def gen_rolling_feature(self,
window_size,
settings="comprehensive",
full_settings=None,
n_jobs=1):
'''
Generate aggregation feature for each sample.
This method will be implemented by tsfresh.
TODO: relationship with scale should be figured out.
:param window_size: int, generate feature according to the rolling result.
:param settings: str or dict. If a string is set, then it must be one of "comprehensive"
"minimal" and "efficient". If a dict is set, then it should follow the instruction
for default_fc_parameters in tsfresh. The value is defaulted to "comprehensive".
:param full_settings: dict. It should follow the instruction for kind_to_fc_parameters in
tsfresh. The value is defaulted to None.
:param n_jobs: int. The number of processes to use for parallelization.
:return: the tsdataset instance.
'''
assert not self._has_generate_agg_feature,\
"Only one of gen_global_feature and gen_rolling_feature should be called."
if isinstance(settings, str):
assert settings in ["comprehensive", "minimal", "efficient"], \
f"settings str should be one of \"comprehensive\", \"minimal\", \"efficient\"\
, but found {settings}."
default_fc_parameters = DEFAULT_PARAMS[settings]
else:
default_fc_parameters = settings
df_rolled = roll_time_series(self.df,
column_id=self.id_col,
column_sort=self.dt_col,
max_timeshift=window_size - 1,
min_timeshift=window_size - 1,
n_jobs=n_jobs)
if not full_settings:
self.roll_feature_df = extract_features(
df_rolled,
column_id=self.id_col,
column_sort=self.dt_col,
default_fc_parameters=default_fc_parameters,
n_jobs=n_jobs)
else:
self.roll_feature_df = extract_features(
df_rolled,
column_id=self.id_col,
column_sort=self.dt_col,
kind_to_fc_parameters=full_settings,
n_jobs=n_jobs)
impute_tsfresh(self.roll_feature_df)
self.feature_col += list(self.roll_feature_df.columns)
self.roll_additional_feature = list(self.roll_feature_df.columns)
self._has_generate_agg_feature = True
return self
def gen_rolling_feature(self,
window_size,
settings="comprehensive",
full_settings=None,
n_jobs=1):
'''
Generate aggregation feature for each sample.
This method will be implemented by tsfresh.
Make sure that the specified column name does not contain '__'.
TODO: relationship with scale should be figured out.
:param window_size: int, generate feature according to the rolling result.
:param settings: str or dict. If a string is set, then it must be one of "comprehensive"
"minimal" and "efficient". If a dict is set, then it should follow the instruction
for default_fc_parameters in tsfresh. The value is defaulted to "comprehensive".
:param full_settings: dict. It should follow the instruction for kind_to_fc_parameters in
tsfresh. The value is defaulted to None.
:param n_jobs: int. The number of processes to use for parallelization.
:return: the tsdataset instance.
'''
from tsfresh.utilities.dataframe_functions import roll_time_series
from tsfresh.utilities.dataframe_functions import impute as impute_tsfresh
from tsfresh import extract_features
from tsfresh.feature_extraction import ComprehensiveFCParameters, \
MinimalFCParameters, EfficientFCParameters
DEFAULT_PARAMS = {
"comprehensive": ComprehensiveFCParameters(),
"minimal": MinimalFCParameters(),
"efficient": EfficientFCParameters()
}
assert not self._has_generate_agg_feature,\
"Only one of gen_global_feature and gen_rolling_feature should be called."
if isinstance(settings, str):
assert settings in ['comprehensive', 'minimal', 'efficient'], \
"settings str should be one of 'comprehensive', 'minimal', 'efficient'"\
f", but found {settings}."
default_fc_parameters = DEFAULT_PARAMS[settings]
else:
default_fc_parameters = settings
assert window_size < self.df.groupby(self.id_col).size().min() + 1, "gen_rolling_feature "\
"should have a window_size smaller than shortest time series length."
df_rolled = roll_time_series(self.df,
column_id=self.id_col,
column_sort=self.dt_col,
max_timeshift=window_size - 1,
min_timeshift=window_size - 1,
n_jobs=n_jobs)
if not full_settings:
self.roll_feature_df = extract_features(
df_rolled,
column_id=self.id_col,
column_sort=self.dt_col,
default_fc_parameters=default_fc_parameters,
n_jobs=n_jobs)
else:
self.roll_feature_df = extract_features(
df_rolled,
column_id=self.id_col,
column_sort=self.dt_col,
kind_to_fc_parameters=full_settings,
n_jobs=n_jobs)
impute_tsfresh(self.roll_feature_df)
self.feature_col += list(self.roll_feature_df.columns)
self.roll_additional_feature = list(self.roll_feature_df.columns)
self._has_generate_agg_feature = True
return self
