import numpy as np
import pytest
from scipy import sparse

from sklearn import base, datasets, linear_model, svm
from sklearn.datasets import load_digits, make_blobs, make_classification
from sklearn.exceptions import ConvergenceWarning
from sklearn.svm.tests import test_svm
from sklearn.utils._testing import (
    assert_allclose,
    assert_array_almost_equal,
    assert_array_equal,
    ignore_warnings,
    skip_if_32bit,
)
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.utils.fixes import (
    CSR_CONTAINERS,
    DOK_CONTAINERS,
    LIL_CONTAINERS,
)

# test sample 1
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
Y = [1, 1, 1, 2, 2, 2]
T = np.array([[-1, -1], [2, 2], [3, 2]])
true_result = [1, 2, 2]

# test sample 2
X2 = np.array(
    [
        [0, 0, 0],
        [1, 1, 1],
        [2, 0, 0],
        [0, 0, 2],
        [3, 3, 3],
    ]
)
Y2 = [1, 2, 2, 2, 3]
T2 = np.array([[-1, -1, -1], [1, 1, 1], [2, 2, 2]])
true_result2 = [1, 2, 3]

iris = datasets.load_iris()
rng = np.random.RandomState(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]

X_blobs, y_blobs = make_blobs(n_samples=100, centers=10, random_state=0)


def check_svm_model_equal(dense_svm, X_train, y_train, X_test):
    # Use the original svm model for dense fit and clone an exactly same
    # svm model for sparse fit
    sparse_svm = base.clone(dense_svm)

    dense_svm.fit(X_train.toarray(), y_train)
    if sparse.issparse(X_test):
        X_test_dense = X_test.toarray()
    else:
        X_test_dense = X_test
    sparse_svm.fit(X_train, y_train)
    assert sparse.issparse(sparse_svm.support_vectors_)
    assert sparse.issparse(sparse_svm.dual_coef_)
    assert_allclose(dense_svm.support_vectors_, sparse_svm.support_vectors_.toarray())
    assert_allclose(dense_svm.dual_coef_, sparse_svm.dual_coef_.toarray())
    if dense_svm.kernel == "linear":
        assert sparse.issparse(sparse_svm.coef_)
        assert_array_almost_equal(dense_svm.coef_, sparse_svm.coef_.toarray())
    assert_allclose(dense_svm.support_, sparse_svm.support_)
    assert_allclose(dense_svm.predict(X_test_dense), sparse_svm.predict(X_test))

    assert_array_almost_equal(
        dense_svm.decision_function(X_test_dense), sparse_svm.decision_function(X_test)
    )
    assert_array_almost_equal(
        dense_svm.decision_function(X_test_dense),
        sparse_svm.decision_function(X_test_dense),
    )
    if isinstance(dense_svm, svm.OneClassSVM):
        msg = "cannot use sparse input in 'OneClassSVM' trained on dense data"
    else:
        if hasattr(dense_svm, "predict_proba"):
            assert_array_almost_equal(
                dense_svm.predict_proba(X_test_dense),
                sparse_svm.predict_proba(X_test),
                decimal=4,
            )
        msg = "cannot use sparse input in 'SVC' trained on dense data"
    if sparse.issparse(X_test):
        with pytest.raises(ValueError, match=msg):
            dense_svm.predict(X_test)


# XXX: probability=True is not thread-safe:
# https://github.com/scikit-learn/scikit-learn/issues/31885
@pytest.mark.thread_unsafe
@skip_if_32bit
@pytest.mark.parametrize(
    "X_train, y_train, X_test",
    [
        [X, Y, T],
        [X2, Y2, T2],
        [X_blobs[:80], y_blobs[:80], X_blobs[80:]],
        [iris.data, iris.target, iris.data],
    ],
)
@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
def test_svc(X_train, y_train, X_test, kernel, sparse_container):
    """Check that sparse SVC gives the same result as SVC."""
    X_train = sparse_container(X_train)

    clf = svm.SVC(
        gamma=1,
        kernel=kernel,
        probability=True,
        random_state=0,
        decision_function_shape="ovo",
    )
    check_svm_model_equal(clf, X_train, y_train, X_test)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_unsorted_indices(csr_container):
    # test that the result with sorted and unsorted indices in csr is the same
    # we use a subset of digits as iris, blobs or make_classification didn't
    # show the problem
    X, y = load_digits(return_X_y=True)
    X_test = csr_container(X[50:100])
    X, y = X[:50], y[:50]
    tols = dict(rtol=1e-12, atol=1e-14)

