490 lines
16 KiB
Python
490 lines
16 KiB
Python
|
import re
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from sklearn.datasets import make_classification
|
||
|
|
from sklearn.kernel_approximation import (
|
||
|
|
AdditiveChi2Sampler,
|
||
|
|
Nystroem,
|
||
|
|
PolynomialCountSketch,
|
||
|
|
RBFSampler,
|
||
|
|
SkewedChi2Sampler,
|
||
|
|
)
|
||
|
|
from sklearn.metrics.pairwise import (
|
||
|
|
chi2_kernel,
|
||
|
|
kernel_metrics,
|
||
|
|
polynomial_kernel,
|
||
|
|
rbf_kernel,
|
||
|
|
)
|
||
|
|
from sklearn.utils._testing import (
|
||
|
|
assert_allclose,
|
||
|
|
assert_array_almost_equal,
|
||
|
|
assert_array_equal,
|
||
|
|
)
|
||
|
|
from sklearn.utils.fixes import CSR_CONTAINERS
|
||
|
|
|
||
|
|
# generate data
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample(size=(300, 50))
|
||
|
|
Y = rng.random_sample(size=(300, 50))
|
||
|
|
X /= X.sum(axis=1)[:, np.newaxis]
|
||
|
|
Y /= Y.sum(axis=1)[:, np.newaxis]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("gamma", [0.1, 1, 2.5])
|
||
|
|
@pytest.mark.parametrize("degree, n_components", [(1, 500), (2, 500), (3, 5000)])
|
||
|
|
@pytest.mark.parametrize("coef0", [0, 2.5])
|
||
|
|
def test_polynomial_count_sketch(gamma, degree, coef0, n_components):
|
||
|
|
# test that PolynomialCountSketch approximates polynomial
|
||
|
|
# kernel on random data
|
||
|
|
|
||
|
|
# compute exact kernel
|
||
|
|
kernel = polynomial_kernel(X, Y, gamma=gamma, degree=degree, coef0=coef0)
|
||
|
|
|
||
|
|
# approximate kernel mapping
|
||
|
|
ps_transform = PolynomialCountSketch(
|
||
|
|
n_components=n_components,
|
||
|
|
gamma=gamma,
|
||
|
|
coef0=coef0,
|
||
|
|
degree=degree,
|
||
|
|
random_state=42,
|
||
|
|
)
|
||
|
|
X_trans = ps_transform.fit_transform(X)
|
||
|
|
Y_trans = ps_transform.transform(Y)
|
||
|
|
kernel_approx = np.dot(X_trans, Y_trans.T)
|
||
|
|
|
||
|
|
error = kernel - kernel_approx
|
||
|
|
assert np.abs(np.mean(error)) <= 0.05 # close to unbiased
|
||
|
|
np.abs(error, out=error)
|
||
|
|
assert np.max(error) <= 0.1 # nothing too far off
|
||
|
|
assert np.mean(error) <= 0.05 # mean is fairly close
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
@pytest.mark.parametrize("gamma", [0.1, 1.0])
|
||
|
|
@pytest.mark.parametrize("degree", [1, 2, 3])
|
||
|
|
@pytest.mark.parametrize("coef0", [0, 2.5])
|
||
|
|
def test_polynomial_count_sketch_dense_sparse(gamma, degree, coef0, csr_container):
|
||
|
|
"""Check that PolynomialCountSketch results are the same for dense and sparse
|
||
|
|
input.
