def test_scenario4(self): """ Scenario 4: Successfully creating a local deepnet from an exported file: 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 deepnet And I wait until the deepnet is ready less than <time_3> secs And I export the deepnet to "<exported_file>" When I create a local deepnet from the file "<exported_file>" Then the deepnet ID and the local deepnet ID match Examples: | data | time_1 | time_2 | time_3 | exported_file | ../data/iris.csv | 10 | 10 | 50 | ./tmp/deepnet.json """ print self.test_scenario4.__doc__ examples = [ ['data/iris.csv', '10', '10', '500', './tmp/deepnet.json']] 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_deepnet(self) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) model_create.i_export_deepnet(self, example[4]) model_create.i_create_local_deepnet_from_file(self, example[4]) model_create.check_deepnet_id_local_id(self)
def test_scenario4(self): """ Scenario4: Successfully creating an evaluation for a deepnet: 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 deepnet And I wait until the deepnet is ready less than <time_3> secs When I create an evaluation for the deepnet with the dataset And I wait until the evaluation is ready less than <time_4> secs Then the measured "<measure>" is <value> Examples: | data | time_1 | time_2 | time_3 | time_4 | measure | value | | ../data/iris.csv | 30 | 30 | 50 | 30 | average_phi | 0.95007 | """ print self.test_scenario4.__doc__ examples = [ ['data/iris.csv', '50', '50', '800', '80', 'average_phi', '0.95007']] 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_deepnet(self) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) evaluation_create.i_create_an_evaluation_deepnet(self) evaluation_create.the_evaluation_is_finished_in_less_than(self, example[4]) evaluation_create.the_measured_measure_is_value(self, example[5], example[6])
def test_scenario4(self): """ Scenario 4: Successfully creating a local deepnet from an exported file: 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 deepnet And I wait until the deepnet is ready less than <time_3> secs And I export the deepnet to "<exported_file>" When I create a local deepnet from the file "<exported_file>" Then the deepnet ID and the local deepnet ID match Examples: | data | time_1 | time_2 | time_3 | exported_file | ../data/iris.csv | 10 | 10 | 50 | ./tmp/deepnet.json """ print self.test_scenario4.__doc__ examples = [ ['data/iris.csv', '10', '10', '500', './tmp/deepnet.json']] 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_deepnet(self) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) model_create.i_export_deepnet(self, example[4]) model_create.i_create_local_deepnet_from_file(self, example[4]) model_create.check_deepnet_id_local_id(self)
def test_scenario1(self): """ Scenario: Successfully comparing predictions for deepnets: 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 deepnet with objective "<objective>" and "<params>" And I wait until the deepnet is ready less than <time_3> secs And I create a local deepnet 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 | data_input | objective | prediction | params, """ examples = [[ 'data/iris.csv', '30', '50', '30000', '{"petal width": 4}', '000004', 'Iris-virginica', '{}' ], [ 'data/iris.csv', '30', '50', '30000', '{"sepal length": 4.1, "sepal width": 2.4}', '000004', 'Iris-setosa', '{}' ], [ 'data/iris_missing2.csv', '30', '50', '30000', '{}', '000004', 'Iris-setosa', '{}' ], [ 'data/grades.csv', '30', '50', '30000', '{}', '000005', 42.15473, '{}' ], [ 'data/spam.csv', '30', '50', '30000', '{}', '000000', 'ham', '{}' ]] show_doc(self.test_scenario1, 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_deepnet_with_objective_and_params( self, example[5], example[7]) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_deepnet(self) prediction_create.i_create_a_deepnet_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_local_deepnet_prediction( self, example[4]) prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self): """ Scenario: Successfully comparing predictions for deepnets with operating kind: 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 deepnet with objective "<objective>" and "<params>" And I wait until the deepnet is ready less than <time_3> secs And I create a local deepnet 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": 2.46}', '000004', 'Iris-setosa', '{}', "probability" ], [ 'data/iris.csv', '10', '50', '30000', '{"petal length": 2}', '000004', 'Iris-setosa', '{}', "probability" ]] show_doc(self.test_scenario6, 