我刚开始使用ML和Apache Spark,所以我一直在尝试基于Spark示例的线性回归。除了示例中的示例之外,我似乎无法为任何数据生成适当的模型,并且无论输入数据如何,截距始终为0.0。
我已根据功能准备了一个简单的训练数据集:
y =(2 * x1)+(3 * x2)+ 4
即。我希望截距为4,权重为(2,3)。
如果我对原始数据运行LinearRegressionWithSGD.train(...),则模型为:
Model intercept: 0.0, weights: [NaN,NaN]
预测都是NaN:
Features: [1.0,1.0], Predicted: NaN, Actual: 9.0
Features: [1.0,2.0], Predicted: NaN, Actual: 12.0
等
如果我先缩放数据,我会得到:
Model intercept: 0.0, weights: [17.407863391511754,2.463212481736855]
Features: [1.0,1.0], Predicted: 19.871075873248607, Actual: 9.0
Features: [1.0,2.0], Predicted: 22.334288354985464, Actual: 12.0
Features: [1.0,3.0], Predicted: 24.797500836722318, Actual: 15.0
等
要么我做错了,要么我不明白这个模型的输出应该是什么,那么有人可以建议我在这里出错吗?
我的代码如下:
// Load and parse the dummy data (y, x1, x2) for y = (2*x1) + (3*x2) + 4
// i.e. intercept should be 4, weights (2, 3)?
val data = sc.textFile("data/dummydata.txt")
// LabeledPoint is (label, [features])
val parsedData = data.map { line =>
val parts = line.split(',')
val label = parts(0).toDouble
val features = Array(parts(1), parts(2)) map (_.toDouble)
LabeledPoint(label, Vectors.dense(features))
}
// Scale the features
val scaler = new StandardScaler(withMean = true, withStd = true)
.fit(parsedData.map(x => x.features))
val scaledData = parsedData
.map(x =>
LabeledPoint(x.label,
scaler.transform(Vectors.dense(x.features.toArray))))
// Building the model: SGD = stochastic gradient descent
val numIterations = 1000
val step = 0.2
val model = LinearRegressionWithSGD.train(scaledData, numIterations, step)
println(s">>>> Model intercept: ${model.intercept}, weights: ${model.weights}")`
// Evaluate model on training examples
val valuesAndPreds = scaledData.map { point =>
val prediction = model.predict(point.features)
(point.label, point.features, prediction)
}
// Print out features, actual and predicted values...
valuesAndPreds.take(10).foreach({case (v, f, p) =>
println(s"Features: ${f}, Predicted: ${p}, Actual: ${v}")})
答案 0 :(得分:11)
@Noah:谢谢 - 您的建议促使我再次查看此内容,并且我发现some example code here允许您生成拦截并通过优化器设置其他参数,例如迭代次数。< / p>
这是我修改过的模型生成代码,它似乎对我的虚拟数据运行正常:
// Building the model: SGD = stochastic gradient descent:
// Need to setIntercept = true, and seems only to work with scaled data
val numIterations = 600
val stepSize = 0.1
val algorithm = new LinearRegressionWithSGD()
algorithm.setIntercept(true)
algorithm.optimizer
.setNumIterations(numIterations)
.setStepSize(stepSize)
val model = algorithm.run(scaledData)
它似乎仍然需要缩放数据而不是原始数据作为输入,但这对我的目的来说还不错。
答案 1 :(得分:9)
您使用的train
方法是一种快捷方式,可将截距设置为零,并且不会尝试查找截距。如果使用基础类,则可以获得非零截距:
val model = new LinearRegressionWithSGD(step, numIterations, 1.0).
setIntercept(true).
run(scaledData)
现在应该给你一个拦截。