Spark mllib predicting weird number or NaN

I am new to Apache Spark and trying to use the machine learning library to predict some data. My dataset right now is only about 350 points. Here are 7 of those points:

"365","4",41401.387,5330569
"364","3",51517.886,5946290
"363","2",55059.838,6097388
"362","1",43780.977,5304694
"361","7",46447.196,5471836
"360","6",50656.121,5849862
"359","5",44494.476,5460289

Here's my code:

def parsePoint(line):
    split = map(sanitize, line.split(','))
    rev = split.pop(-2)
    return LabeledPoint(rev, split)

def sanitize(value):
    return float(value.strip('"'))

parsedData = textFile.map(parsePoint)
model = LinearRegressionWithSGD.train(parsedData, iterations=10)

print model.predict(parsedData.first().features)

The prediction is something totally crazy, like -6.92840330273e+136. If I don't set iterations in train(), then I get nan as a result. What am I doing wrong? Is it my data set (the size of it, maybe?) or my configuration?

Solution 1:

The problem is that LinearRegressionWithSGD uses stochastic gradient descent (SGD) to optimize the weight vector of your linear model. SGD is really sensitive to the provided stepSize which is used to update the intermediate solution.

What SGD does is to calculate the gradient g of the cost function given a sample of the input points and the current weights w. In order to update the weights w you go for a certain distance in the opposite direction of g. The distance is your step size s.

w(i+1) = w(i) - s * g   

Since you're not providing an explicit step size value, MLlib assumes stepSize = 1. This seems to not work for your use case. I'd recommend you to try different step sizes, usually lower values, to see how LinearRegressionWithSGD behaves:

LinearRegressionWithSGD.train(parsedData, numIterartions = 10, stepSize = 0.001)