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)
def set_pipeline(self):
dist = self.kwargs.get("distance_type", "haversine")
feateng_steps = self.kwargs.get("feateng",
["distance", "time_features"])
# 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),
StandardScaler())
pipe_geohash = make_pipeline(AddGeohash(), ce.HashingEncoder())
# Add new feature engineer Above
#pipe_direction =
#pipe_distance_to_center =
# 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())], )
def set_pipeline(self):
time_features = make_pipeline(TimeFeaturesEncoder(time_column='pickup_datetime'),
OneHotEncoder(handle_unknown='ignore'))
pipe_geohash = make_pipeline(AddGeohash(), ce.HashingEncoder())
features_encoder = ColumnTransformer([
('distance', DistanceTransformer(**DIST_ARGS), list(DIST_ARGS.values())),
('time_features', time_features, ['pickup_datetime']),
('geohash', pipe_geohash, list(DIST_ARGS.values()))
])
self.pipeline = Pipeline(steps=[
('features', features_encoder),
('rgs', self.get_estimator())])
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)])
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"])
if memory:
memory = mkdtemp()
time_pipe = Pipeline([
("time_enc", TimeFeaturesEncoder("pickup_datetime")),
("ohe", OneHotEncoder(handle_unknown="ignore")),
])
dist_pipe = Pipeline([
("dist_trans", DistanceTransformer(distance_type=dist,
**DIST_ARGS)),
("stdscaler", StandardScaler()),
])
center_pipe = Pipeline([("distance_center", DistanceToCenter()),
("stdscaler", StandardScaler())])
geohash_pipe = Pipeline([("deohash_add", AddGeohash()),
("hash_encode", ce.HashingEncoder())])
direction_pipe = Pipeline([("direction_add", Direction()),
("stdscaler", StandardScaler())])
feateng_blocks = [
("distance", dist_pipe, list(DIST_ARGS.values())),
("time_features", time_pipe, ["pickup_datetime"]),
#("geohash", geohash_pipe, list(DIST_ARGS.values())),
("direction", direction_pipe, list(DIST_ARGS.values())),
("distance_to_center", center_pipe, 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),
("df_clener", DataframeCleaner(verbose=False)),
("rgs", self.get_estimator())],
memory=memory,
)
def set_pipeline(self):
time_features = make_pipeline(TimeFeaturesEncoder(time_column='pickup_datetime'),
OneHotEncoder(handle_unknown='ignore'))
pipe_geohash = make_pipeline(AddGeohash(), ce.HashingEncoder())
dist_args = dict(start_lat="pickup_latitude",
start_lon="pickup_longitude",
end_lat="dropoff_latitude",
end_lon="dropoff_longitude")
features_encoder = ColumnTransformer([
('distance', DistanceTransformer(**dist_args), list(dist_args.values())),
('time_features', time_features, ['pickup_datetime']),
('geohash', pipe_geohash, list(dist_args.values()))
])
self.pipeline = Pipeline(steps=[
('features', features_encoder),
('rgs', self.get_estimator())])