Source code for autogluon.features.generators.datetime
import logging
import pandas as pd
from pandas import DataFrame
from autogluon.core.features.types import R_DATETIME, S_DATETIME_AS_OBJECT
from .abstract import AbstractFeatureGenerator
logger = logging.getLogger(__name__)
[docs]class DatetimeFeatureGenerator(AbstractFeatureGenerator):
"""Transforms datetime features into numeric features."""
def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict):
X_out = self._transform(X)
type_family_groups_special = dict(
datetime_as_int=list(X_out.columns)
)
return X_out, type_family_groups_special
def _transform(self, X: DataFrame) -> DataFrame:
return self._generate_features_datetime(X)
@staticmethod
def get_default_infer_features_in_args() -> dict:
return dict(required_raw_special_pairs=[
(R_DATETIME, None),
(None, [S_DATETIME_AS_OBJECT])
])
# TODO: Improve handling of missing datetimes
def _generate_features_datetime(self, X: DataFrame) -> DataFrame:
X_datetime = DataFrame(index=X.index)
for datetime_feature in self.features_in:
# TODO: Be aware: When converted to float32 by downstream models, the seconds value will be up to 3 seconds off the true time due to rounding error. If seconds matter, find a separate way to generate (Possibly subtract smallest datetime from all values).
X_datetime[datetime_feature] = pd.to_datetime(X[datetime_feature])
X_datetime[datetime_feature] = pd.to_numeric(X_datetime[datetime_feature]) # TODO: Use actual date info
# X_datetime[datetime_feature] = pd.to_timedelta(X_datetime[datetime_feature]).dt.total_seconds()
# TODO: Add fastai date features
return X_datetime