|
||
|
|
"""
|
||
|
|
ps_dense = PolynomialCountSketch(
|
||
|
|
n_components=500, gamma=gamma, degree=degree, coef0=coef0, random_state=42
|
||
|
|
)
|
||
|
|
Xt_dense = ps_dense.fit_transform(X)
|
||
|
|
Yt_dense = ps_dense.transform(Y)
|
||
|
|
|
||
|
|
ps_sparse = PolynomialCountSketch(
|
||
|
|
n_components=500, gamma=gamma, degree=degree, coef0=coef0, random_state=42
|
||
|
|
)
|
||
|
|
Xt_sparse = ps_sparse.fit_transform(csr_container(X))
|
||
|
|
Yt_sparse = ps_sparse.transform(csr_container(Y))
|
||
|
|
|
||
|
|
assert_allclose(Xt_dense, Xt_sparse)
|
||
|
|
assert_allclose(Yt_dense, Yt_sparse)
|
||
|
|
|
||
|
|
|
||
|
|
def _linear_kernel(X, Y):
|
||
|
|
return np.dot(X, Y.T)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_additive_chi2_sampler(csr_container):
|
||
|
|
# test that AdditiveChi2Sampler approximates kernel on random data
|
||
|
|
|
||
|
|
# compute exact kernel
|
||
|
|
# abbreviations for easier formula
|
||
|
|
X_ = X[:, np.newaxis, :]
|
||
|
|
Y_ = Y[np.newaxis, :, :]
|
||
|
|
|
||
|
|
large_kernel = 2 * X_ * Y_ / (X_ + Y_)
|
||
|
|
|
||
|
|
# reduce to n_samples_x x n_samples_y by summing over features
|
||
|
|
kernel = large_kernel.sum(axis=2)
|
||
|
|
|
||
|
|
# approximate kernel mapping
|
||
|
|
transform = AdditiveChi2Sampler(sample_steps=3)
|
||
|
|
X_trans = transform.fit_transform(X)
|
||
|
|
Y_trans = transform.transform(Y)
|
||
|
|
|
||
|
|
kernel_approx = np.dot(X_trans, Y_trans.T)
|
||
|
|
|
||
|
|
assert_array_almost_equal(kernel, kernel_approx, 1)
|
||
|
|
|
||
|
|
X_sp_trans = transform.fit_transform(csr_container(X))
|
||
|
|
Y_sp_trans = transform.transform(csr_container(Y))
|
||
|
|
|
||
|
|
assert_array_equal(X_trans, X_sp_trans.toarray())
|
||
|
|
assert_array_equal(Y_trans, Y_sp_trans.toarray())
|
||
|
|
|
||
|
|
# test error is raised on negative input
|
||
|
|
Y_neg = Y.copy()
|
||
|
|
Y_neg[0, 0] = -1
|
||
|
|
msg = "Negative values in data passed to"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
transform.fit(Y_neg)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["fit", "fit_transform", "transform"])
|
||
|
|
@pytest.mark.parametrize("sample_steps", range(1, 4))
|
||
|
|
def test_additive_chi2_sampler_sample_steps(method, sample_steps):
|
||
|
|
"""Check that the input sample step doesn't raise an error
|
||
|
|
and that sample interval doesn't change after fit.
|
||
|
|
"""
|
||
|
|
transformer = AdditiveChi2Sampler(sample_steps=sample_steps)
|
||
|
|
getattr(transformer, method)(X)
|
||
|
|
|
||
|
|
sample_interval = 0.5
|
||
|
|
transformer = AdditiveChi2Sampler(
|
||
|
|
sample_steps=sample_steps,
|
||
|
|
sample_interval=sample_interval,
|
||
|
|
)
|
||
|
|
getattr(transformer, method)(X)
|
||
|
|
assert transformer.sample_interval == sample_interval
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["fit", "fit_transform", "transform"])
|
||
|
|
def test_additive_chi2_sampler_wrong_sample_steps(method):
|
||
|
|
"""Check that we raise a ValueError on invalid sample_steps"""
|
||
|
|
transformer = AdditiveChi2Sampler(sample_steps=4)
|
||
|
|
msg = re.escape(
|
||
|
|
"If sample_steps is not in [1, 2, 3], you need to provide sample_interval"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
getattr(transformer, method)(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_skewed_chi2_sampler():
