def test_compute_mean_std_from_runs(mock_model, iteration_results):
vol = BaseVolatilityEstimator(mock_model)
results = vol._compute_mean_std_from_runs(
iteration_results[iteration_results['metric_name'] == 'roc_auc'])
expected_results = [0.8, 0.08164, 0.7, 0.08164, 0.1, 0]
for idx, item in enumerate(results):
assert pytest.approx(item, 0.01) == expected_results[idx]
def test_compute_stats_tests_values(mock_model, iteration_results):
vol = BaseVolatilityEstimator(mock_model, stats_tests_to_apply=['KS'])
with patch.object(DistributionStatistics,
'compute',
return_value=(0.1, 0.05)):
stats = vol._compute_stats_tests_values(iteration_results)
assert stats[0] == 0.1
assert stats[1] == 0.05
def test_get_samples_to_plot(mock_model, iteration_results, iterations_train,
iterations_test, iterations_delta):
vol = BaseVolatilityEstimator(mock_model)
vol.fitted = True
vol.iterations_results = iteration_results
train, test, delta = vol._get_samples_to_plot(metric_name='roc_auc')
pd.testing.assert_series_equal(train, iterations_train)
pd.testing.assert_series_equal(test, iterations_test)
pd.testing.assert_series_equal(delta, iterations_delta)
def test_base_fit(mock_model, X_df, y_series):
vol = BaseVolatilityEstimator(mock_model, random_state=1)
with patch('numpy.random.seed') as mock_seed:
vol.fit(X_df, y_series)
mock_seed.assert_called_with(1)
assert vol.iterations_results is None
assert vol.report is None
assert vol.fitted is True
def test_fit_compute(mock_model, report, X_df, y_series):
vol = BaseVolatilityEstimator(mock_model)
with patch.object(BaseVolatilityEstimator, 'fit') as mock_fit:
with patch.object(BaseVolatilityEstimator,
'compute',
return_value=report) as mock_compute:
result = vol.fit_compute(X_df, y_series)
mock_fit.assert_called_with(X_df, y_series)
mock_compute.assert_called_with()
pd.testing.assert_frame_equal(result, report)
def test_plot(report, mock_model, iterations_train, iterations_test,
iterations_delta):
with patch.object(BaseVolatilityEstimator,
'compute',
return_value=report.loc[['roc_auc']]) as mock_compute:
with patch.object(BaseVolatilityEstimator,
'_get_samples_to_plot',
return_value=(iterations_train, iterations_test,
iterations_delta)) as mock_get_samples:
vol = BaseVolatilityEstimator(mock_model)
vol.fitted = True
vol.plot(metrics='roc_auc')
mock_compute.assert_called_with(metrics='roc_auc')
mock_get_samples.assert_called_with(metric_name='roc_auc')
def test_plot(report, mock_model, iterations_train, iterations_test,
iterations_delta):
"""
Test.
"""
with patch.object(BaseVolatilityEstimator,
"compute",
return_value=report.loc[["roc_auc"]]) as mock_compute:
with patch.object(
BaseVolatilityEstimator,
"_get_samples_to_plot",
return_value=(iterations_train, iterations_test,
iterations_delta),
) as mock_get_samples:
vol = BaseVolatilityEstimator(mock_model)
vol.fitted = True
vol.plot(metrics="roc_auc")
mock_compute.assert_called_with(metrics="roc_auc")
mock_get_samples.assert_called_with(metric_name="roc_auc")
def test_create_report(mock_model, iteration_results, report):
vol = BaseVolatilityEstimator(mock_model)
vol.fitted = True
vol.iterations_results = iteration_results
vol._create_report()
pd.testing.assert_frame_equal(vol.report, report, check_less_precise=3)
def test_compute(report, mock_model):
"""
Test.
"""
vol = BaseVolatilityEstimator(mock_model)
with pytest.raises(NotFittedError):
vol.compute()
vol.fit()
with pytest.raises(ValueError):
vol.compute()
vol.report = report
pd.testing.assert_frame_equal(vol.compute(), report)
pd.testing.assert_frame_equal(vol.compute(metrics=["roc_auc"]),
report.loc[["roc_auc"]])
pd.testing.assert_frame_equal(vol.compute(metrics="roc_auc"),
report.loc[["roc_auc"]])
