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Normalize integer fixed time horizons #292

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Merged
uriahf merged 1 commit into from
Aug 11, 2026
Merged

Normalize integer fixed time horizons #292

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8 changes: 6 additions & 2 deletions src/rtichoke/performance_data/performance_data_times.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,8 @@ def prepare_performance_data_times(
The event or censoring times corresponding to the `reals`. Can be a
single array or a dictionary.
fixed_time_horizons : list[float]
A list of time points at which to evaluate the model's performance.
A list of numeric time points at which to evaluate the model's
performance. Integer inputs are accepted and normalized to floats.
heuristics_sets : list[Dict], optional
A list of dictionaries, each specifying how to handle censored data
and competing events. The default is
Expand Down Expand Up @@ -134,7 +135,8 @@ def prepare_binned_classification_data_times(
times : Union[np.ndarray, Dict[str, np.ndarray]]
The event or censoring times.
fixed_time_horizons : list[float]
A list of time points for performance evaluation.
A list of numeric time points for performance evaluation. Integer
inputs are accepted and normalized to floats.
heuristics_sets : list[Dict], optional
Specifies how to handle censored data and competing events.
stratified_by : Sequence[str], optional
Expand All @@ -152,6 +154,8 @@ def prepare_binned_classification_data_times(
represents a unique combination of dataset, bin, time horizon,
heuristic, and other strata.
"""
fixed_time_horizons = [float(horizon) for horizon in fixed_time_horizons]

breaks = create_breaks_values(None, "probability_threshold", by)

aj_data_combinations = create_aj_data_combinations(
Expand Down
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