"""
Testing for Neighborhood Component Analysis module (xlearn.neighbors.nca)
"""

# Authors: The jax-sklearn developers
# SPDX-License-Identifier: BSD-3-Clause

import re

import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal, assert_array_equal
from scipy.optimize import check_grad

from xlearn import clone
from xlearn.datasets import load_iris, make_blobs, make_classification
from xlearn.exceptions import ConvergenceWarning
from xlearn.metrics import pairwise_distances
from xlearn.neighbors import NeighborhoodComponentsAnalysis
from xlearn.preprocessing import LabelEncoder
from xlearn.utils import check_random_state
from xlearn.utils.validation import validate_data

rng = check_random_state(0)
# Load and shuffle the iris dataset.
iris = load_iris()
perm = rng.permutation(iris.target.size)
iris_data = iris.data[perm]
iris_target = iris.target[perm]
# Avoid having test data introducing dependencies between tests.
iris_data.flags.writeable = False
iris_target.flags.writeable = False
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 = validate_data(self.fake_nca, X, y, ensure_min_samples=2)
            y = LabelEncoder().fit_transform(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], 0.0)
    assert abs(loss_storer.loss + 1) < 1e-10


def test_finite_differences(global_random_seed):
    """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(global_random_seed)
    X, y = make_classification(random_state=global_random_seed)
    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]

    # compare the gradient to a finite difference approximation
    diff = check_grad(fun, grad, M.ravel())
    assert diff == pytest.approx(0.0, abs=1e-4)


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)

    init = rng.rand(5, 3)
    msg = (
        f"The output dimensionality ({init.shape[0]}) "
        "of the given linear transformation `init` cannot be "
        f"greater than its input dimensionality ({init.shape[1]})."
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        NCA(init=init).fit(X, y)
    n_components = 10
    msg = (
        "The preferred dimensionality of the projected space "
        f"`n_components` ({n_components}) cannot be greater "
        f"than the given data dimensionality ({X.shape[1]})!"
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        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]])
    with pytest.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])
    with pytest.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)
    msg = (
        "The preferred dimensionality of the projected space "
        f"`n_components` ({n_components}) does not match the output "
        "dimensionality of the given linear transformation "
        f"`init` ({init.shape[0]})!"
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        nca.fit(X, y)

    # n_components > X.shape[1]
    n_components = X.shape[1] + 2
    nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
    msg = (
        "The preferred dimensionality of the projected space "
        f"`n_components` ({n_components}) cannot be greater than "
        f"the given data dimensionality ({X.shape[1]})!"
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        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)
    msg = (
        f"The input dimensionality ({init.shape[1]}) of the given "
        "linear transformation `init` must match the "
        f"dimensionality of the given inputs `X` ({X.shape[1]})."
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        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)
    msg = (
        f"The output dimensionality ({init.shape[0]}) of the given "
        "linear transformation `init` cannot be "
        f"greater than its input dimensionality ({init.shape[1]})."
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        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)
    msg = (
        "The preferred dimensionality of the "
        f"projected space `n_components` ({n_components}) "
        "does not match the output dimensionality of the given "
        f"linear transformation `init` ({init.shape[0]})!"
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        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,
    )
    msg = (
        f"The new inputs dimensionality ({X_less_features.shape[1]}) "
        "does not match the input dimensionality of the previously learned "
        f"transformation ({nca.components_.shape[1]})."
    )
    with pytest.raises(ValueError, match=re.escape(msg)):
        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\ *\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.copy()
    y = iris_target.copy()

    # 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):
    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 = validate_data(self.fake_nca, X, y, ensure_min_samples=2)
            y = LabelEncoder().fit_transform(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 transformation_storer.transformation.size == X.shape[1] ** 2


def test_convergence_warning():
    nca = NeighborhoodComponentsAnalysis(max_iter=2, verbose=1)
    cls_name = nca.__class__.__name__
    msg = "[{}] NCA did not converge".format(cls_name)
    with pytest.warns(ConvergenceWarning, match=re.escape(msg)):
        nca.fit(iris_data, iris_target)


@pytest.mark.parametrize(
    "param, value",
    [
        ("n_components", np.int32(3)),
        ("max_iter", np.int32(100)),
        ("tol", np.float32(0.0001)),
    ],
)
def test_parameters_valid_types(param, value):
    # check that no error is raised when parameters have numpy integer or
    # floating types.
    nca = NeighborhoodComponentsAnalysis(**{param: value})

    X = iris_data
    y = iris_target

    nca.fit(X, y)


@pytest.mark.parametrize("n_components", [None, 2])
def test_nca_feature_names_out(n_components):
    """Check `get_feature_names_out` for `NeighborhoodComponentsAnalysis`.

    Non-regression test for:
    https://github.com/chenxingqiang/jax-sklearn/issues/28293
    """

    X = iris_data
    y = iris_target

    est = NeighborhoodComponentsAnalysis(n_components=n_components).fit(X, y)
    names_out = est.get_feature_names_out()

    class_name_lower = est.__class__.__name__.lower()

    if n_components is not None:
        expected_n_features = n_components
    else:
        expected_n_features = X.shape[1]

    expected_names_out = np.array(
        [f"{class_name_lower}{i}" for i in range(expected_n_features)],
        dtype=object,
    )

    assert_array_equal(names_out, expected_names_out)
