我试图在~20k LabeledPoints的集合上测试各种候选模型，其中有〜5000个特征用于二进制分类。
我在输入空间上使用VectorAssembler创建了一个包含数字特征，稀疏矢量等的特征向量，然后将这些向量（以及标签）转换为LabeledPoints。
assembler = VectorAssembler(inputCols = ["fileSize", "hour", "day", "month", "title_tfidf", "ct_tfidf", "excerpt_tfidf", "regex_tfidf"], outputCol="features")
training_set_vector = assembler.transform(training_set).select("Target", "features")
training_set_labeled = training_set_vector.rdd.map(lambda x: Row(label=x[0],features=DenseVector(x[1].toArray()))).map(lambda row: LabeledPoint(row[1], [row[0]]))
training_set_vector和training_set_labeled的输出如下：
[Row（Target = 0，features = SparseVector（2273，{0：8397.0,1：13.0,2： 12.0,3：1.0,8：3.3147,82：5.721,370：0.2546,410：7.0356,418：5.3786,429：12.4219,623：7.3906,1061：9.3078,1637：2.3682,1647：2.716}））] [ LabeledPoint（0.0，[8397.0,13.0,12.0,1.0,0.0,0.0,0.0,0.0,3.31468140319,0.0,0.0,0.0,0.0,0.0，....，0.0,0.0,0.0,0.0,0.0,0.0， 0.0,0.0,0.0）]
然后我拿了LabeledPoints并适合RandomForest，SVM和GBT模型。
RF_model = RandomForest.trainClassifier(training_set_labeled, numClasses=2, categoricalFeaturesInfo = {}, numTrees=10, featureSubsetStrategy = "auto", impurity='gini', maxDepth=4, maxBins=32)
SVM_model = SVMWithSGD.train(training_set_labeled, iterations=500)
GBT_model = GradientBoostedTrees.trainClassifier(training_set_labeled, categoricalFeaturesInfo = {}, maxDepth=4, maxBins=32, numIterations=5)
到目前为止，一切都顺利进行，当我尝试将这些模型应用于测试集时（与训练集相同的维度）出现了问题。以下是我用于测试集的代码：
predictions_RF = RF_model.predict(test_set_labeled.map(lambda r: r.features))
predictions_SVM = SVM_model.predict(test_set_labeled.map(lambda r: r.features))
predictions_GBT = GBT_model.predict(test_set_labeled.map(lambda r: r.features))
RF和GBT模型成功完成：
print predictions_RF.take(5)
print predictions_GBT.take(5)
[0.0,0.0,0.0,0.0,0.0] [0.0,0.0,1.0,0.0,0.0]
但是在应用SVM模型时，我收到以下错误：
AssertionError：尺寸不匹配
运行＆＃34;打印SVM_model＆＃34;显示只有~500个重量，但有~5k的功能。我假设这是问题，但我不完全确定如何处理这个问题。任何人都有类似的问题，能够指出我如何将这个SVM模型（或其他一些SVM模型）应用于测试集？