使用PySpark进行多类分类的Logistic回归问题 [英] Issues with Logistic Regression for multiclass classification using PySpark

问题描述

我正在尝试使用Logistic Regression分类特征向量中具有稀疏向量的数据集:

I am trying to use Logistic Regression to classify the datasets which has Sparse Vector in feature vector:

# imported library from ML
from pyspark.ml.feature import HashingTF
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression

print(type(trainingData)) # for checking only
print(trainingData.take(2)) # for of data type
lr = LogisticRegression(labelCol="label", featuresCol="features", maxIter=maximumIteration,     regParam=re
gParamValue)
pipeline = Pipeline(stages=[lr])
# Train model
model = pipeline.fit(trainingData)

收到以下错误:

<class 'pyspark.sql.dataframe.DataFrame'>
[Row(label=2.0, features=SparseVector(2000, {51: 1.0, 160: 1.0, 341: 1.0, 417: 1.0, 561: 1.0, 656: 1.0, 863: 1.0, 939: 1.0, 1021: 1.0, 1324: 1.0, 1433: 1.0, 1573: 1.0, 1604: 1.0, 1720: 1.0})), Row(label=3.0, features=SparseVector(2000, {24: 1.0, 51: 2.0, 119: 1.0, 167: 1.0, 182: 1.0, 190: 1.0, 195: 1.0, 285: 1.0, 432: 1.0, 539: 1.0, 571: 1.0, 630: 1.0, 638: 1.0, 656: 1.0, 660: 2.0, 751: 1.0, 785: 1.0, 794: 1.0, 801: 1.0, 823: 1.0, 893: 1.0, 900: 1.0, 915: 1.0, 956: 1.0, 966: 1.0, 1025: 1.0, 1029: 1.0, 1035: 1.0, 1038: 1.0, 1093: 1.0, 1115: 2.0, 1147: 1.0, 1206: 1.0, 1252: 1.0, 1261: 1.0, 1262: 1.0, 1268: 1.0, 1304: 1.0, 1351: 1.0, 1378: 1.0, 1423: 1.0, 1437: 1.0, 1441: 1.0, 1530: 1.0, 1534: 1.0, 1556: 1.0, 1562: 1.0, 1604: 1.0, 1711: 1.0, 1737: 1.0, 1750: 1.0, 1776: 1.0, 1858: 1.0, 1865: 1.0, 1923: 1.0, 1926: 1.0, 1959: 1.0, 1999: 1.0}))]
16/08/25 19:14:07 ERROR org.apache.spark.ml.classification.LogisticRegression: Currently, LogisticRegression with E
lasticNet in ML package only supports binary classification. Found 5 in the input dataset.
Traceback (most recent call last):
  File "/home/LR/test.py", line 260, in <module>
    accuracy = TrainLRCModel(trainData, testData)
  File "/home/LR/test.py", line 211, in TrainLRCModel
    model = pipeline.fit(trainingData)
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/ml/pipeline.py", line 69, in fit
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/ml/pipeline.py", line 213, in _fit
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/ml/pipeline.py", line 69, in fit
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/ml/wrapper.py", line 133, in _fit
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/ml/wrapper.py", line 130, in _fit_java
  File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in __call__
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 45, in deco
  File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line 308, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o207.fit.
: org.apache.spark.SparkException: Currently, LogisticRegression with ElasticNet in ML package only supports binary
 classification. Found 5 in the input dataset.
        at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:290)
        at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:159)
        at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
        at org.apache.spark.ml.Predictor.fit(Predictor.scala:71)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
        at py4j.Gateway.invoke(Gateway.java:259)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:209)
        at java.lang.Thread.run(Thread.java:745)

案例2 :我搜索了上面一种可能的替代解决方案，并得到了LogisticRegressionWithLBFGS将适用于多类分类的方法，我尝试如下:

Case 2: I search the possible alternate solution of above one and got that LogisticRegressionWithLBFGS will work on multi-class classificaton, I tried as follow:

#imported library
from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel, LogisticRegressionWithSGD
print(type(trainingData)) # to check the dataset type
print(trainingData.take(2)) # To see the data
model = LogisticRegressionWithLBFGS.train(trainingData, numClasses=5)
print(type(model))

收到以下错误:

