import pytest

import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from scipy import sparse

from sklearn import datasets, svm, linear_model, base
from sklearn.datasets import make_classification, load_digits, make_blobs
from sklearn.svm.tests import test_svm
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.utils._testing import ignore_warnings, skip_if_32bit


# test sample 1
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
X_sp = sparse.lil_matrix(X)
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],
    ]
)
X2_sp = sparse.dok_matrix(X2)
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()
# permute
rng = np.random.RandomState(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
# sparsify
iris.data = sparse.csr_matrix(iris.data)


def check_svm_model_equal(dense_svm, sparse_svm, X_train, y_train, X_test):
    dense_svm.fit(X_train.toarray(), y_train)
    if sparse.isspmatrix(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_array_almost_equal(
        dense_svm.support_vectors_, sparse_svm.support_vectors_.toarray()
    )
    assert_array_almost_equal(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_array_almost_equal(dense_svm.support_, sparse_svm.support_)
    assert_array_almost_equal(
        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:
        assert_array_almost_equal(
            dense_svm.predict_proba(X_test_dense), sparse_svm.predict_proba(X_test), 4
        )
        msg = "cannot use sparse input in 'SVC' trained on dense data"
    if sparse.isspmatrix(X_test):
        with pytest.raises(ValueError, match=msg):
            dense_svm.predict(X_test)


@skip_if_32bit
def test_svc():
    """Check that sparse SVC gives the same result as SVC"""
    # many class dataset:
    X_blobs, y_blobs = make_blobs(n_samples=100, centers=10, random_state=0)
    X_blobs = sparse.csr_matrix(X_blobs)

    datasets = [
        [X_sp, Y, T],
        [X2_sp, Y2, T2],
        [X_blobs[:80], y_blobs[:80], X_blobs[80:]],
        [iris.data, iris.target, iris.data],
    ]
    kernels = ["linear", "poly", "rbf", "sigmoid"]
    for dataset in datasets:
        for kernel in kernels:
            clf = svm.SVC(
                gamma=1,
                kernel=kernel,
                probability=True,
                random_state=0,
                decision_function_shape="ovo",
            )
            sp_clf = svm.SVC(
                gamma=1,
                kernel=kernel,
                probability=True,
                random_state=0,
                decision_function_shape="ovo",
            )
            check_svm_model_equal(clf, sp_clf, *dataset)


def test_unsorted_indices():
    # 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 = sparse.csr_matrix(X[50:100])
    X, y = X[:50], y[:50]

    X_sparse = sparse.csr_matrix(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_array_almost_equal(coef_dense, coef_sorted.toarray())

    # 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 sparse.csr_matrix((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_array_almost_equal(coef_unsorted.toarray(), coef_sorted.toarray())
    assert_array_almost_equal(
        sparse_svc.predict_proba(X_test_unsorted), sparse_svc.predict_proba(X_test)
    )


def test_svc_with_custom_kernel():
    def kfunc(x, y):
        return safe_sparse_dot(x, y.T)

    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
def test_svc_iris():
    # Test the sparse SVC with the iris dataset
    for k in ("linear", "poly", "rbf"):
        sp_clf = svm.SVC(kernel=k).fit(iris.data, iris.target)
        clf = svm.SVC(kernel=k).fit(iris.data.toarray(), iris.target)

        assert_array_almost_equal(
            clf.support_vectors_, sp_clf.support_vectors_.toarray()
        )
        assert_array_almost_equal(clf.dual_coef_, sp_clf.dual_coef_.toarray())
        assert_array_almost_equal(
            clf.predict(iris.data.toarray()), sp_clf.predict(iris.data)
        )
        if k == "linear":
            assert_array_almost_equal(clf.coef_, sp_clf.coef_.toarray())


def test_sparse_decision_function():
    # Test decision_function

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

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

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

    assert_array_almost_equal(dec, clf.decision_function(iris.data))

    # binary:
    clf.fit(X, Y)
    dec = np.dot(X, clf.coef_.T) + clf.intercept_
    prediction = clf.predict(X)
    assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
    assert_array_almost_equal(
        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, 2)


def test_error():
    # Test that it gives proper exception on deficient input
    clf = svm.SVC()

    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)


def test_linearsvc():
    # Similar to test_SVC
    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_array_almost_equal(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)


def test_linearsvc_iris():
    # Test the sparse LinearSVC with the iris dataset

    sp_clf = svm.LinearSVC(random_state=0).fit(iris.data, iris.target)
    clf = svm.LinearSVC(random_state=0).fit(iris.data.toarray(), 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_array_almost_equal(
        clf.predict(iris.data.toarray()), sp_clf.predict(iris.data)
    )

    # check decision_function
    pred = np.argmax(sp_clf.decision_function(iris.data), 1)
    assert_array_almost_equal(pred, clf.predict(iris.data.toarray()))

    # 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_clf.sparsify()
    assert_array_equal(pred, sp_clf.predict(iris.data))


def test_weight():
    # Test class weights
    X_, y_ = make_classification(
        n_samples=200, n_features=100, weights=[0.833, 0.167], random_state=0
    )

    X_ = sparse.csr_matrix(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


def test_sample_weights():
    # Test weights on individual samples
    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("datasets_index", range(4))
@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
@skip_if_32bit
def test_sparse_oneclasssvm(datasets_index, kernel):
    # Check that sparse OneClassSVM gives the same result as dense OneClassSVM
    # many class dataset:
    X_blobs, _ = make_blobs(n_samples=100, centers=10, random_state=0)
    X_blobs = sparse.csr_matrix(X_blobs)
    datasets = [
        [X_sp, None, T],
        [X2_sp, None, T2],
        [X_blobs[:80], None, X_blobs[80:]],
        [iris.data, None, iris.data],
    ]
    dataset = datasets[datasets_index]
    clf = svm.OneClassSVM(gamma=1, kernel=kernel)
    sp_clf = svm.OneClassSVM(gamma=1, kernel=kernel)
    check_svm_model_equal(clf, sp_clf, *dataset)


def test_sparse_realdata():
    # 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])
    indices = np.array([6, 5, 35, 31])
    indptr = np.array(
        [
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            1,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            4,
            4,
            4,
        ]
    )
    X = sparse.csr_matrix((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(sparse.coo_matrix(X), y)

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


def test_sparse_svc_clone_with_callable_kernel():
    # 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)

    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


def test_timeout():
    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(X_sp, Y)


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_array_almost_equal(proba_1, proba_2)