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50334f2c52
* add basic support to Spark dataframe add support to SynapseML LightGBM model update to pyspark>=3.2.0 to leverage pandas_on_Spark API * clean code, add TODOs * add sample_train_data for pyspark.pandas dataframe, fix bugs * improve some functions, fix bugs * fix dict change size during iteration * update model predict * update LightGBM model, update test * update SynapseML LightGBM params * update synapseML and tests * update TODOs * Added support to roc_auc for spark models * Added support to score of spark estimator * Added test for automl score of spark estimator * Added cv support to pyspark.pandas dataframe * Update test, fix bugs * Added tests * Updated docs, tests, added a notebook * Fix bugs in non-spark env * Fix bugs and improve tests * Fix uninstall pyspark * Fix tests error * Fix java.lang.OutOfMemoryError: Java heap space * Fix test_performance * Update test_sparkml to test_0sparkml to use the expected spark conf * Remove unnecessary widgets in notebook * Fix iloc java.lang.StackOverflowError * fix pre-commit * Added params check for spark dataframes * Refactor code for train_test_split to a function * Update train_test_split_pyspark * Refactor if-else, remove unnecessary code * Remove y from predict, remove mem control from n_iter compute * Update workflow * Improve _split_pyspark * Fix test failure of too short training time * Fix typos, improve docstrings * Fix index errors of pandas_on_spark, add spark loss metric * Fix typo of ndcgAtK * Update NDCG metrics and tests * Remove unuseful logger * Use cache and count to ensure consistent indexes * refactor for merge maain * fix errors of refactor * Updated SparkLightGBMEstimator and cache * Updated config2params * Remove unused import * Fix unknown parameters * Update default_estimator_list * Add unit tests for spark metrics
98 lines
2.1 KiB
Python
98 lines
2.1 KiB
Python
ParamList_LightGBM_Base = [
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"baggingFraction",
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"baggingFreq",
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"baggingSeed",
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"binSampleCount",
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"boostFromAverage",
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"boostingType",
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"catSmooth",
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"categoricalSlotIndexes",
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"categoricalSlotNames",
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"catl2",
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"chunkSize",
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"dataRandomSeed",
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"defaultListenPort",
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"deterministic",
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"driverListenPort",
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"dropRate",
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"dropSeed",
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"earlyStoppingRound",
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"executionMode",
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"extraSeed" "featureFraction",
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"featureFractionByNode",
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"featureFractionSeed",
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"featuresCol",
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"featuresShapCol",
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"fobj" "improvementTolerance",
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"initScoreCol",
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"isEnableSparse",
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"isProvideTrainingMetric",
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"labelCol",
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"lambdaL1",
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"lambdaL2",
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"leafPredictionCol",
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"learningRate",
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"matrixType",
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"maxBin",
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"maxBinByFeature",
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"maxCatThreshold",
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"maxCatToOnehot",
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"maxDeltaStep",
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"maxDepth",
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"maxDrop",
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"metric",
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"microBatchSize",
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"minDataInLeaf",
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"minDataPerBin",
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"minDataPerGroup",
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"minGainToSplit",
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"minSumHessianInLeaf",
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"modelString",
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"monotoneConstraints",
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"monotoneConstraintsMethod",
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"monotonePenalty",
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"negBaggingFraction",
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"numBatches",
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"numIterations",
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"numLeaves",
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"numTasks",
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"numThreads",
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"objectiveSeed",
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"otherRate",
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"parallelism",
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"passThroughArgs",
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"posBaggingFraction",
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"predictDisableShapeCheck",
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"predictionCol",
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"repartitionByGroupingColumn",
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"seed",
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"skipDrop",
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"slotNames",
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"timeout",
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"topK",
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"topRate",
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"uniformDrop",
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"useBarrierExecutionMode",
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"useMissing",
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"useSingleDatasetMode",
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"validationIndicatorCol",
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"verbosity",
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"weightCol",
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"xGBoostDartMode",
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"zeroAsMissing",
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"objective",
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]
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ParamList_LightGBM_Classifier = ParamList_LightGBM_Base + [
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"isUnbalance",
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"probabilityCol",
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"rawPredictionCol",
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"thresholds",
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]
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ParamList_LightGBM_Regressor = ParamList_LightGBM_Base + ["tweedieVariancePower"]
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ParamList_LightGBM_Ranker = ParamList_LightGBM_Base + [
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"groupCol",
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"evalAt",
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"labelGain",
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"maxPosition",
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]
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