88 lines
2.7 KiB
Python
88 lines
2.7 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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import voluptuous
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from monasca_analytics.sml.base import BaseSML
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from monasca_analytics.util.validation_utils import NoSpaceCharacter
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logger = logging.getLogger(__name__)
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ANOMALY = -1
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NON_ANOMALY = 1
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N_SAMPLES = 1000
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class IsolationForest(BaseSML):
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"""Anomaly detection based on the IsolationForest algorithm"""
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def __init__(self, _id, _config):
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super(IsolationForest, self).__init__(_id, _config)
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self._nb_samples = int(_config['nb_samples'])
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@staticmethod
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def validate_config(_config):
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isolation_schema = voluptuous.Schema({
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'module': voluptuous.And(
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basestring, NoSpaceCharacter()),
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'nb_samples': voluptuous.Or(float, int)
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}, required=True)
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return isolation_schema(_config)
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@staticmethod
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def get_default_config():
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return {
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'module': IsolationForest.__name__,
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'nb_samples': N_SAMPLES
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}
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@staticmethod
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def get_params():
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return [
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params.ParamDescriptor('nb_samples', type_util.Number(), N_SAMPLES)
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]
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def number_of_samples_required(self):
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return self._nb_samples
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def _generate_train_test_sets(self, samples, ratio_train):
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num_samples_train = int(len(samples) * ratio_train)
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X_train = np.array(samples[:num_samples_train])
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X_test = np.array(samples[num_samples_train:])
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return X_train, X_test
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def _get_best_detector(self, train):
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detector = ensemble.IsolationForest()
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detector.fit(train)
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return detector
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def learn_structure(self, samples):
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X_train, X_test = self._generate_train_test_sets(samples, 0.75)
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logger.info('Trainig with ' + str(len(X_train)) +
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'samples; testing with ' + str(len(X_test)) + ' samples.')
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if_detector = self._get_best_detector(X_train)
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Y_test = if_detector.predict(X_test)
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num_anomalies = Y_test[Y_test == ANOMALY].size
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logger.info('Found ' + str(num_anomalies) +
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' anomalies in testing set')
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return if_detector
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