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_deepnet_with_objective_and_params( self, example[5], example[7]) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_deepnet(self) prediction_create.i_create_a_deepnet_prediction_op_kind( self, example[4], example[8]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_local_deepnet_prediction_op_kind( self, example[4], example[8]) prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario1(self): """ Scenario: Successfully comparing predictions for deepnets: 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 deepnet with objective "<objective>" and "<params>" And I wait until the deepnet is ready less than <time_3> secs And I create a local deepnet 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 | data_input | objective | prediction | params, """ examples = [ ['data/iris.csv', '30', '50', '30000', '{"petal width": 4}', '000004', 'Iris-virginica', '{}'], ['data/iris.csv', '30', '50', '30000', '{"sepal length": 4.1, "sepal width": 2.4}', '000004', 'Iris-setosa', '{}'], ['data/iris_missing2.csv', '30', '50', '30000', '{}', '000004', 'Iris-setosa', '{}'], ['data/grades.csv', '30', '50', '30000', '{}', '000005', 42.15473, '{}'], ['data/spam.csv', '30', '50', '30000', '{}', '000000', 'ham', '{}']] show_doc(self.test_scenario1, 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_deepnet_with_objective_and_params(self, example[5], example[7]) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_deepnet(self) prediction_create.i_create_a_deepnet_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_local_deepnet_prediction(self, example[4]) prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self): """ Scenario: Successfully comparing predictions for deepnets with operating kind: 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 deepnet with objective "<objective>" and "<params>" And I wait until the deepnet is ready less than <time_3> secs And I create a local deepnet 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": 2.46}', '000004', 'Iris-setosa', '{}', "probability"], ['data/iris.csv', '10', '50', '30000', '{"petal length": 2}', '000004', 'Iris-setosa', '{}', "probability"]] show_doc(self.test_scenario6, 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_deepnet_with_objective_and_params(self, example[5], example[7]) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_deepnet(self) prediction_create.i_create_a_deepnet_prediction_op_kind(self, example[4], example[8]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_local_deepnet_prediction_op_kind(self, example[4], example[8]) prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario12(self): """ Scenario: Successfully comparing remote and local predictions with raw date input for deepnet: 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 deepnet And I wait until the deepnet is ready less than <time_3> secs And I create a local deepnet 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|data_input|objective|prediction ['data/dates2.csv', '20', '45', '60', '{"time-1": "1910-05-08T19:10:23.106", "cat-0":"cat2"}', '000002', 0.04082], ['data/dates2.csv', '20', '45', '60', '{"time-1": "2011-04-01T00:16:45.747", "cat-0":"cat2"}', '000002', 0.02919], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1920-06-45T20:21:20.320", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "2001-01-05T23:04:04.693", "cat-0":"cat2"}', '000002', 0.28517], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1932-01-30T19:24:11.440", "cat-0":"cat2"}', '000002', 0.16183], ['data/dates2.csv', '20', '45', '60', '{"time-1": "Mon Jul 14 17:36 +0000 1969", "cat-0":"cat1"}', '000002', 0.0199] """ examples = [ ['data/dates2.csv', '20', '45', '60', '{"time-1": "1910-05-08T19:10:23.106", "cat-0":"cat2"}', '000002', 0.04082], ['data/dates2.csv', '20', '45', '60', '{"time-1": "2011-04-01T00:16:45.747", "cat-0":"cat2"}', '000002', 0.02919], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1920-06-45T20:21:20.320", "cat-0":"cat1"}', '000002', 0.0199], ['data/dates2.csv', '20', '45', '60', '{"time-1": "2001-01-05T23:04:04.693", "cat-0":"cat2"}', '000002', 0.28517], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}', '000002', -0.05673], ['data/dates2.csv', '20', '45', '60', '{"time-1": "1932-01-30T19:24:11.440", "cat-0":"cat2"}', '000002', 0.16183], ['data/dates2.csv', '20', '45', '60', '{"time-1": "Mon Jul 14 17:36 +0000 1969", "cat-0":"cat1"}', '000002', 0.0199] ] 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_no_suggest_deepnet(self) model_create.the_deepnet_is_finished_in_less_than(self, example[3]) prediction_compare.i_create_a_local_deepnet(self) prediction_create.i_create_a_deepnet_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6]) prediction_compare.i_create_a_local_deepnet_prediction(self, example[4]) prediction_compare.the_local_prediction_is(self, example[6])