def test_scenario3(self): """ Scenario: Successfully comparing predictions 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 a model And I wait until the model is ready less than <time_3> secs And I create a local model 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 | """ examples = [[ 'data/iris.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.2629' ], [ 'data/grades.csv', '10', '10', '10', '{}', '000005', '68.62224', '27.5358' ], [ 'data/grades.csv', '10', '10', '10', '{"Midterm": 20}', '000005', '40.46667', '54.89713' ], [ 'data/grades.csv', '10', '10', '10', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '28.06', '25.65806' ]] show_doc(self.test_scenario3, 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_model(self) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_model(self) prediction_create.i_create_a_proportional_prediction( self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_compare.i_create_a_proportional_local_prediction( self, example[4]) prediction_compare.the_local_prediction_is(self, example[6]) prediction_compare.the_local_prediction_confidence_is( self, example[7])
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_scenario10(self): """ Scenario: Successfully comparing predictions with proportional missing strategy and balanced models: 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 balanced model And I wait until the model is ready less than <time_3> secs And I create a local model 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>" And I create local probabilities for "<data_input>" Then the local probabilities are "<probabilities>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | """ examples = [ [ 'data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]' ], [ 'data/iris_unbalanced.csv', '10', '10', '10', '{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}', '000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]' ] ] show_doc(self.test_scenario10, 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_balanced_model(self) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_model(self) prediction_create.i_create_a_proportional_prediction( self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_proportional_local_prediction( self, example[4]) prediction_compare.the_local_prediction_is(self, example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_compare.the_local_prediction_confidence_is( self, example[7]) prediction_compare.i_create_local_probabilities(self, example[4]) prediction_compare.the_local_probabilities_are(self, example[8])
def test_scenario6(self): """ Scenario: Successfully comparing predictions with proportional missing strategy for missing_splits models: 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 model with missing splits And I wait until the model is ready less than <time_3> secs And I create a local model 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 | | ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1} | 000004 | Iris-setosa | 0.8064 | | ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1, "petal length": 4} | 000004 | Iris-versicolor | 0.7847 | """ print self.test_scenario6.__doc__ examples = [[ 'data/iris_missing2.csv', '10', '10', '10', '{"petal width": 1}', '000004', 'Iris-setosa', '0.8064' ], [ 'data/iris_missing2.csv', '10', '10', '10', '{"petal width": 1, "petal length": 4}', '000004', 'Iris-versicolor', '0.7847' ]] 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_model_with_missing_splits(self) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_model(self) prediction_create.i_create_a_proportional_prediction( self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_compare.i_create_a_proportional_local_prediction( self, example[4]) prediction_compare.the_local_prediction_is(self, example[6]) prediction_compare.the_local_prediction_confidence_is( self, example[7])
def test_scenario6(self): """ Scenario: Successfully comparing predictions with proportional missing strategy for missing_splits models: 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 model with missing splits And I wait until the model is ready less than <time_3> secs And I create a local model 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 | | ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1} | 000004 | Iris-setosa | 0.8064 | | ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1, "petal length": 4} | 000004 | Iris-versicolor | 0.7847 | """ print self.test_scenario6.__doc__ examples = [ ["data/iris_missing2.csv", "10", "10", "10", '{"petal width": 1}', "000004", "Iris-setosa", "0.8064"], [ "data/iris_missing2.csv", "10", "10", "10", '{"petal width": 1, "petal length": 4}', "000004", "Iris-versicolor", "0.7847", ], ] 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_model_with_missing_splits(self) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_model(self) prediction_create.i_create_a_proportional_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_compare.i_create_a_proportional_local_prediction(self, example[4]) prediction_compare.the_local_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_scenario10(self): """ Scenario: Successfully comparing predictions with proportional missing strategy and balanced models: 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 balanced model And I wait until the model is ready less than <time_3> secs And I create a local model 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>" And I create local probabilities for "<data_input>" Then the local probabilities are "<probabilities>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | """ examples = [ ['data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]'], ['data/iris_unbalanced.csv', '10', '10', '10', '{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}', '000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]']] show_doc(self.test_scenario10, 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_balanced_model(self) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_model(self) prediction_create.i_create_a_proportional_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_proportional_local_prediction(self, example[4]) prediction_compare.the_local_prediction_is(self, example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_compare.the_local_prediction_confidence_is(self, example[7]) prediction_compare.i_create_local_probabilities(self, example[4]) prediction_compare.the_local_probabilities_are(self, example[8])
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])
def test_scenario3(self): """ Scenario: Successfully comparing predictions 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 a model And I wait until the model is ready less than <time_3> secs And I create a local model 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 | """ examples = [ ['data/iris.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.2629'], ['data/grades.csv', '10', '10', '10', '{}', '000005', '68.62224', '27.5358'], ['data/grades.csv', '10', '10', '10', '{"Midterm": 20}', '000005', '40.46667', '54.89713'], ['data/grades.csv', '10', '10', '10', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '28.06', '25.65806']] show_doc(self.test_scenario3, 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_model(self) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_model(self) prediction_create.i_create_a_proportional_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_create.the_confidence_is(self, example[7]) prediction_compare.i_create_a_proportional_local_prediction(self, example[4]) prediction_compare.the_local_prediction_is(self, example[6]) prediction_compare.the_local_prediction_confidence_is(self, example[7])