def test_scenario3(self): """ Scenario: Successfully creating a Fields object and a modified fields structure from a file: Given I create a data source uploading a "<data>" file And I wait until the source is ready less than <time_1> secs And I create a dataset And I wait until the dataset is ready less than <time_2> secs And I create a Fields object from the dataset with objective column "<objective_column>" And I import a summary fields file "<summary_file>" as a fields structure Then I check the new field structure has field "<field_id>" as "<optype>" Examples: | data | time_1 | objective_column | summary_file| field_id | optype | time_2 | ../data/iris.csv | 10 | 0 | fields_summary_modified.csv | 000000 | categorical | 10 """ print self.test_scenario3.__doc__ examples = [ ['data/iris.csv', '10', '0', 'data/fields/fields_summary_modified.csv', '000000', 'categorical', '10']] for example in examples: print "\nTesting with:\n", example source_create.i_upload_a_file(self, example[0]) source_create.the_source_is_finished(self, example[1]) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than(self, example[6]) fields_steps.create_fields_from_dataset(self, example[2]) fields_steps.import_summary_file(self, example[3]) fields_steps.check_field_type(self, example[4], example[5])
def test_scenario3(self): """ Scenario: Successfully creating a Fields object and a modified fields structure from a file: Given I create a data source uploading a "<data>" file And I wait until the source is ready less than <time_1> secs And I create a dataset And I wait until the dataset is ready less than <time_2> secs And I create a Fields object from the dataset with objective column "<objective_column>" And I import a summary fields file "<summary_file>" as a fields structure Then I check the new field structure has field "<field_id>" as "<optype>" Examples: | data | time_1 | objective_column | summary_file| field_id | optype | time_2 | ../data/iris.csv | 10 | 0 | fields_summary_modified.csv | 000000 | categorical | 10 """ print self.test_scenario3.__doc__ examples = [[ 'data/iris.csv', '10', '0', 'data/fields/fields_summary_modified.csv', '000000', 'categorical', '10' ]] for example in examples: print "\nTesting with:\n", example source_create.i_upload_a_file(self, example[0]) source_create.the_source_is_finished(self, example[1]) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than( self, example[6]) fields_steps.create_fields_from_dataset(self, example[2]) fields_steps.import_summary_file(self, example[3]) fields_steps.check_field_type(self, example[4], example[5])
def test_scenario2(self): """ Scenario: Successfully creating a Fields object and a summary fields file: Given I create a data source uploading a "<data>" file And I wait until the source is ready less than <time_1> secs And I create a dataset And I wait until the dataset is ready less than <time_2> secs And I create a Fields object from the dataset with objective column "<objective_column>" And I export a summary fields file "<summary_file>" Then I check that the file "<summary_file>" is like "<expected_file>" Examples: | data | time_1 | objective_column | summary_file| expected_file | time_2 | ../data/iris.csv | 10 | 0 | fields_summary.csv | data/fields/fields_summary.csv | 10 """ print self.test_scenario2.__doc__ examples = [ ['data/iris.csv', '10', '0', 'fields_summary.csv', 'data/fields/fields_summary.csv', '10']] for example in examples: print "\nTesting with:\n", example source_create.i_upload_a_file(self, example[0]) source_create.the_source_is_finished(self, example[1]) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than(self, example[5]) fields_steps.create_fields_from_dataset(self, example[2]) fields_steps.generate_summary(self, example[3]) fields_steps.check_summary_like_expected(self, example[3], example[4])
def test_scenario2(self): """ Scenario: Successfully creating a Fields object and a summary fields file: Given I create a data source uploading a "<data>" file And I wait until the source is ready less than <time_1> secs And I create a dataset And I wait until the dataset is ready less than <time_2> secs And I create a Fields object from the dataset with objective column "<objective_column>" And I export a summary fields file "<summary_file>" Then I check that the file "<summary_file>" is like "<expected_file>" Examples: | data | time_1 | objective_column | summary_file| expected_file | time_2 | ../data/iris.csv | 10 | 0 | fields_summary.csv | data/fields/fields_summary.csv | 10 """ print self.test_scenario2.__doc__ examples = [[ 'data/iris.csv', '10', '0', 'fields_summary.csv', 'data/fields/fields_summary.csv', '10' ]] for example in examples: print "\nTesting with:\n", example source_create.i_upload_a_file(self, example[0]) source_create.the_source_is_finished(self, example[1]) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than( self, example[5]) fields_steps.create_fields_from_dataset(self, example[2]) fields_steps.generate_summary(self, example[3]) fields_steps.check_summary_like_expected(self, example[3], example[4])