Question

我有一个5000行和401列的训练集，其中第1列是标签，其余400列是特征。我正在尝试使用pyspark mllib进行多类逻辑回归。请在下面找到我的代码。我必须承认，这不是一个优化/编写良好的代码，因为我仍然是python / pyspark领域的新手。

tset=sio.loadmat('ex3data1.mat') # load the training set from a mat file
X=tset['X']                      # read the X,y values
y=tset['y']
print(X.shape) # works!
print(y.shape)
sp= 
SparkSession.builder.master("local").appName("multiclassifier").getOrCreate()
sc=sp.sparkContext
XY=np.concatenate((y,X),axis=1) # 5000x401 where the first col is the label.
print(XY[0:2])

上面打印的样本输出。请注意，我只打印第一行

[[  1.00000000e+01   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    8.56059680e-06   1.94035948e-06  -7.37438725e-04  -8.13403799e-03
   -1.86104473e-02  -1.87412865e-02  -1.87572508e-02  -1.90963542e-02
   -1.64039011e-02  -3.78191381e-03   3.30347316e-04   1.27655229e-05
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.16421569e-04
    1.20052179e-04  -1.40444581e-02  -2.84542484e-02   8.03826593e-02
    2.66540339e-01   2.73853746e-01   2.78729541e-01   2.74293607e-01
    2.24676403e-01   2.77562977e-02  -7.06315478e-03   2.34715414e-04
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   1.28335523e-17  -3.26286765e-04
   -1.38651604e-02   8.15651552e-02   3.82800381e-01   8.57849775e-01
    1.00109761e+00   9.69710638e-01   9.30928598e-01   1.00383757e+00
    9.64157356e-01   4.49256553e-01  -5.60408259e-03  -3.78319036e-03
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    5.10620915e-06   4.36410675e-04  -3.95509940e-03  -2.68537241e-02
    1.00755014e-01   6.42031710e-01   1.03136838e+00   8.50968614e-01
    5.43122379e-01   3.42599738e-01   2.68918777e-01   6.68374643e-01
    1.01256958e+00   9.03795598e-01   1.04481574e-01  -1.66424973e-02
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    2.59875260e-05  -3.10606987e-03   7.52456076e-03   1.77539831e-01
    7.92890120e-01   9.65626503e-01   4.63166079e-01   6.91720680e-02
   -3.64100526e-03  -4.12180405e-02  -5.01900656e-02   1.56102907e-01
    9.01762651e-01   1.04748346e+00   1.51055252e-01  -2.16044665e-02
    0.00000000e+00   0.00000000e+00   0.00000000e+00   5.87012352e-05
   -6.40931373e-04  -3.23305249e-02   2.78203465e-01   9.36720163e-01
    1.04320956e+00   5.98003217e-01  -3.59409041e-03  -2.16751770e-02
   -4.81021923e-03   6.16566793e-05  -1.23773318e-02   1.55477482e-01
    9.14867477e-01   9.20401348e-01   1.09173902e-01  -1.71058007e-02
    0.00000000e+00   0.00000000e+00   1.56250000e-04  -4.27724104e-04
   -2.51466503e-02   1.30532561e-01   7.81664862e-01   1.02836583e+00
    7.57137601e-01   2.84667194e-01   4.86865128e-03  -3.18688725e-03
    0.00000000e+00   8.36492601e-04  -3.70751123e-02   4.52644165e-01
    1.03180133e+00   5.39028101e-01  -2.43742611e-03  -4.80290033e-03
    0.00000000e+00   0.00000000e+00  -7.03635621e-04  -1.27262443e-02
    1.61706648e-01   7.79865383e-01   1.03676705e+00   8.04490400e-01
    1.60586724e-01  -1.38173339e-02   2.14879493e-03  -2.12622549e-04
    2.04248366e-04  -6.85907627e-03   4.31712963e-04   7.20680947e-01
    8.48136063e-01   1.51383408e-01  -2.28404366e-02   1.98971950e-04
    0.00000000e+00   0.00000000e+00  -9.40410539e-03   3.74520505e-02
    6.94389110e-01   1.02844844e+00   1.01648066e+00   8.80488426e-01
    3.92123945e-01  -1.74122413e-02  -1.20098039e-04   5.55215142e-05
   -2.23907271e-03  -2.76068376e-02   3.68645493e-01   9.36411169e-01
    4.59006723e-01  -4.24701797e-02   1.17356610e-03   1.88929739e-05
    0.00000000e+00   0.00000000e+00  -1.93511951e-02   1.29999794e-01
    9.79821705e-01   9.41862388e-01   7.75147704e-01   8.73632241e-01
    2.12778350e-01  -1.72353349e-02   0.00000000e+00   1.09937426e-03
   -2.61793751e-02   1.22872879e-01   8.30812662e-01   7.26501773e-01
    5.24441863e-02  -6.18971913e-03   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00  -9.36563862e-03   3.68349741e-02
    6.99079299e-01   1.00293583e+00   6.05704402e-01   3.27299224e-01
   -3.22099249e-02  -4.83053002e-02  -4.34069138e-02  -5.75151144e-02
    9.55674190e-02   7.26512627e-01   6.95366966e-01   1.47114481e-01
   -1.20048679e-02  -3.02798203e-04   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00  -6.76572712e-04  -6.51415556e-03
    1.17339359e-01   4.21948410e-01   9.93210937e-01   8.82013974e-01
    7.45758734e-01   7.23874268e-01   7.23341725e-01   7.20020340e-01
    8.45324959e-01   8.31859739e-01   6.88831870e-02  -2.77765012e-02
    3.59136710e-04   7.14869281e-05   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   1.53186275e-04   3.17353553e-04
   -2.29167177e-02  -4.14402914e-03   3.87038450e-01   5.04583435e-01
    7.74885876e-01   9.90037446e-01   1.00769478e+00   1.00851440e+00
    7.37905042e-01   2.15455291e-01  -2.69624864e-02   1.32506127e-03
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    2.36366422e-04  -2.26031454e-03  -2.51994485e-02  -3.73889910e-02
    6.62121228e-02   2.91134498e-01   3.23055726e-01   3.06260315e-01
    8.76070942e-02  -2.50581917e-02   2.37438725e-04   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   6.20939216e-18   6.72618320e-04
   -1.13151411e-02  -3.54641066e-02  -3.88214912e-02  -3.71077412e-02
   -1.33524928e-02   9.90964718e-04   4.89176960e-05   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00]]

