# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.
import sys
if sys.version >= '3':
basestring = str
from pyspark import SparkContext, SQLContext
from pyspark.sql import DataFrame
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from synapse.ml.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.evaluation import JavaEvaluator
from pyspark.ml.common import inherit_doc
from synapse.ml.core.schema.Utils import *
from pyspark.ml.param import TypeConverters
from synapse.ml.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
from synapse.ml.lightgbm.LightGBMClassificationModel import LightGBMClassificationModel
[docs]@inherit_doc
class LightGBMClassifier(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
baggingFraction (float): Bagging fraction
baggingFreq (int): Bagging frequency
baggingSeed (int): Bagging seed
binSampleCount (int): Number of samples considered at computing histogram bins
boostFromAverage (bool): Adjusts initial score to the mean of labels for faster convergence
boostingType (object): Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling).
categoricalSlotIndexes (list): List of categorical column indexes, the slot index in the features column
categoricalSlotNames (list): List of categorical column slot names, the slot name in the features column
chunkSize (int): Advanced parameter to specify the chunk size for copying Java data to native. If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.
defaultListenPort (int): The default listen port on executors, used for testing
driverListenPort (int): The listen port on a driver. Default value is 0 (random)
dropRate (float): Dropout rate: a fraction of previous trees to drop during the dropout
earlyStoppingRound (int): Early stopping round
featureFraction (float): Feature fraction
featuresCol (object): features column name
featuresShapCol (object): Output SHAP vector column name after prediction containing the feature contribution values
fobj (object): Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).
improvementTolerance (float): Tolerance to consider improvement in metric
initScoreCol (object): The name of the initial score column, used for continued training
isProvideTrainingMetric (bool): Whether output metric result over training dataset.
isUnbalance (bool): Set to true if training data is unbalanced in binary classification scenario
labelCol (object): label column name
lambdaL1 (float): L1 regularization
lambdaL2 (float): L2 regularization
leafPredictionCol (object): Predicted leaf indices's column name
learningRate (float): Learning rate or shrinkage rate
matrixType (object): Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense. Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.
maxBin (int): Max bin
maxBinByFeature (list): Max number of bins for each feature
maxDeltaStep (float): Used to limit the max output of tree leaves
maxDepth (int): Max depth
maxDrop (int): Max number of dropped trees during one boosting iteration
metric (object): Metrics to be evaluated on the evaluation data. Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv.
minDataInLeaf (int): Minimal number of data in one leaf. Can be used to deal with over-fitting.
minGainToSplit (float): The minimal gain to perform split
minSumHessianInLeaf (float): Minimal sum hessian in one leaf
modelString (object): LightGBM model to retrain
negBaggingFraction (float): Negative Bagging fraction
numBatches (int): If greater than 0, splits data into separate batches during training
numIterations (int): Number of iterations, LightGBM constructs num_class * num_iterations trees
numLeaves (int): Number of leaves
numTasks (int): Advanced parameter to specify the number of tasks. SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.
numThreads (int): Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.
objective (object): The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.
parallelism (object): Tree learner parallelism, can be set to data_parallel or voting_parallel
posBaggingFraction (float): Positive Bagging fraction
predictionCol (object): prediction column name
probabilityCol (object): Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities
rawPredictionCol (object): raw prediction (a.k.a. confidence) column name
repartitionByGroupingColumn (bool): Repartition training data according to grouping column, on by default.
skipDrop (float): Probability of skipping the dropout procedure during a boosting iteration
slotNames (list): List of slot names in the features column
thresholds (list): Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold
timeout (float): Timeout in seconds
topK (int): The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0
uniformDrop (bool): Set this to true to use uniform drop in dart mode
useBarrierExecutionMode (bool): Barrier execution mode which uses a barrier stage, off by default.
useSingleDatasetMode (bool): Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.
