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Posted to issues@spark.apache.org by "Edward Ma (JIRA)" <ji...@apache.org> on 2016/06/28 05:27:57 UTC
[jira] [Updated] (SPARK-16247) Using pyspark dataframe with
pipeline and cross validator
[ https://issues.apache.org/jira/browse/SPARK-16247?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Edward Ma updated SPARK-16247:
------------------------------
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 [python/pyspark/ml/tuning.py] (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 [python/pyspark/ml/tuning.py] (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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