<gh_stars>1-10
# -*- coding: utf-8 -*-

from __future__ import division
from __future__ import print_function

import os
import sys

# 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__), '..')))

import numpy as np

import unittest
from sklearn.utils.testing import assert_equal
# noinspection PyProtectedMember
from sklearn.utils.testing import assert_allclose
from sklearn.utils.testing import assert_less_equal
from sklearn.utils.testing import assert_raises
from sklearn.metrics import precision_score

from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print

from pyod.utils.utility import check_parameter
from pyod.utils.utility import standardizer
from pyod.utils.utility import get_label_n
from pyod.utils.utility import precision_n_scores
from pyod.utils.utility import argmaxn
from pyod.utils.utility import invert_order


class TestUtils(unittest.TestCase):
    def setUp(self):
        self.n_train = 1000
        self.n_test = 500
        self.contamination = 0.1

        self.value_lists = [0.1, 0.3, 0.2, -2, 1.5, 0, 1, -1, -0.5, 11]

    def test_data_generate(self):
        X_train, y_train, X_test, y_test = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          contamination=self.contamination)

        assert_equal(y_train.shape[0], X_train.shape[0])
        assert_equal(y_test.shape[0], X_test.shape[0])

        assert_less_equal(self.n_train - X_train.shape[0], 1)
        assert_equal(X_train.shape[1], 2)

        assert_less_equal(self.n_test - X_test.shape[0], 1)
        assert_equal(X_test.shape[1], 2)

        out_perc = np.sum(y_train) / self.n_train
        assert_allclose(self.contamination, out_perc, atol=0.01)

        out_perc = np.sum(y_test) / self.n_test
        assert_allclose(self.contamination, out_perc, atol=0.01)

    def test_data_generate2(self):
        X_train, y_train, X_test, y_test = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          n_features=3,
                          contamination=self.contamination)
        assert_allclose(X_train.shape, (self.n_train, 3))
        assert_allclose(X_test.shape, (self.n_test, 3))

    def test_data_generate3(self):
        X_train, y_train, X_test, y_test = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          n_features=2,
                          contamination=self.contamination,
                          random_state=42)

        X_train2, y_train2, X_test2, y_test2 = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          n_features=2,
                          contamination=self.contamination,
                          random_state=42)

        assert_allclose(X_train, X_train2)
        assert_allclose(X_test, X_test2)
        assert_allclose(y_train, y_train2)
        assert_allclose(y_test, y_test2)

    def test_argmaxn(self):
        ind = argmaxn(self.value_lists, 3)
        assert_equal(len(ind), 3)

        ind = argmaxn(self.value_lists, 3)
        assert_equal(np.sum(ind), np.sum([4, 6, 9]))

        ind = argmaxn(self.value_lists, 3, order='asc')
        assert_equal(np.sum(ind), np.sum([3, 7, 8]))

        with assert_raises(ValueError):
            argmaxn(self.value_lists, -1)
        with assert_raises(ValueError):
            argmaxn(self.value_lists, 20)

    def test_evaluate_print(self):
        X_train, y_train, X_test, y_test = generate_data(n_train=self.n_train,
                                                         n_test=self.n_test,
                                                         contamination=self.contamination)
        evaluate_print('dummy', y_train, y_train * 0.1)

    # TODO: remove temporarily for pleasing Travis integration
    # def test_visualize(self):
    #     X_train, y_train, X_test, y_test = generate_data(
    #         n_train=self.n_train, n_test=self.n_test,
    #         contamination=self.contamination)
    #     visualize('dummy', X_train, y_train, X_test, y_test, y_train * 0.1,
    #               y_test * 0.1, show_figure=False, save_figure=False)

    def tearDown(self):
        pass


class TestParameters(unittest.TestCase):
    def setUp(self):
        pass

    def test_check_parameter_range(self):
        # verify parameter type correction
        with assert_raises(TypeError):
            check_parameter('f', 0, 100)

        with assert_raises(TypeError):
            check_parameter(1, 'f', 100)

        with assert_raises(TypeError):
            check_parameter(1, 0, 'f')

        with assert_raises(TypeError):
            check_parameter(argmaxn(value_list=[1, 2, 3], n=1), 0, 100)

        # if low and high are both unset
        with assert_raises(ValueError):
            check_parameter(50)

        # if low <= high
        with assert_raises(ValueError):
            check_parameter(50, 100, 99)

        with assert_raises(ValueError):
            check_parameter(50, 100, 100)

