618 lines
23 KiB
Python
618 lines
23 KiB
Python
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import pytest
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import numpy as np
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import scipy.sparse as sp
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from scipy import linalg
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from numpy.testing import assert_array_almost_equal, assert_array_equal
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from numpy.random import RandomState
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from sklearn.datasets import make_classification
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from sklearn.utils.sparsefuncs import (mean_variance_axis,
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incr_mean_variance_axis,
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inplace_column_scale,
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inplace_row_scale,
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inplace_swap_row, inplace_swap_column,
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min_max_axis,
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count_nonzero, csc_median_axis_0)
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from sklearn.utils.sparsefuncs_fast import (assign_rows_csr,
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inplace_csr_row_normalize_l1,
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inplace_csr_row_normalize_l2,
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csr_row_norms)
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from sklearn.utils._testing import assert_allclose
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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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with pytest.raises(TypeError):
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mean_variance_axis(X_lil, axis=0)
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X_csr = sp.csr_matrix(X_lil)
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X_csc = sp.csc_matrix(X_lil)
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expected_dtypes = [(np.float32, np.float32),
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(np.float64, np.float64),
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(np.int32, np.float64),
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(np.int64, np.float64)]
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for input_dtype, output_dtype in expected_dtypes:
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X_test = X.astype(input_dtype)
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for X_sparse in (X_csr, X_csc):
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X_sparse = X_sparse.astype(input_dtype)
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X_means, X_vars = mean_variance_axis(X_sparse, axis=0)
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assert X_means.dtype == output_dtype
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assert X_vars.dtype == output_dtype
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assert_array_almost_equal(X_means, np.mean(X_test, axis=0))
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assert_array_almost_equal(X_vars, np.var(X_test, axis=0))
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def test_mean_variance_axis1():
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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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with pytest.raises(TypeError):
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mean_variance_axis(X_lil, axis=1)
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X_csr = sp.csr_matrix(X_lil)
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X_csc = sp.csc_matrix(X_lil)
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expected_dtypes = [(np.float32, np.float32),
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(np.float64, np.float64),
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(np.int32, np.float64),
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(np.int64, np.float64)]
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for input_dtype, output_dtype in expected_dtypes:
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X_test = X.astype(input_dtype)
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for X_sparse in (X_csr, X_csc):
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X_sparse = X_sparse.astype(input_dtype)
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X_means, X_vars = mean_variance_axis(X_sparse, axis=0)
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assert X_means.dtype == output_dtype
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assert X_vars.dtype == output_dtype
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assert_array_almost_equal(X_means, np.mean(X_test, axis=0))
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assert_array_almost_equal(X_vars, np.var(X_test, axis=0))
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def test_incr_mean_variance_axis():
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for axis in [0, 1]:
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rng = np.random.RandomState(0)
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n_features = 50
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n_samples = 10
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data_chunks = [rng.randint(0, 2, size=n_features)
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for i in range(n_samples)]
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# default params for incr_mean_variance
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last_mean = np.zeros(n_features)
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last_var = np.zeros_like(last_mean)
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last_n = np.zeros_like(last_mean, dtype=np.int64)
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# Test errors
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X = np.array(data_chunks[0])
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X = np.atleast_2d(X)
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X_lil = sp.lil_matrix(X)
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X_csr = sp.csr_matrix(X_lil)
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with pytest.raises(TypeError):
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incr_mean_variance_axis(X=axis, axis=last_mean, last_mean=last_var,
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last_var=last_n)
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with pytest.raises(TypeError):
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incr_mean_variance_axis(X_lil, axis=axis, last_mean=last_mean,
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last_var=last_var, last_n=last_n)
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# Test _incr_mean_and_var with a 1 row input
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X_means, X_vars = mean_variance_axis(X_csr, axis)
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X_means_incr, X_vars_incr, n_incr = \
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incr_mean_variance_axis(X_csr, axis=axis, last_mean=last_mean,
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last_var=last_var, last_n=last_n)
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assert_array_almost_equal(X_means, X_means_incr)
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assert_array_almost_equal(X_vars, X_vars_incr)
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# X.shape[axis] picks # samples
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assert_array_equal(X.shape[axis], n_incr)
