Merge pull request #88 from washingtonpost/release/2.0.2

Release/2.0.2

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 2.0.2 (11/02/2023)
+- fix: allow bootstrap model parameters to be of type int as well as float [#86](https://github.com/washingtonpost/elex-live-model/pull/86)
+- fix: pass alpha to national summary client function [#87](https://github.com/washingtonpost/elex-live-model/pull/87)
+
 ## 2.0.1 (10/23/2023)
 - chore: updating all required packages to their latest versions and addressing some warnings that surfaced during testing [#81](https://github.com/washingtonpost/elex-live-model/pull/81)
 - fix: CLI no longer throws an error if `aggregates` are missing or specified with columns that don't exist in the data [#83](https://github.com/washingtonpost/elex-live-model/pull/83)

setup.py

@@ -20,7 +20,7 @@
     LONG_DESCRIPTION = f.read()
 
 # The full version, including alpha/beta/rc tags
-RELEASE = "2.0.1"
+RELEASE = "2.0.2"
 # The short X.Y version
 VERSION = ".".join(RELEASE.split(".")[:2])
 

src/elexmodel/client.py

@@ -104,11 +104,11 @@ def _check_input_parameters(
             ):
                 raise ValueError("lambda is not valid. It has to be numeric and greater than zero.")
             if "turnout_factor_lower" in model_parameters and not isinstance(
-                model_parameters["turnout_factor_lower"], float
+                model_parameters["turnout_factor_lower"], (float, int)
             ):
                 raise ValueError("turnout_factor_lower is not valid. Has to be a float.")
             if "turnout_factor_upper" in model_parameters and not isinstance(
-                model_parameters["turnout_factor_upper"], float
+                model_parameters["turnout_factor_upper"], (float, int)
             ):
                 raise ValueError("turnout_factor_upper is not valid. Has to be a float.")
             if pi_method == "gaussian":
@@ -130,32 +130,32 @@ def _check_input_parameters(
                     model_parameters["agg_model_hard_threshold"], bool
                 ):
                     raise ValueError("agg_model_hard_threshold is not valid. Has to be a boolean.")
-                if "y_LB" in model_parameters and not isinstance(model_parameters["y_LB"], float):
+                if "y_LB" in model_parameters and not isinstance(model_parameters["y_LB"], (float, int)):
                     raise ValueError("y_LB is not valid. Has to be a float.")
-                if "y_UB" in model_parameters and not isinstance(model_parameters["y_UB"], float):
+                if "y_UB" in model_parameters and not isinstance(model_parameters["y_UB"], (float, int)):
                     raise ValueError("y_UB is not valid. Has to be a float.")
-                if "z_LB" in model_parameters and not isinstance(model_parameters["z_LB"], float):
+                if "z_LB" in model_parameters and not isinstance(model_parameters["z_LB"], (float, int)):
                     raise ValueError("z_LB is not valid. Has to be a float.")
-                if "z_UB" in model_parameters and not isinstance(model_parameters["z_UB"], float):
+                if "z_UB" in model_parameters and not isinstance(model_parameters["z_UB"], (float, int)):
                     raise ValueError("z_UB is not valid. Has to be a float.")
                 if "y_unobserved_upper_bound" in model_parameters and not isinstance(
-                    model_parameters["y_unobserved_upper_bound"], float
+                    model_parameters["y_unobserved_upper_bound"], (float, int)
                 ):
                     raise ValueError("y_unobserved_upper_bound is not valid. Has to be a float.")
                 if "y_unobserved_lower_bound" in model_parameters and not isinstance(
-                    model_parameters["y_unobserved_lower_bound"], float
+                    model_parameters["y_unobserved_lower_bound"], (float, int)
                 ):
                     raise ValueError("y_unobserved_lower_bound is not valid. Has to be a float.")
                 if "percent_expected_vote_error_bound" in model_parameters and not isinstance(
-                    model_parameters["percent_expected_vote_error_bound"], float
+                    model_parameters["percent_expected_vote_error_bound"], (float, int)
                 ):
                     raise ValueError("z_UB is not valid. Has to be a float.")
                 if "z_unobserved_upper_bound" in model_parameters and not isinstance(
-                    model_parameters["z_unobserved_upper_bound"], float
+                    model_parameters["z_unobserved_upper_bound"], (float, int)
                 ):
                     raise ValueError("z_unobserved_upper_bound is not valid. Has to be a float.")
                 if "z_unobserved_lower_bound" in model_parameters and not isinstance(
-                    model_parameters["z_unobserved_lower_bound"], float
+                    model_parameters["z_unobserved_lower_bound"], (float, int)
                 ):
                     raise ValueError("z_unobserved_lower_bound is not valid. Has to be a float.")
         if handle_unreporting not in {"drop", "zero"}:
@@ -169,8 +169,8 @@ def get_aggregate_list(self, office, aggregate):
         raw_aggregate_list = base_aggregate + [aggregate]
         return sorted(list(set(raw_aggregate_list)), key=lambda x: AGGREGATE_ORDER.index(x))
 
