def test_scenario2(self): """ Scenario: Successfully building remote test centroid predictions from scratch to dataset: Given I create BigML resources uploading train "<data>" file to find centroids for "<test>" remotely to dataset with no CSV and log resources in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the cluster has been created And I check that the source has been created from the test file And I check that the dataset has been created from the test file And I check that the batch centroid prediction has been created Then I check that the batch centroids dataset exists And no local CSV file is created Examples: | data | test | output_dir | | ../data/grades.csv | ../data/test_grades.csv | ./scenario_cb_2 | """ print self.test_scenario2.__doc__ examples = [ ['data/grades.csv', 'data/test_grades.csv', 'scenario_cb_2']] for example in examples: print "\nTesting with:\n", example test_cluster.i_create_all_cluster_resources_to_dataset(self, data=example[0], test=example[1], output_dir=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_cluster(self) test_pred.i_check_create_test_source(self) test_pred.i_check_create_test_dataset(self) batch_pred.i_check_create_batch_centroid(self) batch_pred.i_check_create_batch_centroids_dataset(self) test_anomaly.i_check_no_local_CSV(self)
def test_scenario2(self): """ Scenario: Successfully building remote test centroid predictions from scratch to dataset: Given I create BigML resources uploading train "<data>" file to find centroids for "<test>" remotely to dataset with no CSV and log resources in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the cluster has been created And I check that the source has been created from the test file And I check that the dataset has been created from the test file And I check that the batch centroid prediction has been created Then I check that the batch centroids dataset exists And no local CSV file is created Examples: | data | test | output_dir | | ../data/grades.csv | ../data/test_grades.csv | ./scenario_cb_2 | """ print self.test_scenario2.__doc__ examples = [[ 'data/grades.csv', 'data/test_grades.csv', 'scenario_cb_2' ]] for example in examples: print "\nTesting with:\n", example test_cluster.i_create_all_cluster_resources_to_dataset( self, data=example[0], test=example[1], output_dir=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_cluster(self) test_pred.i_check_create_test_source(self) test_pred.i_check_create_test_dataset(self) batch_pred.i_check_create_batch_centroid(self) batch_pred.i_check_create_batch_centroids_dataset(self) test_anomaly.i_check_no_local_CSV(self)
def test_scenario4(self): """ Scenario: Successfully building test anomaly score predictions from training set as a dataset: Given I create BigML resources uploading train "<data>" file to find anomaly scores for the training set remotely saved to dataset with no CSV output and log resources in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the anomaly detector has been created And I check that the batch anomaly scores prediction has been created Then I check that the batch anomaly scores dataset exists And no local CSV file is created Examples: | data | output_dir | | ../data/iris.csv | ./scenario_ab_4 | """ print self.test_scenario3.__doc__ examples = [ ['data/iris.csv', 'scenario_ab_4']] for example in examples: print "\nTesting with:\n", example test_anomaly.i_create_all_anomaly_resources_without_test_split(self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self) test_anomaly.i_check_create_anomaly(self) test_batch.i_check_create_batch_anomaly_scores(self) test_anomaly.i_check_create_batch_anomaly_score_dataset(self) test_anomaly.i_check_no_local_CSV(self)
def test_scenario6(self): """ Scenario 6: Successfully building remote test predictions from scratch to a dataset: Given I create BigML resources uploading train "<data>" file to test "<test>" remotely to a dataset with no CSV output and log resources in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created And I check that the source has been created from the test file And I check that the dataset has been created from the test file And I check that the batch prediction has been created Then I check that the batch predictions dataset exists And no local CSV file is created Examples: | data | test | output_dir | | ../data/iris.csv | ../data/test_iris.csv | ./scenario_r5 | """ print self.test_scenario6.__doc__ examples = [ ['data/iris.csv', 'data/test_iris.csv', 'scenario_r5']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_all_resources_batch_to_dataset(self, data=example[0], test=example[1], output_dir=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_model(self) test_batch_pred.i_check_create_test_source(self) test_batch_pred.i_check_create_test_dataset(self) test_batch_pred.i_check_create_batch_prediction(self) test_batch_pred.i_check_create_batch_predictions_dataset(self) anomaly_pred.i_check_no_local_CSV(self)
def test_scenario6(self): """ Scenario 6: Successfully building remote test predictions from scratch to a dataset: Given I create BigML resources uploading train "<data>" file to test "<test>" remotely to a dataset with no CSV output and log resources in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created And I check that the source has been created from the test file And I check that the dataset has been created from the test file And I check that the batch prediction has been created Then I check that the batch predictions dataset exists And no local CSV file is created Examples: | data | test | output_dir | | ../data/iris.csv | ../data/test_iris.csv | ./scenario_r5 | """ print self.test_scenario6.__doc__ examples = [['data/iris.csv', 'data/test_iris.csv', 'scenario_r5']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_all_resources_batch_to_dataset( self, data=example[0], test=example[1], output_dir=example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_model(self) test_batch_pred.i_check_create_test_source(self) test_batch_pred.i_check_create_test_dataset(self) test_batch_pred.i_check_create_batch_prediction(self) test_batch_pred.i_check_create_batch_predictions_dataset(self) anomaly_pred.i_check_no_local_CSV(self)
def test_scenario4(self): """ Scenario: Successfully building test anomaly score predictions from training set as a dataset: Given I create BigML resources uploading train "<data>" file to find anomaly scores for the training set remotely saved to dataset with no CSV output and log resources in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the anomaly detector has been created And I check that the batch anomaly scores prediction has been created Then I check that the batch anomaly scores dataset exists And no local CSV file is created Examples: | data | output_dir | | ../data/iris.csv | ./scenario_ab_4 | """ print self.test_scenario3.__doc__ examples = [['data/iris.csv', 'scenario_ab_4']] for example in examples: print "\nTesting with:\n", example test_anomaly.i_create_all_anomaly_resources_without_test_split( self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self) test_anomaly.i_check_create_anomaly(self) test_batch.i_check_create_batch_anomaly_scores(self) test_anomaly.i_check_create_batch_anomaly_score_dataset(self) test_anomaly.i_check_no_local_CSV(self)