqMFuZ+e6HQhB7ektRE232FfFOUzOxxXgBtTe5reBYGAT4I0aEF+ewEKqBfSuA7sUnQ6Dua5a7SG6c5WHBA39CL/rn8KWk4YhQP3QJoTAGBwDioIt5wg4bGqwnJ2KsKkD0ByF2aTPHqidJ2r6KfRegRSnbqLgrznO9IkoLSDW+4RWS7CmKAoGr4r/M77NWuPbrLW2Xue6G6lz3Y3aeuiAwYQOGKytB/Xoy4CkEuzFRYA6iK/TnBXYnb22ANFjJ1GafQpTcCgAXtGNsOXlYgpWq2zY8nLVY529zkXHUrHs/AvvmFgArCkHOPzpm/g2b0v06ImUZGWy5T+3oBgMDNivjjdY3T0SW0EecSv34d2gMTsfuxtrykGaPDadgA7dObHkO/KTdhHcayD+7bthTU2mMO0QnhHReNVvhKOszFm32XRJr68kSdc2GfDWAFZrIXFxcezbtw+DwYjpsr7RK1T0zHZ1LuXudy7FwBGq7iVOQ83/1QH5wK/OY2c5f94FHHD+HsDZ84hjnI9L15ry9AZHWbFLz+/56ExeuIXG4hYaq21TFIW6E1cAao6vPS9d6yU2RamD3xxFFtzCW6tBsL0Unad635WkbiIv8f1Kz2MIjiHyGfUetu5YTMnRrRj8ozBFdcIU3uofv+4rQefmhs5NTZvQm9wJ6tHX5fFGk551WW85/WOX9VYzvqTZC+9rOcChN96BV0wspqAQADzCo6g//hEtYBZ2G+bWnVAUBUVREA4HtoI8EELrabbs3Ex+0i7sRWqec/qKHznxv28x+gXg374bJ5Z+x/43niTijnG0ev1zTiYsY8v4m/Hv0J2uC9dQnH6MLRMGYwoOpcMn6uDHpBlPo3f3IGrMgxh9fMnevBaEwKdJS4y+ZuwlJejc3GRahiRJGhnwViOr1YrVamXYsGFYLBZGjBjBF198wYkTJ9i0aRONGjWie/fu+Pr6XuGWuQONnEtVynuIDcB84DhQPkuWP2AGLEC2c9lexTmeBF4BFqEGyzcCU4BTwC7UwLge4PZvXohUA5UHuRcT7F4IRafH4BeOwS8c6ld8kDMGNSDi8e0AOEqsWhBkqtcBv37TXNMoLMcw+FakWlh3/UTBpq8A8Os3TQt4j73ZBWEvwxAQ6dJD7N6gu1aT+Gqk6HQYfSrebzwjovGMiNbWfRo1I/bJN1we7/7jRpfj++8txJZnwS0wGIDmL36glnBzpmbUiR+Im18gvs3bAWA0++PTpCUeEWpVjfLUDJ3JA1BrM1t2bMLNGXQ7bDaSP3gFgMgR/wfA39PGU3BwL53n/U5g197sfHg0J379nhYvfUjkiP/jyPxPOfbTXOrecAdRo+4nb+9O0pf9gHdMLGGDhmGzFpC7fSNu/oH4NmujPY/O8M//RJ5tymw5lbYkVY8aEfB+8MEHvPHGG6Snp9O6dWvee+89OnXqdNb9Fy5cyDPPPENKSgqNGjXitdde44Ybbqhy3wkTJvDRRx/x9ttvM2XKlMv0Ci5eaWkpycnJxMXFYbGob/CrV6+mdevWJCQkMHz4cOrUqcPgwYP54YcfANi8eTPR0dEEBQVVZ9Op6CH2AIae8Vj5Hz8Lam9wVT3EqUC0c78dwEqgfFT9WuDW056nLmfvJW5GDbmFpauEzuSl/e5evwvu9V0Hpwm7DUdJvrbuGdsfnZsntpw03CLaqvs4HJQe246wlVB6dKvL8SH3/ICh9WAcRRaOzuigVZsIuuMDdG6e2AtOYS/MxuBXD52bx2V8pdVHb3JHH1wR0AV06unyePjgUYQPHqWtR42cQNTICdp62M3DqdPnJm3mPI+IaNp//FPFtNZ2G/XvmaoO+HP2IntEROOwlWH0V98by/JyweHQguaC5H1kb/gDc8sOAOTt3srBd18kOH4AYYOGYT2UxKa7+uJeN4Lr1h3BXlLC8qbu6D08uW7DcYy+ZrY9OAxbQR6xT7+Fd8OmHFv8DcXpR6lz3U34NGmB9fABSrNP4hERjXtIWMVU2raKGQAVg5EBewsv1aWWJOkiVHu0sGDBAqZOncrs2bPp3LkzM2fOpH///iQlJVGnTp1K+69bt47hw4czffp0brrpJubNm8ett97K1q1badGihcu+ixcvZsOGDYSFhVU6T3Vzc3OjYcOG7N+/n2HDhrF69Wp69+7N/PnzsVqtfPDBBzRs2JC4uDhAzVW89dZbOXbsGEOHDmX+/PkAFBUV4eFRE/9wmoGWzuVchqEGuw2d6w6gMWpgXIzae3wc2FDFsTmok3DchjpY703nsRtQe6EjUQNmOXhGujCK3oDe019b924/HO/2wyvtF/bwJpfBdeW/G4JiANT1kwexnTyIYjARPFydWMO6/XtOfadWfND71FErS/hHYopoi3//p9VjLSdQdHp03sHX5Ffyil6vlXcDMPqaCel3s7auN7kT+/SbLsd0/OIXl/X2H/9IWV4uBi/1m6fw28dgbtVBq73sVb8xkXfdj3fDptox3o2ba6kb5b3M9qJCLa86a/1qSrMyafL4awAc/f4Lstb9jntYJD5NWpA65wNSvniHBvc9QdPHpgMgbGUIm+1fXxNJkv49RVTzXJqdO3emY8eOvP++mkvncDioV68eDz74IE888USl/YcOHYrVamXJkiXati5dutCmTRtmz56tbTt27BidO3dm+fLl3HjjjUyZMuWCe3jz8vIwm81YLJYrkk6QmZlJ48aNOXDgAMHBwS6PORwOdDodubm5dO/enT179vDQQw/x1ltvAdCjRw8yMjK47bbbeP311y97W68cAZzk7HnE2cAh577eqIPoDqDmBvcE1jgfMwIRVN1D3JSzD+6TpH/OUWKl5MgWbNmpOIrzMMdNBCD397fI+eUZRKlrL5+pfjfCH1JLsZ2cdy/5Gz5DMbrjP+B5/Po9DoAl8QN0Jm+t19jgF4FilAO7LgfhcFCWl4st36JNYJKx8mfKcrMI6T8Yo48vhz59i/ykXUSPfgBzy/bsn/k8xxfPIXrcFKLHqrWnf21kdAl4FYOBgQfUHl97USFp8z8hoEMPfFu0uyY/3EjSv3Ux8Vq19vCWlpayZcsWpk2bpm3T6XT07duX9evXV3nM+vXrmTp1qsu2/v378+OPP2rrDoeDUaNG8eijj9K8eXNqOi8vLxITE/H09Kz0mM45utrPz4/du3dz8uRJbM430JKSErZs2UJxcTGHDh3SjnnooYc4efIkcXFxxMXF0aRJk6vwzVRBnTWuDtDxHPs5UEurpVIRvIahBrXHUOsWn60m8d3AZ8AqYBrQC3gddTDeKioC4wBkCTbpYuhMXnjExFXa7nfdVMy9H8JRmONafu20XuXylApRVoziTMEQQpD9v8cqBcp631CC7/oaz6b9cJQWkb/+Uy0gdgtvLUuD/UOKToebXwBufhUDa0P6DnLZp8G9rn+HGk95XptF70Lk7tjE3hen4B4aTu91R3CUlZE861X823cjsEsvOXmIJF1i1Rrwnjp1CrvdTkhIiMv2kJAQ9u2rqhwWpKenV7l/enq6tv7aa69hMBiYNGnSBbWjpKSEkpISbT0vL+9CX8Il4eXlRdOmTXFzO/8ArdN7gE0mEydOnGDdunWYzWoumxCC7777juPHjzN37lwA6tSpQ1xcHFOmTKF79+6X50VUGx0w4IxtC5w/bajpEGfLIy7/OjMJ+As1UAbYg5omUc6LqmsSRwHtUGsWS9KFURQFvVcAeq8ATBFtKj0eMm4B4q6vsVmOoXN39ljYSvFuP8IlSBZlRdjz0tGZ1K/tbdkpZP3gfM/TGaj/llqWzbpjEXlrP3YOsDt9FrsGGPzlNxyXk2IwnnVdMRgJ7n0D7qERKIqCZe8ODrz9LAZfP/pty0IIQdLr0zC36khIn0Fa9Q1Jkv6Zas/hvdS2bNnCO++8w9atWy+4V3P69Om88MILl7ll53YhwW5V/Pz8XAbsCSH4+uuvSUxMJDExkQ0bNpCZmcn333/P2LFjAbU6xIgRI+jZsyc333wzjRs3vhQvoQYyoAankUCPc+x3M2qwW97LZkftVU4FMlFnsduL6xTQ5faiBs4jgKPAdKA7ah5xLhXBsVcVx0pS1RSjCWNQA5f14OGfaOtCCBzWLGzZqRjLy7QpOrzaDMGWnQqKos04V3J0G0X7lld6DlNUJ8IfVgeZ5q56g5IjWzD4R+HV8mbcG6gfjB3F+Sgm76vwG6LqZy8prnKAWnmVhoAO3Qn4fKm2XWd0I+yWEejdPVF0OgqS93Fo9mvo3D24fkcuAHteegifxi2oO2gYBk/5niJJF6NaA96goCD0ej0ZGRku2zMyMggNDa3ymNDQ0HPu/+eff5KZmUlkZKT2uN1u5+GHH2bmzJmkpKRUOue0adNc0iTy8vKoV+/q7PnQ6XT06dOHPn36AGrv9V9//UViYqLWu7tu3Tr+97//8b///Q9PT08aN26MEII333yTzp0706lTp8tcC7imiXAu5bqhTqAB6lTNRzh7L3H5fbIRNae4PCX+TeD7084ZSNW9xE0A18GWknQ+iqKg9w5C711RscUtpAkhdy+stK93u2EYAxtUmsXOGFzxQbdo/+8U7V0GgMEcpgW8qc9GgBCVyq95tx+OMTAa4XCAcKDoa13fyb92ttJjZ9vuG9uKNjPnauuK3kDkXfchysrQublRdCyNlM9nouj11L1JrY6z+/lJeIRHEXHHOJf0C0mSKqsRg9Y6derEe++9B6j5t5GRkTzwwANnHbRWWFjIzz//rG3r1q0brVq1Yvbs2WRlZXHixAmXY/r378+oUaMYN24cTZo0OW+brvSgtSstLS2NhQsXkpiYyKuvvkpsbCz79+/Xrs0TTzzB9OnqKOMNGzbQsmVLvLxkb8K5bUINeAegVo54BPgNNTC2nOO4G4ElqLWKhwLtgXmo6RhzqQiMI5A1iaXLpXDfCkqP78KWk4Z3u2G41++Co8hCyuN+Ve4fNmUN7g26U5Z5gCOvNEXvF44xsAF1H1yNoiiUZuzDdipZC5B1HrXvffRKK844TurXH1CafZKW0z+mNCeLle3UDzx9t5zELSCIv5+5Hzf/ICJHTsA9pOZVJ5KkS+2qGbQGMHXqVMaMGUOHDh3o1KkTM2fOxGq1Mm7cOABGjx5NeHi4FoBNnjyZ+Ph43nzzTW688Ubmz5/P5s2b+fhjdcagwMBAAgMDXZ7DaDQSGhp6QcHutSAyMpKHH36Yhx9+WNtms9m44447SEhI0Eqh5efn0717d3Q6He3btyc+Pp64uDi6d++On59fNbW+purkXMrNOO338prEVfUQt3HucwjYT0VaxVHUqZ/LldckriqPuCty1jrp3/Bs2g/Ppv1ctuk8zETPsDp7hdMqeohzUjEEqWUEbTlpIBzYc44AaKkP1q0LyPn1eZdzGfwj8Wjcl8DBaoWZkrQtCFsxBv8o9Oa6WgqGVDX3kDCaPPpKxQZFocnjr1F09DBuAUHYCq0c+fZjhN1OvaH3AvD30/ehGIxEj5uMV1TDs5xZkq4N1R7wDh06lJMnT/Lss8+Snp5OmzZtWLZsmTYwLS0tTatUAGpv7rx583j66ad58sknadSoET/++GOlGrzSxWnWrBnfffcdQgjKO/1TUlIIDw/nyJEjbNy4kY0bN/L666+jKAoHDx6kQYMGWCwWSktLK5VTk053ITWJewO/U1ENohToS0VwXEJFTeIzK5j87jz+/1Bzh59HHXS3CbU6RXlwHIqsSSxdDJ2bJ24hTXELaVrl4+6NehP50jFs2WmIUmvFcZ4BuIW3wZaThqMwG0eRhdKiXVqdYoCcX5+ncLdaXjLgljfw6/MIACcXTEDvFVRRfs0/EkNAJDqT92V8pVcfN78AGk54rGKDEDR/aRb5SbvwCI/EUVbG0R++wlFcRNRdau3nXU9NwFFUSP3xj+AbW7OnyZakS63aUxpqotqe0nCxUlJStEFwiYmJWCwW0tPTURSF2bNnc9999xEbG8sPP/xAbGwsQgg5yOWSEqiD587WS7wYNaDthhoMLwSGAA8AH5x2HiNqzvGZvcSNUGsXS9Kl5yjOx5ZzRC2/5u6Le4NuAJycdw9F+3/HlnuUOmPm4d32DhwlBaQ86lPleepOSsAjJg6b5QS5K6Y7A+FovNsOuZIv56phLykhc8VP5O7YRNNpao32le2CKLPk0G3xRvzadOLvZ+6n+MRRGt73BP7tu1VziyXp4l1MvCYD3irIgPfc8vLytOvy6KOPMmPGDPR6PTk5Ofj4+LB06VImT55MXFwcTzzxRC2uAlHTHAAOopZKCwHeQg2G01BrEtvPclx7YLNzn/aos96tQe1t/sh5rvIg2Z9/X5PY4TyHQPY4S8Jhdw58M+IozidvzSzXGexyUnEUWaj3bDLGoAYUHfiDE+/1BkDvW5eol48DaqWJvDWzMQREufQOu9VtUWkK6WuRsNs5te53cjavIeaBp1EMBlZ3q0dx+jE6z/udwK692f3cg+Tt3UHMxCcJjh8gOy+kGu+qyuGVrj6n31RvvPEGTzzxBDt37sTHR+2ZSUxMJDk5meTkZG1SkX379vHSSy9pk2E0bdpUvpFeco2cS7mpzgUqahKf3jNc/nt5bnsqkAG4owakRcCEM57Dm8oz1kWh9hBfSGUTB5CHOunH54AvMui9tqm5u2r+rs7dB7++j1Xax1FkQXHWG9abw/Dr+wS2nDSU09Icyk4exJZ1CFvWIZdjPVsMIvQ//wPUXuWSYzsw+Efi2/N+PJv0BaDkyFb05nD0PnVq7fuSotcT3LMfwT3VXG0hBO1mLSJ78xrMrdXxB6fWrsSavA9HmTob3N5XHiZrzUoaPvAUYTcNxVFWhs5oPOtzSFJNJnt4qyB7eP+dvLw81q1bx6ZNm3jmmWdQFIUPPviABx54QNsnODiYnj170qdPH+6///5qbK1UoQi1rnABEAdkoeYFlwfImec49ltgGPAoakrFY8D9wDbUST0igVaok3R0cz5PLLAOGfRKl4ItLx3byYPYctIoO62H2L1BD/yvVz94H32jA6VHtgCoaRTth+MoLSLlEXXyGMVg0nqG9f6R+Pd/GmNQAxylRdhzj6L3r4fOWHVZsbNxlBWjM7prP2uygkP7ydm8htD+t2E0+7Pm5g7k7dpCm3e/JWzQMJLeeIpjP86h4f1PEjVyAvbiInRuJjmjn1RtZA+vVK18fX0ZMGAAAwZUzIAWFxfHc889R2JiIuvXr+fkyZMsWrSIEydOaAHv559/TlZWFnFxcXTq1KnW9rTUXB6o6RDlAnGtJVxek7iqPOLyXuJk57rDub4UeAa1p7gNFcEuzp/dgETU3mc5iEb65wy+oRh8q67fXq7OXV9RduoQtpw0TNFqmoPDegq9ORx73nGErYSykwcoO3kAAL9+amnM0iNbOP6OmudurNucetP+BtT6xaXHdrqkUei8g1zeu3RGdw4/Zqb+6+cqT1gzeDdojHeDihS0jp8tJWfLWvw7qq89d9t6io8fQeesu3z485kcmv0aDf7vMWImPonNWoCi16N396iW9kvVryZ/wJMBr3RFtGzZkpYt1SoFJSUlbNmyhYSEBOrWravtM2vWLDZv3kxERARpaWkAbNq0ieLiYjp16oS7e836z3Pt8QAaO5ezmYXay1s+8UsD4BbUwXNDqDxb3V7gP1QE1qWoNYcjUatP+KL2HvsC0c5F1oSW/hm3us1xq9vcZZvBvx5RLx1F2EqxWY45S7CpPcQGf/U+thfmoLh5IkoLUQwV70PW7T+Qt+ZDl/MpRg+8O95F8LCPKzba1RSBsqwUdcIOWynW7d+DotMWRaf+dI/phd7TD0eRheLD69XHDCY8GsUDqAMAs1MrjtXpUBQdiskHtxD1g6ct9xiO4jxQdOoEJV6B2rEI4TxOr55b0aFz90UxmtQqPcX54GyLW1AdQgcM1qr3tP/0Z3K3b8SnUTMALDs2Ycu3oHfO+nZ04Rfsm/4IUaMeIPbpN7EV5OMoK8XN37VUqFR71eQPeDLgla44k8lEt27d6NbNdVTwqFGjCA8PJzw8XOshee2111i0aBE+Pj5kZWVhNBo5efIkHh4eeHvLMkU1T4hzKTfCuThQc3b34Rr0xgKzgW+A0ag9yCeBfKB8tP59uE7eEUxF8Fu+1EftQa6LJP0TisENY2B9jIH1Kz3m1XIQ0W8UqCXWivO07W712uPV5g5tcJ09Lx1hKyHgpop6ubbcowiHDVvOEYyB0YA6UC9zzigQjjOfivDHtqH3bENpZhLpswcCoPMOJvq/akpR/qavyVn6dKXjTPW7Ef7QWgCylzxFwaavAPAb8BwBNzwPwLE3O2PPO1Hp2Dqj5+LdYQSitJCUx83a9qhXMtD71KFw549kfDbYeaF05DmD7cAgf2L+txlTSBiWhHexb32Uhr1L0RX/AcCJXxaSteAevMM9cfMPQqCAQ6AY3fDtcR9+16ljDE582B97Xjro9AQP+xhTZAdsOUfI+GKoFtBrHwwUHXXGzkfvHURx6iZyl70Iig5DQBRBQ9QJrPI3fkXh3l+1/cuPNdVrjzn+QQAsiR9Qlr4HdDq82t6JR0O1Fzvrp8fVl+n8MFF+rE/H0RiDG2IvzCVvzSzneRXMfR5VJ1s5/jdFB1ZXPKfzWJ2HH95t7wDUXPHS9L0oOh3G4MaYItsD6jcFjuJ8lw8+KHqMIU0wBkQBUJy8Rrv+bhFt0Ll54igpoCxzv8u1Kf8QZKzTBEVRcBTnY7dmqR+KjO7ofeoA4CjKQzjK1P1RtDYrOiOK0eS8Tx3qNOUX+22r8wNeTSMDXqnGmDRpEpMmTXLZFhISQmhoKDExMRidgyX++9//8t5771WaDMPf37+q00o1gg61l3YdVefwjnbuVw/YgZovrKD2+PYGUpxLLmpAfBI1N/h0bwNTUHuZvwLuQi3NdhT4GzUojkTtqZaki6MoCnqvQK23FMC36934dr1bWxdlJdhyj6L3DubwY2awlyEcNrCXkfZiQxSdAfRG6r9uwaNpP4S9DJzTMyMcCOHQ6g3rjB64RbQD4UDn4ac9h87TH2NwI4RwPa48kAHQuXmh8wpUj3XzrHgNBhOK0V2rjKEuAsoDmjMDcKU8N/e0oT7lxwHYSzG3VIO24h1FKJSidwP3iHAArCkH0LuBjkJ1kpLTOIpysBVasR7eT2n6Xuy56uQljtJC7WdJypk1x51NcAZU9rwMCncvBcBYt6IWf8mRLVi3Lqh0nKM4Twt4C3cv0abTdgtppgW8lt9nVPlBxL1hT4zBDXEU5ZCz5Elte/kgy+LDa8n6YVKl44x1GmsBb8GWeVh+fxMA354TtYA3a9EUSo/vqnRs4OCZmHtNBuDEh/0QZcUARDy5B7fQWEqPbtdSbc5U/+0y0Buwbv+ek/PUe9Q9phdhk1YDcHLB/2HdOr/ScT7dxmvfThx7s1NFzvu4hXi3HYK9MIe0Z8JO+4bBQL1puzD4RQBgz89EOGzY8zMrgusakt4gA16pRvvwww/54IMPsFgqevgOHjyI3W5n06ZNbNq0iTfeeANFUZg2bRqvvKL2rOTn52tVI6Sa4vSg92xVGtxwzeV1Qy2tVi4XNUf4MBVBcPlSnke8E9gIlM8ctgwYf9o5QqncQ1y+NEIOoJP+KcVoUoOismLtK117fiapz0YQ9eJRlwCg7n3Lznkut7CWRDy2pdJ2c8/7Mfc890DfoDs/IOjODyptj3z+cKVtp49bV0zeRL9ZpAW1ilENlj2b3UjUf09qwTXCAQ7XMoc+3cbj2fo2Z5CtBu1NH5uOdf+tOIotGM3+HP9xDqlzPyS03634dL2X7I0JbL77RoLaNaLFK7/hKCmi4GgOxjArBnMYIff+6PqczqX8A4ApvDXBIz5HOOwuHwq8Wt+OMTgGHK7HGoMrJj7x7jASU1RnEA7cnIEngPm6R0DYKx1r8FOr0OjcvPHpco/62GkfBAyBDfBqNxQc9tOukQO9uWKKZ2NwIzya9AUhMNapmPnVLawViptXxQcY54eg0z/EGIMbIWwlCOFA0Tunmdcb0ftFuFyb8mO1DzGKDsXoof57Gk6bnr6KoL58/9NujtM267TjygNv9YIYMPhFVPqAl/pshMsHvJpAVmmogqzSUPOlpqby559/kpiYSEJCAvv37+fTTz/lnnvuweFwEBwcTJ06dfjPf/7DQw89VN3NlVxc7jq8B1B7dBsBLYA5wBuoQXLBOY4zog7M0wMDUWsOv4UaIG9FTbGIBEyXoc1SbXb4YU/qv1lY3c2oEezFRdgLrbgFBHHku8/Z+/JDhA4cQqvXPiNrQwIbh/fCIzyK3mtScNhsZK76Gf/23TEF1Tn/yaULJoRQA9rTviVQA2Wd1hvrKLKovenCgeLui8757YA995jLhwG9b110JjWP+2wf8C5XD6+s0iDVelFRUURFRXHXXXcBkJ6ejoeH+lX1wYMHyc7OJjs7m9zcXO2YQYMGERgYyKBBg7j99turo9kSUBHkXq4qHGfWIx7lXASQQ0WP8Jm9xOX1YAtQe4UBygckjQCSnG0O4+w9xJGovdKSdBq9rF1bTu/uoVVxqHfn3UTcPgabNR+AMks27nUj8GvTGYD8vTvYOmEwRrM/fbeeAkXh2KI5+Lfrimd0jKzk8y8oiuLsBVbfj6u6kjoPc6Vtik6PISCyir1Vep86KDqDa4pNDUhnABnwSrVEaGhFOaLGjRuTlZXFmjVraNJE/dooOzubpUuXal/flQe8b7zxBt7e3sTFxdGsWTP5BlqrKUCAc2l3jv30qLWEjwJ+zm3eqDWEC1FnpDsGrK3i2MeBV1ED5rlAX2AMahCdiZqjLIOfa0l5ekNNyWOsaRS9HqOvHwCh/W8j5PpbcZSoX5mX5VvwbtICz/BoFJ2OguR97HxkDDqTO9fvtKC4uZE27yN8m7fD3KIdil5fja9E0tTQD3gy4JVqpYCAAG6++WZt3cvLi19//ZXExES6d+8OgN1u55VXXtHyg4OCgujZsydxcXHccccdhIeHV0vbpermgVpC7XSbUXuIT1E5d/j0nuJo5/4bUStPeKAGvH8Ag1B7U8KpqCwRfcYSgQyIa5fyIFcGuxdGURStBzio23XELduFw2YDwGYtwL9DD/Qenujc3Cg6foS/n5qAotfTb0cuBi9vUr54F6+YWAI6xaE3yfSjK60mf8CTObxVkDm814bCwkJmzJhBYmIi69ato6ioSHvsjz/+ID4+nqysLD755BPi4+Np3749bm7y62rpbARgR+1H2Iga5LZCzQeegzpwruQ859ChBtX+wFOowe9/UNMoslEH+cl+CunaJoRAURQKDu5l3/THcJSW0GnOb5TmZrOyrVpFo8/mTEyBwRz6eAYe4VEExw/A4C0HMtc2FxOvyYC3CjLgvfaUlpaydetWEhISWLNmDd999x0eHh4sXryYwYPV+pNfffUVo0ePxm638+eff9K5c2ctb1iSzs+BmtaQQtU5xCmo+b/ldV59UVMh9qFWoOgFrEFNi4imcg9xfdTAWH6tK12bio6lkTTjKUpPZdBpzm/YCq2saO2HsNno/WcKHhFRJM96FaNfIKEDBssJMWoBGfD+SzLglcolJCTwzjvvkJiYyJYtW4iKimLLli106NABNzc3PvroI8aOHQuA1WrFy0vOAib9Uw4gC3ViDRvwOmoQ/C7gjjoQ7+B5zmEAJgMzgF3Aj6j5yjc6zy+QAbF0rSjJOsnBd17AmnKAjl8tQ9hs/NbKjKO4iJ7L/8ancXMOfvBfdAYjdW8ejkfdiOpusnSRZJUGSbpE4uPjiY+Px+FwoHPWIczIyCAsLIzjx4/TsGFDADIzMwkLC6Nt27baZBg9evQgICCgOpsvXVV0qMEuqG/NT57xeBJwgrPnD6cBZVTMULcWeBY1d/hG1NJq3VArSURTdS9xXWQdYqm2MAUG0/zF97V1e0kxDe59GMuebXjHxCIcDg5/+iZludkEdI7Ho24EB99/BXtxERG3j8GrfqNznF262sge3irIHl7pfIQQHDp0iHr16uHm5saSJUsYNGhQpf1atmzJ2rVr8fHxcQmaJenSs6MGxG5AHWAVMA9oizrj3PfAHec5hxsQBaxw/pyLWt2ir/OcklR72EtKSP3qPXK3baDNu9+iMxr5vXskxceP0GnuKoK6XcfB91+hJPME9Ybdi2+zNtXdZOkMMqXhX5IBr/RPHDlyxGUyjH379tGgQQOSk5MBmDFjBp988glxcXG8+uqrBAbK/DHpSrIDx6k6d/gwcMS5D0A+aim2GCAZSADigLHAeirnDpf/HsLlq68sSZeXsNs5+v2XZG9eQ/MX3sfg6UVC31isyfto/8n/COk7iIMfTqdg/24iR/wfAZ2qntZXunJkwPsvyYBXuhQyMzNJS0ujQ4cOgDrxxZIlS/Dw8CA3Nxc3Nzd++eUX5s+fr5VCM5srF/qWpCvDhlpfOA0o/0P+H2A/aom1CKAzsOkc53BHDXyHAC8B6ajBcmPUnmZJunoIIUhftoicv/4k5sFncPMPZO0tHbHs3Eybd+YRdvNwkme/TvZfiUSNvI86191Y3U2uAS73TJquZMD7L8mAV7occnJyWLt2LUePHmXChAkATJw4kQ8/VGfzOnbsGGFhYezbt49Vq1YRFxdH8+bNZRqEVIOkAYeoupf4KOofO4AJwCzUQXO3AR1RA+WjQD+qrjARDQRRG3uIS0tLZUnDWiJr/WqyNiYQOfw/uIeEsWFYL7I3JtBy+ifUG3Yvhz97m/RlPxA58j7Cbx2plVC7NjhQq8zcDXyOWmnm8v79koPWJKkG8vf356abbnLZdtdddxEQEEBycjJhYWEA/PTTTzzxxBOAOoFG+WQYvXv3pm1b2UsmVadI59KrisdKUQPaFNTAFdQe3x6o9YhBDZT3OZeqeFIRBL+FWo5tE+ofzaaoaRZXF6vVSnJyMg0bNpRVXGqBwK69CezaW1uPffotsjclEtSjHwCn1q0iZ/NaQgeqk9ekzvmQtLmziBwxgegxDyDsdtDpamEQXB7sdgP2ov4fX8eVCHovlOzhrYLs4ZWq04IFC/jss89Yt24dVqtV296vXz9+++03AJYtW4aPjw8dO3aUPUfSVcQC/EXVOcTHz9j3INAQNWBeCywA7kQt1/YnVVeZ8Kem9RBnZmbSuHFj9u/fT506cuBfbWdNTSbnrz/x79gTr6iGbHtgKCeWfkfjh18m5oGnOLLwCw689Qz1ho2n0eTncJSVoeh0V2haZIGaulQMmFAHqWagTnYTCISifouzG3WSnGLnUnLGz2IqSieCOrFOf9QPwntPe75YLnfQK3t4JekqNnToUIYOHUpZWRnbtm3TBsH17dtX22fq1Kns3buX1q1bs337dgDS0tIICgrC09OzmlouSedjRq34UJUS1D+2Kc6lnnN7MGq5tGjn+lpgyVnO4UNF8NsTeBQoRC3pFo0aEF+84uJikpKSsFgsWCwWcnNztd8fe+wxjEYjCQkJTJ8+ndzcXD7//HOioqIoKChg+PDhWCwWhg0bxvz58/Hy8uLJJ59kz549+Pv74+fnpy233XYbsbGxOBwONm7ciJ+fH6Ghofj7/7N2S1eeV1RDvKIaauvNXnifujcPx6dRE8BK3p5VFKcfw15SDCSRteEndkx9mbo3jqX58yNxlB0BUYjODc4ecLZAHUD6N/AiaqD6Lmod7xupHJyefnx52tH3wO3Ac8BHwAuoZQx/AiZd4Kt9DfUD5kjUvP29Zzy+FzW94YcLPN/lJQNeSaqhjEYjnTp1olOnTjzyyCPa9rKyMpo1a8apU6fo3Lmztn38+PGsXr2a6667jmXLlgFcY/lj0tXNhDq5xpm1Txefsf44cAOVe4nTUatL7AJ2IYQOq/U+LJbfsVhuITc3BIvlCwwGB/36fQJE88MPFn7/PZ28PD1ff/0NiuLHwoULefjhh7FYLKxcuZKOHTuSlJREmzZtqmz1//3f/xEUFER2djbLly8HIDo6moMHDxIXF4fFYgFg9erVNG7cmMTERJ5++ukqe3ubNGlCbGwsubm5dOvWDYBHH32U119Xe9K6d+9OdnY2vXr1YtasWYA6A2RSUpJL4Ozn50dMTAwNGjQAwG63o78iPYg1kZ2KgE8H+AFW4IBzvRVqOs4yzt6befq20UAH4DPgf6il/u5CDepernS8KbCY0OtLKK+A0vw5CO2/ClOdCKAHwT1PYtIK9kxDZ1x9Aa/pNtSANwdYiDooFNTgc+MFXpdi508/1BSk8m8K66JOVuOO+n/y9J9nbiufSn0N8DFqGsOZPbyfo/YsV//fIRnwStJVxmg08v333yOEoLCwEFAD27S0NMrKylwC3Pvuu4+//vqLuLg44uPj6dGjB0FBQWc7tSTVGCUlJVpvqqenJxER6ixYCxce48gRK/7+jRk3bjoAb731Fj///BPFxRbWr38VSOG997YyebLPaWfMAG6gWbMYdu9WZ6xbswacY0b58EN/fHz8cDjMHDlyBIDc3J1AR/z8SqlTJxg/P3/MZjN+fn6YzWbMZrP2/61Dhw589dVXmM1mCgsLadiwIfv372fYsGGsXr2a3r17s2DBAjw9Pdm6dStz5swhNzfXZWncWA1cioqKqF+/Prm5uS6T1xw4cICTJ0/SqFHFh4Lvv/+eJUsq93hPmjSJd955B4A2bdpw+PBhevXqpe37/vvvs3HjRvz9/XnnnXdQFIX9+/fz999/uwTO/v7++Pr6/oOA2YFr8OcDeKHmeR9D7ZWMQv2w8heVA82qAs4PUMOWu1FrTr+PmvZyH5B4luNtp7XpFtSBlNtRU2ViUAPfQudjF6IjasD7N2rAG+vcnus87/kFdukKeABhCGGkzftvoejaAu9RfOIg1pQjGP3D8W3agZKsfE7+8TtGvzBC+gxBOIyUZofiFihQlBjgPSpy5n1Re2irCk6r2gbwqnMpN8S5XIw41H/vdVTk8F7+dIaLJQNeSbpKKYqiDYJRFIU9e/aQkpJCQUGBts/q1avZv38/W7duZebMmQA0b96cp59+mmHDhlVHs6VrgM2mBhkGg4GysjLWrl2LxWIhOjqa1q1bA/DKK6+QkpJCdHQ0Tz31FAAPPvggCxcuJDc3l5KSEu18EyZM0Ho0//vf/7J9+3batm3LuHHjADUQ/OOPRABKS6/Dzc0Ns/kr4BP0er0WnKo9n/WAh4AU+vffgI9PMn5+FvR6K5BL3765bNoEfn4QEVEfgKio18jIOIn6te99wCLU1Ipo1D/q0dSrF83o0aNdroOXlwfz58+ncePGLFgwn+DgQEBHz5496dnz7DVcw8PDOXToUKXtK1euJCcnB2/visF7gwcPpmHDhlrQnJOTQ25uDtHRIahBfjG5ublYrVbKyrJR85/bkJCQwPfff4+vrwfvvtsaKOHnn5fxyCM/V3re1q3D2L79GJDEa6/dzK+/5uLv342FC7/EYOjOzp0WVq/Ox8/Pzo03CoKCSikpKSMjQ72OPj6gKF+h9o7OBN4EHkP9Svx34J6zXgtXb6GGLb8Dqahf4TdETYXZcwHHl99T3qg9meUzG3qgltw7V3BYvq2F85g7UIO6Ns71/qi9xOc73kTF9N7bURTwiSlv33uYQt/FYU9F2GxADOm/zmL3M79T98YehPR5h7zdW1h7cwe8Gn5G3Io9wETydm/Dp6kNncEA3HwB1+Fy0KEGt+u4klUaLoYMeCWpllAUhfr167tsW7VqlTYZRmJiInv27GH37t1ar1RpaSkdO3akY8eOjBgxguuuu646mi7VMKWlpWRkZGCxWKhXrx5ms5mCggK++eYbcnNz6dKlC7169QJgxIgRpKamuuS2FhQUMH/+fIYOHUpxcTG9e6uj2qdMmcLbb78NwLx589izZw9dunTRAl6r1UpGRoZLW3x8fFx6FwcMGECzZs2IidGiBMaOHUt8fDxms1kr4zd06FCGDBmCp6fnWdN6BgxQF5UVSCUwMIXAwPKyay2dj+U4f0Y4fy4DPqnijIFU5BAPA3rj5XWAxMQEPD33o/7J9UPtDfuOcw8MKv85BogHPqdVq3moX2dPRO3Je5Rx4848vtTZll2Ael137EghJ8eGovRC7Y1bz913302XLntwOPYA9wJQpw507w45OZCbq/4sKgI/vyLtGu3atZ+EBDCZfsVg8AB2k5gIU6aoe2zbBkFBsGsXdOyobtPpwM/vfvz9X8TPr5iEhHp4efmwefNmFiz4BT+/aEaNiiAy0p/CQj3btxfh5+dNeLi/szZ5ecBYHjy95nyt0c71l4FHOH/AWh7ytMZ1kKQJ2FDFv+e5dHMu5SKouD/+OUVR8IyI1taj7rqPujfeic2aD4A19SA6Nzc8IxugKAoFyUmsHdQet4Ag+mxKR9Hryd6YiG+Ldhi8rnRVk/Kg9weuVB3eiyEDXkmqxSIiIhg+fDjDhw8H4OTJk/z555/ExcUBsGXLFnbu3MnOnTtp0qSJFvA+9NBDNGvWjD59+mh5gFLNJ4SguLgYd3d3FEUhMzOT7du3Y7FYiIuLIyQkhJMnT/LSSy+5BKjlPzds2EBISAi//fabNlX2okWLuO222yguLua+++4D4PHHH9cC3o0bN1bZG5mbmwuAt7c3sbGx+Pr6amkJoKbbWCwWlw9pTz/9NJMnT9ZSBs4MdgGmT59e6bk6d+7sks8O4O7ufpFXzwto5lzOtAo1P9joXL8J9Sv6lNOWbNQexyzUr717AD3x8nKnadPluLldjxqUrnM+18gLbFcn1IA3xdmO8q/Qi1G/jj8fdwICvAgICELN9fQE9AwcOJCBA9cDYZQHhaNGuTNqlGvAWFJioLg40nmuaKZOfY5Bg/IpLm7lvB6raNBgK0OH/kZubiF16rwGRFFQsAWj8Q7KyspwOCA720p2djKKouDhYQN0bN36MTNmqAOa+vadT2RkZ5KTd9G9u1rGbubMmUyePBmA+vXrYzLNPSNXeT2TJ08mNrYthYWF/Pzzz/j5+REbG0tkpNrmsrIyjMbyf7erk5t/IG7+aqJv2E1DCel3C2U5WQAUHT2MwceMd0wzFL2eohNH2TAsHp2biX47ctC7e3AyYRm+zdpgCg69Aq0tD3KrP2f3TDLglaRrSHBwMIMHD9bWW7ZsydKlS0lMTGTgwIEAHD16VEt/eOCBB3jvvfcA+OGHH2jUqBEtWrSQk2FcRnl5eZw8eRKLxULbtm1RFIV9+/axfPnySlUCvL29+fLLLwF4/vnnmT59OqWlpWRnZ+Pv78+ff/7JkCFqPt4vv/zCwIEDKSkp0f5Nz5STk0NISAhmsxmDwYDZbMZuVwfbmM1mbrvtNsxmM+3bt9eOeeutt7Db7S5pA+U/oSLd5kwPPPBApW01+8PV6fnAN1P5q2ML6tfsaaiD6ipGrbu5nT5ArXzU+g3O9fN9/V0eyN+OWou4iXP9OtT0hHMdb8Q18Fh1RptfPM9rBpNJXVQBtGv3PO3anb7Hddxww3XccMMjLsf16hVBSUkJRUVFLnnK+fn52vtHy5YteeSRR8jNzSU8PBxQ02EaNmxITk6Olr9cWlpKSkpKle0bMWIEsbGxHD16VEvTeu2113jssccACAkJobi4mOHDh/PZZ58B6v+VgwcPVspV7tChg5Zyk5aWhpeXl/Z/oSbRm9zRh6rXKzh+AP22Z1PqDICL04/iHhaJW0AQencPSnOz+Wus+t7ed+sp3PwDSf/tR7wbxuLVoPE1Nai5Zv0rSpJ0RXl7e3PDDTdwww03aNsMBgPPPvssiYmJ9OnTB1AHEN11110UFxfj7+9Pjx49iIuLIy4ujrZt2171PSiXgsPhoLi4WCsLt2/fPpKTk11KWOXm5tKiRQtGjRoFwL333su6desICwtj5cqVgNrLWR6QFhQU4OXlxdatW5lS/p3xaU4f6W80GiktVb/Ozs3Nxd/fn9DQUFq1aoXZbNbaFRgYyFNPPVUpQDWbzURFRQHQo0cPSktLXf4YGo1GFi1aVKkNt9xyoYN9ajsz6oj/VqgpC59z7lHrSy/y/K2dS7lgKnJQayZFUfD09MTT01ObWOd0Xbt2pWvXri7b2rZty8GDB1226fV6duzYUWmQX05ODg0bNtSeq1evXuTm5lKvnlrSzuFwYLFYcDgcnD7lwPLly9mwoXIKw4svvqgFvJ07dyY9PZ0bbriBpUvVf6upU6eyZcsWIiMjmTNnDgBr1qxh8+bNLoGzn58fwcHBVb7my0HR6TAFqveCf9suXLc2FVuBmgJRknEcn6atELYy3PwDsRcVsm3iHQibjV5/HsYzIpoTS77DIzwK3xbt0NXi93IZ8EqS5CI0NJQXXnjBZVtWVhZxcXGsXbuWnJwcfv75Z37+WR3csnnzZtq3b09WVhZ79uyhU6dOmCq6hKpUU6dazcjIIDs7u1JPao8ePWjWrBl2u52xY8disVi47rrrtCC0R48erFu3jnbt2rF582YA3n77bT7++ONKzzFkyBAt4E1LS2Pv3r0uAw3Lg1M/Pz+sViteXl7ExMQwdOhQl+DUz8+PwECtnhETJkxg1KhRmM1mrQB79+7d2bFjh8vze3h48PLLL5/zOlxLvT6Xx+kDeGruqPWrhV6vp1WrVufcp1GjRqxe7VrSS1EUTp06RW5ursv7zaOPPkpKSsoZA/1yadasIp2lrKwMQPumAtDqop/+TcTSpUt59dXTqxyo+vTpo32IHT9+PKtWrSI8PJw///wTgBUrVrBs2TL8/f25//77CQgIICcnh927d7sEzufKQT8Xg7f6jYRPkxb0/HUHDueH4ZKsTPzbdaM48zge4VE4ysrY+dg47EWF9Fz+Nz6Nm3Ns8Te4BQTj37EHBs/aMzugDHglSTqvsLAwli9fjs1mc5kMY8eOHVqPyC+//MLo0aMxmUwsX76c+Ph4bDYbJSUlLlOqXo6pVktLS7UgtXzp3LkzXl5epKam8sUXX2CxWBgxYgQdO3bEarVqNVLvuusunn/+eQDi4+NJSkqqdP7333+fZs2aodfr+e677ygtLXUOplHpdDqEEFrNVYCYmBg6dOhQqRe13WnfB7/88stMmzbNpfTUiy++yEsvveTy/J06dWL+/PnnvAaBgYEuAbBU3Wr+qPXaTlEU/P39K03ccXpa19mcOnWKsrIy7VsTUPPHjxw54pJX3qpVK4YPH64FzeVLaGhFvuyRI0c4fPiwy7nWrFnDW2+9BaiDLgMCAti4caOWWlbOYDAQFBTE8ePHURSFJUuWMGfOHPz8/Hj22WcJDw/n5MmTrFq1Cj8/P9q0aUNoaChCCEpLS7XOB50z4PeMiKbLggSEw4GiKJRacgjs3peCg3vwjolFOBzseWkKZTlZdFu0Ab+2nTn6w1foTO4E9eiHm18A51NTOzQQUiUWi0UAwmKxVHdTJKlGczgc2u+zZs0SderUEYDIzMwUQgjxxx9/CIPBILp06SL+/PNPIYQQGRkZwmw2i4yMDCGEEHa7XeTk5IijR49q59qzZ4+YM2eOeO+990R+fr4QQojt27eLO++8U1x//fUiMTFRCCHEyZMnhYeHh0D9jthl2blzpxBCiPXr12vbPvnkE+05FUURgLjnnnu05+3atavw8/MT0dHRonXr1iI+Pl7cfPPN4qefftL2ef/998Unn3yivR4hhDhy5Ig4ceKEKCwsvHQXV6ol7EIIh/OndC06dOiQWLdunUhISNC2/fbbb+LRRx8V48ePF1arVQghxLJly0RMTIwICgoSer1ee98KCgrSjnvllVe07QcPHhRCCPH7779r27755hshhBCFhYUCEO7u7iI0NFTExsaKrl27ioEDB4qkpCQhhBDp6eni9ddfFx9//LHYt2+fEEKIsvw88fPdt4mf4huL4oICIYQQq7rVE0ujESfXrhJCCJG24DOROvcjUXSi4j3bVlwkhBCioKBA7NixQxQ4jy3ffrlcTLwmA94qyIBXkv4Zh8OhvQkLIcSMGTMEINq2bSsyMjJERkaG6N27twBE7969RUpKivjtt99EmzZthL+/v3bcq6++qr2B79+/XwihBs/l277++mshhBBFRUUuQa63t7cIDw8XzZs3F9u3bxdCCHH06FExYcIE8cQTT4iNGzdqz7Fs2TKxZs0akZqa6tJ+SZKk6uZwOER+fr44cuSIFowKIcTmzZvFu+++K1588UWRl5cnhFA/1Pfu3Vu0bdtWrF69WgghxPHjx6vsCADE7t27hRBCrF27Vtv22WefCSGEKCsr07aNHz9e2EtKxN/PPSj6hvuL7t26iptuuklcH2oWg3wQD428U2zcuFGkffe5EEKIJZPHiGOpKcJsNosTqSli+6PjLvt1uuoC3vfff19ERUUJk8kkOnXq5PJHqSrfffedaNKkiTCZTKJFixZi6dKl2mOlpaXiscceEy1atBCenp6ibt26YtSoUeLYsWMX3B4Z8ErSpXP48GHtU7/ZbHZ54zWbzWL79u0iMzNTeHl5aQHnokWLRL9+/cSQIUPE4cOHhRBCnDhxQrzzzjviyy+/FMnJydr5Dx06JLKyskRZWVl1vDxJkqQax+FwiJycHHH48GGxbds2sXr1arFo0SLx+eefa4Hy7t27xejRo8WgQYO03ufs7Gzh7e0tAPHoo49q54uJiakyeP7www/F9odHi4KCApGSkuLSoXE8NUXr6b1cLiZeU4Q4behiNViwYAGjR49m9uzZdO7cmZkzZ7Jw4UKSkpKqnGt83bp1xMXFMX36dG666SbmzZvHa6+9xtatW2nRogUWi4UhQ4Ywfvx4WrduTU5ODpMnT8Zut2uDSc4nLy8Ps9mMxWLRBn9IkvTvWK1WrFary1Sr8+