<class 'pyspark.sql.dataframe.DataFrame'>
[Row(label=3.0, features=SparseVector(2000, {24: 1.0, 51: 2.0, 119: 1.0, 167: 1.0, 182: 1.0, 190: 1.0, 195: 1.0, 28
5: 1.0, 432: 1.0, 539: 1.0, 571: 1.0, 630: 1.0, 638: 1.0, 656: 1.0, 660: 2.0, 751: 1.0, 785: 1.0, 794: 1.0, 801: 1.
0, 823: 1.0, 893: 1.0, 900: 1.0, 915: 1.0, 956: 1.0, 966: 1.0, 1025: 1.0, 1029: 1.0, 1035: 1.0, 1038: 1.0, 1093: 1.
0, 1115: 2.0, 1147: 1.0, 1206: 1.0, 1252: 1.0, 1261: 1.0, 1262: 1.0, 1268: 1.0, 1304: 1.0, 1351: 1.0, 1378: 1.0, 14
23: 1.0, 1437: 1.0, 1441: 1.0, 1530: 1.0, 1534: 1.0, 1556: 1.0, 1562: 1.0, 1604: 1.0, 1711: 1.0, 1737: 1.0, 1750: 1
.0, 1776: 1.0, 1858: 1.0, 1865: 1.0, 1923: 1.0, 1926: 1.0, 1959: 1.0, 1999: 1.0})), Row(label=5.0, features=SparseV
ector(2000, {103: 1.0, 310: 1.0, 601: 1.0, 817: 1.0, 866: 1.0, 940: 1.0, 1023: 1.0, 1118: 1.0, 1339: 1.0, 1447: 1.0
, 1634: 1.0, 1776: 1.0}))]
Traceback (most recent call last):
  File "/home/LR/test.py", line 260, in <module>
    accuracy = TrainLRCModel(trainData, testData)
  File "/home/LR/test.py", line 230, in TrainLRCModel
    model = LogisticRegressionWithLBFGS.train(trainingData, numClasses=5)
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/mllib/classification.py", line 382, in train
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/mllib/regression.py", line 206, in _regression_train_wrapper
TypeError: data should be an RDD of LabeledPoint, but got <class 'pyspark.sql.types.Row'>

同样，我尝试将数据集转换为Labeled Point的RDD，例如案例3:

Again I tried to convert the dataset into RDD of Labeled Point as follow i.e case 3:

    #imported libraries
    from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel, LogisticRegressionWithSGD
    from pyspark.mllib.regression import LabeledPoint

    print(type(trainingData))
    print(trainingData.take(2))
    trainingData = trainingData.map(lambda row:[LabeledPoint(row.label,row.features)])
    print('type of trainingData')
    print(type(trainingData))
    print(trainingData.take(2))
    model = LogisticRegressionWithLBFGS.train(trainingData, numClasses=5)
    print(type(model))

收到以下错误:

<class 'pyspark.sql.dataframe.DataFrame'>
[Row(label=2.0, features=SparseVector(2000, {51: 1.0, 160: 1.0, 341: 1.0, 417: 1.0, 561: 1.0, 656: 1.0, 863: 1.0, 9
39: 1.0, 1021: 1.0, 1324: 1.0, 1433: 1.0, 1573: 1.0, 1604: 1.0, 1720: 1.0})), Row(label=3.0, features=SparseVector(
2000, {24: 1.0, 51: 2.0, 119: 1.0, 167: 1.0, 182: 1.0, 190: 1.0, 195: 1.0, 285: 1.0, 432: 1.0, 539: 1.0, 571: 1.0, 
630: 1.0, 638: 1.0, 656: 1.0, 660: 2.0, 751: 1.0, 785: 1.0, 794: 1.0, 801: 1.0, 823: 1.0, 893: 1.0, 900: 1.0, 915: 
1.0, 956: 1.0, 966: 1.0, 1025: 1.0, 1029: 1.0, 1035: 1.0, 1038: 1.0, 1093: 1.0, 1115: 2.0, 1147: 1.0, 1206: 1.0, 12
52: 1.0, 1261: 1.0, 1262: 1.0, 1268: 1.0, 1304: 1.0, 1351: 1.0, 1378: 1.0, 1423: 1.0, 1437: 1.0, 1441: 1.0, 1530: 1
.0, 1534: 1.0, 1556: 1.0, 1562: 1.0, 1604: 1.0, 1711: 1.0, 1737: 1.0, 1750: 1.0, 1776: 1.0, 1858: 1.0, 1865: 1.0, 1
923: 1.0, 1926: 1.0, 1959: 1.0, 1999: 1.0}))]
type of trainingData
<class 'pyspark.rdd.PipelinedRDD'>
[[LabeledPoint(2.0, (2000,[51,160,341,417,561,656,863,939,1021,1324,1433,1573,1604,1720],[1.0,1.0,1.0,1.0,1.0,1.0,1
.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]))], [LabeledPoint(3.0, (2000,[24,51,119,167,182,190,195,285,432,539,571,630,638,656
,660,751,785,794,801,823,893,900,915,956,966,1025,1029,1035,1038,1093,1115,1147,1206,1252,1261,1262,1268,1304,1351,
1378,1423,1437,1441,1530,1534,1556,1562,1604,1711,1737,1750,1776,1858,1865,1923,1926,1959,1999],[1.0,2.0,1.0,1.0,1.
0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,1.0,1
.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]))]]
Traceback (most recent call last):
  File "/home/LR/test.py", line 260, in <module>
    accuracy = TrainLRCModel(trainData, testData)
  File "/home/LR/test.py", line 230, in TrainLRCModel
    model = LogisticRegressionWithLBFGS.train(trainingData, numClasses=5)
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/mllib/classification.py", line 381, in train
AttributeError: 'list' object has no attribute 'features'

