spark决策树API分析

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Contents

1 此版本是ml版本，区别于mllib版本的决策树api
2 输入
3 输出

此版本是ml版本，区别于mllib版本的决策树api

输入

Param name	Type(s)	Default	Description
labelCol	Double	“label”	标签
featuresCol	Vector	“features”	特征向量

输出

Param name	Type(s)	Default	Description	Notes
predictionCol	Double	“prediction”	预测结果标签
rawPredictionCol	Vector	“rawPrediction”	Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction	仅限分类
probabilityCol	Vector	“probability”	Vector of length # classes equal to rawPrediction normalized to a multinomial distribution	仅限分类
varianceCol	Double		预测结果方差	仅限回归

API函数

决策树分类器
class pyspark.ml.classification.DecisionTreeClassifier(self, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, probabilityCol=”probability”, rawPredictionCol=”rawPrediction”, maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity=”gini”)

maxDepth=5表示树的深度最大为5

maxBins=32表示离散化连续变量分区个数最大值

imputity=”gini”决策树节点特征选择方式，类似还有c45等

代码实例

>>> from pyspark.mllib.linalg import Vectors
>>> from pyspark.ml.feature import StringIndexer
>>> df = sqlContext.createDataFrame([
...     (1.0, Vectors.dense(1.0)),
...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
>>> si_model = stringIndexer.fit(df)
>>> td = si_model.transform(df)
>>> dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed")
>>> model = dt.fit(td)
>>> model.numNodes
3
>>> model.depth
1
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> result = model.transform(test0).head()
>>> result.prediction
0.0
>>> result.probability
DenseVector([1.0, 0.0])
>>> result.rawPrediction
DenseVector([1.0, 0.0])
>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> model.transform(test1).head().prediction
1.0

决策树模型

class pyspark.ml.classification.DecisionTreeClassificationModel(java_model)

模型是通过决策树分类器训练获得

主要的内部成员函数是transform，负责对测试样本预测分类，返回结果是dataframe格式
transform(dataset, params=None)

Transforms 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.
Returns:	transformed dataset