cursus.processing.numerical.minmax_scaling_processor

MinMax Scaling Processor for Numerical Features

This module provides atomic min-max scaling with learned parameters. Extracted from TSA preprocessing scaling logic.

class MinMaxScalingProcessor(feature_range=(0, 1), learned_params=None, columns=None, clip_values=True)

Bases: Processor

Min-max scaling with learned parameters.

Extracted from TSA preprocessing: - seq_num_mtx[:, :-2] = seq_num_mtx[:, :-2] * np.array(seq_num_scale_) + np.array(seq_num_min_)

Parameters:

• feature_range (Tuple[float, float]) – Target range for scaling

• learned_params (Dict[str, Dict[str, float]] | None) – Pre-computed scaling parameters

• columns (List[str] | None) – Specific columns to scale

• clip_values (bool) – Whether to clip to feature_range

fit(data)

Learn scaling parameters from data.

If params were pre-supplied at construction (learned_params), fit is intentionally a no-op that REUSES them (load-a-prior-fit pattern) — it logs that it is skipping recomputation so the no-op is not mistaken for a silent failure. Pass learned_params=None to force learning from data.

process(input_data)

Apply min-max scaling

inverse_transform(scaled_data)

Inverse transform scaled data back to original scale

get_data_range(column=None)

Get the original data range(s)

get_scaling_info()

Get detailed scaling information

get_config()

Return processor configuration