"""Testing for Spectral Biclustering methods"""

import numpy as np
import pytest
from scipy.sparse import csr_matrix, issparse

from sklearn.model_selection import ParameterGrid

from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal

from sklearn.base import BaseEstimator, BiclusterMixin

from sklearn.cluster import SpectralCoclustering
from sklearn.cluster import SpectralBiclustering
from sklearn.cluster._bicluster import _scale_normalize
from sklearn.cluster._bicluster import _bistochastic_normalize
from sklearn.cluster._bicluster import _log_normalize

from sklearn.metrics import (consensus_score, v_measure_score)

from sklearn.datasets import make_biclusters, make_checkerboard


class MockBiclustering(BiclusterMixin, BaseEstimator):
    # Mock object for testing get_submatrix.
    def __init__(self):
        pass

    def get_indices(self, i):
        # Overridden to reproduce old get_submatrix test.
        return (np.where([True, True, False, False, True])[0],
                np.where([False, False, True, True])[0])


def test_get_submatrix():
    data = np.arange(20).reshape(5, 4)
    model = MockBiclustering()

    for X in (data, csr_matrix(data), data.tolist()):
        submatrix = model.get_submatrix(0, X)
        if issparse(submatrix):
            submatrix = submatrix.toarray()
        assert_array_equal(submatrix, [[2, 3],
                                       [6, 7],
                                       [18, 19]])
        submatrix[:] = -1
        if issparse(X):
            X = X.toarray()
        assert np.all(X != -1)


def _test_shape_indices(model):
    # Test get_shape and get_indices on fitted model.
    for i in range(model.n_clusters):
        m, n = model.get_shape(i)
        i_ind, j_ind = model.get_indices(i)
        assert len(i_ind) == m
        assert len(j_ind) == n


def test_spectral_coclustering():
    # Test Dhillon's Spectral CoClustering on a simple problem.
    param_grid = {'svd_method': ['randomized', 'arpack'],
                  'n_svd_vecs': [None, 20],
                  'mini_batch': [False, True],
                  'init': ['k-means++'],
                  'n_init': [10]}
    random_state = 0
    S, rows, cols = make_biclusters((30, 30), 3, noise=0.5,
                                    random_state=random_state)
    S -= S.min()  # needs to be nonnegative before making it sparse
    S = np.where(S < 1, 0, S)  # threshold some values
    for mat in (S, csr_matrix(S)):
        for kwargs in ParameterGrid(param_grid):
            model = SpectralCoclustering(n_clusters=3,
                                         random_state=random_state,
                                         **kwargs)
            model.fit(mat)

            assert model.rows_.shape == (3, 30)
            assert_array_equal(model.rows_.sum(axis=0), np.ones(30))
            assert_array_equal(model.columns_.sum(axis=0), np.ones(30))
            assert consensus_score(model.biclusters_,
                                   (rows, cols)) == 1

            _test_shape_indices(model)


def test_spectral_biclustering():
    # Test Kluger methods on a checkerboard dataset.
    S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
                                      random_state=0)

    non_default_params = {'method': ['scale', 'log'],
                          'svd_method': ['arpack'],
                          'n_svd_vecs': [20],
                          'mini_batch': [True]}

    for mat in (S, csr_matrix(S)):
        for param_name, param_values in non_default_params.items():
            for param_value in param_values:

                model = SpectralBiclustering(
                    n_clusters=3,
                    n_init=3,
                    init='k-means++',
                    random_state=0,
                )
                model.set_params(**dict([(param_name, param_value)]))

                if issparse(mat) and model.get_params().get('method') == 'log':
                    # cannot take log of sparse matrix
                    with pytest.raises(ValueError):
                        model.fit(mat)
                    continue
                else:
                    model.fit(mat)

                assert model.rows_.shape == (9, 30)
                assert model.columns_.shape == (9, 30)
                assert_array_equal(model.rows_.sum(axis=0),
                                   np.repeat(3, 30))
                assert_array_equal(model.columns_.sum(axis=0),
                                   np.repeat(3, 30))
                assert consensus_score(model.biclusters_,
                                       (rows, cols)) == 1

