def read_file(
path=os.path.join(get_data_path(), 'input/<My_data>.csv'), **kwargs):
try:
df = pd.read_csv(path, **kwargs)
return df
except FileNotFoundError:
print('Data file not found. Path was ' + path)
def read_model(type: str = 'regressor'):
"""
Method for reading in pickled models
:param type: A string representing if type of model is related to duration, false booking or direction prediction
:return: model instance
"""
if type == 'regressor':
path = os.path.join(get_data_path(), 'output/duration.pkl')
with open(path, 'rb') as f:
model = pickle.load(f)
elif type == 'booking_filter':
path = os.path.join(get_data_path(), 'output/booking_filter.pkl')
with open(path, 'rb') as f:
model = pickle.load(f)
elif type == 'classifier':
path = os.path.join(get_data_path(), 'output/direction.pkl')
with open(path, 'rb') as f:
model = pickle.load(f)
return model
def read_df(path: str = os.path.join(get_data_path(), 'input/<My_data>.csv'),
**kwargs) -> pd.DataFrame:
"""
Method importing a DataFrame from a specified path
:param path: A str pointing to the respective csv file
:param kwargs: Additional kwargs for pandas' read_csv method
:return: None
"""
try:
df = pd.read_csv(path, **kwargs)
return df
except FileNotFoundError:
print('Data file not found. Path was ' + path)
def read_encoder(type: str = 'label') -> LabelEncoder or OneHotEncoder:
"""
Mehthod to read the classes of an encoder object for later use
:return: Encoder Object containing the correct classes
"""
path = os.path.join(get_data_path(), 'output')
if type == 'label':
encoder = joblib.load(os.path.join(path, 'classes.joblib'))
elif type == 'season':
encoder = joblib.load(os.path.join(path, 'season.joblib'))
elif type == 'station':
encoder = joblib.load(os.path.join(path, 'station.joblib'))
return encoder
def save_model(model):
pickle.dump(model, open(os.path.join(get_data_path(), 'output/model.pkl'), 'wb'))
def read_model():
path = os.path.join(get_data_path(), 'output/model.pkl')
with open(path, 'rb') as f:
model = pickle.load(f)
return model