Release/2.1.1 🎉

CHANGELOG.md

@@ -1,5 +1,8 @@
 # Changelog
 
+## 2.1.1 (10/10/2024)
+- fix: allow multiple `alpha` values passed in to `ModelClient.get_national_summary_votes_estimates()` and change that method to return a `pandas.DataFrame` [#111](https://github.com/washingtonpost/elex-live-model/pull/111)
+
 ## 2.1.0 (09/23/2024)
 - fix: model evaluation functions and margin estimand rounding [#94](https://github.com/washingtonpost/elex-live-model/pull/94)
 - chore: updated requirements to their latest versions [#95](https://github.com/washingtonpost/elex-live-model/pull/95), [#103](https://github.com/washingtonpost/elex-live-model/pull/103)

setup.py

@@ -20,7 +20,7 @@
     LONG_DESCRIPTION = f.read()
 
 # The full version, including alpha/beta/rc tags
-RELEASE = "2.1.0"
+RELEASE = "2.1.1"
 # The short X.Y version
 VERSION = ".".join(RELEASE.split(".")[:2])
 

src/elexmodel/cli.py

@@ -186,7 +186,7 @@ def cli(
 
         if kwargs.get("national_summary", False):
             # TODO: get_national_summary_votes_estimates() arguments via CLI
-            model_client.get_national_summary_votes_estimates(None, 0, 0.99)
+            model_client.get_national_summary_votes_estimates(None, 0, [0.99])
 
         for aggregate_level, estimates in result.items():
             print(aggregate_level, "\n", estimates, "\n")

src/elexmodel/client.py

@@ -174,21 +174,23 @@ 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, base_to_add=0, alpha=0.99):
+    def get_national_summary_votes_estimates(self, nat_sum_data_dict=None, base_to_add=0, alphas=[0.99]):
         if self.model is None:
             raise ModelClientException(
                 "Must call the get_estimands() method before get_national_summary_votes_estimates()."
             )
 
-        nat_sum_estimates = self.model.get_national_summary_estimates(nat_sum_data_dict, base_to_add, alpha)
-        self.results_handler.add_national_summary_estimates(nat_sum_estimates)
+        nat_sum_estimates_dict = {}
+        for alpha in alphas:
+            nat_sum_estimates = self.model.get_national_summary_estimates(nat_sum_data_dict, base_to_add, alpha)
+            nat_sum_estimates_dict[alpha] = nat_sum_estimates
+        self.results_handler.add_national_summary_estimates(nat_sum_estimates_dict)
 
         if APP_ENV != "local" and self.save_results:
             self.results_handler.write_data(
                 self.election_id, self.office, self.geographic_unit_type, keys=["nat_sum_data"]
             )
-
-        return nat_sum_estimates
+        return self.results_handler.final_results["nat_sum_data"]
 
     def get_estimates(
         self,

src/elexmodel/handlers/data/ModelResults.py

@@ -106,10 +106,14 @@ def process_final_results(self):
                 lambda x, y: pd.merge(x, y, how="inner", on=merge_on), self.unit_data.values()
             )
 
-    def add_national_summary_estimates(self, national_summary_dict):
-        df = pd.DataFrame.from_dict(
-            national_summary_dict, orient="index", columns=["agg_pred", "agg_lower", "agg_upper"]
-        )
+    def add_national_summary_estimates(self, nat_sum_estimates_dict):
+        df = pd.DataFrame(index=["margin"])
+        for alpha, data in nat_sum_estimates_dict.items():
+            if "agg_pred" not in df.columns:
+                df["agg_pred"] = [data["margin"][0]]
+            df[f"lower_{alpha}"] = [data["margin"][1]]
+            df[f"upper_{alpha}"] = [data["margin"][2]]
+
         df.index.name = "estimand"
         self.final_results["nat_sum_data"] = df.reset_index()
 

tests/test_client.py

@@ -838,7 +838,7 @@ def test_estimandizer_input(model_client, va_governor_county_data, va_config):
 
 def test_get_national_summary_votes_estimates(model_client, va_governor_county_data, va_config):
     expected = {"margin": [1.0, 1.0, 1.0]}
-    expected_df = pd.DataFrame.from_dict(expected, orient="index", columns=["agg_pred", "agg_lower", "agg_upper"])
+    expected_df = pd.DataFrame.from_dict(expected, orient="index", columns=["agg_pred", "lower_0.99", "upper_0.99"])
     expected_df.index.name = "estimand"
     expected_df = expected_df.reset_index()
 
@@ -874,7 +874,7 @@ def test_get_national_summary_votes_estimates(model_client, va_governor_county_d
         **kwargs,
     )
 
-    current = model_client.get_national_summary_votes_estimates(None, 0, 0.99)
+    current = model_client.get_national_summary_votes_estimates(None, 0, [0.99])
 
-    assert expected == current
+    pd.testing.assert_frame_equal(current, model_client.results_handler.final_results["nat_sum_data"])
     pd.testing.assert_frame_equal(expected_df, model_client.results_handler.final_results["nat_sum_data"])