# coding: utf-8
"""
Testing for Neighborhood Component Analysis module (sklearn.neighbors.nca)
"""

# Authors: William de Vazelhes <wdevazelhes@gmail.com>
#          John Chiotellis <ioannis.chiotellis@in.tum.de>
# License: BSD 3 clause

import pytest
import re
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
from scipy.optimize import check_grad
from sklearn import clone
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_raises, assert_equal,
                                   assert_raise_message, assert_warns_message)
from sklearn.datasets import load_iris, make_classification, make_blobs
from sklearn.neighbors.nca import NeighborhoodComponentsAnalysis
from sklearn.metrics import pairwise_distances


rng = check_random_state(0)
# load and shuffle iris dataset
iris = load_iris()
perm = rng.permutation(iris.target.size)
iris_data = iris.data[perm]
iris_target = iris.target[perm]
EPS = np.finfo(float).eps


def test_simple_example():
    """Test on a simple example.

    Puts four points in the input space where the opposite labels points are
    next to each other. After transform the samples from the same class
    should be next to each other.

    """
    X = np.array([[0, 0], [0, 1], [2, 0], [2, 1]])
    y = np.array([1, 0, 1, 0])
    nca = NeighborhoodComponentsAnalysis(n_components=2, init='identity',
                                         random_state=42)
    nca.fit(X, y)
    X_t = nca.transform(X)
    assert_array_equal(pairwise_distances(X_t).argsort()[:, 1],
                       np.array([2, 3, 0, 1]))


def test_toy_example_collapse_points():
    """Test on a toy example of three points that should collapse

    We build a simple example: two points from the same class and a point from
    a different class in the middle of them. On this simple example, the new
    (transformed) points should all collapse into one single point. Indeed, the
    objective is 2/(1 + exp(d/2)), with d the euclidean distance between the
    two samples from the same class. This is maximized for d=0 (because d>=0),
    with an objective equal to 1 (loss=-1.).

    """
    rng = np.random.RandomState(42)
    input_dim = 5
    two_points = rng.randn(2, input_dim)
    X = np.vstack([two_points, two_points.mean(axis=0)[np.newaxis, :]])
    y = [0, 0, 1]

    class LossStorer:

        def __init__(self, X, y):
            self.loss = np.inf  # initialize the loss to very high
            # Initialize a fake NCA and variables needed to compute the loss:
            self.fake_nca = NeighborhoodComponentsAnalysis()
            self.fake_nca.n_iter_ = np.inf
            self.X, y, _ = self.fake_nca._validate_params(X, y)
            self.same_class_mask = y[:, np.newaxis] == y[np.newaxis, :]

        def callback(self, transformation, n_iter):
            """Stores the last value of the loss function"""
            self.loss, _ = self.fake_nca._loss_grad_lbfgs(transformation,
                                                          self.X,
                                                          self.same_class_mask,
                                                          -1.0)

    loss_storer = LossStorer(X, y)
    nca = NeighborhoodComponentsAnalysis(random_state=42,
                                         callback=loss_storer.callback)
    X_t = nca.fit_transform(X, y)
    print(X_t)
    # test that points are collapsed into one point
    assert_array_almost_equal(X_t - X_t[0], 0.)
    assert abs(loss_storer.loss + 1) < 1e-10


def test_finite_differences():
    """Test gradient of loss function

    Assert that the gradient is almost equal to its finite differences
    approximation.
    """
    # Initialize the transformation `M`, as well as `X` and `y` and `NCA`
    rng = np.random.RandomState(42)
    X, y = make_classification()
    M = rng.randn(rng.randint(1, X.shape[1] + 1),
                  X.shape[1])
    nca = NeighborhoodComponentsAnalysis()
    nca.n_iter_ = 0
    mask = y[:, np.newaxis] == y[np.newaxis, :]

    def fun(M):
        return nca._loss_grad_lbfgs(M, X, mask)[0]

    def grad(M):
        return nca._loss_grad_lbfgs(M, X, mask)[1]