validationIndicatorCol (object): Indicates whether the row is for training or validation
verbosity (int): Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug
weightCol (object): The name of the weight column
xgboostDartMode (bool): Set this to true to use xgboost dart mode
"""
baggingFraction = Param(Params._dummy(), "baggingFraction", "Bagging fraction", typeConverter=TypeConverters.toFloat)
baggingFreq = Param(Params._dummy(), "baggingFreq", "Bagging frequency", typeConverter=TypeConverters.toInt)
baggingSeed = Param(Params._dummy(), "baggingSeed", "Bagging seed", typeConverter=TypeConverters.toInt)
binSampleCount = Param(Params._dummy(), "binSampleCount", "Number of samples considered at computing histogram bins", typeConverter=TypeConverters.toInt)
boostFromAverage = Param(Params._dummy(), "boostFromAverage", "Adjusts initial score to the mean of labels for faster convergence", typeConverter=TypeConverters.toBoolean)
boostingType = Param(Params._dummy(), "boostingType", "Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling). ")
categoricalSlotIndexes = Param(Params._dummy(), "categoricalSlotIndexes", "List of categorical column indexes, the slot index in the features column", typeConverter=TypeConverters.toListInt)
categoricalSlotNames = Param(Params._dummy(), "categoricalSlotNames", "List of categorical column slot names, the slot name in the features column", typeConverter=TypeConverters.toListString)
chunkSize = Param(Params._dummy(), "chunkSize", "Advanced parameter to specify the chunk size for copying Java data to native. If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.", typeConverter=TypeConverters.toInt)
defaultListenPort = Param(Params._dummy(), "defaultListenPort", "The default listen port on executors, used for testing", typeConverter=TypeConverters.toInt)
driverListenPort = Param(Params._dummy(), "driverListenPort", "The listen port on a driver. Default value is 0 (random)", typeConverter=TypeConverters.toInt)
dropRate = Param(Params._dummy(), "dropRate", "Dropout rate: a fraction of previous trees to drop during the dropout", typeConverter=TypeConverters.toFloat)
earlyStoppingRound = Param(Params._dummy(), "earlyStoppingRound", "Early stopping round", typeConverter=TypeConverters.toInt)
featureFraction = Param(Params._dummy(), "featureFraction", "Feature fraction", typeConverter=TypeConverters.toFloat)
featuresCol = Param(Params._dummy(), "featuresCol", "features column name")
featuresShapCol = Param(Params._dummy(), "featuresShapCol", "Output SHAP vector column name after prediction containing the feature contribution values")
fobj = Param(Params._dummy(), "fobj", "Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).")
improvementTolerance = Param(Params._dummy(), "improvementTolerance", "Tolerance to consider improvement in metric", typeConverter=TypeConverters.toFloat)
initScoreCol = Param(Params._dummy(), "initScoreCol", "The name of the initial score column, used for continued training")
isProvideTrainingMetric = Param(Params._dummy(), "isProvideTrainingMetric", "Whether output metric result over training dataset.", typeConverter=TypeConverters.toBoolean)
isUnbalance = Param(Params._dummy(), "isUnbalance", "Set to true if training data is unbalanced in binary classification scenario", typeConverter=TypeConverters.toBoolean)
labelCol = Param(Params._dummy(), "labelCol", "label column name")
lambdaL1 = Param(Params._dummy(), "lambdaL1", "L1 regularization", typeConverter=TypeConverters.toFloat)
lambdaL2 = Param(Params._dummy(), "lambdaL2", "L2 regularization", typeConverter=TypeConverters.toFloat)
leafPredictionCol = Param(Params._dummy(), "leafPredictionCol", "Predicted leaf indices's column name")
learningRate = Param(Params._dummy(), "learningRate", "Learning rate or shrinkage rate", typeConverter=TypeConverters.toFloat)
matrixType = Param(Params._dummy(), "matrixType", "Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense. Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.")
maxBin = Param(Params._dummy(), "maxBin", "Max bin", typeConverter=TypeConverters.toInt)
maxBinByFeature = Param(Params._dummy(), "maxBinByFeature", "Max number of bins for each feature", typeConverter=TypeConverters.toListInt)
maxDeltaStep = Param(Params._dummy(), "maxDeltaStep", "Used to limit the max output of tree leaves", typeConverter=TypeConverters.toFloat)
maxDepth = Param(Params._dummy(), "maxDepth", "Max depth", typeConverter=TypeConverters.toInt)
maxDrop = Param(Params._dummy(), "maxDrop", "Max number of dropped trees during one boosting iteration", typeConverter=TypeConverters.toInt)
metric = Param(Params._dummy(), "metric", "Metrics to be evaluated on the evaluation data. Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv. ")
minDataInLeaf = Param(Params._dummy(), "minDataInLeaf", "Minimal number of data in one leaf. Can be used to deal with over-fitting.", typeConverter=TypeConverters.toInt)
