preprocessing.checking.check_residuals_input

preprocessing.checking.check_residuals_input(
    forecaster_name,
    use_in_sample_residuals,
    in_sample_residuals_,
    out_sample_residuals_,
    use_binned_residuals,
    in_sample_residuals_by_bin_,
    out_sample_residuals_by_bin_,
    levels=None,
    encoding=None,
)

Check residuals input arguments in Forecasters.

Parameters

Name	Type	Description	Default
forecaster_name	str	Forecaster name.	required
use_in_sample_residuals	bool	Indicates if in-sample or out-of-sample residuals are used.	required
in_sample_residuals_	np.ndarray \| dict[str, np.ndarray] \| None	Residuals of the model when predicting training data.	required
out_sample_residuals_	np.ndarray \| dict[str, np.ndarray] \| None	Residuals of the model when predicting non-training data.	required
use_binned_residuals	bool	Indicates if residuals are binned.	required
in_sample_residuals_by_bin_	dict[str \| int, np.ndarray \| dict[int, np.ndarray]] \| None	In-sample residuals binned according to the predicted value each residual is associated with.	required
out_sample_residuals_by_bin_	dict[str \| int, np.ndarray \| dict[int, np.ndarray]] \| None	Out-of-sample residuals binned according to the predicted value each residual is associated with.	required
levels	list[str] \| None	Names of the series (levels) to be predicted (multiseries forecasters). Defaults to None.	`None`
encoding	str \| None	Encoding used to identify the different series (`ForecasterRecursiveMultiSeries`). Defaults to None.	`None`

Returns

Name	Type	Description
	None	None

Raises

Name	Type	Description
	ValueError	If the requested residuals store is None or empty.

Examples

import numpy as np

from spotforecast2_safe.preprocessing.checking import check_residuals_input

residuals = np.array([0.1, -0.2, 0.3, -0.1, 0.2])

# Success path: in-sample residuals present
check_residuals_input(
    forecaster_name="ForecasterRecursive",
    use_in_sample_residuals=True,
    in_sample_residuals_=residuals,
    out_sample_residuals_=None,
    use_binned_residuals=False,
    in_sample_residuals_by_bin_=None,
    out_sample_residuals_by_bin_=None,
)
print("Success: residuals accepted.")

# Failure path: in-sample residuals are None
try:
    check_residuals_input(
        forecaster_name="ForecasterRecursive",
        use_in_sample_residuals=True,
        in_sample_residuals_=None,
        out_sample_residuals_=None,
        use_binned_residuals=False,
        in_sample_residuals_by_bin_=None,
        out_sample_residuals_by_bin_=None,
    )
except ValueError as e:
    print(f"ValueError (no residuals): {type(e).__name__}")

# Failure path: out-of-sample residuals not set
try:
    check_residuals_input(
        forecaster_name="ForecasterRecursive",
        use_in_sample_residuals=False,
        in_sample_residuals_=residuals,
        out_sample_residuals_=None,
        use_binned_residuals=False,
        in_sample_residuals_by_bin_=None,
        out_sample_residuals_by_bin_=None,
    )
except ValueError as e:
    print(f"ValueError (out-sample missing): {type(e).__name__}")

Success: residuals accepted.
ValueError (no residuals): ValueError
ValueError (out-sample missing): ValueError