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X_csc = sp.csc_matrix(X_lil)
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X_means, X_vars = mean_variance_axis(X_csc, axis)
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assert_array_almost_equal(X_means, X_means_incr)
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assert_array_almost_equal(X_vars, X_vars_incr)
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assert_array_equal(X.shape[axis], n_incr)
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# Test _incremental_mean_and_var with whole data
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X = np.vstack(data_chunks)
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X_lil = sp.lil_matrix(X)
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X_csr = sp.csr_matrix(X_lil)
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X_csc = sp.csc_matrix(X_lil)
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expected_dtypes = [(np.float32, np.float32),
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(np.float64, np.float64),
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(np.int32, np.float64),
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(np.int64, np.float64)]
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for input_dtype, output_dtype in expected_dtypes:
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for X_sparse in (X_csr, X_csc):
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X_sparse = X_sparse.astype(input_dtype)
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last_mean = last_mean.astype(output_dtype)
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last_var = last_var.astype(output_dtype)
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X_means, X_vars = mean_variance_axis(X_sparse, axis)
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X_means_incr, X_vars_incr, n_incr = \
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incr_mean_variance_axis(X_sparse, axis=axis,
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last_mean=last_mean,
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last_var=last_var,
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last_n=last_n)
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assert X_means_incr.dtype == output_dtype
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assert X_vars_incr.dtype == output_dtype
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assert_array_almost_equal(X_means, X_means_incr)
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assert_array_almost_equal(X_vars, X_vars_incr)
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assert_array_equal(X.shape[axis], n_incr)
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@pytest.mark.parametrize(
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"X1, X2",
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[
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(sp.random(5, 2, density=0.8, format='csr', random_state=0),
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sp.random(13, 2, density=0.8, format='csr', random_state=0)),
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(sp.random(5, 2, density=0.8, format='csr', random_state=0),
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sp.hstack([sp.csr_matrix(np.full((13, 1), fill_value=np.nan)),
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sp.random(13, 1, density=0.8, random_state=42)],
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format="csr"))
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]
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)
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def test_incr_mean_variance_axis_equivalence_mean_variance(X1, X2):
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# non-regression test for:
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# https://github.com/scikit-learn/scikit-learn/issues/16448
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# check that computing the incremental mean and variance is equivalent to
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# computing the mean and variance on the stacked dataset.
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axis = 0
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last_mean, last_var = np.zeros(X1.shape[1]), np.zeros(X1.shape[1])
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last_n = np.zeros(X1.shape[1], dtype=np.int64)
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updated_mean, updated_var, updated_n = incr_mean_variance_axis(
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X1, axis=axis, last_mean=last_mean, last_var=last_var, last_n=last_n
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)
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updated_mean, updated_var, updated_n = incr_mean_variance_axis(
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X2, axis=axis, last_mean=updated_mean, last_var=updated_var,
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last_n=updated_n
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)
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X = sp.vstack([X1, X2])
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assert_allclose(updated_mean, np.nanmean(X.A, axis=axis))
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assert_allclose(updated_var, np.nanvar(X.A, axis=axis))
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assert_allclose(updated_n, np.count_nonzero(~np.isnan(X.A), axis=0))
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def test_incr_mean_variance_no_new_n():
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# check the behaviour when we update the variance with an empty matrix
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axis = 0
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X1 = sp.random(5, 1, density=0.8, random_state=0).tocsr()
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X2 = sp.random(0, 1, density=0.8, random_state=0).tocsr()
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last_mean, last_var = np.zeros(X1.shape[1]), np.zeros(X1.shape[1])
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last_n = np.zeros(X1.shape[1], dtype=np.int64)
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last_mean, last_var, last_n = incr_mean_variance_axis(
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X1, axis=axis, last_mean=last_mean, last_var=last_var, last_n=last_n
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)
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# update statistic with a column which should ignored
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updated_mean, updated_var, updated_n = incr_mean_variance_axis(
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X2, axis=axis, last_mean=last_mean, last_var=last_var, last_n=last_n
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)
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assert_allclose(updated_mean, last_mean)
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assert_allclose(updated_var, last_var)
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assert_allclose(updated_n, last_n)
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@pytest.mark.parametrize("axis", [0, 1])
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@pytest.mark.parametrize("sparse_constructor", [sp.csc_matrix, sp.csr_matrix])
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def test_incr_mean_variance_axis_ignore_nan(axis, sparse_constructor):
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old_means = np.array([535., 535., 535., 535.])
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old_variances = np.array([4225., 4225., 4225., 4225.])
