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import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_almost_equal
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import mean_variance_axis0
def test_mean_variance_axis0():
X, _ = make_classification(5, 4, random_state=0)
# Sparsify the array a little bit
X[0, 0] = 0
X[2, 1] = 0
X[4, 3] = 0
X_csr = sp.csr_matrix(X)
X_csr[1, 0] = 0
X[1, 0] = 0
X_means, X_vars = mean_variance_axis0(X_csr)
assert_array_almost_equal(X_means, np.mean(X, axis=0))
assert_array_almost_equal(X_vars, np.var(X, axis=0))
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