datetime_format_data_check¶
Module Contents¶
Classes Summary¶
Checks if the datetime column has equally spaced intervals and is monotonically increasing or decreasing in order |
Contents¶
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class
evalml.data_checks.datetime_format_data_check.DateTimeFormatDataCheck(datetime_column='index')[source]¶ Checks if the datetime column has equally spaced intervals and is monotonically increasing or decreasing in order to be supported by time series estimators.
- Parameters
datetime_column (str, int) – The name of the datetime column. If the datetime values are in the index, then pass “index”.
Methods
Returns a name describing the data check.
Checks if the target data has equal intervals and is sorted.
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name(cls)¶ Returns a name describing the data check.
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validate(self, X, y)[source]¶ Checks if the target data has equal intervals and is sorted.
- 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)
Example
>>> from pandas as pd >>> X = pd.DataFrame(pd.date_range("January 1, 2021", periods=8), columns=["dates"]) >>> y = pd.Series([1, 2, 4, 2, 1, 2, 3, 1]) >>> X.iloc[7] = "January 9, 2021" >>> datetime_format_check = DateTimeFormatDataCheck() >>> assert datetime_format_check.validate(X, y) == {"errors": [{"message": "No frequency could be detected in dates, possibly due to uneven intervals.", "data_check_name": "EqualIntervalDataCheck", "level": "error", "code": "DATETIME_HAS_UNEVEN_INTERVALS", "details": {}}], "warnings": [], "actions": []}