def test_scenario2(self): """ Scenario: Successfully associating resources to an existing project: Given I create a BigML source uploading train "<data>" file and associate it to a new project named "<project>" storing results in "<output_dir>" And I check that the project has been created And I check that the source has been created And I create a BigML source uploading train "<data>" file and associate it to the last created project id storing results in "<output_dir2>" Then the source is associated to the project Examples: | data | project | output_dir | output_dir2 | ../data/iris.csv | My new project | ./scenario_p_2 | ./scenario_p_2_1 """ print self.test_scenario2.__doc__ examples = [[ 'data/iris.csv', 'My new project', 'scenario_p_2', 'scenario_p_2_1' ]] for example in examples: print "\nTesting with:\n", example test_project.i_create_source_with_project(self, data=example[0], project=example[1], output_dir=example[2]) test_project.i_check_create_project(self) test_pred.i_check_create_source(self) test_project.i_create_source_with_project_id(self, data=example[0], output_dir=example[3]) test_project.check_source_in_project(self)
def test_scenario1(self): """ Scenario: Successfully creating a project and associating resources to it: Given I create a BigML project in an organization named "<project>" storing results in "<output_dir>" And I create a BigML source uploading train "<data>" file and associate it to the organization project storing results in "<output_dir>" And I check that the project has been created And I check that the source has been created Then the source is associated to the project Examples: | data | project | output_dir | ../data/iris.csv | My new project | ./scenario_org_1 """ print self.test_scenario1.__doc__ examples = [['data/iris.csv', 'My new project', 'scenario_org_1']] for example in examples: print "\nTesting with:\n", example test_project.i_create_project_in_org( self, name=example[1], output_dir=example[2], organization=BIGML_ORGANIZATION) test_project.i_check_create_project(self, organization=True) test_project.i_create_source_with_org_project( self, data=example[0], output_dir=example[2]) test_pred.i_check_create_source(self) test_project.check_source_in_project(self)
def test_scenario1(self): """ Scenario: Successfully creating a project and associating resources to it: Given I create a BigML source uploading train "<data>" file and associate it to a new project named "<project>" storing results in "<output_dir>" And I check that the project has been created And I check that the source has been created Then the source is associated to the project Examples: | data | project | output_dir | ../data/iris.csv | My new project | ./scenario_p_1 """ print self.test_scenario1.__doc__ examples = [ ['data/iris.csv', 'My new project', 'scenario_p_1']] for example in examples: print "\nTesting with:\n", example test_project.i_create_source_with_project(self, data=example[0], project=example[1], output_dir=example[2]) test_project.i_check_create_project(self) test_pred.i_check_create_source(self) test_project.check_source_in_project(self)