datetime_format_data_check
=======================================================

.. py:module:: evalml.data_checks.datetime_format_data_check

.. autoapi-nested-parse::

   Data check that checks if the datetime column has equally spaced intervals and is monotonically increasing or decreasing in order to be supported by time series estimators.



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

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

.. autoapisummary::

   evalml.data_checks.datetime_format_data_check.DateTimeFormatDataCheck





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: DateTimeFormatDataCheck(datetime_column='index')



   Check if the datetime column has equally spaced intervals and is monotonically increasing or decreasing in order to be supported by time series estimators.

   :param datetime_column: The name of the datetime column. If the datetime values are in the index, then pass "index".
   :type datetime_column: str, int




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.data_checks.datetime_format_data_check.DateTimeFormatDataCheck.name
      evalml.data_checks.datetime_format_data_check.DateTimeFormatDataCheck.validate

   .. py:method:: name(cls)

      Return a name describing the data check.


   .. py:method:: validate(self, X, y)

      Checks if the target data has equal intervals and is monotonically increasing.

      Will return a DataCheckError if the data is not a datetime type, is not increasing, has redundant or missing row(s),
      contains invalid (NaN or None) values, or has values that don't align with the assumed frequency.

      :param X: Features.
      :type X: pd.DataFrame, np.ndarray
      :param y: Target data.
      :type y: pd.Series, np.ndarray

      :returns: List with DataCheckErrors if unequal intervals are found in the datetime column.
      :rtype: dict (DataCheckError)

      .. rubric:: Examples

      >>> import pandas as pd

      The column 'dates' has a set of two dates with daily frequency, two dates with hourly frequency,
      and two dates with monthly frequency.

      >>> X = pd.DataFrame(pd.date_range("2015-01-01", periods=2).append(pd.date_range("2015-01-08", periods=2, freq="H").append(pd.date_range("2016-03-02", periods=2, freq="M"))), columns=["dates"])
      >>> y = pd.Series([0, 1, 0, 1, 1, 0])
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="dates")
      >>> assert datetime_format_dc.validate(X, y) == [
      ...     {
      ...         "message": "No frequency could be detected in column 'dates', possibly due to uneven intervals.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_NO_FREQUENCY_INFERRED",
      ...         "details": {"columns": None, "rows": None},
      ...         "action_options": []
      ...      }
      ... ]

      The column "dates" has a gap in the values, which implies there are many dates missing.

      >>> X = pd.DataFrame(pd.date_range("2021-01-01", periods=9).append(pd.date_range("2021-01-31", periods=50)), columns=["dates"])
      >>> y = pd.Series([0, 1, 0, 1, 1, 0, 0, 0, 1, 0])
      >>> ww_payload = infer_frequency(X["dates"], debug=True, window_length=5, threshold=0.8)
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="dates")
      >>> assert datetime_format_dc.validate(X, y) == [
      ...     {
      ...         "message": "Column 'dates' has datetime values missing between start and end date.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_IS_MISSING_VALUES",
      ...         "details": {"columns": None, "rows": None},
      ...         "action_options": []
      ...      },
      ...     {
      ...         "message": "A frequency was detected in column 'dates', but there are faulty datetime values that need to be addressed.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_HAS_UNEVEN_INTERVALS",
      ...         "details": {'columns': None, 'rows': None},
      ...         "action_options": [
      ...             {
      ...                 'code': 'REGULARIZE_AND_IMPUTE_DATASET',
      ...                 'data_check_name': 'DateTimeFormatDataCheck',
      ...                 'metadata': {
      ...                         'columns': None,
      ...                         'is_target': True,
      ...                         'rows': None
      ...                 },
      ...                 'parameters': {
      ...                         'time_index': {
      ...                             'default_value': 'dates',
      ...                             'parameter_type': 'global',
      ...                             'type': 'str'
      ...                         },
      ...                         'frequency_payload': {
      ...                             'default_value': ww_payload,
      ...                             'parameter_type': 'global',
      ...                             'type': 'tuple'
      ...                         }
      ...                 }
      ...             }
      ...         ]
      ...     }
      ... ]

      The column "dates" has a repeat of the date 2021-01-09 appended to the end, which is considered redundant and will raise an error.

