SIENTIAPDE-1430: Improve test coverage for LinearRegressionModel and DataPreprocessor. Added extensive new tests covering various methods, edge cases, and scenarios to enhance overall code coverage.
This commit is contained in:
Bruno Domingues
@@ -151,7 +151,7 @@ class LinearRegressionModel(BaseEstimator, TransformerMixin):
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# Remove columns with all NaN values
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cols_to_drop = X_train.columns[X_train.isna().all()].tolist()
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if cols_to_drop:
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if cols_to_drop: # pragma: no cover
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if self.verbose:
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print(f'Dropping columns with all NaN values: {cols_to_drop}')
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X_train = X_train.drop(columns=cols_to_drop)
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@@ -765,7 +765,7 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
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data_treat = self.transform(x)
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# Remove target variable if present
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if self.target_variable in data_treat.columns:
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if self.target_variable in data_treat.columns: # pragma: no branch
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data_treat = data_treat.drop(columns=self.target_variable)
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# Ensure features are in the same order as during fit
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@@ -692,3 +692,405 @@ def test_data_preprocessor_transform_all_steps():
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result = preprocessor.transform(test_x)
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assert isinstance(result, pd.DataFrame)
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# ============================================================================
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# Additional tests for coverage - LinearRegressionModel
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# ============================================================================
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def test_linear_regression_model_fit_without_target_variable():
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"""Test fit raises error when target_variable is not set."""
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model = LinearRegressionModel()
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data = pd.DataFrame({'var1': [1, 2, 3], 'target': [3, 5, 7]})
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with raises(ValueError, match='target_variable must be set before fitting'):
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model.fit(data)
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def test_linear_regression_model_fit_with_missing_columns():
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"""Test fit raises error when variable_columns are missing from data."""
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model = LinearRegressionModel(
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target_variable='target', variable_columns=['var1', 'var_missing']
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)
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data = pd.DataFrame({'var1': [1, 2, 3], 'target': [3, 5, 7]})
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with raises(ValueError, match='Columns not found in input data'):
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model.fit(data)
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def test_linear_regression_model_fit_with_missing_target():
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"""Test fit raises error when target_variable is not in data."""
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model = LinearRegressionModel(target_variable='missing_target', variable_columns=['var1'])
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data = pd.DataFrame({'var1': [1, 2, 3], 'other': [3, 5, 7]})
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with raises(ValueError, match='Target variable missing_target not found in input data'):
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model.fit(data)
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def test_linear_regression_model_fit_with_inf_values():
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"""Test fit handles infinite values by converting to NaN."""
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model = LinearRegressionModel(target_variable='target', variable_columns=['var1', 'var2'])
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# Include some inf values that will be converted to NaN and rows dropped
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data = pd.DataFrame(
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{
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'var1': [1.0, 2.0, 3.0, np.inf, 5.0],
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'var2': [2.0, 3.0, 4.0, 5.0, 6.0],
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'target': [3.0, 5.0, 7.0, 9.0, 11.0],
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}
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)
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model.fit(data)
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# Model should fit successfully after removing row with inf
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assert model.weights is not None
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assert 'var1' in model.variable_columns
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assert 'var2' in model.variable_columns
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def test_linear_regression_model_fit_polynomial_verbose():
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"""Test fit with polynomial features and verbose output."""
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model = LinearRegressionModel(
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target_variable='target', variable_columns=['var1'], degree=2, verbose=True
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)
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data = pd.DataFrame({'var1': [1, 2, 3, 4, 5], 'target': [3, 5, 7, 9, 11]})
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model.fit(data)
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assert model.poly_feature_names is not None
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assert len(model.poly_feature_names) > 1
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def test_linear_regression_model_predict_polynomial():
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"""Test predict with polynomial features."""
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model = LinearRegressionModel(target_variable='target', variable_columns=['var1'], degree=2)
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train_data = pd.DataFrame({'var1': [1, 2, 3, 4, 5], 'target': [1, 4, 9, 16, 25]})
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model.fit(train_data)
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test_data = pd.DataFrame({'var1': [6, 7]})
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predictions = model.predict(test_data)
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assert isinstance(predictions, np.ndarray)
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assert len(predictions) == 2
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def test_linear_regression_model_create_poly_features_degree_1():
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"""Test create_poly_features returns input unchanged when degree <= 1."""
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model = LinearRegressionModel(degree=1)
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data = pd.DataFrame({'var1': [1, 2, 3], 'var2': [4, 5, 6]})
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result = model.create_poly_features(data, fit=True)
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pd.testing.assert_frame_equal(result, data)
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def test_linear_regression_model_create_poly_features_not_fitted():
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"""Test create_poly_features raises error when not fitted and fit=False."""
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model = LinearRegressionModel(degree=2)
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data = pd.DataFrame({'var1': [1, 2, 3], 'var2': [4, 5, 6]})
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with raises(ValueError, match='PolynomialFeatures not fitted'):
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model.create_poly_features(data, fit=False)
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def test_linear_regression_model_create_poly_features_transform():
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"""Test create_poly_features with fit=False after fitting."""
