GBTClassificationModel¶

class pyspark.ml.classification.GBTClassificationModel(java_model: Optional[JavaObject] = None)[source]¶

Model fitted by GBTClassifier.

New in version 1.4.0.

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`evaluateEachIteration`(dataset)	Method to compute error or loss for every iteration of gradient boosting.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
`getCacheNodeIds`()	Gets the value of cacheNodeIds or its default value.
`getCheckpointInterval`()	Gets the value of checkpointInterval or its default value.
`getFeatureSubsetStrategy`()	Gets the value of featureSubsetStrategy or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getImpurity`()	Gets the value of impurity or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getLeafCol`()	Gets the value of leafCol or its default value.
`getLossType`()	Gets the value of lossType or its default value.
`getMaxBins`()	Gets the value of maxBins or its default value.
`getMaxDepth`()	Gets the value of maxDepth or its default value.
`getMaxIter`()	Gets the value of maxIter or its default value.
`getMaxMemoryInMB`()	Gets the value of maxMemoryInMB or its default value.
`getMinInfoGain`()	Gets the value of minInfoGain or its default value.
`getMinInstancesPerNode`()	Gets the value of minInstancesPerNode or its default value.
`getMinWeightFractionPerNode`()	Gets the value of minWeightFractionPerNode or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getParam`(paramName)	Gets a param by its name.
`getPredictionCol`()	Gets the value of predictionCol or its default value.
`getProbabilityCol`()	Gets the value of probabilityCol or its default value.
`getRawPredictionCol`()	Gets the value of rawPredictionCol or its default value.
`getSeed`()	Gets the value of seed or its default value.
`getStepSize`()	Gets the value of stepSize or its default value.
`getSubsamplingRate`()	Gets the value of subsamplingRate or its default value.
`getThresholds`()	Gets the value of thresholds or its default value.
`getValidationIndicatorCol`()	Gets the value of validationIndicatorCol or its default value.
`getValidationTol`()	Gets the value of validationTol or its default value.
`getWeightCol`()	Gets the value of weightCol or its default value.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`predict`(value)	Predict label for the given features.
`predictLeaf`(value)	Predict the indices of the leaves corresponding to the feature vector.
`predictProbability`(value)	Predict the probability of each class given the features.
`predictRaw`(value)	Raw prediction for each possible label.
`read`()	Returns an MLReader instance for this class.
`save`(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
`set`(param, value)	Sets a parameter in the embedded param map.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setLeafCol`(value)	Sets the value of `leafCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setProbabilityCol`(value)	Sets the value of `probabilityCol`.
`setRawPredictionCol`(value)	Sets the value of `rawPredictionCol`.
`setThresholds`(value)	Sets the value of `thresholds`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`cacheNodeIds`
`checkpointInterval`
`featureImportances`	Estimate of the importance of each feature.
`featureSubsetStrategy`
`featuresCol`
`getNumTrees`	Number of trees in ensemble.
`impurity`
`labelCol`
`leafCol`
`lossType`
`maxBins`
`maxDepth`
`maxIter`
`maxMemoryInMB`
`minInfoGain`
`minInstancesPerNode`
`minWeightFractionPerNode`
`numClasses`	Number of classes (values which the label can take).
`numFeatures`	Returns the number of features the model was trained on.
`params`	Returns all params ordered by name.
`predictionCol`
`probabilityCol`
`rawPredictionCol`
`seed`
`stepSize`
`subsamplingRate`
`supportedFeatureSubsetStrategies`
`supportedImpurities`
`supportedLossTypes`
`thresholds`
`toDebugString`	Full description of model.
`totalNumNodes`	Total number of nodes, summed over all trees in the ensemble.
`treeWeights`	Return the weights for each tree
`trees`	Trees in this ensemble.
`validationIndicatorCol`
`validationTol`
`weightCol`

Methods Documentation

clear(param: pyspark.ml.param.Param) → None¶: Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP¶

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional: Extra parameters to copy to the new instance

Returns

JavaParams: Copy of this instance

evaluateEachIteration(dataset: pyspark.sql.dataframe.DataFrame) → List[float][source]¶

Method to compute error or loss for every iteration of gradient boosting.

New in version 2.4.0.

