datetime_format_data_check#

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#

DateTimeFormatDataCheck

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

Contents#

class evalml.data_checks.datetime_format_data_check.DateTimeFormatDataCheck(datetime_column='index', nan_duplicate_threshold=0.75, series_id=None)[source]#

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

If used for multiseries problem, works specifically on stacked datasets.

Parameters

datetime_column (str, int) – The name of the datetime column. If the datetime values are in the index, then pass “index”.
nan_duplicate_threshold (float) – The percentage of values in the datetime_column that must not be duplicate or nan before DATETIME_NO_FREQUENCY_INFERRED is returned instead of DATETIME_HAS_UNEVEN_INTERVALS. For example, if this is set to 0.80, then only 20% of the values in datetime_column can be duplicate or nan. Defaults to 0.75.
series_id (str) – The name of the series_id column for multiseries. Defaults to None

Methods

`name`	Return a name describing the data check.
`validate`	Checks if the target data has equal intervals and is monotonically increasing.

name(cls)#: Return a name describing the data check.

validate(self, X, y)[source]#

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

Will return DataCheckError(s) 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.

If used for multiseries problem, works specifically on stacked datasets.

Parameters

X (pd.DataFrame, np.ndarray) – Features.
y (pd.Series, np.ndarray) – Target data.

Returns

List with DataCheckErrors if unequal intervals are found in the datetime column.

Return type

dict (DataCheckError)

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 or too many duplicate/missing values.",
...         "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” 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'
...                         }
...                 }
...             }
...         ]
...     }
... ]

For multiseries, the datacheck will go through each series and perform checks on them similar to the single series case To denote that the datacheck is checking a multiseries, pass in the name of the series_id column to the datacheck

>>> X = pd.DataFrame(
...     {
...         "date": pd.date_range("2021-01-01", periods=15).repeat(2),
...         "series_id": pd.Series(list(range(2)) * 15, dtype="str")
...     }
... )
>>> X = X.drop([15])
>>> dc = DateTimeFormatDataCheck(datetime_column="date", series_id="series_id")
>>> ww_payload_expected_series1 = infer_frequency((X[X["series_id"] == "1"]["date"].reset_index(drop=True)), debug=True, window_length=4, threshold=0.4)
>>> xd = dc.validate(X,y)
>>> assert dc.validate(X, y) == [
...     {
...         "message": "Column 'date' for series '1' has datetime values missing between start and end date.",
...         "data_check_name": "DateTimeFormatDataCheck",
...         "level": "error",
...         "details": {"columns": None, "rows": None},
...         "code": "DATETIME_IS_MISSING_VALUES",
...         "action_options": []
...      },
...     {
...         "message": "A frequency was detected in column 'date' for series '1', 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': 'date',
...                             'parameter_type': 'global',
...                             'type': 'str'
...                         },
...                         'frequency_payload': {
...                             'default_value': ww_payload_expected_series1,
...                             'parameter_type': 'global',
...                             'type': 'tuple'
...                         }
...                 }
...             }
...         ]
...     }
... ]