def test_scenario7(self): """ Scenario: Successfully building feature selection for a category from dataset: Given I create BigML dataset uploading train "<data>" file with attributes "<attributes>" in "<output>" And I check that the source has been created And I check that the dataset has been created And I create BigML feature selection <kfold>-fold cross-validations improving "<metric>" for category "<category>" And I check that the <kfold>-datasets have been created And I check that the <kfold>-models have been created And I check that all the <kfold>-fold cross-validations have been created Then the best feature selection is "<selection>", with "<metric>" of <metric_value> Examples: | data | attributes | output | kfold | metric | category | selection | metric_value | ../data/spam.csv | ../data/spam_attributes.json |./scenario_a_9/evaluation | 2 | recall | spam | Message | 61.24% """ print self.test_scenario7.__doc__ examples = [ ['data/spam.csv', 'data/spam_attributes.json', 'scenario_a_9/evaluation', '2', 'recall', 'spam', 'Message', '61.24%']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_dataset_with_attributes(self, data=example[0], attributes=example[1], output=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self) test_pred.i_create_kfold_cross_validation_metric_category(self, k_folds=example[3], metric=example[4], category=example[5]) test_pred.i_check_create_kfold_datasets(self, example[3]) test_pred.i_check_create_kfold_models(self, example[3]) test_pred.i_check_create_all_kfold_cross_validations(self, example[3]) test_pred.i_check_feature_selection(self, example[6], example[4], example[7])
def test_scenario7(self): """ Scenario: Successfully building feature selection for a category from dataset: Given I create BigML dataset uploading train "<data>" file with attributes "<attributes>" in "<output>" And I check that the source has been created And I check that the dataset has been created And I create BigML feature selection <kfold>-fold cross-validations improving "<metric>" for category "<category>" And I check that the <kfold>-datasets have been created And I check that the <kfold>-models have been created And I check that all the <kfold>-fold cross-validations have been created Then the best feature selection is "<selection>", with "<metric>" of <metric_value> Examples: | data | attributes | output | kfold | metric | category | selection | metric_value | ../data/spam.csv | ../data/spam_attributes.json |./scenario_a_9/evaluation | 2 | recall | spam | Message | 58.69% """ print self.test_scenario7.__doc__ examples = [ ['data/spam.csv', 'data/spam_attributes.json', 'scenario_a_9/evaluation', '2', 'recall', 'spam', 'Message', '58.69%']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_dataset_with_attributes(self, data=example[0], attributes=example[1], output=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self) test_pred.i_create_kfold_cross_validation_metric_category(self, k_folds=example[3], metric=example[4], category=example[5]) test_pred.i_check_create_kfold_datasets(self, example[3]) test_pred.i_check_create_kfold_models(self, example[3]) test_pred.i_check_create_all_kfold_cross_validations(self, example[3]) test_pred.i_check_feature_selection(self, example[6], example[4], example[7])
def setup_scenario02(self): """ Scenario: Successfully building text source from local file: Given I create BigML dataset uploading train "<data>" file with attributes "<attributes>" in "<output>" And I check that the source has been created Then I check that the dataset has been created Examples: | data | attributes | output | | ../data/spam.csv | ../data/spam_attributes.json | scenario2_td/topic_distributions.csv | """ print self.setup_scenario02.__doc__ examples = [ ['data/spam.csv', 'data/spam_attributes.json', 'scenario2_td/topic_distributions.csv']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_dataset_with_attributes( \ self, data=example[0], attributes=example[1], output=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None)
def setup_scenario02(self): """ Scenario: Successfully building text source from local file: Given I create BigML dataset uploading train "<data>" file with attributes "<attributes>" in "<output>" And I check that the source has been created Then I check that the dataset has been created Examples: | data | attributes | output | | ../data/spam.csv | ../data/spam_attributes.json | scenario2_td/topic_distributions.csv | """ print self.setup_scenario02.__doc__ examples = [[ 'data/spam.csv', 'data/spam_attributes.json', 'scenario2_td/topic_distributions.csv' ]] for example in examples: print "\nTesting with:\n", example test_pred.i_create_dataset_with_attributes( \ self, data=example[0], attributes=example[1], output=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None)