def test_scenario9(self): """ Scenario: Successfully comparing predictions for ensembles with proportional missing strategy in a 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 an esemble with "<params>" And I wait until the ensemble is ready less than <time_3> secs And I create a local ensemble When I create a proportional missing strategy prediction for "<data_input>" Then the prediction for "<objective>" is "<prediction>" And the confidence for the prediction is "<confidence>" And I create a proportional missing strategy local prediction for "<data_input>" Then the local prediction is "<prediction>" And the local prediction's confidence is "<confidence>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | params ['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'], """ examples = [[ 'data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}', {} ], [ 'data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-versicolor', '0.27261', '{"number_of_models": 5"}', { "operating_kind": "confidence" } ]] show_doc(self.test_scenario7, 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]) ensemble_create.i_create_an_ensemble_with_params(self, example[8]) ensemble_create.the_ensemble_is_finished_in_less_than( self, example[3]) ensemble_create.create_local_supervised_ensemble(self) prediction_create.i_create_an_ensemble_proportional_prediction( self, example[4], example[9]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_create.create_local_ensemble_proportional_prediction_with_confidence( self, example[4], example[9]) prediction_compare.the_local_ensemble_prediction_is( self, example[6]) prediction_compare.the_local_prediction_confidence_is( self, example[7])
def test_scenario7(self): """ Scenario: Successfully comparing predictions for ensembles with proportional missing strategy: 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 an esemble with "<params>" And I wait until the ensemble is ready less than <time_3> secs And I create a local ensemble When I create a proportional missing strategy prediction for "<data_input>" Then the prediction for "<objective>" is "<prediction>" And the confidence for the prediction is "<confidence>" And I create a proportional missing strategy local prediction for "<data_input>" Then the local prediction is "<prediction>" And the local prediction's confidence is "<confidence>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | params ['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'], """ examples = [ ['data/iris.csv', '30', '30', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}', {}], ['data/iris.csv', '30', '30', '50', '{}', '000004', 'Iris-versicolor', '0.27261', '{"number_of_models": 5"}', {"operating_kind": "confidence"}], ['data/grades.csv', '30', '30', '50', '{}', '000005', '70.505792', '30.7161', '{"number_of_models": 5}', {}], ['data/grades.csv', '30', '30', '50', '{"Midterm": 20}', '000005', '54.82214', '25.89672', '{"number_of_models": 5}', {"operating_kind": "confidence"}], ['data/grades.csv', '30', '30', '50', '{"Midterm": 20}', '000005', '45.4573', '29.58403', '{"number_of_models": 5}', {}], ['data/grades.csv', '30', '30', '50', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '42.814', '31.51804', '{"number_of_models": 5}', {}]] show_doc(self.test_scenario7, 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]) ensemble_create.i_create_an_ensemble_with_params(self, example[8]) ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3]) ensemble_create.create_local_ensemble(self) prediction_create.i_create_an_ensemble_proportional_prediction(self, example[4], example[9]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_create.create_local_ensemble_proportional_prediction_with_confidence(self, example[4], example[9]) prediction_compare.the_local_ensemble_prediction_is(self, example[6]) prediction_compare.the_local_prediction_confidence_is(self, example[7])
def test_scenario7(self): """ Scenario: Successfully comparing predictions for ensembles with proportional missing strategy: 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 an esemble with "<params>" And I wait until the ensemble is ready less than <time_3> secs And I create a local ensemble When I create a proportional missing strategy prediction for "<data_input>" Then the prediction for "<objective>" is "<prediction>" And the confidence for the prediction is "<confidence>" And I create a proportional missing strategy local prediction for "<data_input>" Then the local prediction is "<prediction>" And the local prediction's confidence is "<confidence>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | params """ examples = [ ['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'], ['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-versicolor', '0.3174', '{"number_of_models": 5}'], ['data/grades.csv', '10', '10', '50', '{}', '000005', '70.505792', '30.7161', '{"number_of_models": 5}'], ['data/grades.csv', '10', '10', '50', '{"Midterm": 20}', '000005', '45.4573', '29.58403', '{"number_of_models": 5}'], ['data/grades.csv', '10', '10', '50', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '42.814', '31.51804', '{"number_of_models": 5}']] show_doc(self.test_scenario7, 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]) ensemble_create.i_create_an_ensemble_with_params(self, example[8]) ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3]) ensemble_create.create_local_ensemble(self) prediction_create.i_create_an_ensemble_proportional_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_create.create_local_ensemble_proportional_prediction_with_confidence(self, example[4]) prediction_compare.the_local_ensemble_prediction_is(self, example[6]) prediction_compare.the_local_prediction_confidence_is(self, example[7])