Videre

linedance-app/venv/lib/python3.12/site-packages/sklearn/decomposition/tests/test_factor_analysis.py

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+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from itertools import combinations
+
+import numpy as np
+import pytest
+
+from sklearn.decomposition import FactorAnalysis
+from sklearn.decomposition._factor_analysis import _ortho_rotation
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal
+
+
+def test_factor_analysis(global_random_seed):
+    # Test FactorAnalysis ability to recover the data covariance structure
+    rng = np.random.RandomState(global_random_seed)
+    n_samples, n_features, n_components = 20, 5, 3
+
+    # Some random settings for the generative model
+    W = rng.randn(n_components, n_features)
+    # latent variable of dim 3, 20 of it
+    h = rng.randn(n_samples, n_components)
+    # using gamma to model different noise variance
+    # per component
+    noise = rng.gamma(1, size=n_features) * rng.randn(n_samples, n_features)
+
+    # generate observations
+    # wlog, mean is 0
+    X = np.dot(h, W) + noise
+
+    fas = []
+    for method in ["randomized", "lapack"]:
+        fa = FactorAnalysis(n_components=n_components, svd_method=method)
+        fa.fit(X)
+        fas.append(fa)
+
+        X_t = fa.transform(X)
+        assert X_t.shape == (n_samples, n_components)
+
+        assert_almost_equal(fa.loglike_[-1], fa.score_samples(X).sum())
+        assert_almost_equal(fa.score_samples(X).mean(), fa.score(X))
+
+        diff = np.all(np.diff(fa.loglike_))
+        assert diff > 0.0, "Log likelihood dif not increase"
+
+        # Sample Covariance
+        scov = np.cov(X, rowvar=0.0, bias=1.0)
+
+        # Model Covariance
+        mcov = fa.get_covariance()
+        diff = np.sum(np.abs(scov - mcov)) / W.size
+        assert diff < 0.2, "Mean absolute difference is %f" % diff
+        fa = FactorAnalysis(
+            n_components=n_components, noise_variance_init=np.ones(n_features)
+        )
+        with pytest.raises(ValueError):
+            fa.fit(X[:, :2])
+
+    def f(x, y):
+        return np.abs(getattr(x, y))  # sign will not be equal
+
+    fa1, fa2 = fas
+    for attr in ["loglike_", "components_", "noise_variance_"]:
+        assert_almost_equal(f(fa1, attr), f(fa2, attr))
+
+    fa1.max_iter = 1
+    fa1.verbose = True
+    with pytest.warns(ConvergenceWarning):
+        fa1.fit(X)
+
+    # Test get_covariance and get_precision with n_components == n_features
+    # with n_components < n_features and with n_components == 0
+    for n_components in [0, 2, X.shape[1]]:
+        fa.n_components = n_components
+        fa.fit(X)
+        cov = fa.get_covariance()
+        precision = fa.get_precision()
+        assert_array_almost_equal(np.dot(cov, precision), np.eye(X.shape[1]), 12)
+
+    # test rotation
+    n_components = 2
+
+    results, projections = {}, {}
+    for method in (None, "varimax", "quartimax"):
+        fa_var = FactorAnalysis(n_components=n_components, rotation=method)
+        results[method] = fa_var.fit_transform(X)
+        projections[method] = fa_var.get_covariance()
+    for rot1, rot2 in combinations([None, "varimax", "quartimax"], 2):
+        assert not np.allclose(results[rot1], results[rot2])
+        assert np.allclose(projections[rot1], projections[rot2], atol=3)
+
+    # test against R's psych::principal with rotate="varimax"
+    # (i.e., the values below stem from rotating the components in R)
+    # R's factor analysis returns quite different values; therefore, we only
+    # test the rotation itself
+    factors = np.array(
+        [
+            [0.89421016, -0.35854928, -0.27770122, 0.03773647],
+            [-0.45081822, -0.89132754, 0.0932195, -0.01787973],
+            [0.99500666, -0.02031465, 0.05426497, -0.11539407],
+            [0.96822861, -0.06299656, 0.24411001, 0.07540887],
+        ]
+    )
+    r_solution = np.array(
+        [[0.962, 0.052], [-0.141, 0.989], [0.949, -0.300], [0.937, -0.251]]
+    )
+    rotated = _ortho_rotation(factors[:, :n_components], method="varimax").T
+    assert_array_almost_equal(np.abs(rotated), np.abs(r_solution), decimal=3)

linedance-app/venv/lib/python3.12/site-packages/sklearn/decomposition/tests/test_fastica.py

@@ -0,0 +1,457 @@
+"""
+Test the fastica algorithm.
+"""
+
+import itertools
+import os
+import warnings
+
+import numpy as np
+import pytest
+from scipy import stats
+
+from sklearn.decomposition import PCA, FastICA, fastica
+from sklearn.decomposition._fastica import _gs_decorrelation
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.utils._testing import assert_allclose, ignore_warnings
+
+
+def center_and_norm(x, axis=-1):
+    """Centers and norms x **in place**
+
+    Parameters
+    -----------
+    x: ndarray
+        Array with an axis of observations (statistical units) measured on
+        random variables.
+    axis: int, optional
+        Axis along which the mean and variance are calculated.
+    """
+    x = np.rollaxis(x, axis)
+    x -= x.mean(axis=0)
+    x /= x.std(axis=0)
+
+
+def test_gs(global_random_seed):
+    # Test gram schmidt orthonormalization
+    # generate a random orthogonal  matrix
+    rng = np.random.RandomState(global_random_seed)
+    W, _, _ = np.linalg.svd(rng.randn(10, 10))
+    w = rng.randn(10)
+    _gs_decorrelation(w, W, 10)
+    assert (w**2).sum() < 1.0e-10
+    w = rng.randn(10)
+    u = _gs_decorrelation(w, W, 5)
+    tmp = np.dot(u, W.T)
+    assert (tmp[:5] ** 2).sum() < 1.0e-10
+
+
+def test_fastica_attributes_dtypes(global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    fica = FastICA(
+        n_components=5, max_iter=1000, whiten="unit-variance", random_state=0
+    ).fit(X)
+    assert fica.components_.dtype == global_dtype
+    assert fica.mixing_.dtype == global_dtype
+    assert fica.mean_.dtype == global_dtype
+    assert fica.whitening_.dtype == global_dtype
+
+
+def test_fastica_return_dtypes(global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    k_, mixing_, s_ = fastica(
+        X, max_iter=1000, whiten="unit-variance", random_state=rng
+    )
+    assert k_.dtype == global_dtype
+    assert mixing_.dtype == global_dtype
+    assert s_.dtype == global_dtype
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_fastica_simple(add_noise, global_random_seed, global_dtype):
+    if (
+        global_random_seed == 20
+        and global_dtype == np.float32
+        and not add_noise
+        and os.getenv("DISTRIB") == "ubuntu"
+    ):
+        pytest.xfail(
+            "FastICA instability with Ubuntu Atlas build with float32 "
+            "global_dtype. For more details, see "
+            "https://github.com/scikit-learn/scikit-learn/issues/24131#issuecomment-1208091119"
+        )
+
+    # Test the FastICA algorithm on very simple data.
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    # Generate two sources:
+    s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
+    s2 = stats.t.rvs(1, size=n_samples, random_state=global_random_seed)
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s = s.astype(global_dtype)
+    s1, s2 = s
+
+    # Mixing angle
+    phi = 0.6
+    mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
+    mixing = mixing.astype(global_dtype)
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(2, 1000)
+
+    center_and_norm(m)
+
+    # function as fun arg
+    def g_test(x):
+        return x**3, (3 * x**2).mean(axis=-1)
+
+    algos = ["parallel", "deflation"]
+    nls = ["logcosh", "exp", "cube", g_test]
+    whitening = ["arbitrary-variance", "unit-variance", False]
+    for algo, nl, whiten in itertools.product(algos, nls, whitening):
+        if whiten:
+            k_, mixing_, s_ = fastica(
+                m.T, fun=nl, whiten=whiten, algorithm=algo, random_state=rng
+            )
+            with pytest.raises(ValueError):
+                fastica(m.T, fun=np.tanh, whiten=whiten, algorithm=algo)
+        else:
+            pca = PCA(n_components=2, whiten=True, random_state=rng)
+            X = pca.fit_transform(m.T)
+            k_, mixing_, s_ = fastica(
+                X, fun=nl, algorithm=algo, whiten=False, random_state=rng
+            )
+            with pytest.raises(ValueError):
+                fastica(X, fun=np.tanh, algorithm=algo)
+        s_ = s_.T
+        # Check that the mixing model described in the docstring holds:
+        if whiten:
+            # XXX: exact reconstruction to standard relative tolerance is not
+            # possible. This is probably expected when add_noise is True but we
+            # also need a non-trivial atol in float32 when add_noise is False.
+            #
+            # Note that the 2 sources are non-Gaussian in this test.
+            atol = 1e-5 if global_dtype == np.float32 else 0
+            assert_allclose(np.dot(np.dot(mixing_, k_), m), s_, atol=atol)
+
+        center_and_norm(s_)
+        s1_, s2_ = s_
+        # Check to see if the sources have been estimated
+        # in the wrong order
+        if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
+            s2_, s1_ = s_
+        s1_ *= np.sign(np.dot(s1_, s1))
+        s2_ *= np.sign(np.dot(s2_, s2))
+
+        # Check that we have estimated the original sources
+        if not add_noise:
+            assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-2)
+            assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-2)
+        else:
+            assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-1)
+            assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-1)
+
+    # Test FastICA class
+    _, _, sources_fun = fastica(
+        m.T, fun=nl, algorithm=algo, random_state=global_random_seed
+    )
+    ica = FastICA(fun=nl, algorithm=algo, random_state=global_random_seed)
+    sources = ica.fit_transform(m.T)
+    assert ica.components_.shape == (2, 2)
+    assert sources.shape == (1000, 2)
+
+    assert_allclose(sources_fun, sources)
+    # Set atol to account for the different magnitudes of the elements in sources
+    # (from 1e-4 to 1e1).
+    atol = np.max(np.abs(sources)) * (1e-5 if global_dtype == np.float32 else 1e-7)
+    assert_allclose(sources, ica.transform(m.T), atol=atol)
+
+    assert ica.mixing_.shape == (2, 2)
+
+    ica = FastICA(fun=np.tanh, algorithm=algo)
+    with pytest.raises(ValueError):
+        ica.fit(m.T)
+
+
+def test_fastica_nowhiten():
+    m = [[0, 1], [1, 0]]
+
+    # test for issue #697
+    ica = FastICA(n_components=1, whiten=False, random_state=0)
+    warn_msg = "Ignoring n_components with whiten=False."
+    with pytest.warns(UserWarning, match=warn_msg):
+        ica.fit(m)
+    assert hasattr(ica, "mixing_")
+
+
+def test_fastica_convergence_fail(global_random_seed):
+    # Test the FastICA algorithm on very simple data
+    # (see test_non_square_fastica).
+    # Ensure a ConvergenceWarning raised if the tolerance is sufficiently low.
+    rng = np.random.RandomState(global_random_seed)
+
+    n_samples = 1000
+    # Generate two sources:
+    t = np.linspace(0, 100, n_samples)
+    s1 = np.sin(t)
+    s2 = np.ceil(np.sin(np.pi * t))
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+
+    # Mixing matrix
+    mixing = rng.randn(6, 2)
+    m = np.dot(mixing, s)
+
+    # Do fastICA with tolerance 0. to ensure failing convergence
+    warn_msg = (
+        "FastICA did not converge. Consider increasing tolerance "
+        "or the maximum number of iterations."
+    )
+    with pytest.warns(ConvergenceWarning, match=warn_msg):
+        ica = FastICA(
+            algorithm="parallel", n_components=2, random_state=rng, max_iter=2, tol=0.0
+        )
+        ica.fit(m.T)
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_non_square_fastica(global_random_seed, add_noise):
+    # Test the FastICA algorithm on very simple data.
