def setUp(self): self.extraction = models.Extraction() self.extraction.main_dataset['source'] = ''.join([ "from sklearn.datasets import load_digits\n", "\n", "\n", "def extract_main_dataset():\n", " X, y = load_digits(return_X_y=True)\n", " return X, y" ])
def setUp(self): self.extraction = models.Extraction() self.extraction.main_dataset['source'] = ''.join([ "from sklearn.datasets import load_digits\n", "\n", "\n", "def extract_main_dataset():\n", " X, y = load_digits(return_X_y=True)\n", " return X, y" ]) self.extraction.test_dataset['method'] = 'split_from_main' self.extraction.test_dataset['split_ratio'] = 0.1 self.extraction.test_dataset['split_seed'] = 8
def setUp(self): self.extraction = models.Extraction() self.extraction.main_dataset['source'] = ''.join([ "from sklearn.datasets import load_digits\n", "\n", "\n", "def extract_main_dataset():\n", " X, y = load_digits(return_X_y=True)\n", " return X, y" ]) self.extraction.meta_feature_generation['method'] = 'holdout_split' self.extraction.meta_feature_generation['seed'] = 8 self.extraction.meta_feature_generation['split_ratio'] = 0.1
def create_new_ensemble(): req_body = request.get_json() ensemble_name = req_body['ensemble_name'] if os.path.exists(ensemble_name): return jsonify(message="File/folder already exists"), 400 os.makedirs(ensemble_name) xcessiv_notebook_path = os.path.join(ensemble_name, app.config['XCESSIV_NOTEBOOK_NAME']) sqlite_url = 'sqlite:///{}'.format(xcessiv_notebook_path) engine = create_engine(sqlite_url) models.Base.metadata.create_all(engine) # Initialize extraction = models.Extraction() with functions.DBContextManager(ensemble_name) as session: session.add(extraction) session.commit() return jsonify(message="Xcessiv notebook created")