import numpy as np
import scipy.sparse as sp
import pytest

from sklearn.utils._testing import (assert_array_almost_equal,
                                   assert_allclose)

from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import make_circles
from sklearn.datasets import make_blobs
from sklearn.linear_model import Perceptron
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.utils.validation import _check_psd_eigenvalues


def test_kernel_pca():
    rng = np.random.RandomState(0)
    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"):
        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():
    with pytest.raises(ValueError):
        KernelPCA(10, fit_inverse_transform=True, kernel='precomputed')


def test_kernel_pca_consistent_transform():
    # X_fit_ needs to retain the old, unmodified copy of X
    state = np.random.RandomState(0)
    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():
    rng = np.random.RandomState(0)
    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))


def test_kernel_pca_sparse():
    rng = np.random.RandomState(0)
    X_fit = sp.csr_matrix(rng.random_sample((5, 4)))
    X_pred = sp.csr_matrix(rng.random_sample((2, 4)))

    for eigen_solver in ("auto", "arpack"):
        for kernel in ("linear", "rbf", "poly"):
            # transform fit data
            kpca = KernelPCA(4, kernel=kernel, eigen_solver=eigen_solver,
                             fit_inverse_transform=False)
            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
            # X_pred2 = kpca.inverse_transform(X_pred_transformed)
            # assert X_pred2.shape == X_pred.shape)


def test_kernel_pca_linear_kernel():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((5, 4))
    X_pred = rng.random_sample((2, 4))

    # 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
    assert_array_almost_equal(
        np.abs(KernelPCA(4).fit(X_fit).transform(X_pred)),
        np.abs(PCA(4).fit(X_fit).transform(X_pred)))


def test_kernel_pca_n_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"):
        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():
    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():
    """This test checks that fit().transform() returns the same result as
    fit_transform() in case of non-removed zero eigenvalue.
    Non-regression test for issue #12141 (PR #12143)"""
    X_fit = np.array([[1, 1], [0, 0]])

    # Assert that even with all np warnings on, there is no div by zero warning
    with pytest.warns(None) as record:
        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))

    for w in record:
        # 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 lets check only this)
        assert not issubclass(w.category, RuntimeWarning)


def test_kernel_pca_precomputed():
    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"):
        X_kpca = KernelPCA(4, eigen_solver=eigen_solver).\
            fit(X_fit).transform(X_pred)
        X_kpca2 = KernelPCA(
            4, eigen_solver=eigen_solver, kernel='precomputed').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').fit_transform(np.dot(X_fit, X_fit.T))
        X_kpca_train2 = KernelPCA(
            4, eigen_solver=eigen_solver, kernel='precomputed').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))


def test_kernel_pca_invalid_kernel():
    rng = np.random.RandomState(0)
    X_fit = rng.random_sample((2, 4))
    kpca = KernelPCA(kernel="tototiti")
    with pytest.raises(ValueError):
        kpca.fit(X_fit)


def test_gridsearch_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=.3, noise=.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. ** 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():
    # Test if we can do a grid-search to find parameters to separate
    # circles with a perceptron model using a precomputed kernel.
    X, y = make_circles(n_samples=400, factor=.3, noise=.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.)
    grid_search.fit(X_kernel, y)
    assert grid_search.best_score_ == 1


def test_nested_circles():
    # Test the linear separability of the first 2D KPCA transform
    X, y = make_circles(n_samples=400, factor=.3, noise=.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.)
    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():
    """ Test that ``_check_psd_eigenvalues`` is correctly called
    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.lambdas_.min() == 0
    assert np.all(kpca.lambdas_ == _check_psd_eigenvalues(kpca.lambdas_))


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_32_64_decomposition_shape():
    """ Test that the decomposition is similar for 32 and 64 bits data """
    # see 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)


# TODO: Remove in 1.1
def test_kernel_pcc_pairwise_is_deprecated():
    kp = KernelPCA(kernel='precomputed')
    msg = r"Attribute _pairwise was deprecated in version 0\.24"
    with pytest.warns(FutureWarning, match=msg):
        kp._pairwise
