# Sebastian Raschka 2014-2023
# mlxtend Machine Learning Library Extensions
# Author: Sebastian Raschka <sebastianraschka.com>
#
# License: BSD 3 clause

import random

import numpy as np
import pytest
from packaging.version import Version
from sklearn import __version__ as sklearn_version
from sklearn import exceptions
from sklearn.base import clone
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier

from mlxtend.classifier import EnsembleVoteClassifier
from mlxtend.data import iris_data
from mlxtend.utils import assert_raises

X, y = iris_data()
X = X[:, 1:3]


def test_EnsembleVoteClassifier():
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting="hard")

    scores = cross_val_score(eclf, X, y, cv=5, scoring="accuracy")
    scores_mean = round(scores.mean(), 2)
    assert scores_mean == 0.94


def test_fit_base_estimators_false():
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()

    clf1.fit(X, y)
    clf2.fit(X, y)
    clf3.fit(X, y)

    eclf = EnsembleVoteClassifier(
        clfs=[clf1, clf2, clf3], voting="hard", fit_base_estimators=False
    )

    eclf.fit(X, y)
    assert round(eclf.score(X, y), 2) == 0.97


def test_use_clones():
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], use_clones=True).fit(X, y)

    assert_raises(
        exceptions.NotFittedError,
        "This RandomForestClassifier instance is not fitted yet."
        " Call 'fit' with appropriate arguments"
        " before using this estimator.",
        clf2.predict,
        X,
    )

    EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], use_clones=False).fit(X, y)

    clf2.predict(X)


def test_sample_weight():
    # with no weight
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting="hard")
    prob1 = eclf.fit(X, y).predict_proba(X)

    # with weight = 1
    w = np.ones(len(y))
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting="hard")
    prob2 = eclf.fit(X, y, sample_weight=w).predict_proba(X)

    # with random weight
    random.seed(87)
    w = np.array([random.random() for _ in range(len(y))])
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting="hard")
    prob3 = eclf.fit(X, y, sample_weight=w).predict_proba(X)

    diff12 = np.max(np.abs(prob1 - prob2))
    diff23 = np.max(np.abs(prob2 - prob3))
    assert diff12 < 1e-3, "max diff is %.4f" % diff12
    assert diff23 > 1e-3, "max diff is %.4f" % diff23


def test_no_weight_support():
    random.seed(87)
    w = np.array([random.random() for _ in range(len(y))])
    logi = LogisticRegression(solver="liblinear", multi_class="ovr")
    rf = RandomForestClassifier(n_estimators=10)
    gnb = GaussianNB()
    knn = KNeighborsClassifier()
    eclf = EnsembleVoteClassifier(clfs=[logi, rf, gnb, knn], voting="hard")
    with pytest.raises(TypeError):
        eclf.fit(X, y, sample_weight=w)


def test_no_weight_support_with_no_weight():
    logi = LogisticRegression(solver="liblinear", multi_class="ovr")
    rf = RandomForestClassifier(n_estimators=10)
    gnb = GaussianNB()
    knn = KNeighborsClassifier()
    eclf = EnsembleVoteClassifier(clfs=[logi, rf, gnb, knn], voting="hard")
    eclf.fit(X, y)


def test_1model_labels():
    clf = LogisticRegression(
        multi_class="multinomial", solver="newton-cg", random_state=123
    )
    ens_clf_1 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=None)
    ens_clf_2 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=[1.0])

    pred_e1 = ens_clf_1.fit(X, y).predict(X)
    pred_e2 = ens_clf_2.fit(X, y).predict(X)
    pred_e3 = clf.fit(X, y).predict(X)

    np.testing.assert_equal(pred_e1, pred_e2)
    np.testing.assert_equal(pred_e1, pred_e3)


def test_1model_probas():
    clf = LogisticRegression(
        multi_class="multinomial", solver="newton-cg", random_state=123
    )
    ens_clf_1 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=None)
    ens_clf_2 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=[1.0])

