Pyspark UDF for Dataframe vs RDD

Question

我的数据帧的架构是：

root
     |-- _10: string (nullable = true)
     |-- _11: string (nullable = true)
     |-- _12: string (nullable = true)
     |-- _13: string (nullable = true)
     |-- _14: string (nullable = true)
     |-- _15: string (nullable = true)
     |-- _16: string (nullable = true)
     |-- _17: string (nullable = true)
     |-- _18: string (nullable = true)
     |-- _19: string (nullable = true)
     |-- _20: string (nullable = true)
     |-- _21: string (nullable = true)
     |-- _22: string (nullable = true)
     |-- _23: string (nullable = true)
     |-- _24: string (nullable = true)
     |-- _25: string (nullable = true)
     |-- id: long (nullable = true)
     |-- features: array (nullable = true)
     |    |-- element: double (containsNull = true)

我想使用features数组进行一些操作，并将结果存储在一个新列中：预测

def addPred(inp):
    global weights, bias
    for j in range(0,len(weights)):
        if j==0:
            out = sigmoid(np.dot(inp,weights[j]) + bias[j])
        elif j==len(weights)-1:
            out = softmax(np.dot(out,weights[j]) + bias[j])
        else:
            out = sigmoid(np.dot(out,weights[j]) + bias[j])

    if out[0]>out[1]:
        return -1*out[0]
    return out[1]

使用此UDF和以下代码我正在尝试直接向数据框添加新列。

udf_addPred = udf(addPred, DoubleType())
test_data = test_data.withColumn('pred', udf_addPred('features'))

但它给了我各种错误。

有时'不可序列化的错误'
有时'RDD是空错误'

但是如果我使用rdd map执行相同的操作，它可以使用以下代码

col_rdd  = test_data.rdd.map(lambda x: addPred(x.features))

我自己尝试调试这个问题，但无法弄清楚错误的来源
以RDD方式执行然后合并列将需要两倍的计算
有人可以指出错误或建议更好的选择吗？

编辑：

test_data.rdd.first（）的输出：

Row(_10=u'Abu Dhabi Global Market', _11=u'Abu Dhabi Media Company', _12=u'Abu Dhabi Global Market (ADGM) BuildingADGM Square Al Maryah Island PO Box 111999', _13=u'Abu Dhabi Media P.O. Box 63', _14=u'Abu Dhabi', _15=u'Abu Dhabi', _16=u'Abu Dhabi', _17=u'Abu Dhabi', _18=u'United Arab Emirates', _19=u'United Arab Emirates', _20=None, _21=None, _22=u'557942700', _23=u'552544884', _24=u'www.adgm.com', _25=u'http://www.admedia.ae', id=4, features=[0.4782608695652174, 0.2592592592592593, 1.0, 1.0, 1.0, 0.14285714285714285, 0.0, 0.19999999999999996])

重量和偏差是Spark的Multilayer Perceptron的相应内容

def extWeights():
    weights = []
    bias = []
    last = 0
    for i in range(0,len(model.layers)-1):
        curr = (model.layers[i]+1)*model.layers[i+1]
        weights.append(np.reshape(model.weights[last:last+curr],((model.layers[i]+1),model.layers[i+1])))
        bias.append(weights[i][model.layers[i]])
        weights[i] = weights[i][:model.layers[i]]
        last += curr
    return weights, bias