    X_sparse = csr_container(X)
    coef_dense = (
        svm.SVC(kernel="linear", probability=True, random_state=0).fit(X, y).coef_
    )
    sparse_svc = svm.SVC(kernel="linear", probability=True, random_state=0).fit(
        X_sparse, y
    )
    coef_sorted = sparse_svc.coef_
    # make sure dense and sparse SVM give the same result
    assert_allclose(coef_dense, coef_sorted.toarray(), **tols)

    # reverse each row's indices
    def scramble_indices(X):
        new_data = []
        new_indices = []
        for i in range(1, len(X.indptr)):
            row_slice = slice(*X.indptr[i - 1 : i + 1])
            new_data.extend(X.data[row_slice][::-1])
            new_indices.extend(X.indices[row_slice][::-1])
        return csr_container((new_data, new_indices, X.indptr), shape=X.shape)

    X_sparse_unsorted = scramble_indices(X_sparse)
    X_test_unsorted = scramble_indices(X_test)

    assert not X_sparse_unsorted.has_sorted_indices
    assert not X_test_unsorted.has_sorted_indices

    unsorted_svc = svm.SVC(kernel="linear", probability=True, random_state=0).fit(
        X_sparse_unsorted, y
    )
    coef_unsorted = unsorted_svc.coef_
    # make sure unsorted indices give same result
    assert_allclose(coef_unsorted.toarray(), coef_sorted.toarray(), **tols)
    assert_allclose(
        sparse_svc.predict_proba(X_test_unsorted),
        sparse_svc.predict_proba(X_test),
        **tols,
    )


@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
def test_svc_with_custom_kernel(lil_container):
    def kfunc(x, y):
        return safe_sparse_dot(x, y.T)

    X_sp = lil_container(X)
    clf_lin = svm.SVC(kernel="linear").fit(X_sp, Y)
    clf_mylin = svm.SVC(kernel=kfunc).fit(X_sp, Y)
    assert_array_equal(clf_lin.predict(X_sp), clf_mylin.predict(X_sp))


@skip_if_32bit
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf"])
def test_svc_iris(csr_container, kernel):
    # Test the sparse SVC with the iris dataset
    iris_data_sp = csr_container(iris.data)

    sp_clf = svm.SVC(kernel=kernel).fit(iris_data_sp, iris.target)
    clf = svm.SVC(kernel=kernel).fit(iris.data, iris.target)

    assert_allclose(clf.support_vectors_, sp_clf.support_vectors_.toarray())
    assert_allclose(clf.dual_coef_, sp_clf.dual_coef_.toarray())
    assert_allclose(clf.predict(iris.data), sp_clf.predict(iris_data_sp))
    if kernel == "linear":
        assert_allclose(clf.coef_, sp_clf.coef_.toarray())


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sparse_decision_function(csr_container):
    # Test decision_function

    # Sanity check, test that decision_function implemented in python
    # returns the same as the one in libsvm

    # multi class:
    iris_data_sp = csr_container(iris.data)
    svc = svm.SVC(kernel="linear", C=0.1, decision_function_shape="ovo")
    clf = svc.fit(iris_data_sp, iris.target)

    dec = safe_sparse_dot(iris_data_sp, clf.coef_.T) + clf.intercept_

    assert_allclose(dec, clf.decision_function(iris_data_sp))

    # binary:
    clf.fit(X, Y)
    dec = np.dot(X, clf.coef_.T) + clf.intercept_
    prediction = clf.predict(X)
    assert_allclose(dec.ravel(), clf.decision_function(X))
    assert_allclose(
        prediction, clf.classes_[(clf.decision_function(X) > 0).astype(int).ravel()]
    )
    expected = np.array([-1.0, -0.66, -1.0, 0.66, 1.0, 1.0])
    assert_array_almost_equal(clf.decision_function(X), expected, decimal=2)


