import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest

from sklearn.manifold import _mds as mds
from sklearn.utils._testing import ignore_warnings


def test_smacof():
    # test metric smacof using the data of "Modern Multidimensional Scaling",
    # Borg & Groenen, p 154
    sim = np.array([[0, 5, 3, 4],
                    [5, 0, 2, 2],
                    [3, 2, 0, 1],
                    [4, 2, 1, 0]])
    Z = np.array([[-.266, -.539],
                  [.451, .252],
                  [.016, -.238],
                  [-.200, .524]])
    X, _ = mds.smacof(sim, init=Z, n_components=2, max_iter=1, n_init=1)
    X_true = np.array([[-1.415, -2.471],
                       [1.633, 1.107],
                       [.249, -.067],
                       [-.468, 1.431]])
    assert_array_almost_equal(X, X_true, decimal=3)


def test_smacof_error():
    # Not symmetric similarity matrix:
    sim = np.array([[0, 5, 9, 4],
                    [5, 0, 2, 2],
                    [3, 2, 0, 1],
                    [4, 2, 1, 0]])

    with pytest.raises(ValueError):
        mds.smacof(sim)

    # Not squared similarity matrix:
    sim = np.array([[0, 5, 9, 4],
                    [5, 0, 2, 2],
                    [4, 2, 1, 0]])

    with pytest.raises(ValueError):
        mds.smacof(sim)

    # init not None and not correct format:
    sim = np.array([[0, 5, 3, 4],
                    [5, 0, 2, 2],
                    [3, 2, 0, 1],
                    [4, 2, 1, 0]])

    Z = np.array([[-.266, -.539],
                  [.016, -.238],
                  [-.200, .524]])
    with pytest.raises(ValueError):
        mds.smacof(sim, init=Z, n_init=1)


def test_MDS():
    sim = np.array([[0, 5, 3, 4],
                    [5, 0, 2, 2],
                    [3, 2, 0, 1],
                    [4, 2, 1, 0]])
    mds_clf = mds.MDS(metric=False, n_jobs=3, dissimilarity="precomputed")
    mds_clf.fit(sim)


# TODO: Remove in 1.1
def test_MDS_pairwise_deprecated():
    mds_clf = mds.MDS(metric='precomputed')
    msg = r"Attribute _pairwise was deprecated in version 0\.24"
    with pytest.warns(FutureWarning, match=msg):
        mds_clf._pairwise


# TODO: Remove in 1.1
@ignore_warnings(category=FutureWarning)
@pytest.mark.parametrize("dissimilarity, expected_pairwise", [
   ("precomputed", True),
   ("euclidean", False),
])
def test_MDS_pairwise(dissimilarity, expected_pairwise):
    # _pairwise attribute is set correctly
    mds_clf = mds.MDS(dissimilarity=dissimilarity)
    assert mds_clf._pairwise == expected_pairwise