Source code for evalml.pipelines.components.transformers.preprocessing.datetime_featurizer

from evalml.pipelines.components.transformers import Transformer
from evalml.utils import (

def _extract_year(col, encode_as_categories=False):
    return col.dt.year, None

_month_to_int_mapping = {"January": 0, "February": 1, "March": 2, "April": 3, "May": 4, "June": 5,
                         "July": 6, "August": 7, "September": 8, "October": 9, "November": 10, "December": 11}

def _extract_month(col, encode_as_categories=False):
    months = col.dt.month_name()
    months_unique = months.unique()
    months_encoded = m: _month_to_int_mapping[m])
    if encode_as_categories:
        months_encoded = months_encoded.astype("category")
    return months_encoded, {m: _month_to_int_mapping[m] for m in months_unique}

_day_to_int_mapping = {"Sunday": 0, "Monday": 1, "Tuesday": 2, "Wednesday": 3, "Thursday": 4, "Friday": 5,
                       "Saturday": 6}

def _extract_day_of_week(col, encode_as_categories=False):
    days = col.dt.day_name()
    days_unique = days.unique()
    days_encoded = d: _day_to_int_mapping[d])
    if encode_as_categories:
        days_encoded = days_encoded.astype("category")
    return days_encoded, {d: _day_to_int_mapping[d] for d in days_unique}

def _extract_hour(col, encode_as_categories=False):
    return col.dt.hour, None

[docs]class DateTimeFeaturizer(Transformer): """Transformer that can automatically featurize DateTime columns.""" name = "DateTime Featurization Component" hyperparameter_ranges = {} _function_mappings = {"year": _extract_year, "month": _extract_month, "day_of_week": _extract_day_of_week, "hour": _extract_hour}
[docs] def __init__(self, features_to_extract=None, encode_as_categories=False, random_state=None, random_seed=0, **kwargs): """Extracts features from DateTime columns Arguments: features_to_extract (list): List of features to extract. Valid options include "year", "month", "day_of_week", "hour". encode_as_categories (bool): Whether day-of-week and month features should be encoded as pandas "category" dtype. This allows OneHotEncoders to encode these features. random_state (int): Deprecated - use random_seed instead. random_seed (int): Seed for the random number generator. Defaults to 0. """ if features_to_extract is None: features_to_extract = ["year", "month", "day_of_week", "hour"] invalid_features = set(features_to_extract) - set(self._function_mappings.keys()) if len(invalid_features) > 0: raise ValueError("{} are not valid options for features_to_extract".format(", ".join([f"'{feature}'" for feature in invalid_features]))) parameters = {"features_to_extract": features_to_extract, "encode_as_categories": encode_as_categories} parameters.update(kwargs) self._date_time_col_names = None self._categories = {} self.encode_as_categories = encode_as_categories super().__init__(parameters=parameters, component_obj=None, random_state=random_state, random_seed=random_seed)
[docs] def fit(self, X, y=None): X = infer_feature_types(X) self._date_time_col_names ="datetime").columns return self
[docs] def transform(self, X, y=None): """Transforms data X by creating new features using existing DateTime columns, and then dropping those DateTime columns Arguments: X (ww.DataTable, pd.DataFrame): Data to transform y (ww.DataColumn, pd.Series, optional): Ignored. Returns: ww.DataTable: Transformed X """ X_ww = infer_feature_types(X) X_t = _convert_woodwork_types_wrapper(X_ww.to_dataframe()) features_to_extract = self.parameters["features_to_extract"] if len(features_to_extract) == 0: return infer_feature_types(X_t) for col_name in self._date_time_col_names: for feature in features_to_extract: name = f"{col_name}_{feature}" features, categories = self._function_mappings[feature](X_t[col_name], self.encode_as_categories) X_t[name] = features if categories: self._categories[name] = categories X_t = X_t.drop(self._date_time_col_names, axis=1) return _retain_custom_types_and_initalize_woodwork(X_ww, X_t)
[docs] def get_feature_names(self): """Gets the categories of each datetime feature. Returns: Dictionary, where each key-value pair is a column name and a dictionary mapping the unique feature values to their integer encoding. """ return self._categories
def _get_feature_provenance(self): provenance = {} for col_name in self._date_time_col_names: provenance[col_name] = [] for feature in self.parameters['features_to_extract']: provenance[col_name].append(f'{col_name}_{feature}') return provenance