Edward Ma created SPARK-16247:
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Summary: Using pyspark dataframe with pipeline and cross validator
Key: SPARK-16247
URL: https://issues.apache.org/jira/browse/SPARK-16247
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 1.6.1
Reporter: Edward Ma
I am using pyspark with dataframe. Using pipeline operation to train and
predict the result. It is alright for single testing.
However, I got issue when using pipeline and CrossValidator. The issue is that
I expect CrossValidator use "indexedLabel" and "indexedMsg" as label and
feature. Those fields are built by StringIndexer and VectorIndex. It suppose to
be existed after executing pipeline.
Then I dig into pyspark library (line 222, _fit function and line 239,
est.fit), I found that it does not execute pipeline stage. Therefore, I cannot
get "indexedLabel" and "indexedMsg".
Would you mind advising whether my usage is correct or not.
Thanks.
Here is code snippet
# Indexing
labelIndexer = StringIndexer(inputCol="label",
outputCol="indexedLabel").fit(extracted_data)
featureIndexer = VectorIndexer(inputCol="extracted_msg",
outputCol="indexedMsg", maxCategories=3000).fit(extracted_data)
# Training
classification_model = RandomForestClassifier(labelCol="indexedLabel",
featuresCol="indexedMsg", numTrees=50, maxDepth=20)
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, classification_model])
# Cross Validation
paramGrid = ParamGridBuilder().addGrid(1000, (10, 100, 1000)).build()
cvEvaluator = MulticlassClassificationEvaluator(metricName="precision")
cv = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid,
evaluator=cvEvaluator, numFolds=10)
cvModel = cv.fit(trainingData)
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