def test_scenario11(self): """ Scenario: Successfully comparing predictions in operating points for fusions: Scenario: Successfully comparing predictions for fusions: 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 "<params>" And I wait until the model is ready less than <time_3> secs And I create a model with "<params>" And I wait until the model is ready less than <time_3> secs And I create a model with "<params>" And I wait until the model is ready less than <time_3> secs And I retrieve a list of remote models tagged with "<tag>" And I create a fusion from a list of models And I wait until the fusion is ready less than <time_4> secs And I create a local fusion When I create a prediction for "<data_input>" in "<operating_point>" Then the prediction for "<objective>" is "<prediction>" And I create a local fusion prediction for "<data_input>" in "<operating_point>" Then the local ensemble prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params | operating_point """ examples = [ ['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11"]}', 'my_fusion_tag_11', '{"petal width": 4}', '000004', 'Iris-virginica', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}], ['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11_b"]}', 'my_fusion_tag_11_b', '{"petal width": 4}', '000004', 'Iris-virginica', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}]] show_doc(self.test_scenario11, 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_with(self, example[5]) model_create.the_model_is_finished_in_less_than(self, example[3]) model_create.i_create_a_model_with(self, example[5]) model_create.the_model_is_finished_in_less_than(self, example[3]) model_create.i_create_a_model_with(self, example[5]) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_retrieve_a_list_of_remote_models(self, example[6]) model_create.i_create_a_fusion(self) model_create.the_fusion_is_finished_in_less_than(self, example[4]) prediction_compare.i_create_a_local_fusion(self) prediction_create.i_create_a_fusion_prediction_op(self, example[7], example[10]) prediction_create.the_prediction_is(self, example[8], example[9]) prediction_compare.i_create_a_local_prediction_op(self, example[7], example[10]) prediction_compare.the_local_prediction_is(self, example[9])
def test_scenario2(self): """ Scenario: Successfully comparing predictions in operating points for 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 And I wait until the model is ready less than <time_3> secs And I create a local model When I create a prediction for "<data_input>" in "<operating_point>" Then the prediction for "<objective>" is "<prediction>" And I create a local prediction for "<data_input>" in "<operating_point>" Then the local prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | data_input | prediction | operating_point """ examples = [ ['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-setosa', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"], ['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-versicolor', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"], ['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-setosa', {"kind": "confidence", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"], ['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-versicolor', {"kind": "confidence", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"]] show_doc(self.test_scenario2, 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_prediction_op(self, example[4], example[6]) prediction_create.the_prediction_is(self, example[7], example[5]) prediction_compare.i_create_a_local_prediction_op(self, example[4], example[6]) prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario2(self): """ Scenario: Successfully comparing predictions in operating points for 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 And I wait until the model is ready less than <time_3> secs And I create a local model When I create a prediction for "<data_input>" in "<operating_point>" Then the prediction for "<objective>" is "<prediction>" And I create a local prediction for "<data_input>" in "<operating_point>" Then the local prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | data_input | prediction | operating_point """ examples = [ ['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-setosa', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"], ['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-versicolor', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"], ['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-setosa', {"kind": "confidence", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"], ['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-versicolor', {"kind": "confidence", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"]] show_doc(self.test_scenario2, 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_prediction_op(self, example[4], example[6]) prediction_create.the_prediction_is(self, example[7], example[5]) prediction_compare.i_create_a_local_prediction_op(self, example[4], example[6]) prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario11(self): """ Scenario: Successfully comparing predictions in operating points for fusions: Scenario: Successfully comparing predictions for fusions: 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 "<params>" And I wait until the model is ready less than <time_3> secs And I create a model with "<params>" And I wait until the model is ready less than <time_3> secs And I create a model with "<params>" And I wait until the model is ready less than <time_3> secs And I retrieve a list of remote models tagged with "<tag>" And I create a fusion from a list of models And I wait until the fusion is ready less than <time_4> secs And I create a local fusion When I create a prediction for "<data_input>" in "<operating_point>" Then the prediction for "<objective>" is "<prediction>" And I create a local fusion prediction for "<data_input>" in "<operating_point>" Then the local ensemble prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params | operating_point """ examples = [[ 'data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11"]}', 'my_fusion_tag_11', '{"petal width": 4}', '000004', 'Iris-virginica', { "kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa" } ], [ 'data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11_b"]}', 'my_fusion_tag_11_b', '{"petal width": 4}', '000004', 'Iris-virginica', { "kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa" } ]] show_doc(self.test_scenario11, 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_with(self, example[5]) model_create.the_model_is_finished_in_less_than(self, example[3]) model_create.i_create_a_model_with(self, example[5]) model_create.the_model_is_finished_in_less_than(self, example[3]) model_create.i_create_a_model_with(self, example[5]) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_compare.i_retrieve_a_list_of_remote_models( self, example[6]) model_create.i_create_a_fusion(self) model_create.the_fusion_is_finished_in_less_than(self, example[4]) prediction_compare.i_create_a_local_fusion(self) prediction_create.i_create_a_fusion_prediction_op( self, example[7], example[10]) prediction_create.the_prediction_is(self, example[8], example[9]) prediction_compare.i_create_a_local_prediction_op( self, example[7], example[10]) prediction_compare.the_local_prediction_is(self, example[9])