minGainToSplit = Param(Params._dummy(), "minGainToSplit", "The minimal gain to perform split", typeConverter=TypeConverters.toFloat)
minSumHessianInLeaf = Param(Params._dummy(), "minSumHessianInLeaf", "Minimal sum hessian in one leaf", typeConverter=TypeConverters.toFloat)
modelString = Param(Params._dummy(), "modelString", "LightGBM model to retrain")
negBaggingFraction = Param(Params._dummy(), "negBaggingFraction", "Negative Bagging fraction", typeConverter=TypeConverters.toFloat)
numBatches = Param(Params._dummy(), "numBatches", "If greater than 0, splits data into separate batches during training", typeConverter=TypeConverters.toInt)
numIterations = Param(Params._dummy(), "numIterations", "Number of iterations, LightGBM constructs num_class * num_iterations trees", typeConverter=TypeConverters.toInt)
numLeaves = Param(Params._dummy(), "numLeaves", "Number of leaves", typeConverter=TypeConverters.toInt)
numTasks = Param(Params._dummy(), "numTasks", "Advanced parameter to specify the number of tasks. SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.", typeConverter=TypeConverters.toInt)
numThreads = Param(Params._dummy(), "numThreads", "Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.", typeConverter=TypeConverters.toInt)
objective = Param(Params._dummy(), "objective", "The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova. ")
parallelism = Param(Params._dummy(), "parallelism", "Tree learner parallelism, can be set to data_parallel or voting_parallel")
posBaggingFraction = Param(Params._dummy(), "posBaggingFraction", "Positive Bagging fraction", typeConverter=TypeConverters.toFloat)
predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name")
probabilityCol = Param(Params._dummy(), "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities")
rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction (a.k.a. confidence) column name")
repartitionByGroupingColumn = Param(Params._dummy(), "repartitionByGroupingColumn", "Repartition training data according to grouping column, on by default.", typeConverter=TypeConverters.toBoolean)
skipDrop = Param(Params._dummy(), "skipDrop", "Probability of skipping the dropout procedure during a boosting iteration", typeConverter=TypeConverters.toFloat)
slotNames = Param(Params._dummy(), "slotNames", "List of slot names in the features column", typeConverter=TypeConverters.toListString)
thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold", typeConverter=TypeConverters.toListFloat)
timeout = Param(Params._dummy(), "timeout", "Timeout in seconds", typeConverter=TypeConverters.toFloat)
topK = Param(Params._dummy(), "topK", "The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0", typeConverter=TypeConverters.toInt)
uniformDrop = Param(Params._dummy(), "uniformDrop", "Set this to true to use uniform drop in dart mode", typeConverter=TypeConverters.toBoolean)
useBarrierExecutionMode = Param(Params._dummy(), "useBarrierExecutionMode", "Barrier execution mode which uses a barrier stage, off by default.", typeConverter=TypeConverters.toBoolean)
useSingleDatasetMode = Param(Params._dummy(), "useSingleDatasetMode", "Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.", typeConverter=TypeConverters.toBoolean)
validationIndicatorCol = Param(Params._dummy(), "validationIndicatorCol", "Indicates whether the row is for training or validation")
verbosity = Param(Params._dummy(), "verbosity", "Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug", typeConverter=TypeConverters.toInt)
weightCol = Param(Params._dummy(), "weightCol", "The name of the weight column")
xgboostDartMode = Param(Params._dummy(), "xgboostDartMode", "Set this to true to use xgboost dart mode", typeConverter=TypeConverters.toBoolean)
@keyword_only
def __init__(
self,
java_obj=None,
baggingFraction=1.0,
baggingFreq=0,
baggingSeed=3,
binSampleCount=200000,
boostFromAverage=True,
boostingType="gbdt",
categoricalSlotIndexes=[],
categoricalSlotNames=[],
chunkSize=10000,
defaultListenPort=12400,
driverListenPort=0,
dropRate=0.1,
earlyStoppingRound=0,
featureFraction=1.0,
featuresCol="features",
featuresShapCol="",
fobj=None,
improvementTolerance=0.0,
initScoreCol=None,
isProvideTrainingMetric=False,
isUnbalance=False,
labelCol="label",
lambdaL1=0.0,
lambdaL2=0.0,
leafPredictionCol="",
learningRate=0.1,
matrixType="auto",
maxBin=255,
maxBinByFeature=[],
maxDeltaStep=0.0,
maxDepth=-1,
maxDrop=50,
metric="",
minDataInLeaf=20,
minGainToSplit=0.0,
minSumHessianInLeaf=0.001,
modelString="",
negBaggingFraction=1.0,
numBatches=0,
numIterations=100,
numLeaves=31,
numTasks=0,
numThreads=0,
objective="binary",
parallelism="data_parallel",
posBaggingFraction=1.0,
predictionCol="prediction",
probabilityCol="probability",
rawPredictionCol="rawPrediction",
repartitionByGroupingColumn=True,
skipDrop=0.5,
slotNames=[],
thresholds=None,
timeout=1200.0,
topK=20,
uniformDrop=False,
useBarrierExecutionMode=False,
useSingleDatasetMode=False,
