59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
#!/usr/bin/env python
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# Copyright (c) 2016 FUJITSU LIMITED
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import logging
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import numpy as np
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from sklearn import ensemble
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from monasca_analytics.sml import random_forest_classifier
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from test.util_for_testing import MonanasTestCase
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logger = logging.getLogger(__name__)
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class TestRandomForestClassifier(MonanasTestCase):
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def setUp(self):
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super(TestRandomForestClassifier, self).setUp()
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self.rf_sml = random_forest_classifier.RandomForestClassifier(
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"fakeid", {"module": "fake", "nb_samples": 1000})
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def tearDown(self):
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super(TestRandomForestClassifier, self).tearDown()
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def get_testing_data(self):
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a = np.random.uniform(size=1000)
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b = np.random.uniform(size=1000)
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c = np.random.uniform(size=1000)
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d = np.random.uniform(size=1000)
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labels = np.random.randint(2, size=1000)
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return np.array([a, b, c, d, labels]).T
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def test_generate_train_test_sets(self):
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data = self.get_testing_data()
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X_train, X_train_labeled, X_test, X_test_labeled =\
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self.rf_sml._generate_train_test_sets(data, 0.6)
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self.assertEqual(600, len(X_train))
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self.assertEqual(600, len(X_train_labeled))
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self.assertEqual(400, len(X_test))
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self.assertEqual(400, len(X_test_labeled))
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def test_learn_structure(self):
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data = self.get_testing_data()
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clf = self.rf_sml.learn_structure(data)
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self.assertIsInstance(clf, ensemble.RandomForestClassifier)
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