    # compute relative error
    rel_diff = check_grad(fun, grad, M.ravel()) / np.linalg.norm(grad(M))
    np.testing.assert_almost_equal(rel_diff, 0., decimal=5)


def test_params_validation():
    # Test that invalid parameters raise value error
    X = np.arange(12).reshape(4, 3)
    y = [1, 1, 2, 2]
    NCA = NeighborhoodComponentsAnalysis
    rng = np.random.RandomState(42)

    # TypeError
    assert_raises(TypeError, NCA(max_iter='21').fit, X, y)
    assert_raises(TypeError, NCA(verbose='true').fit, X, y)
    assert_raises(TypeError, NCA(tol=1).fit, X, y)
    assert_raises(TypeError, NCA(n_components='invalid').fit, X, y)
    assert_raises(TypeError, NCA(warm_start=1).fit, X, y)

    # ValueError
    assert_raise_message(ValueError,
                         "`init` must be 'auto', 'pca', 'lda', 'identity', "
                         "'random' or a numpy array of shape "
                         "(n_components, n_features).",
                         NCA(init=1).fit, X, y)
    assert_raise_message(ValueError,
                         '`max_iter`= -1, must be >= 1.',
                         NCA(max_iter=-1).fit, X, y)

    init = rng.rand(5, 3)
    assert_raise_message(ValueError,
                         'The output dimensionality ({}) of the given linear '
                         'transformation `init` cannot be greater than its '
                         'input dimensionality ({}).'
                         .format(init.shape[0], init.shape[1]),
                         NCA(init=init).fit, X, y)

    n_components = 10
    assert_raise_message(ValueError,
                         'The preferred dimensionality of the '
                         'projected space `n_components` ({}) cannot '
                         'be greater than the given data '
                         'dimensionality ({})!'
                         .format(n_components, X.shape[1]),
                         NCA(n_components=n_components).fit, X, y)


def test_transformation_dimensions():
    X = np.arange(12).reshape(4, 3)
    y = [1, 1, 2, 2]

    # Fail if transformation input dimension does not match inputs dimensions
    transformation = np.array([[1, 2], [3, 4]])
    assert_raises(ValueError,
                  NeighborhoodComponentsAnalysis(init=transformation).fit,
                  X, y)

    # Fail if transformation output dimension is larger than
    # transformation input dimension
    transformation = np.array([[1, 2], [3, 4], [5, 6]])
    # len(transformation) > len(transformation[0])
    assert_raises(ValueError,
                  NeighborhoodComponentsAnalysis(init=transformation).fit,
                  X, y)

    # Pass otherwise
    transformation = np.arange(9).reshape(3, 3)
    NeighborhoodComponentsAnalysis(init=transformation).fit(X, y)


def test_n_components():
    rng = np.random.RandomState(42)
    X = np.arange(12).reshape(4, 3)
    y = [1, 1, 2, 2]

    init = rng.rand(X.shape[1] - 1, 3)

    # n_components = X.shape[1] != transformation.shape[0]
    n_components = X.shape[1]
    nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
    assert_raise_message(ValueError,
                         'The preferred dimensionality of the '
                         'projected space `n_components` ({}) does not match '
                         'the output dimensionality of the given '
                         'linear transformation `init` ({})!'
                         .format(n_components, init.shape[0]),
                         nca.fit, X, y)

    # n_components > X.shape[1]
    n_components = X.shape[1] + 2
    nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
    assert_raise_message(ValueError,
                         'The preferred dimensionality of the '
                         'projected space `n_components` ({}) cannot '
                         'be greater than the given data '
                         'dimensionality ({})!'
                         .format(n_components, X.shape[1]),
                         nca.fit, X, y)

    # n_components < X.shape[1]
    nca = NeighborhoodComponentsAnalysis(n_components=2, init='identity')
    nca.fit(X, y)


def test_init_transformation():
    rng = np.random.RandomState(42)
    X, y = make_blobs(n_samples=30, centers=6, n_features=5, random_state=0)