validationIndicatorCol=None,
verbosity=-1,
weightCol=None,
xgboostDartMode=False
):
super(LightGBMClassifier, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier", self.uid)
else:
self._java_obj = java_obj
self._setDefault(baggingFraction=1.0)
self._setDefault(baggingFreq=0)
self._setDefault(baggingSeed=3)
self._setDefault(binSampleCount=200000)
self._setDefault(boostFromAverage=True)
self._setDefault(boostingType="gbdt")
self._setDefault(categoricalSlotIndexes=[])
self._setDefault(categoricalSlotNames=[])
self._setDefault(chunkSize=10000)
self._setDefault(defaultListenPort=12400)
self._setDefault(driverListenPort=0)
self._setDefault(dropRate=0.1)
self._setDefault(earlyStoppingRound=0)
self._setDefault(featureFraction=1.0)
self._setDefault(featuresCol="features")
self._setDefault(featuresShapCol="")
self._setDefault(improvementTolerance=0.0)
self._setDefault(isProvideTrainingMetric=False)
self._setDefault(isUnbalance=False)
self._setDefault(labelCol="label")
self._setDefault(lambdaL1=0.0)
self._setDefault(lambdaL2=0.0)
self._setDefault(leafPredictionCol="")
self._setDefault(learningRate=0.1)
self._setDefault(matrixType="auto")
self._setDefault(maxBin=255)
self._setDefault(maxBinByFeature=[])
self._setDefault(maxDeltaStep=0.0)
self._setDefault(maxDepth=-1)
self._setDefault(maxDrop=50)
self._setDefault(metric="")
self._setDefault(minDataInLeaf=20)
self._setDefault(minGainToSplit=0.0)
self._setDefault(minSumHessianInLeaf=0.001)
self._setDefault(modelString="")
self._setDefault(negBaggingFraction=1.0)
self._setDefault(numBatches=0)
self._setDefault(numIterations=100)
self._setDefault(numLeaves=31)
self._setDefault(numTasks=0)
self._setDefault(numThreads=0)
self._setDefault(objective="binary")
self._setDefault(parallelism="data_parallel")
self._setDefault(posBaggingFraction=1.0)
self._setDefault(predictionCol="prediction")
self._setDefault(probabilityCol="probability")
self._setDefault(rawPredictionCol="rawPrediction")
self._setDefault(repartitionByGroupingColumn=True)
self._setDefault(skipDrop=0.5)
self._setDefault(slotNames=[])
self._setDefault(timeout=1200.0)
self._setDefault(topK=20)
self._setDefault(uniformDrop=False)
self._setDefault(useBarrierExecutionMode=False)
self._setDefault(useSingleDatasetMode=False)
self._setDefault(verbosity=-1)
self._setDefault(xgboostDartMode=False)
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
if java_obj is None:
for k,v in kwargs.items():
if v is not None:
getattr(self, "set" + k[0].upper() + k[1:])(v)
[docs] @keyword_only
def setParams(
self,
baggingFraction=1.0,
baggingFreq=0,
baggingSeed=3,
binSampleCount=200000,
boostFromAverage=True,
boostingType="gbdt",
categoricalSlotIndexes=[],
categoricalSlotNames=[],
chunkSize=10000,
defaultListenPort=12400,
driverListenPort=0,
dropRate=0.1,
earlyStoppingRound=0,
featureFraction=1.0,
featuresCol="features",
featuresShapCol="",
fobj=None,
improvementTolerance=0.0,
initScoreCol=None,
isProvideTrainingMetric=False,
isUnbalance=False,
labelCol="label",
lambdaL1=0.0,
lambdaL2=0.0,
leafPredictionCol="",
learningRate=0.1,
matrixType="auto",
maxBin=255,
maxBinByFeature=[],
maxDeltaStep=0.0,
maxDepth=-1,
maxDrop=50,
metric="",
minDataInLeaf=20,
minGainToSplit=0.0,
minSumHessianInLeaf=0.001,
modelString="",
negBaggingFraction=1.0,
numBatches=0,
numIterations=100,
numLeaves=31,
numTasks=0,
numThreads=0,
objective="binary",
parallelism="data_parallel",
posBaggingFraction=1.0,
predictionCol="prediction",
probabilityCol="probability",
rawPredictionCol="rawPrediction",
repartitionByGroupingColumn=True,
skipDrop=0.5,
slotNames=[],
thresholds=None,
timeout=1200.0,
topK=20,
uniformDrop=False,
useBarrierExecutionMode=False,
useSingleDatasetMode=False,
validationIndicatorCol=None,
verbosity=-1,
weightCol=None,
xgboostDartMode=False
):
"""
Set the (keyword only) parameters
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier"
@staticmethod
def _from_java(java_stage):
module_name=LightGBMClassifier.__module__
module_name=module_name.rsplit(".", 1)[0] + ".LightGBMClassifier"
return from_java(java_stage, module_name)
[docs] def setBaggingFraction(self, value):
"""
Args:
baggingFraction: Bagging fraction
"""
self._set(baggingFraction=value)
return self
[docs] def setBaggingFreq(self, value):
"""
Args:
baggingFreq: Bagging frequency
"""
self._set(baggingFreq=value)
return self
[docs] def setBaggingSeed(self, value):
"""
Args:
baggingSeed: Bagging seed
"""
self._set(baggingSeed=value)
return self
[docs] def setBinSampleCount(self, value):
"""
Args:
binSampleCount: Number of samples considered at computing histogram bins
"""
self._set(binSampleCount=value)
return self
[docs] def setBoostFromAverage(self, value):
"""
Args:
boostFromAverage: Adjusts initial score to the mean of labels for faster convergence
"""
self._set(boostFromAverage=value)
return self
[docs] def setBoostingType(self, value):
"""
Args:
boostingType: Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling).