-    def get_national_summary_votes_estimates(self, nat_sum_data_dict=None, called_states={}, base_to_add=0):
-        return self.model.get_national_summary_estimates(nat_sum_data_dict, called_states, base_to_add)
+    def get_national_summary_votes_estimates(self, nat_sum_data_dict=None, called_states={}, base_to_add=0, alpha=0.99):
+        return self.model.get_national_summary_estimates(nat_sum_data_dict, called_states, base_to_add, alpha)
 
     def get_estimates(
         self,

tests/test_client.py

@@ -20,12 +20,7 @@
 aggregates = ["postal_code", "county_fips"]
 fixed_effects = []
 pi_method = "gaussian"
-model_parameters = {
-    "beta": 3,
-    "winsorize": False,
-    "robust": True,
-    "lambda_": 0,
-}
+model_parameters = {"beta": 3, "winsorize": False, "robust": True, "lambda_": 0, "y_LB": 1.0, "y_UB": 4}
 handle_unreporting = "drop"
 
 
@@ -47,6 +42,25 @@ def test_check_input_parameters(model_client, va_config):
     )
 
 
+def test_check_input_parameters_bootstrap(model_client, va_config):
+    # this is to test y_LB and y_UB
+    election_id = "2017-11-07_VA_G"
+    config_handler = ConfigHandler(election_id, config=va_config)
+
+    assert model_client._check_input_parameters(
+        config_handler,
+        office,
+        estimands,
+        geographic_unit_type,
+        features,
+        aggregates,
+        fixed_effects,
+        "bootstrap",
+        model_parameters,
+        handle_unreporting,
+    )
+
+
 def test_check_input_parameters_office(model_client, va_config):
     election_id = "2017-11-07_VA_G"
     config_handler = ConfigHandler(election_id, config=va_config)
@@ -297,6 +311,25 @@ def test_check_input_parameters_lambda_(model_client, va_config):
         )
 
 
+def test_check_input_parameters_y_UB_LB(model_client, va_config):
+    election_id = "2017-11-07_VA_G"
+    config_handler = ConfigHandler(election_id, config=va_config)
+
+    with pytest.raises(ValueError):
+        model_client._check_input_parameters(
+            config_handler,
+            office,
+            estimands,
+            geographic_unit_type,
+            features,
+            aggregates,
+            fixed_effects,
+            "bootstrap",
+            {"beta": 3, "winsorize": False, "robust": True, "lambda_": -1, "y_LB": "break", "y_UB": 1},
+            handle_unreporting,
+        )
+
+
 def test_check_input_parameters_handle_unreporting(model_client, va_config):
     election_id = "2017-11-07_VA_G"
     config_handler = ConfigHandler(election_id, config=va_config)