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add unit test
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@R-Palazzo
R-Palazzo committed Feb 7, 2025
1 parent d43f197 commit 4c8117d
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80 changes: 80 additions & 0 deletions tests/unit/single_table/data_augmentation/test_binary_classifier_precision_efficacy.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,9 @@
"""Test for the Binary Classifier Precision Efficacy metrics."""

from unittest.mock import patch

import pandas as pd

from sdmetrics.single_table.data_augmentation.binary_classifier_precision_efficacy import (
BinaryClassifierPrecisionEfficacy,
)
Expand All @@ -15,3 +19,79 @@ def test_class_attributes(self):
# Run and Assert
assert BinaryClassifierPrecisionEfficacy.name == expected_name
assert BinaryClassifierPrecisionEfficacy.metric_name == expected_metric_name

@patch(
'sdmetrics.single_table.data_augmentation.base.BaseDataAugmentationMetric.compute_breakdown'
)
def test_compute_breakdown(self, mock_compute_breakdown):
"""Test the compute_breakdown method."""
# Setup
real_training_data = pd.DataFrame()
synthetic_data = pd.DataFrame()
real_validation_data = pd.DataFrame()
metadata = {}
prediction_column_name = 'prediction_column_name'
minority_class_label = 'minority_class_label'
classifier = 'XGBoost'
fixed_recall_value = 0.8

# Run
BinaryClassifierPrecisionEfficacy.compute_breakdown(
real_training_data=real_training_data,
synthetic_data=synthetic_data,
real_validation_data=real_validation_data,
metadata=metadata,
prediction_column_name=prediction_column_name,
minority_class_label=minority_class_label,
classifier=classifier,
fixed_recall_value=fixed_recall_value,
)

# Assert
mock_compute_breakdown.assert_called_once_with(
real_training_data,
synthetic_data,
real_validation_data,
metadata,
prediction_column_name,
minority_class_label,
classifier,
fixed_recall_value,
)

@patch('sdmetrics.single_table.data_augmentation.base.BaseDataAugmentationMetric.compute')
def test_compute(self, mock_compute):
"""Test the compute method."""
# Setup
real_training_data = pd.DataFrame()
synthetic_data = pd.DataFrame()
real_validation_data = pd.DataFrame()
metadata = {}
prediction_column_name = 'prediction_column_name'
minority_class_label = 'minority_class_label'
classifier = 'XGBoost'
fixed_recall_value = 0.8

# Run
BinaryClassifierPrecisionEfficacy.compute(
real_training_data=real_training_data,
synthetic_data=synthetic_data,
real_validation_data=real_validation_data,
metadata=metadata,
prediction_column_name=prediction_column_name,
minority_class_label=minority_class_label,
classifier=classifier,
fixed_recall_value=fixed_recall_value,
)

# Assert
mock_compute.assert_called_once_with(
real_training_data,
synthetic_data,
real_validation_data,
metadata,
prediction_column_name,
minority_class_label,
classifier,
fixed_recall_value,
)
80 changes: 80 additions & 0 deletions tests/unit/single_table/data_augmentation/test_binary_classifier_recall_efficacy.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,9 @@
"""Test for the Binary Classifier Precision Efficacy metrics."""

from unittest.mock import patch

import pandas as pd

from sdmetrics.single_table.data_augmentation.binary_classifier_recall_efficacy import (
BinaryClassifierRecallEfficacy,
)
Expand All @@ -15,3 +19,79 @@ def test_class_attributes(self):
# Run and Assert
assert BinaryClassifierRecallEfficacy.name == expected_name
assert BinaryClassifierRecallEfficacy.metric_name == expected_metric_name

@patch(
'sdmetrics.single_table.data_augmentation.base.BaseDataAugmentationMetric.compute_breakdown'
)
def test_compute_breakdown(self, mock_compute_breakdown):
"""Test the compute_breakdown method."""
# Setup
real_training_data = pd.DataFrame()
synthetic_data = pd.DataFrame()
real_validation_data = pd.DataFrame()
metadata = {}
prediction_column_name = 'prediction_column_name'
minority_class_label = 'minority_class_label'
classifier = 'XGBoost'
fixed_precision_value = 0.8

# Run
BinaryClassifierRecallEfficacy.compute_breakdown(
real_training_data=real_training_data,
synthetic_data=synthetic_data,
real_validation_data=real_validation_data,
metadata=metadata,
prediction_column_name=prediction_column_name,
minority_class_label=minority_class_label,
classifier=classifier,
fixed_precision_value=fixed_precision_value,
)

# Assert
mock_compute_breakdown.assert_called_once_with(
real_training_data,
synthetic_data,
real_validation_data,
metadata,
prediction_column_name,
minority_class_label,
classifier,
fixed_precision_value,
)

@patch('sdmetrics.single_table.data_augmentation.base.BaseDataAugmentationMetric.compute')
def test_compute(self, mock_compute):
"""Test the compute method."""
# Setup
real_training_data = pd.DataFrame()
synthetic_data = pd.DataFrame()
real_validation_data = pd.DataFrame()
metadata = {}
prediction_column_name = 'prediction_column_name'
minority_class_label = 'minority_class_label'
classifier = 'XGBoost'
fixed_precision_value = 0.8

# Run
BinaryClassifierRecallEfficacy.compute(
real_training_data=real_training_data,
synthetic_data=synthetic_data,
real_validation_data=real_validation_data,
metadata=metadata,
prediction_column_name=prediction_column_name,
minority_class_label=minority_class_label,
classifier=classifier,
fixed_precision_value=fixed_precision_value,
)

# Assert
mock_compute.assert_called_once_with(
real_training_data,
synthetic_data,
real_validation_data,
metadata,
prediction_column_name,
minority_class_label,
classifier,
fixed_precision_value,
)

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