def test_scenario01(self): """ Scenario: Successfully creating an execution from source code: Given I create BigML execution resources from source code "<code>" and log results in "<output_dir>" And I check that the script has been created And I check that the execution has been created And I check that the result is ready Then the result file is like "<result_file>" Examples: | code | output_dir | result_file | (+ 1 1) | scenario1_exe | check_files/results_s1exe.json """ print self.test_scenario01.__doc__ examples = [[ '(+ 1 1)', 'scenario1_exe', 'check_files/results_s1exe.json' ]] for example in examples: print "\nTesting with:\n", example execute.i_create_all_execution_resources(self, example[0], example[1]) execute.i_check_create_script(self) execute.i_check_create_execution(self) execute.i_check_create_result(self) execute.i_check_result_is(self, example[2])
def test_scenario1(self): """ Scenario: Successfully retraining a balanced model Given I create a BigML balanced model from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created And I retrain the model from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created Then I check that the model has doubled its rows And I check that the model is balanced Examples: |data |output_dir | output_dir_ret |../data/iris.csv | ./scenario_rt_1 |./scenario_rt_1b | """ print self.test_scenario1.__doc__ examples = [ ['data/iris.csv', 'scenario_rt_1', 'scenario_rt_1b']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_balanced_model(self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_model(self) test_pred.i_retrain_model(self, data=example[0], output_dir=example[2]) test_pred.i_check_create_source(self) execute_steps.i_check_create_execution(self, number_of_executions=2) test_pred.i_check_create_model_in_execution(self) test_pred.i_check_model_double(self) test_pred.i_check_model_is_balanced(self)
def test_scenario03(self): """ Scenario: Successfully creating an execution with input/outputs from a code file: Given I create BigML execution resources from code in file "<code_file>" with inputs "<inputs_dec>", outputs "<outputs_dec>" and inputs "<inputs>" and log results in "<output_dir>" And I check that the script has been created And I check that the execution has been created And I check that the result is ready Then the result file is like "<result_file>" Examples: | code_file | output_dir | inputs_dec | outputs_dec | inputs | result_file | code.whizzml | scenario3_exe | data/inputs_dec.json | data/outputs_dec.json | data/inputs.json | check_files/results_s3exe.json """ print self.test_scenario03.__doc__ examples = [[ 'data/whizzml/code.whizzml', 'scenario3_exe', 'data/inputs_dec.json', 'data/outputs_dec.json', 'data/inputs.json', 'check_files/results_s3exe.json' ]] for example in examples: print "\nTesting with:\n", example execute.i_create_all_execution_with_io_resources( self, example[0], example[1], example[2], example[3], example[4]) execute.i_check_create_script(self) execute.i_check_create_execution(self) execute.i_check_create_result(self) execute.i_check_result_is(self, example[5])
def test_scenario2(self): """ Scenario: Successfully retraining from a model using sampled dataset Given I create a BigML balanced model from "<data>" sampling 50% of data and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created And I retrain the model from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created Then I check that the model has doubled its rows And I check that the model is balanced Examples: |data |output_dir | output_dir_ret |../data/iris.csv | ./scenario_rt_2 |./scenario_rt_2b | """ print self.test_scenario2.__doc__ examples = [['data/iris.csv', 'scenario_rt_2', 'scenario_rt_2b'], [ 'https://static.bigml.com/csv/iris.csv', 'scenario_rt_2c', 'scenario_rt_2d' ]] for example in examples: print "\nTesting with:\n", example test_pred.i_create_balanced_model_from_sample( self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_dataset(self, suffix='gen ') test_pred.i_check_create_model(self) test_pred.i_retrain_model(self, data=example[0], output_dir=example[2]) if not example[0].startswith("https"): test_pred.i_check_create_source(self) execute_steps.i_check_create_execution(self, number_of_executions=2) test_pred.i_check_create_model_in_execution(self) test_pred.i_check_model_double(self) test_pred.i_check_model_is_balanced(self)