+    rng = np.random.RandomState(global_random_seed)
+
+    n_samples = 1000
+    # Generate two sources:
+    t = np.linspace(0, 100, n_samples)
+    s1 = np.sin(t)
+    s2 = np.ceil(np.sin(np.pi * t))
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s1, s2 = s
+
+    # Mixing matrix
+    mixing = rng.randn(6, 2)
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(6, n_samples)
+
+    center_and_norm(m)
+
+    k_, mixing_, s_ = fastica(
+        m.T, n_components=2, whiten="unit-variance", random_state=rng
+    )
+    s_ = s_.T
+
+    # Check that the mixing model described in the docstring holds:
+    assert_allclose(s_, np.dot(np.dot(mixing_, k_), m))
+
+    center_and_norm(s_)
+    s1_, s2_ = s_
+    # Check to see if the sources have been estimated
+    # in the wrong order
+    if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
+        s2_, s1_ = s_
+    s1_ *= np.sign(np.dot(s1_, s1))
+    s2_ *= np.sign(np.dot(s2_, s2))
+
+    # Check that we have estimated the original sources
+    if not add_noise:
+        assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-3)
+        assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-3)
+
+
+def test_fit_transform(global_random_seed, global_dtype):
+    """Test unit variance of transformed data using FastICA algorithm.
+
+    Check that `fit_transform` gives the same result as applying
+    `fit` and then `transform`.
+
+    Bug #13056
+    """
+    # multivariate uniform data in [0, 1]
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((100, 10)).astype(global_dtype)
+    max_iter = 300
+    for whiten, n_components in [["unit-variance", 5], [False, None]]:
+        n_components_ = n_components if n_components is not None else X.shape[1]
+
+        ica = FastICA(
+            n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
+        )
+        with warnings.catch_warnings():
+            # make sure that numerical errors do not cause sqrt of negative
+            # values
+            warnings.simplefilter("error", RuntimeWarning)
+            # XXX: for some seeds, the model does not converge.
+            # However this is not what we test here.
+            warnings.simplefilter("ignore", ConvergenceWarning)
+            Xt = ica.fit_transform(X)
+        assert ica.components_.shape == (n_components_, 10)
+        assert Xt.shape == (X.shape[0], n_components_)
+
+        ica2 = FastICA(
+            n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
+        )
+        with warnings.catch_warnings():
+            # make sure that numerical errors do not cause sqrt of negative
+            # values
+            warnings.simplefilter("error", RuntimeWarning)
+            warnings.simplefilter("ignore", ConvergenceWarning)
+            ica2.fit(X)
+        assert ica2.components_.shape == (n_components_, 10)
+        Xt2 = ica2.transform(X)
+
+        # XXX: we have to set atol for this test to pass for all seeds when
+        # fitting with float32 data. Is this revealing a bug?
+        if global_dtype:
+            atol = np.abs(Xt2).mean() / 1e6
+        else:
+            atol = 0.0  # the default rtol is enough for float64 data
+        assert_allclose(Xt, Xt2, atol=atol)
+
+
+@pytest.mark.filterwarnings("ignore:Ignoring n_components with whiten=False.")
+@pytest.mark.parametrize(
+    "whiten, n_components, expected_mixing_shape",
+    [
+        ("arbitrary-variance", 5, (10, 5)),
+        ("arbitrary-variance", 10, (10, 10)),
+        ("unit-variance", 5, (10, 5)),
+        ("unit-variance", 10, (10, 10)),
+        (False, 5, (10, 10)),
+        (False, 10, (10, 10)),
+    ],
+)
+def test_inverse_transform(
+    whiten, n_components, expected_mixing_shape, global_random_seed, global_dtype
+):
+    # Test FastICA.inverse_transform
+    n_samples = 100
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((n_samples, 10)).astype(global_dtype)
+
+    ica = FastICA(n_components=n_components, random_state=rng, whiten=whiten)
+    with warnings.catch_warnings():
+        # For some dataset (depending on the value of global_dtype) the model
+        # can fail to converge but this should not impact the definition of
+        # a valid inverse transform.
+        warnings.simplefilter("ignore", ConvergenceWarning)
+        Xt = ica.fit_transform(X)
+    assert ica.mixing_.shape == expected_mixing_shape
+    X2 = ica.inverse_transform(Xt)
+    assert X.shape == X2.shape
+
+    # reversibility test in non-reduction case
+    if n_components == X.shape[1]:
+        # XXX: we have to set atol for this test to pass for all seeds when
+        # fitting with float32 data. Is this revealing a bug?
+        if global_dtype:
+            # XXX: dividing by a smaller number makes
+            # tests fail for some seeds.
+            atol = np.abs(X2).mean() / 1e5
+        else:
+            atol = 0.0  # the default rtol is enough for float64 data
+        assert_allclose(X, X2, atol=atol)
+
+
+def test_fastica_errors():
+    n_features = 3
+    n_samples = 10
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((n_samples, n_features))
+    w_init = rng.randn(n_features + 1, n_features + 1)
+    with pytest.raises(ValueError, match=r"alpha must be in \[1,2\]"):
+        fastica(X, fun_args={"alpha": 0})
+    with pytest.raises(
+        ValueError, match=r"w_init has invalid shape.+should be \(3L?, 3L?\)"
+    ):
+        fastica(X, w_init=w_init)
+
+
+def test_fastica_whiten_unit_variance(global_random_seed):
+    """Test unit variance of transformed data using FastICA algorithm.
+
+    Bug #13056
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((100, 10))
+    n_components = X.shape[1]
+    ica = FastICA(n_components=n_components, whiten="unit-variance", random_state=0)
+    Xt = ica.fit_transform(X)
+
+    assert np.var(Xt) == pytest.approx(1.0)
+
+
+@pytest.mark.parametrize("whiten", ["arbitrary-variance", "unit-variance", False])
+@pytest.mark.parametrize("return_X_mean", [True, False])
+@pytest.mark.parametrize("return_n_iter", [True, False])
+def test_fastica_output_shape(whiten, return_X_mean, return_n_iter):
+    n_features = 3
+    n_samples = 10
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((n_samples, n_features))
+
+    expected_len = 3 + return_X_mean + return_n_iter
+
+    out = fastica(
+        X, whiten=whiten, return_n_iter=return_n_iter, return_X_mean=return_X_mean
+    )
+
+    assert len(out) == expected_len
+    if not whiten:
+        assert out[0] is None
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_fastica_simple_different_solvers(add_noise, global_random_seed):
+    """Test FastICA is consistent between whiten_solvers."""
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    # Generate two sources:
+    s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
+    s2 = stats.t.rvs(1, size=n_samples, random_state=rng)
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s1, s2 = s
+
+    # Mixing angle
+    phi = rng.rand() * 2 * np.pi
+    mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(2, 1000)
+
+    center_and_norm(m)
+
+    outs = {}
+    for solver in ("svd", "eigh"):
+        ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver=solver)
+        sources = ica.fit_transform(m.T)
+        outs[solver] = sources
+        assert ica.components_.shape == (2, 2)
+        assert sources.shape == (1000, 2)
+
+    # compared numbers are not all on the same magnitude. Using a small atol to
+    # make the test less brittle
+    assert_allclose(outs["eigh"], outs["svd"], atol=1e-12)
+
+
+def test_fastica_eigh_low_rank_warning(global_random_seed):
+    """Test FastICA eigh solver raises warning for low-rank data."""
+    rng = np.random.RandomState(global_random_seed)
+    A = rng.randn(10, 2)
+    X = A @ A.T
+    ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver="eigh")
+    msg = "There are some small singular values"
+
+    with pytest.warns(UserWarning, match=msg):
+        with ignore_warnings(category=ConvergenceWarning):
+            # The FastICA solver may not converge for some data with specific
+            # random seeds but this happens after the whiten step so this is
+            # not want we want to test here.
+            ica.fit(X)

linedance-app/venv/lib/python3.12/site-packages/sklearn/decomposition/tests/test_incremental_pca.py

@@ -0,0 +1,487 @@
+"""Tests for Incremental PCA."""
+
+import itertools
+import warnings
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose, assert_array_equal
+
+from sklearn import datasets
+from sklearn.decomposition import PCA, IncrementalPCA
+from sklearn.utils._testing import (
+    assert_allclose_dense_sparse,
+    assert_almost_equal,
+    assert_array_almost_equal,
+)
+from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS, LIL_CONTAINERS
+
+iris = datasets.load_iris()
+
+
+def test_incremental_pca():
+    # Incremental PCA on dense arrays.
+    X = iris.data
+    batch_size = X.shape[0] // 3
+    ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
+    pca = PCA(n_components=2)
+    pca.fit_transform(X)
+
+    X_transformed = ipca.fit_transform(X)
+
+    assert X_transformed.shape == (X.shape[0], 2)
+    np.testing.assert_allclose(
+        ipca.explained_variance_ratio_.sum(),
+        pca.explained_variance_ratio_.sum(),
+        rtol=1e-3,
+    )
+
+    for n_components in [1, 2, X.shape[1]]:
+        ipca = IncrementalPCA(n_components, batch_size=batch_size)
+        ipca.fit(X)
+        cov = ipca.get_covariance()
+        precision = ipca.get_precision()
+        np.testing.assert_allclose(
+            np.dot(cov, precision), np.eye(X.shape[1]), atol=1e-13
+        )
+
+
+@pytest.mark.parametrize(
+    "sparse_container", CSC_CONTAINERS + CSR_CONTAINERS + LIL_CONTAINERS
+)
+def test_incremental_pca_sparse(sparse_container):
+    # Incremental PCA on sparse arrays.
+    X = iris.data
+    pca = PCA(n_components=2)
+    pca.fit_transform(X)
+    X_sparse = sparse_container(X)
+    batch_size = X_sparse.shape[0] // 3
+    ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
+
+    X_transformed = ipca.fit_transform(X_sparse)
+
+    assert X_transformed.shape == (X_sparse.shape[0], 2)
+    np.testing.assert_allclose(
+        ipca.explained_variance_ratio_.sum(),
+        pca.explained_variance_ratio_.sum(),
+        rtol=1e-3,
+    )
+
+    for n_components in [1, 2, X.shape[1]]:
+        ipca = IncrementalPCA(n_components, batch_size=batch_size)
+        ipca.fit(X_sparse)
+        cov = ipca.get_covariance()
+        precision = ipca.get_precision()
+        np.testing.assert_allclose(
+            np.dot(cov, precision), np.eye(X_sparse.shape[1]), atol=1e-13
+        )
+
+    with pytest.raises(
+        TypeError,
+        match=(
+            "IncrementalPCA.partial_fit does not support "
+            "sparse input. Either convert data to dense "
+            "or use IncrementalPCA.fit to do so in batches."
+        ),
+    ):
+        ipca.partial_fit(X_sparse)
+
+
+def test_incremental_pca_check_projection(global_random_seed):
+    # Test that the projection of data is correct.
+    rng = np.random.RandomState(global_random_seed)
+    n, p = 100, 3
+    X = rng.randn(n, p) * 0.1
+    X[:10] += np.array([3, 4, 5])
+    Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
+
+    # Get the reconstruction of the generated data X
+    # Note that Xt has the same "components" as X, just separated
+    # This is what we want to ensure is recreated correctly
+    Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt)
+
+    # Normalize
+    Yt /= np.sqrt((Yt**2).sum())
+
+    # Make sure that the first element of Yt is ~1, this means
+    # the reconstruction worked as expected
+    assert_almost_equal(np.abs(Yt[0][0]), 1.0, 1)
+
+
+def test_incremental_pca_inverse(global_random_seed):
+    # Test that the projection of data can be inverted.
+    rng = np.random.RandomState(global_random_seed)
+    n, p = 50, 3
+    X = rng.randn(n, p)  # spherical data
+    X[:, 1] *= 0.00001  # make middle component relatively small
+    X += [5, 4, 3]  # make a large mean
+
+    # same check that we can find the original data from the transformed
+    # signal (since the data is almost of rank n_components)
+    ipca = IncrementalPCA(n_components=2, batch_size=10).fit(X)
+    Y = ipca.transform(X)
+    Y_inverse = ipca.inverse_transform(Y)
+    assert_almost_equal(X, Y_inverse, decimal=3)
+
+
+def test_incremental_pca_validation():
+    # Test that n_components is <= n_features.
+    X = np.array([[0, 1, 0], [1, 0, 0]])
+    n_samples, n_features = X.shape
+    n_components = 4
+    with pytest.raises(
+        ValueError,
+        match=(
+            "n_components={} invalid"
+            " for n_features={}, need more rows than"
+            " columns for IncrementalPCA"
+            " processing".format(n_components, n_features)
+        ),
+    ):
+        IncrementalPCA(n_components, batch_size=10).fit(X)
+
+    # Test that n_components is also <= n_samples in first call to partial fit.
