ts_parameters_data_check¶
Data check that checks whether the time series parameters are compatible with the data size.
Module Contents¶
Classes Summary¶
Checks whether the time series parameters are compatible with data splitting. |
Contents¶
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class
evalml.data_checks.ts_parameters_data_check.TimeSeriesParametersDataCheck(problem_configuration, n_splits)[source]¶ 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.
- Parameters
problem_configuration (dict) – Dict containing problem_configuration parameters.
n_splits (int) – Number of time series splits.
Methods
Return a name describing the data check.
Check if the time series parameters are compatible with data splitting.
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name(cls)¶ Return a name describing the data check.
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validate(self, X, y=None)[source]¶ Check if the time series parameters are compatible with data splitting.
- Parameters
X (pd.DataFrame, np.ndarray) – Features.
y (pd.Series, np.ndarray) – Ignored. Defaults to None.
- Returns
dict with a DataCheckError if parameters are too big for the split sizes.
- Return type
dict