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https://issues.apache.org/jira/browse/SPARK-16247?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Edward Ma updated SPARK-16247:
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Description:
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)
was:
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)
> 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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