【Spark机器学习速成宝典】模型篇06随机森林【Random Forests】（Python版）

目录

　　随机森林原理

　　随机森林代码(Spark Python)

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随机森林原理

　　参考：http://www.cnblogs.com/itmorn/p/8269334.html

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随机森林代码(Spark Python)

　　

　　代码里数据：https://pan.baidu.com/s/1jHWKG4I 密码：acq1

# -*-coding=utf-8 -*-

from pyspark import SparkConf, SparkContext

sc = SparkContext('local')

from pyspark.mllib.tree import RandomForest, RandomForestModel

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')

'''

每一行使用以下格式表示一个标记的稀疏特征向量

label index1:value1 index2:value2 ...

tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0")

>>> tempFile.flush()

>>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()

>>> tempFile.close()

>>> examples[0]

LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0]))

>>> examples[1]

LabeledPoint(-1.0, (6,[],[]))

>>> examples[2]

LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0]))

'''

# Split the data into training and test sets (30% held out for testing) 分割数据集，留30%作为测试集

(trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model. 训练决策树模型

#  Empty categoricalFeaturesInfo indicates all features are continuous. 空的categoricalFeaturesInfo意味着所有的特征都是连续的

#  Note: Use larger numTrees in practice. 注意：在实践中可以使用更多棵树

#  Setting featureSubsetStrategy="auto" lets the algorithm choose. featureSubsetStrategy="auto"的意思是让算法自己选择

model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},

                                     numTrees=3, featureSubsetStrategy="auto",

                                     impurity='gini', maxDepth=4, maxBins=32)

# Evaluate model on test instances and compute test error 评估模型

predictions = model.predict(testData.map(lambda x: x.features))

labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)

testErr = labelsAndPredictions.filter(

    lambda lp: lp[0] != lp[1]).count() / float(testData.count())

print('Test Error = ' + str(testErr)) #Test Error = 0.0

print('Learned classification forest model:')

print(model.toDebugString())

'''

TreeEnsembleModel classifier with 3 trees

  Tree 0:

    If (feature 517 <= 116.0)

     If (feature 489 <= 11.0)

      If (feature 437 <= 218.0)

       Predict: 0.0

      Else (feature 437 > 218.0)

       Predict: 1.0

     Else (feature 489 > 11.0)

      Predict: 1.0

    Else (feature 517 > 116.0)

     Predict: 1.0

  Tree 1:

    If (feature 456 <= 0.0)

     If (feature 471 <= 0.0)

      Predict: 1.0

     Else (feature 471 > 0.0)

      Predict: 0.0

    Else (feature 456 > 0.0)

     Predict: 0.0

  Tree 2:

    If (feature 377 <= 3.0)

     If (feature 212 <= 253.0)

      Predict: 0.0

     Else (feature 212 > 253.0)

      Predict: 1.0

    Else (feature 377 > 3.0)

     If (feature 299 <= 204.0)

      Predict: 1.0

     Else (feature 299 > 204.0)

      Predict: 0.0

'''

# Save and load model

model.save(sc, "myRandomForestClassificationModel")

sameModel = RandomForestModel.load(sc, "myRandomForestClassificationModel")

print sameModel.predict(data.collect()[0].features) #0.0

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