我是新来的火花(使用pyspark)。我尝试从此处(链接)运行决策树教程。我执行代码:
from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.mllib.util import MLUtils
# Load and parse the data file, converting it to a DataFrame.
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
# Now this line fails
featureIndexer =\
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
我收到错误消息:IllegalArgumentException:u'requirement失败:列要素必须为org.apache.spark.ml.linalg.VectorUDT@3bfc3ba7类型,但实际上为org.apache.spark.mllib.linalg.VectorUDT@f71b0bce。
谷歌搜索此错误时,我找到了一个回答,说:
use from pyspark.ml.linalg import Vectors, VectorUDT
instead of
from pyspark.mllib.linalg import Vectors, VectorUDT
这很奇怪,因为我没有使用过。另外,将此导入添加到我的代码中并不能解决任何问题,并且仍然会出现相同的错误。
对于如何调试这种情况,我不太清楚。当查看原始数据时,我看到:
data.show()
+--------------------+-----+
| features|label|
+--------------------+-----+
|(692,[127,128,129...| 0.0|
|(692,[158,159,160...| 1.0|
|(692,[124,125,126...| 1.0|
|(692,[152,153,154...| 1.0|
看起来像一个列表,以'('开头。
我不确定如何解决此问题,甚至无法调试...关于我做错了什么的建议?
谢谢
问题的根源似乎是在执行spark 1.5.2。spark 2.0.0上的示例(请参见下面对spark 2.0示例的引用)。
spark.ml和spark.mllib之间的区别
从Spark 2.0开始,spark.mllib软件包中基于RDD的API已进入维护模式。Spark的主要机器学习API现在是spark.ml软件包中基于DataFrame的API。
可在此处找到更多详细信息:http : //spark.apache.org/docs/latest/ml-guide.html
使用Spark 2.0,请尝试使用Spark 2.0.0示例(https://spark.apache.org/docs/2.0.0/mllib-decision-tree.html)
from pyspark.mllib.tree import DecisionTree, DecisionTreeModel
from pyspark.mllib.util import MLUtils
# Load and parse the data file into an RDD of LabeledPoint.
data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])
# Train a DecisionTree model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
model = DecisionTree.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},
impurity='gini', maxDepth=5, maxBins=32)
# Evaluate model on test instances and compute test error
predictions = model.predict(testData.map(lambda x: x.features))
labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count())
print('Test Error = ' + str(testErr))
print('Learned classification tree model:')
print(model.toDebugString())
# Save and load model
model.save(sc, "target/tmp/myDecisionTreeClassificationModel")
sameModel = DecisionTreeModel.load(sc, "target/tmp/myDecisionTreeClassificationModel")
在Spark存储库中的“ examples / src / main / python / mllib / decision_tree_classification_example.py”中找到完整的示例代码。
本文收集自互联网,转载请注明来源。
如有侵权,请联系 [email protected] 删除。
我来说两句