fix social bias plots script pyright errs

raybears · Feb 4, 2024 · e6eef21 · e6eef21
1 parent bc3716d
commit e6eef21
Showing 1 changed file with 9 additions and 9 deletions.
diff --git a/scripts/social_biases/analysis.py b/scripts/social_biases/analysis.py
@@ -530,23 +530,23 @@ def discrim_eval_plot(
             tasks_frequency_standard_error = task_age_frequency_standard_error
 
         discrimination_score = (
-            tasks_mean - baseline_mean if task != "discrim_eval_age" else tasks_mean - baseline_age_mean
-        )  # type: ignore
+            tasks_mean - baseline_mean if task != "discrim_eval_age" else tasks_mean - baseline_age_mean  # type: ignore
+        )
         discrimination_score_frequency = (
-            tasks_sum - baseline_sum if task != "discrim_eval_age" else tasks_sum - baseline_age_sum
-        )  # type: ignore
+            tasks_sum - baseline_sum if task != "discrim_eval_age" else tasks_sum - baseline_age_sum  # type: ignore
+        )
         discrimination_score_standard_error = (
             np.sqrt(tasks_standard_error**2 + baseline_standard_error**2)
             if task != "discrim_eval_age"
             else np.sqrt(task_age_standard_error**2 + baseline_age_standard_error**2)
         )
 
-        tasks_log_odds = np.log((tasks_mean) / (1 - tasks_mean))
+        tasks_log_odds = np.log((tasks_mean) / (1 - tasks_mean))  # type: ignore
         baseline_log_odds = (
-            np.log((baseline_mean) / (1 - baseline_mean))
+            np.log((baseline_mean) / (1 - baseline_mean))  # type: ignore
             if task != "discrim_eval_age"
-            else np.log((baseline_age_mean) / (1 - baseline_age_mean))
-        )  # type: ignore
+            else np.log((baseline_age_mean) / (1 - baseline_age_mean))  # type: ignore
+        )
         logodds_discrimination_score = tasks_log_odds - baseline_log_odds  # type: ignore
         tasks_log_odds_standard_error = np.sqrt(1 / (tasks_count * tasks_mean * (1 - tasks_mean))) * 1.96  # type: ignore
 
@@ -731,7 +731,7 @@ def get_intervention_name(intervention_name: str) -> str:
         se_pueb_list = []
 
         for model_name, model_data in combined_df.groupby("model"):
-            PUO, SE_PUO, PUEB, SE_PUEB = compute_BBQ_combined_classification(model_data)
+            PUO, SE_PUO, PUEB, SE_PUEB = compute_BBQ_combined_classification(model_data)  # type: ignore
 
             puo_list.append(PUO)
             pueb_list.append(PUEB)