# -*- coding: utf-8 -*-

from __future__ import division
from __future__ import print_function

import os
import sys
from os import path

import unittest
# noinspection PyProtectedMember
from sklearn.utils.testing import assert_allclose
from sklearn.utils.testing import assert_array_less
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_greater_equal
from sklearn.utils.testing import assert_less_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.estimator_checks import check_estimator

from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.utils.validation import check_X_y
from scipy.io import loadmat
from scipy.stats import rankdata

# temporary solution for relative imports in case pyod is not installed
# if pyod is installed, no need to use the following line
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

from pyod.models.xgbod import XGBOD
from pyod.utils.data import generate_data


class TestXGBOD(unittest.TestCase):
    def setUp(self):
        # Define data file and read X and y
        # Generate some data if the source data is missing
        this_directory = path.abspath(path.dirname(__file__))
        mat_file = 'pima.mat'
        try:
            mat = loadmat(path.join(*[this_directory, 'data', mat_file]))

        except TypeError:
            print('{data_file} does not exist. Use generated data'.format(
                data_file=mat_file))
            X, y = generate_data(train_only=True)  # load data
        except IOError:
            print('{data_file} does not exist. Use generated data'.format(
                data_file=mat_file))
            X, y = generate_data(train_only=True)  # load data
        else:
            X = mat['X']
            y = mat['y'].ravel()
            X, y = check_X_y(X, y)

        self.X_train, self.X_test, self.y_train, self.y_test = \
            train_test_split(X, y, test_size=0.4, random_state=42)

        self.clf = XGBOD(random_state=42)
        self.clf.fit(self.X_train, self.y_train)

        self.roc_floor = 0.75

    def test_parameters(self):
        assert (hasattr(self.clf, 'clf_') and
                self.clf.decision_scores_ is not None)
        assert (hasattr(self.clf, '_scalar') and
                self.clf.labels_ is not None)
        assert (hasattr(self.clf, 'n_detector_') and
                self.clf.labels_ is not None)
        assert (hasattr(self.clf, 'X_train_add_') and
                self.clf.labels_ is not None)
        assert (hasattr(self.clf, 'decision_scores_') and
                self.clf.decision_scores_ is not None)
        assert (hasattr(self.clf, 'labels_') and
                self.clf.labels_ is not None)

    def test_train_scores(self):
        assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])

    def test_prediction_scores(self):
        pred_scores = self.clf.decision_function(self.X_test)

        # check score shapes
        assert_equal(pred_scores.shape[0], self.X_test.shape[0])

        # check performance
        assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)

    def test_prediction_labels(self):
        pred_labels = self.clf.predict(self.X_test)
        assert_equal(pred_labels.shape, self.y_test.shape)

    def test_prediction_proba(self):
        pred_proba = self.clf.predict_proba(self.X_test)
        assert_greater_equal(pred_proba.min(), 0)
        assert_less_equal(pred_proba.max(), 1)
        # check performance
        assert_greater(roc_auc_score(self.y_test, pred_proba), self.roc_floor)

    def test_fit_predict(self):
        pred_labels = self.clf.fit_predict(self.X_train, self.y_train)
        assert_equal(pred_labels.shape, self.y_train.shape)

    def test_fit_predict_score(self):
        self.clf.fit_predict_score(self.X_test, self.y_test)
        self.clf.fit_predict_score(self.X_test, self.y_test,
                                   scoring='roc_auc_score')
        self.clf.fit_predict_score(self.X_test, self.y_test,
                                   scoring='prc_n_score')
        with assert_raises(NotImplementedError):
            self.clf.fit_predict_score(self.X_test, self.y_test,
                                       scoring='something')

    def test_predict_rank(self):
        pred_socres = self.clf.decision_function(self.X_test)
        pred_ranks = self.clf._predict_rank(self.X_test)
        print(pred_ranks)

        # assert the order is reserved
        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), rtol=4)
        assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
        assert_array_less(-0.1, pred_ranks)

    def test_predict_rank_normalized(self):
        pred_socres = self.clf.decision_function(self.X_test)
        pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)

        # assert the order is reserved
        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), rtol=4)
        assert_array_less(pred_ranks, 1.01)
        assert_array_less(-0.1, pred_ranks)

    def tearDown(self):
        pass


if __name__ == '__main__':
    unittest.main()