"""
self._set(boostingType=value)
return self
[docs] def setCategoricalSlotIndexes(self, value):
"""
Args:
categoricalSlotIndexes: List of categorical column indexes, the slot index in the features column
"""
self._set(categoricalSlotIndexes=value)
return self
[docs] def setCategoricalSlotNames(self, value):
"""
Args:
categoricalSlotNames: List of categorical column slot names, the slot name in the features column
"""
self._set(categoricalSlotNames=value)
return self
[docs] def setChunkSize(self, value):
"""
Args:
chunkSize: Advanced parameter to specify the chunk size for copying Java data to native. If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.
"""
self._set(chunkSize=value)
return self
[docs] def setDefaultListenPort(self, value):
"""
Args:
defaultListenPort: The default listen port on executors, used for testing
"""
self._set(defaultListenPort=value)
return self
[docs] def setDriverListenPort(self, value):
"""
Args:
driverListenPort: The listen port on a driver. Default value is 0 (random)
"""
self._set(driverListenPort=value)
return self
[docs] def setDropRate(self, value):
"""
Args:
dropRate: Dropout rate: a fraction of previous trees to drop during the dropout
"""
self._set(dropRate=value)
return self
[docs] def setEarlyStoppingRound(self, value):
"""
Args:
earlyStoppingRound: Early stopping round
"""
self._set(earlyStoppingRound=value)
return self
[docs] def setFeatureFraction(self, value):
"""
Args:
featureFraction: Feature fraction
"""
self._set(featureFraction=value)
return self
[docs] def setFeaturesCol(self, value):
"""
Args:
featuresCol: features column name
"""
self._set(featuresCol=value)
return self
[docs] def setFeaturesShapCol(self, value):
"""
Args:
featuresShapCol: Output SHAP vector column name after prediction containing the feature contribution values
"""
self._set(featuresShapCol=value)
return self
[docs] def setFobj(self, value):
"""
Args:
fobj: Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).
"""
self._set(fobj=value)
return self
[docs] def setImprovementTolerance(self, value):
"""
Args:
improvementTolerance: Tolerance to consider improvement in metric
"""
self._set(improvementTolerance=value)
return self
[docs] def setInitScoreCol(self, value):
"""
Args:
initScoreCol: The name of the initial score column, used for continued training
"""
self._set(initScoreCol=value)
return self
[docs] def setIsProvideTrainingMetric(self, value):
"""
Args:
isProvideTrainingMetric: Whether output metric result over training dataset.
"""
self._set(isProvideTrainingMetric=value)
return self
[docs] def setIsUnbalance(self, value):
"""
Args:
isUnbalance: Set to true if training data is unbalanced in binary classification scenario
"""
self._set(isUnbalance=value)
return self
[docs] def setLabelCol(self, value):
"""
Args:
labelCol: label column name
"""
self._set(labelCol=value)
return self
[docs] def setLambdaL1(self, value):
"""
Args:
lambdaL1: L1 regularization
"""
self._set(lambdaL1=value)
return self
[docs] def setLambdaL2(self, value):
"""
Args:
lambdaL2: L2 regularization
"""
self._set(lambdaL2=value)
return self
[docs] def setLeafPredictionCol(self, value):
"""
Args:
leafPredictionCol: Predicted leaf indices's column name
"""
self._set(leafPredictionCol=value)
return self
[docs] def setLearningRate(self, value):
"""
Args:
learningRate: Learning rate or shrinkage rate
"""
self._set(learningRate=value)
return self
[docs] def setMatrixType(self, value):
"""
Args:
matrixType: Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense. Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.
"""
self._set(matrixType=value)
return self
[docs] def setMaxBin(self, value):
"""
Args:
maxBin: Max bin
"""
self._set(maxBin=value)
return self
[docs] def setMaxBinByFeature(self, value):
"""
Args:
maxBinByFeature: Max number of bins for each feature
"""
self._set(maxBinByFeature=value)
return self
[docs] def setMaxDeltaStep(self, value):
"""
Args:
maxDeltaStep: Used to limit the max output of tree leaves
"""
self._set(maxDeltaStep=value)
return self
[docs] def setMaxDepth(self, value):
"""
Args:
maxDepth: Max depth
"""
self._set(maxDepth=value)
return self
[docs] def setMaxDrop(self, value):
"""
Args:
maxDrop: Max number of dropped trees during one boosting iteration
"""
self._set(maxDrop=value)
return self
[docs] def setMetric(self, value):
"""
Args:
metric: Metrics to be evaluated on the evaluation data. Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv.
"""
self._set(metric=value)
return self
[docs] def setMinDataInLeaf(self, value):
"""
Args:
minDataInLeaf: Minimal number of data in one leaf. Can be used to deal with over-fitting.