|
||
|
|
# test that RBFSampler approximates kernel on random data
|
||
|
|
|
||
|
|
# compute exact kernel
|
||
|
|
c = 0.03
|
||
|
|
# set on negative component but greater than c to ensure that the kernel
|
||
|
|
# approximation is valid on the group (-c; +\infty) endowed with the skewed
|
||
|
|
# multiplication.
|
||
|
|
Y[0, 0] = -c / 2.0
|
||
|
|
|
||
|
|
# abbreviations for easier formula
|
||
|
|
X_c = (X + c)[:, np.newaxis, :]
|
||
|
|
Y_c = (Y + c)[np.newaxis, :, :]
|
||
|
|
|
||
|
|
# we do it in log-space in the hope that it's more stable
|
||
|
|
# this array is n_samples_x x n_samples_y big x n_features
|
||
|
|
log_kernel = (
|
||
|
|
(np.log(X_c) / 2.0) + (np.log(Y_c) / 2.0) + np.log(2.0) - np.log(X_c + Y_c)
|
||
|
|
)
|
||
|
|
# reduce to n_samples_x x n_samples_y by summing over features in log-space
|
||
|
|
kernel = np.exp(log_kernel.sum(axis=2))
|
||
|
|
|
||
|
|
# approximate kernel mapping
|
||
|
|
transform = SkewedChi2Sampler(skewedness=c, n_components=1000, random_state=42)
|
||
|
|
X_trans = transform.fit_transform(X)
|
||
|
|
Y_trans = transform.transform(Y)
|
||
|
|
|
||
|
|
kernel_approx = np.dot(X_trans, Y_trans.T)
|
||
|
|
assert_array_almost_equal(kernel, kernel_approx, 1)
|
||
|
|
assert np.isfinite(kernel).all(), "NaNs found in the Gram matrix"
|
||
|
|
assert np.isfinite(kernel_approx).all(), "NaNs found in the approximate Gram matrix"
|
||
|
|
|
||
|
|
# test error is raised on when inputs contains values smaller than -c
|
||
|
|
Y_neg = Y.copy()
|
||
|
|
Y_neg[0, 0] = -c * 2.0
|
||
|
|
msg = "X may not contain entries smaller than -skewedness"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
transform.transform(Y_neg)
|
||
|
|
|
||
|
|
|
||
|
|
def test_additive_chi2_sampler_exceptions():
|
||
|
|
"""Ensures correct error message"""
|
||
|
|
transformer = AdditiveChi2Sampler()
|
||
|
|
X_neg = X.copy()
|
||
|
|
X_neg[0, 0] = -1
|
||
|
|
with pytest.raises(ValueError, match="X in AdditiveChi2Sampler.fit"):
|
||
|
|
transformer.fit(X_neg)
|
||
|
|
with pytest.raises(ValueError, match="X in AdditiveChi2Sampler.transform"):
|
||
|
|
transformer.fit(X)
|
||
|
|
transformer.transform(X_neg)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rbf_sampler():
|
||
|
|
# test that RBFSampler approximates kernel on random data
|
||
|
|
# compute exact kernel
|
||
|
|
gamma = 10.0
|
||
|
|
kernel = rbf_kernel(X, Y, gamma=gamma)
|
||
|
|
|
||
|
|
# approximate kernel mapping
|
||
|
|
rbf_transform = RBFSampler(gamma=gamma, n_components=1000, random_state=42)
|
||
|
|
X_trans = rbf_transform.fit_transform(X)
|
||
|
|
Y_trans = rbf_transform.transform(Y)
|
||
|
|
kernel_approx = np.dot(X_trans, Y_trans.T)
|
||
|
|
|
||
|
|
error = kernel - kernel_approx
|
||
|
|
assert np.abs(np.mean(error)) <= 0.01 # close to unbiased
|
||
|
|
np.abs(error, out=error)
|
||
|
|
assert np.max(error) <= 0.1 # nothing too far off
|
||
|
|
assert np.mean(error) <= 0.05 # mean is fairly close
|
||
|
|
|
||
|
|
|
||
|
|
def test_rbf_sampler_fitted_attributes_dtype(global_dtype):
|
||
|
|
"""Check that the fitted attributes are stored accordingly to the
|
||
|
|
data type of X."""
|
||
|
|
rbf = RBFSampler()
|
||
|
|
|
||
|
|
X = np.array([[1, 2], [3, 4], [5, 6]], dtype=global_dtype)
|
||
|
|
|
||
|
|
rbf.fit(X)
|
||
|
|
|
||
|
|
assert rbf.random_offset_.dtype == global_dtype
|
||
|
|
assert rbf.random_weights_.dtype == global_dtype
|
||
|
|
|
||
|
|
|
||
|
|
def test_rbf_sampler_dtype_equivalence():
|
||
|
|
"""Check the equivalence of the results with 32 and 64 bits input."""