@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
def test_error(lil_container):
    # Test that it gives proper exception on deficient input
    clf = svm.SVC()
    X_sp = lil_container(X)

    Y2 = Y[:-1]  # wrong dimensions for labels
    with pytest.raises(ValueError):
        clf.fit(X_sp, Y2)

    clf.fit(X_sp, Y)
    assert_array_equal(clf.predict(T), true_result)


@pytest.mark.parametrize(
    "lil_container, dok_container", zip(LIL_CONTAINERS, DOK_CONTAINERS)
)
def test_linearsvc(lil_container, dok_container):
    # Similar to test_SVC
    X_sp = lil_container(X)
    X2_sp = dok_container(X2)

    clf = svm.LinearSVC(random_state=0).fit(X, Y)
    sp_clf = svm.LinearSVC(random_state=0).fit(X_sp, Y)

    assert sp_clf.fit_intercept

    assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
    assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)

    assert_allclose(clf.predict(X), sp_clf.predict(X_sp))

    clf.fit(X2, Y2)
    sp_clf.fit(X2_sp, Y2)

    assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
    assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_linearsvc_iris(csr_container):
    # Test the sparse LinearSVC with the iris dataset
    iris_data_sp = csr_container(iris.data)

    sp_clf = svm.LinearSVC(random_state=0).fit(iris_data_sp, iris.target)
    clf = svm.LinearSVC(random_state=0).fit(iris.data, iris.target)

    assert clf.fit_intercept == sp_clf.fit_intercept

    assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=1)
    assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=1)
    assert_allclose(clf.predict(iris.data), sp_clf.predict(iris_data_sp))

    # check decision_function
    pred = np.argmax(sp_clf.decision_function(iris_data_sp), axis=1)
    assert_allclose(pred, clf.predict(iris.data))

    # sparsify the coefficients on both models and check that they still
    # produce the same results
    clf.sparsify()
    assert_array_equal(pred, clf.predict(iris_data_sp))
    sp_clf.sparsify()
    assert_array_equal(pred, sp_clf.predict(iris_data_sp))


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_weight(csr_container):
    # Test class weights
    X_, y_ = make_classification(
        n_samples=200, n_features=100, weights=[0.833, 0.167], random_state=0
    )

    X_ = csr_container(X_)
    for clf in (
        linear_model.LogisticRegression(),
        svm.LinearSVC(random_state=0),
        svm.SVC(),
    ):
        clf.set_params(class_weight={0: 5})
        clf.fit(X_[:180], y_[:180])
        y_pred = clf.predict(X_[180:])
        assert np.sum(y_pred == y_[180:]) >= 11


@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
def test_sample_weights(lil_container):
    # Test weights on individual samples
    X_sp = lil_container(X)

    clf = svm.SVC()
    clf.fit(X_sp, Y)
    assert_array_equal(clf.predict([X[2]]), [1.0])

    sample_weight = [0.1] * 3 + [10] * 3
    clf.fit(X_sp, Y, sample_weight=sample_weight)
    assert_array_equal(clf.predict([X[2]]), [2.0])


def test_sparse_liblinear_intercept_handling():
    # Test that sparse liblinear honours intercept_scaling param
    test_svm.test_dense_liblinear_intercept_handling(svm.LinearSVC)


@pytest.mark.parametrize(
    "X_train, y_train, X_test",
    [
        [X, None, T],
        [X2, None, T2],
        [X_blobs[:80], None, X_blobs[80:]],
        [iris.data, None, iris.data],
    ],
)
@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
@skip_if_32bit
def test_sparse_oneclasssvm(X_train, y_train, X_test, kernel, sparse_container):
    # Check that sparse OneClassSVM gives the same result as dense OneClassSVM
    X_train = sparse_container(X_train)

    clf = svm.OneClassSVM(gamma=1, kernel=kernel)
    check_svm_model_equal(clf, X_train, y_train, X_test)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sparse_realdata(csr_container):
    # Test on a subset from the 20newsgroups dataset.
    # This catches some bugs if input is not correctly converted into
    # sparse format or weights are not correctly initialized.
    data = np.array([0.03771744, 0.1003567, 0.01174647, 0.027069])