    pred_e1 = ens_clf_1.fit(X, y).predict_proba(X)
    pred_e2 = ens_clf_2.fit(X, y).predict_proba(X)
    pred_e3 = clf.fit(X, y).predict_proba(X)

    np.testing.assert_almost_equal(pred_e1, pred_e2, decimal=8)
    np.testing.assert_almost_equal(pred_e1, pred_e3, decimal=8)


def test_EnsembleVoteClassifier_weights():
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(
        clfs=[clf1, clf2, clf3], voting="soft", weights=[1, 2, 10]
    )

    scores = cross_val_score(eclf, X, y, cv=5, scoring="accuracy")
    scores_mean = round(scores.mean(), 2)
    assert scores_mean == 0.93


def test_EnsembleVoteClassifier_gridsearch():
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr", random_state=1)
    clf2 = RandomForestClassifier(random_state=1)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting="soft")

    params = {
        "logisticregression__C": [1.0, 100.0],
        "randomforestclassifier__n_estimators": [20, 200],
    }

    if Version(sklearn_version) < Version("0.24.1"):
        grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5, iid=False)
    else:
        grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)

    X, y = iris_data()
    grid.fit(X, y)

    mean_scores = [round(s, 2) for s in grid.cv_results_["mean_test_score"]]

    assert mean_scores == [0.95, 0.96, 0.96, 0.95]


def test_EnsembleVoteClassifier_gridsearch_enumerate_names():
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr", random_state=1)
    clf2 = RandomForestClassifier(random_state=1)
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf1, clf2])

    params = {
        "logisticregression-1__C": [1.0, 100.0],
        "logisticregression-2__C": [1.0, 100.0],
        "randomforestclassifier__n_estimators": [5, 20],
        "voting": ["hard", "soft"],
    }

    if Version(sklearn_version) < Version("0.24.1"):
        grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5, iid=False)
    else:
        grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)

    X, y = iris_data()
    grid = grid.fit(X, y)


def test_get_params():
    clf1 = KNeighborsClassifier(n_neighbors=1)
    clf2 = RandomForestClassifier(random_state=1, n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3])

    got = sorted(list({s.split("__")[0] for s in eclf.get_params().keys()}))
    expect = [
        "clfs",
        "fit_base_estimators",
        "gaussiannb",
        "kneighborsclassifier",
        "randomforestclassifier",
        "use_clones",
        "verbose",
        "voting",
        "weights",
    ]
    assert got == expect, got


def test_classifier_gridsearch():
    clf1 = KNeighborsClassifier(n_neighbors=1)
    clf2 = RandomForestClassifier(random_state=1, n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1])

    params = {"clfs": [[clf1, clf1, clf1], [clf2, clf3]]}

    if Version(sklearn_version) < Version("0.24.1"):
        grid = GridSearchCV(
            estimator=eclf, param_grid=params, iid=False, cv=5, refit=True
        )
    else:
        grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5, refit=True)
    grid.fit(X, y)

    assert len(grid.best_params_["clfs"]) == 2


def test_string_labels_numpy_array():
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting="hard")

    y_str = y.copy()
    y_str = y_str.astype(str)
    y_str[:50] = "a"
    y_str[50:100] = "b"
    y_str[100:150] = "c"

    scores = cross_val_score(eclf, X, y_str, cv=5, scoring="accuracy")
    scores_mean = round(scores.mean(), 2)
    assert scores_mean == 0.94


def test_string_labels_python_list():
    np.random.seed(123)
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting="hard")

    y_str = (
        ["a" for a in range(50)] + ["b" for a in range(50)] + ["c" for a in range(50)]
    )

    scores = cross_val_score(eclf, X, y_str, cv=5, scoring="accuracy")
    scores_mean = round(scores.mean(), 2)
    assert scores_mean == 0.94


def test_clone():
    clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
    clf2 = RandomForestClassifier(n_estimators=10)
    clf3 = GaussianNB()
    eclf = EnsembleVoteClassifier(
        clfs=[clf1, clf2, clf3], voting="hard", fit_base_estimators=False
    )
    clone(eclf)
