def setup_scenario02(self): """ Scenario: Successfully building test predictions from start: Given I create BigML logistic regression resources uploading train "<data>" file to test "<test>" 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 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 | ./scenario1_lr/predictions.csv | ./check_files/predictions_iris_lr.csv | """ print self.setup_scenario02.__doc__ examples = [[ 'data/iris.csv', 'data/test_iris.csv', 'scenario1_lr/predictions.csv', 'check_files/predictions_iris_lr.csv' ]] for example in examples: print "\nTesting with:\n", example lr_pred.i_create_all_lr_resources(self, example[0], example[1], example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) lr_pred.i_check_create_lr_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[3])
def test_scenario10(self): """ Scenario: Successfully building logistic regression from a sampled dataset Given I create a BigML dataset from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I create a BigML logistic regression with params "<params>" from dataset in "<output_dir>" And I check that the logistic regression has been created And the logistic regression params are "<params_json>" Examples: |data |output_dir | params | params_json |../data/iris.csv | ./scenario_d_10 | "--sample-rate 0.2 --replacement" | {"sample-rate": 0.2, "replacement": true} """ print self.test_scenario10.__doc__ examples = [[ 'data/iris.csv', 'scenario_d_10', '--sample-rate 0.2 --replacement', '{"sample_rate": 0.2, "replacement": true}' ]] for example in examples: print "\nTesting with:\n", example dataset_adv.i_create_dataset(self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) dataset_adv.i_create_logistic_with_params_from_dataset( \ self, params=example[2], output_dir=example[1]) test_logistic.i_check_create_lr_model(self) dataset_adv.i_check_logistic_params(self, params_json=example[3])
def test_scenario04(self): """ Scenario: Successfully building test predictions from dataset Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML logistic regression resources using dataset to test "<test>" and log predictions in "<output>" And I check that the model has been created And I check that the predictions are ready Then the local prediction file is like "<predictions_file>" Examples: |scenario | kwargs | test | output |predictions_file | | scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario3/predictions.csv | ./check_files/predictions_iris.csv | """ print self.test_scenario04.__doc__ examples = [[ 'scenario1_lr', '{"data": "data/iris.csv", "output": "scenario1_lr/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario3_lr/predictions.csv', 'check_files/predictions_iris_lr.csv' ]] for example in examples: print "\nTesting with:\n", example test_pred.i_have_previous_scenario_or_reproduce_it( self, example[0], example[1]) lr_pred.i_create_lr_resources_from_dataset(self, None, test=example[2], output=example[3]) lr_pred.i_check_create_lr_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def setup_scenario02(self): """ Scenario: Successfully building test predictions from start: Given I create BigML logistic regression resources uploading train "<data>" file to test "<test>" 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 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 | ./scenario1_lr/predictions.csv | ./check_files/predictions_iris_lr.csv | """ print self.setup_scenario02.__doc__ examples = [ ['data/iris.csv', 'data/test_iris.csv', 'scenario1_lr/predictions.csv', 'check_files/predictions_iris_lr.csv']] for example in examples: print "\nTesting with:\n", example lr_pred.i_create_all_lr_resources(self, example[0], example[1], example[2]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) lr_pred.i_check_create_lr_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[3])
def test_scenario03(self): """ Scenario: Successfully building test predictions from source Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML logistic regression resources using source to test "<test>" 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 predictions are ready Then the local prediction file is like "<predictions_file>" Examples: |scenario | kwargs | test | output |predictions_file | | scenario1| {"data": "../data/iris.csv", "output": "./scenario1_lr/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario2/predictions.csv | ./check_files/predictions_iris.csv | """ print self.test_scenario03.__doc__ examples = [ ['scenario1_lr', '{"data": "data/iris.csv", "output": "scenario1_lr/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario2_lr/predictions.csv', 'check_files/predictions_iris_lr.csv']] for example in examples: print "\nTesting with:\n", example test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1]) lr_pred.i_create_lr_resources_from_source(self, None, test=example[2], output=example[3]) test_pred.i_check_create_dataset(self, suffix=None) lr_pred.i_check_create_lr_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def test_scenario8(self): """ Scenario: Successfully building evaluations for logistic regression from start: Given I create BigML logistic regression resources uploading train "<data>" file to evaluate and log evaluation 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 logistic regression has been created And I check that the evaluation has been created Then the evaluation file is like "<json_evaluation_file>" Examples: | data | output | json_evaluation_file | | ../data/iris.csv | ./scenario_e8/evaluation | ./check_files/evaluation_iris_lr.json | """ print self.test_scenario8.__doc__ examples = [[ 'data/iris.csv', 'scenario_e8/evaluation', 'check_files/evaluation_iris_lr.json' ]] for example in examples: print "\nTesting with:\n", example lr_pred.i_create_all_lr_resources_to_evaluate(self, data=example[0], output=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) lr_pred.i_check_create_lr_model(self) test_pred.i_check_create_evaluation(self) evaluation.then_the_evaluation_file_is_like(self, example[2])
def test_scenario10(self): """ Scenario: Successfully building logistic regression from a sampled dataset Given I create a BigML dataset from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I create a BigML logistic regression with params "<params>" from dataset in "<output_dir>" And I check that the logistic regression has been created And the logistic regression params are "<params_json>" Examples: |data |output_dir | params | params_json |../data/iris.csv | ./scenario_d_10 | "--sample-rate 0.2 --replacement" | {"sample-rate": 0.2, "replacement": true} """ print self.test_scenario10.__doc__ examples = [ ['data/iris.csv', 'scenario_d_10', '--sample-rate 0.2 --replacement', '{"sample_rate": 0.2, "replacement": true}']] for example in examples: print "\nTesting with:\n", example dataset_adv.i_create_dataset(self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) dataset_adv.i_create_logistic_with_params_from_dataset( \ self, params=example[2], output_dir=example[1]) test_logistic.i_check_create_lr_model(self) dataset_adv.i_check_logistic_params(self, params_json=example[3])
def test_scenario8(self): """ Scenario: Successfully building evaluations for logistic regression from start: Given I create BigML logistic regression resources uploading train "<data>" file to evaluate and log evaluation 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 logistic regression has been created And I check that the evaluation has been created Then the evaluation file is like "<json_evaluation_file>" Examples: | data | output | json_evaluation_file | | ../data/iris.csv | ./scenario_e8/evaluation | ./check_files/evaluation_iris_lr.json | """ print self.test_scenario8.__doc__ examples = [ ['data/iris.csv', 'scenario_e8/evaluation', 'check_files/evaluation_iris_lr.json']] for example in examples: print "\nTesting with:\n", example lr_pred.i_create_all_lr_resources_to_evaluate(self, data=example[0], output=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) lr_pred.i_check_create_lr_model(self) test_pred.i_check_create_evaluation(self) evaluation.then_the_evaluation_file_is_like(self, example[2])