Python getPrecisionandRecallの例

コード例 #1

ファイル: classifyTweets.py プロジェクト: nidishrajendran/Diablo

def main():
	fm = []
	fileName = './data/simMats' + str(15) +'.pickle'
	for j in xrange(10):
		xData,yData = loadData(fileName)
		xTrain,yTrain,xTest,yTest = testTrainSplit(xData,yData)
		print fileName
		bestClassifier = None
		minF = 0
		P = R = F = 0
		for xCVTrain,yCVTrain,xCVTest,yCVTest in splitData(xTrain,yTrain):
			#xTrain,yTrain,xTest,yTest = splitData(xData,yData)
			classifier = trainClassifier(xCVTrain,yCVTrain)
			Y_1 = classifier.predict(xCVTest)
			ret = metrics.getPrecisionandRecall(Y_1,yCVTest)
			P += ret[0]
			R += ret[1]
			F += ret[2]

		print 'Accuracy - ', accuracy_score(yCVTest,Y_1)
		print 'P R F  - ', P/10,R/10,F/10

	#	if F/10 > minF:
	#		bestClassifier = classifier


		print 'Overall Test Accuracy'
		Y = classifier.predict(xTest)
		print 'Accuracy - ', accuracy_score(yTest,Y)
		print 'P R F  - ', metrics.getPrecisionandRecall(Y,yTest)
		fm.append(metrics.getPrecisionandRecall(Y,yTest)[2])
	print 'Average fmeasure',sum(fm)/len(fm)

コード例 #2

def main():

    xTrain, yTrain = getSet1()
    # xTrain,yTrain = getSet2()

    print xTrain.shape
    print xTrain, yTrain, sum(yTrain)
    # raw_input()

    # xTrain = analyzeFeatures(ETC(), xTrain, yTrain)
    # analyzeFeatures(ETC(), xTrain, yTrain)
    # print xTrain.shape
    # print xTrain,yTrain
    # return

    xTest = loadTest()

    bestClassifier = None
    minF = 0
    P = R = F = 0
    i = 1
    for xCVTrain, yCVTrain, xCVTest, yCVTest in splitData(xTrain, yTrain):
        print "\nCross Validation: Training and testing instance", i
        #xTrain,yTrain,xTest,yTest = splitData(xData,yData)
        classifier = trainClassifier(xCVTrain, yCVTrain)
        Y_1 = classifier.predict(xCVTest)
        # Y_1 = np.array([0]*len(yCVTest))
        ret = metrics.getPrecisionandRecall(Y_1, yCVTest)
        P += ret[0]
        R += ret[1]
        F += ret[2]
        i += 1

    print 'Accuracy - ', accuracy_score(yCVTest, Y_1)

コード例 #3

ファイル: main.py プロジェクト: deeptikochar/Detecting-Native-Advertising

def main():

    xTrain, yTrain = getSet1()
    # xTrain,yTrain = getSet2()

    print xTrain.shape
    print xTrain, yTrain, sum(yTrain)
    # raw_input()

    # xTrain = analyzeFeatures(ETC(), xTrain, yTrain)
    # analyzeFeatures(ETC(), xTrain, yTrain)
    # print xTrain.shape
    # print xTrain,yTrain
    # return

    xTest = loadTest()

    bestClassifier = None
    minF = 0
    P = R = F = 0
    i = 1
    for xCVTrain, yCVTrain, xCVTest, yCVTest in splitData(xTrain, yTrain):
        print "\nCross Validation: Training and testing instance", i
        # xTrain,yTrain,xTest,yTest = splitData(xData,yData)
        classifier = trainClassifier(xCVTrain, yCVTrain)
        Y_1 = classifier.predict(xCVTest)
        # Y_1 = np.array([0]*len(yCVTest))
        ret = metrics.getPrecisionandRecall(Y_1, yCVTest)
        P += ret[0]
        R += ret[1]
        F += ret[2]
        i += 1

    print "Accuracy - ", accuracy_score(yCVTest, Y_1)

コード例 #4

def main2():
    xTrain, yTrain = getSetBow()
    classifier = trainClassifier(xTrain, yTrain)
    Y_1 = classifier.predict(xCVTest)
    # Y_1 = np.array([0]*len(yCVTest))
    ret = metrics.getPrecisionandRecall(Y_1, yCVTest)
    P += ret[0]
    R += ret[1]
    F += ret[2]

    print 'Accuracy - ', accuracy_score(yCVTest, Y_1)

コード例 #5

ファイル: main.py プロジェクト: deeptikochar/Detecting-Native-Advertising

def main2():
    xTrain, yTrain = getSetBow()
    classifier = trainClassifier(xTrain, yTrain)
    Y_1 = classifier.predict(xCVTest)
    # Y_1 = np.array([0]*len(yCVTest))
    ret = metrics.getPrecisionandRecall(Y_1, yCVTest)
    P += ret[0]
    R += ret[1]
    F += ret[2]

    print "Accuracy - ", accuracy_score(yCVTest, Y_1)