projektAI/venv/Lib/site-packages/sklearn/utils/tests/test_extmath.py
2021-06-06 22:13:05 +02:00

812 lines
30 KiB
Python

# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <denis-alexander.engemann@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from scipy.special import expit
import pytest
from sklearn.utils import gen_batches
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import assert_allclose_dense_sparse
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import skip_if_32bit
from sklearn.utils.extmath import density, _safe_accumulator_op
from sklearn.utils.extmath import randomized_svd
from sklearn.utils.extmath import row_norms
from sklearn.utils.extmath import weighted_mode
from sklearn.utils.extmath import cartesian
from sklearn.utils.extmath import log_logistic
from sklearn.utils.extmath import svd_flip
from sklearn.utils.extmath import _incremental_mean_and_var
from sklearn.utils.extmath import _incremental_weighted_mean_and_var
from sklearn.utils.extmath import _deterministic_vector_sign_flip
from sklearn.utils.extmath import softmax
from sklearn.utils.extmath import stable_cumsum
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.datasets import make_low_rank_matrix
def test_density():
rng = np.random.RandomState(0)
X = rng.randint(10, size=(10, 5))
X[1, 2] = 0
X[5, 3] = 0
X_csr = sparse.csr_matrix(X)
X_csc = sparse.csc_matrix(X)
X_coo = sparse.coo_matrix(X)
X_lil = sparse.lil_matrix(X)
for X_ in (X_csr, X_csc, X_coo, X_lil):
assert density(X_) == density(X)
def test_uniform_weights():
# with uniform weights, results should be identical to stats.mode
rng = np.random.RandomState(0)
x = rng.randint(10, size=(10, 5))
weights = np.ones(x.shape)
for axis in (None, 0, 1):
mode, score = stats.mode(x, axis)
mode2, score2 = weighted_mode(x, weights, axis=axis)
assert_array_equal(mode, mode2)
assert_array_equal(score, score2)
def test_random_weights():
# set this up so that each row should have a weighted mode of 6,
# with a score that is easily reproduced
mode_result = 6
rng = np.random.RandomState(0)
x = rng.randint(mode_result, size=(100, 10))
w = rng.random_sample(x.shape)
x[:, :5] = mode_result
w[:, :5] += 1
mode, score = weighted_mode(x, w, axis=1)
assert_array_equal(mode, mode_result)
assert_array_almost_equal(score.ravel(), w[:, :5].sum(1))
def check_randomized_svd_low_rank(dtype):
# Check that extmath.randomized_svd is consistent with linalg.svd
n_samples = 100
n_features = 500
rank = 5
k = 10
decimal = 5 if dtype == np.float32 else 7
dtype = np.dtype(dtype)
# generate a matrix X of approximate effective rank `rank` and no noise
# component (very structured signal):
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=0.0,
random_state=0).astype(dtype, copy=False)
assert X.shape == (n_samples, n_features)
# compute the singular values of X using the slow exact method
U, s, Vt = linalg.svd(X, full_matrices=False)
# Convert the singular values to the specific dtype
U = U.astype(dtype, copy=False)
s = s.astype(dtype, copy=False)
Vt = Vt.astype(dtype, copy=False)
for normalizer in ['auto', 'LU', 'QR']: # 'none' would not be stable
# compute the singular values of X using the fast approximate method
Ua, sa, Va = randomized_svd(
X, k, power_iteration_normalizer=normalizer, random_state=0)
# If the input dtype is float, then the output dtype is float of the
# same bit size (f32 is not upcast to f64)
# But if the input dtype is int, the output dtype is float64
if dtype.kind == 'f':
assert Ua.dtype == dtype
assert sa.dtype == dtype
