ts_parameters_data_check
=====================================================

.. py:module:: evalml.data_checks.ts_parameters_data_check

.. autoapi-nested-parse::

   Data check that checks whether the time series parameters are compatible with the data size.



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

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

.. autoapisummary::

   evalml.data_checks.ts_parameters_data_check.TimeSeriesParametersDataCheck





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: TimeSeriesParametersDataCheck(problem_configuration, n_splits)



   Checks whether the time series parameters are compatible with data splitting.

   If `gap + max_delay + forecast_horizon > X.shape[0] // (n_splits + 1)`

   then the feature engineering window is larger than the smallest split. This will cause the
   pipeline to create features from data that does not exist, which will cause errors.

   :param problem_configuration: Dict containing problem_configuration parameters.
   :type problem_configuration: dict
   :param n_splits: Number of time series splits.
   :type n_splits: int




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.data_checks.ts_parameters_data_check.TimeSeriesParametersDataCheck.name
      evalml.data_checks.ts_parameters_data_check.TimeSeriesParametersDataCheck.validate

   .. py:method:: name(cls)

      Return a name describing the data check.


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

      Check if the time series parameters are compatible with data splitting.

      :param X: Features.
      :type X: pd.DataFrame, np.ndarray
      :param y: Ignored. Defaults to None.
      :type y: pd.Series, np.ndarray

      :returns: dict with a DataCheckError if parameters are too big for the split sizes.
      :rtype: dict

      .. rubric:: Examples

      >>> import pandas as pd

      The time series parameters have to be compatible with the data passed. If the window size (gap + max_delay +
      forecast_horizon) is greater than or equal to the split size, then an error will be raised.

      >>> X = pd.DataFrame({
      ...    "dates": pd.date_range("1/1/21", periods=100),
      ...    "first": [i for i in range(100)],
      ... })
      >>> y = pd.Series([i for i in range(100)])
      ...
      >>> problem_config = {"gap": 7, "max_delay": 2, "forecast_horizon": 12, "time_index": "dates"}
      >>> ts_parameters_check = TimeSeriesParametersDataCheck(problem_configuration=problem_config, n_splits=7)
      >>> assert ts_parameters_check.validate(X, y) == [
      ...     {
      ...         "message": "Since the data has 100 observations, n_splits=7, and a forecast horizon of 12, the smallest "
      ...                    "split would have 16 observations. Since 21 (gap + max_delay + forecast_horizon)"
      ...                    " >= 16, then at least one of the splits would be empty by the time it reaches "
      ...                    "the pipeline. Please use a smaller number of splits, reduce one or more these "
      ...                    "parameters, or collect more data.",
      ...         "data_check_name": "TimeSeriesParametersDataCheck",
      ...         "level": "error",
      ...         "code": "TIMESERIES_PARAMETERS_NOT_COMPATIBLE_WITH_SPLIT",
      ...         "details": {
      ...             "columns": None,
      ...             "rows": None,
      ...             "max_window_size": 21,
      ...             "min_split_size": 16,
      ...             "n_obs": 100,
      ...             "n_splits": 7
      ...         },
      ...         "action_options": []
      ...     }
      ... ]