def combine_predictions() -> None:
"""
Method combining duration and direction prediction and saving the file to disc.
:return: None
"""
# Trying to load data from duration prediction
try:
duration_predictions = pd.read_csv(
os.path.join(get_data_path(), 'output/duration_predictions.csv'))
except Exception as e:
print('Data from duration prediction could not be loaded.')
raise e
# Trying to load data from direction prediction
try:
direction_predictions = pd.read_csv(
os.path.join(get_data_path(), 'output/direction_predictions.csv'))
except Exception as e:
print('Data from direction prediction could not be loaded.')
raise e
# Concatenating both DataFrames and save them to disk
final_df = pd.concat(
[duration_predictions, direction_predictions['direction']], axis=1)
final_df.to_csv(
os.path.join(get_data_path(), 'output/final_predictions.csv'))
print('Prediction data was combined and saved to disc.')
def execute_geo_filtering(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
# Load the GeoJSON boundary of Mannheim
mannheim_boundary_gdf = gpd.read_file(os.path.join(
get_data_path(), 'input/mannheim_boundary.geojson'),
crs='EPSG:4326')
# Remove all trips which are not within Mannheim (using native shapely is faster than geopandas' spatial join)
return gdf[gdf.within(mannheim_boundary_gdf['geometry'][0])]
def train(whatmodel, resolution):
"""
This command allows for training several machine-learning models for different scopes on preprocessed Nextbike data.
A trips-indexed Nextbike file for the city of Bremen must exist in data/processed/.
Duration, direction and demand models are available.
Models are saved after training as pre-trained models in pickle format under /models/.
"""
if not os.path.isfile(os.path.join(get_data_path(), 'processed/bremen.csv')):
click.echo(
'Could not find /data/processed/bremen.csv - please run preprocessing first using " nextbike transform".', err=True)
sys.exit(0)
m = Model('bremen.csv')
if whatmodel == 'duration':
m.train_duration()
elif whatmodel == 'direction':
m.train_direction_uni()
m.train_direction_main_station()
elif whatmodel == 'demand':
if resolution is None:
click.echo(
'No temporal resolution defined, please specify using the -t/--resolution parameter.')
sys.exit(0)
else:
m.train_demand(resolution +'H')
def train(filename):
"""
Trains a model based on a given data frame and saves it to disk at {project_dir}/data/output
:param filename: Path to the data frame which should be used for training
:return: None
"""
with yaspin(color='blue') as spinner:
spinner.text = 'Conducting Pre-Processing and Transformation steps ...\t'
preprocessor = Preprocessor()
preprocessor.load_gdf(filename)
preprocessor.clean_gdf()
transformer = Transformer(preprocessor)
transformer.transform()
spinner.text = 'Training duration model ...\t'
duration_model = DurationModel()
duration_model.load_from_transformer(transformer, training=True)
duration_model.train()
duration_model.predict()
duration_model.training_score()
spinner.text = 'Training direction model ...\t'
direction_model = DirectionModel()
direction_model.load_from_transformer(transformer, training=True)
direction_model.train()
direction_model.predict()
direction_model.training_score()
spinner.text = 'Models trained and saved to disk at {}.'.format(
os.path.join(get_data_path(), 'output'))
spinner.ok('✅ ')
def __init__(self, filename, refresh=False):
self._refresh = refresh
self._filename = filename
self._prettyfilename = filename.replace('.csv', '')
self._datapath = get_data_path()
self._raw = io.read_file(path=os.path.join(self._datapath,
'raw/' + filename),
datetime_cols=['datetime'])
self.plz_df = gpd.read_file(self._datapath +
'/external/plz_bremen.geojson')
def save_model(model, type: str = 'regressor') -> None:
"""
Method for saving trained models to disc.