fPx83NDU9Pz5qZ8yVJknQNstls2Gw2rZ51YmIimZmZLoP8cnNzGTlyJO1atWL/oUPauIhyZrOZxMREmjZtetne3y8mXqv2gLdz58507NiR999/H1DLiNSrV48HH3yQJ554otL+Q4cOxWq1uswd3qVLF9q0acPs2bOrfI6//vqLTp06kZqaqhWjPhcZ8ErS5ZOZmUnjxo05cOAAwcE1u6ySJEmSdH5Wq5X8rFOMGDtO69CY99WX+AQEXrLByVW5mHitWoeLlpaWsmXLFvr27att0+l09O3bl/Xr11d5zPr16132B+jfv/9Z9wewWCwoiuJSXuR0JSUl5OXluSySJF0eXl5eJCYmanVhJUmSpKubl5cXGe+/wNwvv8BsNjPvqy/JePe5yxrsXqxqDXhPnTqF3W4nJCTEZXtISAjp6elVHpOenn5R+xcXF/P4448zfPjws0b/06dP10oGmc1mrWi1JEmXnpeXF02bNq1Rb4SSJEnSP2cvKab165/jGxhEYmIiPgGBtH7jC+wlxdXdNE2tLghYVlbGnXfeiRCCWbNmnXW/adOmudTvPHLkyBVspSRde2S+riRJUu2hN6m5vmd2aJRvrwmqdeKJoKAg9Ho9GRkZLtszMjJcijafLjQ09IL2Lw92U1NT+f3338+Z22Eymc47M5QkSZIkSZJ0bjW1Q6Nae3jd3Nxo3749q1at0rY5HA5WrVpVaX7tcl27dnXZH9Qp+k7fvzzYPXDgACtXrpSzD0mSJEmSJF3Dqn1q4alTpzJmzBg6dOhAp06dmDlzJlarlXHjxgEwevRowsPDmT59OgCTJ08mPj6eN998kxtvvJH58+ezefNmbc76srIyhgwZwtatW1myZAl2u13L7w0ICKixnzwkSZIkSZKky6PaA96hQ4dy8uRJnn32WdLT02nTpg3Lli3TBqalpaWh01V0RHfr1o158+bx9NNP8+STT9KoUSN+/PFHWrRoAcCxY8f43//+B0CbNm1cnmv16tX06tXrirwuSZIkSZIkqWao9jq8NZGswytJkiRJklSzXTV1eCVJkiRJkiTpcpMBryRJkiRJklSryYBXkiRJkiRJqtVkwCtJkiRJkiTVajLglSRJkiRJkmo1GfBKkiRJkiRJtZoMeCVJkiRJkqRardonnqiJyksT5+XlVXNLJEmSJEmSpKqUx2kXMqWEDHirkJ+fD0C9evWquSWSJEmSJEnSueTn52M2m8+5j5xprQoOh4Pjx4/j4+ODoiiX/fny8vKoV68eR44ckTO7XUHyulcPed2rh7zu1UNe9+ohr3v1uNLXXQhBfn4+YWFh6HTnztKVPbxV0Ol0REREXPHn9fX1lf8xq4G87tVDXvfqIa979ZDXvXrI6149ruR1P1/Pbjk5aE2SJEmSJEmq1WTAK0mSJEmSJNVqMuCtAUwmE8899xwmk6m6m3JNkde9esjrXj3kda8e8rpXD3ndq0dNvu5y0JokSZIkSZJUq8keXkmSJEmSJKlWkwGvJEmSJEmSVKvJgFeSJEmSJEmq1WTAK0mSJEmSJNVqMuC9ghITExk0aBBhYWEoisKPP/7o8rgQgmeffZa6devi4eFB3759OXDgQPU0thY533UfO3YsiqK4LAMGDKiextYi06dPp2PHjvj4+FCnTh1uvfVWkpKSXPYpLi5m4sSJBAYG4u3tze23305GRkY1tbh2uJDr3qtXr0r3/IQJE6qpxbXDrFmzaNWqlVZwv2vXrvz666/a4/Jev/TOd83lfX5lvPrqqyiKwpQpU7RtNfF+lwHvFWS1WmndujUffPBBlY+//vrrvPvuu8yePZuNGzfi5eVF//79KS4uvsItrV3Od90BBgwYwIkTJ7Tl22+/vYItrJ0SEhKYOHEiGzZsYMWKFZSVlXH99ddjtVq1fR566CF+/vlnFi5cSEJCAsePH2fw4MHV2Oqr34Vcd4Dx48e73POvv/56NbW4doiIiODVV19ly5YtbN68meuuu45bbrmF3bt3A/JevxzOd81B3ueX219//cVHH31Eq1atXLbXyPtdSNUCEIsXL9bWHQ6HCA0NFW+88Ya2LTc3V5hMJvHtt99WQwtrpzOvuxBCjBkzRtxyyy3V0p5rSWZmpgBEQkKCEEK9v41Go1i4cKG2z969ewUg1q9fX13NrHXOvO5CCBEfHy8mT55cfY26Rvj7+4tPP/1U3utXUPk1F0Le55dbfn6+aNSokVixYoXLta6p97vs4a0hDh8+THp6On379tW2mc1mOnfuzPr166uxZdeGP/74gzp16tCkSRPuu+8+srKyqrtJtY7FYgEgICAAgC1btlBWVuZyzzdt2pTIyEh5z19CZ173cnPnziUoKIgWLVowbdo0CgsLq6N5tZLdbmf+/PlYrVa6du0q7/Ur4MxrXk7e55fPxIkTufHGG13ua6i57+2GantmyUV6ejoAISEhLttDQkK0x6TLY8CAAQwePJj69euTnJzMk08+ycCBA1m/fj16vb66m1crOBwOpkyZQvfu3WnRogWg3vNubm74+fm57Cvv+UunqusOMGLECKKioggLC2Pnzp08/vjjJCUlsWjRomps7dVv165ddO3aleLiYry9vVm8eDHNmjVj+/bt8l6/TM52zUHe55fT/Pnz2bp1K3/99Velx2rqe7sMeKVr3rBhw7TfW7ZsSatWrWjYsCF//PEHffr0qcaW1R4TJ07k77//Zs2aNdXdlGvK2a77f/7zH+33li1bUrduXfr06UNycjINGza80s2sNZo0acL27duxWCx8//33jBkzhoSEhOpuVq12tmverFkzeZ9fJkeOHGHy5MmsWLECd3f36m7OBZMpDTVEaGgoQKVRjBkZGdpj0pXRoEEDgoKCOHjwYHU3pVZ44IEHWLJkCatXryYiIkLbHhoaSmlpKbm5uS77y3v+0jjbda9K586dAeQ9/y+5ubkRExND+/btmT59Oq1bt+add96R9/pldLZrXhV5n18aW7ZsITMzk3bt2mEwGDAYDCQkJPDuu+9iMBgICQmpkfe7DHhriPr16xMaGsqqVau0bXl5eWzcuNElH0m6/I4ePUpWVhZ169at7qZc1YQQPPDAAyxevJjff/+d+vXruzzevn17jEajyz2flJREWlqavOf/hfNd96ps374dQN7zl5jD4aCkpETe61dQ+TWvirzPL40+ffqwa9cutm/fri0dOnRg5MiR2u818X6XKQ1XUEFBgcsny8OHD7N9+3YCAgKIjIxkypQpvPzyyzRq1Ij69evzzDPPEBYWxq233lp9ja4FznXdAwICeOGFF7j99tsJDQ0lOTmZxx57jJiYGPr371+Nrb76TZw4kXnz5vHTTz/h4+Oj5W6ZzWY8PDwwm83cc889TJ06lYCAAHx9fXnwwQfp2rUrXbp0qebWX73Od92Tk5OZN28eN9xwA4GBgezcuZOHHnqIuLi4SqWFpAs3bdo0Bg4cSGRkJPn5+cybN48//viD5cuXy3v9MjnXNZf3+eXj4+PjMiYAwMvLi8DAQG17jbzfq60+xDVo9erVAqi0jBkzRgihliZ75plnREhIiDCZTKJPnz4iKSmpehtdC5zruhcWForrr79eBAcHC6PRKKKiosT48eNFenp6dTf7qlfVNQfEF198oe1TVFQk7r//fuHv7y88PT3FbbfdJk6cOFF9ja4Fznfd09LSRFxcnAgICBAmk0nExMSIRx99VFgslupt+FXu7rvvFlFRUcLNzU0EBweLPn36iN9++017XN7rl965rrm8z6+sM0vA1cT7XRFCiCsZYEuSJEmSJEnSlSRzeCVJkiRJkqRaTQa8kiRJkiRJUq0mA15JkiRJkiSpVpMBryRJkiRJklSryYBXkiRJkiRJqtVkwCtJkiRJkiTVajLglSRJkiRJkmo1GfBKkiSdhaIo/PjjjwCkpKSgKIo2PenFHl+Vf3LOCxEdHc3MmTMv6Tlroz/++ANFUcjNza3upkiSdJnJgFeSpFpn7Nixl3xK7nr16nHixIlKU2qey4kTJxg4cOAlbcfl9uWXX6IoCoqioNPpiIiIYNy4cWRmZl7y55KBuSRJV4qhuhsgSZJ0NdDr9YSGhl7UMRe7f03h6+tLUlISDoeDHTt2MG7cOI4fP87y5cv/0fnKysowGo2XuJWSJEkXTvbwSpJU6/Xq1YtJkybx2GOPERAQQGhoKM8//7zLPgcOHCAuLg53d3eaNWvGihUrXB4/Pf3A4XAQERHBrFmzXPbZtm0bOp2O1NRUoHJKw6ZNm2jbti3u7u506NCBbdu2uRz/5Zdf4ufn57Ltxx9/RFEUbT05OZlbbrmFkJAQvL296dixIytXrjzraxdC8PzzzxMZGYnJZCIsLIxJkyad83opikJoaChhYWEMHDiQSZMmsXLlSoqKigD49NNPiY2Nxd3dnaZNm/Lhhx9Wuk4LFiwgPj4ed3d35s6de87nO5uffvqJdu3a4e7uToMGDXjhhRew2WwAjBgxgqFDh7rsX1ZWRlBQEF9//TUADoeD6dOnU79+fTw8PGjdujXff//9P2qLJElXN9nDK0nSNeGrr75i6tSpbNy4kfXr1zN27Fi6d+9Ov379cDgcDB48mJCQEDZu3IjFYmHKlClnPZdOp2P48OHMmzeP++67T9s+d+5cunfvTlRUVKVjCgoKuOmmm+jXrx/ffPMNhw8fZvLkyRf9OgoKCrjhhht45ZVXMJlMfP311wwaNIikpCQiIyMr7f/DDz/w9ttvM3/+fJo3b056ejo7duy4qOf08PDA4XBgs9mYO3cuzz77LO+//z5t27Zl27ZtjB8/Hi8vL8aMGaMd88QTT/Dmm29qAf7F+vPPPxk9ejTvvvsuPXv2JDk5mf/85z8APPfcc4wcOZI77riDgoICvL29AVi+fDmFhYXcdtttAEyfPp1vvvmG2bNn06hRIxITE7nrrrsIDg4mPj7+otskSdJVTEiSJNUyY8aMEbfccou2Hh8fL3r06OGyT8eOHcXjjz8uhBBi+fLlwmAwiGPHjmmP//rrrwIQixcvFkIIcfjwYQGIbdu2CSGE2LZtm1AURaSmpgohhLDb7SI8PFzMmjVLO8fpx3/00UciMDBQFBUVaY/PmjXL5ZxffPGFMJvNLu1cvHixON9bdfPmzcV7772nrUdFRYm3335bCCHEm2++KRo3bixKS0vPeY5yZ7Zh//79onHjxqJDhw5CCCEaNmwo5s2b53LMSy+9JLp27SqEqLhOM2fOPO9znd7OM/Xp00f897//ddk2Z84cUbduXSGEEGVlZSIoKEh8/fXX2uPDhw8XQ4cOFUIIUVxcLDw9PcW6detcznHPPfeI4cOHCyGEWL16tQBETk7OedsqSdLVTaY0SJJ0TWjVqpXLet26dbWBWHv37qVevXqEhYVpj3ft2vWc52vTpg2xsbHMmzcPgISEBDIzM7njjjuq3H/v3r20atXKpbfzfM9RlYKCAh555BFiY2Px8/PD29ubvXv3kpaWVuX+d9xxB0VFRTRo0IDx48ezePFiLS3gbCwWC97e3nh6etKkSRNCQkKYO3cuVquV5ORk7rnnHry9vbXl5ZdfJjk52eUcHTp0uOjXdrodO3bw4osvujzP+PHjOXHiBIWFhRgMBu68804tXcJqtfLTTz8xcuRIAA4ePEhhYSH9+vVzOcfXX39dqa2SJNV+MqVBkqRrwpmDphRFweFw/Ktzjhw5knnz5vHEE08wb948BgwYQGBg4D8+n06nQwjhsq2srMxl/ZFHHmHFihXMmDGDmJgYPDw8GDJkCKWlpVWes169eiQlJbFy5UpWrFjB/fffzxtvvEFCQsJZB5L5+PiwdetWdDoddevWxcPDA4CMjAwAPvnkEzp37uxyjF6vd1n38vK68BdehYKCAl544QUGDx5c6bHyDw0jR44kPj6ezMxMVqxYgYeHBwMGDNCOB1i6dCnh4eEux5tMpn/VNkmSrj4y4JUk6ZoXGxvLkSNHOHHiBHXr1gVgw4YN5z1uxIgRPP3002zZsoXvv/+e2bNnn/M55syZQ3FxsRawnfkcwcHB5OfnY7VatYDxzBq9a9euZezYsVqeakFBASkpKedsp4eHB4MGDWLQoEFMnDiRpk2bsmvXLtq1a1fl/jqdjpiYmErbQ0JCCAsL49ChQ1pP6uXSrl07kpKSqmxHuW7dulGvXj0WLFjAr7/+yh133KEF8c2aNcNkMpGWlibzdSVJkgGvJElS3759ady4MWPGjOGNN94gLy+Pp5566rzHRUdH061bN+655x7sdjs333zzWfcdMWIETz31FOPHj2fatGmkpKQwY8YMl306d+6Mp6cnTz75JJMmTWLjxo18+eWXLvs0atSIRYsWMWjQIBRF4ZlnnjlnT/WXX36J3W7Xzv3NN9/g4eFR5cC6C/HCCy8wadIkzGYzAwYMoKSkhM2bN5OTk8PUqVMv+nzHjh2rFNRHRUXx7LPPctNNNxEZGcmQIUPQ6XTs2LGDv//+m5dfflnbd8SIEcyePZv9+/ezevVqbbuPjw+PPPIIDz30EA6Hgx49emCxWFi7di2+vr4uA+wkSar9ZA6vJEnXPJ1Ox+LFiykqKqJTp07ce++9vPLKKxd07MiRI9mxYwe33Xab9tV/Vby9vfn555/ZtWsXbdu25amnnuK1115z2ScgIIBvvvmGX375hZYtW/Ltt99WKp/21ltv4e/vT7du3Rg0aBD9+/c/a08tgJ+fH5988gndu3enVatWrFy5kp9//vkfp17ce++9fPrpp3zxxRe0bNmS+Ph4vvzyS+rXr/+Pzjdjxgzatm3rsixdupT+/fuzZMkSfvvtNzp27EiXLl14++23KwXqI0eOZM+ePYSHh9O9e3eXx1566SWeeeYZpk+fTmxsLAMGDGDp0qX/uK2SJF29FHFmwpgkSZIkSZIk1SKyh1eSJEmSJEmq1WTAK0mSJEmSJNVqMuCVJEmSJEmSajUZ8EqSJEmSJEm1mgx4JUmSJEmSpFpNBrySJEmSJElSrSYDXkmSJEmSJKlWkwGvJEmSJEmSVKvJgFeSJEmSJEmq1WTAK0mSJEmSJNVqMuCVJEmSJEmSajUZ8EqSJEmSJEm12v8DVov/uQJdrx8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 5), dpi=100)\n",
-    "sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n",
-    "plt.xlabel(\"Individuals Per Level\")\n",
-    "plt.ylabel(col_name)\n",
-    "plt.legend(title=\"N Surrogates\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "6dda5ebd-45d9-41b7-bfee-2c0cca1d8250",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>large_sample_se</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Statistical Parity</th>\n",
-       "      <th>fair</th>\n",
-       "      <td>0.005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <th>fair</th>\n",
-       "      <td>0.009</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Equality</th>\n",
-       "      <th>fair</th>\n",
-       "      <td>0.006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Average Odds</th>\n",
-       "      <th>fair</th>\n",
-       "      <td>0.006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Statistical Parity</th>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>0.005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>0.013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Equality</th>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>0.005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Average Odds</th>\n",
-       "      <th>slightly_unfair</th>\n",
-       "      <td>0.006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Statistical Parity</th>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>0.004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>0.023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Equality</th>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>0.004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Average Odds</th>\n",
-       "      <th>moderately_unfair</th>\n",
-       "      <td>0.011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Statistical Parity</th>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>0.005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>0.027</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Equality</th>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>0.005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Average Odds</th>\n",
-       "      <th>very_unfair</th>\n",
-       "      <td>0.013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Statistical Parity</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>0.005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>0.029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Equality</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>0.004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Average Odds</th>\n",
-       "      <th>extremely_unfair</th>\n",
-       "      <td>0.015</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       large_sample_se\n",
-       "Metric              simulation                        \n",
-       "Statistical Parity  fair                         0.005\n",
-       "Equal Opportunity   fair                         0.009\n",
-       "Predictive Equality fair                         0.006\n",
-       "Average Odds        fair                         0.006\n",
-       "Statistical Parity  slightly_unfair              0.005\n",
-       "Equal Opportunity   slightly_unfair              0.013\n",
-       "Predictive Equality slightly_unfair              0.005\n",
-       "Average Odds        slightly_unfair              0.006\n",
-       "Statistical Parity  moderately_unfair            0.004\n",
-       "Equal Opportunity   moderately_unfair            0.023\n",
-       "Predictive Equality moderately_unfair            0.004\n",
-       "Average Odds        moderately_unfair            0.011\n",
-       "Statistical Parity  very_unfair                  0.005\n",
-       "Equal Opportunity   very_unfair                  0.027\n",
-       "Predictive Equality very_unfair                  0.005\n",
-       "Average Odds        very_unfair                  0.013\n",
-       "Statistical Parity  extremely_unfair             0.005\n",
-       "Equal Opportunity   extremely_unfair             0.029\n",
-       "Predictive Equality extremely_unfair             0.004\n",
-       "Average Odds        extremely_unfair             0.015"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#In order to show all of the standard deviations on the same graph, \n",
-    "#We compare the standard deviation of the statistic at each sample size \n",
-    "#To the large sample statistics reported in the first section. \n",
-    "large_sample_se=pd.DataFrame.from_dict({\"simulation\":[\"fair\",\"fair\",\"fair\",\"fair\",\n",
-    "                                        \"slightly_unfair\",\"slightly_unfair\",\"slightly_unfair\",\"slightly_unfair\",\n",
-    "                                        \"moderately_unfair\",\"moderately_unfair\",\"moderately_unfair\",\"moderately_unfair\",\n",
-    "                                        \"very_unfair\",\"very_unfair\",\"very_unfair\",\"very_unfair\",\n",
-    "                                        \"extremely_unfair\",\"extremely_unfair\",\"extremely_unfair\",\"extremely_unfair\"],\n",
-    "                                        \"Metric\":[\"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
-    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
-    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
-    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\",\n",
-    "                                        \"Statistical Parity\",\"Equal Opportunity\",\"Predictive Equality\",\"Average Odds\"],\n",
-    "                                        \"large_sample_se\":[0.005,0.009,0.006,0.006,\n",
-    "                                         0.005,0.013,0.005,0.006,\n",
-    "                                         0.004,0.023,0.004,0.011,\n",
-    "                                         0.005,0.027,0.005,0.013,\n",
-    "                                         0.005,0.029,0.004,0.015]}).set_index([\"Metric\",\"simulation\"])\n",
-    "large_sample_se\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "d78afe80-64b4-4db8-9bcc-db09dba2fe98",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "both          592\n",
-      "left_only       0\n",
-      "right_only      0\n",
-      "Name: _merge, dtype: int64\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>probabilistic_sum_of_squares</th>\n",
-       "      <th>probabilistic_count</th>\n",
-       "      <th>df</th>\n",
-       "      <th>probabilistic_se</th>\n",
-       "      <th>oracle_mean</th>\n",
-       "      <th>oracle_var</th>\n",
-       "      <th>oracle_std</th>\n",
-       "      <th>oracle_count</th>\n",
-       "      <th>oracle_sum_of_squares</th>\n",
-       "      <th>std_ratio</th>\n",
-       "      <th>std_diff</th>\n",
-       "      <th>ss_ratio</th>\n",
-       "      <th>prob_se_oracle_val_ratio</th>\n",
-       "      <th>large_sample_se</th>\n",
-       "      <th>_merge</th>\n",
-       "      <th>small_to_large_se</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Metric</th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Average Odds</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">extremely_unfair</th>\n",
-       "      <th rowspan=\"4\" valign=\"top\">50</th>\n",
-       "      <th>10</th>\n",
-       "      <td>1.400771</td>\n",
-       "      <td>26</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.374269</td>\n",
-       "      <td>-0.332308</td>\n",
-       "      <td>0.016266</td>\n",
-       "      <td>0.127536</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0.406638</td>\n",
-       "      <td>2.934606</td>\n",
-       "      <td>0.246732</td>\n",
-       "      <td>3.444766</td>\n",
-       "      <td>1.126272</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>both</td>\n",
-       "      <td>24.951250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1.013655</td>\n",
-       "      <td>22</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.411026</td>\n",
-       "      <td>-0.332000</td>\n",
-       "      <td>0.009308</td>\n",
-       "      <td>0.096480</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0.195476</td>\n",
-       "      <td>4.260224</td>\n",
-       "      <td>0.314546</td>\n",
-       "      <td>5.185573</td>\n",
-       "      <td>1.238031</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>both</td>\n",
-       "      <td>27.401744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.616997</td>\n",
-       "      <td>21</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.351283</td>\n",
-       "      <td>-0.334714</td>\n",
-       "      <td>0.005208</td>\n",
-       "      <td>0.072164</td>\n",
-       "      <td>21</td>\n",
-       "      <td>0.104152</td>\n",
-       "      <td>4.867849</td>\n",
-       "      <td>0.279119</td>\n",
-       "      <td>5.923989</td>\n",
-       "      <td>1.049500</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>both</td>\n",
-       "      <td>23.418834</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.406700</td>\n",
-       "      <td>25</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.212577</td>\n",
-       "      <td>-0.358400</td>\n",
-       "      <td>0.003703</td>\n",
-       "      <td>0.060852</td>\n",
-       "      <td>25</td>\n",
-       "      <td>0.088870</td>\n",
-       "      <td>3.493364</td>\n",
-       "      <td>0.151725</td>\n",
-       "      <td>4.576347</td>\n",
-       "      <td>0.593127</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>both</td>\n",
-       "      <td>14.171786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <th>5</th>\n",
-       "      <td>1.299259</td>\n",
-       "      <td>22</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.465342</td>\n",
-       "      <td>-0.307455</td>\n",
-       "      <td>0.016769</td>\n",
-       "      <td>0.129493</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0.352139</td>\n",
-       "      <td>3.593557</td>\n",
-       "      <td>0.335849</td>\n",
-       "      <td>3.689615</td>\n",
-       "      <td>1.513531</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>both</td>\n",
-       "      <td>31.022799</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">Statistical Parity</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">very_unfair</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
-       "      <th>5</th>\n",
-       "      <td>0.027871</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.035593</td>\n",
-       "      <td>-0.413200</td>\n",
-       "      <td>0.000311</td>\n",
-       "      <td>0.017649</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.009033</td>\n",
-       "      <td>2.016753</td>\n",
-       "      <td>0.017944</td>\n",
-       "      <td>3.085533</td>\n",
-       "      <td>0.086140</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>both</td>\n",
-       "      <td>7.118606</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.014474</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.025650</td>\n",
-       "      <td>-0.413967</td>\n",
-       "      <td>0.000136</td>\n",
-       "      <td>0.011666</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.003947</td>\n",
-       "      <td>2.198620</td>\n",
-       "      <td>0.013983</td>\n",
-       "      <td>3.667120</td>\n",
-       "      <td>0.061961</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>both</td>\n",
-       "      <td>5.129948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>0.006801</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.017996</td>\n",
-       "      <td>-0.413448</td>\n",
-       "      <td>0.000064</td>\n",
-       "      <td>0.007985</td>\n",
-       "      <td>29</td>\n",
-       "      <td>0.001785</td>\n",
-       "      <td>2.253802</td>\n",
-       "      <td>0.010011</td>\n",
-       "      <td>3.809716</td>\n",
-       "      <td>0.043527</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>both</td>\n",
-       "      <td>3.599206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.004502</td>\n",
-       "      <td>30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.014305</td>\n",
-       "      <td>-0.412267</td>\n",
-       "      <td>0.000055</td>\n",
-       "      <td>0.007437</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.001604</td>\n",
-       "      <td>1.923562</td>\n",
-       "      <td>0.006868</td>\n",
-       "      <td>2.806966</td>\n",
-       "      <td>0.034699</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>both</td>\n",
-       "      <td>2.861023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.003393</td>\n",
-       "      <td>29</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.012711</td>\n",
-       "      <td>-0.413655</td>\n",
-       "      <td>0.000029</td>\n",
-       "      <td>0.005340</td>\n",
-       "      <td>29</td>\n",
-       "      <td>0.000799</td>\n",
-       "      <td>2.380180</td>\n",
-       "      <td>0.007371</td>\n",
-       "      <td>4.248942</td>\n",
-       "      <td>0.030729</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>both</td>\n",
-       "      <td>2.542215</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>592 rows × 16 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                probabilistic_sum_of_squares  \\\n",
-       "Metric             simulation       n_surrogates average_count                                 \n",
-       "Average Odds       extremely_unfair 50           10                                 1.400771   \n",
-       "                                                 20                                 1.013655   \n",