assert Va.dtype == dtype
else:
assert Ua.dtype == np.float64
assert sa.dtype == np.float64
assert Va.dtype == np.float64
assert Ua.shape == (n_samples, k)
assert sa.shape == (k,)
assert Va.shape == (k, n_features)
# ensure that the singular values of both methods are equal up to the
# real rank of the matrix
assert_almost_equal(s[:k], sa, decimal=decimal)
# check the singular vectors too (while not checking the sign)
assert_almost_equal(np.dot(U[:, :k], Vt[:k, :]), np.dot(Ua, Va),
decimal=decimal)
# check the sparse matrix representation
X = sparse.csr_matrix(X)
# compute the singular values of X using the fast approximate method
Ua, sa, Va = \
randomized_svd(X, k, power_iteration_normalizer=normalizer,
random_state=0)
if dtype.kind == 'f':
assert Ua.dtype == dtype
assert sa.dtype == dtype
assert Va.dtype == dtype
else:
assert Ua.dtype.kind == 'f'
assert sa.dtype.kind == 'f'
assert Va.dtype.kind == 'f'
assert_almost_equal(s[:rank], sa[:rank], decimal=decimal)
@pytest.mark.parametrize('dtype',
(np.int32, np.int64, np.float32, np.float64))
def test_randomized_svd_low_rank_all_dtypes(dtype):
check_randomized_svd_low_rank(dtype)
@pytest.mark.parametrize('dtype',
(np.float32, np.float64))
def test_row_norms(dtype):
X = np.random.RandomState(42).randn(100, 100)
if dtype is np.float32:
precision = 4
else:
precision = 5
X = X.astype(dtype, copy=False)
sq_norm = (X ** 2).sum(axis=1)
assert_array_almost_equal(sq_norm, row_norms(X, squared=True),
precision)
assert_array_almost_equal(np.sqrt(sq_norm), row_norms(X), precision)
for csr_index_dtype in [np.int32, np.int64]:
Xcsr = sparse.csr_matrix(X, dtype=dtype)
# csr_matrix will use int32 indices by default,
# up-casting those to int64 when necessary
if csr_index_dtype is np.int64:
Xcsr.indptr = Xcsr.indptr.astype(csr_index_dtype, copy=False)
Xcsr.indices = Xcsr.indices.astype(csr_index_dtype, copy=False)
assert Xcsr.indices.dtype == csr_index_dtype
assert Xcsr.indptr.dtype == csr_index_dtype
assert_array_almost_equal(sq_norm, row_norms(Xcsr, squared=True),
precision)
assert_array_almost_equal(np.sqrt(sq_norm), row_norms(Xcsr),
precision)
def test_randomized_svd_low_rank_with_noise():
# Check that extmath.randomized_svd can handle noisy matrices
n_samples = 100
n_features = 500
rank = 5
k = 10
# generate a matrix X wity structure approximate rank `rank` and an
# important noisy component
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=0.1,
random_state=0)
assert X.shape == (n_samples, n_features)
# compute the singular values of X using the slow exact method
_, s, _ = linalg.svd(X, full_matrices=False)
for normalizer in ['auto', 'none', 'LU', 'QR']:
# compute the singular values of X using the fast approximate
# method without the iterated power method
_, sa, _ = randomized_svd(X, k, n_iter=0,
power_iteration_normalizer=normalizer,
random_state=0)
# the approximation does not tolerate the noise:
assert np.abs(s[:k] - sa).max() > 0.01
# compute the singular values of X using the fast approximate
# method with iterated power method
_, sap, _ = randomized_svd(X, k,
power_iteration_normalizer=normalizer,
random_state=0)
# the iterated power method is helping getting rid of the noise:
assert_almost_equal(s[:k], sap, decimal=3)
def test_randomized_svd_infinite_rank():
# Check that extmath.randomized_svd can handle noisy matrices
n_samples = 100
n_features = 500
rank = 5
k = 10
# let us try again without 'low_rank component': just regularly but slowly
# decreasing singular values: the rank of the data matrix is infinite
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=1.0,