+    n_components = 3
+    with pytest.raises(
+        ValueError,
+        match=(
+            f"n_components={n_components} must be less or equal to the batch "
+            f"number of samples {n_samples} for the first partial_fit call."
+        ),
+    ):
+        IncrementalPCA(n_components=n_components).partial_fit(X)
+
+
+def test_n_samples_equal_n_components():
+    # Ensures no warning is raised when n_samples==n_components
+    # Non-regression test for gh-19050
+    ipca = IncrementalPCA(n_components=5)
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        ipca.partial_fit(np.random.randn(5, 7))
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        ipca.fit(np.random.randn(5, 7))
+
+
+def test_n_components_none():
+    # Ensures that n_components == None is handled correctly
+    rng = np.random.RandomState(1999)
+    for n_samples, n_features in [(50, 10), (10, 50)]:
+        X = rng.rand(n_samples, n_features)
+        ipca = IncrementalPCA(n_components=None)
+
+        # First partial_fit call, ipca.n_components_ is inferred from
+        # min(X.shape)
+        ipca.partial_fit(X)
+        assert ipca.n_components_ == min(X.shape)
+
+        # Second partial_fit call, ipca.n_components_ is inferred from
+        # ipca.components_ computed from the first partial_fit call
+        ipca.partial_fit(X)
+        assert ipca.n_components_ == ipca.components_.shape[0]
+
+
+def test_incremental_pca_set_params():
+    # Test that components_ sign is stable over batch sizes.
+    rng = np.random.RandomState(1999)
+    n_samples = 100
+    n_features = 20
+    X = rng.randn(n_samples, n_features)
+    X2 = rng.randn(n_samples, n_features)
+    X3 = rng.randn(n_samples, n_features)
+    ipca = IncrementalPCA(n_components=20)
+    ipca.fit(X)
+    # Decreasing number of components
+    ipca.set_params(n_components=10)
+    with pytest.raises(ValueError):
+        ipca.partial_fit(X2)
+    # Increasing number of components
+    ipca.set_params(n_components=15)
+    with pytest.raises(ValueError):
+        ipca.partial_fit(X3)
+    # Returning to original setting
+    ipca.set_params(n_components=20)
+    ipca.partial_fit(X)
+
+
+def test_incremental_pca_num_features_change():
+    # Test that changing n_components will raise an error.
+    rng = np.random.RandomState(1999)
+    n_samples = 100
+    X = rng.randn(n_samples, 20)
+    X2 = rng.randn(n_samples, 50)
+    ipca = IncrementalPCA(n_components=None)
+    ipca.fit(X)
+    with pytest.raises(ValueError):
+        ipca.partial_fit(X2)
+
+
+def test_incremental_pca_batch_signs(global_random_seed):
+    # Test that components_ sign is stable over batch sizes.
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 100
+    n_features = 3
+    X = rng.randn(n_samples, n_features)
+    all_components = []
+    batch_sizes = np.arange(10, 20)
+    for batch_size in batch_sizes:
+        ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
+        all_components.append(ipca.components_)
+
+    for i, j in itertools.pairwise(all_components):
+        assert_almost_equal(np.sign(i), np.sign(j), decimal=6)
+
+
+def test_incremental_pca_partial_fit_small_batch():
+    # Test that there is no minimum batch size after the first partial_fit
+    # Non-regression test
+    rng = np.random.RandomState(1999)
+    n, p = 50, 3
+    X = rng.randn(n, p)  # spherical data
+    X[:, 1] *= 0.00001  # make middle component relatively small
+    X += [5, 4, 3]  # make a large mean
+
+    n_components = p
+    pipca = IncrementalPCA(n_components=n_components)
+    pipca.partial_fit(X[:n_components])
+    for idx in range(n_components, n):
+        pipca.partial_fit(X[idx : idx + 1])
+
+    pca = PCA(n_components=n_components)
+    pca.fit(X)
+
+    assert_allclose(pca.components_, pipca.components_, atol=1e-3)
+
+
+def test_incremental_pca_batch_values(global_random_seed):
+    # Test that components_ values are stable over batch sizes.
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 100
+    n_features = 3
+    X = rng.randn(n_samples, n_features)
+    all_components = []
+    batch_sizes = np.arange(20, 40, 3)
+    for batch_size in batch_sizes:
+        ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
+        all_components.append(ipca.components_)
+
+    for i, j in itertools.pairwise(all_components):
+        assert_almost_equal(i, j, decimal=1)
+
+
+def test_incremental_pca_batch_rank():
+    # Test sample size in each batch is always larger or equal to n_components
+    rng = np.random.RandomState(1999)
+    n_samples = 100
+    n_features = 20
+    X = rng.randn(n_samples, n_features)
+    all_components = []
+    batch_sizes = np.arange(20, 90, 3)
+    for batch_size in batch_sizes:
+        ipca = IncrementalPCA(n_components=20, batch_size=batch_size).fit(X)
+        all_components.append(ipca.components_)
+
+    for components_i, components_j in itertools.pairwise(all_components):
+        assert_allclose_dense_sparse(components_i, components_j)
+
+
+def test_incremental_pca_partial_fit(global_random_seed):
+    # Test that fit and partial_fit get equivalent results.
+    rng = np.random.RandomState(global_random_seed)
+    n, p = 50, 3
+    X = rng.randn(n, p)  # spherical data
+    X[:, 1] *= 0.00001  # make middle component relatively small
+    X += [5, 4, 3]  # make a large mean
+
+    # same check that we can find the original data from the transformed
+    # signal (since the data is almost of rank n_components)
+    batch_size = 10
+    ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X)
+    pipca = IncrementalPCA(n_components=2, batch_size=batch_size)
+    # Add one to make sure endpoint is included
+    batch_itr = np.arange(0, n + 1, batch_size)
+    for i, j in itertools.pairwise(batch_itr):
+        pipca.partial_fit(X[i:j, :])
+    assert_almost_equal(ipca.components_, pipca.components_, decimal=3)
+
+
+def test_incremental_pca_against_pca_iris():
+    # Test that IncrementalPCA and PCA are approximate (to a sign flip).
+    X = iris.data
+
+    Y_pca = PCA(n_components=2).fit_transform(X)
+    Y_ipca = IncrementalPCA(n_components=2, batch_size=25).fit_transform(X)
+
+    assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)
+
+
+def test_incremental_pca_against_pca_random_data(global_random_seed):
+    # Test that IncrementalPCA and PCA are approximate (to a sign flip).
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 100
+    n_features = 3
+    X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features)
+
+    Y_pca = PCA(n_components=3).fit_transform(X)
+    Y_ipca = IncrementalPCA(n_components=3, batch_size=25).fit_transform(X)
+
+    assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)
+
+
+def test_explained_variances():
+    # Test that PCA and IncrementalPCA calculations match
+    X = datasets.make_low_rank_matrix(
+        1000, 100, tail_strength=0.0, effective_rank=10, random_state=1999
+    )
+    prec = 3
+    n_samples, n_features = X.shape
+    for nc in [None, 99]:
+        pca = PCA(n_components=nc).fit(X)
+        ipca = IncrementalPCA(n_components=nc, batch_size=100).fit(X)
+        assert_almost_equal(
+            pca.explained_variance_, ipca.explained_variance_, decimal=prec
+        )
+        assert_almost_equal(
+            pca.explained_variance_ratio_, ipca.explained_variance_ratio_, decimal=prec
+        )
+        assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec)
+
+
+def test_singular_values(global_random_seed):
+    # Check that the IncrementalPCA output has the correct singular values
+
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    n_features = 100
+
+    X = datasets.make_low_rank_matrix(
+        n_samples, n_features, tail_strength=0.0, effective_rank=10, random_state=rng
+    )
+
+    pca = PCA(n_components=10, svd_solver="full", random_state=rng).fit(X)
+    ipca = IncrementalPCA(n_components=10, batch_size=150).fit(X)
+    assert_array_almost_equal(pca.singular_values_, ipca.singular_values_, 2)
+
+    # Compare to the Frobenius norm
+    X_pca = pca.transform(X)
+    X_ipca = ipca.transform(X)
+    assert_array_almost_equal(
+        np.sum(pca.singular_values_**2.0), np.linalg.norm(X_pca, "fro") ** 2.0, 12
+    )
+    assert_array_almost_equal(
+        np.sum(ipca.singular_values_**2.0), np.linalg.norm(X_ipca, "fro") ** 2.0, 2
+    )
+
+    # Compare to the 2-norms of the score vectors
+    assert_array_almost_equal(
+        pca.singular_values_, np.sqrt(np.sum(X_pca**2.0, axis=0)), 12
+    )
+    assert_array_almost_equal(
+        ipca.singular_values_, np.sqrt(np.sum(X_ipca**2.0, axis=0)), 2
+    )
+
+    # Set the singular values and see what we get back
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 100
+    n_features = 110
+
+    X = datasets.make_low_rank_matrix(
+        n_samples, n_features, tail_strength=0.0, effective_rank=3, random_state=rng
+    )
+
+    pca = PCA(n_components=3, svd_solver="full", random_state=rng)
+    ipca = IncrementalPCA(n_components=3, batch_size=100)
+
+    X_pca = pca.fit_transform(X)
+    X_pca /= np.sqrt(np.sum(X_pca**2.0, axis=0))
+    X_pca[:, 0] *= 3.142
+    X_pca[:, 1] *= 2.718
+
+    X_hat = np.dot(X_pca, pca.components_)
+    pca.fit(X_hat)
+    ipca.fit(X_hat)
+    assert_array_almost_equal(pca.singular_values_, [3.142, 2.718, 1.0], 14)
+    assert_array_almost_equal(ipca.singular_values_, [3.142, 2.718, 1.0], 14)
+
+
+def test_whitening(global_random_seed):
+    # Test that PCA and IncrementalPCA transforms match to sign flip.
+    X = datasets.make_low_rank_matrix(
+        1000, 10, tail_strength=0.0, effective_rank=2, random_state=global_random_seed
+    )
+    atol = 1e-3
+    for nc in [None, 9]:
+        pca = PCA(whiten=True, n_components=nc).fit(X)
+        ipca = IncrementalPCA(whiten=True, n_components=nc, batch_size=250).fit(X)
+
+        # Since the data is rank deficient, some components are pure noise. We
+        # should not expect those dimensions to carry any signal and their
+        # values might be arbitrarily changed by implementation details of the
+        # internal SVD solver. We therefore filter them out before comparison.
+        stable_mask = pca.explained_variance_ratio_ > 1e-12
+
+        Xt_pca = pca.transform(X)
+        Xt_ipca = ipca.transform(X)
+        assert_allclose(
+            np.abs(Xt_pca)[:, stable_mask],
+            np.abs(Xt_ipca)[:, stable_mask],
+            atol=atol,
+        )
+
+        # The noisy dimensions are in the null space of the inverse transform,
+        # so they are not influencing the reconstruction. We therefore don't
+        # need to apply the mask here.
+        Xinv_ipca = ipca.inverse_transform(Xt_ipca)
+        Xinv_pca = pca.inverse_transform(Xt_pca)
+        assert_allclose(X, Xinv_ipca, atol=atol)
+        assert_allclose(X, Xinv_pca, atol=atol)
+        assert_allclose(Xinv_pca, Xinv_ipca, atol=atol)
+
+
+def test_incremental_pca_partial_fit_float_division():
+    # Test to ensure float division is used in all versions of Python
+    # (non-regression test for issue #9489)
+
+    rng = np.random.RandomState(0)
+    A = rng.randn(5, 3) + 2
+    B = rng.randn(7, 3) + 5
+
+    pca = IncrementalPCA(n_components=2)
+    pca.partial_fit(A)
+    # Set n_samples_seen_ to be a floating point number instead of an int
+    pca.n_samples_seen_ = float(pca.n_samples_seen_)
+    pca.partial_fit(B)
+    singular_vals_float_samples_seen = pca.singular_values_
+
+    pca2 = IncrementalPCA(n_components=2)
+    pca2.partial_fit(A)
+    pca2.partial_fit(B)
+    singular_vals_int_samples_seen = pca2.singular_values_
+
+    np.testing.assert_allclose(
+        singular_vals_float_samples_seen, singular_vals_int_samples_seen
+    )
+
+
+def test_incremental_pca_fit_overflow_error():
+    # Test for overflow error on Windows OS
+    # (non-regression test for issue #17693)
+    rng = np.random.RandomState(0)
+    A = rng.rand(500000, 2)
+
+    ipca = IncrementalPCA(n_components=2, batch_size=10000)
+    ipca.fit(A)
+
+    pca = PCA(n_components=2)
+    pca.fit(A)
+
+    np.testing.assert_allclose(ipca.singular_values_, pca.singular_values_)
+
+
+def test_incremental_pca_feature_names_out():
+    """Check feature names out for IncrementalPCA."""