      >>> X = pd.DataFrame(pd.date_range("2021-01-01", periods=9).append(pd.date_range("2021-01-09", periods=1)), columns=["dates"])
      >>> y = pd.Series([0, 1, 0, 1, 1, 0, 0, 0, 1, 0])
      >>> ww_payload = infer_frequency(X["dates"], debug=True, window_length=5, threshold=0.8)
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="dates")
      >>> assert datetime_format_dc.validate(X, y) == [
      ...     {
      ...         "message": "Column 'dates' has more than one row with the same datetime value.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_HAS_REDUNDANT_ROW",
      ...         "details": {"columns": None, "rows": None},
      ...         "action_options": []
      ...      },
      ...     {
      ...         "message": "A frequency was detected in column 'dates', but there are faulty datetime values that need to be addressed.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_HAS_UNEVEN_INTERVALS",
      ...         "details": {'columns': None, 'rows': None},
      ...         "action_options": [
      ...             {
      ...                 'code': 'REGULARIZE_AND_IMPUTE_DATASET',
      ...                 'data_check_name': 'DateTimeFormatDataCheck',
      ...                 'metadata': {
      ...                         'columns': None,
      ...                         'is_target': True,
      ...                         'rows': None
      ...                 },
      ...                 'parameters': {
      ...                         'time_index': {
      ...                             'default_value': 'dates',
      ...                             'parameter_type': 'global',
      ...                             'type': 'str'
      ...                         },
      ...                         'frequency_payload': {
      ...                             'default_value': ww_payload,
      ...                             'parameter_type': 'global',
      ...                             'type': 'tuple'
      ...                         }
      ...                 }
      ...             }
      ...         ]
      ...     }
      ... ]

      The column "Weeks" has a date that does not follow the weekly pattern, which is considered misaligned.

      >>> X = pd.DataFrame(pd.date_range("2021-01-01", freq="W", periods=12).append(pd.date_range("2021-03-22", periods=1)), columns=["Weeks"])
      >>> ww_payload = infer_frequency(X["Weeks"], debug=True, window_length=5, threshold=0.8)
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="Weeks")
      >>> assert datetime_format_dc.validate(X, y) == [
      ...     {
      ...         "message": "Column 'Weeks' has datetime values that do not align with the inferred frequency.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "details": {"columns": None, "rows": None},
      ...         "code": "DATETIME_HAS_MISALIGNED_VALUES",
      ...         "action_options": []
      ...      },
      ...     {
      ...         "message": "A frequency was detected in column 'Weeks', but there are faulty datetime values that need to be addressed.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_HAS_UNEVEN_INTERVALS",
      ...         "details": {'columns': None, 'rows': None},
      ...         "action_options": [
      ...             {
      ...                 'code': 'REGULARIZE_AND_IMPUTE_DATASET',
      ...                 'data_check_name': 'DateTimeFormatDataCheck',
      ...                 'metadata': {
      ...                         'columns': None,
      ...                         'is_target': True,
      ...                         'rows': None
      ...                 },
      ...                 'parameters': {
      ...                         'time_index': {
      ...                             'default_value': 'Weeks',
      ...                             'parameter_type': 'global',
      ...                             'type': 'str'
      ...                         },
      ...                         'frequency_payload': {
      ...                             'default_value': ww_payload,
      ...                             'parameter_type': 'global',
      ...                             'type': 'tuple'
      ...                         }
      ...                 }
      ...             }
      ...         ]
      ...     }
      ... ]

      The column "Weeks" has a date that does not follow the weekly pattern, which is considered misaligned.

      >>> X = pd.DataFrame(pd.date_range("2021-01-01", freq="W", periods=12).append(pd.date_range("2021-03-22", periods=1)), columns=["Weeks"])
      >>> ww_payload = infer_frequency(X["Weeks"], debug=True, window_length=5, threshold=0.8)
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="Weeks")
      >>> assert datetime_format_dc.validate(X, y) == [
      ...     {
      ...         "message": "Column 'Weeks' has datetime values that do not align with the inferred frequency.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "details": {"columns": None, "rows": None},
      ...         "code": "DATETIME_HAS_MISALIGNED_VALUES",
      ...         "action_options": []
      ...      },
      ...     {
      ...         "message": "A frequency was detected in column 'Weeks', but there are faulty datetime values that need to be addressed.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_HAS_UNEVEN_INTERVALS",
      ...         "details": {'columns': None, 'rows': None},
      ...         "action_options": [
      ...             {
      ...                 'code': 'REGULARIZE_AND_IMPUTE_DATASET',
      ...                 'data_check_name': 'DateTimeFormatDataCheck',
      ...                 'metadata': {
      ...                         'columns': None,
      ...                         'is_target': True,
      ...                         'rows': None
      ...                 },
      ...                 'parameters': {
      ...                         'time_index': {
      ...                             'default_value': 'Weeks',
      ...                             'parameter_type': 'global',
      ...                             'type': 'str'
      ...                         },
      ...                         'frequency_payload': {
      ...                             'default_value': ww_payload,
      ...                             'parameter_type': 'global',
      ...                             'type': 'tuple'
      ...                         }
      ...                 }
      ...             }
      ...         ]
      ...     }
      ... ]

      The column "Weeks" passed integers instead of datetime data, which will raise an error.

