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")
