FoldSelectionConfig# class causalpy.checks.placebo_in_time.FoldSelectionConfig[source]# Configuration for placebo fold selection. n_folds# Number of placebo folds. Type: int selection_method# How to choose placebo windows (“sequential” or “random”). Type: str min_training_pct# Minimum fraction of training data before each fold (random mode). Type: float min_gap# Minimum gap between folds in observations (random mode). Type: int allow_overlap# Whether folds can overlap (random mode). Type: bool exclude_periods# Period labels to exclude (random mode). Type: set[str] | None Methods Attributes allow_overlap exclude_periods min_gap min_training_pct n_folds selection_method __init__(n_folds=3, selection_method='sequential', min_training_pct=0.3, min_gap=1, allow_overlap=False, exclude_periods=None)# Parameters: n_folds (int) selection_method (Literal['sequential', 'random']) min_training_pct (float) min_gap (int) allow_overlap (bool) exclude_periods (set[str] | None) Return type: None classmethod __new__(*args, **kwargs)#