-       "                                                 30                                 0.616997   \n",
-       "                                                 40                                 0.406700   \n",
-       "                                    100          5                                  1.299259   \n",
-       "...                                                                                      ...   \n",
-       "Statistical Parity very_unfair      1000         5                                  0.027871   \n",
-       "                                                 10                                 0.014474   \n",
-       "                                                 20                                 0.006801   \n",
-       "                                                 30                                 0.004502   \n",
-       "                                                 40                                 0.003393   \n",
-       "\n",
-       "                                                                probabilistic_count  \\\n",
-       "Metric             simulation       n_surrogates average_count                        \n",
-       "Average Odds       extremely_unfair 50           10                              26   \n",
-       "                                                 20                              22   \n",
-       "                                                 30                              21   \n",
-       "                                                 40                              25   \n",
-       "                                    100          5                               22   \n",
-       "...                                                                             ...   \n",
-       "Statistical Parity very_unfair      1000         5                               30   \n",
-       "                                                 10                              30   \n",
-       "                                                 20                              29   \n",
-       "                                                 30                              30   \n",
-       "                                                 40                              29   \n",
-       "\n",
-       "                                                                df  \\\n",
-       "Metric             simulation       n_surrogates average_count       \n",
-       "Average Odds       extremely_unfair 50           10             16   \n",
-       "                                                 20             16   \n",
-       "                                                 30             16   \n",
-       "                                                 40             16   \n",
-       "                                    100          5              16   \n",
-       "...                                                             ..   \n",
-       "Statistical Parity very_unfair      1000         5               8   \n",
-       "                                                 10              8   \n",
-       "                                                 20              8   \n",
-       "                                                 30              8   \n",
-       "                                                 40              8   \n",
-       "\n",
-       "                                                                probabilistic_se  \\\n",
-       "Metric             simulation       n_surrogates average_count                     \n",
-       "Average Odds       extremely_unfair 50           10                     0.374269   \n",
-       "                                                 20                     0.411026   \n",
-       "                                                 30                     0.351283   \n",
-       "                                                 40                     0.212577   \n",
-       "                                    100          5                      0.465342   \n",
-       "...                                                                          ...   \n",
-       "Statistical Parity very_unfair      1000         5                      0.035593   \n",
-       "                                                 10                     0.025650   \n",
-       "                                                 20                     0.017996   \n",
-       "                                                 30                     0.014305   \n",
-       "                                                 40                     0.012711   \n",
-       "\n",
-       "                                                                oracle_mean  \\\n",
-       "Metric             simulation       n_surrogates average_count                \n",
-       "Average Odds       extremely_unfair 50           10               -0.332308   \n",
-       "                                                 20               -0.332000   \n",
-       "                                                 30               -0.334714   \n",
-       "                                                 40               -0.358400   \n",
-       "                                    100          5                -0.307455   \n",
-       "...                                                                     ...   \n",
-       "Statistical Parity very_unfair      1000         5                -0.413200   \n",
-       "                                                 10               -0.413967   \n",
-       "                                                 20               -0.413448   \n",
-       "                                                 30               -0.412267   \n",
-       "                                                 40               -0.413655   \n",
-       "\n",
-       "                                                                oracle_var  \\\n",
-       "Metric             simulation       n_surrogates average_count               \n",
-       "Average Odds       extremely_unfair 50           10               0.016266   \n",
-       "                                                 20               0.009308   \n",
-       "                                                 30               0.005208   \n",
-       "                                                 40               0.003703   \n",
-       "                                    100          5                0.016769   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      1000         5                0.000311   \n",
-       "                                                 10               0.000136   \n",
-       "                                                 20               0.000064   \n",
-       "                                                 30               0.000055   \n",
-       "                                                 40               0.000029   \n",
-       "\n",
-       "                                                                oracle_std  \\\n",
-       "Metric             simulation       n_surrogates average_count               \n",
-       "Average Odds       extremely_unfair 50           10               0.127536   \n",
-       "                                                 20               0.096480   \n",
-       "                                                 30               0.072164   \n",
-       "                                                 40               0.060852   \n",
-       "                                    100          5                0.129493   \n",
-       "...                                                                    ...   \n",
-       "Statistical Parity very_unfair      1000         5                0.017649   \n",
-       "                                                 10               0.011666   \n",
-       "                                                 20               0.007985   \n",
-       "                                                 30               0.007437   \n",
-       "                                                 40               0.005340   \n",
-       "\n",
-       "                                                                oracle_count  \\\n",
-       "Metric             simulation       n_surrogates average_count                 \n",
-       "Average Odds       extremely_unfair 50           10                       26   \n",
-       "                                                 20                       22   \n",
-       "                                                 30                       21   \n",
-       "                                                 40                       25   \n",
-       "                                    100          5                        22   \n",
-       "...                                                                      ...   \n",
-       "Statistical Parity very_unfair      1000         5                        30   \n",
-       "                                                 10                       30   \n",
-       "                                                 20                       29   \n",
-       "                                                 30                       30   \n",
-       "                                                 40                       29   \n",
-       "\n",
-       "                                                                oracle_sum_of_squares  \\\n",
-       "Metric             simulation       n_surrogates average_count                          \n",
-       "Average Odds       extremely_unfair 50           10                          0.406638   \n",
-       "                                                 20                          0.195476   \n",
-       "                                                 30                          0.104152   \n",
-       "                                                 40                          0.088870   \n",
-       "                                    100          5                           0.352139   \n",
-       "...                                                                               ...   \n",
-       "Statistical Parity very_unfair      1000         5                           0.009033   \n",
-       "                                                 10                          0.003947   \n",
-       "                                                 20                          0.001785   \n",
-       "                                                 30                          0.001604   \n",
-       "                                                 40                          0.000799   \n",
-       "\n",
-       "                                                                std_ratio  \\\n",
-       "Metric             simulation       n_surrogates average_count              \n",
-       "Average Odds       extremely_unfair 50           10              2.934606   \n",
-       "                                                 20              4.260224   \n",
-       "                                                 30              4.867849   \n",
-       "                                                 40              3.493364   \n",
-       "                                    100          5               3.593557   \n",
-       "...                                                                   ...   \n",
-       "Statistical Parity very_unfair      1000         5               2.016753   \n",
-       "                                                 10              2.198620   \n",
-       "                                                 20              2.253802   \n",
-       "                                                 30              1.923562   \n",
-       "                                                 40              2.380180   \n",
-       "\n",
-       "                                                                std_diff  \\\n",
-       "Metric             simulation       n_surrogates average_count             \n",
-       "Average Odds       extremely_unfair 50           10             0.246732   \n",
-       "                                                 20             0.314546   \n",
-       "                                                 30             0.279119   \n",
-       "                                                 40             0.151725   \n",
-       "                                    100          5              0.335849   \n",
-       "...                                                                  ...   \n",
-       "Statistical Parity very_unfair      1000         5              0.017944   \n",
-       "                                                 10             0.013983   \n",
-       "                                                 20             0.010011   \n",
-       "                                                 30             0.006868   \n",
-       "                                                 40             0.007371   \n",
-       "\n",
-       "                                                                ss_ratio  \\\n",
-       "Metric             simulation       n_surrogates average_count             \n",
-       "Average Odds       extremely_unfair 50           10             3.444766   \n",
-       "                                                 20             5.185573   \n",
-       "                                                 30             5.923989   \n",
-       "                                                 40             4.576347   \n",
-       "                                    100          5              3.689615   \n",
-       "...                                                                  ...   \n",
-       "Statistical Parity very_unfair      1000         5              3.085533   \n",
-       "                                                 10             3.667120   \n",
-       "                                                 20             3.809716   \n",
-       "                                                 30             2.806966   \n",
-       "                                                 40             4.248942   \n",
-       "\n",
-       "                                                                prob_se_oracle_val_ratio  \\\n",
-       "Metric             simulation       n_surrogates average_count                             \n",
-       "Average Odds       extremely_unfair 50           10                             1.126272   \n",
-       "                                                 20                             1.238031   \n",
-       "                                                 30                             1.049500   \n",
-       "                                                 40                             0.593127   \n",
-       "                                    100          5                              1.513531   \n",
-       "...                                                                                  ...   \n",
-       "Statistical Parity very_unfair      1000         5                              0.086140   \n",
-       "                                                 10                             0.061961   \n",
-       "                                                 20                             0.043527   \n",
-       "                                                 30                             0.034699   \n",
-       "                                                 40                             0.030729   \n",
-       "\n",
-       "                                                                large_sample_se  \\\n",
-       "Metric             simulation       n_surrogates average_count                    \n",
-       "Average Odds       extremely_unfair 50           10                       0.015   \n",
-       "                                                 20                       0.015   \n",
-       "                                                 30                       0.015   \n",
-       "                                                 40                       0.015   \n",
-       "                                    100          5                        0.015   \n",
-       "...                                                                         ...   \n",
-       "Statistical Parity very_unfair      1000         5                        0.005   \n",
-       "                                                 10                       0.005   \n",
-       "                                                 20                       0.005   \n",
-       "                                                 30                       0.005   \n",
-       "                                                 40                       0.005   \n",
-       "\n",
-       "                                                               _merge  \\\n",
-       "Metric             simulation       n_surrogates average_count          \n",
-       "Average Odds       extremely_unfair 50           10              both   \n",
-       "                                                 20              both   \n",
-       "                                                 30              both   \n",
-       "                                                 40              both   \n",
-       "                                    100          5               both   \n",
-       "...                                                               ...   \n",
-       "Statistical Parity very_unfair      1000         5               both   \n",
-       "                                                 10              both   \n",
-       "                                                 20              both   \n",
-       "                                                 30              both   \n",
-       "                                                 40              both   \n",
-       "\n",
-       "                                                                small_to_large_se  \n",
-       "Metric             simulation       n_surrogates average_count                     \n",
-       "Average Odds       extremely_unfair 50           10                     24.951250  \n",
-       "                                                 20                     27.401744  \n",
-       "                                                 30                     23.418834  \n",
-       "                                                 40                     14.171786  \n",
-       "                                    100          5                      31.022799  \n",
-       "...                                                                           ...  \n",
-       "Statistical Parity very_unfair      1000         5                       7.118606  \n",
-       "                                                 10                      5.129948  \n",
-       "                                                 20                      3.599206  \n",
-       "                                                 30                      2.861023  \n",
-       "                                                 40                      2.542215  \n",
-       "\n",
-       "[592 rows x 16 columns]"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compare_to_large=compare_errors.drop(columns=[\"_merge\"]).merge(large_sample_se,left_index=True,right_index=True,how=\"outer\",indicator=True)\n",
-    "print(compare_to_large[\"_merge\"].value_counts())\n",
-    "compare_to_large[\"small_to_large_se\"]=compare_to_large[\"probabilistic_se\"]/compare_to_large[\"large_sample_se\"]\n",
-    "compare_to_large\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "c557719c-a3c0-4f5d-ac4b-4b1595760133",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>50</th>\n",
-       "      <th>100</th>\n",
-       "      <th>300</th>\n",
-       "      <th>400</th>\n",
-       "      <th>500</th>\n",
-       "      <th>1000</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>25.896489</td>\n",
-       "      <td>17.617785</td>\n",
-       "      <td>14.603082</td>\n",
-       "      <td>13.777349</td>\n",
-       "      <td>8.457351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>31.982684</td>\n",
-       "      <td>16.590574</td>\n",
-       "      <td>10.792551</td>\n",
-       "      <td>12.214599</td>\n",
-       "      <td>9.682458</td>\n",
-       "      <td>6.408889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>22.797279</td>\n",
-       "      <td>14.947161</td>\n",
-       "      <td>7.556927</td>\n",
-       "      <td>8.315340</td>\n",
