Refer to the  and  for more details on the API.

from pyspark.mllib.tree import DecisionTree, DecisionTreeModel from pyspark.mllib.util import MLUtils # Load and parse the data file into an RDD of LabeledPoint. data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt') # Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3]) # Train a DecisionTree model. # Empty categoricalFeaturesInfo indicates all features are continuous. model = DecisionTree.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, impurity='gini', maxDepth=5, maxBins=32) # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count()) print('Test Error = ' + str(testErr)) print('Learned classification tree model:') print(model.toDebugString()) # Save and load model model.save(sc, "target/tmp/myDecisionTreeClassificationModel") sameModel = DecisionTreeModel.load(sc, "target/tmp/myDecisionTreeClassificationModel")

Find full example code at "examples/src/main/python/mllib/decision_tree_classification_example.py" in the Spark repo.

class pyspark.mllib.tree.DecisionTree

Learning algorithm for a decision tree model for classification or regression.

New in version 1.1.0.

classmethod 

trainClassifier

(

data, 

numClasses, 

categoricalFeaturesInfo, 

impurity='gini', 

maxDepth=5, 

maxBins=32, 

minInstancesPerNode=1, 

minInfoGain=0.0

)

Train a decision tree model for classification.

Parameters:

data – Training data: RDD of LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}. numClasses – Number of classes for classification. categoricalFeaturesInfo – Map storing arity of categorical features. An entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}. impurity – Criterion used for information gain calculation. Supported values: “gini” or “entropy”. (default: “gini”) maxDepth – Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 5) maxBins – Number of bins used for finding splits at each node. (default: 32) minInstancesPerNode – Minimum number of instances required at child nodes to create the parent split. (default: 1) minInfoGain – Minimum info gain required to create a split. (default: 0.0)

Returns:

DecisionTreeModel.

Example usage:

>>> from numpy import array>>> from pyspark.mllib.regression import LabeledPoint >>> from pyspark.mllib.tree import DecisionTree >>> >>> data = [ ... LabeledPoint(0.0, [0.0]), ... LabeledPoint(1.0, [1.0]), ... LabeledPoint(1.0, [2.0]), ... LabeledPoint(1.0, [3.0]) ... ] >>> model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {}) >>> print(model) DecisionTreeModel classifier of depth 1 with 3 nodes

>>> print(model.toDebugString()) DecisionTreeModel classifier of depth 1 with 3 nodes If (feature 0 <= 0.0) Predict: 0.0 Else (feature 0 > 0.0) Predict: 1.0 >>> model.predict(array([1.0])) 1.0 >>> model.predict(array([0.0])) 0.0 >>> rdd = sc.parallelize([[1.0], [0.0]]) >>> model.predict(rdd).collect() [1.0, 0.0]

 

摘自：https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.tree.DecisionTree