+    ipca = IncrementalPCA(n_components=2).fit(iris.data)
+
+    names = ipca.get_feature_names_out()
+    assert_array_equal([f"incrementalpca{i}" for i in range(2)], names)

linedance-app/venv/lib/python3.12/site-packages/sklearn/decomposition/tests/test_kernel_pca.py

@@ -0,0 +1,566 @@
+import warnings
+
+import numpy as np
+import pytest
+
+import sklearn
+from sklearn.datasets import load_iris, make_blobs, make_circles
+from sklearn.decomposition import PCA, KernelPCA
+from sklearn.exceptions import NotFittedError
+from sklearn.linear_model import Perceptron
+from sklearn.metrics.pairwise import rbf_kernel
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+from sklearn.utils.fixes import CSR_CONTAINERS
+from sklearn.utils.validation import _check_psd_eigenvalues
+
+
+def test_kernel_pca(global_random_seed):
+    """Nominal test for all solvers and all known kernels + a custom one
+
+    It tests
+     - that fit_transform is equivalent to fit+transform
+     - that the shapes of transforms and inverse transforms are correct
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X_fit = rng.random_sample((5, 4))
+    X_pred = rng.random_sample((2, 4))
+
+    def histogram(x, y, **kwargs):
+        # Histogram kernel implemented as a callable.
+        assert kwargs == {}  # no kernel_params that we didn't ask for
+        return np.minimum(x, y).sum()
+
+    for eigen_solver in ("auto", "dense", "arpack", "randomized"):
+        for kernel in ("linear", "rbf", "poly", histogram):
+            # histogram kernel produces singular matrix inside linalg.solve
+            # XXX use a least-squares approximation?
+            inv = not callable(kernel)
+
+            # transform fit data
+            kpca = KernelPCA(
+                4, kernel=kernel, eigen_solver=eigen_solver, fit_inverse_transform=inv
+            )
+            X_fit_transformed = kpca.fit_transform(X_fit)
+            X_fit_transformed2 = kpca.fit(X_fit).transform(X_fit)
+            assert_array_almost_equal(
+                np.abs(X_fit_transformed), np.abs(X_fit_transformed2)
+            )
+
+            # non-regression test: previously, gamma would be 0 by default,
+            # forcing all eigenvalues to 0 under the poly kernel
+            assert X_fit_transformed.size != 0
+
+            # transform new data
+            X_pred_transformed = kpca.transform(X_pred)
+            assert X_pred_transformed.shape[1] == X_fit_transformed.shape[1]
+
+            # inverse transform
+            if inv:
+                X_pred2 = kpca.inverse_transform(X_pred_transformed)
+                assert X_pred2.shape == X_pred.shape
+
+
+def test_kernel_pca_invalid_parameters():
+    """Check that kPCA raises an error if the parameters are invalid
+
+    Tests fitting inverse transform with a precomputed kernel raises a
+    ValueError.
+    """
+    estimator = KernelPCA(
+        n_components=10, fit_inverse_transform=True, kernel="precomputed"
+    )
+    err_ms = "Cannot fit_inverse_transform with a precomputed kernel"
+    with pytest.raises(ValueError, match=err_ms):
+        estimator.fit(np.random.randn(10, 10))
+
+
+def test_kernel_pca_consistent_transform(global_random_seed):
+    """Check robustness to mutations in the original training array
+
+    Test that after fitting a kPCA model, it stays independent of any
+    mutation of the values of the original data object by relying on an
+    internal copy.
+    """
+    # X_fit_ needs to retain the old, unmodified copy of X
+    state = np.random.RandomState(global_random_seed)
+    X = state.rand(10, 10)
+    kpca = KernelPCA(random_state=state).fit(X)
+    transformed1 = kpca.transform(X)
+
+    X_copy = X.copy()
+    X[:, 0] = 666
+    transformed2 = kpca.transform(X_copy)
+    assert_array_almost_equal(transformed1, transformed2)
+
+
+def test_kernel_pca_deterministic_output(global_random_seed):
+    """Test that Kernel PCA produces deterministic output
+
+    Tests that the same inputs and random state produce the same output.
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.rand(10, 10)
+    eigen_solver = ("arpack", "dense")
+
+    for solver in eigen_solver:
+        transformed_X = np.zeros((20, 2))
+        for i in range(20):
+            kpca = KernelPCA(n_components=2, eigen_solver=solver, random_state=rng)
+            transformed_X[i, :] = kpca.fit_transform(X)[0]
+        assert_allclose(transformed_X, np.tile(transformed_X[0, :], 20).reshape(20, 2))
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_kernel_pca_sparse(csr_container, global_random_seed):
+    """Test that kPCA works on a sparse data input.
+
+    Same test as ``test_kernel_pca except inverse_transform`` since it's not
+    implemented for sparse matrices.
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X_fit = csr_container(rng.random_sample((5, 4)))
+    X_pred = csr_container(rng.random_sample((2, 4)))
+
+    for eigen_solver in ("auto", "arpack", "randomized"):
+        for kernel in ("linear", "rbf", "poly"):
+            # transform fit data
+            kpca = KernelPCA(
+                4,
+                kernel=kernel,
+                eigen_solver=eigen_solver,
+                fit_inverse_transform=False,
+                random_state=0,
+            )
+            X_fit_transformed = kpca.fit_transform(X_fit)
+            X_fit_transformed2 = kpca.fit(X_fit).transform(X_fit)
+            assert_array_almost_equal(
+                np.abs(X_fit_transformed), np.abs(X_fit_transformed2)
+            )
+
+            # transform new data
+            X_pred_transformed = kpca.transform(X_pred)
+            assert X_pred_transformed.shape[1] == X_fit_transformed.shape[1]
+
+            # inverse transform: not available for sparse matrices
+            # XXX: should we raise another exception type here? For instance:
+            # NotImplementedError.
+            with pytest.raises(NotFittedError):
+                kpca.inverse_transform(X_pred_transformed)
+
+
+@pytest.mark.parametrize("solver", ["auto", "dense", "arpack", "randomized"])
+@pytest.mark.parametrize("n_features", [4, 10])
+def test_kernel_pca_linear_kernel(solver, n_features, global_random_seed):
+    """Test that kPCA with linear kernel is equivalent to PCA for all solvers.
+
+    KernelPCA with linear kernel should produce the same output as PCA.
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X_fit = rng.random_sample((5, n_features))
+    X_pred = rng.random_sample((2, n_features))
+
+    # for a linear kernel, kernel PCA should find the same projection as PCA
+    # modulo the sign (direction)
+    # fit only the first four components: fifth is near zero eigenvalue, so
+    # can be trimmed due to roundoff error
+    n_comps = 3 if solver == "arpack" else 4
+    assert_array_almost_equal(
+        np.abs(KernelPCA(n_comps, eigen_solver=solver).fit(X_fit).transform(X_pred)),
+        np.abs(
+            PCA(n_comps, svd_solver=solver if solver != "dense" else "full")
+            .fit(X_fit)
+            .transform(X_pred)
+        ),
+    )
+
+
+def test_kernel_pca_n_components():
+    """Test that `n_components` is correctly taken into account for projections
+
+    For all solvers this tests that the output has the correct shape depending
+    on the selected number of components.
+    """
+    rng = np.random.RandomState(0)
+    X_fit = rng.random_sample((5, 4))
+    X_pred = rng.random_sample((2, 4))
+
+    for eigen_solver in ("dense", "arpack", "randomized"):
+        for c in [1, 2, 4]:
+            kpca = KernelPCA(n_components=c, eigen_solver=eigen_solver)
+            shape = kpca.fit(X_fit).transform(X_pred).shape
+
+            assert shape == (2, c)
+
+
+def test_remove_zero_eig():
+    """Check that the ``remove_zero_eig`` parameter works correctly.
+
+    Tests that the null-space (Zero) eigenvalues are removed when
+    remove_zero_eig=True, whereas they are not by default.
+    """
+    X = np.array([[1 - 1e-30, 1], [1, 1], [1, 1 - 1e-20]])
+
+    # n_components=None (default) => remove_zero_eig is True
+    kpca = KernelPCA()
+    Xt = kpca.fit_transform(X)
+    assert Xt.shape == (3, 0)
+
+    kpca = KernelPCA(n_components=2)
+    Xt = kpca.fit_transform(X)
+    assert Xt.shape == (3, 2)
+
+    kpca = KernelPCA(n_components=2, remove_zero_eig=True)
+    Xt = kpca.fit_transform(X)
+    assert Xt.shape == (3, 0)
+
+
+def test_leave_zero_eig():
+    """Non-regression test for issue #12141 (PR #12143)
+
+    This test checks that fit().transform() returns the same result as
+    fit_transform() in case of non-removed zero eigenvalue.
+    """
+    X_fit = np.array([[1, 1], [0, 0]])
+
+    # Assert that even with all np warnings on, there is no div by zero warning
+    with warnings.catch_warnings():
+        # There might be warnings about the kernel being badly conditioned,
+        # but there should not be warnings about division by zero.
+        # (Numpy division by zero warning can have many message variants, but
+        # at least we know that it is a RuntimeWarning so let's check only this)
+        warnings.simplefilter("error", RuntimeWarning)
+        with np.errstate(all="warn"):
+            k = KernelPCA(n_components=2, remove_zero_eig=False, eigen_solver="dense")
+            # Fit, then transform
+            A = k.fit(X_fit).transform(X_fit)
+            # Do both at once
+            B = k.fit_transform(X_fit)
+            # Compare
+            assert_array_almost_equal(np.abs(A), np.abs(B))
+
+
+def test_kernel_pca_precomputed(global_random_seed):
+    """Test that kPCA works with a precomputed kernel, for all solvers"""
+    rng = np.random.RandomState(global_random_seed)
+    X_fit = rng.random_sample((5, 4))
+    X_pred = rng.random_sample((2, 4))
+
+    for eigen_solver in ("dense", "arpack", "randomized"):
+        X_kpca = (
+            KernelPCA(4, eigen_solver=eigen_solver, random_state=0)
+            .fit(X_fit)
+            .transform(X_pred)
+        )
+
+        X_kpca2 = (
+            KernelPCA(
+                4, eigen_solver=eigen_solver, kernel="precomputed", random_state=0
+            )
+            .fit(np.dot(X_fit, X_fit.T))
+            .transform(np.dot(X_pred, X_fit.T))
+        )
+
+        X_kpca_train = KernelPCA(
+            4, eigen_solver=eigen_solver, kernel="precomputed", random_state=0
+        ).fit_transform(np.dot(X_fit, X_fit.T))
+
+        X_kpca_train2 = (
+            KernelPCA(
+                4, eigen_solver=eigen_solver, kernel="precomputed", random_state=0
+            )
+            .fit(np.dot(X_fit, X_fit.T))
+            .transform(np.dot(X_fit, X_fit.T))
+        )
+
+        assert_array_almost_equal(np.abs(X_kpca), np.abs(X_kpca2))
+
+        assert_array_almost_equal(np.abs(X_kpca_train), np.abs(X_kpca_train2))
+
+
+@pytest.mark.parametrize("solver", ["auto", "dense", "arpack", "randomized"])
+def test_kernel_pca_precomputed_non_symmetric(solver):
+    """Check that the kernel centerer works.
+
+    Tests that a non symmetric precomputed kernel is actually accepted
+    because the kernel centerer does its job correctly.