|
||
|
|
rbf32 = RBFSampler(random_state=42)
|
||
|
|
X32 = np.array([[1, 2], [3, 4], [5, 6]], dtype=np.float32)
|
||
|
|
rbf32.fit(X32)
|
||
|
|
|
||
|
|
rbf64 = RBFSampler(random_state=42)
|
||
|
|
X64 = np.array([[1, 2], [3, 4], [5, 6]], dtype=np.float64)
|
||
|
|
rbf64.fit(X64)
|
||
|
|
|
||
|
|
assert_allclose(rbf32.random_offset_, rbf64.random_offset_)
|
||
|
|
assert_allclose(rbf32.random_weights_, rbf64.random_weights_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rbf_sampler_gamma_scale():
|
||
|
|
"""Check the inner value computed when `gamma='scale'`."""
|
||
|
|
X, y = [[0.0], [1.0]], [0, 1]
|
||
|
|
rbf = RBFSampler(gamma="scale")
|
||
|
|
rbf.fit(X, y)
|
||
|
|
assert rbf._gamma == pytest.approx(4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_skewed_chi2_sampler_fitted_attributes_dtype(global_dtype):
|
||
|
|
"""Check that the fitted attributes are stored accordingly to the
|
||
|
|
data type of X."""
|
||
|
|
skewed_chi2_sampler = SkewedChi2Sampler()
|
||
|
|
|
||
|
|
X = np.array([[1, 2], [3, 4], [5, 6]], dtype=global_dtype)
|
||
|
|
|
||
|
|
skewed_chi2_sampler.fit(X)
|
||
|
|
|
||
|
|
assert skewed_chi2_sampler.random_offset_.dtype == global_dtype
|
||
|
|
assert skewed_chi2_sampler.random_weights_.dtype == global_dtype
|
||
|
|
|
||
|
|
|
||
|
|
def test_skewed_chi2_sampler_dtype_equivalence():
|
||
|
|
"""Check the equivalence of the results with 32 and 64 bits input."""
|
||
|
|
skewed_chi2_sampler_32 = SkewedChi2Sampler(random_state=42)
|
||
|
|
X_32 = np.array([[1, 2], [3, 4], [5, 6]], dtype=np.float32)
|
||
|
|
skewed_chi2_sampler_32.fit(X_32)
|
||
|
|
|
||
|
|
skewed_chi2_sampler_64 = SkewedChi2Sampler(random_state=42)
|
||
|
|
X_64 = np.array([[1, 2], [3, 4], [5, 6]], dtype=np.float64)
|
||
|
|
skewed_chi2_sampler_64.fit(X_64)
|
||
|
|
|
||
|
|
assert_allclose(
|
||
|
|
skewed_chi2_sampler_32.random_offset_, skewed_chi2_sampler_64.random_offset_
|
||
|
|
)
|
||
|
|
assert_allclose(
|
||
|
|
skewed_chi2_sampler_32.random_weights_, skewed_chi2_sampler_64.random_weights_
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_input_validation(csr_container):
|
||
|
|
# Regression test: kernel approx. transformers should work on lists
|
||
|
|
# No assertions; the old versions would simply crash
|
||
|
|
X = [[1, 2], [3, 4], [5, 6]]
|
||
|
|
AdditiveChi2Sampler().fit(X).transform(X)
|
||
|
|
SkewedChi2Sampler().fit(X).transform(X)
|
||
|
|
RBFSampler().fit(X).transform(X)
|
||
|
|
|
||
|
|
X = csr_container(X)
|
||
|
|
RBFSampler().fit(X).transform(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nystroem_approximation():
|
||
|
|
# some basic tests
|
||
|
|
rnd = np.random.RandomState(0)
|
||
|
|
X = rnd.uniform(size=(10, 4))
|
||
|
|
|
||
|
|
# With n_components = n_samples this is exact
|
||
|
|
X_transformed = Nystroem(n_components=X.shape[0]).fit_transform(X)
|
||
|
|
K = rbf_kernel(X)
|
||
|
|
assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