random_state=0)
assert X.shape == (n_samples, n_features)
# compute the singular values of X using the slow exact method
_, s, _ = linalg.svd(X, full_matrices=False)
for normalizer in ['auto', 'none', 'LU', 'QR']:
# compute the singular values of X using the fast approximate method
# without the iterated power method
_, sa, _ = randomized_svd(X, k, n_iter=0,
power_iteration_normalizer=normalizer)
# the approximation does not tolerate the noise:
assert np.abs(s[:k] - sa).max() > 0.1
# compute the singular values of X using the fast approximate method
# with iterated power method
_, sap, _ = randomized_svd(X, k, n_iter=5,
power_iteration_normalizer=normalizer)
# the iterated power method is still managing to get most of the
# structure at the requested rank
assert_almost_equal(s[:k], sap, decimal=3)
def test_randomized_svd_transpose_consistency():
# Check that transposing the design matrix has limited impact
n_samples = 100
n_features = 500
rank = 4
k = 10
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=0.5,
random_state=0)
assert X.shape == (n_samples, n_features)
U1, s1, V1 = randomized_svd(X, k, n_iter=3, transpose=False,
random_state=0)
U2, s2, V2 = randomized_svd(X, k, n_iter=3, transpose=True,
random_state=0)
U3, s3, V3 = randomized_svd(X, k, n_iter=3, transpose='auto',
random_state=0)
U4, s4, V4 = linalg.svd(X, full_matrices=False)
assert_almost_equal(s1, s4[:k], decimal=3)
assert_almost_equal(s2, s4[:k], decimal=3)
assert_almost_equal(s3, s4[:k], decimal=3)
assert_almost_equal(np.dot(U1, V1), np.dot(U4[:, :k], V4[:k, :]),
decimal=2)
assert_almost_equal(np.dot(U2, V2), np.dot(U4[:, :k], V4[:k, :]),
decimal=2)
# in this case 'auto' is equivalent to transpose
assert_almost_equal(s2, s3)
def test_randomized_svd_power_iteration_normalizer():
# randomized_svd with power_iteration_normalized='none' diverges for
# large number of power iterations on this dataset
rng = np.random.RandomState(42)
X = make_low_rank_matrix(100, 500, effective_rank=50, random_state=rng)
X += 3 * rng.randint(0, 2, size=X.shape)
n_components = 50
# Check that it diverges with many (non-normalized) power iterations
U, s, Vt = randomized_svd(X, n_components, n_iter=2,
power_iteration_normalizer='none')
A = X - U.dot(np.diag(s).dot(Vt))
error_2 = linalg.norm(A, ord='fro')
U, s, Vt = randomized_svd(X, n_components, n_iter=20,
power_iteration_normalizer='none')
A = X - U.dot(np.diag(s).dot(Vt))
error_20 = linalg.norm(A, ord='fro')
assert np.abs(error_2 - error_20) > 100
for normalizer in ['LU', 'QR', 'auto']:
U, s, Vt = randomized_svd(X, n_components, n_iter=2,
power_iteration_normalizer=normalizer,
random_state=0)
A = X - U.dot(np.diag(s).dot(Vt))
error_2 = linalg.norm(A, ord='fro')
for i in [5, 10, 50]:
U, s, Vt = randomized_svd(X, n_components, n_iter=i,
power_iteration_normalizer=normalizer,
random_state=0)
A = X - U.dot(np.diag(s).dot(Vt))
error = linalg.norm(A, ord='fro')
assert 15 > np.abs(error_2 - error)
def test_randomized_svd_sparse_warnings():
# randomized_svd throws a warning for lil and dok matrix
rng = np.random.RandomState(42)
X = make_low_rank_matrix(50, 20, effective_rank=10, random_state=rng)
n_components = 5
for cls in (sparse.lil_matrix, sparse.dok_matrix):
X = cls(X)
assert_warns_message(
sparse.SparseEfficiencyWarning,
"Calculating SVD of a {} is expensive. "
"csr_matrix is more efficient.".format(cls.__name__),
randomized_svd, X, n_components, n_iter=1,
power_iteration_normalizer='none')
def test_svd_flip():
