def test_scenario1(self): """ Scenario: Successfully creating an anomaly detector from a dataset and a dataset list: 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 Then I create an anomaly detector from a dataset And I wait until the anomaly detector is ready less than <time_4> secs And I check the anomaly detector stems from the original dataset And I store the dataset id in a list And I create a dataset And I wait until the dataset is ready less than <time_3> secs And I store the dataset id in a list Then I create an anomaly detector from a dataset list And I wait until the anomaly detector is ready less than <time_4> secs And I check the anomaly detector stems from the original dataset list Examples: | data | time_1 | time_2 | time_3 | time_4 | | ../data/tiny_kdd.csv | 40 | 40 | 80 | 100 """ print self.test_scenario1.__doc__ examples = [ ['data/tiny_kdd.csv', '40', '40', '40', '100']] 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[2]) anomaly_create.i_create_an_anomaly_from_dataset(self) anomaly_create.the_anomaly_is_finished_in_less_than(self, example[4]) anomaly_create.i_check_anomaly_dataset_and_datasets_ids(self) mm_create.i_store_dataset_id(self) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than(self, example[3]) mm_create.i_store_dataset_id(self) anomaly_create.i_create_an_anomaly_from_dataset_list(self) anomaly_create.the_anomaly_is_finished_in_less_than(self, example[4]) anomaly_create.i_check_anomaly_datasets_and_datasets_ids(self)
def test_scenario1(self): """ Scenario: Successfully creating an anomaly detector from a dataset and a dataset list: 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 Then I create an anomaly detector from a dataset And I wait until the anomaly detector is ready less than <time_4> secs And I check the anomaly detector stems from the original dataset And I store the dataset id in a list And I create a dataset And I wait until the dataset is ready less than <time_3> secs And I store the dataset id in a list Then I create an anomaly detector from a dataset list And I wait until the anomaly detector is ready less than <time_4> secs And I check the anomaly detector stems from the original dataset list Examples: | data | time_1 | time_2 | time_3 | time_4 | | ../data/tiny_kdd.csv | 40 | 40 | 40 | 100 """ print self.test_scenario1.__doc__ examples = [ ['data/tiny_kdd.csv', '40', '40', '40', '100']] 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[2]) anomaly_create.i_create_an_anomaly_from_dataset(self) anomaly_create.the_anomaly_is_finished_in_less_than(self, example[4]) anomaly_create.i_check_anomaly_dataset_and_datasets_ids(self) mm_create.i_store_dataset_id(self) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than(self, example[3]) mm_create.i_store_dataset_id(self) anomaly_create.i_create_an_anomaly_from_dataset_list(self) anomaly_create.the_anomaly_is_finished_in_less_than(self, example[4]) anomaly_create.i_check_anomaly_datasets_and_datasets_ids(self)