pyspark.sql.utils.IllegalArgumentException：u'字段“功能”不存在。'

我正在尝试执行随机森林分类器并使用交叉验证评估模型。我使用 pySpark 工作。输入 CSV 文件以 Spark DataFrame 格式加载。 但我在构建模型时遇到了一个问题。

下面是代码。

from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.ml import Pipeline
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.mllib.evaluation import BinaryClassificationMetrics
sc = SparkContext()
sqlContext = SQLContext(sc)
trainingData =(sqlContext.read
         .format("com.databricks.spark.csv")
         .option("header", "true")
         .option("inferSchema", "true")
         .load("/PATH/CSVFile"))
numFolds = 10 
rf = RandomForestClassifier(numTrees=100, maxDepth=5, maxBins=5, labelCol="V5409",featuresCol="features",seed=42)
evaluator = MulticlassClassificationEvaluator().setLabelCol("V5409").setPredictionCol("prediction").setMetricName("accuracy")
paramGrid = ParamGridBuilder().build()

pipeline = Pipeline(stages=[rf])
paramGrid=ParamGridBuilder().build()
crossval = CrossValidator(
    estimator=pipeline,
    estimatorParamMaps=paramGrid,
    evaluator=evaluator,
    numFolds=numFolds)
model = crossval.fit(trainingData)
print accuracy

我遇到以下错误

Traceback (most recent call last):
  File "SparkDF.py", line 41, in <module>
    model = crossval.fit(trainingData)
  File "/usr/local/spark-2.1.1/python/pyspark/ml/base.py", line 64, in fit
    return self._fit(dataset)
  File "/usr/local/spark-2.1.1/python/pyspark/ml/tuning.py", line 236, in _fit
    model = est.fit(train, epm[j])
  File "/usr/local/spark-2.1.1/python/pyspark/ml/base.py", line 64, in fit
    return self._fit(dataset)
  File "/usr/local/spark-2.1.1/python/pyspark/ml/pipeline.py", line 108, in _fit
    model = stage.fit(dataset)
  File "/usr/local/spark-2.1.1/python/pyspark/ml/base.py", line 64, in fit
    return self._fit(dataset)
  File "/usr/local/spark-2.1.1/python/pyspark/ml/wrapper.py", line 236, in _fit
    java_model = self._fit_java(dataset)
  File "/usr/local/spark-2.1.1/python/pyspark/ml/wrapper.py", line 233, in _fit_java
    return self._java_obj.fit(dataset._jdf)
  File "/home/hadoopuser/anaconda2/lib/python2.7/site-packages/py4j/java_gateway.py", line 1160, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/local/spark-2.1.1/python/pyspark/sql/utils.py", line 79, in deco
    raise IllegalArgumentException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.IllegalArgumentException: u'Field "features" does not exist.'
hadoopuser@rackserver-PowerEdge-R220:~/workspace/RandomForest_CV$ 

请帮我解决 pySpark 中的这个问题。 谢谢。

我在这里展示数据集的详细信息。 不，我没有专门的功能栏。下面是 TrainingData.take(5) 的输出，它显示数据集的前 5 行。

[行(V4366=0.0、V4460=0.232、V4916=-0.017、V1495=-0.104、V1639=0.005、V1967=-0.008、V3049=0.177、V3746=-0.675、V3869=-3.451、V524=0。 004、V5409= 0)、行(V4366=0.0、V4460=0.111、V4916=-0.003、V1495=-0.137、V1639=0.001、V1967=-0.01、V3049=0.01、V3746=-0.867、V3869=-2.759、V524=0。 0, V5409=0)、行(V4366=0.0、V4460=-0.391、V4916=-0.003、V1495=-0.155、V1639=-0.006、V1967=-0.019、V3049=-0.706、V3746=0.166、V3869=0.189 , V524 =0.001，V5409=0)，行(V4366=0.0，V4460=0.098，V4916=-0.012，V1495=-0.108，V1639=0.005，V1967=-0.002，V3049=0.033，V3746=-0.787，V3869= -0.926 , V524=0.002, V5409=0), 行(V4366=0.0, V4460=0.026, V4916=-0.004, V1495=-0.139, V1639=0.003, V1967=-0.006, V3049=-0.045, V3746=-0.208 , V3869 =-0.782，V524=0.001，V5409=0)]

其中 V433 到 V524 是功能。 V5409 是类别标签。

Spark 数据帧的使用方式与 Spark ML 不同；你所有的特征都需要是向量single列，通常命名为features。以下是使用您提供的 5 行作为示例的方法：

spark.version
# u'2.2.0'

from pyspark.sql import Row
from pyspark.ml.linalg import Vectors

# your sample data:
temp_df = spark.createDataFrame([Row(V4366=0.0, V4460=0.232, V4916=-0.017, V1495=-0.104, V1639=0.005, V1967=-0.008, V3049=0.177, V3746=-0.675, V3869=-3.451, V524=0.004, V5409=0), Row(V4366=0.0, V4460=0.111, V4916=-0.003, V1495=-0.137, V1639=0.001, V1967=-0.01, V3049=0.01, V3746=-0.867, V3869=-2.759, V524=0.0, V5409=0), Row(V4366=0.0, V4460=-0.391, V4916=-0.003, V1495=-0.155, V1639=-0.006, V1967=-0.019, V3049=-0.706, V3746=0.166, V3869=0.189, V524=0.001, V5409=0), Row(V4366=0.0, V4460=0.098, V4916=-0.012, V1495=-0.108, V1639=0.005, V1967=-0.002, V3049=0.033, V3746=-0.787, V3869=-0.926, V524=0.002, V5409=0), Row(V4366=0.0, V4460=0.026, V4916=-0.004, V1495=-0.139, V1639=0.003, V1967=-0.006, V3049=-0.045, V3746=-0.208, V3869=-0.782, V524=0.001, V5409=0)])

trainingData=temp_df.rdd.map(lambda x:(Vectors.dense(x[0:-1]), x[-1])).toDF(["features", "label"])
trainingData.show()
# +--------------------+-----+ 
# |            features|label|
# +--------------------+-----+
# |[-0.104,0.005,-0....|    0| 
# |[-0.137,0.001,-0....|    0|
# |[-0.155,-0.006,-0...|    0|
# |[-0.108,0.005,-0....|    0|
# |[-0.139,0.003,-0....|    0|
# +--------------------+-----+

之后，您的管道应该运行良好（我假设您确实有多类分类，因为您的样本仅包含 0 作为标签），只需更改您的标签列rf and evaluator如下：

rf = RandomForestClassifier(numTrees=100, maxDepth=5, maxBins=5, labelCol="label",featuresCol="features",seed=42)
evaluator = MulticlassClassificationEvaluator().setLabelCol("label").setPredictionCol("prediction").setMetricName("accuracy")

最后，print accuracy行不通 - 你需要model.avgMetrics反而。