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SVM.py
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SVM.py
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Linear Support Vector Machines (SVMs)
from pyspark.mllib.classification import SVMWithSGD, SVMModel
from pyspark.mllib.regression import LabeledPoint
def parsePoint(line):
values = [float(x) for x in line.split(' ')]
return LabeledPoint(values[0], values[1:])
data = sc.textFile("/Users/arrowlittle/Desktop/data/pima-indians-diabetes.data.txt")
parsedData = data.map(parsePoint)
model1 = SVMWithSGD.train(parsedData, iterations=10)
labelsAndPreds = parsedData.map(lambda p: (p.label, model1.predict(p.features)))
trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
print("Training Error = " + str(trainErr))
model1.save(sc, "/Users/arrowlittle/Desktop/data/australianSVMWithSGDModel")
Linear least squares, Lasso, and ridge regression
from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel
from pyspark.mllib.regression import LabeledPoint
def parsePoint(line):
values = [float(x) for x in line.split(' ')]
return LabeledPoint(values[0], values[1:])
data = sc.textFile("/Users/arrowlittle/Desktop/data/pima-indians-diabetes.data.txt")
parsedData = data.map(parsePoint)
model2 = LinearRegressionWithSGD.train(parsedData, iterations=100, step=0.00000001)
valuesAndPreds = parsedData.map(lambda p: (p.label, model2.predict(p.features)))
MSE = valuesAndPreds \
.map(lambda (v, p): (v - p)**2) \
.reduce(lambda x, y: x + y) / valuesAndPreds.count()
print("Mean Squared Error = " + str(MSE))
model2.save(sc, "/Users/arrowlittle/Desktop/data/australianLinearRegressionWithSGDModel")