69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
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import math, sklearn.metrics.pairwise as sk
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from sklearn import svm
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import numpy as np
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import random, sys
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class Model(object):
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def __init__(self):
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self.model = None
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self.weight = 0.0
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"""
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Initialize training of a new weighted SVM model by choosing best parameters.
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Sets the trained model for this object.
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"""
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def trainUsingKLIEPWeights(self, traindata, trainLabels, weightSrcData, maxvar, svmC, svmGamma, svmKernel):
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self.model = svm.SVC(decision_function_shape='ovr', probability=True, C=svmC, gamma=svmGamma, kernel=svmKernel)
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self.model.fit(traindata, trainLabels)
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"""
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Test the weighted SVM to predict labels of a given test data.
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Returns the result of prediction, and confidence behind the prediction
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"""
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def test(self, testdata):
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#predict and gather results
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#predictedClass = ["" for x in range(len(testdata))]
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#confidences = np.zeros(len(testdata))
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confidences = []
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#reshapedData = np.reshape(testdata, (1,-1))
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if len(testdata)==1:
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testdata = np.reshape(testdata, (1,-1))
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predictions = self.model.predict(testdata)
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probs = self.model.predict_proba(testdata)
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for i in range(0, len(testdata)):
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#curData = np.reshape(testdata[i], (1,-1))
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#predictedClass[i] = self.model.predict(curData)[0]
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for j in range(len(self.model.classes_)):
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if self.model.classes_[j] == predictions[i]:
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#confidences[i] = prob[j]
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confidences.append(probs[i][j])
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break
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"""
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scores = self.model.decision_function(curData)
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if len(self.model.classes_)<=2:
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confidences[i] = min(1.0, math.fabs(scores[0]))
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else:
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# we calculate the confidence by taking normalized score
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totScore = 0.0
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for x, y in zip(self.model.classes_, scores[0]):
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totScore += math.fabs(y)
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if predictedClass[i] == x:
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confidences[i] = math.fabs(y)
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confidences[i] /= totScore
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"""
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return predictions, confidences
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"""
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Set model weights using test prediction.
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For source weight, use error rate with known source data labels.
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For target weight, use confidence (or probability) measure on target data.
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"""
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def computeModelWeightKLIEP(self, data, maxvar):
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totConf = 0.0
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predictedClass, confidences = self.test(data)
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for i in range(0, len(confidences)):
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totConf += confidences[i]
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return totConf/len(data)
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