-       "      <td>5.878505</td>\n",
-       "      <td>4.835357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>16.568213</td>\n",
-       "      <td>11.538793</td>\n",
-       "      <td>5.952940</td>\n",
-       "      <td>4.917443</td>\n",
-       "      <td>4.387482</td>\n",
-       "      <td>3.195789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>13.931345</td>\n",
-       "      <td>9.773428</td>\n",
-       "      <td>4.922121</td>\n",
-       "      <td>3.953925</td>\n",
-       "      <td>4.354334</td>\n",
-       "      <td>2.819877</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "n_surrogates        50         100        300        400        500       1000\n",
-       "average_count                                                                 \n",
-       "5                    NaN  25.896489  17.617785  14.603082  13.777349  8.457351\n",
-       "10             31.982684  16.590574  10.792551  12.214599   9.682458  6.408889\n",
-       "20             22.797279  14.947161   7.556927   8.315340   5.878505  4.835357\n",
-       "30             16.568213  11.538793   5.952940   4.917443   4.387482  3.195789\n",
-       "40             13.931345   9.773428   4.922121   3.953925   4.354334  2.819877"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_name=\"small_to_large_se\"\n",
-    "pivoted_errors=pivot_errors(compare_to_large,\"Statistical Parity\",\"moderately_unfair\",col_name)\n",
-    "pivoted_errors\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "6b579b0a-70bf-444e-8781-39b6d80a5706",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/3273741071.py:2: UserWarning: \n",
-      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
-      "  sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 5), dpi=100)\n",
-    "sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n",
-    "plt.xlabel(\"Individuals Per Level\")\n",
-    "plt.ylabel(col_name)\n",
-    "plt.legend(title=\"Levels of Z\")\n",
-    "plt.savefig(\"standard_error_ratio.png\",dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "81974a1a-7c30-477a-9da5-86c5153072e8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/s_/9wh_dsgx63vgx9fj7w1tsprx597k19/T/ipykernel_14094/3377951155.py:3: UserWarning: \n",
-      "The palette list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
-      "  sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pivoted_errors=pivot_errors(compare_to_large,\"Statistical Parity\",\"moderately_unfair\",\"small_to_large_se\")\n",
-    "plt.figure(figsize=(8, 5), dpi=100)\n",
-    "sns.lineplot(data=(pivoted_errors),markers=True,palette=my_colors)\n",
-    "plt.xlabel(\"Individuals Per Level\")\n",
-    "plt.ylabel(\"Standard Error Ratio\")\n",
-    "plt.legend(title=\"Levels of Z\")\n",
-    "plt.title(\"Change in Standard Deviation as Sample Size Increases\")\n",
-    "plt.savefig(\"standard_error_ratio.png\",dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "dd166db5-14b4-4833-ba02-db3c7202e287",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Metric               simulation       \n",
-       "Average Odds         extremely_unfair     31.022799\n",
-       "                     fair                 34.018526\n",
-       "                     moderately_unfair    54.912440\n",
-       "                     slightly_unfair      73.258999\n",
-       "                     very_unfair          42.324813\n",
-       "Equal Opportunity    extremely_unfair     21.039590\n",
-       "                     fair                 33.880035\n",
-       "                     moderately_unfair    34.391143\n",
-       "                     slightly_unfair      46.786413\n",
-       "                     very_unfair          26.406198\n",
-       "Predictive Equality  extremely_unfair     20.138865\n",
-       "                     fair                 30.098801\n",
-       "                     moderately_unfair    29.344982\n",
-       "                     slightly_unfair      27.736733\n",
-       "                     very_unfair          33.032923\n",
-       "Statistical Parity   extremely_unfair     26.942090\n",
-       "                     fair                 34.281561\n",
-       "                     moderately_unfair    31.982684\n",
-       "                     slightly_unfair      33.178651\n",
-       "                     very_unfair          41.666613\n",
-       "Name: small_to_large_se, dtype: float64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compare_to_large.groupby([\"Metric\",\"simulation\"])[\"small_to_large_se\"].max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "40b6b4c3-2cfa-4de9-93c6-9a3882d2ebbc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Metric               simulation       \n",
-       "Average Odds         extremely_unfair     2.899097\n",
-       "                     fair                 2.416051\n",
-       "                     moderately_unfair    2.559562\n",
-       "                     slightly_unfair      4.312220\n",
-       "                     very_unfair          3.493931\n",
-       "Equal Opportunity    extremely_unfair     2.313933\n",
-       "                     fair                 2.359640\n",
-       "                     moderately_unfair    2.029978\n",
-       "                     slightly_unfair      3.301059\n",
-       "                     very_unfair          2.603045\n",
-       "Predictive Equality  extremely_unfair     2.279479\n",
-       "                     fair                 1.848764\n",
-       "                     moderately_unfair    3.112018\n",
-       "                     slightly_unfair      2.186737\n",
-       "                     very_unfair          2.335497\n",
-       "Statistical Parity   extremely_unfair     2.689035\n",
-       "                     fair                 2.300198\n",
-       "                     moderately_unfair    2.819877\n",
-       "                     slightly_unfair      2.161649\n",
-       "                     very_unfair          2.542215\n",
-       "Name: small_to_large_se, dtype: float64"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compare_to_large.groupby([\"Metric\",\"simulation\"])[\"small_to_large_se\"].min()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "ae2bdbcb-c5f3-4298-b303-8ce219cf14a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>probabilistic_se</th>\n",
-       "      <th>large_sample_se</th>\n",
-       "      <th>se_ratio</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>average_count</th>\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"6\" valign=\"top\">5</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.315713</td>\n",
-       "      <td>0.00875</td>\n",
-       "      <td>36.081469</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>0.294178</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>29.417778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.170695</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>17.069506</td>\n",
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-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.141937</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>14.193727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.124460</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>12.445997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.078519</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>7.851926</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.284701</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>28.470057</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>0.187965</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>18.796539</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.114504</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>11.450357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.088459</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>8.845898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.076873</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>7.687338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.051142</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>5.114184</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"6\" valign=\"top\">20</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.212469</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>21.246893</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>0.137857</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>13.785687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.074436</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>7.443642</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.069738</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>6.973847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.058116</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>5.811568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.038450</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>3.845032</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"6\" valign=\"top\">30</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.170757</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>17.075735</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>0.109066</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>10.906633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.059213</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>5.921254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.050148</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>5.014825</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.040596</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>4.059623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.030429</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>3.042903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"6\" valign=\"top\">40</th>\n",
-       "      <th>50</th>\n",
-       "      <td>0.146803</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>14.680332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>0.096735</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>9.673524</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>0.048268</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>4.826794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>0.041226</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>4.122641</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>0.034852</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>3.485152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>0.026180</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>2.617997</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            probabilistic_se  large_sample_se   se_ratio\n",
-       "average_count n_surrogates                                              \n",
-       "5             50                    0.315713          0.00875  36.081469\n",
-       "              100                   0.294178          0.01000  29.417778\n",
-       "              300                   0.170695          0.01000  17.069506\n",
-       "              400                   0.141937          0.01000  14.193727\n",
-       "              500                   0.124460          0.01000  12.445997\n",
-       "              1000                  0.078519          0.01000   7.851926\n",
-       "10            50                    0.284701          0.01000  28.470057\n",
-       "              100                   0.187965          0.01000  18.796539\n",
-       "              300                   0.114504          0.01000  11.450357\n",
-       "              400                   0.088459          0.01000   8.845898\n",
-       "              500                   0.076873          0.01000   7.687338\n",
-       "              1000                  0.051142          0.01000   5.114184\n",
-       "20            50                    0.212469          0.01000  21.246893\n",
-       "              100                   0.137857          0.01000  13.785687\n",
-       "              300                   0.074436          0.01000   7.443642\n",
-       "              400                   0.069738          0.01000   6.973847\n",
-       "              500                   0.058116          0.01000   5.811568\n",
-       "              1000                  0.038450          0.01000   3.845032\n",
-       "30            50                    0.170757          0.01000  17.075735\n",
-       "              100                   0.109066          0.01000  10.906633\n",
-       "              300                   0.059213          0.01000   5.921254\n",
-       "              400                   0.050148          0.01000   5.014825\n",
-       "              500                   0.040596          0.01000   4.059623\n",
-       "              1000                  0.030429          0.01000   3.042903\n",
-       "40            50                    0.146803          0.01000  14.680332\n",
-       "              100                   0.096735          0.01000   9.673524\n",
-       "              300                   0.048268          0.01000   4.826794\n",
-       "              400                   0.041226          0.01000   4.122641\n",
-       "              500                   0.034852          0.01000   3.485152\n",
-       "              1000                  0.026180          0.01000   2.617997"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_ratio=compare_to_large.groupby([\"average_count\",\"n_surrogates\"])[[\"probabilistic_se\",\"large_sample_se\"]].mean()\n",
-    "average_ratio[\"se_ratio\"]=average_ratio[\"probabilistic_se\"]/average_ratio[\"large_sample_se\"]\n",
-    "average_ratio\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "d5940108-cf14-4e76-a80b-2500688b82cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>n_surrogates</th>\n",
-       "      <th>50</th>\n",
-       "      <th>100</th>\n",
-       "      <th>300</th>\n",
-       "      <th>400</th>\n",
-       "      <th>500</th>\n",
-       "      <th>1000</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>average_count</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>36.081469</td>\n",
-       "      <td>29.417778</td>\n",
-       "      <td>17.069506</td>\n",
-       "      <td>14.193727</td>\n",
-       "      <td>12.445997</td>\n",
-       "      <td>7.851926</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>28.470057</td>\n",
-       "      <td>18.796539</td>\n",
-       "      <td>11.450357</td>\n",
-       "      <td>8.845898</td>\n",
-       "      <td>7.687338</td>\n",
-       "      <td>5.114184</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>21.246893</td>\n",
-       "      <td>13.785687</td>\n",
-       "      <td>7.443642</td>\n",
-       "      <td>6.973847</td>\n",
-       "      <td>5.811568</td>\n",
-       "      <td>3.845032</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>17.075735</td>\n",
-       "      <td>10.906633</td>\n",
-       "      <td>5.921254</td>\n",
-       "      <td>5.014825</td>\n",
-       "      <td>4.059623</td>\n",
-       "      <td>3.042903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>14.680332</td>\n",
-       "      <td>9.673524</td>\n",
-       "      <td>4.826794</td>\n",
-       "      <td>4.122641</td>\n",
-       "      <td>3.485152</td>\n",
-       "      <td>2.617997</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "n_surrogates        50         100        300        400        500       1000\n",
-       "average_count                                                                 \n",
-       "5              36.081469  29.417778  17.069506  14.193727  12.445997  7.851926\n",
-       "10             28.470057  18.796539  11.450357   8.845898   7.687338  5.114184\n",
-       "20             21.246893  13.785687   7.443642   6.973847   5.811568  3.845032\n",
-       "30             17.075735  10.906633   5.921254   5.014825   4.059623  3.042903\n",
-       "40             14.680332   9.673524   4.826794   4.122641   3.485152  2.617997"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_name=\"se_ratio\"\n",
-    "pivoted_errors=pd.pivot(average_ratio.reset_index(),index=\"average_count\",columns=\"n_surrogates\",values=col_name)\n",
-    "pivoted_errors\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "953205b3-c6f5-4e78-bb9a-1cf0ed36677e",
-   "metadata": {},
-   "source": [
-    "Standard errors continue to decrease as the number of levels of $Z$ and the number of individuals per level increase. Note that the estimates for Equal Opportunity, and therefore to a lesser extent Average Odds, are particulary poor for small samples. This is consistent with the higher standard errors for those probabilistic estimates (see Table ). For applications where Equal Opportunity is of particular interest, minimum sample size recommendations should be heeded with particular interest, and it is suggested that practitioners use the higher range of the recommendations described below. See: Rodolfa, Kit T., et al. \"Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions.\" Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020. for an analysis of the most critical fairness metrics under different use cases. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "ae132028-86bf-4042-aefd-635ba5ae9438",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 5), dpi=100)\n",
-    "sns.lineplot(data=(pivoted_errors),markers=True)\n",
-    "plt.xlabel(\"Individuals Per Level\")\n",
-    "plt.ylabel(col_name)\n",
-    "plt.legend(title=\"N Surrogates\")\n",
-    "plt.savefig(\"change_in_standard_deviation.png\",dpi=300)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/probabilistic_fairness/python_scripts/simulation.py b/examples/probabilistic_fairness/python_scripts/simulation.py
deleted file mode 100644
index a3bc4ef..0000000
--- a/examples/probabilistic_fairness/python_scripts/simulation.py
+++ /dev/null
@@ -1,62 +0,0 @@
-#Simulations
-import pandas as pd
-import numpy as np
-import math
-import sys
-sys.path.append('../../jurity/tests')
-sys.path.append('../../jurity/jurity')
-from jurity.fairness import BinaryFairnessMetrics as bfm
-from test_utils_proba import UtilsProbaSimulator
-
-output_path='~/Documents/data/jurity_tests/simulations/'
-
-testing_simulation=False
-n_runs=30
-avg_counts=[30,50]
-fair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2},'protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}},surrogate_name="ZIP")
-slightly_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.1, 'fnr': 0.35, 'fpr': 0.1}},surrogate_name="ZIP")
-moderately_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.3, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.1, 'fnr': 0.45, 'fpr': 0.1}},surrogate_name="ZIP")
-very_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.4, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.1}},surrogate_name="ZIP")
-extremely_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.5, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.05}},surrogate_name="ZIP")
-
-scenarios={"fair":fair_sim,
-           "slightly_unfair":slightly_unfair_sim,
-           "moderately_unfair":moderately_unfair_sim,
-           "very_unfair":very_unfair_sim,
-           "extremely_unfair":extremely_unfair_sim}
-surrogates=pd.read_csv('../input_data/surrogate_inputs.csv')
-if testing_simulation:
-    output_string = output_path+'{0}_simulation_count_{1}_surrogates_{2}_test.csv'
-else:
-    output_string = output_path+'{0}_simulation_count_{1}_surrogates_{2}.csv'
-
-def run_one_sim(simulator, membership_df,count_mean,rng=np.random.default_rng()):
-    membership_df["count"]=pd.Series(rng.poisson(lam=count_mean,size=membership_df.shape[0]))
-    test_data=simulator.explode_dataframe(membership_df)
-    oracle_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
-                                    (test_data["class"]=="protected").astype(int).values).rename(columns={"Value":"oracle_value"})
-    prob_metrics=bfm.get_all_scores(test_data["label"],test_data["prediction"],
-                   membership_df.set_index("ZIP")[["not_protected","protected"]],
-                   test_data["ZIP"],[1]).rename(columns={"Value":"probabilistic_estimate"})
-    predicted_class=test_data[["not_protected","protected"]].values.tolist()
-    argmax_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
-                                     predicted_class).rename(columns={"Value":"argmax_estimate"})
-    return pd.concat([oracle_metrics["oracle_value"],prob_metrics["probabilistic_estimate"], argmax_metrics["argmax_estimate"]], axis=1)
-
-if __name__=="__main__":
-    n_surrogates=surrogates.shape[0]
