mismatched_series_length_data_check
================================================================

.. py:module:: evalml.data_checks.mismatched_series_length_data_check

.. autoapi-nested-parse::

   Data check that checks if one or more unique series in a multiseres data is a different length than the others.



Module Contents
---------------

Classes Summary
~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.data_checks.mismatched_series_length_data_check.MismatchedSeriesLengthDataCheck





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: MismatchedSeriesLengthDataCheck(series_id)



   Check if one or more unique series in a multiseries dataset is of a different length than the others.

   Currently works specifically on stacked data

   :param series_id: The name of the series_id column for the dataset.
   :type series_id: str




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.data_checks.mismatched_series_length_data_check.MismatchedSeriesLengthDataCheck.name
      evalml.data_checks.mismatched_series_length_data_check.MismatchedSeriesLengthDataCheck.validate

   .. py:method:: name(cls)

      Return a name describing the data check.


   .. py:method:: validate(self, X, y=None)

      Check if one or more unique series in a multiseries dataset is of a different length than the other.

      Currently works specifically on stacked data

      :param X: The input features to check. Must have a series_id column.
      :type X: pd.DataFrame, np.ndarray
      :param y: The target. Defaults to None. Ignored.
      :type y: pd.Series

      :returns:

                List with DataCheckWarning if there are mismatch series length in the datasets
                      or list with DataCheckError if the given series_id is not in the dataset
      :rtype: dict (DataCheckWarning, DataCheckError)

      .. rubric:: Examples

      >>> import pandas as pd

      For multiseries time series datasets, each seriesID should ideally have the same number of datetime entries as
      each other. If they don't, then a warning will be raised denoting which seriesID have mismatched lengths.

      >>> X = pd.DataFrame(
      ...     {
      ...         "date": pd.date_range(start="1/1/2018", periods=20).repeat(5),
      ...         "series_id": pd.Series(list(range(5)) * 20, dtype="str"),
      ...         "feature_a": range(100),
      ...         "feature_b": reversed(range(100)),
      ...     },
      ... )
      >>> X = X.drop(labels=0, axis=0)
      >>> mismatched_series_length_check = MismatchedSeriesLengthDataCheck("series_id")
      >>> assert mismatched_series_length_check.validate(X) == [
      ...      {
      ...         "message": "Series ID ['0'] do not match the majority length of the other series, which is 20",
      ...         "data_check_name": "MismatchedSeriesLengthDataCheck",
      ...         "level": "warning",
      ...         "details": {
      ...             "columns": None,
      ...             "rows": None,
      ...             "series_id": ['0'],
      ...             "majority_length": 20
      ...         },
      ...         "code": "MISMATCHED_SERIES_LENGTH",
      ...         "action_options": [],
      ...     }
      ... ]

      If MismatchedSeriesLengthDataCheck is passed in an invalid series_id column name, then an error will be raised.

      >>> X = pd.DataFrame(
      ...     {
      ...         "date": pd.date_range(start="1/1/2018", periods=20).repeat(5),
      ...         "series_id": pd.Series(list(range(5)) * 20, dtype="str"),
      ...         "feature_a": range(100),
      ...         "feature_b": reversed(range(100)),
      ...     },
      ... )
      >>> X = X.drop(labels=0, axis=0)
      >>> mismatched_series_length_check = MismatchedSeriesLengthDataCheck("not_series_id")
      >>> assert mismatched_series_length_check.validate(X) == [
      ...      {
      ...         "message": "series_id 'not_series_id' is not in the dataset.",
      ...         "data_check_name": "MismatchedSeriesLengthDataCheck",
      ...         "level": "error",
      ...         "details": {
      ...             "columns": None,
      ...             "rows": None,
      ...             "series_id": "not_series_id",
      ...         },
      ...         "code": "INVALID_SERIES_ID_COL",
      ...         "action_options": [],
      ...     }
      ... ]

      If there are multiple lengths that have the same number of series (e.g. two series have length 20 and two series have length 19),
      this datacheck will consider the higher length to be the majority length (e.g. from the previous example length 20 would be the majority length)
      >>> X = pd.DataFrame(
      ...     {
      ...         "date": pd.date_range(start="1/1/2018", periods=20).repeat(4),
      ...         "series_id": pd.Series(list(range(4)) * 20, dtype="str"),
      ...         "feature_a": range(80),
      ...         "feature_b": reversed(range(80)),
      ...     },
      ... )
      >>> X = X.drop(labels=[0, 1], axis=0)
      >>> mismatched_series_length_check = MismatchedSeriesLengthDataCheck("series_id")
      >>> assert mismatched_series_length_check.validate(X) == [
      ...      {
      ...         "message": "Series ID ['0', '1'] do not match the majority length of the other series, which is 20",
      ...         "data_check_name": "MismatchedSeriesLengthDataCheck",
      ...         "level": "warning",
      ...         "details": {
      ...             "columns": None,
      ...             "rows": None,
      ...             "series_id": ['0', '1'],
      ...             "majority_length": 20
      ...         },
      ...         "code": "MISMATCHED_SERIES_LENGTH",
      ...         "action_options": [],
      ...     }
      ... ]