def test_permutation_test_not_fitted(self):
"""Ensures permutation test can't be run before fitting the model."""
model = models.InferenceElasticNet(random_state=18)
data = _prepare_data_and_target()
with self.assertRaises(RuntimeError):
model.permutation_test(data)
def test_permutation_test(self):
"""Ensures the permutation test computes the expected results."""
data = _prepare_data_and_target()
model = models.InferenceElasticNet(random_state=18)
model.fit(data)
expected_result = pd.DataFrame(
data=[[-0.20383230, np.nan, np.nan, np.nan, True],
[-0.13463647, np.nan, np.nan, np.nan, True],
[0.010821799, np.nan, np.nan, np.nan, True],
[0.010061159, np.nan, np.nan, np.nan, True],
[0.000000000, np.nan, np.nan, np.nan, False],
[0.000000000, np.nan, np.nan, np.nan, False]],
columns=[
'effect', 'bootstrap_std', 'confidence_interval',
'significant_bootstrap', 'significant_permutation'],
index=[1, 'Intercept', 0, 4, 3, 2])
model.permutation_test(n_permutations=5, verbose=False, n_jobs=1)
result = model.get_results()
pd.testing.assert_frame_equal(
result,
expected_result,
check_dtype=False,
check_index_type=False)
def test_predict(self):
data = _prepare_data_and_target()
model = models.InferenceElasticNet(random_state=18)
model.fit(data)
predictions = model.predict(data)
self.assertIsInstance(predictions, pd.Series)
self.assertEqual(len(predictions), len(data.data))
pd.testing.assert_index_equal(predictions.index, data.data.index)
def test_metrics_calculates_r2_and_mape(self):
data = _prepare_data_and_target()
model = models.InferenceElasticNet(random_state=18)
model.fit(data)
fit_metrics = ('rmse', 'r2')
metrics = model.calculate_fit_metrics(data, fit_metrics=fit_metrics)
self.assertTrue(0 < metrics['r2'] < 1)
self.assertLess(0, metrics['rmse'])
self.assertCountEqual(metrics.keys(), fit_metrics)
def test_fit(self):
data = _prepare_data_and_target()
model = models.InferenceElasticNet(random_state=18)
expected_result = pd.DataFrame(
data=[[-0.203832],
[-0.134636],
[0.0108217],
[0.0100611],
[0.0000000],
[0.0000000],],
columns=['effect'],
index=[1, 'Intercept', 0, 4, 3, 2])
model.fit(data)
result = model.get_results()
pd.testing.assert_frame_equal(
result[['effect']],
expected_result,
check_less_precise=2,
check_index_type=False)
def test_fit_bootstrap(self):
data = _prepare_data_and_target()
model = models.InferenceElasticNet(random_state=18)
expected_result = pd.DataFrame(
data=[[-0.173, 0.2715, 0.4477, False],
[-0.135, 0.0784, 0.1287, True],
[0.0445, 0.0893, 0.1473, False],
[0.0357, 0.0548, 0.0883, False],
[0.0123, 0.0384, 0.0643, False],
[-0.010, 0.0332, 0.0541, False]],
columns=['effect', 'bootstrap_std',
'confidence_interval', 'significant_bootstrap'],
index=['Intercept', 1, 0, 4, 2, 3])
model.fit(data)
model.fit_bootstrap(bootstraps=10, n_jobs=1, verbose=False)
result = model.get_results()
pd.testing.assert_frame_equal(
result[expected_result.columns],
expected_result,
check_less_precise=1,
check_index_type=False)
def test_fit_with_data_not_ready_throw_error(self):
data = _prepare_data_and_target(ready_for_modelling=False)
model = models.InferenceElasticNet(random_state=18)
with self.assertRaises(data_preparation.InferenceDataError):
model.fit(data)
