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57 lines
2.4 KiB
Python
57 lines
2.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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Tencent is pleased to support the open source community by making Metis available.
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Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
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Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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https://opensource.org/licenses/BSD-3-Clause
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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.
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"""
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import numpy as np
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from sklearn.linear_model import Ridge
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.pipeline import make_pipeline
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from time_series_detector.common.tsd_common import *
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class PolynomialInterpolation(object):
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"""
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In statistics, polynomial regression is a form of regression analysis in which the relationship
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between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x.
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WIKIPEDIA: https://en.wikipedia.org/wiki/Polynomial_regression
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"""
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def __init__(self, threshold=0.15, degree=4):
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"""
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:param threshold: The critical point of normal.
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:param degree: Depth of iteration.
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"""
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self.degree = degree
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self.threshold = threshold
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def predict(self, X, window=DEFAULT_WINDOW):
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"""
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Predict if a particular sample is an outlier or not.
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:param X: the time series to detect of
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:param type X: pandas.Series
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:param window: the length of window
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:param type window: int
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:return: 1 denotes normal, 0 denotes abnormal
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"""
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x_train = list(range(0, 2 * window + 1)) + list(range(0, 2 * window + 1)) + list(range(0, window + 1))
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x_train = np.array(x_train)
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x_train = x_train[:, np.newaxis]
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avg_value = np.mean(X[-(window + 1):])
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if avg_value > 1:
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y_train = X / avg_value
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else:
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y_train = X
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model = make_pipeline(PolynomialFeatures(self.degree), Ridge())
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model.fit(x_train, y_train)
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if abs(y_train[-1] - model.predict(np.array(x_train[-1]).reshape(1, -1))) > self.threshold:
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return 0
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return 1
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