# Check that svd_flip works in both situations, and reconstructs input.
rs = np.random.RandomState(1999)
n_samples = 20
n_features = 10
X = rs.randn(n_samples, n_features)
# Check matrix reconstruction
U, S, Vt = linalg.svd(X, full_matrices=False)
U1, V1 = svd_flip(U, Vt, u_based_decision=False)
assert_almost_equal(np.dot(U1 * S, V1), X, decimal=6)
# Check transposed matrix reconstruction
XT = X.T
U, S, Vt = linalg.svd(XT, full_matrices=False)
U2, V2 = svd_flip(U, Vt, u_based_decision=True)
assert_almost_equal(np.dot(U2 * S, V2), XT, decimal=6)
# Check that different flip methods are equivalent under reconstruction
U_flip1, V_flip1 = svd_flip(U, Vt, u_based_decision=True)
assert_almost_equal(np.dot(U_flip1 * S, V_flip1), XT, decimal=6)
U_flip2, V_flip2 = svd_flip(U, Vt, u_based_decision=False)
assert_almost_equal(np.dot(U_flip2 * S, V_flip2), XT, decimal=6)
def test_randomized_svd_sign_flip():
a = np.array([[2.0, 0.0], [0.0, 1.0]])
u1, s1, v1 = randomized_svd(a, 2, flip_sign=True, random_state=41)
for seed in range(10):
u2, s2, v2 = randomized_svd(a, 2, flip_sign=True, random_state=seed)
assert_almost_equal(u1, u2)
assert_almost_equal(v1, v2)
assert_almost_equal(np.dot(u2 * s2, v2), a)
assert_almost_equal(np.dot(u2.T, u2), np.eye(2))
assert_almost_equal(np.dot(v2.T, v2), np.eye(2))
def test_randomized_svd_sign_flip_with_transpose():
# Check if the randomized_svd sign flipping is always done based on u
# irrespective of transpose.
# See https://github.com/scikit-learn/scikit-learn/issues/5608
# for more details.
def max_loading_is_positive(u, v):
"""
returns bool tuple indicating if the values maximising np.abs
are positive across all rows for u and across all columns for v.
"""
u_based = (np.abs(u).max(axis=0) == u.max(axis=0)).all()
v_based = (np.abs(v).max(axis=1) == v.max(axis=1)).all()
return u_based, v_based
mat = np.arange(10 * 8).reshape(10, -1)
# Without transpose
u_flipped, _, v_flipped = randomized_svd(mat, 3, flip_sign=True)
u_based, v_based = max_loading_is_positive(u_flipped, v_flipped)
assert u_based
assert not v_based
# With transpose
u_flipped_with_transpose, _, v_flipped_with_transpose = randomized_svd(
mat, 3, flip_sign=True, transpose=True)
u_based, v_based = max_loading_is_positive(
u_flipped_with_transpose, v_flipped_with_transpose)
assert u_based
assert not v_based
def test_cartesian():
# Check if cartesian product delivers the right results
axes = (np.array([1, 2, 3]), np.array([4, 5]), np.array([6, 7]))
true_out = np.array([[1, 4, 6],
[1, 4, 7],
[1, 5, 6],
[1, 5, 7],
[2, 4, 6],
[2, 4, 7],
[2, 5, 6],
[2, 5, 7],
[3, 4, 6],
[3, 4, 7],
[3, 5, 6],
[3, 5, 7]])
out = cartesian(axes)
assert_array_equal(true_out, out)
# check single axis
x = np.arange(3)
assert_array_equal(x[:, np.newaxis], cartesian((x,)))
def test_logistic_sigmoid():
# Check correctness and robustness of logistic sigmoid implementation
def naive_log_logistic(x):
return np.log(expit(x))
x = np.linspace(-2, 2, 50)
assert_array_almost_equal(log_logistic(x), naive_log_logistic(x))
extreme_x = np.array([-100., 100.])
