sktime.utils.validation.forecasting¶
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sktime.utils.validation.forecasting.
check_X
(X, allow_empty=False, enforce_univariate=False, enforce_index_type=None)[source]¶ Validate input data.
- Parameters
- Returns
X – Validated input data.
- Return type
pd.Series, pd.DataFrame
- Raises
ValueError, TypeError – If X is an invalid input
UserWarning – Warning that X is given and model can’t use it
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sktime.utils.validation.forecasting.
check_alpha
(alpha)[source]¶ Check that a confidence level alpha (or list of alphas) is valid. All alpha values must lie in the open interval (0, 1). :param alpha: :type alpha: float, list of float
- Raises
ValueError – If alpha is outside the range (0, 1).
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sktime.utils.validation.forecasting.
check_cutoffs
(cutoffs)[source]¶ Validates the cutoff
- Parameters
cutoffs (np.ndarray or pd.Index) –
- Returns
- Return type
cutoffs (Sorted array)
- Raises
ValueError – If cutoffs is not a instance of np.array or pd.Index If cutoffs array is empty.
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sktime.utils.validation.forecasting.
check_cv
(cv)[source]¶ Check CV generators.
- Parameters
cv (CV generator) –
- Raises
ValueError – if cv does not have the required attributes.
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sktime.utils.validation.forecasting.
check_fh
(fh, enforce_relative=False)[source]¶ Validate forecasting horizon.
- Parameters
fh (int, list, np.array, pd.Index or ForecastingHorizon) – Forecasting horizon specifying the time points to predict.
enforce_relative (bool, optional (default=False)) – If True, checks if fh is relative.
- Returns
fh – Validated forecasting horizon.
- Return type
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sktime.utils.validation.forecasting.
check_scoring
(scoring)[source]¶ Validates the performace scoring
- Parameters
scoring (object of class MetricFunctionWrapper from sktime.performance_metrics.) –
- Returns
scoring (object of class MetricFunctionWrapper of sktime.performance_metrics.)
sMAPE(mean percentage error) – if the object is None.
- Raises
TypeError – if object is not callable from current scope. if object is not an instance of class MetricFunctionWrapper of sktime.performance_metrics.
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sktime.utils.validation.forecasting.
check_sp
(sp, enforce_list=False)[source]¶ Validate seasonal periodicity.
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sktime.utils.validation.forecasting.
check_step_length
(step_length)[source]¶ Validate window length. :param step_length: :type step_length: step length for data set.
- Returns
step_length – if step_length in not none and is int and greater than or equal to 1.
- Return type
- Raises
ValueError – if step_length is negative or not an integer or is None.
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sktime.utils.validation.forecasting.
check_y
(y, allow_empty=False, allow_constant=True, enforce_index_type=None)[source]¶ Validate input data.
- Parameters
- Returns
y
- Return type
pd.Series
- Raises
ValueError, TypeError – If y is an invalid input
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sktime.utils.validation.forecasting.
check_y_X
(y, X=None, allow_empty=False, allow_constant=True, enforce_index_type=None)[source]¶ Validate input data.
- Parameters
y (pd.Series) –
X (pd.DataFrame, optional (default=None)) –
allow_empty (bool, optional (default=False)) – If True, empty y does not raise an error.
allow_constant (bool, optional (default=True)) – If True, constant y does not raise an error.
enforce_index_type (type, optional (default=None)) – type of time index
- Raises
ValueError – If y or X are invalid inputs