def test_scenario9(self): """ Scenario: Successfully comparing predictions for logistic regressions with operating kind and supervised model: Given I create a data source uploading a "<data>" file And I wait until the source is ready less than <time_1> secs And I create a dataset And I wait until the dataset is ready less than <time_2> secs And I create a logistic regression with objective "<objective>" And I wait until the logistic regression is ready less than <time_3> secs And I create a local supervised model When I create a prediction with operating kind "<operating_kind>" for "<data_input>" Then the prediction for "<objective>" is "<prediction>" And I create a local prediction with operating point "<operating_kind>" for "<data_input>" Then the local prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | params | operating_point, """ examples = [[ 'data/iris.csv', '10', '50', '30000', '{"petal length": 5}', '000004', 'Iris-versicolor', '{}', "probability" ], [ 'data/iris.csv', '10', '50', '30000', '{"petal length": 2}', '000004', 'Iris-setosa', '{}', "probability" ]] show_doc(self.test_scenario9, examples) for example in examples: print "\nTesting with:\n", example source_create.i_upload_a_file(self, example[0]) source_create.the_source_is_finished(self, example[1]) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than( self, example[2]) model_create.i_create_a_logistic_model(self) model_create.the_logistic_model_is_finished_in_less_than( self, example[3]) prediction_compare.i_create_a_local_supervised_model( self, model_type="logistic_regression") prediction_create.i_create_a_logistic_prediction_with_op_kind( self, example[4], example[8]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_local_logistic_prediction_op_kind( self, example[4], example[8]) prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario9(self): """ Scenario: Successfully comparing predictions for logistic regressions with operating kind and supervised model: Given I create a data source uploading a "<data>" file And I wait until the source is ready less than <time_1> secs And I create a dataset And I wait until the dataset is ready less than <time_2> secs And I create a logistic regression with objective "<objective>" And I wait until the logistic regression is ready less than <time_3> secs And I create a local supervised model When I create a prediction with operating kind "<operating_kind>" for "<data_input>" Then the prediction for "<objective>" is "<prediction>" And I create a local prediction with operating point "<operating_kind>" for "<data_input>" Then the local prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | params | operating_point, """ examples = [ ['data/iris.csv', '10', '50', '30000', '{"petal length": 5}', '000004', 'Iris-versicolor', '{}', "probability"], ['data/iris.csv', '10', '50', '30000', '{"petal length": 2}', '000004', 'Iris-setosa', '{}', "probability"]] show_doc(self.test_scenario9, examples) for example in examples: print "\nTesting with:\n", example source_create.i_upload_a_file(self, example[0]) source_create.the_source_is_finished(self, example[1]) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than(self, example[2]) model_create.i_create_a_logistic_model(self) model_create.the_logistic_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_supervised_model(self, model_type="logistic_regression") prediction_create.i_create_a_logistic_prediction_with_op_kind(self, example[4], example[8]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_local_logistic_prediction_op_kind(self, example[4], example[8]) prediction_compare.the_local_prediction_is(self, example[6])