def set_pipeline(self):
memory = self.kwargs.get("pipeline_memory", None)
dist = self.kwargs.get("distance_type", "euclidian")
feateng_steps = self.kwargs.get(
"feateng",
["distance", "time_features", 'direction', 'distance_to_center'])
if memory:
memory = mkdtemp()
# Define feature engineering pipeline blocks here
pipe_time_features = make_pipeline(
TimeFeaturesEncoder(time_column='pickup_datetime'),
OneHotEncoder(handle_unknown='ignore'))
pipe_distance = make_pipeline(
DistanceTransformer(distance_type=dist, **DIST_ARGS),
RobustScaler())
pipe_geohash = make_pipeline(AddGeohash(), ce.HashingEncoder())
pipe_direction = make_pipeline(Direction(), RobustScaler())
pipe_distance_to_center = make_pipeline(DistanceToCenter(),
RobustScaler())
# Define default feature engineering blocs
feateng_blocks = [
('distance', pipe_distance, list(DIST_ARGS.values())),
('time_features', pipe_time_features, ['pickup_datetime']),
('geohash', pipe_geohash, list(DIST_ARGS.values())),
('direction', pipe_direction, list(DIST_ARGS.values())),
('distance_to_center', pipe_distance_to_center,
list(DIST_ARGS.values())),
]
# Filter out some bocks according to input parameters
for bloc in feateng_blocks:
if bloc[0] not in feateng_steps:
feateng_blocks.remove(bloc)
features_encoder = ColumnTransformer(feateng_blocks,
n_jobs=None,
remainder="drop")
self.pipeline = Pipeline(steps=[('features', features_encoder),
('rgs', self.get_estimator())],
memory=memory)
if self.optimize:
self.pipeline.steps.insert(
-1,
['optimize_size', OptimizeSize(verbose=False)])
def set_pipeline(self):
memory = self.kwargs.get('pipeline_memory', None)
dist = self.kwargs.get('distance_type', 'haversine')
feateng_steps = self.kwargs.get('feateng',
['distance', 'time_features'])
if memory:
memory = mkdtemp()
# Define feature engineering pipeline blocks here
pipe_time_features = make_pipeline(
TimeFeaturesEncoder(time_column='pickup_datetime'),
OneHotEncoder(handle_unknown='ignore'))
pipe_distance = make_pipeline(
DistanceTransformer(distance_type=dist, **DIST_ARGS),
StandardScaler())
pipe_geohash = make_pipeline(AddGeohash(), ce.HashingEncoder())
pipe_direction = make_pipeline(Direction(), StandardScaler())
pipe_distance_to_center = make_pipeline(DistanceToCenter(),
StandardScaler())
# Combine pipes
feateng_blocks = [
('distance', pipe_distance, list(DIST_ARGS.values())),
('time_features', pipe_time_features, ['pickup_datetime']),
#('geohash', pipe_geohash, list(DIST_ARGS.values())),
('direction', pipe_direction, list(DIST_ARGS.values())),
('distance_to_center', pipe_distance_to_center,
list(DIST_ARGS.values())),
]
for bloc in feateng_blocks:
if bloc[0] not in feateng_steps:
feateng_blocks.remove(bloc)
features_encoder = ColumnTransformer(feateng_blocks,
n_jobs=None,
remainder="drop")
self.pipeline = Pipeline(steps=[('features', features_encoder),
('rgs', self.get_estimator())],
memory=memory)
if self.optimize:
self.pipeline.steps.insert(
-1,
['optimize_size', OptimizeSize(verbose=False)])