def setup_scenario2(self): """ Scenario 2: Successfully building test predictions from scratch: Given I create BigML resources uploading train "<data>" file to test "<test>" remotely and log predictions in "<output>" 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 And I check that the predictions are ready Then the local prediction file is like "<predictions_file>" Examples: | data | test | output |predictions_file | | ../data/iris.csv | ../data/test_iris.csv | ./scenario_r1/predictions.csv | ./check_files/predictions_iris.csv | """ print self.setup_scenario2.__doc__ examples = [ ['data/iris.csv', 'data/test_iris.csv', 'scenario_r1/predictions.csv', 'check_files/predictions_iris.csv']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_all_resources_batch(self, data=example[0], test=example[1], output=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_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[3])
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_scenario3(self): """ Scenario 3: Successfully building test predictions from source Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML resources using source to test the previous test source remotely and log predictions in "<output>" And I check that the dataset has been created And I check that the model has been created And I check that the dataset has been created from the test file And I check that the batch prediction has been created And I check that the predictions are ready Then the local prediction file is like "<predictions_file>" Examples: |scenario | kwargs | output |predictions_file | | scenario_r1| {"data": "../data/iris.csv", "output": "./scenario_r1/predictions.csv", "test": "../data/test_iris.csv"} |./scenario_r2/predictions.csv | ./check_files/predictions_iris.csv | """ print self.test_scenario3.__doc__ examples = [ ['scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'scenario_r2/predictions.csv', 'check_files/predictions_iris.csv']] for example in examples: print "\nTesting with:\n", example test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1]) test_pred.i_create_resources_from_source_batch(self, output=example[2]) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_model(self) test_batch_pred.i_check_create_test_dataset(self) test_batch_pred.i_check_create_batch_prediction(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[3])
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_scenario3(self): """ Scenario 3: Successfully building test predictions from source Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML resources using source to test the previous test source remotely and log predictions in "<output>" And I check that the dataset has been created And I check that the model has been created And I check that the dataset has been created from the test file And I check that the batch prediction has been created And I check that the predictions are ready Then the local prediction file is like "<predictions_file>" Examples: |scenario | kwargs | output |predictions_file | | scenario_r1| {"data": "../data/iris.csv", "output": "./scenario_r1/predictions.csv", "test": "../data/test_iris.csv"} |./scenario_r2/predictions.csv | ./check_files/predictions_iris.csv | """ print self.test_scenario3.__doc__ examples = [[ 'scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'scenario_r2/predictions.csv', 'check_files/predictions_iris.csv' ]] for example in examples: print "\nTesting with:\n", example test_pred.i_have_previous_scenario_or_reproduce_it( self, example[0], example[1]) test_pred.i_create_resources_from_source_batch(self, output=example[2]) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_model(self) test_batch_pred.i_check_create_test_dataset(self) test_batch_pred.i_check_create_batch_prediction(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[3])