DecisionTreeRegressor

class pyspark.ml.regression.DecisionTreeRegressor(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxDepth: int = 5, maxBins: int = 32, minInstancesPerNode: int = 1, minInfoGain: float = 0.0, maxMemoryInMB: int = 256, cacheNodeIds: bool = False, checkpointInterval: int = 10, impurity: str = 'variance', seed: Optional[int] = None, varianceCol: Optional[str] = None, weightCol: Optional[str] = None, leafCol: str = '', minWeightFractionPerNode: float = 0.0)

Decision tree learning algorithm for regression. It supports both continuous and categorical features.

New in version 1.4.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([
...     (1.0, Vectors.dense(1.0)),
...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> dt = DecisionTreeRegressor(maxDepth=2)
>>> dt.setVarianceCol("variance")
DecisionTreeRegressor...
>>> model = dt.fit(df)
>>> model.getVarianceCol()
'variance'
>>> model.setLeafCol("leafId")
DecisionTreeRegressionModel...
>>> model.depth
1
>>> model.numNodes
3
>>> model.featureImportances
SparseVector(1, {0: 1.0})
>>> model.numFeatures
1
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.predict(test0.head().features)
0.0
>>> result = model.transform(test0).head()
>>> result.prediction
0.0
>>> model.predictLeaf(test0.head().features)
0.0
>>> result.leafId
0.0
>>> test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> model.transform(test1).head().prediction
1.0
>>> dtr_path = temp_path + "/dtr"
>>> dt.save(dtr_path)
>>> dt2 = DecisionTreeRegressor.load(dtr_path)
>>> dt2.getMaxDepth()
2
>>> model_path = temp_path + "/dtr_model"
>>> model.save(model_path)
>>> model2 = DecisionTreeRegressionModel.load(model_path)
>>> model.numNodes == model2.numNodes
True
>>> model.depth == model2.depth
True
>>> model.transform(test1).head().variance
0.0
>>> model.transform(test0).take(1) == model2.transform(test0).take(1)
True
>>> df3 = spark.createDataFrame([
...     (1.0, 0.2, Vectors.dense(1.0)),
...     (1.0, 0.8, Vectors.dense(1.0)),
...     (0.0, 1.0, Vectors.sparse(1, [], []))], ["label", "weight", "features"])
>>> dt3 = DecisionTreeRegressor(maxDepth=2, weightCol="weight", varianceCol="variance")
>>> model3 = dt3.fit(df3)
>>> print(model3.toDebugString)
DecisionTreeRegressionModel...depth=1, numNodes=3...

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.
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.
fit(dataset[, params])	Fits a model to the input dataset with optional parameters.
fitMultiple(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
getCacheNodeIds()	Gets the value of cacheNodeIds or its default value.
getCheckpointInterval()	Gets the value of checkpointInterval 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.
getMaxBins()	Gets the value of maxBins or its default value.
getMaxDepth()	Gets the value of maxDepth 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.
getSeed()	Gets the value of seed or its default value.
getVarianceCol()	Gets the value of varianceCol 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).
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.
setCacheNodeIds(value)	Sets the value of cacheNodeIds.
setCheckpointInterval(value)	Sets the value of checkpointInterval.
setFeaturesCol(value)	Sets the value of featuresCol.
setImpurity(value)	Sets the value of impurity.
setLabelCol(value)	Sets the value of labelCol.
setLeafCol(value)	Sets the value of leafCol.
setMaxBins(value)	Sets the value of maxBins.
setMaxDepth(value)	Sets the value of maxDepth.
setMaxMemoryInMB(value)	Sets the value of maxMemoryInMB.
setMinInfoGain(value)	Sets the value of minInfoGain.
setMinInstancesPerNode(value)	Sets the value of minInstancesPerNode.
setMinWeightFractionPerNode(value)	Sets the value of minWeightFractionPerNode.
setParams(self, \*[, featuresCol, labelCol, …])	Sets params for the DecisionTreeRegressor.
setPredictionCol(value)	Sets the value of predictionCol.
setSeed(value)	Sets the value of seed.
setVarianceCol(value)	Sets the value of varianceCol.
setWeightCol(value)	Sets the value of weightCol.
write()	Returns an MLWriter instance for this ML instance.

Attributes

cacheNodeIds
checkpointInterval
featuresCol
impurity
labelCol
leafCol
maxBins
maxDepth
maxMemoryInMB
minInfoGain
minInstancesPerNode
minWeightFractionPerNode
params	Returns all params ordered by name.
predictionCol
seed
supportedImpurities
varianceCol
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

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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset.

paramsdict or list or tuple, optional

an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

Transformer or a list of Transformer

fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset.

paramMapscollections.abc.Sequence

A Sequence of param maps.

Returns

_FitMultipleIterator

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

getCacheNodeIds() → bool

Gets the value of cacheNodeIds or its default value.

getCheckpointInterval() → int

Gets the value of checkpointInterval or its default value.

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.

getMaxBins() → int

Gets the value of maxBins or its default value.

getMaxDepth() → int

Gets the value of maxDepth 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.

getSeed() → int

Gets the value of seed or its default value.

getVarianceCol() → str

Gets the value of varianceCol or its default value.

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).

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.

setCacheNodeIds(value: bool) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of cacheNodeIds.

New in version 1.4.0.

setCheckpointInterval(value: int) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of checkpointInterval.

New in version 1.4.0.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

New in version 3.0.0.

setImpurity(value: str) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of impurity.

New in version 1.4.0.

setLabelCol(value: str) → P

Sets the value of labelCol.

New in version 3.0.0.

setLeafCol(value: str) → P

Sets the value of leafCol.

setMaxBins(value: int) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of maxBins.

New in version 1.4.0.

setMaxDepth(value: int) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of maxDepth.

New in version 1.4.0.

setMaxMemoryInMB(value: int) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of maxMemoryInMB.

New in version 1.4.0.

setMinInfoGain(value: float) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of minInfoGain.

New in version 1.4.0.

setMinInstancesPerNode(value: int) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of minInstancesPerNode.

New in version 1.4.0.

setMinWeightFractionPerNode(value: float) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of minWeightFractionPerNode.

New in version 3.0.0.

setParams(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", seed=None, varianceCol=None, weightCol=None, leafCol="", minWeightFractionPerNode=0.0)

Sets params for the DecisionTreeRegressor.

New in version 1.4.0.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

New in version 3.0.0.

setSeed(value: int) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of seed.

setVarianceCol(value: str) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of varianceCol.

New in version 2.0.0.

setWeightCol(value: str) → pyspark.ml.regression.DecisionTreeRegressor

Sets the value of weightCol.

New in version 3.0.0.

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.')

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')

impurity = Param(parent='undefined', name='impurity', doc='Criterion used for information gain calculation (case-insensitive). Supported options: variance')

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')

leafCol = Param(parent='undefined', name='leafCol', doc='Leaf indices column name. Predicted leaf index of each instance in each tree by preorder.')

maxBins = Param(parent='undefined', name='maxBins', doc='Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature.')

maxDepth = Param(parent='undefined', name='maxDepth', doc='Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. Must be in range [0, 30].')

maxMemoryInMB = Param(parent='undefined', name='maxMemoryInMB', doc='Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size.')

minInfoGain = Param(parent='undefined', name='minInfoGain', doc='Minimum information gain for a split to be considered at a tree node.')

minInstancesPerNode = Param(parent='undefined', name='minInstancesPerNode', doc='Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1.')

minWeightFractionPerNode = Param(parent='undefined', name='minWeightFractionPerNode', doc='Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in interval [0.0, 0.5).')

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')

seed = Param(parent='undefined', name='seed', doc='random seed.')

supportedImpurities = ['variance']

varianceCol = Param(parent='undefined', name='varianceCol', doc='column name for the biased sample variance of prediction.')

weightCol = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')

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