public class GBTRegressionModel extends RegressionModel<Vector,GBTRegressionModel> implements GBTRegressorParams, TreeEnsembleModel<DecisionTreeRegressionModel>, MLWritable, scala.Serializable
| Constructor and Description |
|---|
GBTRegressionModel(String uid,
DecisionTreeRegressionModel[] _trees,
double[] _treeWeights)
Construct a GBTRegressionModel
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| Modifier and Type | Method and Description |
|---|---|
BooleanParam |
cacheNodeIds()
If false, the algorithm will pass trees to executors to match instances with nodes.
|
IntParam |
checkpointInterval()
Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
|
GBTRegressionModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
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double[] |
evaluateEachIteration(Dataset<?> dataset,
String loss)
Method to compute error or loss for every iteration of gradient boosting.
|
Vector |
featureImportances() |
Param<String> |
featureSubsetStrategy()
The number of features to consider for splits at each tree node.
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int |
getNumTrees()
Number of trees in ensemble
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Param<String> |
impurity()
Criterion used for information gain calculation (case-insensitive).
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Param<String> |
leafCol()
Leaf indices column name.
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static GBTRegressionModel |
load(String path) |
Param<String> |
lossType()
Loss function which GBT tries to minimize.
|
IntParam |
maxBins()
Maximum number of bins used for discretizing continuous features and for choosing how to split
on features at each node.
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IntParam |
maxDepth()
Maximum depth of the tree (nonnegative).
|
IntParam |
maxIter()
Param for maximum number of iterations (>= 0).
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IntParam |
maxMemoryInMB()
Maximum memory in MB allocated to histogram aggregation.
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DoubleParam |
minInfoGain()
Minimum information gain for a split to be considered at a tree node.
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IntParam |
minInstancesPerNode()
Minimum number of instances each child must have after split.
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DoubleParam |
minWeightFractionPerNode()
Minimum fraction of the weighted sample count that each child must have after split.
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int |
numFeatures()
Returns the number of features the model was trained on.
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double |
predict(Vector features)
Predict label for the given features.
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static MLReader<GBTRegressionModel> |
read() |
LongParam |
seed()
Param for random seed.
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DoubleParam |
stepSize()
Param for Step size (a.k.a.
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DoubleParam |
subsamplingRate()
Fraction of the training data used for learning each decision tree, in range (0, 1].
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String |
toString()
Summary of the model
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int |
totalNumNodes()
Total number of nodes, summed over all trees in the ensemble.
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Dataset<Row> |
transform(Dataset<?> dataset)
Transforms dataset by reading from
featuresCol, calling predict, and storing
the predictions as a new column predictionCol. |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
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DecisionTreeRegressionModel[] |
trees()
Trees in this ensemble.
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double[] |
treeWeights()
Weights for each tree, zippable with
trees |
String |
uid()
An immutable unique ID for the object and its derivatives.
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Param<String> |
validationIndicatorCol()
Param for name of the column that indicates whether each row is for training or for validation.
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DoubleParam |
validationTol()
Threshold for stopping early when fit with validation is used.
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Param<String> |
weightCol()
Param for weight column name.