    # Start learning from scratch
    nca = NeighborhoodComponentsAnalysis(init='identity')
    nca.fit(X, y)

    # Initialize with random
    nca_random = NeighborhoodComponentsAnalysis(init='random')
    nca_random.fit(X, y)

    # Initialize with auto
    nca_auto = NeighborhoodComponentsAnalysis(init='auto')
    nca_auto.fit(X, y)

    # Initialize with PCA
    nca_pca = NeighborhoodComponentsAnalysis(init='pca')
    nca_pca.fit(X, y)

    # Initialize with LDA
    nca_lda = NeighborhoodComponentsAnalysis(init='lda')
    nca_lda.fit(X, y)

    init = rng.rand(X.shape[1], X.shape[1])
    nca = NeighborhoodComponentsAnalysis(init=init)
    nca.fit(X, y)

    # init.shape[1] must match X.shape[1]
    init = rng.rand(X.shape[1], X.shape[1] + 1)
    nca = NeighborhoodComponentsAnalysis(init=init)
    assert_raise_message(ValueError,
                         'The input dimensionality ({}) of the given '
                         'linear transformation `init` must match the '
                         'dimensionality of the given inputs `X` ({}).'
                         .format(init.shape[1], X.shape[1]),
                         nca.fit, X, y)

    # init.shape[0] must be <= init.shape[1]
    init = rng.rand(X.shape[1] + 1, X.shape[1])
    nca = NeighborhoodComponentsAnalysis(init=init)
    assert_raise_message(ValueError,
                         'The output dimensionality ({}) of the given '
                         'linear transformation `init` cannot be '
                         'greater than its input dimensionality ({}).'
                         .format(init.shape[0], init.shape[1]),
                         nca.fit, X, y)

    # init.shape[0] must match n_components
    init = rng.rand(X.shape[1], X.shape[1])
    n_components = X.shape[1] - 2
    nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
    assert_raise_message(ValueError,
                         'The preferred dimensionality of the '
                         'projected space `n_components` ({}) does not match '
                         'the output dimensionality of the given '
                         'linear transformation `init` ({})!'
                         .format(n_components, init.shape[0]),
                         nca.fit, X, y)


@pytest.mark.parametrize('n_samples', [3, 5, 7, 11])
@pytest.mark.parametrize('n_features', [3, 5, 7, 11])
@pytest.mark.parametrize('n_classes', [5, 7, 11])
@pytest.mark.parametrize('n_components', [3, 5, 7, 11])
def test_auto_init(n_samples, n_features, n_classes, n_components):
    # Test that auto choose the init as expected with every configuration
    # of order of n_samples, n_features, n_classes and n_components.
    rng = np.random.RandomState(42)
    nca_base = NeighborhoodComponentsAnalysis(init='auto',
                                              n_components=n_components,
                                              max_iter=1,
                                              random_state=rng)
    if n_classes >= n_samples:
        pass
        # n_classes > n_samples is impossible, and n_classes == n_samples
        # throws an error from lda but is an absurd case
    else:
        X = rng.randn(n_samples, n_features)
        y = np.tile(range(n_classes), n_samples // n_classes + 1)[:n_samples]
        if n_components > n_features:
            # this would return a ValueError, which is already tested in
            # test_params_validation
            pass
        else:
            nca = clone(nca_base)
            nca.fit(X, y)
            if n_components <= min(n_classes - 1, n_features):
                nca_other = clone(nca_base).set_params(init='lda')
            elif n_components < min(n_features, n_samples):
                nca_other = clone(nca_base).set_params(init='pca')
            else:
                nca_other = clone(nca_base).set_params(init='identity')
            nca_other.fit(X, y)
            assert_array_almost_equal(nca.components_, nca_other.components_)


def test_warm_start_validation():
    X, y = make_classification(n_samples=30, n_features=5, n_classes=4,
                               n_redundant=0, n_informative=5, random_state=0)

    nca = NeighborhoodComponentsAnalysis(warm_start=True, max_iter=5)
    nca.fit(X, y)

    X_less_features, y = make_classification(n_samples=30, n_features=4,
                                             n_classes=4, n_redundant=0,
                                             n_informative=4, random_state=0)
    assert_raise_message(ValueError,
                         'The new inputs dimensionality ({}) does not '
                         'match the input dimensionality of the '
                         'previously learned transformation ({}).'
                         .format(X_less_features.shape[1],
                                 nca.components_.shape[1]),
                         nca.fit, X_less_features, y)


def test_warm_start_effectiveness():
    # A 1-iteration second fit on same data should give almost same result
    # with warm starting, and quite different result without warm starting.

    nca_warm = NeighborhoodComponentsAnalysis(warm_start=True, random_state=0)
    nca_warm.fit(iris_data, iris_target)
    transformation_warm = nca_warm.components_
    nca_warm.max_iter = 1
    nca_warm.fit(iris_data, iris_target)
    transformation_warm_plus_one = nca_warm.components_

    nca_cold = NeighborhoodComponentsAnalysis(warm_start=False, random_state=0)
    nca_cold.fit(iris_data, iris_target)
    transformation_cold = nca_cold.components_
    nca_cold.max_iter = 1
    nca_cold.fit(iris_data, iris_target)
    transformation_cold_plus_one = nca_cold.components_

    diff_warm = np.sum(np.abs(transformation_warm_plus_one -
                              transformation_warm))
    diff_cold = np.sum(np.abs(transformation_cold_plus_one -
                              transformation_cold))
    assert diff_warm < 3.0, ("Transformer changed significantly after one "
                             "iteration even though it was warm-started.")

    assert diff_cold > diff_warm, ("Cold-started transformer changed less "
                                   "significantly than warm-started "
                                   "transformer after one iteration.")


@pytest.mark.parametrize('init_name', ['pca', 'lda', 'identity', 'random',
                                       'precomputed'])
def test_verbose(init_name, capsys):
    # assert there is proper output when verbose = 1, for every initialization
    # except auto because auto will call one of the others
    rng = np.random.RandomState(42)
    X, y = make_blobs(n_samples=30, centers=6, n_features=5, random_state=0)
    regexp_init = r'... done in \ *\d+\.\d{2}s'
    msgs = {'pca': "Finding principal components" + regexp_init,
            'lda': "Finding most discriminative components" + regexp_init}
    if init_name == 'precomputed':
        init = rng.randn(X.shape[1], X.shape[1])
    else:
        init = init_name
    nca = NeighborhoodComponentsAnalysis(verbose=1, init=init)
    nca.fit(X, y)
    out, _ = capsys.readouterr()

    # check output
    lines = re.split('\n+', out)
    # if pca or lda init, an additional line is printed, so we test
    # it and remove it to test the rest equally among initializations
    if init_name in ['pca', 'lda']:
        assert re.match(msgs[init_name], lines[0])
        lines = lines[1:]
    assert lines[0] == '[NeighborhoodComponentsAnalysis]'
    header = '{:>10} {:>20} {:>10}'.format('Iteration', 'Objective Value',
                                           'Time(s)')
    assert lines[1] == '[NeighborhoodComponentsAnalysis] {}'.format(header)
    assert lines[2] == ('[NeighborhoodComponentsAnalysis] {}'
                        .format('-' * len(header)))
    for line in lines[3:-2]:
        # The following regex will match for instance:
        # '[NeighborhoodComponentsAnalysis]  0    6.988936e+01   0.01'
        assert re.match(r'\[NeighborhoodComponentsAnalysis\] *\d+ *\d\.\d{6}e'
                        r'[+|-]\d+\ *\d+\.\d{2}', line)
    assert re.match(r'\[NeighborhoodComponentsAnalysis\] Training took\ *'
                    r'\d+\.\d{2}s\.', lines[-2])
    assert lines[-1] == ''