-    for sim_label,simulator in scenarios.items():
-        for c in avg_counts:
-            all_results=[]
-            for i in range(0, n_runs):
-                if testing_simulation:
-                    output_df = run_one_sim(simulator, surrogates.head(10), c)
-                else:
-                    output_df = run_one_sim(simulator, surrogates, c)
-                output_df["run_id"] = i
-                all_results.append(output_df)
-            all_output=pd.concat(all_results)
-            all_output["average_count"] = c
-            all_output["simulation"] = sim_label
-            all_output["n_surrogates"] = n_surrogates
-            all_output[~(all_output["probabilistic_estimate"].apply(np.isnan))].to_csv(output_string.format(sim_label, c,n_surrogates))
\ No newline at end of file
diff --git a/examples/probabilistic_fairness/python_scripts/simulation_compare_to_model.py b/examples/probabilistic_fairness/python_scripts/simulation_compare_to_model.py
deleted file mode 100644
index 6ed51f4..0000000
--- a/examples/probabilistic_fairness/python_scripts/simulation_compare_to_model.py
+++ /dev/null
@@ -1,144 +0,0 @@
-#Simulationed data: Model-based assignment to protected class vs probabilistic fairness
-# One of the claims in the paper is that model-based fairness metrics are biased,
-# and that the degree of bias is a function of the PPV (positive predictive value/precision)
-# and NPV (negative predctive value) of the models that predicts protected status.
-# This simulation demonstrates the difference between probabilistic estimates and
-# model-based estimates for a given input data file (located in ../input_data.surrogate_inputscsv)
-
-import pandas as pd
-import numpy as np
-import math
-import sys
-sys.path.append('../../tests')
-sys.path.append('../../jurity')
-from jurity.fairness import BinaryFairnessMetrics as bfm
-from constants import Constants
-from sklearn.metrics import confusion_matrix
-from test_utils_proba import UtilsProbaSimulator
-
-def performance_measures(ground_truth: np.ndarray,
-                         predictions: np.ndarray) -> dict:
-    """Compute various performance measures, optionally conditioned on protected attribute.
-    Assume that positive label is encoded as 1 and negative label as 0.
-
-    Parameters
-    ---------
-    ground_truth: np.ndarray
-        Ground truth labels (1/0).
-    predictions: np.ndarray
-        Predicted values.
-    group_idx: Union[np.ndarray, List]
-        Indices of the group to consider. Optional.
-    group_membership: bool
-        Restrict performance measures to members of a certain group.
-        If None, the whole population is used.
-        Default value is False.
-
-    Returns
-    ---------
-    Dictionary with performance measure identifiers as keys and their corresponding values.
-    """
-    tn, fp, fn, tp = confusion_matrix(ground_truth, predictions).ravel()
-
-    p = np.sum(ground_truth == 1)
-    n = np.sum(ground_truth == 0)
-
-    return {Constants.TPR: tp / p,
-            Constants.TNR: tn / n,
-            Constants.FPR: fp / n,
-            Constants.FNR: fn / p,
-            Constants.PPV: tp / (tp + fp) if (tp + fp) > 0.0 else Constants.float_null,
-            Constants.NPV: tn / (tn + fn) if (tn + fn) > 0.0 else Constants.float_null,
-            Constants.FDR: fp / (fp + tp) if (fp + tp) > 0.0 else Constants.float_null,
-            Constants.FOR: fn / (fn + tn) if (fn + tn) > 0.0 else Constants.float_null,
-            Constants.ACC: (tp + tn) / (p + n) if (p + n) > 0.0 else Constants.float_null}
-
-#If true, only simulate a small dataframe. Used to test simulation syntax.
-testing_simulation=False
-n_runs=30
-
-# The test_utils_proba.py test file in jurity/tests contains a class called
-# UtilsProbaSimulator, which can simulate the confusion matrix from an unfair model for different classes.
-# Simulation is explained in :
-
-fair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2},'protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}},surrogate_name="ZIP")
-slightly_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.1, 'fnr': 0.35, 'fpr': 0.1}},surrogate_name="ZIP")
-moderately_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.3, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.1, 'fnr': 0.45, 'fpr': 0.1}},surrogate_name="ZIP")
-very_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.4, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.1}},surrogate_name="ZIP")
-extremely_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.5, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.05}},surrogate_name="ZIP")
-if testing_simulation:
-    scenarios = {"moderately_unfair": moderately_unfair_sim,
-           "very_unfair":very_unfair_sim}
-else:
-    scenarios={"fair":fair_sim,
-           "slightly_unfair":slightly_unfair_sim,
-           "moderately_unfair":moderately_unfair_sim,
-           "very_unfair":very_unfair_sim,
-           "extremely_unfair":extremely_unfair_sim}
-#Location of input and output files
-surrogates=pd.read_csv('../input_data/sampled_surrogate_inputs.csv')
-if testing_simulation:
-    prob_output_string = '~/Documents/data/jurity_tests/simulations//model_v_prob/{0}_prob_simulation_{1}_surrogates_{2}_count_test.csv'
-    model_output_string = '~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_model_simulation_{1}_surrogates_{2}_count_test.csv'
-else:
-    prob_output_string = '~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_prob_simulation_{1}_surrogates_{2}_count.csv'
-    model_output_string = '~/Documents/data/jurity_tests/simulations/model_v_prob/{0}_model_simulation_{1}_surrogates_{2}_count.csv'
-
-def generate_test_data(simulator, membership_df,count_mean,rng=np.random.default_rng()):
-    membership_df["count"]=pd.Series(rng.poisson(lam=count_mean,size=membership_df.shape[0]))
-    return simulator.explode_dataframe(membership_df)
-
-def calc_prob_estimate(test_data,membership_df):
-    oracle_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
-                                    (test_data["class"]=="protected").astype(int).values).rename(columns={"Value":"oracle_value"})
-    prob_metrics=bfm.get_all_scores(test_data["label"],test_data["prediction"],
-                   membership_df.set_index("ZIP")[["not_protected","protected"]],
-                   test_data["ZIP"],[1]).rename(columns={"Value":"probabilistic_estimate"})
-    return pd.concat([oracle_metrics["oracle_value"],prob_metrics["probabilistic_estimate"]], axis=1)
-
-def calc_model_estimate(df,rng=np.random.default_rng()):
-    out_dfs=[]
-    for s in [[0.99, 0.99], [0.9, 0.99], [0.8, 0.9], [0.7, 0.8]]:
-        p_given_p = s[0]
-        np_given_np = s[1]
-        prediction_p=rng.choice([0,1],p=[1-p_given_p,p_given_p],size=df.shape[0])
-        prediction_np=rng.choice([0,1],p=[np_given_np,1-np_given_np],size=df.shape[0])
-        class_vec_p=(df["class"]=="protected").astype(int).values
-        class_vec_np=(df["class"]=="not_protected").astype(int).values
-        class_pred=np.multiply(class_vec_p,prediction_p)+np.multiply(class_vec_np,prediction_np)
-        scores=bfm.get_all_scores(df["label"].values,df["prediction"].values,class_pred).rename(columns={"Value":"model_estimate"})
-        scores["p_given_p"]=p_given_p
-        scores["np_given_np"]=np_given_np
-        class_model_performance=performance_measures(class_vec_p,class_pred)
-        scores["p_given_p"]=p_given_p
-        scores["np_given_np"]=np_given_np
-        scores["class_PPV"]=class_model_performance[Constants.PPV]
-        scores["class_NPV"]=class_model_performance[Constants.NPV]
-        scores["class_TPR"]=class_model_performance[Constants.TPR]
-        scores["class_BR"]=np.sum(class_vec_p)
-        out_dfs.append(scores.reset_index()[["Metric","model_estimate","class_PPV","class_NPV","class_TPR","p_given_p","np_given_np"]])
-    return pd.concat(out_dfs,axis=0)
-
-if __name__=="__main__":
-    n_surrogates=surrogates.shape[0]
-    generator=np.random.default_rng()
-    for sim_label,simulator in scenarios.items():
-        prob_results=[]
-        model_results=[]
-        for i in range(0, n_runs):
-            if testing_simulation:
-                test_df = generate_test_data(simulator, surrogates, 50, generator)
-            else:
-                test_df = generate_test_data(simulator, surrogates, 50, generator)
-            p=calc_prob_estimate(test_df,surrogates)
-            p["run_id"]=i
-            prob_results.append(p)
-            m=calc_model_estimate(test_df, generator)
-            m["run_id"]=i
-            model_results.append(m)
-        all_prob_results=pd.concat(prob_results,axis=0)
-        all_prob_results["simulation"]=sim_label
-        all_model_results=pd.concat(model_results,axis=0)
-        all_model_results["simulation"]=sim_label
-        all_prob_results.to_csv(prob_output_string.format(sim_label,50,surrogates.shape[0]))
-        all_model_results.to_csv(model_output_string.format(sim_label,50,surrogates.shape[0]),index=False)
\ No newline at end of file
diff --git a/examples/probabilistic_fairness/python_scripts/simulation_counts.py b/examples/probabilistic_fairness/python_scripts/simulation_counts.py
deleted file mode 100644
index ae8f6d6..0000000
--- a/examples/probabilistic_fairness/python_scripts/simulation_counts.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#Simulation inspecting probabilistic fairness performance for different sample sizes.
-import pandas as pd
-import numpy as np
-import math
-import sys
-sys.path.append('../../jurity/tests')
-sys.path.append('../../jurity/jurity')
-from jurity.fairness import BinaryFairnessMetrics as bfm
-from test_utils_proba import UtilsProbaSimulator
-output_path='~/Documents/data/jurity_tests/simulations/sample_size/min_weight_0/'
-testing_simulation=False
-n_runs=30
-avg_counts=[5,10,20,30,40]
-num_surrogates=[50,100,300,400,500,1000]
-fair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2},'protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}},surrogate_name="ZIP")
-slightly_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.2, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.1, 'fnr': 0.35, 'fpr': 0.1}},surrogate_name="ZIP")
-moderately_unfair_sim=UtilsProbaSimulator({'not_protected': {'pct_positive': 0.3, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.1, 'fnr': 0.45, 'fpr': 0.1}},surrogate_name="ZIP")
-very_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.4, 'fnr': 0.1, 'fpr': 0.3}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.1}},surrogate_name="ZIP")
-extremely_unfair_sim =UtilsProbaSimulator({'not_protected': {'pct_positive': 0.5, 'fnr': 0.1, 'fpr': 0.2}, 'protected': {'pct_positive': 0.10, 'fnr': 0.65, 'fpr': 0.05}},surrogate_name="ZIP")
-
-scenarios={"fair":fair_sim,
-           "slightly_unfair":slightly_unfair_sim,
-           "moderately_unfair":moderately_unfair_sim,
-           "very_unfair":very_unfair_sim,
-           "extremely_unfair":extremely_unfair_sim}
-surrogates=pd.read_csv('./supporting_data/surrogate_inputs.csv')
-surrogates["ZIP"]=surrogates["ZIP"].astype(int)
-if testing_simulation:
-    output_string = output_path+'{0}_simulation_count_{1}_test_surrogates_{2}.csv'
-else:
-    output_string = output_path+'sample_size/min_weight_0/{0}_simulation_count_{1}_surrogates_{2}.csv'
-
-def run_one_sim(test_data,membership_df):
-    #Sometimes the sub-sampling leads to data errors.
-    #Return a dataframe that is all nans in this case.
-    #Keep track--if there are too many of these, stop the simulation
-    global n_errors
-    try:
-        oracle_metrics=bfm.get_all_scores(test_data["label"].values,test_data["prediction"].values,
-                                    (test_data["class"]=="protected").astype(int).values).rename(columns={"Value":"oracle_value"})
-    except:
-        oracle_metrics=pd.DataFrame({"oracle_value":[np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]},
-            index=['Average Odds', 'Disparate Impact', 'Equal Opportunity',
-       'FNR difference', 'FOR difference', 'Generalized Entropy Index',
-       'Predictive Equality', 'Statistical Parity', 'Theil Index']
-        )
-        n_errors=n_errors+1
-    try:
-        prob_metrics=bfm.get_all_scores(test_data["label"],test_data["prediction"],
-                        membership_df.set_index("ZIP")[["not_protected","protected"]],
-                        test_data["ZIP"],[1]).rename(columns={"Value":"probabilistic_estimate"})
-    except:
-        prob_metrics=pd.DataFrame({"probabilistic_estimate": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]},
-                     index=['Average Odds', 'Disparate Impact', 'Equal Opportunity',
-                            'FNR difference', 'FOR difference', 'Generalized Entropy Index',
-                            'Predictive Equality', 'Statistical Parity', 'Theil Index']
-                     )
-        n_errors=n_errors+1
-    return pd.concat([oracle_metrics["oracle_value"],prob_metrics["probabilistic_estimate"]], axis=1)
-
-if __name__=="__main__":
-    n_errors=0
-    rng=np.random.default_rng()
-    for sim_label,simulator in scenarios.items():
-        for c in avg_counts:
-            surrogates["count"]=pd.Series(rng.poisson(lam=c,size=surrogates.shape[0]))
-            if testing_simulation:
-                test_data=simulator.explode_dataframe(surrogates.head(10))
-            else:
-                test_data=simulator.explode_dataframe(surrogates)
-            print("The number of rows in the data data is: ",test_data.shape)
-            for n_surrogates in num_surrogates:
-                all_results = []
-                for i in range(0, n_runs):
-                    #Sample surrogate classes from the dataframe
-                    #Take a sample stratified by p(protected) to get a spread
-                    #along the x axis for the regression
-                    if testing_simulation:
-                        sampled_surrogates=surrogates.head(10)["ZIP"].values
-                    else:
-                        sampled_surrogates=surrogates.groupby("bin").sample(frac=(n_surrogates/surrogates.shape[0]),
-                                                                            replace=True)["ZIP"].values
-                    #only feed sampled surrogate classes into simulation
-                    a=test_data["ZIP"].apply(lambda x: x in sampled_surrogates).values
-                    b=surrogates["ZIP"].apply(lambda x:x in sampled_surrogates).values
-                    input_data=test_data.iloc[a].copy(deep=True)
-                    input_surrogates=surrogates.iloc[b].copy(deep=True)
-                    output_df=run_one_sim(input_data,input_surrogates)
-                    if n_errors>30:
-                        print("Errors limit reached. Stopping simulation.")
-                        break
-                    output_df["run_id"] = i
-                    all_results.append(output_df)
-                all_output=pd.concat(all_results)
-                all_output["average_count"] = c
-                all_output["n_surrogates"] = n_surrogates
-                all_output["simulation"] = sim_label
-                all_output[~(all_output["probabilistic_estimate"].apply(np.isnan))].to_csv(output_string.format(sim_label, c,n_surrogates))
\ No newline at end of file
diff --git a/examples/probabilistic_fairness/readme.md b/examples/probabilistic_fairness/readme.md
deleted file mode 100644
index a45c1c2..0000000
--- a/examples/probabilistic_fairness/readme.md
+++ /dev/null
@@ -1,59 +0,0 @@
-# Probabilistic Fairness Demonstration
-This folder contains the files necessary to reproduce the results from 
-academic papers demonstrating the accuracy of probabilistic 
-fairness under different sample sizes. Probabilistic fairness is a technique 
-unique to jurity that allows users to calculate fairness metrics when protected status is 
-unknown but a surrogate class feature is available. A <i>surrogate</i> class divides 
-the population into groups where 
-the probability of protected class membership is known given the surrogate class membership.
-
-Probabilistic fairness, its accuracy, and the simulation method used in
-these demonstrations are detailed in 
-<a href="https://doi.org/10.1007/978-3-031-44505-7_29">"
-Surrogate Membership for Inferred Metrics in Fairness Evaluation"</a>
-
-## simulation.py
-Demonstrates the accuracy of the probabilistic fairness method, 
-showing that the method gives values that are close to the oracle metrics
-that would be calculated if protected status were known. 
-
-## simulation_compare_to_model.py
-One alternative method for calculating fairness metrics when protected 
-status is unknown is to build a predictive model for protected status, assign 
-individuals to groups based on model results, and then calculate fairness 
-metrics as if protected status were known. This script demonstrates that
-fairness metrics calculated in this way are biased, where the degree of the 
-bias is based on the PPV (positive predictive value/precision) and NPV
-(negative predictive value) of the model for protected status. 
-
-## simulation_counts.py
-The performance of probabilistic fairness metrics is related to the number of 
-individuals per surrogate class and the number of surrogate classes available. 
-This simulation examines performance under different sample size scenarios. 
-
-## Citation 
-If you use this analysis in an article, please cite as:
-```
-@inproceedings{DBLP:conf/lion/ThielbarKZPD23,
-  author       = {Melinda Thielbar and
-                  Serdar Kadioglu and
-                  Chenhui Zhang and
-                  Rick Pack and
-                  Lukas Dannull},
-  editor       = {Meinolf Sellmann and
-                  Kevin Tierney},
-  title        = {Surrogate Membership for Inferred Metrics in Fairness Evaluation},
-  booktitle    = {Learning and Intelligent Optimization - 17th International Conference,
-                  {LION} 17, Nice, France, June 4-8, 2023, Revised Selected Papers},
-  series       = {Lecture Notes in Computer Science},
-  volume       = {14286},
-  pages        = {424--442},
-  publisher    = {Springer},
-  year         = {2023},
-  url          = {https://doi.org/10.1007/978-3-031-44505-7_29},
-  doi          = {10.1007/978-3-031-44505-7\_29},
-  timestamp    = {Thu, 09 Nov 2023 21:13:04 +0100},
-  biburl       = {https://dblp.org/rec/conf/lion/ThielbarKZPD23.bib},
-  bibsource    = {dblp computer science bibliography, https://dblp.org}
-}
-```
\ No newline at end of file
diff --git a/jurity/fairness/__init__.py b/jurity/fairness/__init__.py
index dedfef8..c0ff67e 100644
--- a/jurity/fairness/__init__.py
+++ b/jurity/fairness/__init__.py
@@ -147,7 +147,7 @@ def _get_score_logic(metric, name,
         else:
             if name == "StatisticalParity":
                 score = metric.get_score(predictions, memberships, surrogates, membership_labels, bootstrap_results)
-            elif name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality","EqualOpportunity"]:
+            elif name in ["AverageOdds", "EqualOpportunity", "FNRDifference", "PredictiveEquality"]:
                 score = metric.get_score(labels, predictions, memberships, surrogates,
                                          membership_labels, bootstrap_results)
             else:
diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index 545beb2..bd0c0bd 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -697,8 +697,7 @@ def get_bias_calculator(self, df: pd.DataFrame, min_weight: int = 5, weight_warn
         if self.weight_name() in df.columns:
             subset = df[df[self._weight_name] >= min_weight]
             if weight_warnings:
-                print("{0} rows removed from datafame for insufficient weight values" \
-                      .format(df.shape[0] - subset.shape[0]))
+                warnings.warn("{0} rows removed from datafame for insufficient weight values".format(df.shape[0] - subset.shape[0]))
             if subset.shape[0] < len(self.class_names()):
                 raise WeightTooLarge("Input dataframe does not have enough rows to estimate surrogate classes "
                                      "reduce minimum weight.")
@@ -791,7 +790,7 @@ def summarize(cls,
             likes_df.columns = membership_names
             likes_df = likes_df.reset_index()
         summarizer = cls("surrogates", "surrogates", "predictions", true_name=label_name, test_names=test_names)
-        return summarizer.make_summary_data(perf_df=df, surrogate_df=likes_df,warnings=warnings)
+        return summarizer.make_summary_data(perf_df=df, surrogate_df=likes_df, warnings=warnings)
 