+    """
+
+    # a non symmetric gram matrix
+    K = [[1, 2], [3, 40]]
+    kpca = KernelPCA(
+        kernel="precomputed", eigen_solver=solver, n_components=1, random_state=0
+    )
+    kpca.fit(K)  # no error
+
+    # same test with centered kernel
+    Kc = [[9, -9], [-9, 9]]
+    kpca_c = KernelPCA(
+        kernel="precomputed", eigen_solver=solver, n_components=1, random_state=0
+    )
+    kpca_c.fit(Kc)
+
+    # comparison between the non-centered and centered versions
+    assert_array_equal(kpca.eigenvectors_, kpca_c.eigenvectors_)
+    assert_array_equal(kpca.eigenvalues_, kpca_c.eigenvalues_)
+
+
+def test_gridsearch_pipeline():
+    """Check that kPCA works as expected in a grid search pipeline
+
+    Test if we can do a grid-search to find parameters to separate
+    circles with a perceptron model.
+    """
+    X, y = make_circles(n_samples=400, factor=0.3, noise=0.05, random_state=0)
+    kpca = KernelPCA(kernel="rbf", n_components=2)
+    pipeline = Pipeline([("kernel_pca", kpca), ("Perceptron", Perceptron(max_iter=5))])
+    param_grid = dict(kernel_pca__gamma=2.0 ** np.arange(-2, 2))
+    grid_search = GridSearchCV(pipeline, cv=3, param_grid=param_grid)
+    grid_search.fit(X, y)
+    assert grid_search.best_score_ == 1
+
+
+def test_gridsearch_pipeline_precomputed():
+    """Check that kPCA works as expected in a grid search pipeline (2)
+
+    Test if we can do a grid-search to find parameters to separate
+    circles with a perceptron model. This test uses a precomputed kernel.
+    """
+    X, y = make_circles(n_samples=400, factor=0.3, noise=0.05, random_state=0)
+    kpca = KernelPCA(kernel="precomputed", n_components=2)
+    pipeline = Pipeline([("kernel_pca", kpca), ("Perceptron", Perceptron(max_iter=5))])
+    param_grid = dict(Perceptron__max_iter=np.arange(1, 5))
+    grid_search = GridSearchCV(pipeline, cv=3, param_grid=param_grid)
+    X_kernel = rbf_kernel(X, gamma=2.0)
+    grid_search.fit(X_kernel, y)
+    assert grid_search.best_score_ == 1
+
+
+def test_nested_circles():
+    """Check that kPCA projects in a space where nested circles are separable
+
+    Tests that 2D nested circles become separable with a perceptron when
+    projected in the first 2 kPCA using an RBF kernel, while raw samples
+    are not directly separable in the original space.
+    """
+    X, y = make_circles(n_samples=400, factor=0.3, noise=0.05, random_state=0)
+
+    # 2D nested circles are not linearly separable
+    train_score = Perceptron(max_iter=5).fit(X, y).score(X, y)
+    assert train_score < 0.8
+
+    # Project the circles data into the first 2 components of a RBF Kernel
+    # PCA model.
+    # Note that the gamma value is data dependent. If this test breaks
+    # and the gamma value has to be updated, the Kernel PCA example will
+    # have to be updated too.
+    kpca = KernelPCA(
+        kernel="rbf", n_components=2, fit_inverse_transform=True, gamma=2.0
+    )
+    X_kpca = kpca.fit_transform(X)
+
+    # The data is perfectly linearly separable in that space
+    train_score = Perceptron(max_iter=5).fit(X_kpca, y).score(X_kpca, y)
+    assert train_score == 1.0
+
+
+def test_kernel_conditioning():
+    """Check that ``_check_psd_eigenvalues`` is correctly called in kPCA
+
+    Non-regression test for issue #12140 (PR #12145).
+    """
+
+    # create a pathological X leading to small non-zero eigenvalue
+    X = [[5, 1], [5 + 1e-8, 1e-8], [5 + 1e-8, 0]]
+    kpca = KernelPCA(kernel="linear", n_components=2, fit_inverse_transform=True)
+    kpca.fit(X)
+
+    # check that the small non-zero eigenvalue was correctly set to zero
+    assert kpca.eigenvalues_.min() == 0
+    assert np.all(kpca.eigenvalues_ == _check_psd_eigenvalues(kpca.eigenvalues_))
+
+
+@pytest.mark.parametrize("solver", ["auto", "dense", "arpack", "randomized"])
+def test_precomputed_kernel_not_psd(solver):
+    """Check how KernelPCA works with non-PSD kernels depending on n_components
+
+    Tests for all methods what happens with a non PSD gram matrix (this
+    can happen in an isomap scenario, or with custom kernel functions, or
+    maybe with ill-posed datasets).
+
+    When ``n_component`` is large enough to capture a negative eigenvalue, an
+    error should be raised. Otherwise, KernelPCA should run without error
+    since the negative eigenvalues are not selected.
+    """
+
+    # a non PSD kernel with large eigenvalues, already centered
+    # it was captured from an isomap call and multiplied by 100 for compacity
+    K = [
+        [4.48, -1.0, 8.07, 2.33, 2.33, 2.33, -5.76, -12.78],
+        [-1.0, -6.48, 4.5, -1.24, -1.24, -1.24, -0.81, 7.49],
+        [8.07, 4.5, 15.48, 2.09, 2.09, 2.09, -11.1, -23.23],
+        [2.33, -1.24, 2.09, 4.0, -3.65, -3.65, 1.02, -0.9],
+        [2.33, -1.24, 2.09, -3.65, 4.0, -3.65, 1.02, -0.9],
+        [2.33, -1.24, 2.09, -3.65, -3.65, 4.0, 1.02, -0.9],
+        [-5.76, -0.81, -11.1, 1.02, 1.02, 1.02, 4.86, 9.75],
+        [-12.78, 7.49, -23.23, -0.9, -0.9, -0.9, 9.75, 21.46],
+    ]
+    # this gram matrix has 5 positive eigenvalues and 3 negative ones
+    # [ 52.72,   7.65,   7.65,   5.02,   0.  ,  -0.  ,  -6.13, -15.11]
+
+    # 1. ask for enough components to get a significant negative one
+    kpca = KernelPCA(kernel="precomputed", eigen_solver=solver, n_components=7)
+    # make sure that the appropriate error is raised
+    with pytest.raises(ValueError, match="There are significant negative eigenvalues"):
+        kpca.fit(K)
+
+    # 2. ask for a small enough n_components to get only positive ones
+    kpca = KernelPCA(kernel="precomputed", eigen_solver=solver, n_components=2)
+    if solver == "randomized":
+        # the randomized method is still inconsistent with the others on this
+        # since it selects the eigenvalues based on the largest 2 modules, not
+        # on the largest 2 values.
+        #
+        # At least we can ensure that we return an error instead of returning
+        # the wrong eigenvalues
+        with pytest.raises(
+            ValueError, match="There are significant negative eigenvalues"
+        ):
+            kpca.fit(K)
+    else:
+        # general case: make sure that it works
+        kpca.fit(K)
+
+
+@pytest.mark.parametrize("n_components", [4, 10, 20])
+def test_kernel_pca_solvers_equivalence(n_components):
+    """Check that 'dense' 'arpack' & 'randomized' solvers give similar results"""
+
+    # Generate random data
+    n_train, n_test = 1_000, 100
+    X, _ = make_circles(
+        n_samples=(n_train + n_test), factor=0.3, noise=0.05, random_state=0
+    )
+    X_fit, X_pred = X[:n_train, :], X[n_train:, :]
+
+    # reference (full)
+    ref_pred = (
+        KernelPCA(n_components, eigen_solver="dense", random_state=0)
+        .fit(X_fit)
+        .transform(X_pred)
+    )
+
+    # arpack
+    a_pred = (
+        KernelPCA(n_components, eigen_solver="arpack", random_state=0)
+        .fit(X_fit)
+        .transform(X_pred)
+    )
+    # check that the result is still correct despite the approx
+    assert_array_almost_equal(np.abs(a_pred), np.abs(ref_pred))
+
+    # randomized
+    r_pred = (
+        KernelPCA(n_components, eigen_solver="randomized", random_state=0)
+        .fit(X_fit)
+        .transform(X_pred)
+    )
+    # check that the result is still correct despite the approximation
+    assert_array_almost_equal(np.abs(r_pred), np.abs(ref_pred))
+
+
+def test_kernel_pca_inverse_transform_reconstruction():
+    """Test if the reconstruction is a good approximation.
+
+    Note that in general it is not possible to get an arbitrarily good
+    reconstruction because of kernel centering that does not
+    preserve all the information of the original data.
+    """
+    X, *_ = make_blobs(n_samples=100, n_features=4, random_state=0)
+
+    kpca = KernelPCA(
+        n_components=20, kernel="rbf", fit_inverse_transform=True, alpha=1e-3
+    )
+    X_trans = kpca.fit_transform(X)
+    X_reconst = kpca.inverse_transform(X_trans)
+    assert np.linalg.norm(X - X_reconst) / np.linalg.norm(X) < 1e-1
+
+
+def test_kernel_pca_raise_not_fitted_error():
+    X = np.random.randn(15).reshape(5, 3)
+    kpca = KernelPCA()
+    kpca.fit(X)
+    with pytest.raises(NotFittedError):
+        kpca.inverse_transform(X)
+
+
+def test_32_64_decomposition_shape():
+    """Test that the decomposition is similar for 32 and 64 bits data
+
+    Non regression test for
+    https://github.com/scikit-learn/scikit-learn/issues/18146
+    """
+    X, y = make_blobs(
+        n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, cluster_std=0.1
+    )
+    X = StandardScaler().fit_transform(X)
+    X -= X.min()
+
+    # Compare the shapes (corresponds to the number of non-zero eigenvalues)
+    kpca = KernelPCA()
+    assert kpca.fit_transform(X).shape == kpca.fit_transform(X.astype(np.float32)).shape
+
+
+def test_kernel_pca_feature_names_out():
+    """Check feature names out for KernelPCA."""
+    X, *_ = make_blobs(n_samples=100, n_features=4, random_state=0)
+    kpca = KernelPCA(n_components=2).fit(X)
+
+    names = kpca.get_feature_names_out()
+    assert_array_equal([f"kernelpca{i}" for i in range(2)], names)
+
+
+def test_kernel_pca_inverse_correct_gamma(global_random_seed):
+    """Check that gamma is set correctly when not provided.
+
+    Non-regression test for #26280
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((5, 4))
+
+    kwargs = {
+        "n_components": 2,
+        "random_state": rng,
+        "fit_inverse_transform": True,
+        "kernel": "rbf",
+    }
+
+    expected_gamma = 1 / X.shape[1]
+    kpca1 = KernelPCA(gamma=None, **kwargs).fit(X)
+    kpca2 = KernelPCA(gamma=expected_gamma, **kwargs).fit(X)
+
+    assert kpca1.gamma_ == expected_gamma
+    assert kpca2.gamma_ == expected_gamma
+
+    X1_recon = kpca1.inverse_transform(kpca1.transform(X))
+    X2_recon = kpca2.inverse_transform(kpca1.transform(X))
+
+    assert_allclose(X1_recon, X2_recon)
+
+
+def test_kernel_pca_pandas_output():
+    """Check that KernelPCA works with pandas output when the solver is arpack.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/27579
+    """
+    pytest.importorskip("pandas")
+    X, _ = load_iris(as_frame=True, return_X_y=True)
+    with sklearn.config_context(transform_output="pandas"):
+        KernelPCA(n_components=2, eigen_solver="arpack").fit_transform(X)

linedance-app/venv/lib/python3.12/site-packages/sklearn/decomposition/tests/test_online_lda.py

@@ -0,0 +1,489 @@
+import sys
+from io import StringIO
+
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+from scipy.linalg import block_diag
+from scipy.special import psi
+
+from sklearn.decomposition import LatentDirichletAllocation
+from sklearn.decomposition._online_lda_fast import (
+    _dirichlet_expectation_1d,
+    _dirichlet_expectation_2d,
+)
+from sklearn.exceptions import NotFittedError
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    if_safe_multiprocessing_with_blas,
+)
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+
+def _build_sparse_array(csr_container):
+    # Create 3 topics and each topic has 3 distinct words.
+    # (Each word only belongs to a single topic.)
