def setup_scenario1(self): """ Scenario: Successfully building multi-label test predictions from start: Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and <number_of_labels> labels uploading train "<data>" file with "<training_separator>" field separator and <number_of_models> models ensembles 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 models in the ensembles have been created Then I check that the predictions are ready Examples: |tag |label_separator |number_of_labels | data |training_separator |number_of_models | test | output | |my_multilabel_1|:|7| ../data/multilabel.csv |,|10| ../data/test_multilabel.csv | ./scenario_mle_1/predictions.csv """ print self.setup_scenario1.__doc__ examples = [[ 'my_multilabel_1%s' % PY3, ':', '7', 'data/multilabel.csv', ',', '10', 'data/test_multilabel.csv', 'scenario_mle_1/predictions.csv' ]] for example in examples: print "\nTesting with:\n", example ml_pred.i_create_all_ml_resources_and_ensembles( self, tag=example[0], label_separator=example[1], number_of_labels=example[2], data=example[3], training_separator=example[4], number_of_models=example[5], test=example[6], output=example[7]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self) test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True) test_pred.i_check_create_predictions(self)
def test_scenario2(self): """ Scenario: Successfully building test predictions from source Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML multi-label resources using source and <number_of_models> models ensembles to test "<test>" and log predictions in "<output>" And I check that the dataset has been created And I check that the models in the ensembles have been created Then I check that the predictions are ready Examples: |scenario | kwargs |number_of_models |test | output | | scenario_mle_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_mle_1/predictions.csv", "test": "../data/test_multilabel.csv", "number_of_models": 10} |10| ../data/test_multilabel.csv | ./scenario_mle_2/predictions.csv """ print self.test_scenario2.__doc__ examples = [[ 'scenario_mle_1', '{"tag": "my_multilabel_1%s", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_mle_1/predictions.csv", "test": "data/test_multilabel.csv", "number_of_models": 10}' % PY3, '10', 'data/test_multilabel.csv', 'scenario_mle_2/predictions.csv' ]] for example in examples: print "\nTesting with:\n", example test_pred.i_have_previous_scenario_or_reproduce_it( self, example[0], example[1]) ml_pred.i_create_resources_and_ensembles_from_source( self, multi_label='multi-label ', number_of_models=example[2], test=example[3], output=example[4]) test_pred.i_check_create_dataset(self) test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True) test_pred.i_check_create_predictions(self)
def test_scenario21(self): """ Scenario 1: Successfully building test predictions from ensemble And I create BigML resources from "<data>" using ensemble of <number_of_models> models 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 ensemble has been created And I check that the models in the ensembles have been created And I check that the predictions are ready Then the local prediction file is like "<predictions_file>" Examples: |data | number_of_models | test | output | predictions_file | """ examples = [ ['data/grades.csv', '5', 'data/test_grades.csv', 'scenario21/predictions.csv', 'check_files/predictions_grades_e.csv']] show_doc(self.test_scenario21, examples) for example in examples: print "\nTesting with:\n", example test_pred.i_create_resources_in_prod_from_ensemble( \ self, data=example[0], number_of_models=example[1], test=example[2], output=example[3]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_ensemble(self) test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def test_scenario21(self): """ Scenario 1: Successfully building test predictions from ensemble And I create BigML resources from "<data>" using ensemble of <number_of_models> models 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 ensemble has been created And I check that the models in the ensembles have been created And I check that the predictions are ready Then the local prediction file is like "<predictions_file>" Examples: |data | number_of_models | test | output | predictions_file | """ examples = [[ 'data/grades.csv', '5', 'data/test_grades.csv', 'scenario21/predictions.csv', 'check_files/predictions_grades_e.csv' ]] show_doc(self.test_scenario21, examples) for example in examples: print "\nTesting with:\n", example test_pred.i_create_resources_in_prod_from_ensemble( \ self, data=example[0], number_of_models=example[1], test=example[2], output=example[3]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_ensemble(self) test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def test_scenario3(self): """ Scenario: Successfully building test predictions from dataset Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML multi-label resources using dataset and <number_of_models> models ensembles to test "<test>" and log predictions in "<output>" And I check that the models in the ensembles have been created Then I check that the predictions are ready Examples: |scenario | kwargs | number_of_models |test | output | | scenario_mle_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_mle_1/predictions.csv", "test": "../data/test_multilabel.csv", "number_of_models": 10} |10| ../data/test_multilabel.csv | ./scenario_mle_3/predictions.csv """ print self.test_scenario3.__doc__ examples = [ ['scenario_mle_1', '{"tag": "my_multilabel_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_mle_1/predictions.csv", "test": "data/test_multilabel.csv", "number_of_models": 10}', '10', 'data/test_multilabel.csv', 'scenario_mle_3/predictions.csv']] for example in examples: print "\nTesting with:\n", example test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1]) ml_pred.i_create_resources_and_ensembles_from_source(self, multi_label='multi-label', number_of_models=example[2], test=example[3], output=example[4]) test_pred.i_check_create_dataset(self) test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True) test_pred.i_check_create_predictions(self)
def test_scenario1(self): """ Scenario 1: Successfully building test predictions from ensemble Given I want to use api in DEV mode And I create BigML resources in DEV from "<data>" using ensemble of <number_of_models> models 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 ensemble has been created And I check that the models in the ensembles have been created And I check that the predictions are ready Then the local prediction file is like "<predictions_file>" Examples: |data | number_of_models | test | output | predictions_file | | ../data/grades.csv| 5 | ../data/test_grades.csv | ./scenario1_dev/predictions.csv | ./check_files/predictions_grades_e.csv | """ print self.test_scenario1.__doc__ examples = [ [ "data/grades.csv", "5", "data/test_grades.csv", "scenario1_dev/predictions.csv", "check_files/predictions_grades_e.csv", ] ] for example in examples: print "\nTesting with:\n", example common.i_want_api_dev_mode(self) test_pred.i_create_resources_in_dev_from_ensemble( self, data=example[0], number_of_models=example[1], test=example[2], output=example[3] ) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_ensemble(self) test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def setup_scenario1(self): """ Scenario: Successfully building multi-label test predictions from start: Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and <number_of_labels> labels uploading train "<data>" file with "<training_separator>" field separator and <number_of_models> models ensembles 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 models in the ensembles have been created Then I check that the predictions are ready Examples: |tag |label_separator |number_of_labels | data |training_separator |number_of_models | test | output | |my_multilabel_1|:|7| ../data/multilabel.csv |,|10| ../data/test_multilabel.csv | ./scenario_mle_1/predictions.csv """ print self.setup_scenario1.__doc__ examples = [ ['my_multilabel_1', ':', '7', 'data/multilabel.csv', ',', '10', 'data/test_multilabel.csv', 'scenario_mle_1/predictions.csv']] for example in examples: print "\nTesting with:\n", example ml_pred.i_create_all_ml_resources_and_ensembles(self, tag=example[0], label_separator=example[1], number_of_labels=example[2], data=example[3], training_separator=example[4], number_of_models=example[5], test=example[6], output=example[7]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self) test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True) test_pred.i_check_create_predictions(self)