:param model: A trained model instance
:param type: A string representing if type of model is related to duration, false booking or direction prediction
:return: None
"""
if type == 'regressor':
pickle.dump(
model,
open(os.path.join(get_data_path(), 'output/duration.pkl'), 'wb'))
elif type == 'booking_filter':
pickle.dump(
model,
open(os.path.join(get_data_path(), 'output/booking_filter.pkl'),
'wb'))
elif type == 'classifier':
pickle.dump(
model,
open(os.path.join(get_data_path(), 'output/direction.pkl'), 'wb'))
def _geo_filter_mannheim_trips(self) -> None:
"""
Removes all trips which are geographically outside of Mannheim
:return: None
"""
# Load the GeoJSON boundary of Mannheim
mannheim_boundary_gdf = gpd.read_file(os.path.join(
get_data_path(), 'input/mannheim_boundary.geojson'),
crs='EPSG:4326')
# Remove all trips which are not within Mannheim (using native shapely is faster than geopandas' spatial join)
self._gdf = self._gdf[self._gdf.within(
mannheim_boundary_gdf['geometry'][0])]
def save_encoder(encoder: LabelEncoder, type: str = 'label') -> None:
"""
Mehthod to save the classes of an encoder object for later use
:param encoder: The encoder object that was fit and used to transform target features in classification
:return: None
"""
path = os.path.join(get_data_path(), 'output')
if type == 'label':
joblib.dump(encoder, os.path.join(path, 'classes.joblib'))
elif type == 'season':
joblib.dump(encoder, os.path.join(path, 'season.joblib'))
elif type == 'station':
joblib.dump(encoder, os.path.join(path, 'station.joblib'))
def save(self, filename: str = 'mannheim_transformed.csv') -> None:
"""
Saves the transformed GeoDataFrame as csv-file to the disk.
:return: None
:raises: UserWarning
"""
if self.__gdf is None:
raise UserWarning(
'Attempting to save an empty data set. Did you transform it before?'
)
path = os.path.join(get_data_path(), 'output')
create_dir_if_not_exists(path)
self.__gdf.to_csv(os.path.join(path, filename), index=False)
def save_predictions(predicted_data: pd.DataFrame,
type: str = 'regressor') -> None:
"""
Method that saves DataFrames containing the raw data as well as predictions
:param predicted_data: A DataFrame containing raw data and predictions
:param type: A string representing if type of model is related to duration, false booking or direction prediction
:return: None
"""
path = os.path.join(get_data_path(), 'output')
create_dir_if_not_exists(path)
if type == 'regressor':
predicted_data.to_csv(os.path.join(path, 'duration_predictions.csv'),
index=False)
elif type == 'classifier':
predicted_data.to_csv(os.path.join(path, 'direction_predictions.csv'),
index=False)
def load_gdf(self, path: str = None) -> None:
"""
Reads the raw DataFrame, transforms it to a GeoDataFrame and initializes the __gdf property.
:type path: object A path that points to the .csv file
:return: None
"""
if path:
df = read_df(path, index_col=0, parse_dates=['datetime'])
else:
df = read_df(os.path.join(get_data_path(), 'input/mannheim.csv'),
index_col=0,
parse_dates=['datetime'])
validate_input(df)
self._gdf = gpd.GeoDataFrame(df,
crs='EPSG:4326',
geometry=gpd.points_from_xy(
df['p_lng'], df['p_lat']))
def predict(filename):
"""
Predicts the duration of the trips specified in the given data frame and saves them to disk at
{project_dir}/data/output
:param filename: Path to the data frame which should be used for prediction
:return: None
"""
with yaspin(color='blue') as spinner:
spinner.text = 'Conducting Pre-Processing and Transformation steps ...\t'
preprocessor = Preprocessor()
preprocessor.load_gdf(filename)
preprocessor.clean_gdf()
transformer = Transformer(preprocessor)
transformer.transform()
spinner.text = 'Performing duration prediction ...\t'
duration_predictor = DurationModel()
duration_predictor.load_from_transformer(transformer, training=False)
duration_predictor.predict(save=True)
spinner.text = 'Performing direction prediction ...\t'
direction_predictor = DirectionModel()
direction_predictor.load_from_transformer(transformer, training=False)
direction_predictor.predict(save=True)
spinner.text = 'Predictions performed and saved to disk at {}.'.format(os.path.join(get_data_path(), 'output'))
spinner.ok('✅ ')
def __init__(self, filename):
self._datapath = get_data_path()
self._filename = filename