"""
self._set(minDataInLeaf=value)
return self
[docs] def setMinGainToSplit(self, value):
"""
Args:
minGainToSplit: The minimal gain to perform split
"""
self._set(minGainToSplit=value)
return self
[docs] def setMinSumHessianInLeaf(self, value):
"""
Args:
minSumHessianInLeaf: Minimal sum hessian in one leaf
"""
self._set(minSumHessianInLeaf=value)
return self
[docs] def setModelString(self, value):
"""
Args:
modelString: LightGBM model to retrain
"""
self._set(modelString=value)
return self
[docs] def setNegBaggingFraction(self, value):
"""
Args:
negBaggingFraction: Negative Bagging fraction
"""
self._set(negBaggingFraction=value)
return self
[docs] def setNumBatches(self, value):
"""
Args:
numBatches: If greater than 0, splits data into separate batches during training
"""
self._set(numBatches=value)
return self
[docs] def setNumIterations(self, value):
"""
Args:
numIterations: Number of iterations, LightGBM constructs num_class * num_iterations trees
"""
self._set(numIterations=value)
return self
[docs] def setNumLeaves(self, value):
"""
Args:
numLeaves: Number of leaves
"""
self._set(numLeaves=value)
return self
[docs] def setNumTasks(self, value):
"""
Args:
numTasks: Advanced parameter to specify the number of tasks. SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.
"""
self._set(numTasks=value)
return self
[docs] def setNumThreads(self, value):
"""
Args:
numThreads: Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.
"""
self._set(numThreads=value)
return self
[docs] def setObjective(self, value):
"""
Args:
objective: The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.
"""
self._set(objective=value)
return self
[docs] def setParallelism(self, value):
"""
Args:
parallelism: Tree learner parallelism, can be set to data_parallel or voting_parallel
"""
self._set(parallelism=value)
return self
[docs] def setPosBaggingFraction(self, value):
"""
Args:
posBaggingFraction: Positive Bagging fraction
"""
self._set(posBaggingFraction=value)
return self
[docs] def setPredictionCol(self, value):
"""
Args:
predictionCol: prediction column name
"""
self._set(predictionCol=value)
return self
[docs] def setProbabilityCol(self, value):
"""
Args:
probabilityCol: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities
"""
self._set(probabilityCol=value)
return self
[docs] def setRawPredictionCol(self, value):
"""
Args:
rawPredictionCol: raw prediction (a.k.a. confidence) column name
"""
self._set(rawPredictionCol=value)
return self
[docs] def setRepartitionByGroupingColumn(self, value):
"""
Args:
repartitionByGroupingColumn: Repartition training data according to grouping column, on by default.
"""
self._set(repartitionByGroupingColumn=value)
return self
[docs] def setSkipDrop(self, value):
"""
Args:
skipDrop: Probability of skipping the dropout procedure during a boosting iteration
"""
self._set(skipDrop=value)
return self
[docs] def setSlotNames(self, value):
"""
Args:
slotNames: List of slot names in the features column
"""
self._set(slotNames=value)
return self
[docs] def setThresholds(self, value):
"""
Args:
thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold
"""
self._set(thresholds=value)
return self
[docs] def setTimeout(self, value):
"""
Args:
timeout: Timeout in seconds
"""
self._set(timeout=value)
return self
[docs] def setTopK(self, value):
"""
Args:
topK: The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0
"""
self._set(topK=value)
return self
[docs] def setUseBarrierExecutionMode(self, value):
"""
Args:
useBarrierExecutionMode: Barrier execution mode which uses a barrier stage, off by default.
"""
self._set(useBarrierExecutionMode=value)
return self
[docs] def setUseSingleDatasetMode(self, value):
"""
Args:
useSingleDatasetMode: Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.
"""
self._set(useSingleDatasetMode=value)
return self
[docs] def setValidationIndicatorCol(self, value):
"""
Args:
validationIndicatorCol: Indicates whether the row is for training or validation
"""
self._set(validationIndicatorCol=value)
return self
[docs] def setVerbosity(self, value):
"""
Args:
verbosity: Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug
"""
self._set(verbosity=value)
return self
[docs] def setWeightCol(self, value):
"""
Args:
weightCol: The name of the weight column
"""
self._set(weightCol=value)
return self
[docs] def setXgboostDartMode(self, value):
"""
Args:
xgboostDartMode: Set this to true to use xgboost dart mode
"""
self._set(xgboostDartMode=value)
return self
[docs] def getBaggingFraction(self):
"""
Returns:
baggingFraction: Bagging fraction
"""
return self.getOrDefault(self.baggingFraction)
[docs] def getBaggingFreq(self):
"""
Returns:
baggingFreq: Bagging frequency
"""
return self.getOrDefault(self.baggingFreq)
[docs] def getBaggingSeed(self):
"""
Returns:
baggingSeed: Bagging seed
"""
return self.getOrDefault(self.baggingSeed)
[docs] def getBinSampleCount(self):
"""
Returns:
binSampleCount: Number of samples considered at computing histogram bins
"""
return self.getOrDefault(self.binSampleCount)
[docs] def getBoostFromAverage(self):
"""
Returns:
boostFromAverage: Adjusts initial score to the mean of labels for faster convergence
"""
return self.getOrDefault(self.boostFromAverage)
[docs] def getBoostingType(self):
"""
Returns:
boostingType: Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling).
