具有多个标签的逻辑回归 PySpark MLlib 问题 [英] Logistic Regression PySpark MLlib issue with multiple labels

问题描述

我正在尝试创建一个 LogisticRegression 模型 (LogisticRegressionWithSGD)，但它得到了

I am trying to create a LogisticRegression model (LogisticRegressionWithSGD), but its getting an error of

org.apache.spark.SparkException: Input validation failed.

如果我给它二进制输入(0,1 而不是 0,1,2)，它确实成功了.

If I give it binary input (0,1 instead of 0,1,2) it does succeed.

示例输入:

parsed_data = [LabeledPoint(0.0, [4.6,3.6,1.0,0.2]),
LabeledPoint(0.0, [5.7,4.4,1.5,0.4]),
LabeledPoint(1.0, [6.7,3.1,4.4,1.4]),
LabeledPoint(0.0, [4.8,3.4,1.6,0.2]),
LabeledPoint(2.0, [4.4,3.2,1.3,0.2])]

代码:model = LogisticRegressionWithSGD.train(parsed_data)

spark 中的 Logistic 回归模型是否应该仅用于二元分类?

Is the Logistic Regression model in spark supposed to be for binary classification only?

推荐答案

虽然从文档中不清楚(你必须深入到 源代码来实现)，LogisticRegressionWithSGD 仅适用于二进制数据；对于多项式回归，您应该使用 LogisticRegressionWithLBFGS:

Although not clear from the documentation (you have to dig in to the source code to realize it), LogisticRegressionWithSGD works only with binary data; for multinomial regression, you should use LogisticRegressionWithLBFGS:

 from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel, LogisticRegressionWithSGD
 from pyspark.mllib.regression import LabeledPoint
 parsed_data = [LabeledPoint(0.0, [4.6,3.6,1.0,0.2]),
                LabeledPoint(0.0, [5.7,4.4,1.5,0.4]),
                LabeledPoint(1.0, [6.7,3.1,4.4,1.4]),
                LabeledPoint(0.0, [4.8,3.4,1.6,0.2]),
                LabeledPoint(2.0, [4.4,3.2,1.3,0.2])]     

 model = LogisticRegressionWithSGD.train(sc.parallelize(parsed_data)) # gives error:
 # org.apache.spark.SparkException: Input validation failed.

 model = LogisticRegressionWithLBFGS.train(sc.parallelize(parsed_data), numClasses=3)  # works OK

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