Parameters

datasetpyspark.sql.DataFrame: Test dataset to evaluate model on.

explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str¶: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap¶

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extradict, optional: extra param values

Returns

dict: merged param map

getCacheNodeIds() → bool¶: Gets the value of cacheNodeIds or its default value.

getCheckpointInterval() → int¶: Gets the value of checkpointInterval or its default value.

getFeatureSubsetStrategy() → str¶: Gets the value of featureSubsetStrategy or its default value.

New in version 1.4.0.

getFeaturesCol() → str¶: Gets the value of featuresCol or its default value.

getImpurity() → str¶: Gets the value of impurity or its default value.

New in version 1.4.0.

getLabelCol() → str¶: Gets the value of labelCol or its default value.

getLeafCol() → str¶: Gets the value of leafCol or its default value.

getLossType() → str¶: Gets the value of lossType or its default value.

New in version 1.4.0.

getMaxBins() → int¶: Gets the value of maxBins or its default value.

getMaxDepth() → int¶: Gets the value of maxDepth or its default value.

getMaxIter() → int¶: Gets the value of maxIter or its default value.

getMaxMemoryInMB() → int¶: Gets the value of maxMemoryInMB or its default value.

getMinInfoGain() → float¶: Gets the value of minInfoGain or its default value.

getMinInstancesPerNode() → int¶: Gets the value of minInstancesPerNode or its default value.

getMinWeightFractionPerNode() → float¶: Gets the value of minWeightFractionPerNode or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getPredictionCol() → str¶: Gets the value of predictionCol or its default value.

getProbabilityCol() → str¶: Gets the value of probabilityCol or its default value.

getRawPredictionCol() → str¶: Gets the value of rawPredictionCol or its default value.

getSeed() → int¶: Gets the value of seed or its default value.

getStepSize() → float¶: Gets the value of stepSize or its default value.

getSubsamplingRate() → float¶: Gets the value of subsamplingRate or its default value.

New in version 1.4.0.

getThresholds() → List[float]¶: Gets the value of thresholds or its default value.

getValidationIndicatorCol() → str¶: Gets the value of validationIndicatorCol or its default value.

getValidationTol() → float¶: Gets the value of validationTol or its default value.

New in version 3.0.0.

getWeightCol() → str¶: Gets the value of weightCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param has a default value.

hasParam(paramName: str) → bool¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL¶: Reads an ML instance from the input path, a shortcut of read().load(path).

predict(value: T) → float¶: Predict label for the given features.

New in version 3.0.0.

predictLeaf(value: pyspark.ml.linalg.Vector) → float¶: Predict the indices of the leaves corresponding to the feature vector.

New in version 3.0.0.

predictProbability(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector ¶: Predict the probability of each class given the features.

New in version 3.0.0.

predictRaw(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector ¶: Raw prediction for each possible label.

New in version 3.0.0.

classmethod read() → pyspark.ml.util.JavaMLReader[RL]¶: Returns an MLReader instance for this class.

save(path: str) → None¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶: Sets a parameter in the embedded param map.

setFeaturesCol(value: str) → P¶: Sets the value of featuresCol.

New in version 3.0.0.

setLeafCol(value: str) → P¶: Sets the value of leafCol.

setPredictionCol(value: str) → P¶: Sets the value of predictionCol.

New in version 3.0.0.

setProbabilityCol(value: str) → CM¶: Sets the value of probabilityCol.

New in version 3.0.0.

setRawPredictionCol(value: str) → P¶: Sets the value of rawPredictionCol.

New in version 3.0.0.

setThresholds(value: List[float]) → CM¶: Sets the value of thresholds.

New in version 3.0.0.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

cacheNodeIds = Param(parent='undefined', name='cacheNodeIds', doc='If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval.')¶

checkpointInterval = Param(parent='undefined', name='checkpointInterval', doc='set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.')¶

featureImportances¶

Estimate of the importance of each feature.

Each feature’s importance is the average of its importance across all trees in the ensemble The importance vector is normalized to sum to 1. This method is suggested by Hastie et al. (Hastie, Tibshirani, Friedman. “The Elements of Statistical Learning, 2nd Edition.” 2001.) and follows the implementation from scikit-learn.

New in version 2.0.0.