     def __init__(self, surrogate_surrogate_col_name: str,
                  surrogate_perf_col_name: str,
@@ -896,7 +895,7 @@ def check_read_data(df: pd.DataFrame, needed_names: List[str], df_name: str, id_
             n_unique_ids = df[id_col_name].nunique()
             if not n_rows == n_unique_ids:
                 raise Warning(f"Number of unique ids in {df_name} is: {n_unique_ids} but number of rows is {n_rows}")
-        print(f"There are {n_rows} in {df_name}.")
+        # print(f"There are {n_rows} in {df_name}.")
         names = df.columns
         if not set(needed_names).issubset(set(names)):
             raise ValueError("Some necessary columns not in {0} data: {1} are missing.".format(df_name, list(
@@ -981,7 +980,7 @@ def check_surrogate_confusion_matrix(self, confusion_df, merged_df):
             # return False
         return True
 
-    def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame = None,warnings=True):
+    def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame = None, warnings=True):
         """
         Function that merges two dfs to make a surrogate-based summary file that includes confusion matrix ratios.
         Arguments:
@@ -992,7 +991,7 @@ def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame =
         self.check_surrogate_data(surrogate_df)
         merged_data = perf_df.merge(surrogate_df, left_on=self.surrogate_perf_col_name(),
                                     right_on=self.surrogate_surrogate_col_name())
-        self.check_merged_data(merged_data, perf_df,warnings)
+        self.check_merged_data(merged_data, perf_df, warnings)
 