    # SVC does not support large sparse, so we specify int32 indices
    # In this case, `csr_matrix` automatically uses int32 regardless of the dtypes of
    # `indices` and `indptr` but `csr_array` may or may not use the same dtype as
    # `indices` and `indptr`, which would be int64 if not specified
    indices = np.array([6, 5, 35, 31], dtype=np.int32)
    indptr = np.array([0] * 8 + [1] * 32 + [2] * 38 + [4] * 3, dtype=np.int32)

    X = csr_container((data, indices, indptr))
    y = np.array(
        [
            1.0,
            0.0,
            2.0,
            2.0,
            1.0,
            1.0,
            1.0,
            2.0,
            2.0,
            0.0,
            1.0,
            2.0,
            2.0,
            0.0,
            2.0,
            0.0,
            3.0,
            0.0,
            3.0,
            0.0,
            1.0,
            1.0,
            3.0,
            2.0,
            3.0,
            2.0,
            0.0,
            3.0,
            1.0,
            0.0,
            2.0,
            1.0,
            2.0,
            0.0,
            1.0,
            0.0,
            2.0,
            3.0,
            1.0,
            3.0,
            0.0,
            1.0,
            0.0,
            0.0,
            2.0,
            0.0,
            1.0,
            2.0,
            2.0,
            2.0,
            3.0,
            2.0,
            0.0,
            3.0,
            2.0,
            1.0,
            2.0,
            3.0,
            2.0,
            2.0,
            0.0,
            1.0,
            0.0,
            1.0,
            2.0,
            3.0,
            0.0,
            0.0,
            2.0,
            2.0,
            1.0,
            3.0,
            1.0,
            1.0,
            0.0,
            1.0,
            2.0,
            1.0,
            1.0,
            3.0,
        ]
    )

    clf = svm.SVC(kernel="linear").fit(X.toarray(), y)
    sp_clf = svm.SVC(kernel="linear").fit(X.tocoo(), y)

    assert_array_equal(clf.support_vectors_, sp_clf.support_vectors_.toarray())
    assert_array_equal(clf.dual_coef_, sp_clf.dual_coef_.toarray())


@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
def test_sparse_svc_clone_with_callable_kernel(lil_container):
    # Test that the "dense_fit" is called even though we use sparse input
    # meaning that everything works fine.
    a = svm.SVC(C=1, kernel=lambda x, y: x @ y.T, probability=True, random_state=0)
    b = base.clone(a)

    X_sp = lil_container(X)
    b.fit(X_sp, Y)
    pred = b.predict(X_sp)
    b.predict_proba(X_sp)

    dense_svm = svm.SVC(
        C=1, kernel=lambda x, y: np.dot(x, y.T), probability=True, random_state=0
    )
    pred_dense = dense_svm.fit(X, Y).predict(X)
    assert_array_equal(pred_dense, pred)
    # b.decision_function(X_sp)  # XXX : should be supported


@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
def test_timeout(lil_container):
    sp = svm.SVC(
        C=1, kernel=lambda x, y: x @ y.T, probability=True, random_state=0, max_iter=1
    )
    warning_msg = (
        r"Solver terminated early \(max_iter=1\).  Consider pre-processing "
        r"your data with StandardScaler or MinMaxScaler."
    )
    with pytest.warns(ConvergenceWarning, match=warning_msg):
        sp.fit(lil_container(X), Y)


# XXX: probability=True is not thread-safe:
# https://github.com/scikit-learn/scikit-learn/issues/31885
@pytest.mark.thread_unsafe
def test_consistent_proba():
    a = svm.SVC(probability=True, max_iter=1, random_state=0)
    with ignore_warnings(category=ConvergenceWarning):
        proba_1 = a.fit(X, Y).predict_proba(X)
    a = svm.SVC(probability=True, max_iter=1, random_state=0)
    with ignore_warnings(category=ConvergenceWarning):
        proba_2 = a.fit(X, Y).predict_proba(X)
    assert_allclose(proba_1, proba_2)
