preprocessing.checking.check_predict_input

preprocessing.checking.check_predict_input(
    forecaster_name,
    steps,
    is_fitted,
    exog_in_,
    index_type_,
    index_freq_,
    window_size,
    last_window,
    last_window_exog=None,
    exog=None,
    exog_names_in_=None,
    interval=None,
    alpha=None,
    max_step=None,
    levels=None,
    levels_forecaster=None,
    series_names_in_=None,
    encoding=None,
)

Check all inputs of predict method. This is a helper function to validate that inputs used in predict method match attributes of a forecaster already trained.

Parameters

Name	Type	Description	Default
forecaster_name	str	str Forecaster name.	required
steps	Union[int, List[int]]	int, list Number of future steps predicted.	required
is_fitted	bool	bool Tag to identify if the estimator has been fitted (trained).	required
exog_in_	bool	bool If the forecaster has been trained using exogenous variable/s.	required
index_type_	type	type Type of index of the input used in training.	required
index_freq_	str	str Frequency of Index of the input used in training.	required
window_size	int	int Size of the window needed to create the predictors. It is equal to `max_lag`.	required
last_window	Optional[Union[pd.Series, pd.DataFrame]]	pandas Series, pandas DataFrame, None Values of the series used to create the predictors (lags) need in the first iteration of prediction (t + 1).	required
last_window_exog	Optional[Union[pd.Series, pd.DataFrame]]	pandas Series, pandas DataFrame, default None Values of the exogenous variables aligned with `last_window` in ForecasterStats predictions.	`None`
exog	Optional[Union[pd.Series, pd.DataFrame, Dict[str, Union[pd.Series, pd.DataFrame]]]]	pandas Series, pandas DataFrame, dict, default None Exogenous variable/s included as predictor/s.	`None`
exog_names_in_	Optional[List[str]]	list, default None Names of the exogenous variables used during training.	`None`
interval	Optional[List[float]]	list, tuple, default None Confidence of the prediction interval estimated. Sequence of percentiles to compute, which must be between 0 and 100 inclusive. For example, interval of 95% should be as `interval = [2.5, 97.5]`.	`None`
alpha	Optional[float]	float, default None The confidence intervals used in ForecasterStats are (1 - alpha) %.	`None`
max_step	Optional[int]	int, default None Maximum number of steps allowed (`ForecasterDirect` and `ForecasterDirectMultiVariate`).	`None`
levels	Optional[Union[str, List[str]]]	str, list, default None Time series to be predicted (`ForecasterRecursiveMultiSeries` and `ForecasterRnn). \|`None`\| \| levels_forecaster \| [Optional](`typing.Optional`)\[[Union](`typing.Union`)\[[str](`str`), [List](`typing.List`)\[[str](`str`)\]\]\] \| str, list, default None Time series used as output data of a multiseries problem in a RNN problem (`ForecasterRnn`). \|`None`\| \| series_names_in_ \| [Optional](`typing.Optional`)\[[List](`typing.List`)\[[str](`str`)\]\] \| list, default None Names of the columns used during fit (`ForecasterRecursiveMultiSeries`,`ForecasterDirectMultiVariate`and`ForecasterRnn`). \|`None`\| \| encoding \| [Optional](`typing.Optional`)\[[str](`str`)\] \| str, default None Encoding used to identify the different series (`ForecasterRecursiveMultiSeries`). \|`None`

Returns

Name	Type	Description
	None	None

Examples

import pandas as pd
import numpy as np

from spotforecast2_safe.preprocessing.checking import check_predict_input

last_window = pd.Series(
    [1.0, 2.0, 3.0],
    index=pd.date_range("2020-01-01", periods=3, freq="h"),
)

# Success path: fitted forecaster, no exog
check_predict_input(
    forecaster_name="ForecasterRecursive",
    steps=2,
    is_fitted=True,
    exog_in_=False,
    index_type_=pd.DatetimeIndex,
    index_freq_="h",
    window_size=3,
    last_window=last_window,
)
print("Success: valid predict inputs accepted.")

# Failure path: not fitted
try:
    check_predict_input(
        forecaster_name="ForecasterRecursive",
        steps=2,
        is_fitted=False,
        exog_in_=False,
        index_type_=pd.DatetimeIndex,
        index_freq_="h",
        window_size=3,
        last_window=last_window,
    )
except RuntimeError as e:
    print(f"RuntimeError (not fitted): {type(e).__name__}")

# Failure path: steps < 1
try:
    check_predict_input(
        forecaster_name="ForecasterRecursive",
        steps=0,
        is_fitted=True,
        exog_in_=False,
        index_type_=pd.DatetimeIndex,
        index_freq_="h",
        window_size=3,
        last_window=last_window,
    )
except ValueError as e:
    print(f"ValueError (steps=0): {type(e).__name__}")

# Failure path: exog required but not provided
try:
    check_predict_input(
        forecaster_name="ForecasterRecursive",
        steps=2,
        is_fitted=True,
        exog_in_=True,
        index_type_=pd.DatetimeIndex,
        index_freq_="h",
        window_size=3,
        last_window=last_window,
        exog=None,
    )
except ValueError as e:
    print(f"ValueError (missing exog): {type(e).__name__}")

Success: valid predict inputs accepted.
RuntimeError (not fitted): RuntimeError
ValueError (steps=0): ValueError
ValueError (missing exog): ValueError