"""
return self.getOrDefault(self.boostingType)
[docs] def getCategoricalSlotIndexes(self):
"""
Returns:
categoricalSlotIndexes: List of categorical column indexes, the slot index in the features column
"""
return self.getOrDefault(self.categoricalSlotIndexes)
[docs] def getCategoricalSlotNames(self):
"""
Returns:
categoricalSlotNames: List of categorical column slot names, the slot name in the features column
"""
return self.getOrDefault(self.categoricalSlotNames)
[docs] def getChunkSize(self):
"""
Returns:
chunkSize: Advanced parameter to specify the chunk size for copying Java data to native. If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.
"""
return self.getOrDefault(self.chunkSize)
[docs] def getDefaultListenPort(self):
"""
Returns:
defaultListenPort: The default listen port on executors, used for testing
"""
return self.getOrDefault(self.defaultListenPort)
[docs] def getDriverListenPort(self):
"""
Returns:
driverListenPort: The listen port on a driver. Default value is 0 (random)
"""
return self.getOrDefault(self.driverListenPort)
[docs] def getDropRate(self):
"""
Returns:
dropRate: Dropout rate: a fraction of previous trees to drop during the dropout
"""
return self.getOrDefault(self.dropRate)
[docs] def getEarlyStoppingRound(self):
"""
Returns:
earlyStoppingRound: Early stopping round
"""
return self.getOrDefault(self.earlyStoppingRound)
[docs] def getFeatureFraction(self):
"""
Returns:
featureFraction: Feature fraction
"""
return self.getOrDefault(self.featureFraction)
[docs] def getFeaturesCol(self):
"""
Returns:
featuresCol: features column name
"""
return self.getOrDefault(self.featuresCol)
[docs] def getFeaturesShapCol(self):
"""
Returns:
featuresShapCol: Output SHAP vector column name after prediction containing the feature contribution values
"""
return self.getOrDefault(self.featuresShapCol)
[docs] def getFobj(self):
"""
Returns:
fobj: Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).
"""
return self.getOrDefault(self.fobj)
[docs] def getImprovementTolerance(self):
"""
Returns:
improvementTolerance: Tolerance to consider improvement in metric
"""
return self.getOrDefault(self.improvementTolerance)
[docs] def getInitScoreCol(self):
"""
Returns:
initScoreCol: The name of the initial score column, used for continued training
"""
return self.getOrDefault(self.initScoreCol)
[docs] def getIsProvideTrainingMetric(self):
"""
Returns:
isProvideTrainingMetric: Whether output metric result over training dataset.
"""
return self.getOrDefault(self.isProvideTrainingMetric)
[docs] def getIsUnbalance(self):
"""
Returns:
isUnbalance: Set to true if training data is unbalanced in binary classification scenario
"""
return self.getOrDefault(self.isUnbalance)
[docs] def getLabelCol(self):
"""
Returns:
labelCol: label column name
"""
return self.getOrDefault(self.labelCol)
[docs] def getLambdaL1(self):
"""
Returns:
lambdaL1: L1 regularization
"""
return self.getOrDefault(self.lambdaL1)
[docs] def getLambdaL2(self):
"""
Returns:
lambdaL2: L2 regularization
"""
return self.getOrDefault(self.lambdaL2)
[docs] def getLeafPredictionCol(self):
"""
Returns:
leafPredictionCol: Predicted leaf indices's column name
"""
return self.getOrDefault(self.leafPredictionCol)
[docs] def getLearningRate(self):
"""
Returns:
learningRate: Learning rate or shrinkage rate
"""
return self.getOrDefault(self.learningRate)
[docs] def getMatrixType(self):
"""
Returns:
matrixType: Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense. Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.
"""
return self.getOrDefault(self.matrixType)
[docs] def getMaxBin(self):
"""
Returns:
maxBin: Max bin
"""
return self.getOrDefault(self.maxBin)
[docs] def getMaxBinByFeature(self):
"""
Returns:
maxBinByFeature: Max number of bins for each feature
"""
return self.getOrDefault(self.maxBinByFeature)
[docs] def getMaxDeltaStep(self):
"""
Returns:
maxDeltaStep: Used to limit the max output of tree leaves
"""
return self.getOrDefault(self.maxDeltaStep)
[docs] def getMaxDepth(self):
"""
Returns:
maxDepth: Max depth
"""
return self.getOrDefault(self.maxDepth)
[docs] def getMaxDrop(self):
"""
Returns:
maxDrop: Max number of dropped trees during one boosting iteration
"""
return self.getOrDefault(self.maxDrop)
[docs] def getMetric(self):
"""
Returns:
metric: Metrics to be evaluated on the evaluation data. Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv.
"""
return self.getOrDefault(self.metric)
[docs] def getMinDataInLeaf(self):
"""
Returns:
minDataInLeaf: Minimal number of data in one leaf. Can be used to deal with over-fitting.
