目录
梯度提升树原理
梯度提升树代码(Spark Python)
梯度提升树原理 |
待续...
梯度提升树代码(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 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