def test_scenario2(self): """ Scenario: Successfully retraining from a model using sampled dataset Given I create a BigML balanced model from "<data>" sampling 50% of data and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created And I retrain the model from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created Then I check that the model has doubled its rows And I check that the model is balanced Examples: |data |output_dir | output_dir_ret |../data/iris.csv | ./scenario_rt_2 |./scenario_rt_2b | """ print self.test_scenario2.__doc__ examples = [ ['data/iris.csv', 'scenario_rt_2', 'scenario_rt_2b'], ['https://static.bigml.com/csv/iris.csv', 'scenario_rt_2c', 'scenario_rt_2d']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_balanced_model_from_sample(self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_dataset(self, suffix='gen ') test_pred.i_check_create_model(self) test_pred.i_retrain_model(self, data=example[0], output_dir=example[2]) if not example[0].startswith("https"): test_pred.i_check_create_source(self) execute_steps.i_check_create_execution(self, number_of_executions=2) test_pred.i_check_create_model_in_execution(self) test_pred.i_check_model_double(self) test_pred.i_check_model_is_balanced(self)
def test_scenario1(self): """ Scenario: Successfully retraining a balanced model Given I create a BigML balanced model from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created And I retrain the model from "<data>" and store logs in "<output_dir>" And I check that the source has been created And I check that the dataset has been created And I check that the model has been created Then I check that the model has doubled its rows And I check that the model is balanced Examples: |data |output_dir | output_dir_ret |../data/iris.csv | ./scenario_rt_1 |./scenario_rt_1b | """ print self.test_scenario1.__doc__ examples = [['data/iris.csv', 'scenario_rt_1', 'scenario_rt_1b']] for example in examples: print "\nTesting with:\n", example test_pred.i_create_balanced_model(self, data=example[0], output_dir=example[1]) test_pred.i_check_create_source(self) test_pred.i_check_create_dataset(self, suffix=None) test_pred.i_check_create_model(self) test_pred.i_retrain_model(self, data=example[0], output_dir=example[2]) test_pred.i_check_create_source(self) execute_steps.i_check_create_execution(self, number_of_executions=2) test_pred.i_check_create_model_in_execution(self) test_pred.i_check_model_double(self) test_pred.i_check_model_is_balanced(self)
def test_scenario01(self): """ Scenario: Successfully creating an execution from source code: Given I create BigML execution resources from source code "<code>" and log results in "<output_dir>" And I check that the script has been created And I check that the execution has been created And I check that the result is ready Then the result file is like "<result_file>" Examples: | code | output_dir | result_file | (+ 1 1) | scenario1_exe | check_files/results_s1exe.json """ print self.test_scenario01.__doc__ examples = [ ['(+ 1 1)', 'scenario1_exe', 'check_files/results_s1exe.json' ]] for example in examples: print "\nTesting with:\n", example execute.i_create_all_execution_resources(self, example[0], example[1]) execute.i_check_create_script(self) execute.i_check_create_execution(self) execute.i_check_create_result(self) execute.i_check_result_is(self, example[2])
def test_scenario03(self): """ Scenario: Successfully creating an execution with input/outputs from a code file: Given I create BigML execution resources from code in file "<code_file>" with inputs "<inputs_dec>", outputs "<outputs_dec>" and inputs "<inputs>" and log results in "<output_dir>" And I check that the script has been created And I check that the execution has been created And I check that the result is ready Then the result file is like "<result_file>" Examples: | code_file | output_dir | inputs_dec | outputs_dec | inputs | result_file | code.whizzml | scenario3_exe | data/inputs_dec.json | data/outputs_dec.json | data/inputs.json | check_files/results_s3exe.json """ print self.test_scenario03.__doc__ examples = [ ['data/whizzml/code.whizzml', 'scenario3_exe', 'data/inputs_dec.json', 'data/outputs_dec.json', 'data/inputs.json', 'check_files/results_s3exe.json']] for example in examples: print "\nTesting with:\n", example execute.i_create_all_execution_with_io_resources(self, example[0], example[1], example[2], example[3], example[4]) execute.i_check_create_script(self) execute.i_check_create_execution(self) execute.i_check_create_result(self) execute.i_check_result_is(self, example[5])