有人可以建议我在哪里缺少什么吗?我想在PySpark中使用Logistic回归并对多类分类进行分类.

Can someone please suggest where I am missing something, I wanted to use the Logistic Regression in PySpark and classify the multi-class classification.

当前，我在Google云上使用的是Spark版本version 1.6.2和python版本Python 2.7.9.

Currently I am using spark version version 1.6.2 and python version Python 2.7.9 on google cloud.

在此先感谢您的帮助.

推荐答案

案例1 :这里并没有什么奇怪的，只是(如错误消息所言)LogisticRegression不支持多类分类，如

Case 1: There is nothing strange here, simply (as the error message says) LogisticRegression does not support multi-class classification, as clearly stated in the documentation.

案例2 :在这里，您已从ML切换到MLlib，但是它不适用于数据框，但需要输入作为LabeledPoint的RDD(

Case 2: Here you have switched from ML to MLlib, which however does not work with dataframes but needs the input as RDD of LabeledPoint (documentation), hence again the error message is expected.

案例3 :这是有趣的地方.首先，您应该从map函数中删除括号，即应该是

Case 3: Here is where things get interesting. First, you should remove the brackets from your map function, i.e. it should be

trainingData = trainingData.map(lambda row: LabeledPoint(row.label, row.features)) # no brackets after "row:"

尽管如此，从您提供的代码片段中猜测，很可能现在您将遇到另一个错误:

Nevertheless, guessing from the code snippets you have provided, most probably you are going to get a different error now:

model = LogisticRegressionWithLBFGS.train(trainingData, numClasses=5)
[...]
: org.apache.spark.SparkException: Input validation failed.

这是正在发生的事情(花了我一些时间来弄清楚)，使用了一些虚拟数据(最好将一些示例数据与您的问题一起提供):

Here is what happening (it took me some time to figure it out), using some dummy data (it's always a good idea to provide some sample data with your question):

# 3-class classification
data = sc.parallelize([
     LabeledPoint(3.0, SparseVector(100,[10, 98],[1.0, 1.0])),
     LabeledPoint(1.0, SparseVector(100,[1, 22],[1.0, 1.0])),
     LabeledPoint(2.0, SparseVector(100,[36, 54],[1.0, 1.0]))
])

lrm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3) # throws exception
[...]
: org.apache.spark.SparkException: Input validation failed.

问题在于，您的标签必须从0开始(并且没有记录在这里-您必须在

The problem is that your labels must start from 0 (and this is nowhere documented - you have to dig in the Scala source code to see that this is the case!); so, mapping the labels in my dummy data above from (1.0, 2.0, 3.0) to (0.0, 1.0, 2.0), we finally get:

# 3-class classification
data = sc.parallelize([
     LabeledPoint(2.0, SparseVector(100,[10, 98],[1.0, 1.0])),
     LabeledPoint(0.0, SparseVector(100,[1, 22],[1.0, 1.0])),
     LabeledPoint(1.0, SparseVector(100,[36, 54],[1.0, 1.0]))
])

lrm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3) # no error now

从您的numClasses=5参数以及其中一个打印记录中的label=5.0判断，我想您的代码很可能遭受相同的问题.将标签更改为[0.0, 4.0]，就可以了.

Judging from your numClasses=5 argument, as well as from the label=5.0 in one of your printed records, I guess that most probably your code suffers from the same issue. Change your labels to [0.0, 4.0] and you should be fine.

(我建议您删除打开的其他相同问题

(I suggest that you delete the other identical question you have opened here, for reducing clutter...)

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