"""Tests for Incremental PCA."""

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():
    # Test that the projection of data is correct.
    rng = np.random.RandomState(1999)
    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():
    # Test that the projection of data can be inverted.
    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

    # 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)

    # Tests that n_components is also <= n_samples.
    n_components = 3
    with pytest.raises(
        ValueError,
        match=(
            "n_components={} must be"
            " less or equal to the batch number of"
            " samples {}".format(n_components, n_samples)
        ),
    ):
        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():
    # Test that components_ sign is stable over batch sizes.
    rng = np.random.RandomState(1999)
    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 zip(all_components[:-1], all_components[1:]):
        assert_almost_equal(np.sign(i), np.sign(j), decimal=6)


def test_incremental_pca_batch_values():
    # Test that components_ values are stable over batch sizes.
    rng = np.random.RandomState(1999)
    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 zip(all_components[:-1], all_components[1:]):
        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 zip(all_components[:-1], all_components[1:]):
        assert_allclose_dense_sparse(components_i, components_j)


def test_incremental_pca_partial_fit():
    # Test that fit and partial_fit get equivalent results.
    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

    # 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 zip(batch_itr[:-1], batch_itr[1:]):
        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():
    # Test that IncrementalPCA and PCA are approximate (to a sign flip).
    rng = np.random.RandomState(1999)
    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():
    # Check that the IncrementalPCA output has the correct singular values

    rng = np.random.RandomState(0)
    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=100).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(0)
    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)