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¶
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¶
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class
evalml.data_checks.datetime_format_data_check.DateTimeFormatDataCheck(datetime_column='index')[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.
- Parameters
datetime_column (str, int) – The name of the datetime column. If the datetime values are in the index, then pass “index”.
Methods
Return a name describing the data check.
Checks if the target data has equal intervals and is sorted.
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name(cls)¶ Return 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": []}