                _test_shape_indices(model)


def _do_scale_test(scaled):
    """Check that rows sum to one constant, and columns to another."""
    row_sum = scaled.sum(axis=1)
    col_sum = scaled.sum(axis=0)
    if issparse(scaled):
        row_sum = np.asarray(row_sum).squeeze()
        col_sum = np.asarray(col_sum).squeeze()
    assert_array_almost_equal(row_sum, np.tile(row_sum.mean(), 100),
                              decimal=1)
    assert_array_almost_equal(col_sum, np.tile(col_sum.mean(), 100),
                              decimal=1)


def _do_bistochastic_test(scaled):
    """Check that rows and columns sum to the same constant."""
    _do_scale_test(scaled)
    assert_almost_equal(scaled.sum(axis=0).mean(),
                        scaled.sum(axis=1).mean(),
                        decimal=1)


def test_scale_normalize():
    generator = np.random.RandomState(0)
    X = generator.rand(100, 100)
    for mat in (X, csr_matrix(X)):
        scaled, _, _ = _scale_normalize(mat)
        _do_scale_test(scaled)
        if issparse(mat):
            assert issparse(scaled)


def test_bistochastic_normalize():
    generator = np.random.RandomState(0)
    X = generator.rand(100, 100)
    for mat in (X, csr_matrix(X)):
        scaled = _bistochastic_normalize(mat)
        _do_bistochastic_test(scaled)
        if issparse(mat):
            assert issparse(scaled)


def test_log_normalize():
    # adding any constant to a log-scaled matrix should make it
    # bistochastic
    generator = np.random.RandomState(0)
    mat = generator.rand(100, 100)
    scaled = _log_normalize(mat) + 1
    _do_bistochastic_test(scaled)


def test_fit_best_piecewise():
    model = SpectralBiclustering(random_state=0)
    vectors = np.array([[0, 0, 0, 1, 1, 1],
                        [2, 2, 2, 3, 3, 3],
                        [0, 1, 2, 3, 4, 5]])
    best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2)
    assert_array_equal(best, vectors[:2])


def test_project_and_cluster():
    model = SpectralBiclustering(random_state=0)
    data = np.array([[1, 1, 1],
                     [1, 1, 1],
                     [3, 6, 3],
                     [3, 6, 3]])
    vectors = np.array([[1, 0],
                        [0, 1],
                        [0, 0]])
    for mat in (data, csr_matrix(data)):
        labels = model._project_and_cluster(mat, vectors,
                                            n_clusters=2)
        assert_almost_equal(v_measure_score(labels, [0, 0, 1, 1]), 1.0)


def test_perfect_checkerboard():
    # XXX Previously failed on build bot (not reproducible)
    model = SpectralBiclustering(3, svd_method="arpack", random_state=0)

    S, rows, cols = make_checkerboard((30, 30), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert consensus_score(model.biclusters_,
                           (rows, cols)) == 1

    S, rows, cols = make_checkerboard((40, 30), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert consensus_score(model.biclusters_,
                           (rows, cols)) == 1

    S, rows, cols = make_checkerboard((30, 40), 3, noise=0,
                                      random_state=0)
    model.fit(S)
    assert consensus_score(model.biclusters_,
                           (rows, cols)) == 1


@pytest.mark.parametrize(
    "args",
    [{'n_clusters': (3, 3, 3)},
     {'n_clusters': 'abc'},
     {'n_clusters': (3, 'abc')},
     {'method': 'unknown'},
     {'n_components': 0},
     {'n_best': 0},
     {'svd_method': 'unknown'},
     {'n_components': 3, 'n_best': 4}]
)
def test_errors(args):
    data = np.arange(25).reshape((5, 5))

    model = SpectralBiclustering(**args)
    with pytest.raises(ValueError):
        model.fit(data)


def test_wrong_shape():
    model = SpectralBiclustering()
    data = np.arange(27).reshape((3, 3, 3))
    with pytest.raises(ValueError):
        model.fit(data)


@pytest.mark.parametrize('est',
                         (SpectralBiclustering(), SpectralCoclustering()))
def test_n_features_in_(est):

    X, _, _ = make_biclusters((3, 3), 3, random_state=0)

    assert not hasattr(est, 'n_features_in_')
    est.fit(X)
    assert est.n_features_in_ == 3


@pytest.mark.parametrize("klass", [SpectralBiclustering, SpectralCoclustering])
@pytest.mark.parametrize("n_jobs", [None, 1])
def test_n_jobs_deprecated(klass, n_jobs):
    # FIXME: remove in 1.0
    depr_msg = ("'n_jobs' was deprecated in version 0.23 and will be removed "
                "in 1.0")
    S, _, _ = make_biclusters((30, 30), 3, noise=0.5, random_state=0)
    est = klass(random_state=0, n_jobs=n_jobs)

    with pytest.warns(FutureWarning, match=depr_msg):
        est.fit(S)