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MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
featuresCol, labelCol, predictionCol, setFeaturesCol, setPredictionColtransform, transform, transformparamsconvertToOldLossType, getLossType, getOldLossTypegetOldBoostingStrategy, getValidationTolgetMaxItergetStepSizegetValidationIndicatorColvalidateAndTransformSchemagetFeatureSubsetStrategy, getOldStrategy, getSubsamplingRategetCacheNodeIds, getLeafCol, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getMinWeightFractionPerNode, getOldStrategy, setLeafColextractInstances, extractInstancesgetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwngetCheckpointIntervalgetWeightColgetImpurity, getOldImpuritygetLeafField, javaTreeWeights, predictLeaf, toDebugStringsave$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic GBTRegressionModel(String uid,
DecisionTreeRegressionModel[] _trees,
double[] _treeWeights)
_trees - Decision trees in the ensemble._treeWeights - Weights for the decision trees in the ensemble.uid - (undocumented)public static MLReader<GBTRegressionModel> read()
public static GBTRegressionModel load(String path)
public int totalNumNodes()
TreeEnsembleModeltotalNumNodes in interface TreeEnsembleModel<DecisionTreeRegressionModel>public Param<String> lossType()
GBTRegressorParamslossType in interface GBTRegressorParamspublic final Param<String> impurity()
HasVarianceImpurityimpurity in interface HasVarianceImpuritypublic final DoubleParam validationTol()
GBTParamsvalidationTol in interface GBTParamsvalidationIndicatorColpublic final DoubleParam stepSize()
GBTParamsstepSize in interface HasStepSizestepSize in interface GBTParamspublic final Param<String> validationIndicatorCol()
HasValidationIndicatorColvalidationIndicatorCol in interface HasValidationIndicatorColpublic final IntParam maxIter()
HasMaxItermaxIter in interface HasMaxIterpublic final DoubleParam subsamplingRate()
TreeEnsembleParamssubsamplingRate in interface TreeEnsembleParamspublic final Param<String> featureSubsetStrategy()
TreeEnsembleParamsThese various settings are based on the following references: - log2: tested in Breiman (2001) - sqrt: recommended by Breiman manual for random forests - The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.
featureSubsetStrategy in interface TreeEnsembleParamspublic final Param<String> leafCol()
DecisionTreeParamsleafCol in interface DecisionTreeParamspublic final IntParam maxDepth()
DecisionTreeParamsmaxDepth in interface DecisionTreeParamspublic final IntParam maxBins()
DecisionTreeParamsmaxBins in interface DecisionTreeParamspublic final IntParam minInstancesPerNode()
DecisionTreeParamsminInstancesPerNode in interface DecisionTreeParamspublic final DoubleParam minWeightFractionPerNode()
DecisionTreeParamsminWeightFractionPerNode in interface DecisionTreeParamspublic final DoubleParam minInfoGain()
DecisionTreeParamsminInfoGain in interface DecisionTreeParamspublic final IntParam maxMemoryInMB()
DecisionTreeParamsmaxMemoryInMB in interface DecisionTreeParamspublic final BooleanParam cacheNodeIds()
DecisionTreeParamscacheNodeIds in interface DecisionTreeParamspublic final Param<String> weightCol()
HasWeightColweightCol in interface HasWeightColpublic final LongParam seed()
HasSeedpublic final IntParam checkpointInterval()
HasCheckpointIntervalcheckpointInterval in interface HasCheckpointIntervalpublic String uid()
Identifiableuid in interface Identifiablepublic int numFeatures()
PredictionModelnumFeatures in class PredictionModel<Vector,GBTRegressionModel>public DecisionTreeRegressionModel[] trees()
TreeEnsembleModeltrees in interface TreeEnsembleModel<DecisionTreeRegressionModel>public int getNumTrees()
public double[] treeWeights()
TreeEnsembleModeltreestreeWeights in interface TreeEnsembleModel<DecisionTreeRegressionModel>public StructType transformSchema(StructType schema)
PipelineStage
We check validity for interactions between parameters during transformSchema and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema in class PredictionModel<Vector,GBTRegressionModel>schema - (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
PredictionModelfeaturesCol, calling predict, and storing
the predictions as a new column predictionCol.
transform in class PredictionModel<Vector,GBTRegressionModel>dataset - input datasetpredictionCol of type Doublepublic double predict(Vector features)
PredictionModeltransform() and output predictionCol.predict in class PredictionModel<Vector,GBTRegressionModel>features - (undocumented)public GBTRegressionModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<GBTRegressionModel>extra - (undocumented)public String toString()
TreeEnsembleModeltoString in interface TreeEnsembleModel<DecisionTreeRegressionModel>toString in interface IdentifiabletoString in class Objectpublic Vector featureImportances()
public double[] evaluateEachIteration(Dataset<?> dataset, String loss)
dataset - Dataset for validation.loss - The loss function used to compute error. Supported options: squared, absolutepublic MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable