splitter.split_base

splitter.split_base

Base class for time series cross-validation splitting.

Classes

Name	Description
BaseFold	Base class for all Fold classes in spotforecast. All fold classes should specify

BaseFold

splitter.split_base.BaseFold(
    steps=None,
    initial_train_size=None,
    fold_stride=None,
    window_size=None,
    differentiation=None,
    refit=False,
    fixed_train_size=True,
    gap=0,
    skip_folds=None,
    allow_incomplete_fold=True,
    return_all_indexes=False,
    verbose=True,
)

Base class for all Fold classes in spotforecast. All fold classes should specify all the parameters that can be set at the class level in their __init__.

Parameters

Name	Type	Description	Default
steps	int	Number of observations used to be predicted in each fold. This is also commonly referred to as the forecast horizon or test size. Defaults to None.	`None`
initial_train_size	int \| str \| pd.Timestamp	Number of observations used for initial training. - If an integer, the number of observations used for initial training. - If a date string or pandas Timestamp, it is the last date included in the initial training set. Defaults to None.	`None`
fold_stride	int	Number of observations that the start of the test set advances between consecutive folds. - If `None`, it defaults to the same value as `steps`, meaning that folds are placed back-to-back without overlap. - If `fold_stride < steps`, test sets overlap and multiple forecasts will be generated for the same observations. - If `fold_stride > steps`, gaps are left between consecutive test sets. Defaults to None.	`None`
window_size	int	Number of observations needed to generate the autoregressive predictors. Defaults to None.	`None`
differentiation	int	Number of observations to use for differentiation. This is used to extend the `last_window` as many observations as the differentiation order. Defaults to None.	`None`
refit	bool \| int	Whether to refit the forecaster in each fold. - If `True`, the forecaster is refitted in each fold. - If `False`, the forecaster is trained only in the first fold. - If an integer, the forecaster is trained in the first fold and then refitted every `refit` folds. Defaults to False.	`False`
fixed_train_size	bool	Whether the training size is fixed or increases in each fold. Defaults to True.	`True`
gap	int	Number of observations between the end of the training set and the start of the test set. Defaults to 0.	`0`
skip_folds	int \| list	Number of folds to skip. - If an integer, every ‘skip_folds’-th is returned. - If a list, the indexes of the folds to skip. For example, if `skip_folds=3` and there are 10 folds, the returned folds are 0, 3, 6, and 9. If `skip_folds=[1, 2, 3]`, the returned folds are 0, 4, 5, 6, 7, 8, and 9. Defaults to None.	`None`
allow_incomplete_fold	bool	Whether to allow the last fold to include fewer observations than `steps`. If `False`, the last fold is excluded if it is incomplete. Defaults to True.	`True`
return_all_indexes	bool	Whether to return all indexes or only the start and end indexes of each fold. Defaults to False.	`False`
verbose	bool	Whether to print information about generated folds. Defaults to True.	`True`

Attributes

Name	Type	Description
initial_train_size	int	Number of observations used for initial training.
window_size	int	Number of observations needed to generate the autoregressive predictors.
differentiation	int	Number of observations to use for differentiation. This is used to extend the `last_window` as many observations as the differentiation order.
return_all_indexes	bool	Whether to return all indexes or only the start and end indexes of each fold.
verbose	bool	Whether to print information about generated folds.

Functions

Validates the input parameters to ensure correctness.

Extracts and returns the index from the input data X.

Set the parameters of the Fold object after validating them.

Examples

import pandas as pd
import numpy as np
from spotforecast2_safe.splitter import TimeSeriesFold
from spotforecast2_safe.splitter.split_base import BaseFold

# BaseFold is the shared base; TimeSeriesFold is its concrete subclass.
rng = np.random.default_rng(0)
idx = pd.date_range("2020-01-01", periods=120, freq="h")
series = pd.Series(rng.standard_normal(120), index=idx)

cv = TimeSeriesFold(
    steps=6,
    initial_train_size=100,
    window_size=10,
    verbose=False,
)
assert isinstance(cv, BaseFold)
assert cv.initial_train_size == 100
assert cv.window_size == 10

# Inspect index extracted from the series.
idx_extracted = cv._extract_index(series)
assert len(idx_extracted) == 120
print(f"BaseFold subclass: {type(cv).__name__}")
print(f"initial_train_size={cv.initial_train_size}, window_size={cv.window_size}")

BaseFold subclass: TimeSeriesFold
initial_train_size=100, window_size=10

Methods

Name	Description
set_params	Set the parameters of the Fold object. Before overwriting the current

set_params

splitter.split_base.BaseFold.set_params(params)

Set the parameters of the Fold object. Before overwriting the current parameters, the input parameters are validated to ensure correctness.

Parameters

Name	Type	Description	Default
params	dict	Dictionary with the parameters to set.	required

Examples

from spotforecast2_safe.splitter import TimeSeriesFold

cv = TimeSeriesFold(steps=1)
cv.set_params({
    "steps": 2,
    "initial_train_size": 10,
    "fold_stride": 2,
    "window_size": 5,
    "differentiation": 1,
    "refit": True,
    "fixed_train_size": False,
    "gap": 1,
    "skip_folds": 2,
    "allow_incomplete_fold": False,
    "return_all_indexes": True,
    "verbose": False,
})
assert cv.initial_train_size == 10
assert cv.window_size == 5