make_regression.py

"""
This file contains a modified version of the make_regression() function of
scikit-learn.

We modify the function in order to create a regression dataset with
standard-uniform coefficients. The original scikit-learn implementation scales
the standard-uniformly generated coefficients with a factor of 100.

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    BSD 3-Clause License

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"""

import numpy as np
from sklearn.utils import check_random_state
from sklearn.utils import shuffle as util_shuffle
from sklearn.datasets import make_low_rank_matrix

def make_regression(
    n_samples=100,
    n_features=100,
    *,
    n_informative=10,
    n_targets=1,
    bias=0.0,
    effective_rank=None,
    tail_strength=0.5,
    noise=0.0,
    shuffle=True,
    coef=False,
    random_state=None,
):
    n_informative = min(n_features, n_informative)
    generator = check_random_state(random_state)

    if effective_rank is None:
        # Randomly generate a well conditioned input set
        X = generator.standard_normal(size=(n_samples, n_features))

    else:
        # Randomly generate a low rank, fat tail input set
        X = make_low_rank_matrix(
            n_samples=n_samples,
            n_features=n_features,
            effective_rank=effective_rank,
            tail_strength=tail_strength,
            random_state=generator,
        )

    # Generate a ground truth model with only n_informative features being non
    # zeros (the other features are not correlated to y and should be ignored
    # by a sparsifying regularizers such as L1 or elastic net)
    # CHANGES: Sklearn multiplies the uniformly created coefficients with a
    # factor of 100. We avoid that.
    ground_truth = np.zeros((n_features, n_targets))
    ground_truth[:n_informative, :] = generator.uniform(
        size=(n_informative, n_targets)
    )

    y = np.dot(X, ground_truth) + bias

    # Add noise
    if noise > 0.0:
        y += generator.normal(scale=noise, size=y.shape)

    # Randomly permute samples and features
    if shuffle:
        X, y = util_shuffle(X, y, random_state=generator)

        indices = np.arange(n_features)
        generator.shuffle(indices)
        X[:, :] = X[:, indices]
        ground_truth = ground_truth[indices]

    y = np.squeeze(y)

    if coef:
        return X, y, np.squeeze(ground_truth)

    else:
        return X, y