def test_no_verbose(capsys):
    # assert by default there is no output (verbose=0)
    nca = NeighborhoodComponentsAnalysis()
    nca.fit(iris_data, iris_target)
    out, _ = capsys.readouterr()
    # check output
    assert(out == '')


def test_singleton_class():
    X = iris_data
    y = iris_target

    # one singleton class
    singleton_class = 1
    ind_singleton, = np.where(y == singleton_class)
    y[ind_singleton] = 2
    y[ind_singleton[0]] = singleton_class

    nca = NeighborhoodComponentsAnalysis(max_iter=30)
    nca.fit(X, y)

    # One non-singleton class
    ind_1, = np.where(y == 1)
    ind_2, = np.where(y == 2)
    y[ind_1] = 0
    y[ind_1[0]] = 1
    y[ind_2] = 0
    y[ind_2[0]] = 2

    nca = NeighborhoodComponentsAnalysis(max_iter=30)
    nca.fit(X, y)

    # Only singleton classes
    ind_0, = np.where(y == 0)
    ind_1, = np.where(y == 1)
    ind_2, = np.where(y == 2)
    X = X[[ind_0[0], ind_1[0], ind_2[0]]]
    y = y[[ind_0[0], ind_1[0], ind_2[0]]]

    nca = NeighborhoodComponentsAnalysis(init='identity', max_iter=30)
    nca.fit(X, y)
    assert_array_equal(X, nca.transform(X))


def test_one_class():
    X = iris_data[iris_target == 0]
    y = iris_target[iris_target == 0]

    nca = NeighborhoodComponentsAnalysis(max_iter=30,
                                         n_components=X.shape[1],
                                         init='identity')
    nca.fit(X, y)
    assert_array_equal(X, nca.transform(X))


def test_callback(capsys):
    X = iris_data
    y = iris_target

    nca = NeighborhoodComponentsAnalysis(callback='my_cb')
    assert_raises(ValueError, nca.fit, X, y)

    max_iter = 10

    def my_cb(transformation, n_iter):
        assert transformation.shape == (iris_data.shape[1]**2,)
        rem_iter = max_iter - n_iter
        print('{} iterations remaining...'.format(rem_iter))

    # assert that my_cb is called
    nca = NeighborhoodComponentsAnalysis(max_iter=max_iter,
                                         callback=my_cb, verbose=1)
    nca.fit(iris_data, iris_target)
    out, _ = capsys.readouterr()

    # check output
    assert('{} iterations remaining...'.format(max_iter - 1) in out)


def test_expected_transformation_shape():
    """Test that the transformation has the expected shape."""
    X = iris_data
    y = iris_target

    class TransformationStorer:

        def __init__(self, X, y):
            # Initialize a fake NCA and variables needed to call the loss
            # function:
            self.fake_nca = NeighborhoodComponentsAnalysis()
            self.fake_nca.n_iter_ = np.inf
            self.X, y, _ = self.fake_nca._validate_params(X, y)
            self.same_class_mask = y[:, np.newaxis] == y[np.newaxis, :]

        def callback(self, transformation, n_iter):
            """Stores the last value of the transformation taken as input by
            the optimizer"""
            self.transformation = transformation

    transformation_storer = TransformationStorer(X, y)
    cb = transformation_storer.callback
    nca = NeighborhoodComponentsAnalysis(max_iter=5, callback=cb)
    nca.fit(X, y)
    assert_equal(transformation_storer.transformation.size, X.shape[1]**2)


def test_convergence_warning():
    nca = NeighborhoodComponentsAnalysis(max_iter=2, verbose=1)
    cls_name = nca.__class__.__name__
    assert_warns_message(ConvergenceWarning,
                         '[{}] NCA did not converge'.format(cls_name),
                         nca.fit, iris_data, iris_target)