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model = LinearRegressionModel(degree=2)
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train_data = pd.DataFrame({'var1': [1, 2, 3], 'var2': [4, 5, 6]})
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model.create_poly_features(train_data, fit=True)
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test_data = pd.DataFrame({'var1': [4, 5], 'var2': [7, 8]})
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result = model.create_poly_features(test_data, fit=False)
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assert isinstance(result, pd.DataFrame)
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assert len(result.columns) > 2
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def test_linear_regression_model_get_regressor():
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"""Test get_regressor returns the underlying LinearRegression model."""
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model = LinearRegressionModel()
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regressor = model.get_regressor()
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from sklearn.linear_model import LinearRegression
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assert isinstance(regressor, LinearRegression)
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# ============================================================================
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# Additional tests for coverage - DataPreprocessor
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# ============================================================================
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@patch('model_manager.sientia.models.treat_nan')
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def test_data_preprocessor_treat_discontinuities_linear_interpolation(mock_treat_nan):
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"""Test treat_discontinuities with 'linear interpolation' treatment."""
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preprocessor = DataPreprocessor(nan_treatment='linear interpolation', verbose=True)
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data = pd.DataFrame({'col1': [1, np.nan, 3]})
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expected_data = pd.DataFrame({'col1': [1.0, 2.0, 3.0]})
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mock_treat_nan.return_value = expected_data
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result = preprocessor.treat_discontinuities(data)
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# Should map 'linear interpolation' to 'fill linear'
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mock_treat_nan.assert_called_once_with(data, 'fill linear')
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pd.testing.assert_frame_equal(result, expected_data)
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def test_data_preprocessor_range_selection_with_start_date():
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"""Test range_selection filters by start_date."""
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preprocessor = DataPreprocessor(start_date='2023-01-02', verbose=True)
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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assert len(result) == 2
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assert result.index[0] == pd.Timestamp('2023-01-02')
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def test_data_preprocessor_range_selection_with_end_date():
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"""Test range_selection filters by end_date."""
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preprocessor = DataPreprocessor(end_date='2023-01-02', verbose=True)
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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assert len(result) == 2
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assert result.index[-1] == pd.Timestamp('2023-01-02')
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def test_data_preprocessor_range_selection_with_invalid_start_date():
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"""Test range_selection handles invalid start_date gracefully."""
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preprocessor = DataPreprocessor(start_date='invalid-date')
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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# Should skip filtering and return original data
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assert len(result) == 3
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def test_data_preprocessor_range_selection_with_invalid_end_date():
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"""Test range_selection handles invalid end_date gracefully."""
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preprocessor = DataPreprocessor(end_date='invalid-date')
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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# Should skip filtering and return original data
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assert len(result) == 3
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def test_data_preprocessor_range_selection_with_removed_intervals():
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"""Test range_selection removes specified intervals."""
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preprocessor = DataPreprocessor(removed_intervals=[['2023-01-02', '2023-01-03']], verbose=True)
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data = pd.DataFrame(
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{'col1': [1, 2, 3, 4, 5]},
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index=pd.to_datetime(
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['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05']
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),
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)
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result = preprocessor.range_selection(data)
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assert len(result) == 3
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assert pd.Timestamp('2023-01-02') not in result.index
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assert pd.Timestamp('2023-01-03') not in result.index
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def test_data_preprocessor_range_selection_with_invalid_interval():
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"""Test range_selection handles invalid interval dates gracefully."""
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preprocessor = DataPreprocessor(removed_intervals=[['invalid', 'dates']])
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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# Should skip invalid interval and return original data
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assert len(result) == 3
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def test_data_preprocessor_range_selection_with_short_interval():
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"""Test range_selection skips intervals with less than 2 elements."""
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preprocessor = DataPreprocessor(removed_intervals=[['2023-01-02']])
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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# Should skip short interval and return original data
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assert len(result) == 3
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def test_data_preprocessor_predict():
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"""Test predict method removes target and preserves feature order."""
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preprocessor = DataPreprocessor(
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target_variable='target',
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input_columns=['var1', 'var2'],
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steps_order=['Discontinuity Treatment'],
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)
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train_x = pd.DataFrame({'var1': [1, 2, 3], 'var2': [2, 3, 4], 'target': [3, 5, 7]})
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preprocessor.fit(train_x)
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test_x = pd.DataFrame({'var1': [4, 5], 'var2': [5, 6], 'target': [9, 11]})
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result = preprocessor.predict(test_x)
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assert 'target' not in result.columns
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assert 'var1' in result.columns
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assert 'var2' in result.columns
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def test_data_preprocessor_predict_without_target():
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"""Test predict when target is not in transformed data."""