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old_sample_count = np.array([2, 2, 2, 2], dtype=np.int64)
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X = sparse_constructor(
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np.array([[170, 170, 170, 170],
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[430, 430, 430, 430],
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[300, 300, 300, 300]]))
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X_nan = sparse_constructor(
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np.array([[170, np.nan, 170, 170],
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[np.nan, 170, 430, 430],
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[430, 430, np.nan, 300],
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[300, 300, 300, np.nan]]))
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# we avoid creating specific data for axis 0 and 1: translating the data is
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# enough.
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if axis:
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X = X.T
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X_nan = X_nan.T
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# take a copy of the old statistics since they are modified in place.
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X_means, X_vars, X_sample_count = incr_mean_variance_axis(
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X, axis=axis, last_mean=old_means.copy(),
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last_var=old_variances.copy(), last_n=old_sample_count.copy())
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X_nan_means, X_nan_vars, X_nan_sample_count = incr_mean_variance_axis(
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X_nan, axis=axis, last_mean=old_means.copy(),
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last_var=old_variances.copy(), last_n=old_sample_count.copy())
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assert_allclose(X_nan_means, X_means)
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assert_allclose(X_nan_vars, X_vars)
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assert_allclose(X_nan_sample_count, X_sample_count)
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def test_mean_variance_illegal_axis():
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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_csr = sp.csr_matrix(X)
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with pytest.raises(ValueError):
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mean_variance_axis(X_csr, axis=-3)
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with pytest.raises(ValueError):
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mean_variance_axis(X_csr, axis=2)
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with pytest.raises(ValueError):
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mean_variance_axis(X_csr, axis=-1)
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with pytest.raises(ValueError):
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incr_mean_variance_axis(X_csr, axis=-3, last_mean=None, last_var=None,
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last_n=None)
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with pytest.raises(ValueError):
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incr_mean_variance_axis(X_csr, axis=2, last_mean=None, last_var=None,
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last_n=None)
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with pytest.raises(ValueError):
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incr_mean_variance_axis(X_csr, axis=-1, last_mean=None, last_var=None,
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last_n=None)
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def test_densify_rows():
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for dtype in (np.float32, np.float64):
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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=dtype)
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X_rows = np.array([0, 2, 3], dtype=np.intp)
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out = np.ones((6, X.shape[1]), dtype=dtype)
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out_rows = np.array([1, 3, 4], dtype=np.intp)
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expect = np.ones_like(out)
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expect[out_rows] = X[X_rows, :].toarray()
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assign_rows_csr(X, X_rows, out_rows, out)
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assert_array_equal(out, expect)
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def test_inplace_column_scale():
