[GitHub] [spark] zhengruifeng commented on pull request #29018: [SPARK-32202][ML][WIP] tree models auto infer compact integer type

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zhengruifeng commented on pull request #29018:
URL: https://github.com/apache/spark/pull/29018#issuecomment-654587121


   test code:
   ```scala
   import org.apache.spark.ml.classification._
   import org.apache.spark.storage.StorageLevel
   
   
   val df = spark.read.option("numFeatures", 
"2000").format("libsvm").load("/data1/Datasets/epsilon/epsilon_normalized.t").withColumn("label",
 (col("label")+1)/2)
   df.persist(StorageLevel.MEMORY_AND_DISK)
   df.count
   
   new RandomForestClassifier().fit(df)
   
   
   val dt = new RandomForestClassifier().setMaxDepth(10).setNumTrees(100)
   
   val start = System.currentTimeMillis; Seq.range(0, 10).foreach{_ => val 
model = dt.fit(df)}; val end = System.currentTimeMillis; end - start
   
   ```
   
   
   results:
   this PR:
   duration = 1356042
   
![image](https://user-images.githubusercontent.com/7322292/86707054-2212ed00-c04a-11ea-8806-67d94c73dae5.png)
   
   Master:
   duration = 1245078
   
![image](https://user-images.githubusercontent.com/7322292/86707096-2b03be80-c04a-11ea-8ace-8e0dfd6e4d4a.png)
   
   
   The RAM to persisit training datset was reduced from 777M to 237M. However, 
there was a performance regression, this PR is about 8% slower than master in 
above test.
   
   @srowen @WeichenXu123 @huaxingao How do you think about this? and is there 
some way to avoid this performace regression?
   


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