assert_array_almost_equal(log_logistic(extreme_x), [-100, 0])
@pytest.fixture()
def rng():
return np.random.RandomState(42)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_incremental_weighted_mean_and_variance_simple(rng, dtype):
mult = 10
X = rng.rand(1000, 20).astype(dtype)*mult
sample_weight = rng.rand(X.shape[0]) * mult
mean, var, _ = _incremental_weighted_mean_and_var(X, sample_weight,
0, 0, 0)
expected_mean = np.average(X, weights=sample_weight, axis=0)
expected_var = np.average(X**2, weights=sample_weight, axis=0) - \
expected_mean**2
assert_almost_equal(mean, expected_mean)
assert_almost_equal(var, expected_var)
@pytest.mark.parametrize("mean", [0, 1e7, -1e7])
@pytest.mark.parametrize("var", [1, 1e-8, 1e5])
@pytest.mark.parametrize("weight_loc, weight_scale", [
(0, 1), (0, 1e-8), (1, 1e-8), (10, 1), (1e7, 1)])
def test_incremental_weighted_mean_and_variance(mean, var, weight_loc,
weight_scale, rng):
# Testing of correctness and numerical stability
def _assert(X, sample_weight, expected_mean, expected_var):
n = X.shape[0]
for chunk_size in [1, n//10 + 1, n//4 + 1, n//2 + 1, n]:
last_mean, last_weight_sum, last_var = 0, 0, 0
for batch in gen_batches(n, chunk_size):
last_mean, last_var, last_weight_sum = \
_incremental_weighted_mean_and_var(X[batch],
sample_weight[batch],
last_mean,
last_var,
last_weight_sum)
assert_allclose(last_mean, expected_mean)
assert_allclose(last_var, expected_var, atol=1e-6)
size = (100, 20)
weight = rng.normal(loc=weight_loc, scale=weight_scale, size=size[0])
# Compare to weighted average: np.average
X = rng.normal(loc=mean, scale=var, size=size)
expected_mean = _safe_accumulator_op(np.average, X, weights=weight, axis=0)
expected_var = _safe_accumulator_op(
np.average, (X - expected_mean) ** 2, weights=weight, axis=0)
_assert(X, weight, expected_mean, expected_var)
# Compare to unweighted mean: np.mean
X = rng.normal(loc=mean, scale=var, size=size)
ones_weight = np.ones(size[0])
expected_mean = _safe_accumulator_op(np.mean, X, axis=0)
expected_var = _safe_accumulator_op(np.var, X, axis=0)
_assert(X, ones_weight, expected_mean, expected_var)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_incremental_weighted_mean_and_variance_ignore_nan(dtype):
old_means = np.array([535., 535., 535., 535.])
old_variances = np.array([4225., 4225., 4225., 4225.])
old_weight_sum = np.array([2, 2, 2, 2], dtype=np.int32)
sample_weights_X = np.ones(3)
sample_weights_X_nan = np.ones(4)
X = np.array([[170, 170, 170, 170],
[430, 430, 430, 430],
[300, 300, 300, 300]]).astype(dtype)
X_nan = np.array([[170, np.nan, 170, 170],
[np.nan, 170, 430, 430],
[430, 430, np.nan, 300],
[300, 300, 300, np.nan]]).astype(dtype)
X_means, X_variances, X_count = \
_incremental_weighted_mean_and_var(X,
sample_weights_X,
old_means,
old_variances,
old_weight_sum)
X_nan_means, X_nan_variances, X_nan_count = \
_incremental_weighted_mean_and_var(X_nan,
sample_weights_X_nan,
old_means,
old_variances,
old_weight_sum)
assert_allclose(X_nan_means, X_means)
assert_allclose(X_nan_variances, X_variances)
assert_allclose(X_nan_count, X_count)
def test_incremental_variance_update_formulas():
# Test Youngs and Cramer incremental variance formulas.
# Doggie data from https://www.mathsisfun.com/data/standard-deviation.html
A = np.array([[600, 470, 170, 430, 300],
[600, 470, 170, 430, 300],
[600, 470, 170, 430, 300],
[600, 470, 170, 430, 300]]).T
idx = 2
X1 = A[:idx, :]
X2 = A[idx:, :]
old_means = X1.mean(axis=0)
old_variances = X1.var(axis=0)
old_sample_count = np.full(X1.shape[1], X1.shape[0], dtype=np.int32)
final_means, final_variances, final_count = \
_incremental_mean_and_var(X2, old_means, old_variances,
old_sample_count)
assert_almost_equal(final_means, A.mean(axis=0), 6)
assert_almost_equal(final_variances, A.var(axis=0), 6)
assert_almost_equal(final_count, A.shape[0])
def test_incremental_mean_and_variance_ignore_nan():
old_means = np.array([535., 535., 535., 535.])
old_variances = np.array([4225., 4225., 4225., 4225.])
old_sample_count = np.array([2, 2, 2, 2], dtype=np.int32)
X = np.array([[170, 170, 170, 170],
[430, 430, 430, 430],
[300, 300, 300, 300]])
X_nan = np.array([[170, np.nan, 170, 170],
[np.nan, 170, 430, 430],
[430, 430, np.nan, 300],
[300, 300, 300, np.nan]])
X_means, X_variances, X_count = _incremental_mean_and_var(
X, old_means, old_variances, old_sample_count)
X_nan_means, X_nan_variances, X_nan_count = _incremental_mean_and_var(
X_nan, old_means, old_variances, old_sample_count)
assert_allclose(X_nan_means, X_means)
assert_allclose(X_nan_variances, X_variances)
assert_allclose(X_nan_count, X_count)
@skip_if_32bit
def test_incremental_variance_numerical_stability():
# Test Youngs and Cramer incremental variance formulas.
def np_var(A):
return A.var(axis=0)
# Naive one pass variance computation - not numerically stable
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
def one_pass_var(X):
n = X.shape[0]
exp_x2 = (X ** 2).sum(axis=0) / n
expx_2 = (X.sum(axis=0) / n) ** 2
return exp_x2 - expx_2
# Two-pass algorithm, stable.
# We use it as a benchmark. It is not an online algorithm
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Two-pass_algorithm
def two_pass_var(X):
mean = X.mean(axis=0)
Y = X.copy()
return np.mean((Y - mean)**2, axis=0)
# Naive online implementation
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm
# This works only for chunks for size 1
def naive_mean_variance_update(x, last_mean, last_variance,
last_sample_count):
updated_sample_count = (last_sample_count + 1)
samples_ratio = last_sample_count / float(updated_sample_count)
updated_mean = x / updated_sample_count + last_mean * samples_ratio
updated_variance = last_variance * samples_ratio + \
(x - last_mean) * (x - updated_mean) / updated_sample_count
return updated_mean, updated_variance, updated_sample_count
# We want to show a case when one_pass_var has error > 1e-3 while
# _batch_mean_variance_update has less.
tol = 200
n_features = 2
n_samples = 10000
x1 = np.array(1e8, dtype=np.float64)
x2 = np.log(1e-5, dtype=np.float64)
A0 = np.full((n_samples // 2, n_features), x1, dtype=np.float64)
A1 = np.full((n_samples // 2, n_features), x2, dtype=np.float64)
A = np.vstack((A0, A1))
# Naive one pass var: >tol (=1063)
assert np.abs(np_var(A) - one_pass_var(A)).max() > tol
# Starting point for online algorithms: after A0
# Naive implementation: >tol (436)
mean, var, n = A0[0, :], np.zeros(n_features), n_samples // 2
for i in range(A1.shape[0]):
mean, var, n = \
naive_mean_variance_update(A1[i, :], mean, var, n)
assert n == A.shape[0]
# the mean is also slightly unstable
assert np.abs(A.mean(axis=0) - mean).max() > 1e-6
assert np.abs(np_var(A) - var).max() > tol
# Robust implementation: <tol (177)
mean, var = A0[0, :], np.zeros(n_features)
n = np.full(n_features, n_samples // 2, dtype=np.int32)
for i in range(A1.shape[0]):
mean, var, n = \
_incremental_mean_and_var(A1[i, :].reshape((1, A1.shape[1])),
mean, var, n)
assert_array_equal(n, A.shape[0])
assert_array_almost_equal(A.mean(axis=0), mean)
assert tol > np.abs(np_var(A) - var).max()
def test_incremental_variance_ddof():