+    n_components = 3
+    block = np.full((3, 3), n_components, dtype=int)
+    blocks = [block] * n_components
+    X = block_diag(*blocks)
+    X = csr_container(X)
+    return (n_components, X)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_default_prior_params(csr_container):
+    # default prior parameter should be `1 / topics`
+    # and verbose params should not affect result
+    n_components, X = _build_sparse_array(csr_container)
+    prior = 1.0 / n_components
+    lda_1 = LatentDirichletAllocation(
+        n_components=n_components,
+        doc_topic_prior=prior,
+        topic_word_prior=prior,
+        random_state=0,
+    )
+    lda_2 = LatentDirichletAllocation(n_components=n_components, random_state=0)
+    topic_distr_1 = lda_1.fit_transform(X)
+    topic_distr_2 = lda_2.fit_transform(X)
+    assert_almost_equal(topic_distr_1, topic_distr_2)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_fit_batch(csr_container):
+    # Test LDA batch learning_offset (`fit` method with 'batch' learning)
+    rng = np.random.RandomState(0)
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        evaluate_every=1,
+        learning_method="batch",
+        random_state=rng,
+    )
+    lda.fit(X)
+
+    correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
+    for component in lda.components_:
+        # Find top 3 words in each LDA component
+        top_idx = set(component.argsort()[-3:][::-1])
+        assert tuple(sorted(top_idx)) in correct_idx_grps
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_fit_online(csr_container):
+    # Test LDA online learning (`fit` method with 'online' learning)
+    rng = np.random.RandomState(0)
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        learning_offset=10.0,
+        evaluate_every=1,
+        learning_method="online",
+        random_state=rng,
+    )
+    lda.fit(X)
+
+    correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
+    for component in lda.components_:
+        # Find top 3 words in each LDA component
+        top_idx = set(component.argsort()[-3:][::-1])
+        assert tuple(sorted(top_idx)) in correct_idx_grps
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_partial_fit(csr_container):
+    # Test LDA online learning (`partial_fit` method)
+    # (same as test_lda_batch)
+    rng = np.random.RandomState(0)
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        learning_offset=10.0,
+        total_samples=100,
+        random_state=rng,
+    )
+    for i in range(3):
+        lda.partial_fit(X)
+
+    correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
+    for c in lda.components_:
+        top_idx = set(c.argsort()[-3:][::-1])
+        assert tuple(sorted(top_idx)) in correct_idx_grps
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_dense_input(csr_container):
+    # Test LDA with dense input.
+    rng = np.random.RandomState(0)
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components, learning_method="batch", random_state=rng
+    )
+    lda.fit(X.toarray())
+
+    correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
+    for component in lda.components_:
+        # Find top 3 words in each LDA component
+        top_idx = set(component.argsort()[-3:][::-1])
+        assert tuple(sorted(top_idx)) in correct_idx_grps
+
+
+def test_lda_transform():
+    # Test LDA transform.
+    # Transform result cannot be negative and should be normalized by default
+    rng = np.random.RandomState(0)
+    X = rng.randint(5, size=(20, 10))
+    n_components = 3
+    lda = LatentDirichletAllocation(n_components=n_components, random_state=rng)
+    X_trans = lda.fit_transform(X)
+    assert (X_trans > 0.0).any()
+    assert_array_almost_equal(np.sum(X_trans, axis=1), np.ones(X_trans.shape[0]))
+
+    X_trans_unnormalized = lda.transform(X, normalize=False)
+    assert_array_almost_equal(
+        X_trans, X_trans_unnormalized / X_trans_unnormalized.sum(axis=1)[:, np.newaxis]
+    )
+
+
+@pytest.mark.parametrize("method", ("online", "batch"))
+def test_lda_fit_transform(method):
+    # Test LDA fit_transform & transform
+    # fit_transform and transform result should be the same
+    rng = np.random.RandomState(0)
+    X = rng.randint(10, size=(50, 20))
+    lda = LatentDirichletAllocation(
+        n_components=5, learning_method=method, random_state=rng
+    )
+    X_fit = lda.fit_transform(X)
+    X_trans = lda.transform(X)
+    assert_array_almost_equal(X_fit, X_trans, 4)
+
+
+def test_lda_negative_input():
+    # test pass dense matrix with sparse negative input.
+    X = np.full((5, 10), -1.0)
+    lda = LatentDirichletAllocation()
+    regex = r"^Negative values in data passed"
+    with pytest.raises(ValueError, match=regex):
+        lda.fit(X)
+
+
+def test_lda_no_component_error():
+    # test `perplexity` before `fit`
+    rng = np.random.RandomState(0)
+    X = rng.randint(4, size=(20, 10))
+    lda = LatentDirichletAllocation()
+    regex = (
+        "This LatentDirichletAllocation instance is not fitted yet. "
+        "Call 'fit' with appropriate arguments before using this "
+        "estimator."
+    )
+    with pytest.raises(NotFittedError, match=regex):
+        lda.perplexity(X)
+
+
+# TODO: remove mark once loky bug is fixed:
+# https://github.com/joblib/loky/issues/458
+@pytest.mark.thread_unsafe
+@if_safe_multiprocessing_with_blas
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+@pytest.mark.parametrize("method", ("online", "batch"))
+def test_lda_multi_jobs(method, csr_container):
+    n_components, X = _build_sparse_array(csr_container)
+    # Test LDA batch training with multi CPU
+    rng = np.random.RandomState(0)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        n_jobs=2,
+        learning_method=method,
+        evaluate_every=1,
+        random_state=rng,
+    )
+    lda.fit(X)
+
+    correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
+    for c in lda.components_:
+        top_idx = set(c.argsort()[-3:][::-1])
+        assert tuple(sorted(top_idx)) in correct_idx_grps
+
+
+# TODO: remove mark once loky bug is fixed:
+# https://github.com/joblib/loky/issues/458
+@pytest.mark.thread_unsafe
+@if_safe_multiprocessing_with_blas
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_partial_fit_multi_jobs(csr_container):
+    # Test LDA online training with multi CPU
+    rng = np.random.RandomState(0)
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        n_jobs=2,
+        learning_offset=5.0,
+        total_samples=30,
+        random_state=rng,
+    )
+    for i in range(2):
+        lda.partial_fit(X)
+
+    correct_idx_grps = [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
+    for c in lda.components_:
+        top_idx = set(c.argsort()[-3:][::-1])
+        assert tuple(sorted(top_idx)) in correct_idx_grps
+
+
+def test_lda_preplexity_mismatch():
+    # test dimension mismatch in `perplexity` method
+    rng = np.random.RandomState(0)
+    n_components = rng.randint(3, 6)
+    n_samples = rng.randint(6, 10)
+    X = np.random.randint(4, size=(n_samples, 10))
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        learning_offset=5.0,
+        total_samples=20,
+        random_state=rng,
+    )
+    lda.fit(X)
+    # invalid samples
+    invalid_n_samples = rng.randint(4, size=(n_samples + 1, n_components))
+    with pytest.raises(ValueError, match=r"Number of samples"):
+        lda._perplexity_precomp_distr(X, invalid_n_samples)
+    # invalid topic number
+    invalid_n_components = rng.randint(4, size=(n_samples, n_components + 1))
+    with pytest.raises(ValueError, match=r"Number of topics"):
+        lda._perplexity_precomp_distr(X, invalid_n_components)
+
+
+@pytest.mark.parametrize("method", ("online", "batch"))
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_perplexity(method, csr_container):
+    # Test LDA perplexity for batch training
+    # perplexity should be lower after each iteration
+    n_components, X = _build_sparse_array(csr_container)
+    lda_1 = LatentDirichletAllocation(
+        n_components=n_components,
+        max_iter=1,
+        learning_method=method,
+        total_samples=100,
+        random_state=0,
+    )
+    lda_2 = LatentDirichletAllocation(
+        n_components=n_components,
+        max_iter=10,
+        learning_method=method,
+        total_samples=100,
+        random_state=0,
+    )
+    lda_1.fit(X)
+    perp_1 = lda_1.perplexity(X, sub_sampling=False)
+
+    lda_2.fit(X)
+    perp_2 = lda_2.perplexity(X, sub_sampling=False)
+    assert perp_1 >= perp_2
+
+    perp_1_subsampling = lda_1.perplexity(X, sub_sampling=True)
+    perp_2_subsampling = lda_2.perplexity(X, sub_sampling=True)
+    assert perp_1_subsampling >= perp_2_subsampling
+
+
+@pytest.mark.parametrize("method", ("online", "batch"))
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_score(method, csr_container):
+    # Test LDA score for batch training
+    # score should be higher after each iteration
+    n_components, X = _build_sparse_array(csr_container)
+    lda_1 = LatentDirichletAllocation(
+        n_components=n_components,
+        max_iter=1,
+        learning_method=method,
+        total_samples=100,
+        random_state=0,
+    )
+    lda_2 = LatentDirichletAllocation(
+        n_components=n_components,
+        max_iter=10,
+        learning_method=method,
+        total_samples=100,
+        random_state=0,
+    )
+    lda_1.fit_transform(X)
+    score_1 = lda_1.score(X)
+
+    lda_2.fit_transform(X)
+    score_2 = lda_2.score(X)
+    assert score_2 >= score_1
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_perplexity_input_format(csr_container):
+    # Test LDA perplexity for sparse and dense input
+    # score should be the same for both dense and sparse input
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        max_iter=1,
+        learning_method="batch",
+        total_samples=100,
+        random_state=0,
+    )
+    lda.fit(X)
+    perp_1 = lda.perplexity(X)
+    perp_2 = lda.perplexity(X.toarray())
+    assert_almost_equal(perp_1, perp_2)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_score_perplexity(csr_container):
+    # Test the relationship between LDA score and perplexity
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components, max_iter=10, random_state=0
+    )
+    lda.fit(X)
+    perplexity_1 = lda.perplexity(X, sub_sampling=False)
+
+    score = lda.score(X)
+    perplexity_2 = np.exp(-1.0 * (score / np.sum(X.data)))
+    assert_almost_equal(perplexity_1, perplexity_2)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_fit_perplexity(csr_container):
+    # Test that the perplexity computed during fit is consistent with what is
+    # returned by the perplexity method
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        max_iter=1,
+        learning_method="batch",
+        random_state=0,
+        evaluate_every=1,
+    )
+    lda.fit(X)
+
+    # Perplexity computed at end of fit method
+    perplexity1 = lda.bound_
+
+    # Result of perplexity method on the train set
+    perplexity2 = lda.perplexity(X)
+
+    assert_almost_equal(perplexity1, perplexity2)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_empty_docs(csr_container):
+    """Test LDA on empty document (all-zero rows)."""
+    Z = np.zeros((5, 4))
+    for X in [Z, csr_container(Z)]:
+        lda = LatentDirichletAllocation(max_iter=750).fit(X)
+        assert_almost_equal(
+            lda.components_.sum(axis=0), np.ones(lda.components_.shape[1])
+        )
+
+
+def test_dirichlet_expectation():
+    """Test Cython version of Dirichlet expectation calculation."""
+    x = np.logspace(-100, 10, 10000)
+    expectation = np.empty_like(x)
+    _dirichlet_expectation_1d(x, 0, expectation)
+    assert_allclose(expectation, np.exp(psi(x) - psi(np.sum(x))), atol=1e-19)
+
+    x = x.reshape(100, 100)
+    assert_allclose(
+        _dirichlet_expectation_2d(x),
+        psi(x) - psi(np.sum(x, axis=1)[:, np.newaxis]),
+        rtol=1e-11,
+        atol=3e-9,
+    )
+
+
+def check_verbosity(
+    verbose, evaluate_every, expected_lines, expected_perplexities, csr_container
+):
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(
+        n_components=n_components,
+        max_iter=3,
+        learning_method="batch",
+        verbose=verbose,
+        evaluate_every=evaluate_every,
+        random_state=0,
+    )
+    out = StringIO()
+    old_out, sys.stdout = sys.stdout, out
+    try:
+        lda.fit(X)
+    finally:
+        sys.stdout = old_out
+
+    n_lines = out.getvalue().count("\n")
+    n_perplexity = out.getvalue().count("perplexity")
+    assert expected_lines == n_lines
+    assert expected_perplexities == n_perplexity
+
+
+@pytest.mark.parametrize(
+    "verbose,evaluate_every,expected_lines,expected_perplexities",
+    [
+        (False, 1, 0, 0),
+        (False, 0, 0, 0),
+        (True, 0, 3, 0),
+        (True, 1, 3, 3),
+        (True, 2, 3, 1),
+    ],
+)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+@pytest.mark.thread_unsafe  # manually captured stdout
+def test_verbosity(
+    verbose, evaluate_every, expected_lines, expected_perplexities, csr_container
+):
+    check_verbosity(
+        verbose, evaluate_every, expected_lines, expected_perplexities, csr_container
+    )
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_lda_feature_names_out(csr_container):
+    """Check feature names out for LatentDirichletAllocation."""
