Large prediction results unless using repartition(1) in databricks with lgbm model

[user673]

I'm using mmlspark lgbm model for regression problem and faced strange thing. If using all normal code as in the example, results will be terrible, becouse predictions are huge (around 10^37 , while target is in range from 0 to 200). Testing, I found that using dataset.repartition(1).cache() fixed this problem,but with one detail - modelling began to take longer (around 1h, while 20m earlier). This is logical since all the data (about 4m rows and 150 columns) is collected before learning in one partition.

I tried changing lgbm param useBarrierExecutionMode to True and different parallelism params, but this changes doesn't affect result.

Is there a way not to use such workaround with repartition and still having normal results?

Code, used for training

      repartitioned_data = data_train.repartition(1).cache() #  want to delete this line

      # Define model
      model = LightGBMRegressor(
          objective='regression',
          labelCol='label',
          featuresCol="features"
      )

      # Define grid params
      paramGrid = ParamGridBuilder() \
              .addGrid(model.numIterations, [100, 250])\
            .build()

      # Define cross validation for grid params
      evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="mae")
      crossval = CrossValidator(estimator=model,
                          estimatorParamMaps=paramGrid,
                          evaluator=evaluator,   
                          numFolds=2)

      # Train model
      pipeline = crossval.fit(data_train)

• Databricks Runtime Version 6.4 (includes Apache Spark 2.4.5, Scala 2.11)
• 3 worker nodes Standard_DS4_v2
• driver node Standard_DS4_v2
• mmlspark version mmlspark_2.11:1.0.0-rc3

AB#1984587

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[AllardJM]

I am seeing something similar - enormous predictions that are sometimes positive and sometimes negative (there are no negative values in the target). It seems that if I use mse as the objective the predictions are all extremely negative and extremely positive if using a tweedie objective. The rank order / discrimination is relatively good but the estimates are orders of magnitude uncalibrated

Using Data Bricks 7.3 LTS Spark 3.0.1 Lightgbm: com.microsoft.ml.spark:mmlspark_2.12:1.0.0-rc3-24-495af3e4-SNAPSHOT

[mhamilton723]

@AllardJM @user673 Could you possibly share an example dataset for us to repro? Adding @imatiach-msft who built LightGBM on Spark

[imatiach-msft]

interesting, I've seen similar issues reported where setting the tree depth or number of leaves seemed to resolve the large predictions, not sure if it's related to the specific issue(s) @user673 and @AllardJM saw

[imatiach-msft]

@user673 @AllardJM I've found the last iteration is sometimes creating a bad tree that seems to predict inf values, I've created an issue in lightgbm repo to track this: https://github.com/microsoft/LightGBM/issues/4178 this seems to happen when I see the following logging: ... [LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 1 and max_depth = 1 [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements

the very last tree with 1 leaf and 1 depth seems to output the high values; limiting the number of iterations to be one less than this seems to prevent the issue

[imatiach-msft]

this might be different from what @AllardJM is seeing though since I only see inf values, I don't see negative values, and this is just the LightGBMRegressor with regression objective function

[imatiach-msft]

@user673 @AllardJM FYI I believe this issue has been fixed with this PR in lightgbm repository: https://github.com/microsoft/LightGBM/pull/4185 I am waiting for it to be merged to validate/release it

[shiyu1994]

@imatiach-msft The PR has been merged, please check.

[imatiach-msft]

thanks, I've updated code on latest master and have confirmed issue is fixed. I will leave this github issue open since it's pretty bad until the next release, in case others see it, so it's more easy to find.

[shsab]

@imatiach-msft Any update on this issue? We are facing the same issue and using the reparation(1) workaround however it is not feasible for large datasets.

[adesh14]

hey, I am also encountering the same error like [LightGBM] [Warning] No further splits with positive gain, best gain: -inf my dataset contains 360 rows and 19999 col can you please guide what could be the problem, does setting different value of hyperparameter works here?