def test_scenario1(self): """ Scenario: Successfully building test predictions from dataset specifying objective field and model fields 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 BigML resources using dataset, objective field <objective> and model fields <fields> 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: |data | output_dir | test | output |predictions_file | objective | fields | | ../data/iris_2fb.csv| ./scénario1 | ../data/test_iris2fb.csv | ./scénario1/predictions.csv | ./check_files/predictions_iris_2fb.csv | spécies | "pétal width" | """ print self.test_scenario1.__doc__ examples = [ ['data/iris_2fb.csv', u'scénario1', 'data/test_iris2fb.csv', u'scénario1/predictions.csv', 'check_files/predictions_iris_2fb.csv', u'spécies', u'"pétal width"']] 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) test_pred.i_create_resources_from_dataset_objective_model(self, objective=example[5], fields=example[6], test=example[2], output=example[3]) test_pred.i_check_create_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def test_scenario11(self): """ Scenario: Successfully building test predictions from dataset specifying objective field and model fields Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML resources using dataset, objective field <objective> and model fields <fields> 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 | objective | fields | | scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario11/predictions.csv | ./check_files/predictions_iris_b.csv | 0 | "petal length","petal width" | """ print self.test_scenario11.__doc__ examples = [[ 'scenario1', '{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario11/predictions.csv', 'check_files/predictions_iris_b.csv', '0', '"petal length","petal width"' ]] 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_dataset_objective_model( self, objective=example[5], fields=example[6], test=example[2], output=example[3]) test_pred.i_check_create_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def test_scenario1(self): """ Scenario: Successfully building test predictions from dataset specifying objective field and model fields 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 BigML resources using dataset, objective field <objective> and model fields <fields> 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: |data | output_dir | test | output |predictions_file | objective | fields | | ../data/iris_2fb.csv| ./scénario1 | ../data/test_iris2fb.csv | ./scénario1/predictions.csv | ./check_files/predictions_iris_2fb.csv | spécies | "pétal width" | """ print self.test_scenario1.__doc__ examples = [[ 'data/iris_2fb.csv', u'scénario1', 'data/test_iris2fb.csv', u'scénario1/predictions.csv', 'check_files/predictions_iris_2fb.csv', u'spécies', u'"pétal width"' ]] 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) test_pred.i_create_resources_from_dataset_objective_model( self, objective=example[5], fields=example[6], test=example[2], output=example[3]) test_pred.i_check_create_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])
def test_scenario11(self): """ Scenario: Successfully building test predictions from dataset specifying objective field and model fields Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs> And I create BigML resources using dataset, objective field <objective> and model fields <fields> 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 | objective | fields | """ examples = [ ['scenario1', '{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario11/predictions.csv', 'check_files/predictions_iris_b.csv', '0', '"petal length","petal width"']] show_doc(self.test_scenario11, examples) 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_dataset_objective_model(self, objective=example[5], fields=example[6], test=example[2], output=example[3]) test_pred.i_check_create_model(self) test_pred.i_check_create_predictions(self) test_pred.i_check_predictions(self, example[4])