apply Black 2024 style in fbcode (10/16)

Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: aleivag

Differential Revision: D54447733

fbshipit-source-id: 11ac742489579bb1dfec025514aa956159cf4959

balance/stats_and_plots/weighted_comparisons_plots.py

@@ -773,9 +773,11 @@ def plotly_plot_qq(
             weighted_quantile(
                 dict_of_dfs["target"][variable],
                 np.arange(0, 1, 0.001),
-                dict_of_dfs["target"]["weight"]
-                if "weight" in dict_of_dfs["target"].columns
-                else None,
+                (
+                    dict_of_dfs["target"]["weight"]
+                    if "weight" in dict_of_dfs["target"].columns
+                    else None
+                ),
             )["col1"]
         )
 
@@ -798,9 +800,11 @@ def plotly_plot_qq(
                         weighted_quantile(
                             dict_of_dfs[name][variable],
                             np.arange(0, 1, 0.001),
-                            dict_of_dfs[name]["weight"]
-                            if "weight" in dict_of_dfs[name].columns
-                            else None,
+                            (
+                                dict_of_dfs[name]["weight"]
+                                if "weight" in dict_of_dfs[name].columns
+                                else None
+                            ),
                         )["col1"]
                     ),
                     marker={
@@ -1068,9 +1072,11 @@ def plotly_plot_bar(
             df_plot_data = relative_frequency_table(
                 dict_of_dfs[name],
                 variable,
-                dict_of_dfs[name]["weight"]
-                if "weight" in dict_of_dfs[name].columns
-                else None,
+                (
+                    dict_of_dfs[name]["weight"]
+                    if "weight" in dict_of_dfs[name].columns
+                    else None
+                ),
             )
 
             variable_specific_dict_of_plots[name] = go.Bar(

balance/util.py

@@ -1181,9 +1181,11 @@ def _return_type_creation_function(x: Any) -> Union[Callable, Any]:
 
     # Reapply the index for pd.Series
     r = [
-        pd.Series(data, index=pd.Series(orig_data)[nonmissing_mask].index)
-        if isinstance(orig_data, pd.Series)
-        else data
+        (
+            pd.Series(data, index=pd.Series(orig_data)[nonmissing_mask].index)
+            if isinstance(orig_data, pd.Series)
+            else data
+        )
         for data, orig_data in zip(r, args)
     ]
 

balance/weighting_methods/cbps.py

@@ -305,10 +305,7 @@ def _reverse_svd_and_centralization(beta, U, s, Vh, X_matrix_mean, X_matrix_std)
     beta_new = np.insert(
         beta_new,
         0,
-        beta[
-            0,
-        ]
-        - np.matmul(X_matrix_mean, beta_new),
+        beta[0,] - np.matmul(X_matrix_mean, beta_new),
     )
     return beta_new
 
@@ -715,14 +712,14 @@ def cbps(  # noqa
             # The following are the results of the optimizations
             "rescale_initial_result": rescale_initial_result,
             "balance_optimize_result": balance_optimize_result,
-            "gmm_optimize_result_glm_init": gmm_optimize_result_glm_init
-            if cbps_method == "over"
-            else None,
+            "gmm_optimize_result_glm_init": (
+                gmm_optimize_result_glm_init if cbps_method == "over" else None
+            ),
             # pyre-fixme[61]: `gmm_optimize_result_bal_init` is undefined, or not
             #  always defined.
-            "gmm_optimize_result_bal_init": gmm_optimize_result_bal_init
-            if cbps_method == "over"
-            else None,
+            "gmm_optimize_result_bal_init": (
+                gmm_optimize_result_bal_init if cbps_method == "over" else None
+            ),
         },
     }
     logger.info("Done cbps function")

balance/weighting_methods/ipw.py

@@ -553,9 +553,7 @@ def ipw(
         raise NotImplementedError()
     logger.debug(f"fit['lambda_1se']: {fit['lambda_1se']}")
 
-    X_matrix_sample = X_matrix[
-        :sample_n,
-    ].toarray()
+    X_matrix_sample = X_matrix[:sample_n,].toarray()
 
     logger.info(f"max_de: {max_de}")
     if max_de is not None: