DecisionTreeRegressor¶
-
class
pyspark.ml.regression.
DecisionTreeRegressor
(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)[source]¶ Decision tree learning algorithm for regression. It supports both continuous and categorical features.
>>> 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
>>> 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...
New in version 1.4.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra=None)¶ 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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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extractParamMap
(extra=None)¶ 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
extra – extra param values
- Returns
merged param map
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fit
(dataset, params=None)¶ Fits a model to the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – 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
fitted model(s)
New in version 1.3.0.
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fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
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.
New in version 2.3.0.
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getCacheNodeIds
()¶ Gets the value of cacheNodeIds or its default value.
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getCheckpointInterval
()¶ Gets the value of checkpointInterval or its default value.
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getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
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getImpurity
()¶ Gets the value of impurity or its default value.
New in version 1.4.0.
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getLabelCol
()¶ Gets the value of labelCol or its default value.
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getLeafCol
()¶ Gets the value of leafCol or its default value.
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getMaxBins
()¶ Gets the value of maxBins or its default value.
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getMaxDepth
()¶ Gets the value of maxDepth or its default value.
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getMaxMemoryInMB
()¶ Gets the value of maxMemoryInMB or its default value.
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getMinInfoGain
()¶ Gets the value of minInfoGain or its default value.
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getMinInstancesPerNode
()¶ Gets the value of minInstancesPerNode or its default value.
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getMinWeightFractionPerNode
()¶ Gets the value of minWeightFractionPerNode or its default value.
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getOrDefault
(param)¶ Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getSeed
()¶ Gets the value of seed or its default value.
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getVarianceCol
()¶ Gets the value of varianceCol or its default value.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setCacheNodeIds
(value)[source]¶ Sets the value of
cacheNodeIds
.New in version 1.4.0.
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setCheckpointInterval
(value)[source]¶ Sets the value of
checkpointInterval
.New in version 1.4.0.
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setFeaturesCol
(value)¶ Sets the value of
featuresCol
.New in version 3.0.0.
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setMaxMemoryInMB
(value)[source]¶ Sets the value of
maxMemoryInMB
.New in version 1.4.0.
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setMinInfoGain
(value)[source]¶ Sets the value of
minInfoGain
.New in version 1.4.0.
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setMinInstancesPerNode
(value)[source]¶ Sets the value of
minInstancesPerNode
.New in version 1.4.0.
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setMinWeightFractionPerNode
(value)[source]¶ Sets the value of
minWeightFractionPerNode
.New in version 3.0.0.
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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)[source]¶ Sets params for the DecisionTreeRegressor.
New in version 1.4.0.
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setPredictionCol
(value)¶ Sets the value of
predictionCol
.New in version 3.0.0.
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setVarianceCol
(value)[source]¶ Sets the value of
varianceCol
.New in version 2.0.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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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.')¶
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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.')¶
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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impurity
= Param(parent='undefined', name='impurity', doc='Criterion used for information gain calculation (case-insensitive). Supported options: variance')¶
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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leafCol
= Param(parent='undefined', name='leafCol', doc='Leaf indices column name. Predicted leaf index of each instance in each tree by preorder.')¶
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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.')¶
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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.')¶
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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.')¶
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minInfoGain
= Param(parent='undefined', name='minInfoGain', doc='Minimum information gain for a split to be considered at a tree node.')¶
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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.')¶
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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).')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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seed
= Param(parent='undefined', name='seed', doc='random seed.')¶
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supportedImpurities
= ['variance']¶
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varianceCol
= Param(parent='undefined', name='varianceCol', doc='column name for the biased sample variance of prediction.')¶
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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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