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rng = np.random.RandomState(0)
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X = sp.rand(100, 200, 0.05)
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Xr = X.tocsr()
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Xc = X.tocsc()
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XA = X.toarray()
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scale = rng.rand(200)
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XA *= scale
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inplace_column_scale(Xc, scale)
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inplace_column_scale(Xr, scale)
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assert_array_almost_equal(Xr.toarray(), Xc.toarray())
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assert_array_almost_equal(XA, Xc.toarray())
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assert_array_almost_equal(XA, Xr.toarray())
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with pytest.raises(TypeError):
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inplace_column_scale(X.tolil(), scale)
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X = X.astype(np.float32)
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scale = scale.astype(np.float32)
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Xr = X.tocsr()
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Xc = X.tocsc()
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XA = X.toarray()
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XA *= scale
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inplace_column_scale(Xc, scale)
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inplace_column_scale(Xr, scale)
|
||
|
|
assert_array_almost_equal(Xr.toarray(), Xc.toarray())
|
||
|
|
assert_array_almost_equal(XA, Xc.toarray())
|
||
|
|
assert_array_almost_equal(XA, Xr.toarray())
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
inplace_column_scale(X.tolil(), scale)
|
||
|
|
|
||
|
|
|
||
|
|
def test_inplace_row_scale():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = sp.rand(100, 200, 0.05)
|
||
|
|
Xr = X.tocsr()
|
||
|
|
Xc = X.tocsc()
|
||
|
|
XA = X.toarray()
|
||
|
|
scale = rng.rand(100)
|
||
|
|
XA *= scale.reshape(-1, 1)
|
||
|
|
|
||
|
|
inplace_row_scale(Xc, scale)
|
||
|
|
inplace_row_scale(Xr, scale)
|
||
|
|
assert_array_almost_equal(Xr.toarray(), Xc.toarray())
|
||
|
|
assert_array_almost_equal(XA, Xc.toarray())
|
||
|
|
assert_array_almost_equal(XA, Xr.toarray())
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
inplace_column_scale(X.tolil(), scale)
|
||
|
|
|
||
|
|
X = X.astype(np.float32)
|
||
|
|
scale = scale.astype(np.float32)
|
||
|
|
Xr = X.tocsr()
|
||
|
|
Xc = X.tocsc()
|
||
|
|
XA = X.toarray()
|
||
|
|
XA *= scale.reshape(-1, 1)
|
||
|
|
inplace_row_scale(Xc, scale)
|
||
|
|
inplace_row_scale(Xr, scale)
|
||
|
|
assert_array_almost_equal(Xr.toarray(), Xc.toarray())
|
||
|
|
assert_array_almost_equal(XA, Xc.toarray())
|
||
|
|
assert_array_almost_equal(XA, Xr.toarray())
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
inplace_column_scale(X.tolil(), scale)
|
||
|
|
|
||
|
|
|
||
|
|
def test_inplace_swap_row():
|
||
|
|
X = np.array([[0, 3, 0],
|
||
|
|
[2, 4, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[9, 8, 7],
|
||
|
|
[4, 0, 5]], dtype=np.float64)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
X_csc = sp.csc_matrix(X)
|
||
|
|
|
||
|
|
swap = linalg.get_blas_funcs(('swap',), (X,))
|
||
|
|
swap = swap[0]
|
||
|
|
X[0], X[-1] = swap(X[0], X[-1])
|
||
|
|
inplace_swap_row(X_csr, 0, -1)
|
||
|
|
inplace_swap_row(X_csc, 0, -1)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
|
||
|
|
X[2], X[3] = swap(X[2], X[3])
|
||
|
|
inplace_swap_row(X_csr, 2, 3)
|
||
|
|
inplace_swap_row(X_csc, 2, 3)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
inplace_swap_row(X_csr.tolil())
|
||
|
|
|
||
|
|
X = np.array([[0, 3, 0],
|
||
|
|
[2, 4, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[9, 8, 7],
|
||
|
|
[4, 0, 5]], dtype=np.float32)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
X_csc = sp.csc_matrix(X)
|
||
|
|
swap = linalg.get_blas_funcs(('swap',), (X,))
|
||
|
|
swap = swap[0]
|
||
|
|
X[0], X[-1] = swap(X[0], X[-1])
|
||
|
|
inplace_swap_row(X_csr, 0, -1)
|
||
|
|
inplace_swap_row(X_csc, 0, -1)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
X[2], X[3] = swap(X[2], X[3])
|
||
|
|
inplace_swap_row(X_csr, 2, 3)
|
||
|
|
inplace_swap_row(X_csc, 2, 3)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
inplace_swap_row(X_csr.tolil())
|
||
|
|
|
||
|
|
|
||
|
|
def test_inplace_swap_column():
|
||
|
|
X = np.array([[0, 3, 0],
|
||
|
|
[2, 4, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[9, 8, 7],
|
||
|
|
[4, 0, 5]], dtype=np.float64)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
X_csc = sp.csc_matrix(X)
|
||
|
|
|
||
|
|
