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　　梯度提升树原理

　　梯度提升树代码(Spark Python)

　　待续...

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　　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 GradientBoostedTrees, GradientBoostedTreesModel
from pyspark.mllib.util import MLUtils

# Load and parse the data file.
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 GradientBoostedTrees model. 训练决策树模型
#  Notes: (a) Empty categoricalFeaturesInfo indicates all features are continuous. 空的categoricalFeaturesInfo意味着所有的特征都是连续的
#         (b) Use more iterations in practice. 在实践中使用更多的迭代步数
model = GradientBoostedTrees.trainClassifier(trainingData,
                                             categoricalFeaturesInfo={}, numIterations=30)

# 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 GBT model:‘)
print(model.toDebugString())
‘‘‘
TreeEnsembleModel classifier with 30 trees

  Tree 0:
    If (feature 434 <= 0.0)
     If (feature 100 <= 165.0)
      Predict: -1.0
     Else (feature 100 > 165.0)
      Predict: 1.0
    Else (feature 434 > 0.0)
     Predict: 1.0
  Tree 1:
    If (feature 490 <= 0.0)
     If (feature 549 <= 253.0)
      If (feature 184 <= 0.0)
       Predict: -0.4768116880884702
      Else (feature 184 > 0.0)
       Predict: -0.47681168808847024
     Else (feature 549 > 253.0)
      Predict: 0.4768116880884694
    Else (feature 490 > 0.0)
     If (feature 215 <= 251.0)
      Predict: 0.4768116880884701
     Else (feature 215 > 251.0)
      Predict: 0.4768116880884712
  ...
  Tree 29:
    If (feature 434 <= 0.0)
     If (feature 209 <= 4.0)
      Predict: 0.1335953290513215
     Else (feature 209 > 4.0)
      If (feature 372 <= 84.0)
       Predict: -0.13359532905132146
      Else (feature 372 > 84.0)
       Predict: -0.1335953290513215
    Else (feature 434 > 0.0)
     Predict: 0.13359532905132146
‘‘‘
# Save and load model
model.save(sc, "myGradientBoostingClassificationModel")
sameModel = GradientBoostedTreesModel.load(sc,"myGradientBoostingClassificationModel")
print sameModel.predict(data.collect()[0].features) #0.0

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