打印输出结束。

pXYdf=pd.DataFrame(XY)
sXYdf=sp.createDataFrame(pXYdf)

from pyspark.mllib.classification import LogisticRegressionWithLBFGS, 
LogisticRegressionModel
import pyspark.mllib.regression as reg

trainingData = sXYdf.rdd.map(lambda x: reg.LabeledPoint(x[0],x[1:]))
trainingData.take(2) # this works!!

以LabeledPoint格式输出1条记录:(我无法在此处正确格式化，因为这里有400个功能。

[LabeledPoint(10.0,[0.0,0.0,0.0,0.0,0.0,0.0,....,8.56059679589e-06, 1.94035947712e06,.........]),

lrm=LogisticRegressionWithLBFGS.train(trainingData)

我收到以下错误：

Py4JJavaError: An error occurred while calling o168.trainLogisticRegressionModelWithLBFGS. 
: org.apache.spark.SparkException: Multinomial models contain a matrix of coefficients, use coefficientMatrix instead.
[...]

Answer 1

对于多类分类，LogisticRegressionWithLBFGS needs类参数numClasses的数量，您没有提供。

始终提供至少一个数据样本以及您的问题总是一个好主意;既然你没有，这是我尝试用我自己的虚拟数据重现你的错误：

from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
from pyspark.mllib.regression import LabeledPoint

parsed_data = [LabeledPoint(0, [4.6,3.6,1.0,0.2]),  # 3 classes
                LabeledPoint(0, [5.7,4.4,1.5,0.4]),
                LabeledPoint(1, [6.7,3.1,4.4,1.4]),
                LabeledPoint(2, [4.8,3.4,1.6,0.2]),
                LabeledPoint(1, [4.4,3.2,1.3,0.2])]

model = LogisticRegressionWithLBFGS.train(sc.parallelize(parsed_data)) # this will reproduce your error:
[...]
Py4JJavaError: An error occurred while calling o168.trainLogisticRegressionModelWithLBFGS. 
: org.apache.spark.SparkException: Multinomial models contain a matrix of coefficients, use coefficientMatrix instead.
[...]

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

（使用Spark 2.1.1测试）

调用LogisticRegressionModelWithLBFGS.train时的Py4JavaError

1 个答案: