Cheburakshu created SPARK-22277:
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Summary: Chi Square selector garbling Vector content.
Key: SPARK-22277
URL: https://issues.apache.org/jira/browse/SPARK-22277
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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