Source code for scitex_ml.sampling.undersample

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Time-stamp: "2024-11-24 10:13:17 (ywatanabe)"
# File: ./scitex_repo/src/scitex/ai/sampling/undersample.py

THIS_FILE = "/home/ywatanabe/proj/scitex_repo/src/scitex/ai/sampling/undersample.py"

from typing import Tuple

from scitex_types import ArrayLike

try:
    from imblearn.under_sampling import RandomUnderSampler

    IMBLEARN_AVAILABLE = True
except ImportError:
    IMBLEARN_AVAILABLE = False



[docs]
def undersample(
    X: ArrayLike, y: ArrayLike, random_state: int = 42
) -> Tuple[ArrayLike, ArrayLike]:
    """Undersample data preserving input type.

    Args:
        X: Features array-like of shape (n_samples, n_features)
        y: Labels array-like of shape (n_samples,)
    Returns:
        Resampled X, y of same type as input

    Raises:
        ImportError: If imblearn is not installed
    """
    if not IMBLEARN_AVAILABLE:
        raise ImportError(
            "The undersample function requires the imbalanced-learn package. "
            "Install it with: pip install imbalanced-learn"
        )

    rus = RandomUnderSampler(random_state=random_state)
    X_resampled, y_resampled = rus.fit_resample(X, y)
    return X_resampled, y_resampled



# EOF