|
||
|
|
|
||
|
|
trans = Nystroem(n_components=2, random_state=rnd)
|
||
|
|
X_transformed = trans.fit(X).transform(X)
|
||
|
|
assert X_transformed.shape == (X.shape[0], 2)
|
||
|
|
|
||
|
|
# test callable kernel
|
||
|
|
trans = Nystroem(n_components=2, kernel=_linear_kernel, random_state=rnd)
|
||
|
|
X_transformed = trans.fit(X).transform(X)
|
||
|
|
assert X_transformed.shape == (X.shape[0], 2)
|
||
|
|
|
||
|
|
# test that available kernels fit and transform
|
||
|
|
kernels_available = kernel_metrics()
|
||
|
|
for kern in kernels_available:
|
||
|
|
trans = Nystroem(n_components=2, kernel=kern, random_state=rnd)
|
||
|
|
X_transformed = trans.fit(X).transform(X)
|
||
|
|
assert X_transformed.shape == (X.shape[0], 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nystroem_default_parameters():
|
||
|
|
rnd = np.random.RandomState(42)
|
||
|
|
X = rnd.uniform(size=(10, 4))
|
||
|
|
|
||
|
|
# rbf kernel should behave as gamma=None by default
|
||
|
|
# aka gamma = 1 / n_features
|
||
|
|
nystroem = Nystroem(n_components=10)
|
||
|
|
X_transformed = nystroem.fit_transform(X)
|
||
|
|
K = rbf_kernel(X, gamma=None)
|
||
|
|
K2 = np.dot(X_transformed, X_transformed.T)
|
||
|
|
assert_array_almost_equal(K, K2)
|
||
|
|
|
||
|
|
# chi2 kernel should behave as gamma=1 by default
|
||
|
|
nystroem = Nystroem(kernel="chi2", n_components=10)
|
||
|
|
X_transformed = nystroem.fit_transform(X)
|
||
|
|
K = chi2_kernel(X, gamma=1)
|
||
|
|
K2 = np.dot(X_transformed, X_transformed.T)
|
||
|
|
assert_array_almost_equal(K, K2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nystroem_singular_kernel():
|
||
|
|
# test that nystroem works with singular kernel matrix
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.rand(10, 20)
|
||
|
|
X = np.vstack([X] * 2) # duplicate samples
|
||
|
|
|
||
|
|
gamma = 100
|
||
|
|
N = Nystroem(gamma=gamma, n_components=X.shape[0]).fit(X)
|
||
|
|
X_transformed = N.transform(X)
|
||
|
|
|
||
|
|
K = rbf_kernel(X, gamma=gamma)
|
||
|
|
|
||
|
|
assert_array_almost_equal(K, np.dot(X_transformed, X_transformed.T))
|
||
|
|
assert np.all(np.isfinite(Y))
|
||
|
|
|
||
|
|
|
||
|
|
def test_nystroem_poly_kernel_params():
|
||
|
|
# Non-regression: Nystroem should pass other parameters beside gamma.
|
||
|
|
rnd = np.random.RandomState(37)
|
||
|
|
X = rnd.uniform(size=(10, 4))
|
||
|
|
|
||
|
|
K = polynomial_kernel(X, degree=3.1, coef0=0.1)
|
||
|
|
nystroem = Nystroem(
|
||
|
|
kernel="polynomial", n_components=X.shape[0], degree=3.1, coef0=0.1
|
||
|
|
)
|
||
|
|
X_transformed = nystroem.fit_transform(X)
|
||
|
|
assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nystroem_callable():
|
||
|
|
# Test Nystroem on a callable.
|
||
|
|
rnd = np.random.RandomState(42)
|
||
|
|
n_samples = 10
|
||
|
|
X = rnd.uniform(size=(n_samples, 4))
|
||
|
|
|
||
|
|
def logging_histogram_kernel(x, y, log):
|
||
|
|
"""Histogram kernel that writes to a log."""