+    n_components, X = _build_sparse_array(csr_container)
+    lda = LatentDirichletAllocation(n_components=n_components).fit(X)
+
+    names = lda.get_feature_names_out()
+    assert_array_equal(
+        [f"latentdirichletallocation{i}" for i in range(n_components)], names
+    )
+
+
+@pytest.mark.parametrize("learning_method", ("batch", "online"))
+def test_lda_dtype_match(learning_method, global_dtype):
+    """Check data type preservation of fitted attributes."""
+    rng = np.random.RandomState(0)
+    X = rng.uniform(size=(20, 10)).astype(global_dtype, copy=False)
+
+    lda = LatentDirichletAllocation(
+        n_components=5, random_state=0, learning_method=learning_method
+    )
+    lda.fit(X)
+    assert lda.components_.dtype == global_dtype
+    assert lda.exp_dirichlet_component_.dtype == global_dtype
+
+
+@pytest.mark.parametrize("learning_method", ("batch", "online"))
+def test_lda_numerical_consistency(learning_method, global_random_seed):
+    """Check numerical consistency between np.float32 and np.float64."""
+    rng = np.random.RandomState(global_random_seed)
+    X64 = rng.uniform(size=(20, 10))
+    X32 = X64.astype(np.float32)
+
+    lda_64 = LatentDirichletAllocation(
+        n_components=5, random_state=global_random_seed, learning_method=learning_method
+    ).fit(X64)
+    lda_32 = LatentDirichletAllocation(
+        n_components=5, random_state=global_random_seed, learning_method=learning_method
+    ).fit(X32)
+
+    assert_allclose(lda_32.components_, lda_64.components_)
+    assert_allclose(lda_32.transform(X32), lda_64.transform(X64))

linedance-app/venv/lib/python3.12/site-packages/sklearn/decomposition/tests/test_sparse_pca.py

@@ -0,0 +1,350 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from sklearn.datasets import make_low_rank_matrix
+from sklearn.decomposition import PCA, MiniBatchSparsePCA, SparsePCA
+from sklearn.utils import check_random_state
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_array_almost_equal,
+    if_safe_multiprocessing_with_blas,
+)
+from sklearn.utils.extmath import svd_flip
+
+
+def generate_toy_data(n_components, n_samples, image_size, random_state=None):
+    n_features = image_size[0] * image_size[1]
+
+    rng = check_random_state(random_state)
+    U = rng.randn(n_samples, n_components)
+    V = rng.randn(n_components, n_features)
+
+    centers = [(3, 3), (6, 7), (8, 1)]
+    sz = [1, 2, 1]
+    for k in range(n_components):
+        img = np.zeros(image_size)
+        xmin, xmax = centers[k][0] - sz[k], centers[k][0] + sz[k]
+        ymin, ymax = centers[k][1] - sz[k], centers[k][1] + sz[k]
+        img[xmin:xmax][:, ymin:ymax] = 1.0
+        V[k, :] = img.ravel()
+
+    # Y is defined by : Y = UV + noise
+    Y = np.dot(U, V)
+    Y += 0.1 * rng.randn(Y.shape[0], Y.shape[1])  # Add noise
+    return Y, U, V
+
+
+# SparsePCA can be a bit slow. To avoid having test times go up, we
+# test different aspects of the code in the same test
+
+
+def test_correct_shapes():
+    rng = np.random.RandomState(0)
+    X = rng.randn(12, 10)
+    spca = SparsePCA(n_components=8, random_state=rng)
+    U = spca.fit_transform(X)
+    assert spca.components_.shape == (8, 10)
+    assert U.shape == (12, 8)
+    # test overcomplete decomposition
+    spca = SparsePCA(n_components=13, random_state=rng)
+    U = spca.fit_transform(X)
+    assert spca.components_.shape == (13, 10)
+    assert U.shape == (12, 13)
+
+
+def test_fit_transform(global_random_seed):
+    alpha = 1
+    rng = np.random.RandomState(global_random_seed)
+    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
+    spca_lars = SparsePCA(
+        n_components=3, method="lars", alpha=alpha, random_state=global_random_seed
+    )
+    spca_lars.fit(Y)
+
+    # Test that CD gives similar results
+    spca_lasso = SparsePCA(
+        n_components=3, method="cd", random_state=global_random_seed, alpha=alpha
+    )
+    spca_lasso.fit(Y)
+    assert_allclose(spca_lasso.components_, spca_lars.components_, rtol=5e-4, atol=2e-4)
+
+
+# TODO: remove mark once loky bug is fixed:
+# https://github.com/joblib/loky/issues/458
+@pytest.mark.thread_unsafe
+@if_safe_multiprocessing_with_blas
+def test_fit_transform_parallel(global_random_seed):
+    alpha = 1
+    rng = np.random.RandomState(global_random_seed)
+    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
+    spca_lars = SparsePCA(
+        n_components=3, method="lars", alpha=alpha, random_state=global_random_seed
+    )
+    spca_lars.fit(Y)
+    U1 = spca_lars.transform(Y)
+    # Test multiple CPUs
+    spca = SparsePCA(
+        n_components=3,
+        n_jobs=2,
+        method="lars",
+        alpha=alpha,
+        random_state=global_random_seed,
+    ).fit(Y)
+    U2 = spca.transform(Y)
+    assert not np.all(spca_lars.components_ == 0)
+    assert_array_almost_equal(U1, U2)
+
+
+def test_transform_nan(global_random_seed):
+    # Test that SparsePCA won't return NaN when there is 0 feature in all
+    # samples.
+    rng = np.random.RandomState(global_random_seed)
+    Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)  # wide array
+    Y[:, 0] = 0
+    estimator = SparsePCA(n_components=8, random_state=global_random_seed)
+    assert not np.any(np.isnan(estimator.fit_transform(Y)))
+
+
+def test_fit_transform_tall(global_random_seed):
+    rng = np.random.RandomState(global_random_seed)
+    Y, _, _ = generate_toy_data(3, 65, (8, 8), random_state=rng)  # tall array
+    spca_lars = SparsePCA(n_components=3, method="lars", random_state=rng)
+    U1 = spca_lars.fit_transform(Y)
+    spca_lasso = SparsePCA(n_components=3, method="cd", random_state=rng)
+    U2 = spca_lasso.fit(Y).transform(Y)
+    assert_allclose(U1, U2, rtol=1e-4, atol=2e-5)
+
+
+def test_initialization(global_random_seed):
+    rng = np.random.RandomState(global_random_seed)
+    U_init = rng.randn(5, 3)
+    V_init = rng.randn(3, 4)
+    model = SparsePCA(
+        n_components=3, U_init=U_init, V_init=V_init, max_iter=0, random_state=rng
+    )
+    model.fit(rng.randn(5, 4))
+
+    expected_components = V_init / np.linalg.norm(V_init, axis=1, keepdims=True)
+    expected_components = svd_flip(u=expected_components.T, v=None)[0].T
+    assert_allclose(model.components_, expected_components)
+
+
+def test_mini_batch_correct_shapes():
+    rng = np.random.RandomState(0)
+    X = rng.randn(12, 10)
+    pca = MiniBatchSparsePCA(n_components=8, max_iter=1, random_state=rng)
+    U = pca.fit_transform(X)
+    assert pca.components_.shape == (8, 10)
+    assert U.shape == (12, 8)
+    # test overcomplete decomposition
+    pca = MiniBatchSparsePCA(n_components=13, max_iter=1, random_state=rng)
+    U = pca.fit_transform(X)
+    assert pca.components_.shape == (13, 10)
+    assert U.shape == (12, 13)
+
+
+def test_scaling_fit_transform(global_random_seed):
+    alpha = 1
+    rng = np.random.RandomState(global_random_seed)
+    Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
+    spca_lars = SparsePCA(n_components=3, method="lars", alpha=alpha, random_state=rng)
+    results_train = spca_lars.fit_transform(Y)
+    results_test = spca_lars.transform(Y[:10])
+    assert_allclose(results_train[0], results_test[0])
+
+
+def test_pca_vs_spca(global_random_seed):
+    rng = np.random.RandomState(global_random_seed)
+    Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
+    Z, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
+    spca = SparsePCA(alpha=0, ridge_alpha=0, n_components=2, random_state=rng)
+    pca = PCA(n_components=2, random_state=rng)
+    pca.fit(Y)
+    spca.fit(Y)
+    results_test_pca = pca.transform(Z)
+    results_test_spca = spca.transform(Z)
+    assert_allclose(
+        np.abs(spca.components_.dot(pca.components_.T)), np.eye(2), atol=1e-4
+    )
+    results_test_pca *= np.sign(results_test_pca[0, :])
+    results_test_spca *= np.sign(results_test_spca[0, :])
+    assert_allclose(results_test_pca, results_test_spca, atol=1e-4)
+
+
+@pytest.mark.parametrize("SPCA", [SparsePCA, MiniBatchSparsePCA])
+@pytest.mark.parametrize("n_components", [None, 3])
+def test_spca_n_components_(SPCA, n_components):
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 12, 10
+    X = rng.randn(n_samples, n_features)
+
+    model = SPCA(n_components=n_components).fit(X)
+
+    if n_components is not None:
+        assert model.n_components_ == n_components
+    else:
+        assert model.n_components_ == n_features
+
+
+@pytest.mark.parametrize("SPCA", (SparsePCA, MiniBatchSparsePCA))
+@pytest.mark.parametrize("method", ("lars", "cd"))
+@pytest.mark.parametrize(
+    "data_type, expected_type",
+    (
+        (np.float32, np.float32),
+        (np.float64, np.float64),
+        (np.int32, np.float64),
+        (np.int64, np.float64),
+    ),
+)
+def test_sparse_pca_dtype_match(SPCA, method, data_type, expected_type):
+    # Verify output matrix dtype
+    n_samples, n_features, n_components = 12, 10, 3
+    rng = np.random.RandomState(0)
+    input_array = rng.randn(n_samples, n_features).astype(data_type)
+    model = SPCA(n_components=n_components, method=method)
+    transformed = model.fit_transform(input_array)
+
+    assert transformed.dtype == expected_type
+    assert model.components_.dtype == expected_type
+
+
+@pytest.mark.parametrize("SPCA", (SparsePCA, MiniBatchSparsePCA))
+@pytest.mark.parametrize("method", ("lars", "cd"))
+def test_sparse_pca_numerical_consistency(SPCA, method, global_random_seed):
+    # Verify numericall consistentency among np.float32 and np.float64
+    n_samples, n_features, n_components = 20, 20, 5
+    input_array = make_low_rank_matrix(
+        n_samples=n_samples,
+        n_features=n_features,
+        effective_rank=n_components,
+        random_state=global_random_seed,
+    )
+
+    model_32 = SPCA(
+        n_components=n_components,
+        method=method,
+        random_state=global_random_seed,
+    )
+    transformed_32 = model_32.fit_transform(input_array.astype(np.float32))
+
+    model_64 = SPCA(
+        n_components=n_components,
+        method=method,
+        random_state=global_random_seed,
+    )
+    transformed_64 = model_64.fit_transform(input_array.astype(np.float64))
+    assert_allclose(transformed_64, transformed_32)
+    assert_allclose(model_64.components_, model_32.components_)
+
+
+@pytest.mark.parametrize("SPCA", [SparsePCA, MiniBatchSparsePCA])
+def test_spca_feature_names_out(SPCA):
+    """Check feature names out for *SparsePCA."""
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 12, 10
+    X = rng.randn(n_samples, n_features)
+
+    model = SPCA(n_components=4).fit(X)
+    names = model.get_feature_names_out()
+
+    estimator_name = SPCA.__name__.lower()
+    assert_array_equal([f"{estimator_name}{i}" for i in range(4)], names)
+
+
+def test_spca_early_stopping(global_random_seed):
+    """Check that `tol` and `max_no_improvement` act as early stopping."""
