Inzynierka/Lib/site-packages/sklearn/svm/tests/test_bounds.py

import numpy as np
from scipy import sparse as sp
from scipy import stats

import pytest

from sklearn.svm._bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.svm._newrand import set_seed_wrap, bounded_rand_int_wrap


dense_X = [[-1, 0], [0, 1], [1, 1], [1, 1]]
sparse_X = sp.csr_matrix(dense_X)

Y1 = [0, 1, 1, 1]
Y2 = [2, 1, 0, 0]


@pytest.mark.parametrize("loss", ["squared_hinge", "log"])
@pytest.mark.parametrize("X_label", ["sparse", "dense"])
@pytest.mark.parametrize("Y_label", ["two-classes", "multi-class"])
@pytest.mark.parametrize("intercept_label", ["no-intercept", "fit-intercept"])
def test_l1_min_c(loss, X_label, Y_label, intercept_label):
    Xs = {"sparse": sparse_X, "dense": dense_X}
    Ys = {"two-classes": Y1, "multi-class": Y2}
    intercepts = {
        "no-intercept": {"fit_intercept": False},
        "fit-intercept": {"fit_intercept": True, "intercept_scaling": 10},
    }

    X = Xs[X_label]
    Y = Ys[Y_label]
    intercept_params = intercepts[intercept_label]
    check_l1_min_c(X, Y, loss, **intercept_params)


def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=1.0):
    min_c = l1_min_c(
        X,
        y,
        loss=loss,
        fit_intercept=fit_intercept,
        intercept_scaling=intercept_scaling,
    )

    clf = {
        "log": LogisticRegression(penalty="l1", solver="liblinear"),
        "squared_hinge": LinearSVC(loss="squared_hinge", penalty="l1", dual=False),
    }[loss]

    clf.fit_intercept = fit_intercept
    clf.intercept_scaling = intercept_scaling

    clf.C = min_c
    clf.fit(X, y)
    assert (np.asarray(clf.coef_) == 0).all()
    assert (np.asarray(clf.intercept_) == 0).all()

    clf.C = min_c * 1.01
    clf.fit(X, y)
    assert (np.asarray(clf.coef_) != 0).any() or (np.asarray(clf.intercept_) != 0).any()


def test_ill_posed_min_c():
    X = [[0, 0], [0, 0]]
    y = [0, 1]
    with pytest.raises(ValueError):
        l1_min_c(X, y)


_MAX_UNSIGNED_INT = 4294967295


@pytest.mark.parametrize("seed, val", [(None, 81), (0, 54), (_MAX_UNSIGNED_INT, 9)])
def test_newrand_set_seed(seed, val):
    """Test that `set_seed` produces deterministic results"""
    if seed is not None:
        set_seed_wrap(seed)
    x = bounded_rand_int_wrap(100)
    assert x == val, f"Expected {val} but got {x} instead"


@pytest.mark.parametrize("seed", [-1, _MAX_UNSIGNED_INT + 1])
def test_newrand_set_seed_overflow(seed):
    """Test that `set_seed_wrap` is defined for unsigned 32bits ints"""
    with pytest.raises(OverflowError):
        set_seed_wrap(seed)


@pytest.mark.parametrize("range_, n_pts", [(_MAX_UNSIGNED_INT, 10000), (100, 25)])
def test_newrand_bounded_rand_int(range_, n_pts):
    """Test that `bounded_rand_int` follows a uniform distribution"""
    n_iter = 100
    ks_pvals = []
    uniform_dist = stats.uniform(loc=0, scale=range_)
    # perform multiple samplings to make chance of outlier sampling negligible
    for _ in range(n_iter):
        # Deterministic random sampling
        sample = [bounded_rand_int_wrap(range_) for _ in range(n_pts)]
        res = stats.kstest(sample, uniform_dist.cdf)
        ks_pvals.append(res.pvalue)
    # Null hypothesis = samples come from an uniform distribution.
    # Under the null hypothesis, p-values should be uniformly distributed
    # and not concentrated on low values
    # (this may seem counter-intuitive but is backed by multiple refs)
    # So we can do two checks:

    # (1) check uniformity of p-values
    uniform_p_vals_dist = stats.uniform(loc=0, scale=1)
    res_pvals = stats.kstest(ks_pvals, uniform_p_vals_dist.cdf)
    assert res_pvals.pvalue > 0.05, (
        "Null hypothesis rejected: generated random numbers are not uniform."
        " Details: the (meta) p-value of the test of uniform distribution"
        f" of p-values is {res_pvals.pvalue} which is not > 0.05"
    )

    # (2) (safety belt) check that 90% of p-values are above 0.05
    min_10pct_pval = np.percentile(ks_pvals, q=10)
    # lower 10th quantile pvalue <= 0.05 means that the test rejects the
    # null hypothesis that the sample came from the uniform distribution
    assert min_10pct_pval > 0.05, (
        "Null hypothesis rejected: generated random numbers are not uniform. "
        f"Details: lower 10th quantile p-value of {min_10pct_pval} not > 0.05."
    )


@pytest.mark.parametrize("range_", [-1, _MAX_UNSIGNED_INT + 1])
def test_newrand_bounded_rand_int_limits(range_):
    """Test that `bounded_rand_int_wrap` is defined for unsigned 32bits ints"""
    with pytest.raises(OverflowError):
        bounded_rand_int_wrap(range_)