        # check one side
        with assert_raises(ValueError):
            check_parameter(50, low=100)
        with assert_raises(ValueError):
            check_parameter(50, high=0)

        assert_equal(True, check_parameter(50, low=10))
        assert_equal(True, check_parameter(50, high=100))

        # if check fails
        with assert_raises(ValueError):
            check_parameter(-1, 0, 100)

        with assert_raises(ValueError):
            check_parameter(101, 0, 100)

        with assert_raises(ValueError):
            check_parameter(0.5, 0.2, 0.3)

        # if check passes
        assert_equal(True, check_parameter(50, 0, 100))

        assert_equal(True, check_parameter(0.5, 0.1, 0.8))

        # if includes left or right bounds
        with assert_raises(ValueError):
            check_parameter(100, 0, 100, include_left=False,
                            include_right=False)
        assert_equal(True, check_parameter(0, 0, 100, include_left=True,
                                           include_right=False))
        assert_equal(True, check_parameter(0, 0, 100, include_left=True,
                                           include_right=True))
        assert_equal(True, check_parameter(100, 0, 100, include_left=False,
                                           include_right=True))
        assert_equal(True, check_parameter(100, 0, 100, include_left=True,
                                           include_right=True))

    def tearDown(self):
        pass


class TestScaler(unittest.TestCase):

    def setUp(self):
        self.X_train = np.random.rand(500, 5)
        self.X_test = np.random.rand(50, 5)
        self.scores1 = [0.1, 0.3, 0.5, 0.7, 0.2, 0.1]
        self.scores2 = np.array([0.1, 0.3, 0.5, 0.7, 0.2, 0.1])

    def test_normalization(self):
        norm_X_train, norm_X_test = standardizer(self.X_train, self.X_train)
        assert_allclose(norm_X_train.mean(), 0, atol=0.05)
        assert_allclose(norm_X_train.std(), 1, atol=0.05)

        assert_allclose(norm_X_test.mean(), 0, atol=0.05)
        assert_allclose(norm_X_test.std(), 1, atol=0.05)

        # test when X_t is not presented
        norm_X_train = standardizer(self.X_train)
        assert_allclose(norm_X_train.mean(), 0, atol=0.05)
        assert_allclose(norm_X_train.std(), 1, atol=0.05)

    def test_invert_order(self):
        target = np.array([-0.1, -0.3, -0.5, -0.7, -0.2, -0.1]).ravel()
        scores1 = invert_order(self.scores1)
        assert_allclose(scores1, target)

        scores2 = invert_order(self.scores2)
        assert_allclose(scores2, target)

        target = np.array([0.6, 0.4, 0.2, 0, 0.5, 0.6]).ravel()
        scores2 = invert_order(self.scores2, method='subtraction')
        assert_allclose(scores2, target)

    def tearDown(self):
        pass


class TestMetrics(unittest.TestCase):

    def setUp(self):
        self.y = [0, 0, 1, 1, 1, 0, 0, 0, 1, 0]
        self.labels_ = [0.1, 0.2, 0.2, 0.8, 0.2, 0.5, 0.7, 0.9, 1, 0.3]
        self.labels_short_ = [0.1, 0.2, 0.2, 0.8, 0.2, 0.5, 0.7, 0.9, 1]
        self.manual_labels = [0, 0, 0, 1, 0, 0, 1, 1, 1, 0]
        self.outliers_fraction = 0.3

    def test_precision_n_scores(self):
        assert_equal(precision_score(self.y, self.manual_labels),
                     precision_n_scores(self.y, self.labels_))

    def test_get_label_n(self):
        assert_allclose(self.manual_labels,
                        get_label_n(self.y, self.labels_))

    def test_get_label_n_equal_3(self):
        manual_labels = [0, 0, 0, 1, 0, 0, 0, 1, 1, 0]
        assert_allclose(manual_labels,
                        get_label_n(self.y, self.labels_, n=3))

    def test_inconsistent_length(self):
        with assert_raises(ValueError):
            get_label_n(self.y, self.labels_short_)

    def tearDown(self):
        pass


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

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

from __future__ import division
from __future__ import print_function

import os
import sys

# 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__), '..')))

import numpy as np

import unittest
from sklearn.utils.testing import assert_equal
# noinspection PyProtectedMember
from sklearn.utils.testing import assert_allclose
from sklearn.utils.testing import assert_less_equal
from sklearn.utils.testing import assert_raises
from sklearn.metrics import precision_score