swap = linalg.get_blas_funcs(('swap',), (X,))
|
||
|
|
swap = swap[0]
|
||
|
|
X[:, 0], X[:, -1] = swap(X[:, 0], X[:, -1])
|
||
|
|
inplace_swap_column(X_csr, 0, -1)
|
||
|
|
inplace_swap_column(X_csc, 0, -1)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
|
||
|
|
X[:, 0], X[:, 1] = swap(X[:, 0], X[:, 1])
|
||
|
|
inplace_swap_column(X_csr, 0, 1)
|
||
|
|
inplace_swap_column(X_csc, 0, 1)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
inplace_swap_column(X_csr.tolil())
|
||
|
|
|
||
|
|
X = np.array([[0, 3, 0],
|
||
|
|
[2, 4, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[9, 8, 7],
|
||
|
|
[4, 0, 5]], dtype=np.float32)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
X_csc = sp.csc_matrix(X)
|
||
|
|
swap = linalg.get_blas_funcs(('swap',), (X,))
|
||
|
|
swap = swap[0]
|
||
|
|
X[:, 0], X[:, -1] = swap(X[:, 0], X[:, -1])
|
||
|
|
inplace_swap_column(X_csr, 0, -1)
|
||
|
|
inplace_swap_column(X_csc, 0, -1)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
X[:, 0], X[:, 1] = swap(X[:, 0], X[:, 1])
|
||
|
|
inplace_swap_column(X_csr, 0, 1)
|
||
|
|
inplace_swap_column(X_csc, 0, 1)
|
||
|
|
assert_array_equal(X_csr.toarray(), X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csc.toarray())
|
||
|
|
assert_array_equal(X, X_csr.toarray())
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
inplace_swap_column(X_csr.tolil())
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("axis", [0, 1, None])
|
||
|
|
@pytest.mark.parametrize("sparse_format", [sp.csr_matrix, sp.csc_matrix])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"missing_values, min_func, max_func, ignore_nan",
|
||
|
|
[(0, np.min, np.max, False),
|
||
|
|
(np.nan, np.nanmin, np.nanmax, True)]
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("large_indices", [True, False])
|
||
|
|
def test_min_max(dtype, axis, sparse_format, missing_values, min_func,
|
||
|
|
max_func, ignore_nan, large_indices):
|
||
|
|
X = np.array([[0, 3, 0],
|
||
|
|
[2, -1, missing_values],
|
||
|
|
[0, 0, 0],
|
||
|
|
[9, missing_values, 7],
|
||
|
|
[4, 0, 5]], dtype=dtype)
|
||
|
|
X_sparse = sparse_format(X)
|
||
|
|
if large_indices:
|
||
|
|
X_sparse.indices = X_sparse.indices.astype('int64')
|
||
|
|
X_sparse.indptr = X_sparse.indptr.astype('int64')
|
||
|
|
|
||
|
|
mins_sparse, maxs_sparse = min_max_axis(X_sparse, axis=axis,
|
||
|
|
ignore_nan=ignore_nan)
|
||
|
|
assert_array_equal(mins_sparse, min_func(X, axis=axis))
|
||
|
|
assert_array_equal(maxs_sparse, max_func(X, axis=axis))
|
||
|
|
|
||
|
|
|
||
|
|
def test_min_max_axis_errors():
|
||
|
|
X = np.array([[0, 3, 0],
|
||
|
|
[2, -1, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[9, 8, 7],
|
||
|
|
[4, 0, 5]], dtype=np.float64)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
X_csc = sp.csc_matrix(X)
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
min_max_axis(X_csr.tolil(), axis=0)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
min_max_axis(X_csr, axis=2)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
min_max_axis(X_csc, axis=-3)
|
||
|
|
|
||
|
|
|
||
|
|
def test_count_nonzero():
|
||
|
|
X = np.array([[0, 3, 0],
|
||
|
|
[2, -1, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[9, 8, 7],
|
||
|
|
[4, 0, 5]], dtype=np.float64)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
X_csc = sp.csc_matrix(X)
|
||
|
|
X_nonzero = X != 0
|
||
|
|
sample_weight = [.5, .2, .3, .1, .1]
|
||
|
|
X_nonzero_weighted = X_nonzero * np.array(sample_weight)[:, None]
|
||
|
|
|
||
|
|
for axis in [0, 1, -1, -2, None]:
|
||
|
|
assert_array_almost_equal(count_nonzero(X_csr, axis=axis),
|
||
|
|
X_nonzero.sum(axis=axis))
|
||
|
|
assert_array_almost_equal(count_nonzero(X_csr, axis=axis,
|
||
|
|
sample_weight=sample_weight),
|
||
|
|
X_nonzero_weighted.sum(axis=axis))
|
||
|
|
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
count_nonzero(X_csc)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
count_nonzero(X_csr, axis=2)
|
||
|
|
|
||
|
|
assert (count_nonzero(X_csr, axis=0).dtype ==
|
||
|
|
count_nonzero(X_csr, axis=1).dtype)
|
||
|
|
assert (count_nonzero(X_csr, axis=0, sample_weight=sample_weight).dtype ==
|
||
|
|
count_nonzero(X_csr, axis=1, sample_weight=sample_weight).dtype)
|
||
|
|
|
||
|
|
# Check dtypes with large sparse matrices too
|
||
|
|
# XXX: test fails on 32bit (Windows/Linux)
|
||
|
|
try:
|
||
|
|
X_csr.indices = X_csr.indices.astype(np.int64)
|
||
|
|
X_csr.indptr = X_csr.indptr.astype(np.int64)
|
||
|
|
assert (count_nonzero(X_csr, axis=0).dtype ==
|
||
|
|
count_nonzero(X_csr, axis=1).dtype)
|
||
|
|
assert (count_nonzero(X_csr, axis=0,
|
||
|
|
sample_weight=sample_weight).dtype ==
|
||
|
|
count_nonzero(X_csr, axis=1,
|
||
|
|
sample_weight=sample_weight).dtype)
|
||
|
|
except TypeError as e:
|
||
|
|
assert ("according to the rule 'safe'" in e.args[0]
|
||
|
|
and np.intp().nbytes < 8), e
|
||
|
|
|
||
|
|
|
||
|
|
def test_csc_row_median():
