42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import numpy as np
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import scipy.sparse as sp
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from numpy.testing import assert_array_almost_equal, assert_array_equal
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from sklearn.datasets import make_classification
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from sklearn.utils.sparsefuncs import assign_rows_csr, mean_variance_axis0
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def test_mean_variance_axis0():
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X, _ = make_classification(5, 4, random_state=0)
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# Sparsify the array a little bit
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X[0, 0] = 0
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X[2, 1] = 0
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X[4, 3] = 0
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X_lil = sp.lil_matrix(X)
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X_lil[1, 0] = 0
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X[1, 0] = 0
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X_csr = sp.csr_matrix(X_lil)
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X_means, X_vars = mean_variance_axis0(X_csr)
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assert_array_almost_equal(X_means, np.mean(X, axis=0))
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assert_array_almost_equal(X_vars, np.var(X, axis=0))
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X_csc = sp.csc_matrix(X_lil)
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X_means, X_vars = mean_variance_axis0(X_csc)
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assert_array_almost_equal(X_means, np.mean(X, axis=0))
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assert_array_almost_equal(X_vars, np.var(X, axis=0))
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def test_densify_rows():
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X = sp.csr_matrix([[0, 3, 0],
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[2, 4, 0],
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[0, 0, 0],
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[9, 8, 7],
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[4, 0, 5]], dtype=np.float64)
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rows = np.array([0, 2, 3], dtype=np.int)
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out = np.ones((rows.shape[0], X.shape[1]), dtype=np.float64)
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assign_rows_csr(X, rows, np.arange(out.shape[0])[::-1], out)
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assert_array_equal(out, X[rows].toarray()[::-1])
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