automatminer.preprocessing.tests package

Submodules

automatminer.preprocessing.tests.test_core module

class automatminer.preprocessing.tests.test_core.TestFeatureReduction(methodName='runTest')

Bases: unittest.case.TestCase

setUp()

Hook method for setting up the test fixture before exercising it.

test_TreeBasedFeatureReduction()

test_lower_corr_clf()

test_rebate()

class automatminer.preprocessing.tests.test_core.TestPreprocess(methodName='runTest')

Bases: unittest.case.TestCase

setUp()

Hook method for setting up the test fixture before exercising it.

test_DataCleaner()

A basic test ensuring Preprocess can handle numerical features and features/targets that may be strings but should be numbers.

Returns: None

test_DataCleaner_big_nan_handler_warning()

Ensure the DataCleaner throws a warning or error when the number of nan samples and fraction is high (i.e., something has gone horribly wrong in featurization!)

test_DataCleaner_emergency_na_transform_imputation()

For the case where a fit DataCleaner must include feature X, but in the df-to-be-transformed that feature is all nan, which makes it unable to be imputed correctly.

Current implementation dictates this “emergency” be resolved by imputing with the mean of feature x from the fitted_df.

test_DataCleaner_feature_na_method()

test_DataCleaner_na_method_feature_sample_interaction()

test_DataCleaner_sample_na_method()

test_FeatureReducer_advanced()

test_FeatureReducer_basic()

test_FeatureReducer_classification()

test_FeatureReducer_combinations()

test_FeatureReducer_pca()

test_FeatureReducer_transferability()

property test_df

Prevent any memory problems or accidental overwrites.

Returns

A pandas deataframe deepcopy of the testing df.

Return type

(pd.DataFrame)

test_manual_feature_reduction()

test_saving_feature_from_removal()

Module contents