from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print

from pyod.utils.utility import check_parameter
from pyod.utils.utility import standardizer
from pyod.utils.utility import get_label_n
from pyod.utils.utility import precision_n_scores
from pyod.utils.utility import argmaxn
from pyod.utils.utility import invert_order


class TestUtils(unittest.TestCase):
    def setUp(self):
        self.n_train = 1000
        self.n_test = 500
        self.contamination = 0.1

        self.value_lists = [0.1, 0.3, 0.2, -2, 1.5, 0, 1, -1, -0.5, 11]

    def test_data_generate(self):
        X_train, y_train, X_test, y_test = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          contamination=self.contamination)

        assert_equal(y_train.shape[0], X_train.shape[0])
        assert_equal(y_test.shape[0], X_test.shape[0])

        assert_less_equal(self.n_train - X_train.shape[0], 1)
        assert_equal(X_train.shape[1], 2)

        assert_less_equal(self.n_test - X_test.shape[0], 1)
        assert_equal(X_test.shape[1], 2)

        out_perc = np.sum(y_train) / self.n_train
        assert_allclose(self.contamination, out_perc, atol=0.01)

        out_perc = np.sum(y_test) / self.n_test
        assert_allclose(self.contamination, out_perc, atol=0.01)

    def test_data_generate2(self):
        X_train, y_train, X_test, y_test = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          n_features=3,
                          contamination=self.contamination)
        assert_allclose(X_train.shape, (self.n_train, 3))
        assert_allclose(X_test.shape, (self.n_test, 3))

    def test_data_generate3(self):
        X_train, y_train, X_test, y_test = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          n_features=2,
                          contamination=self.contamination,
                          random_state=42)

        X_train2, y_train2, X_test2, y_test2 = \
            generate_data(n_train=self.n_train,
                          n_test=self.n_test,
                          n_features=2,
                          contamination=self.contamination,
                          random_state=42)

        assert_allclose(X_train, X_train2)
        assert_allclose(X_test, X_test2)
        assert_allclose(y_train, y_train2)
        assert_allclose(y_test, y_test2)

    def test_argmaxn(self):
        ind = argmaxn(self.value_lists, 3)
        assert_equal(len(ind), 3)

        ind = argmaxn(self.value_lists, 3)
        assert_equal(np.sum(ind), np.sum([4, 6, 9]))

        ind = argmaxn(self.value_lists, 3, order='asc')
        assert_equal(np.sum(ind), np.sum([3, 7, 8]))

        with assert_raises(ValueError):
            argmaxn(self.value_lists, -1)
        with assert_raises(ValueError):
            argmaxn(self.value_lists, 20)

    def test_evaluate_print(self):
        X_train, y_train, X_test, y_test = generate_data(n_train=self.n_train,
                                                         n_test=self.n_test,
                                                         contamination=self.contamination)
        evaluate_print('dummy', y_train, y_train * 0.1)

    # TODO: remove temporarily for pleasing Travis integration
    # def test_visualize(self):
    #     X_train, y_train, X_test, y_test = generate_data(
    #         n_train=self.n_train, n_test=self.n_test,
    #         contamination=self.contamination)
    #     visualize('dummy', X_train, y_train, X_test, y_test, y_train * 0.1,
    #               y_test * 0.1, show_figure=False, save_figure=False)

    def tearDown(self):
        pass


class TestParameters(unittest.TestCase):
    def setUp(self):
        pass

    def test_check_parameter_range(self):
        # verify parameter type correction
        with assert_raises(TypeError):
            check_parameter('f', 0, 100)

        with assert_raises(TypeError):
            check_parameter(1, 'f', 100)

        with assert_raises(TypeError):
            check_parameter(1, 0, 'f')

        with assert_raises(TypeError):
            check_parameter(argmaxn(value_list=[1, 2, 3], n=1), 0, 100)

        # if low and high are both unset
        with assert_raises(ValueError):
            check_parameter(50)

        # if low <= high
        with assert_raises(ValueError):
            check_parameter(50, 100, 99)

        with assert_raises(ValueError):
            check_parameter(50, 100, 100)

        # check one side
        with assert_raises(ValueError):
            check_parameter(50, low=100)
        with assert_raises(ValueError):
            check_parameter(50, high=0)

        assert_equal(True, check_parameter(50, low=10))
        assert_equal(True, check_parameter(50, high=100))

