def test_scenario10(self): """ 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>" Then the prediction for "<objective>" is "<prediction>" And I create a local prediction for "<data_input>" Then the local prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params """ examples = [[ 'data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag"]}', 'my_fusion_tag', '{"petal width": 4}', '000004', 'Iris-virginica' ], [ 'data/grades.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_reg"]}', 'my_fusion_tag_reg', '{"Midterm": 20}', '000005', 43.65286 ]] 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_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(self, example[7]) prediction_create.the_prediction_is(self, example[8], example[9]) prediction_compare.i_create_a_local_prediction(self, example[7]) prediction_compare.the_local_prediction_is(self, example[9])
def test_scenario6(self): """ Scenario 6: Successfully creating a fusion: 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 logistic regression with "<params>" And I wait until the logistic regression is ready less than <time_3> secs And I create a logistic regression with "<params>" And I wait until the logistic regression is ready less than <time_3> secs And I retrieve a list of remote logistic regression tagged with "<tag>" And I create a fusion from a list of models and weights And I wait until the fusion is ready less than <time_4> secs When I create a prediction for "<data_input>" Then the prediction for "<objective>" is "<prediction>" And the fusion probability for the prediction is "<probability>" And I create a local fusion prediction for "<data_input>" Then the local fusion prediction is "<prediction>" And the local fusion probability for the prediction is "<probability>" Examples: | data | time_1 | time_2 | time_3 | time_4 | data_input | objective | prediction | ../data/iris.csv | 10 | 10 | 20 | 20 | {"petal length": 1, "petal width": 1} | "000004" | "Iris-setosa" """ print self.test_scenario6.__doc__ examples = [ ['data/iris.csv', '10', '10', '20', '20', '{"tags":["my_fusion_6_tag"], "missing_numerics": true}', 'my_fusion_6_tag', '{"petal width": 1.75, "petal length": 2.45}', "000004", "Iris-setosa", '0.4727', '{"tags":["my_fusion_6_tag"], "missing_numerics": false, "balance_fields": false }', '[1, 2]']] 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_with_objective_and_parms(self, example[8], example[5]) model_create.the_logistic_model_is_finished_in_less_than(self, example[3]) model_create.i_create_a_logistic_model_with_objective_and_parms(self, example[8], example[11]) model_create.the_logistic_model_is_finished_in_less_than(self, example[3]) compare_pred.i_retrieve_a_list_of_remote_logistic_regressions(self, example[6]) model_create.i_create_a_fusion_with_weights(self, example[12]) model_create.the_fusion_is_finished_in_less_than(self, example[3]) compare_pred.i_create_a_local_fusion(self) prediction_create.i_create_a_fusion_prediction(self, example[7]) prediction_create.the_prediction_is(self, example[8], example[9]) prediction_create.the_fusion_probability_is(self, example[10]) compare_pred.i_create_a_local_prediction(self, example[7]) compare_pred.the_local_prediction_is(self, example[9]) compare_pred.the_local_probability_is(self, example[10])
def test_scenario13(self): """ 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>" Then the prediction for "<objective>" is "<prediction>" And I create a local prediction for "<data_input>" Then the local prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params """ examples = [[ 'data/grades.csv', '30', '30', '120', '120', 'my_fusion_tag_lreg', '{"000000": 10, "000001": 10, "000002": 10, "000003": 10, "000004": 10}', '000005', 21.01712 ]] show_doc(self.test_scenario13, examples) for example in examples: print "\nTesting with:\n", example tag = "%s_%s" % (example[5], PY3) tag_args = '{"tags":["%s"]}' % tag 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]) linear_create.i_create_a_linear_regression_with_params( self, tag_args) linear_create.the_linear_regression_is_finished_in_less_than( self, example[3]) prediction_compare.i_retrieve_a_list_of_remote_linear_regressions( self, tag) 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(self, example[6]) prediction_create.the_prediction_is(self, example[7], example[8]) prediction_compare.i_create_a_local_prediction(self, example[6]) prediction_compare.the_local_prediction_is(self, example[8])
def test_scenario12(self): """ 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>" Then the prediction for "<objective>" is "<prediction>" And I create a local prediction for "<data_input>" Then the local prediction is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params """ tag = "my_fusion_tag_12_%s" % PY3 tag_reg = "my_fusion_tag_12_reg_%s" % PY3 examples = [ ['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["%s"], "sample_rate": 0.8, "seed": "bigml"}' % tag, tag, '{"petal width": 4}', '000004', 'Iris-virginica'], ['data/grades.csv', '30', '30', '120', '120', '{"tags":["%s"], "sample_rate": 0.8, "seed": "bigml"}' % tag_reg, tag_reg, '{"Midterm": 20}', '000005', 44.37625]] show_doc(self.test_scenario12, 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_with_weights(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(self, example[7]) prediction_create.the_prediction_is(self, example[8], example[9]) prediction_compare.i_create_a_local_prediction(self, example[7]) prediction_compare.the_local_prediction_is(self, example[9])
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_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])