# Test that degrees of freedom parameter for calculations are correct.
rng = np.random.RandomState(1999)
X = rng.randn(50, 10)
n_samples, n_features = X.shape
for batch_size in [11, 20, 37]:
steps = np.arange(0, X.shape[0], batch_size)
if steps[-1] != X.shape[0]:
steps = np.hstack([steps, n_samples])
for i, j in zip(steps[:-1], steps[1:]):
batch = X[i:j, :]
if i == 0:
incremental_means = batch.mean(axis=0)
incremental_variances = batch.var(axis=0)
# Assign this twice so that the test logic is consistent
incremental_count = batch.shape[0]
sample_count = np.full(batch.shape[1], batch.shape[0],
dtype=np.int32)
else:
result = _incremental_mean_and_var(
batch, incremental_means, incremental_variances,
sample_count)
(incremental_means, incremental_variances,
incremental_count) = result
sample_count += batch.shape[0]
calculated_means = np.mean(X[:j], axis=0)
calculated_variances = np.var(X[:j], axis=0)
assert_almost_equal(incremental_means, calculated_means, 6)
assert_almost_equal(incremental_variances,
calculated_variances, 6)
assert_array_equal(incremental_count, sample_count)
def test_vector_sign_flip():
# Testing that sign flip is working & largest value has positive sign
data = np.random.RandomState(36).randn(5, 5)
max_abs_rows = np.argmax(np.abs(data), axis=1)
data_flipped = _deterministic_vector_sign_flip(data)
max_rows = np.argmax(data_flipped, axis=1)
assert_array_equal(max_abs_rows, max_rows)
signs = np.sign(data[range(data.shape[0]), max_abs_rows])
assert_array_equal(data, data_flipped * signs[:, np.newaxis])
def test_softmax():
rng = np.random.RandomState(0)
X = rng.randn(3, 5)
exp_X = np.exp(X)
sum_exp_X = np.sum(exp_X, axis=1).reshape((-1, 1))
assert_array_almost_equal(softmax(X), exp_X / sum_exp_X)
def test_stable_cumsum():
assert_array_equal(stable_cumsum([1, 2, 3]), np.cumsum([1, 2, 3]))
r = np.random.RandomState(0).rand(100000)
assert_warns(RuntimeWarning, stable_cumsum, r, rtol=0, atol=0)
# test axis parameter
A = np.random.RandomState(36).randint(1000, size=(5, 5, 5))
assert_array_equal(stable_cumsum(A, axis=0), np.cumsum(A, axis=0))
assert_array_equal(stable_cumsum(A, axis=1), np.cumsum(A, axis=1))
assert_array_equal(stable_cumsum(A, axis=2), np.cumsum(A, axis=2))
@pytest.mark.parametrize("A_array_constr", [np.array, sparse.csr_matrix],
ids=["dense", "sparse"])
@pytest.mark.parametrize("B_array_constr", [np.array, sparse.csr_matrix],
ids=["dense", "sparse"])
def test_safe_sparse_dot_2d(A_array_constr, B_array_constr):
rng = np.random.RandomState(0)
A = rng.random_sample((30, 10))
B = rng.random_sample((10, 20))
expected = np.dot(A, B)
A = A_array_constr(A)
B = B_array_constr(B)
actual = safe_sparse_dot(A, B, dense_output=True)
assert_allclose(actual, expected)
def test_safe_sparse_dot_nd():
rng = np.random.RandomState(0)
# dense ND / sparse
A = rng.random_sample((2, 3, 4, 5, 6))
B = rng.random_sample((6, 7))
expected = np.dot(A, B)
B = sparse.csr_matrix(B)
actual = safe_sparse_dot(A, B)
assert_allclose(actual, expected)
# sparse / dense ND
A = rng.random_sample((2, 3))
B = rng.random_sample((4, 5, 3, 6))
expected = np.dot(A, B)
A = sparse.csr_matrix(A)
actual = safe_sparse_dot(A, B)
assert_allclose(actual, expected)
@pytest.mark.parametrize("A_array_constr", [np.array, sparse.csr_matrix],
ids=["dense", "sparse"])
def test_safe_sparse_dot_2d_1d(A_array_constr):
rng = np.random.RandomState(0)
B = rng.random_sample((10))
# 2D @ 1D
A = rng.random_sample((30, 10))
expected = np.dot(A, B)
A = A_array_constr(A)
actual = safe_sparse_dot(A, B)
assert_allclose(actual, expected)
# 1D @ 2D
A = rng.random_sample((10, 30))
expected = np.dot(B, A)
A = A_array_constr(A)
actual = safe_sparse_dot(B, A)
assert_allclose(actual, expected)
@pytest.mark.parametrize("dense_output", [True, False])
def test_safe_sparse_dot_dense_output(dense_output):
rng = np.random.RandomState(0)
A = sparse.random(30, 10, density=0.1, random_state=rng)
B = sparse.random(10, 20, density=0.1, random_state=rng)
expected = A.dot(B)
actual = safe_sparse_dot(A, B, dense_output=dense_output)
assert sparse.issparse(actual) == (not dense_output)
if dense_output:
expected = expected.toarray()
assert_allclose_dense_sparse(actual, expected)