Elex 4455 add race call contest agg

README.md

@@ -69,6 +69,7 @@ Parameters for the CLI tool:
 | aggregates           | list    | list of geographies for which to calculate predictions beyond the original `postal_code`, `county_fips`, `district`, `county_classification` |
 | pi_method            | string  | method for constructing prediction intervals (`nonparametric` or `gaussian`) |
 | model_parameters     | dict    | dictionary of model specific parameters e.g. `--model_parameters='{"robust":True}'` |
+| called_contests      | dict    | a dictionary of called contests. specific to Bootstrap model for now. e.g. `--called_contests='{"VA": -1}'` |
 | save_output          | list    | `results`, `data`, `config` |
 | unexpected_units     | int     | number of unexpected units to simulate; only used for testing and does not work with historical run |
 

src/elexmodel/cli.py

@@ -52,6 +52,9 @@ def type_cast_value(self, ctx, value):
     cls=PythonLiteralOption,
     help="A dictionary of model parameters",
 )
+@click.option(
+    "--called_contests", "called_contests", default="{}", cls=PythonLiteralOption, help="A dictionary with race calls"
+)
 @click.option(
     "--percent_reporting",
     "percent_reporting",

src/elexmodel/client.py

@@ -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, alpha=0.99):
-        return self.model.get_national_summary_estimates(nat_sum_data_dict, called_states, base_to_add, alpha)
+    def get_national_summary_votes_estimates(self, nat_sum_data_dict=None, base_to_add=0, alpha=0.99):
+        return self.model.get_national_summary_estimates(nat_sum_data_dict, base_to_add, alpha)
 
     def get_estimates(
         self,
@@ -200,6 +200,7 @@ def get_estimates(
         aggregates = kwargs.get("aggregates", DEFAULT_AGGREGATES[office])
         fixed_effects = kwargs.get("fixed_effects", {})
         pi_method = kwargs.get("pi_method", "nonparametric")
+        called_contests = kwargs.get("called_contests", None)
         save_output = kwargs.get("save_output", ["results"])
         save_results = "results" in save_output
         save_data = "data" in save_output
@@ -359,6 +360,7 @@ def get_estimates(
                     results_handler.unexpected_units,
                     aggregate_list,
                     estimand,
+                    called_contests=called_contests,
                 )
                 alpha_to_agg_prediction_intervals = {}
                 for alpha in prediction_intervals:
@@ -370,6 +372,7 @@ def get_estimates(
                         alpha,
                         alpha_to_unit_prediction_intervals[alpha],
                         estimand,
+                        called_contests=called_contests,
                     )
                     if isinstance(self.model, ConformalElectionModel):
                         self.all_conformalization_data_agg_dict[alpha][

src/elexmodel/models/BaseElectionModel.py

@@ -98,7 +98,7 @@ def get_aggregate_predictions(
         unexpected_units: pd.DataFrame,
         aggregate: list,
         estimand: str,
-        *kwargs,
+        **kwargs,
     ) -> pd.DataFrame:
         """
         Aggregate predictions and results by aggregate (ie. postal_code, county_fips etc.). Add results from reporting
@@ -158,7 +158,7 @@ def get_aggregate_prediction_intervals(
         unexpected_units: pd.DataFrame,
         aggregate: list,
         alpha: float,
-        *kwargs,
+        **kwargs,
     ) -> PredictionIntervals:
         """
         Generates and returns aggregate prediction intervals for arbitrary aggregates

src/elexmodel/models/GaussianElectionModel.py

@@ -114,6 +114,7 @@ def get_aggregate_prediction_intervals(
         alpha: float,
         unit_prediction_intervals: PredictionIntervals,
         estimand: str,
+        **kwargs,
     ) -> PredictionIntervals:
         """
         Get aggregate prediction intervals. Adjust aggregate prediction intervals based on Gaussian models

src/elexmodel/models/NonparametricElectionModel.py

@@ -128,6 +128,7 @@ def get_aggregate_prediction_intervals(
         alpha: float,
         unit_prediction_intervals: PredictionIntervals,
         estimand: str,
+        **kwargs,
     ) -> PredictionIntervals:
         """
         Get aggregate prediction intervals. In the non-parametric case prediction intervals just sum.