        # if check fails
        with assert_raises(ValueError):
            check_parameter(-1, 0, 100)

        with assert_raises(ValueError):
            check_parameter(101, 0, 100)

        with assert_raises(ValueError):
            check_parameter(0.5, 0.2, 0.3)

        # if check passes
        assert_equal(True, check_parameter(50, 0, 100))

        assert_equal(True, check_parameter(0.5, 0.1, 0.8))

        # if includes left or right bounds
        with assert_raises(ValueError):
            check_parameter(100, 0, 100, include_left=False,
                            include_right=False)
        assert_equal(True, check_parameter(0, 0, 100, include_left=True,
                                           include_right=False))
        assert_equal(True, check_parameter(0, 0, 100, include_left=True,
                                           include_right=True))
        assert_equal(True, check_parameter(100, 0, 100, include_left=False,
                                           include_right=True))
        assert_equal(True, check_parameter(100, 0, 100, include_left=True,
                                           include_right=True))

    def tearDown(self):
        pass


class TestScaler(unittest.TestCase):

    def setUp(self):
        self.X_train = np.random.rand(500, 5)
        self.X_test = np.random.rand(50, 5)
        self.scores1 = [0.1, 0.3, 0.5, 0.7, 0.2, 0.1]
        self.scores2 = np.array([0.1, 0.3, 0.5, 0.7, 0.2, 0.1])

    def test_normalization(self):
        norm_X_train, norm_X_test = standardizer(self.X_train, self.X_train)
        assert_allclose(norm_X_train.mean(), 0, atol=0.05)
        assert_allclose(norm_X_train.std(), 1, atol=0.05)

        assert_allclose(norm_X_test.mean(), 0, atol=0.05)
        assert_allclose(norm_X_test.std(), 1, atol=0.05)

        # test when X_t is not presented
        norm_X_train = standardizer(self.X_train)
        assert_allclose(norm_X_train.mean(), 0, atol=0.05)
        assert_allclose(norm_X_train.std(), 1, atol=0.05)

    def test_invert_order(self):
        target = np.array([-0.1, -0.3, -0.5, -0.7, -0.2, -0.1]).ravel()
        scores1 = invert_order(self.scores1)
        assert_allclose(scores1, target)

        scores2 = invert_order(self.scores2)
        assert_allclose(scores2, target)

        target = np.array([0.6, 0.4, 0.2, 0, 0.5, 0.6]).ravel()
        scores2 = invert_order(self.scores2, method='subtraction')
        assert_allclose(scores2, target)

    def tearDown(self):
        pass


class TestMetrics(unittest.TestCase):

    def setUp(self):
        self.y = [0, 0, 1, 1, 1, 0, 0, 0, 1, 0]
        self.labels_ = [0.1, 0.2, 0.2, 0.8, 0.2, 0.5, 0.7, 0.9, 1, 0.3]
        self.labels_short_ = [0.1, 0.2, 0.2, 0.8, 0.2, 0.5, 0.7, 0.9, 1]
        self.manual_labels = [0, 0, 0, 1, 0, 0, 1, 1, 1, 0]
        self.outliers_fraction = 0.3

    def test_precision_n_scores(self):
        assert_equal(precision_score(self.y, self.manual_labels),
                     precision_n_scores(self.y, self.labels_))

    def test_get_label_n(self):
        assert_allclose(self.manual_labels,
                        get_label_n(self.y, self.labels_))

    def test_get_label_n_equal_3(self):
        manual_labels = [0, 0, 0, 1, 0, 0, 0, 1, 1, 0]
        assert_allclose(manual_labels,
                        get_label_n(self.y, self.labels_, n=3))

    def test_inconsistent_length(self):
        with assert_raises(ValueError):
            get_label_n(self.y, self.labels_short_)

    def tearDown(self):
        pass


if __name__ == '__main__':
    unittest.main()
# -*- coding: utf-8 -*- # temporary solution for relative imports in case pyod is not installed # if pyod is installed, no need to use the following line # noinspection PyProtectedMember # TODO: remove temporarily for pleasing Travis integration # def test_visualize(self): #     X_train, y_train, X_test, y_test = generate_data( #         n_train=self.n_train, n_test=self.n_test, #         contamination=self.contamination) #     visualize('dummy', X_train, y_train, X_test, y_test, y_train * 0.1, #               y_test * 0.1, show_figure=False, save_figure=False) # verify parameter type correction # if low and high are both unset # if low <= high # check one side # if check fails # if check passes # if includes left or right bounds # test when X_t is not presented