print removed

Author: Julien Roussel

qolmat/benchmark/comparator.py

@@ -69,7 +69,6 @@ def get_errors(
         for name_metric in self.metrics:
             fun_metric = metrics.get_metric(name_metric)
             dict_errors[name_metric] = fun_metric(df_origin, df_imputed, df_mask)
-        print(dict_errors)
         errors = pd.concat(dict_errors.values(), keys=dict_errors.keys())
         return errors
 

qolmat/benchmark/metrics.py

@@ -1077,15 +1077,10 @@ def pattern_based_weighted_mean_metric(
             continue
         df2_pattern = df2.loc[ind_pattern, list(tup_pattern)]
         df_mask_pattern = df_mask.loc[ind_pattern, list(tup_pattern)]
-        print(df1_pattern.shape, df2_pattern.shape)
         weights.append(len(df1_pattern) / len(df1))
         scores.append(metric(df1_pattern, df2_pattern, df_mask_pattern, **kwargs))
     if len(scores) == 0:
         raise NotEnoughSamples(max_num_row, min_n_rows)
-    print("scores:")
-    print(scores)
-    print("weights:")
-    print(weights)
     return pd.Series(sum([s * w for s, w in zip(scores, weights)]), index=["All"])
 
 

tests/benchmark/test_metrics.py

@@ -354,7 +354,6 @@ def test_value_error_get_correlation_f_oneway_matrix(
 @pytest.mark.parametrize("df_mask", [df_mask])
 def test_distance_anticorr(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> None:
     result = metrics.distance_anticorr(df1, df2, df_mask)
-    print(result)
     np.testing.assert_allclose(result, 1.1e-4, rtol=1e-2)