#!/usr/bin/env python

# Copyright (c) 2016 Hewlett Packard Enterprise Development Company, L.P.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.

import logging

import numpy as np
from sklearn import svm

from monasca_analytics.sml import svm_one_class
from test.util_for_testing import MonanasTestCase

logger = logging.getLogger(__name__)


class TestSvmOneClass(MonanasTestCase):

    def setUp(self):
        super(TestSvmOneClass, self).setUp()
        self.svm = svm_one_class.SvmOneClass("fakeid", {
            "module": "fake",
            "nb_samples": 1000
        })

    def tearDown(self):
        super(TestSvmOneClass, self).tearDown()

    def get_testing_data(self):
        a = np.random.uniform(size=1000)
        b = np.random.uniform(size=1000)
        c = np.random.uniform(size=1000)
        d = np.random.uniform(size=1000)
        return np.array([a, b, c, d]).T

    def test_generate_train_test_sets(self):
        data = self.get_testing_data()
        train, test = self.svm._generate_train_test_sets(data, 0.6)
        self.assertEqual(600, len(train))
        self.assertEqual(400, len(test))

    def test_learn_structure(self):
        data = self.get_testing_data()
        clf = self.svm.learn_structure(data)
        self.assertIsInstance(clf, svm.OneClassSVM)