|
||
|
|
# Test csc_row_median actually calculates the median.
|
||
|
|
|
||
|
|
# Test that it gives the same output when X is dense.
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.rand(100, 50)
|
||
|
|
dense_median = np.median(X, axis=0)
|
||
|
|
csc = sp.csc_matrix(X)
|
||
|
|
sparse_median = csc_median_axis_0(csc)
|
||
|
|
assert_array_equal(sparse_median, dense_median)
|
||
|
|
|
||
|
|
# Test that it gives the same output when X is sparse
|
||
|
|
X = rng.rand(51, 100)
|
||
|
|
X[X < 0.7] = 0.0
|
||
|
|
ind = rng.randint(0, 50, 10)
|
||
|
|
X[ind] = -X[ind]
|
||
|
|
csc = sp.csc_matrix(X)
|
||
|
|
dense_median = np.median(X, axis=0)
|
||
|
|
sparse_median = csc_median_axis_0(csc)
|
||
|
|
assert_array_equal(sparse_median, dense_median)
|
||
|
|
|
||
|
|
# Test for toy data.
|
||
|
|
X = [[0, -2], [-1, -1], [1, 0], [2, 1]]
|
||
|
|
csc = sp.csc_matrix(X)
|
||
|
|
assert_array_equal(csc_median_axis_0(csc), np.array([0.5, -0.5]))
|
||
|
|
X = [[0, -2], [-1, -5], [1, -3]]
|
||
|
|
csc = sp.csc_matrix(X)
|
||
|
|
assert_array_equal(csc_median_axis_0(csc), np.array([0., -3]))
|
||
|
|
|
||
|
|
# Test that it raises an Error for non-csc matrices.
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
csc_median_axis_0(sp.csr_matrix(X))
|
||
|
|
|
||
|
|
|
||
|
|
def test_inplace_normalize():
|
||
|
|
ones = np.ones((10, 1))
|
||
|
|
rs = RandomState(10)
|
||
|
|
|
||
|
|
for inplace_csr_row_normalize in (inplace_csr_row_normalize_l1,
|
||
|
|
inplace_csr_row_normalize_l2):
|
||
|
|
for dtype in (np.float64, np.float32):
|
||
|
|
X = rs.randn(10, 5).astype(dtype)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
for index_dtype in [np.int32, np.int64]:
|
||
|
|
# csr_matrix will use int32 indices by default,
|
||
|
|
# up-casting those to int64 when necessary
|
||
|
|
if index_dtype is np.int64:
|
||
|
|
X_csr.indptr = X_csr.indptr.astype(index_dtype)
|
||
|
|
X_csr.indices = X_csr.indices.astype(index_dtype)
|
||
|
|
assert X_csr.indices.dtype == index_dtype
|
||
|
|
assert X_csr.indptr.dtype == index_dtype
|
||
|
|
inplace_csr_row_normalize(X_csr)
|
||
|
|
assert X_csr.dtype == dtype
|
||
|
|
if inplace_csr_row_normalize is inplace_csr_row_normalize_l2:
|
||
|
|
X_csr.data **= 2
|
||
|
|
assert_array_almost_equal(np.abs(X_csr).sum(axis=1), ones)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
|
||
|
|
def test_csr_row_norms(dtype):
|
||
|
|
# checks that csr_row_norms returns the same output as
|
||
|
|
# scipy.sparse.linalg.norm, and that the dype is the same as X.dtype.
|
||
|
|
X = sp.random(100, 10, format='csr', dtype=dtype, random_state=42)
|
||
|
|
|
||
|
|
scipy_norms = sp.linalg.norm(X, axis=1)**2
|
||
|
|
norms = csr_row_norms(X)
|
||
|
|
|
||
|
|
assert norms.dtype == dtype
|
||
|
|
rtol = 1e-6 if dtype == np.float32 else 1e-7
|
||
|
|
assert_allclose(norms, scipy_norms, rtol=rtol)
|