         # Create accuracy columns that measure true positive, true negative etc
         accuracy_df = pd.concat([merged_data[self.surrogate_surrogate_col_name()],
diff --git a/tests/test_mitigation_binary.py b/tests/test_mitigation_binary.py
index e3a29c5..5e8a0e0 100644
--- a/tests/test_mitigation_binary.py
+++ b/tests/test_mitigation_binary.py
@@ -155,11 +155,11 @@ def test_numerical_stability_mixing_rate_small(self):
         p2p_prob_1 = mitigation.p2p_prob_1
         n2p_prob_1 = mitigation.n2p_prob_1
 
-        # Convert types
-        p2p_prob_0 = p2p_prob_0
-        n2p_prob_0 = n2p_prob_0
-        p2p_prob_1 = p2p_prob_1
-        n2p_prob_1 = n2p_prob_1
+        # # Convert types
+        # p2p_prob_0 = p2p_prob_0
+        # n2p_prob_0 = n2p_prob_0
+        # p2p_prob_1 = p2p_prob_1
+        # n2p_prob_1 = n2p_prob_1
 
         self.assertAlmostEqual(p2p_prob_0, 0.8429378)
         self.assertAlmostEqual(n2p_prob_0, 1.)
@@ -188,11 +188,11 @@ def test_numerical_stability_mixing_rate_large(self):
         p2p_prob_1 = mitigation.p2p_prob_1
         n2p_prob_1 = mitigation.n2p_prob_1
 
