[jira] [Created] (SPARK-22295) Chi Square selector not recognizing field in Data frame

["Cheburakshu (JIRA)" <ji...@apache.org>]

Cheburakshu created SPARK-22295:
-----------------------------------

             Summary: Chi Square selector not recognizing field in Data frame
                 Key: SPARK-22295
                 URL: https://issues.apache.org/jira/browse/SPARK-22295
             Project: Spark
          Issue Type: Bug
          Components: MLlib
    Affects Versions: 2.1.1
            Reporter: Cheburakshu


There is a difference in behavior when Chisquare selector is used v direct feature use in decision tree classifier. 
In the below code, I have used chisquare selector as a thru' pass but the decision tree classifier is unable to process it. But, it is able to process when the features are used directly.

The example is pulled out directly from Apache spark python documentation.

Kindly help.

{code:python}
from pyspark.ml.feature import ChiSqSelector
from pyspark.ml.linalg import Vectors
import sys

df = spark.createDataFrame([
    (7, Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0,),
    (8, Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0,),
    (9, Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0,)], ["id", "features", 
"clicked"])

# ChiSq selector will just be a pass-through. All four featuresin the i/p will be in output also.
selector = ChiSqSelector(numTopFeatures=4, featuresCol="features",
                     outputCol="selectedFeatures", labelCol="clicked")
result = selector.fit(df).transform(df)
print("ChiSqSelector output with top %d features selected" % 
selector.getNumTopFeatures())

from pyspark.ml.classification import DecisionTreeClassifier

try:
# Fails
    dt = DecisionTreeClassifier(labelCol="clicked",featuresCol="selectedFeatures")
    model = dt.fit(result)
except:
    print(sys.exc_info())
#Works    
    dt = DecisionTreeClassifier(labelCol="clicked",featuresCol="features")
    model = dt.fit(df)
    
# Make predictions. Using same dataset, not splitting!!
predictions = model.transform(result)

# Select example rows to display.
predictions.select("prediction", "clicked", "features").show(5)

# Select (prediction, true label) and compute test error
evaluator = MulticlassClassificationEvaluator(
    labelCol="clicked", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g " % (1.0 - accuracy))
{code}

Output:

ChiSqSelector output with top 4 features selected
(<class 'pyspark.sql.utils.IllegalArgumentException'>, IllegalArgumentException('Feature 0 is marked as Nominal (categorical), but it does not have the number of values specified.', 'org.apache.spark.ml.util.MetadataUtils$$anonfun$getCategoricalFeatures$1.apply(MetadataUtils.scala:69)\n\t at org.apache.spark.ml.util.MetadataUtils$$anonfun$getCategoricalFeatures$1.apply(MetadataUtils.scala:59)\n\t at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)\n\t at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)\n\t at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)\n\t at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)\n\t at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)\n\t at scala.collection.mutable.ArrayOps$ofRef.flatMap(ArrayOps.scala:186)\n\t at org.apache.spark.ml.util.MetadataUtils$.getCategoricalFeatures(MetadataUtils.scala:59)\n\t at org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:101)\n\t at org.apache.spark.ml.classification.DecisionTreeClassifier.train(DecisionTreeClassifier.scala:45)\n\t at org.apache.spark.ml.Predictor.fit(Predictor.scala:96)\n\t at org.apache.spark.ml.Predictor.fit(Predictor.scala:72)\n\t at sun.reflect.GeneratedMethodAccessor280.invoke(Unknown Source)\n\t at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\t at java.lang.reflect.Method.invoke(Method.java:498)\n\t at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\t at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\t at py4j.Gateway.invoke(Gateway.java:280)\n\t at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\t at py4j.commands.CallCommand.execute(CallCommand.java:79)\n\t at py4j.GatewayConnection.run(GatewayConnection.java:214)\n\t at java.lang.Thread.run(Thread.java:745)'), <traceback object at 0x0A87D878>)
+----------+-------+------------------+
|prediction|clicked|          features|
+----------+-------+------------------+
|       1.0|    1.0|[0.0,0.0,18.0,1.0]|
|       0.0|    0.0|[0.0,1.0,12.0,0.0]|
|       0.0|    0.0|[1.0,0.0,15.0,0.1]|
+----------+-------+------------------+

Test Error = 0 



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