address comments + fix defaults values

Author: R-Palazzo

sdmetrics/single_table/data_augmentation/base.py

@@ -245,8 +245,8 @@ def compute(
         metadata,
         prediction_column_name,
         minority_class_label,
-        classifier=None,
-        fixed_value=0.9,
+        classifier,
+        fixed_value,
     ):
         """Compute the score of the metric.
 
@@ -265,10 +265,10 @@ def compute(
                 The minority class label.
             classifier (str):
                 The ML algorithm to use when building a Binary Classfication.
-                Supported options are ``XGBoost``. Defaults to ``XGBoost``.
+                Supported options are ``XGBoost``.
             fixed_value (float):
-                A float in the range (0, 1.0) describing the value to fix for the metric to fix
-                when building the Binary Classification model. Defaults to ``0.9``.
+                A float value in the range (0, 1.0) that specifies the metric value
+                to fix when building the Binary Classification model.
 
         Returns:
             float:

sdmetrics/single_table/data_augmentation/binary_classifier_recall_efficacy.py

@@ -18,8 +18,8 @@ def compute_breakdown(
         metadata,
         prediction_column_name,
         minority_class_label,
-        classifier,
-        fixed_precision_value,
+        classifier='XGBoost',
+        fixed_precision_value=0.9,
     ):
         """Compute the score breakdown of the metric."""
         return super().compute_breakdown(
@@ -42,8 +42,8 @@ def compute(
         metadata,
         prediction_column_name,
         minority_class_label,
-        classifier,
-        fixed_precision_value,
+        classifier='XGBoost',
+        fixed_precision_value=0.9,
     ):
         """Compute the score of the metric.
 
@@ -65,6 +65,7 @@ def compute(
                 Supported options are ``XGBoost``. Defaults to ``XGBoost``.
             fixed_precision_value (float):
                 The fixed precision value to be used when calculating the recall score.
+                Defaults to 0.9.
 
         Returns:
             float:

sdmetrics/single_table/data_augmentation/utils.py

@@ -104,7 +104,7 @@ def _validate_data_and_metadata(
         raise ValueError(
             f"The metric can't be computed because the value `{minority_class_label}` "
             f'is not present in the column `{prediction_column_name}` for the real validation data.'
-            ' The `precision`and `recall` are undefined for this case.'
+            ' The `precision` and `recall` are undefined for this case.'
         )
 
     synthetic_labels = set(synthetic_data[prediction_column_name].unique())

tests/integration/single_table/data_augmentation/test_binary_classifier_precision_efficacy.py

@@ -92,7 +92,7 @@ def test_with_no_minority_class_in_validation(self):
         real_validation['gender'] = 'M'
         expected_error = re.escape(
             "The metric can't be computed because the value `F` is not present in the column "
-            '`gender` for the real validation data. The `precision`and `recall` are undefined'
+            '`gender` for the real validation data. The `precision` and `recall` are undefined'
             ' for this case.'
         )
 

tests/integration/single_table/data_augmentation/test_binary_classifier_recall_efficacy.py

@@ -83,7 +83,7 @@ def test_with_no_minority_class_in_validation(self):
         real_validation['gender'] = 'M'
         expected_error = re.escape(
             "The metric can't be computed because the value `F` is not present in the column "
-            '`gender` for the real validation data. The `precision`and `recall` are undefined'
+            '`gender` for the real validation data. The `precision` and `recall` are undefined'
             ' for this case.'
         )