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feat: add interface function for updating learning_rate per each iteration in LightGBMDelegate #849
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imatiach-msft
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ocworld:feat-add-update-learningrate
Apr 7, 2020
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7829a95
feat: add update learning_rate by using LightGBMDelegate
bd6800f
feat: add update learning_rate by using LightGBMDelegate
e99e2bd
feat: add update learning_rate by using LightGBMDelegate
c937457
feat: add update learning_rate by using LightGBMDelegate
c9194a8
fix minor
affb069
fix serialization error
fa62274
fix serialization error
280157c
change LightGBMDelegate to trait for scala style
31d40bd
change LightGBMDelegate to trait for scala style
5e7151d
change LightGBMDelegate to trait for scala style
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Original file line number | Diff line number | Diff line change |
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@@ -6,6 +6,7 @@ package com.microsoft.ml.spark.lightgbm.split1 | |
import java.io.File | ||
import java.nio.file.{Files, Path, Paths} | ||
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import com.microsoft.ml.lightgbm.SWIGTYPE_p_void | ||
import com.microsoft.ml.spark.core.test.base.TestBase | ||
import com.microsoft.ml.spark.core.test.benchmarks.{Benchmarks, DatasetUtils} | ||
import com.microsoft.ml.spark.core.test.fuzzing.{EstimatorFuzzing, TestObject} | ||
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@@ -23,6 +24,7 @@ import org.apache.spark.ml.{Estimator, Model} | |
import org.apache.spark.sql.{DataFrame, Row} | ||
import org.apache.spark.sql.catalyst.encoders.RowEncoder | ||
import org.apache.spark.sql.functions._ | ||
import org.slf4j.Logger | ||
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// scalastyle:off magic.number | ||
trait LightGBMTestUtils extends TestBase { | ||
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@@ -360,6 +362,47 @@ class VerifyLightGBMClassifier extends Benchmarks with EstimatorFuzzing[LightGBM | |
assert(metric > 0.8) | ||
} | ||
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test("Verify LightGBM Classifier updating learning_rate on training by using LightGBMDelegate") { | ||
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class TrainDelegate extends LightGBMDelegate { | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. maybe the test is failing because this class is not serializable? |
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override def beforeTrainIteration(partitionId: Int, curIters: Int, log: Logger, trainParams: TrainParams, | ||
boosterPtr: Option[SWIGTYPE_p_void], hasValid: Boolean): Unit = { | ||
// nothing | ||
} | ||
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override def afterTrainIteration(partitionId: Int, curIters: Int, log: Logger, trainParams: TrainParams, | ||
boosterPtr: Option[SWIGTYPE_p_void], hasValid: Boolean, isFinished: Boolean, | ||
trainEvalResults: Option[Map[String, Double]], | ||
validEvalResults: Option[Map[String, Double]]): Unit = { | ||
// nothing | ||
} | ||
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override def getLearningRate(partitionId: Int, curIters: Int, log: Logger, trainParams: TrainParams, | ||
previousLearningRate: Double): Double = { | ||
if (curIters == 0) { | ||
previousLearningRate | ||
} else { | ||
previousLearningRate * 0.05 | ||
} | ||
} | ||
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} | ||
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val Array(train, _) = indexedBankTrainDF.randomSplit(Array(0.8, 0.2), seed) | ||
val delegate = new TrainDelegate() | ||
val untrainedModel = baseModel | ||
.setCategoricalSlotNames(indexedBankTrainDF.columns.filter(_.startsWith("c_"))) | ||
.setDelegate(delegate) | ||
.setLearningRate(0.1) | ||
.setNumIterations(2) // expected learning_rate: iters 0 => 0.1, iters 1 => 0.005 | ||
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val model = untrainedModel.fit(train) | ||
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// Verify updating learning_rate | ||
assert(model.getModel.model.contains("learning_rate: 0.005")) | ||
} | ||
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test("Verify LightGBM Classifier leaf prediction") { | ||
val Array(train, test) = indexedBankTrainDF.randomSplit(Array(0.8, 0.2), seed) | ||
val untrainedModel = baseModel | ||
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nice logging!