tests.metrics.utility#
tests.metrics.utility.test_univariate#
- tests.metrics.utility.test_univariate.test_distance_divergence(distance_function)[source]#
Test the distance and divergence functions.
- Parameters:
distance_function (
Callable) – the function to test- Return type:
None- Returns:
None
- tests.metrics.utility.test_univariate.univariate_metrics_results(request, df_wbcd, df_mock_wbcd, metadata_wbcd)[source]#
Compute the univariate metrics in different settings.
- Parameters:
request – the number of continuous and categorical columns to test
df_wbcd (
dict[str,DataFrame]) – the real Wisconsin Breast Cancer Dataset fixture, split into train and test setsdf_mock_wbcd (
dict[str,DataFrame]) – the mock wbcd dataset fixture, split into train and test setsmetadata_wbcd (
dict) – the wbcd metadata fixture
- Return type:
Tuple[Type[Metric],str,dict]- Returns:
a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores
- tests.metrics.utility.test_univariate.test_univariate_metrics_summary(univariate_metrics_results)[source]#
Test the univariate metrics average scores.
- Parameters:
univariate_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
- tests.metrics.utility.test_univariate.test_univariate_metrics_detailed(univariate_metrics_results)[source]#
Test the univariate metrics detailed scores.
- Parameters:
univariate_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
tests.metrics.utility.test_bivariate#
- tests.metrics.utility.test_bivariate.bivariate_metrics_results(request, df_wbcd, df_mock_wbcd, metadata_wbcd)[source]#
Compute the bivariate metrics in different settings.
- Parameters:
request – the number of continuous and categorical columns to test
df_wbcd (
dict[str,DataFrame]) – the real Wisconsin Breast Cancer Dataset fixture, split into train and test setsdf_mock_wbcd (
dict[str,DataFrame]) – the mock wbcd dataset fixture, split into train and test setsmetadata_wbcd (
dict) – the wbcd metadata fixture
- Return type:
Tuple[Type[Metric],str,dict]- Returns:
a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores
- tests.metrics.utility.test_bivariate.test_bivariate_metrics_summary(bivariate_metrics_results)[source]#
Test the bivariate metrics average scores.
- Parameters:
bivariate_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
- tests.metrics.utility.test_bivariate.test_bivariate_metrics_detailed(bivariate_metrics_results)[source]#
Test the bivariate metrics detailed scores.
- Parameters:
bivariate_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
tests.metrics.utility.test_population#
- tests.metrics.utility.test_population.test_propensity_score()[source]#
Test the propensity score function.
- Return type:
None- Returns:
None
- tests.metrics.utility.test_population.population_metrics_results(request, df_wbcd, df_mock_wbcd, metadata_wbcd)[source]#
Compute the population metrics in different settings.
- Parameters:
request – the number of continuous and categorical columns to test
df_wbcd (
dict[str,DataFrame]) – the real Wisconsin Breast Cancer Dataset fixture, split into train and test setsdf_mock_wbcd (
dict[str,DataFrame]) – the mock wbcd dataset fixture, split into train and test setsmetadata_wbcd (
dict) – the wbcd metadata fixture
- Return type:
Tuple[Type[Metric],str,dict]- Returns:
a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores
- tests.metrics.utility.test_population.test_population_metrics_summary(population_metrics_results)[source]#
Test the population metrics average scores.
- Parameters:
population_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
- tests.metrics.utility.test_population.test_population_metrics_detailed(population_metrics_results)[source]#
Test the population metrics detailed scores.
- Parameters:
population_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
tests.metrics.utility.test_application#
- tests.metrics.utility.test_application.test_fscore()[source]#
Test the F-score function.
- Return type:
None- Returns:
None
- tests.metrics.utility.test_application.application_metrics_results(request, df_wbcd, df_mock_wbcd, metadata_wbcd)[source]#
Compute the application metrics in different settings.
- Parameters:
request – the number of continuous and categorical columns to test
df_wbcd (
dict[str,DataFrame]) – the real Wisconsin Breast Cancer Dataset fixture, split into train and test setsdf_mock_wbcd (
dict[str,DataFrame]) – the mock wbcd dataset fixture, contains train and test setsmetadata_wbcd (
dict) – the wbcd metadata fixture
- Return type:
Tuple[Type[Metric],str,dict]- Returns:
a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores
- tests.metrics.utility.test_application.test_application_metrics_summary(application_metrics_results)[source]#
Test the application metrics average scores.
- Parameters:
application_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
- tests.metrics.utility.test_application.test_application_metrics_detailed(application_metrics_results)[source]#
Test the application metrics detailed scores.
- Parameters:
application_metrics_results (
Tuple[Type[Metric],str,dict]) – a tuple containing the metric class, the dataset type and a dictionary containing the average scores of the metric and the detailed scores- Return type:
None- Returns:
None
tests.metrics.utility.test_report#
- tests.metrics.utility.test_report.utility_report(request, df_wbcd, df_mock_wbcd)[source]#
Compute the utility report in different settings.
- Parameters:
request – the number of continuous and categorical columns to test
df_wbcd (
dict[str,DataFrame]) – the real Wisconsin Breast Cancer Dataset fixture, split into train and test setsdf_mock_wbcd (
dict[str,DataFrame]) – the mock wbcd dataset fixture, split into train and test sets
- Return type:
UtilityReport- Returns:
an instance of the report
- tests.metrics.utility.test_report.test_summary_report(utility_report)[source]#
Test the summary report.
- Parameters:
utility_report (
UtilityReport) – the computed report fixture- Return type:
None- Returns:
None