|
||
|
|
log.append(1)
|
||
|
|
return np.minimum(x, y).sum()
|
||
|
|
|
||
|
|
kernel_log = []
|
||
|
|
X = list(X) # test input validation
|
||
|
|
Nystroem(
|
||
|
|
kernel=logging_histogram_kernel,
|
||
|
|
n_components=(n_samples - 1),
|
||
|
|
kernel_params={"log": kernel_log},
|
||
|
|
).fit(X)
|
||
|
|
assert len(kernel_log) == n_samples * (n_samples - 1) / 2
|
||
|
|
|
||
|
|
# if degree, gamma or coef0 is passed, we raise a ValueError
|
||
|
|
msg = "Don't pass gamma, coef0 or degree to Nystroem"
|
||
|
|
params = ({"gamma": 1}, {"coef0": 1}, {"degree": 2})
|
||
|
|
for param in params:
|
||
|
|
ny = Nystroem(kernel=_linear_kernel, n_components=(n_samples - 1), **param)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
ny.fit(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nystroem_precomputed_kernel():
|
||
|
|
# Non-regression: test Nystroem on precomputed kernel.
|
||
|
|
# PR - 14706
|
||
|
|
rnd = np.random.RandomState(12)
|
||
|
|
X = rnd.uniform(size=(10, 4))
|
||
|
|
|
||
|
|
K = polynomial_kernel(X, degree=2, coef0=0.1)
|
||
|
|
nystroem = Nystroem(kernel="precomputed", n_components=X.shape[0])
|
||
|
|
X_transformed = nystroem.fit_transform(K)
|
||
|
|
assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
|
||
|
|
|
||
|
|
# if degree, gamma or coef0 is passed, we raise a ValueError
|
||
|
|
msg = "Don't pass gamma, coef0 or degree to Nystroem"
|
||
|
|
params = ({"gamma": 1}, {"coef0": 1}, {"degree": 2})
|
||
|
|
for param in params:
|
||
|
|
ny = Nystroem(kernel="precomputed", n_components=X.shape[0], **param)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
ny.fit(K)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nystroem_component_indices():
|
||
|
|
"""Check that `component_indices_` corresponds to the subset of
|
||
|
|
training points used to construct the feature map.
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/20474
|
||
|
|
"""
|
||
|
|
X, _ = make_classification(n_samples=100, n_features=20)
|
||
|
|
feature_map_nystroem = Nystroem(
|
||
|
|
n_components=10,
|
||
|
|
random_state=0,
|
||
|
|
)
|
||
|
|
feature_map_nystroem.fit(X)
|
||
|
|
assert feature_map_nystroem.component_indices_.shape == (10,)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"Estimator", [PolynomialCountSketch, RBFSampler, SkewedChi2Sampler, Nystroem]
|
||
|
|
)
|
||
|
|
def test_get_feature_names_out(Estimator):
|
||
|
|
"""Check get_feature_names_out"""
|
||
|
|
est = Estimator().fit(X)
|
||
|
|
X_trans = est.transform(X)
|
||
|
|
|
||
|
|
names_out = est.get_feature_names_out()
|
||
|
|
class_name = Estimator.__name__.lower()
|
||
|
|
expected_names = [f"{class_name}{i}" for i in range(X_trans.shape[1])]
|
||
|
|
assert_array_equal(names_out, expected_names)
|
||
|
|
|
||
|
|
|
||
|
|
def test_additivechi2sampler_get_feature_names_out():
|
||
|
|
"""Check get_feature_names_out for AdditiveChi2Sampler."""
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample(size=(300, 3))
|
||
|
|
|
||
|
|
chi2_sampler = AdditiveChi2Sampler(sample_steps=3).fit(X)
|
||
|
|
input_names = ["f0", "f1", "f2"]
|
||
|
|
suffixes = [
|
||
|
|
"f0_sqrt",
|
||
|
|
"f1_sqrt",
|
||
|
|
"f2_sqrt",
|
||
|
|
"f0_cos1",
|
||
|
|
"f1_cos1",
|
||
|
|
"f2_cos1",
|
||
|
|
"f0_sin1",
|
||
|
|
"f1_sin1",
|
||
|
|
"f2_sin1",
|
||
|
|
"f0_cos2",
|
||
|
|
"f1_cos2",
|
||
|
|
"f2_cos2",
|
||
|
|
"f0_sin2",
|
||
|
|
"f1_sin2",
|
||
|
|
"f2_sin2",
|
||
|
|
]
|
||
|
|
|
||
|
|
names_out = chi2_sampler.get_feature_names_out(input_features=input_names)
|
||
|
|
expected_names = [f"additivechi2sampler_{suffix}" for suffix in suffixes]
|
||
|
|
assert_array_equal(names_out, expected_names)
|