-        # Convert types
-        p2p_prob_0 = p2p_prob_0
-        n2p_prob_0 = n2p_prob_0
-        p2p_prob_1 = p2p_prob_1
-        n2p_prob_1 = n2p_prob_1
+        # # Convert types
+        # p2p_prob_0 = p2p_prob_0
+        # n2p_prob_0 = n2p_prob_0
+        # p2p_prob_1 = p2p_prob_1
+        # n2p_prob_1 = n2p_prob_1
 
         self.assertAlmostEqual(p2p_prob_0, 0.819513)
         self.assertAlmostEqual(n2p_prob_0, 1.)

From 4830f9b25737fa4d06122421c7f817495ed49d5b Mon Sep 17 00:00:00 2001
From: skadio <serdar.kadioglu@fmr.com>
Date: Wed, 24 Jan 2024 16:37:17 -0500
Subject: [PATCH 23/25] update

---
 jurity/mitigation/equalized_odds.py | 23 ++++++++++-------
 tests/test_mitigation_binary.py     | 38 ++++++-----------------------
 2 files changed, 22 insertions(+), 39 deletions(-)

diff --git a/jurity/mitigation/equalized_odds.py b/jurity/mitigation/equalized_odds.py
index 7525da5..614809e 100644
--- a/jurity/mitigation/equalized_odds.py
+++ b/jurity/mitigation/equalized_odds.py
@@ -97,19 +97,24 @@ def fit(self,
         # Solve
         prob.solve()
 
-        # Save fairness probabilities
+        # Save fairness probabilities (cvxpy value is a numpy array or None)
         self.p2p_prob_0 = variables_0["p2p"].value
-        if isinstance(self.p2p_prob_0,np.ndarray):
-            self.p2p_prob_0=self.p2p_prob_0.item()
         self.n2p_prob_0 = variables_0["n2p"].value
-        if isinstance(self.n2p_prob_0,np.ndarray):
-            self.n2p_prob_0=self.n2p_prob_0.item()
         self.p2p_prob_1 = variables_1["p2p"].value
-        if isinstance(self.p2p_prob_1,np.ndarray):
-            self.p2p_prob_1=self.p2p_prob_1.item()
         self.n2p_prob_1 = variables_1["n2p"].value
-        if isinstance(self.n2p_prob_1,np.ndarray):
-            self.n2p_prob_1=self.n2p_prob_1.item()
+
+        # Get the scalar/primitive value unless it is None
+        if isinstance(self.p2p_prob_0, np.ndarray):
+            self.p2p_prob_0 = self.p2p_prob_0[0]
+
+        if isinstance(self.n2p_prob_0, np.ndarray):
+            self.n2p_prob_0 = self.n2p_prob_0[0]
+
+        if isinstance(self.p2p_prob_1, np.ndarray):
+            self.p2p_prob_1 = self.p2p_prob_1[0]
+
+        if isinstance(self.n2p_prob_1, np.ndarray):
+            self.n2p_prob_1 = self.n2p_prob_1[0]
 
     def fit_transform(self,
                       labels: Union[List, np.ndarray, pd.Series],
diff --git a/tests/test_mitigation_binary.py b/tests/test_mitigation_binary.py
index 5e8a0e0..bb6d4a8 100644
--- a/tests/test_mitigation_binary.py
+++ b/tests/test_mitigation_binary.py
@@ -150,21 +150,10 @@ def test_numerical_stability_mixing_rate_small(self):
 
         mitigation.fit(labels, predictions, likelihoods, is_member)
 
-        p2p_prob_0 = mitigation.p2p_prob_0
-        n2p_prob_0 = mitigation.n2p_prob_0
-        p2p_prob_1 = mitigation.p2p_prob_1
-        n2p_prob_1 = mitigation.n2p_prob_1
-
-        # # Convert types
-        # p2p_prob_0 = p2p_prob_0
-        # n2p_prob_0 = n2p_prob_0
-        # p2p_prob_1 = p2p_prob_1
-        # n2p_prob_1 = n2p_prob_1
-
-        self.assertAlmostEqual(p2p_prob_0, 0.8429378)
-        self.assertAlmostEqual(n2p_prob_0, 1.)
-        self.assertAlmostEqual(p2p_prob_1, 1.)
-        self.assertAlmostEqual(n2p_prob_1, 0.8893096)
+        self.assertAlmostEqual(mitigation.p2p_prob_0, 0.8429378)
+        self.assertAlmostEqual(mitigation.n2p_prob_0, 1.)
+        self.assertAlmostEqual(mitigation.p2p_prob_1, 1.)
+        self.assertAlmostEqual(mitigation.n2p_prob_1, 0.8893096)
 
     def test_numerical_stability_mixing_rate_large(self):
 
@@ -183,21 +172,10 @@ def test_numerical_stability_mixing_rate_large(self):
 
         mitigation.fit(labels, predictions, likelihoods, is_member)
 
-        p2p_prob_0 = mitigation.p2p_prob_0
-        n2p_prob_0 = mitigation.n2p_prob_0
-        p2p_prob_1 = mitigation.p2p_prob_1
-        n2p_prob_1 = mitigation.n2p_prob_1
-
-        # # Convert types
-        # p2p_prob_0 = p2p_prob_0
-        # n2p_prob_0 = n2p_prob_0
-        # p2p_prob_1 = p2p_prob_1
-        # n2p_prob_1 = n2p_prob_1
-
-        self.assertAlmostEqual(p2p_prob_0, 0.819513)
-        self.assertAlmostEqual(n2p_prob_0, 1.)
-        self.assertAlmostEqual(p2p_prob_1, 0.644566)
-        self.assertAlmostEqual(n2p_prob_1, 1.)
+        self.assertAlmostEqual(mitigation.p2p_prob_0, 0.819513)
+        self.assertAlmostEqual(mitigation.n2p_prob_0, 1.)
+        self.assertAlmostEqual(mitigation.p2p_prob_1, 0.644566)
+        self.assertAlmostEqual(mitigation.n2p_prob_1, 1.)
 
     def test_numerical_stability_bias_mitigation(self):
 

From 02f0497650409ca6bba0b83121753656ed495659 Mon Sep 17 00:00:00 2001
From: skadio <serdar.kadioglu@fmr.com>
Date: Wed, 24 Jan 2024 16:40:31 -0500
Subject: [PATCH 24/25] update

---
 tests/test_utils_proba.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/test_utils_proba.py b/tests/test_utils_proba.py
index e1e4e3b..806c154 100644
--- a/tests/test_utils_proba.py
+++ b/tests/test_utils_proba.py
@@ -183,7 +183,7 @@ def test_make_bias_calculator_filter(self):
         """
         Test that make_bias_calculator filters rows with small counts
         """
-        bc_filtered = self.bcfd.get_bias_calculator(self.summarized_df, 7,weight_warnings=False)
+        bc_filtered = self.bcfd.get_bias_calculator(self.summarized_df, 7, weight_warnings=False)
         self.assertEqual(bc_filtered.X().shape[0], 3)
 
     def test_make_bias_calculator_names(self):
@@ -217,7 +217,7 @@ def test_bias_maker_bad_data(self):
                                     [Constants.false_positive_ratio, Constants.true_positive_ratio,
                                      Constants.false_negative_ratio,
                                      Constants.true_negative_ratio])
-        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1,weight_warnings=False)
+        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1, weight_warnings=False)
         fac = BiasCalcFromDataFrame(["W", "B", "O"], "N", [1, 2],
                                     [Constants.false_positive_ratio, Constants.true_positive_ratio,
                                      Constants.false_negative_ratio,
@@ -227,7 +227,7 @@ def test_bias_maker_bad_data(self):
                                     [Constants.false_positive_ratio, Constants.true_positive_ratio,
                                      Constants.false_negative_ratio,
                                      Constants.true_negative_ratio])
-        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1,weight_warnings=False)
+        self.assertRaises(ValueError, fac.get_bias_calculator, self.summarized_df, 1, weight_warnings=False)
 
     def test_summary(self):
         predictions = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1]

From 8897d666902c771fe59214c924e817fbe0fdf7bd Mon Sep 17 00:00:00 2001
From: skadio <serdar.kadioglu@fmr.com>
Date: Thu, 25 Jan 2024 12:38:00 -0500
Subject: [PATCH 25/25] update

---
 jurity/utils_proba.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/jurity/utils_proba.py b/jurity/utils_proba.py
index bd0c0bd..711b450 100644
--- a/jurity/utils_proba.py
+++ b/jurity/utils_proba.py
@@ -59,7 +59,7 @@ def check_memberships_proba_df(memberships_df: pd.DataFrame, unique_surrogate_li
         membership_names = memberships_df.columns
     sum_to_one = pd.Series(memberships_df.sum(axis=1)).apply(lambda x: math.isclose(x, 1.0))
     check_true(len(unique_surrogate_list) <= memberships_df.shape[0],
-               InputShapeError("", "Memberships dataframe must have one row per surrogate class."))
+               InputShapeError("", "Unique surrogates in cannot exceed the number surrogate memberships."))
     check_true(unique_surrogate_list.issubset(memberships_df.index.values),
                InputShapeError("", "Memberships dataframe must have an index with surrogate values"))
     check_true(memberships_df.shape[1] == len(membership_names),
@@ -697,7 +697,8 @@ def get_bias_calculator(self, df: pd.DataFrame, min_weight: int = 5, weight_warn
         if self.weight_name() in df.columns:
             subset = df[df[self._weight_name] >= min_weight]
             if weight_warnings:
-                warnings.warn("{0} rows removed from datafame for insufficient weight values".format(df.shape[0] - subset.shape[0]))
+                warnings.warn("{0} rows removed from datafame for insufficient weight values".format(
+                    df.shape[0] - subset.shape[0]))
             if subset.shape[0] < len(self.class_names()):
                 raise WeightTooLarge("Input dataframe does not have enough rows to estimate surrogate classes "
                                      "reduce minimum weight.")
@@ -735,7 +736,7 @@ def summarize(cls,
                   surrogates: Union[List, np.ndarray, pd.Series],
                   labels: Union[List, np.ndarray, pd.Series] = None,
                   membership_names: List[str] = None,
-                  warnings:bool = False) -> pd.DataFrame:
+                  warnings: bool = False) -> pd.DataFrame:
         """
         Return a summary dataframe suitable for bootstrap calculations.
         Arguments:
@@ -997,7 +998,7 @@ def make_summary_data(self, perf_df: pd.DataFrame, surrogate_df: pd.DataFrame =
         accuracy_df = pd.concat([merged_data[self.surrogate_surrogate_col_name()],
                                  self.confusion_matrix_actual(merged_data, self.pred_name(), self.true_name())], axis=1)
         # Use calc_accuracy_metrics to create surrogate-level summary
-        #TODO: Accomodate cases where we don't have a binary classifier
+        # TODO: Accomodate cases where we don't have a binary classifier
         confusion_matrix_surrogate_summary = self.calc_accuracy_metrics(accuracy_df)
         self.check_surrogate_confusion_matrix(confusion_matrix_surrogate_summary, merged_data)
         return confusion_matrix_surrogate_summary.join(
@@ -1068,4 +1069,4 @@ def calc_accuracy_metrics(self, test_df):
                                    Constants.false_negative_ratio, Constants.false_positive_ratio]
             # Return a dataframe that has the stats by group. Use these to compare to expected values
         return check_accuracy[out_cols]
-    #TODO: Needs string method
\ No newline at end of file
+    # TODO: Needs string method