"""
return self.getOrDefault(self.minDataInLeaf)
[docs] def getMinGainToSplit(self):
"""
Returns:
minGainToSplit: The minimal gain to perform split
"""
return self.getOrDefault(self.minGainToSplit)
[docs] def getMinSumHessianInLeaf(self):
"""
Returns:
minSumHessianInLeaf: Minimal sum hessian in one leaf
"""
return self.getOrDefault(self.minSumHessianInLeaf)
[docs] def getModelString(self):
"""
Returns:
modelString: LightGBM model to retrain
"""
return self.getOrDefault(self.modelString)
[docs] def getNegBaggingFraction(self):
"""
Returns:
negBaggingFraction: Negative Bagging fraction
"""
return self.getOrDefault(self.negBaggingFraction)
[docs] def getNumBatches(self):
"""
Returns:
numBatches: If greater than 0, splits data into separate batches during training
"""
return self.getOrDefault(self.numBatches)
[docs] def getNumIterations(self):
"""
Returns:
numIterations: Number of iterations, LightGBM constructs num_class * num_iterations trees
"""
return self.getOrDefault(self.numIterations)
[docs] def getNumLeaves(self):
"""
Returns:
numLeaves: Number of leaves
"""
return self.getOrDefault(self.numLeaves)
[docs] def getNumTasks(self):
"""
Returns:
numTasks: Advanced parameter to specify the number of tasks. SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.
"""
return self.getOrDefault(self.numTasks)
[docs] def getNumThreads(self):
"""
Returns:
numThreads: Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.
"""
return self.getOrDefault(self.numThreads)
[docs] def getObjective(self):
"""
Returns:
objective: The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.
"""
return self.getOrDefault(self.objective)
[docs] def getParallelism(self):
"""
Returns:
parallelism: Tree learner parallelism, can be set to data_parallel or voting_parallel
"""
return self.getOrDefault(self.parallelism)
[docs] def getPosBaggingFraction(self):
"""
Returns:
posBaggingFraction: Positive Bagging fraction
"""
return self.getOrDefault(self.posBaggingFraction)
[docs] def getPredictionCol(self):
"""
Returns:
predictionCol: prediction column name
"""
return self.getOrDefault(self.predictionCol)
[docs] def getProbabilityCol(self):
"""
Returns:
probabilityCol: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities
"""
return self.getOrDefault(self.probabilityCol)
[docs] def getRawPredictionCol(self):
"""
Returns:
rawPredictionCol: raw prediction (a.k.a. confidence) column name
"""
return self.getOrDefault(self.rawPredictionCol)
[docs] def getRepartitionByGroupingColumn(self):
"""
Returns:
repartitionByGroupingColumn: Repartition training data according to grouping column, on by default.
"""
return self.getOrDefault(self.repartitionByGroupingColumn)
[docs] def getSkipDrop(self):
"""
Returns:
skipDrop: Probability of skipping the dropout procedure during a boosting iteration
"""
return self.getOrDefault(self.skipDrop)
[docs] def getSlotNames(self):
"""
Returns:
slotNames: List of slot names in the features column
"""
return self.getOrDefault(self.slotNames)
[docs] def getThresholds(self):
"""
Returns:
thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold
"""
return self.getOrDefault(self.thresholds)
[docs] def getTimeout(self):
"""
Returns:
timeout: Timeout in seconds
"""
return self.getOrDefault(self.timeout)
[docs] def getTopK(self):
"""
Returns:
topK: The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0
"""
return self.getOrDefault(self.topK)
[docs] def getUseBarrierExecutionMode(self):
"""
Returns:
useBarrierExecutionMode: Barrier execution mode which uses a barrier stage, off by default.
"""
return self.getOrDefault(self.useBarrierExecutionMode)
[docs] def getUseSingleDatasetMode(self):
"""
Returns:
useSingleDatasetMode: Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.
"""
return self.getOrDefault(self.useSingleDatasetMode)
[docs] def getValidationIndicatorCol(self):
"""
Returns:
validationIndicatorCol: Indicates whether the row is for training or validation
"""
return self.getOrDefault(self.validationIndicatorCol)
[docs] def getVerbosity(self):
"""
Returns:
verbosity: Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug
"""
return self.getOrDefault(self.verbosity)
[docs] def getWeightCol(self):
"""
Returns:
weightCol: The name of the weight column
"""
return self.getOrDefault(self.weightCol)
[docs] def getXgboostDartMode(self):
"""
Returns:
xgboostDartMode: Set this to true to use xgboost dart mode
"""
return self.getOrDefault(self.xgboostDartMode)
def _create_model(self, java_model):
try:
model = LightGBMClassificationModel(java_obj=java_model)
model._transfer_params_from_java()
except TypeError:
model = LightGBMClassificationModel._from_java(java_model)
return model
def _fit(self, dataset):
java_model = self._fit_java(dataset)
return self._create_model(java_model)