class_docstr: Create exogenous date features
  
  Given an exogenous feature of dtype TimeStamp, creates a set of dummy and
  ordinal variables indicating:
  
    * Day of the week
        Particular days of the week may align with quasi-seasonal trends.
  
    * Day of the month
        Useful for modeling things like the end-of-month effect, ie., a
        department spends the remainder of its monthly budget to avoid future
        budget cuts, and the last Friday of the month is heavy on spending.
  
  The motivation for this featurizer comes from a blog post by Rob Hyndman
  [1] on modeling quasi-seasonal patterns in time series. Note that an
  exogenous array _must_ be provided at inference.
  
  Parameters
  ----------
  column_name : str
      The name of the date column. This forces the exogenous array to be a
      Pandas DataFrame, and does not permit a np.ndarray as others may.
  
  with_day_of_week : bool, optional (default=True)
      Whether to include dummy variables for the day of the week (in {0, 1}).
  
  with_day_of_month : bool, optional (default=True)
      Whether to include an ordinal feature for the day of the month (1-31).
  
  prefix : str or None, optional (default=None)
      The feature prefix
  
  Examples
  --------
  >>> from pmdarima.datasets._base import load_date_example
  >>> y, X = load_date_example()
  >>> feat = DateFeaturizer(column_name='date')
  >>> _, X_prime = feat.fit_transform(y, X)
  >>> X_prime.head()
     DATE-WEEKDAY-0  DATE-WEEKDAY-1  ...  DATE-WEEKDAY-6  DATE-DAY-OF-MONTH
  0               0               1  ...               0                  1
  1               0               0  ...               0                  2
  2               0               0  ...               0                  3
  3               0               0  ...               0                  4
  4               0               0  ...               0                  5
  
  Notes
  -----
  * In order to use time series with holes, it is required that an X
    array be provided at prediction time. Other featurizers automatically
    create exog arrays into the future for inference, but this is not
    possible currently with the date featurizer. Your code must provide the
    dates for which you are forecasting as exog features.
  
  * The ``column_name`` field is dropped in the transformed exogenous array.
  
  References
  ----------
  .. [1] https://robjhyndman.com/hyndsight/monthly-seasonality/
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code: def __init__(
      self,
      column_name,
      with_day_of_week=True,
      with_day_of_month=True,
      prefix=None,
  ):
      super().__init__(prefix=prefix)
  
      self.column_name = column_name
      self.with_day_of_week = with_day_of_week
      self.with_day_of_month = with_day_of_month
  
file_path: pmdarima/pmdarima/preprocessing/exog/dates.py
imports: from .base import BaseExogFeaturizer
  import numpy
  import pandas
  import warnings
label_desc: Use image node_id 1 to create a new DateFeaturizer object from inherited base classes: BaseExogFeaturizer with example: obj = DateFeaturizer(column_name, with_day_of_week, with_day_of_month, prefix)