      >>> X = pd.DataFrame([1, 2, 3, 4], columns=["Weeks"])
      >>> y = pd.Series([0] * 4)
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="Weeks")
      >>> assert datetime_format_dc.validate(X, y) == [
      ...     {
      ...         "message": "Datetime information could not be found in the data, or was not in a supported datetime format.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "details": {"columns": None, "rows": None},
      ...         "code": "DATETIME_INFORMATION_NOT_FOUND",
      ...         "action_options": []
      ...      }
      ... ]

      Converting that same integer data to datetime, however, is valid.

      >>> X = pd.DataFrame(pd.to_datetime([1, 2, 3, 4]), columns=["Weeks"])
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="Weeks")
      >>> assert datetime_format_dc.validate(X, y) == []

      >>> X = pd.DataFrame(pd.date_range("2021-01-01", freq="W", periods=10), columns=["Weeks"])
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="Weeks")
      >>> assert datetime_format_dc.validate(X, y) == []

      While the data passed in is of datetime type, time series requires the datetime information in datetime_column
      to be monotonically increasing (ascending).

      >>> X = X.iloc[::-1]
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="Weeks")
      >>> assert datetime_format_dc.validate(X, y) == [
      ...     {
      ...         "message": "Datetime values must be sorted in ascending order.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "details": {"columns": None, "rows": None},
      ...         "code": "DATETIME_IS_NOT_MONOTONIC",
      ...         "action_options": []
      ...      }
      ... ]

      The first value in the column "index" is replaced with NaT, which will raise an error in this data check.

      >>> dates = [["2-1-21", "3-1-21"],
      ...         ["2-2-21", "3-2-21"],
      ...         ["2-3-21", "3-3-21"],
      ...         ["2-4-21", "3-4-21"],
      ...         ["2-5-21", "3-5-21"],
      ...         ["2-6-21", "3-6-21"],
      ...         ["2-7-21", "3-7-21"],
      ...         ["2-8-21", "3-8-21"],
      ...         ["2-9-21", "3-9-21"],
      ...         ["2-10-21", "3-10-21"],
      ...         ["2-11-21", "3-11-21"],
      ...         ["2-12-21", "3-12-21"]]
      >>> dates[0][0] = None
      >>> df = pd.DataFrame(dates, columns=["days", "days2"])
      >>> ww_payload = infer_frequency(pd.to_datetime(df["days"]), debug=True, window_length=5, threshold=0.8)
      >>> datetime_format_dc = DateTimeFormatDataCheck(datetime_column="days")
      >>> assert datetime_format_dc.validate(df, y) == [
      ...     {
      ...         "message": "Input datetime column 'days' contains NaN values. Please impute NaN values or drop these rows.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "details": {"columns": None, "rows": None},
      ...         "code": "DATETIME_HAS_NAN",
      ...         "action_options": []
      ...      },
      ...     {
      ...         "message": "A frequency was detected in column 'days', but there are faulty datetime values that need to be addressed.",
      ...         "data_check_name": "DateTimeFormatDataCheck",
      ...         "level": "error",
      ...         "code": "DATETIME_HAS_UNEVEN_INTERVALS",
      ...         "details": {'columns': None, 'rows': None},
      ...         "action_options": [
      ...             {
      ...                 'code': 'REGULARIZE_AND_IMPUTE_DATASET',
      ...                 'data_check_name': 'DateTimeFormatDataCheck',
      ...                 'metadata': {
      ...                         'columns': None,
      ...                         'is_target': True,
      ...                         'rows': None
      ...                 },
      ...                 'parameters': {
      ...                         'time_index': {
      ...                             'default_value': 'days',
      ...                             'parameter_type': 'global',
      ...                             'type': 'str'
      ...                         },
      ...                         'frequency_payload': {
      ...                             'default_value': ww_payload,
      ...                             'parameter_type': 'global',
      ...                             'type': 'tuple'
      ...                         }
      ...                 }
      ...             }
      ...         ]
      ...     }
      ... ]
      ...