+    rng = np.random.RandomState(global_random_seed)
+    n_samples, n_features = 50, 10
+    X = rng.randn(n_samples, n_features)
+
+    # vary the tolerance to force the early stopping of one of the model
+    model_early_stopped = MiniBatchSparsePCA(
+        max_iter=100, tol=0.5, random_state=global_random_seed
+    ).fit(X)
+    model_not_early_stopped = MiniBatchSparsePCA(
+        max_iter=100, tol=1e-3, random_state=global_random_seed
+    ).fit(X)
+    assert model_early_stopped.n_iter_ < model_not_early_stopped.n_iter_
+
+    # force the max number of no improvement to a large value to check that
+    # it does help to early stop
+    model_early_stopped = MiniBatchSparsePCA(
+        max_iter=100, tol=1e-6, max_no_improvement=2, random_state=global_random_seed
+    ).fit(X)
+    model_not_early_stopped = MiniBatchSparsePCA(
+        max_iter=100, tol=1e-6, max_no_improvement=100, random_state=global_random_seed
+    ).fit(X)
+    assert model_early_stopped.n_iter_ < model_not_early_stopped.n_iter_
+
+
+def test_equivalence_components_pca_spca(global_random_seed):
+    """Check the equivalence of the components found by PCA and SparsePCA.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/23932
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.randn(50, 4)
+
+    n_components = 2
+    pca = PCA(
+        n_components=n_components,
+        svd_solver="randomized",
+        random_state=0,
+    ).fit(X)
+    spca = SparsePCA(
+        n_components=n_components,
+        method="lars",
+        ridge_alpha=0,
+        alpha=0,
+        random_state=0,
+    ).fit(X)
+
+    assert_allclose(pca.components_, spca.components_)
+
+
+def test_sparse_pca_inverse_transform(global_random_seed):
+    """Check that `inverse_transform` in `SparsePCA` and `PCA` are similar."""
+    rng = np.random.RandomState(global_random_seed)
+    n_samples, n_features = 10, 5
+    X = rng.randn(n_samples, n_features)
+
+    n_components = 2
+    spca = SparsePCA(
+        n_components=n_components,
+        alpha=1e-12,
+        ridge_alpha=1e-12,
+        random_state=global_random_seed,
+    )
+    pca = PCA(n_components=n_components, random_state=global_random_seed)
+    X_trans_spca = spca.fit_transform(X)
+    X_trans_pca = pca.fit_transform(X)
+    assert_allclose(
+        spca.inverse_transform(X_trans_spca), pca.inverse_transform(X_trans_pca)
+    )
+
+
+@pytest.mark.parametrize("SPCA", [SparsePCA, MiniBatchSparsePCA])
+def test_transform_inverse_transform_round_trip(SPCA, global_random_seed):
+    """Check the `transform` and `inverse_transform` round trip with no loss of
+    information.
+    """
+    rng = np.random.RandomState(global_random_seed)
+    n_samples, n_features = 10, 5
+    X = rng.randn(n_samples, n_features)
+
+    n_components = n_features
+    spca = SPCA(
+        n_components=n_components,
+        alpha=1e-12,
+        ridge_alpha=1e-12,
+        random_state=global_random_seed,
+    )
+    X_trans_spca = spca.fit_transform(X)
+    assert_allclose(spca.inverse_transform(X_trans_spca), X)

linedance-app/venv/lib/python3.12/site-packages/sklearn/decomposition/tests/test_truncated_svd.py

@@ -0,0 +1,212 @@
+"""Test truncated SVD transformer."""
+
+import numpy as np
+import pytest
+import scipy.sparse as sp
+
+from sklearn.decomposition import PCA, TruncatedSVD
+from sklearn.utils import check_random_state
+from sklearn.utils._testing import assert_allclose, assert_array_less
+
+SVD_SOLVERS = ["arpack", "randomized"]
+
+
+@pytest.fixture(scope="module")
+def X_sparse():
+    # Make an X that looks somewhat like a small tf-idf matrix.
+    rng = check_random_state(42)
+    X = sp.random(60, 55, density=0.2, format="csr", random_state=rng)
+    X.data[:] = 1 + np.log(X.data)
+    return X
+
+
+@pytest.mark.parametrize("solver", ["randomized"])
+@pytest.mark.parametrize("kind", ("dense", "sparse"))
+def test_solvers(X_sparse, solver, kind):
+    X = X_sparse if kind == "sparse" else X_sparse.toarray()
+    svd_a = TruncatedSVD(30, algorithm="arpack")
+    svd = TruncatedSVD(30, algorithm=solver, random_state=42, n_oversamples=100)
+
+    Xa = svd_a.fit_transform(X)[:, :6]
+    Xr = svd.fit_transform(X)[:, :6]
+    assert_allclose(Xa, Xr, rtol=2e-3)
+
+    comp_a = np.abs(svd_a.components_)
+    comp = np.abs(svd.components_)
+    # All elements are equal, but some elements are more equal than others.
+    assert_allclose(comp_a[:9], comp[:9], rtol=1e-3)
+    assert_allclose(comp_a[9:], comp[9:], atol=1e-2)
+
+
+@pytest.mark.parametrize("n_components", (10, 25, 41, 55))
+def test_attributes(n_components, X_sparse):
+    n_features = X_sparse.shape[1]
+    tsvd = TruncatedSVD(n_components).fit(X_sparse)
+    assert tsvd.n_components == n_components
+    assert tsvd.components_.shape == (n_components, n_features)
+
+
+@pytest.mark.parametrize(
+    "algorithm, n_components",
+    [
+        ("arpack", 55),
+        ("arpack", 56),
+        ("randomized", 56),
+    ],
+)
+def test_too_many_components(X_sparse, algorithm, n_components):
+    tsvd = TruncatedSVD(n_components=n_components, algorithm=algorithm)
+    with pytest.raises(ValueError):
+        tsvd.fit(X_sparse)
+
+
+@pytest.mark.parametrize("fmt", ("array", "csr", "csc", "coo", "lil"))
+def test_sparse_formats(fmt, X_sparse):
+    n_samples = X_sparse.shape[0]
+    Xfmt = X_sparse.toarray() if fmt == "dense" else getattr(X_sparse, "to" + fmt)()
+    tsvd = TruncatedSVD(n_components=11)
+    Xtrans = tsvd.fit_transform(Xfmt)
+    assert Xtrans.shape == (n_samples, 11)
+    Xtrans = tsvd.transform(Xfmt)
+    assert Xtrans.shape == (n_samples, 11)
+
+
+@pytest.mark.parametrize("algo", SVD_SOLVERS)
+def test_inverse_transform(algo, X_sparse):
+    # We need a lot of components for the reconstruction to be "almost
+    # equal" in all positions. XXX Test means or sums instead?
+    tsvd = TruncatedSVD(n_components=52, random_state=42, algorithm=algo)
+    Xt = tsvd.fit_transform(X_sparse)
+    Xinv = tsvd.inverse_transform(Xt)
+    assert_allclose(Xinv, X_sparse.toarray(), rtol=1e-1, atol=2e-1)
+
+
+def test_integers(X_sparse):
+    n_samples = X_sparse.shape[0]
+    Xint = X_sparse.astype(np.int64)
+    tsvd = TruncatedSVD(n_components=6)
+    Xtrans = tsvd.fit_transform(Xint)
+    assert Xtrans.shape == (n_samples, tsvd.n_components)
+
+
+@pytest.mark.parametrize("kind", ("dense", "sparse"))
+@pytest.mark.parametrize("n_components", [10, 20])
+@pytest.mark.parametrize("solver", SVD_SOLVERS)
+def test_explained_variance(X_sparse, kind, n_components, solver):
+    X = X_sparse if kind == "sparse" else X_sparse.toarray()
+    svd = TruncatedSVD(n_components, algorithm=solver)
+    X_tr = svd.fit_transform(X)
+    # Assert that all the values are greater than 0
+    assert_array_less(0.0, svd.explained_variance_ratio_)
+
+    # Assert that total explained variance is less than 1
+    assert_array_less(svd.explained_variance_ratio_.sum(), 1.0)
+
+    # Test that explained_variance is correct
+    total_variance = np.var(X_sparse.toarray(), axis=0).sum()
+    variances = np.var(X_tr, axis=0)
+    true_explained_variance_ratio = variances / total_variance
+
+    assert_allclose(
+        svd.explained_variance_ratio_,
+        true_explained_variance_ratio,
+    )
+
+
+@pytest.mark.parametrize("kind", ("dense", "sparse"))
+@pytest.mark.parametrize("solver", SVD_SOLVERS)
+def test_explained_variance_components_10_20(X_sparse, kind, solver):
+    X = X_sparse if kind == "sparse" else X_sparse.toarray()
+    svd_10 = TruncatedSVD(10, algorithm=solver, n_iter=10).fit(X)
+    svd_20 = TruncatedSVD(20, algorithm=solver, n_iter=10).fit(X)
+
+    # Assert the 1st component is equal
+    assert_allclose(
+        svd_10.explained_variance_ratio_,
+        svd_20.explained_variance_ratio_[:10],
+        rtol=5e-3,
+    )
+
+    # Assert that 20 components has higher explained variance than 10
+    assert (
+        svd_20.explained_variance_ratio_.sum() > svd_10.explained_variance_ratio_.sum()
+    )
+
+
+@pytest.mark.parametrize("solver", SVD_SOLVERS)
+def test_singular_values_consistency(solver, global_random_seed):
+    # Check that the TruncatedSVD output has the correct singular values
+    rng = np.random.RandomState(global_random_seed)
+    n_samples, n_features = 100, 80
+    X = rng.randn(n_samples, n_features)
+
+    pca = TruncatedSVD(n_components=2, algorithm=solver, random_state=rng).fit(X)
+
+    # Compare to the Frobenius norm
+    X_pca = pca.transform(X)
+    assert_allclose(
+        np.sum(pca.singular_values_**2.0),
+        np.linalg.norm(X_pca, "fro") ** 2.0,
+        rtol=1e-2,
+    )
+
+    # Compare to the 2-norms of the score vectors
+    assert_allclose(
+        pca.singular_values_, np.sqrt(np.sum(X_pca**2.0, axis=0)), rtol=1e-2
+    )
+
+
+@pytest.mark.parametrize("solver", SVD_SOLVERS)
+def test_singular_values_expected(solver, global_random_seed):
+    # Set the singular values and see what we get back
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 100
+    n_features = 110
+
+    X = rng.randn(n_samples, n_features)
+
+    pca = TruncatedSVD(n_components=3, algorithm=solver, random_state=rng)
+    X_pca = pca.fit_transform(X)
+
+    X_pca /= np.sqrt(np.sum(X_pca**2.0, axis=0))
+    X_pca[:, 0] *= 3.142
+    X_pca[:, 1] *= 2.718
+
+    X_hat_pca = np.dot(X_pca, pca.components_)
+    pca.fit(X_hat_pca)
+    assert_allclose(pca.singular_values_, [3.142, 2.718, 1.0], rtol=1e-14)
+
+
+def test_truncated_svd_eq_pca(X_sparse):
+    # TruncatedSVD should be equal to PCA on centered data
+
+    X_dense = X_sparse.toarray()
+
+    X_c = X_dense - X_dense.mean(axis=0)
+
+    params = dict(n_components=10, random_state=42)
+
+    svd = TruncatedSVD(algorithm="arpack", **params)
+    pca = PCA(svd_solver="arpack", **params)
+
+    Xt_svd = svd.fit_transform(X_c)
+    Xt_pca = pca.fit_transform(X_c)
+
+    assert_allclose(Xt_svd, Xt_pca, rtol=1e-9)
+    assert_allclose(pca.mean_, 0, atol=1e-9)
+    assert_allclose(svd.components_, pca.components_)
+
+
+@pytest.mark.parametrize(
+    "algorithm, tol", [("randomized", 0.0), ("arpack", 1e-6), ("arpack", 0.0)]
+)
+@pytest.mark.parametrize("kind", ("dense", "sparse"))
+def test_fit_transform(X_sparse, algorithm, tol, kind):
+    # fit_transform(X) should equal fit(X).transform(X)
+    X = X_sparse if kind == "sparse" else X_sparse.toarray()
+    svd = TruncatedSVD(
+        n_components=5, n_iter=7, random_state=42, algorithm=algorithm, tol=tol
+    )
+    X_transformed_1 = svd.fit_transform(X)
+    X_transformed_2 = svd.fit(X).transform(X)
+    assert_allclose(X_transformed_1, X_transformed_2)