mllib - CountVectorizer + LogisticRegression unexpected behavior

[pun <pu...@gmail.com>]

Hello,I have a model, which uses CountVectorizer and LogisticRegression. 
*Everything seems to work fine, except that when I am running the last step
to get results and predictions, the document ids (doc_id) are being changed
completely. Do you know why that is? Am I doing something wrong?*
import org.apache.spark.ml.classification.LogisticRegressionimport
org.apache.spark.ml.feature.{CountVectorizer, Tokenizer}val tokenizer = new
Tokenizer()  .setInputCol("text")  .setOutputCol("words")val countVectorizer
= new CountVectorizer()  //.setVocabSize(50000) 
.setInputCol(tokenizer.getOutputCol)  .setOutputCol("features")val lr = new
LogisticRegression()  .setMaxIter(10)  .setRegParam(0.01)val pipeline = new
Pipeline()  .setStages(Array(tokenizer, countVectorizer, lr))// Fit the
pipeline to training documents.val model = pipeline.fit(training)val results
= model.transform(test)
Training and test are two DFs with the following structure:
root |-- doc_id: string (nullable = true) |-- text: string (nullable = true)
|-- label: integer (nullable = false)
Thanks in advance!



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Re: Spark ML - CountVectorizer + LogisticRegression unexpected behavior

[pun <pu...@gmail.com>]

Nm. Rookie error. I wasn't caching the DF.



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