如何使用python在apache spark mllib中设置逻辑回归中的优化器

我现在开始在ApacheSpark mllib上进行一些测试

def mapper(line):
    feats = line.strip().split(',')
    label = feats[len(feats)-1]
    feats = feats[:len(feats)-1]
    feats.insert(0,label)
    return numpy.array([float(feature) for feature in feats])

def test3():
    data = sc.textFile('/home/helxsz/Dropbox/exercise/spark/data_banknote_authentication.txt')
    parsed = data.map(mapper)
    logistic = LogisticRegressionWithSGD()
    logistic.optimizer.setNumIterations(200).setMiniBatchFraction(0.1)
    model = logistic.run(parsed)
    labelsAndPreds = parsed.map(lambda points: (int(points[0]), model.predict( points[1:len(points)]) ))
    trainErr = labelAndPreds.filter(lambda (v,p): v != p).count() / float(parsed.count())
    print 'training error = ' + str(trainErr)

但当我使用逻辑回归时，Gd如下

logistic = LogisticRegressionWithSGD()
logistic.optimizer.setNumIterations(200).setMiniBatchFraction(0.1)

它给出了一个错误，即AttributeError:“LogisticRegressionWithGd”对象没有属性“optimizer”

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这是python API中的API文档和

，您可以在调用“train”时设置这些参数：

model = LogisticRegressionWithSGD.train(parsed, iterations=200, miniBatchFraction=0.1)

我能找到的关于这一点的唯一文档是