Added some tests

nannyml/data_quality/range/calculator.py

@@ -135,8 +135,8 @@ def _fit(self, reference_data: pd.DataFrame, *args, **kwargs):
         continuous_column_names, categorical_column_names = _split_features_by_type(reference_data, self.column_names)
         if not set(self.column_names) == set(continuous_column_names):
             raise InvalidArgumentsException(
-                f"Specified columns_names for NumericalRangeCalculator must all be continuous.\n"
-                f"Categorical columns found:\n{categorical_column_names}"
+                f"Specified columns_names for NumericalRangeCalculator must all be continuous. "
+                f"Categorical columns found: {categorical_column_names}"
             )
 
         for col in self.column_names:

tests/data_quality/test_range.py

@@ -0,0 +1,161 @@
+#  Author:   Niels Nuyttens  <[email protected]>
+#  Author:   Nikolaos Perrakis  <[email protected]>
+#
+#  License: Apache Software License 2.0
+
+"""Tests for Numerical Range Data Quality package."""
+
+import pandas as pd
+import pytest
+
+from nannyml.data_quality.range import NumericalRangeCalculator, Result
+from nannyml.datasets import load_synthetic_car_loan_data_quality_dataset
+from nannyml.exceptions import InvalidArgumentsException
+
+continuous_column_names = ['car_value', 'debt_to_income_ratio', 'loan_length']
+
+
+@pytest.fixture(scope="module")
+def numerical_range_result() -> Result:  # noqa: D103
+    reference, analysis, _ = load_synthetic_car_loan_data_quality_dataset()
+
+    calc = NumericalRangeCalculator(column_names=continuous_column_names).fit(reference)
+    return calc.calculate(data=analysis)
+
+
+def test_numerical_range_calculator_with_default_params_should_not_fail():  # noqa: D103
+    reference, analysis, _ = load_synthetic_car_loan_data_quality_dataset()
+    try:
+        calc = NumericalRangeCalculator(column_names=continuous_column_names).fit(reference)
+        _ = calc.calculate(data=analysis)
+    except Exception:
+        pytest.fail()
+
+
+def test_numerical_range_calculator_raises_invalid_arguments_exception_on_non_continuous_columns():  # noqa: D103
+    reference, analysis, _ = load_synthetic_car_loan_data_quality_dataset()
+    with pytest.raises(InvalidArgumentsException, match=r".*['salary_range'].*"):
+        _ = NumericalRangeCalculator(column_names=continuous_column_names + ['salary_range']).fit(reference)
+
+
+def test_numerical_range_calculator_with_custom_params_should_not_fail():  # noqa: D103
+    reference, analysis, _ = load_synthetic_car_loan_data_quality_dataset()
+    try:
+        calc = NumericalRangeCalculator(
+            column_names=continuous_column_names,
+            chunk_period='M',
+            timestamp_column_name='timestamp',
+            normalize=False,
+        ).fit(reference)
+        _ = calc.calculate(data=analysis)
+    except Exception:
+        pytest.fail()
+
+
+def test_numerical_range_calculator_validates_column_names_list_elements():  # noqa: D103
+    with pytest.raises(InvalidArgumentsException):
+        _ = NumericalRangeCalculator(
+            column_names=[
+                'car_value',
+                {'ab': 1},
+            ],
+            timestamp_column_name='timestamp',
+            normalize=False,
+        )
+
+
+def test_numerical_range_calculator_fit_should_raise_invalid_args_exception_when_no_data_present():  # noqa: D103, F821
+    calc = NumericalRangeCalculator(
+        column_names=continuous_column_names,
+        timestamp_column_name='timestamp',
+        normalize=False,
+    )
+    with pytest.raises(InvalidArgumentsException):
+        _ = calc.fit(pd.DataFrame())
+
+
+def test_numerical_range_calculator_calculate_should_raise_invalid_args_exception_when_no_data_present():  # noqa: D103
+    reference, _, _ = load_synthetic_car_loan_data_quality_dataset()
+    calc = NumericalRangeCalculator(column_names=continuous_column_names).fit(reference_data=reference)
+    with pytest.raises(InvalidArgumentsException):
+        _ = calc.calculate(pd.DataFrame())
+
+
+def test_numerical_range_calculator_fit_should_raise_invalid_args_exception_when_column_missing():  # noqa: D103
+    reference, _, _ = load_synthetic_car_loan_data_quality_dataset()
+    calc = NumericalRangeCalculator(column_names=continuous_column_names)
+    with pytest.raises(InvalidArgumentsException):
+        _ = calc.fit(reference.drop('car_value', axis=1))
+
+
+def test_numerical_range_calculator_calculate_should_raise_invalid_args_exception_when_column_missing():  # noqa: D103
+    reference, analysis, _ = load_synthetic_car_loan_data_quality_dataset()
+    calc = NumericalRangeCalculator(column_names=continuous_column_names).fit(reference)
+    with pytest.raises(InvalidArgumentsException):
+        _ = calc.calculate(analysis.drop('car_value', axis=1))
+
+
+@pytest.mark.parametrize(
+    'normalize, expected_metric', [(True, 'out_of_range_values_rate'), (False, 'out_of_range_values_count')]
+)
+def test_metric_is_set_properly(normalize, expected_metric):  # noqa: D103
+    reference, analysis, _ = load_synthetic_car_loan_data_quality_dataset()
+    calc = NumericalRangeCalculator(column_names=continuous_column_names, normalize=normalize).fit(reference)
+    res = calc.calculate(analysis)
+    assert calc.data_quality_metric == expected_metric
+    assert res.data_quality_metric == expected_metric
+
+
+def test_whether_result_data_dataframe_has_proper_columns(numerical_range_result):  # noqa: D103
+    cols = numerical_range_result.data.columns
+    assert len(cols) == 7 + 3 * 4
+    assert ('chunk', 'key') in cols
+    assert ('chunk', 'chunk_index') in cols
+    assert ('chunk', 'start_index') in cols
+    assert ('chunk', 'start_date') in cols
+    assert ('chunk', 'end_index') in cols
+    assert ('chunk', 'end_date') in cols
+    assert ('chunk', 'period') in cols
+    assert ('car_value', 'value') in cols
+    assert ('car_value', 'upper_threshold') in cols
+    assert ('car_value', 'lower_threshold') in cols
+    assert ('car_value', 'alert') in cols
+
+
+def test_results_filtering_column_as_str(numerical_range_result):  # noqa: D103
+    try:
+        numerical_range_result.filter(column_names='car_value')
+    except Exception:
+        pytest.fail()
+
+
+def test_results_filtering_column_as_list(numerical_range_result):  # noqa: D103
+    try:
+        numerical_range_result.filter(
+            column_names=[
+                'car_value',
+            ]
+        )
+    except Exception:
+        pytest.fail()
+
+
+@pytest.mark.parametrize(
+    'column_name, expected_values',
+    [
+        ('car_value', [0] * 20),
+        (
+            'debt_to_income_ratio',
+            [0.0] * 16 + [0.0004, 0.0, 0.0, 0.0],
+        ),
+        ('loan_length', [0] * 20),
+    ],
+)
+def test_results_repaid_loan_on_prev_car_values(numerical_range_result, column_name, expected_values):  # noqa: D103
+    res = numerical_range_result.filter(column_names=column_name).to_df()
+    assert list(res[(column_name, 'value')]) == expected_values
+
+
+def test_results_alerts(numerical_range_result):  # noqa: D103
+    res = numerical_range_result.filter(column_names='debt_to_income_ratio').to_df()
+    assert list(res[('debt_to_income_ratio', 'alert')]) == [False] * 16 + [True, False, False, False]