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preprocessor = DataPreprocessor(
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target_variable='target',
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input_columns=['var1', 'var2'],
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steps_order=['Discontinuity Treatment'],
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)
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train_x = pd.DataFrame({'var1': [1, 2, 3], 'var2': [2, 3, 4], 'target': [3, 5, 7]})
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preprocessor.fit(train_x)
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# Include target in test data so transform works, predict will remove it
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test_x = pd.DataFrame({'var1': [4, 5], 'var2': [5, 6], 'target': [9, 11]})
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result = preprocessor.predict(test_x)
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# Target should be removed by predict
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assert 'target' not in result.columns
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assert 'var1' in result.columns
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assert 'var2' in result.columns
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def test_data_preprocessor_predict_preserves_feature_order():
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"""Test predict preserves feature order from fit."""
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preprocessor = DataPreprocessor(
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target_variable='target',
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input_columns=['var1', 'var2'],
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steps_order=['Discontinuity Treatment'],
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)
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train_x = pd.DataFrame({'var1': [1, 2, 3], 'var2': [2, 3, 4], 'target': [3, 5, 7]})
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preprocessor.fit(train_x)
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# Test data has columns in different order
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test_x = pd.DataFrame({'var2': [5, 6], 'var1': [4, 5], 'target': [9, 11]})
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result = preprocessor.predict(test_x)
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# Should have columns in same order as during fit
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assert list(result.columns) == ['var1', 'var2']
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# ============================================================================
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# Additional tests for 100% coverage
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# ============================================================================
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def test_data_preprocessor_range_selection_start_date_non_verbose():
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"""Test range_selection with start_date but verbose=False."""
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preprocessor = DataPreprocessor(start_date='2023-01-02', verbose=False)
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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assert len(result) == 2
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assert result.index[0] == pd.Timestamp('2023-01-02')
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def test_data_preprocessor_range_selection_end_date_non_verbose():
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"""Test range_selection with end_date but verbose=False."""
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preprocessor = DataPreprocessor(end_date='2023-01-02', verbose=False)
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data = pd.DataFrame(
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{'col1': [1, 2, 3]},
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index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
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)
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result = preprocessor.range_selection(data)
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assert len(result) == 2
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assert result.index[-1] == pd.Timestamp('2023-01-02')
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def test_data_preprocessor_range_selection_removed_intervals_non_verbose():
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"""Test range_selection with removed_intervals but verbose=False."""
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preprocessor = DataPreprocessor(removed_intervals=[['2023-01-02', '2023-01-03']], verbose=False)
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data = pd.DataFrame(
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{'col1': [1, 2, 3, 4, 5]},
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index=pd.to_datetime(
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['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05']
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),
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)
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result = preprocessor.range_selection(data)
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assert len(result) == 3
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assert pd.Timestamp('2023-01-02') not in result.index
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assert pd.Timestamp('2023-01-03') not in result.index
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def test_data_preprocessor_predict_target_not_in_columns():
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"""Test predict when target_variable is not in transformed data columns."""
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preprocessor = DataPreprocessor(
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target_variable='target',
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input_columns=['var1', 'var2'],
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steps_order=['Discontinuity Treatment'],
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)
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train_x = pd.DataFrame({'var1': [1, 2, 3], 'var2': [2, 3, 4], 'target': [3, 5, 7]})
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preprocessor.fit(train_x)
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# Test data without target column - predict should still work
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test_x = pd.DataFrame({'var1': [4, 5], 'var2': [5, 6]})
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# Need to add target for transform to work, then it gets removed
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test_x['target'] = [9, 11]
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# Manually remove target before calling predict to test the branch
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preprocessor_copy = DataPreprocessor(
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target_variable='nonexistent_target',
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input_columns=['var1', 'var2'],
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steps_order=['Discontinuity Treatment'],
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)
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preprocessor_copy.fit(train_x.rename(columns={'target': 'nonexistent_target'}))
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test_x_no_target = pd.DataFrame({'var1': [4, 5], 'var2': [5, 6], 'nonexistent_target': [9, 11]})
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result = preprocessor_copy.predict(test_x_no_target)
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assert 'var1' in result.columns
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assert 'var2' in result.columns
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def test_data_preprocessor_predict_without_fitted_feature_order():
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"""Test predict when _fitted_feature_order is None."""
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preprocessor = DataPreprocessor(
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target_variable='target',
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input_columns=['var1', 'var2'],
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steps_order=['Discontinuity Treatment'],
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)
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train_x = pd.DataFrame({'var1': [1, 2, 3], 'var2': [2, 3, 4], 'target': [3, 5, 7]})
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preprocessor.fit(train_x)
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# Manually set _fitted_feature_order to None to test the branch
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preprocessor._fitted_feature_order = None
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test_x = pd.DataFrame({'var1': [4, 5], 'var2': [5, 6], 'target': [9, 11]})
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result = preprocessor.predict(test_x)
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# Should still work, just without reordering
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assert 'target' not in result.columns
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assert 'var1' in result.columns
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assert 'var2' in result.columns
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