"Spark Mllib crash Treasure" model 07 gradient Lift Tree "gradient-boosted Trees" (Python version)

Source: Internet
Author: User
Tags pyspark spark mllib

Catalog Gradient Lifting Tree principle gradient lifting Tree code (Spark Python)

The principle of gradient lifting tree

to be continued ...

Back to Catalog

Gradient Boost Tree code (Spark Python)

  

Code data: Https://pan.baidu.com/s/1jHWKG4I Password: acq1

#-*-coding=utf-8-*- fromPysparkImportsparkconf, SPARKCONTEXTSC= Sparkcontext ('Local') fromPyspark.mllib.treeImportGradientboostedtrees, Gradientboostedtreesmodel fromPyspark.mllib.utilImportmlutils#Load and parse the data file.data = Mlutils.loadlibsvmfile (SC,"Data/mllib/sample_libsvm_data.txt")" "each row uses the following format to represent a sparse feature vector for a tag label index1:value1 index2:value2 ... tempfile.write (b "+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4 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) splits the dataset, leaving 30% as the test set(Trainingdata, TestData) = Data.randomsplit ([0.7, 0.3])#Train a gradientboostedtrees model. Training Decision Tree Models#Notes: (a) empty categoricalfeaturesinfo indicates all features is continuous. Empty categoricalfeaturesinfo means that all features are of continuous#(b) Use greater iterations in practice. Using more iteration numbers in practiceModel =Gradientboostedtrees.trainclassifier (Trainingdata, Categoricalfeaturesinf o={}, numiterations=30)#Evaluate model on test instances and compute Test error evaluation modelspredictions = Model.predict (Testdata.map (Lambdax:x.features)) Labelsandpredictions= Testdata.map (LambdaLp:lp.label). zip (predictions) Testerr=Labelsandpredictions.filter (LambdaLP:LP[0]! = lp[1]). COUNT ()/Float (testdata.count ())Print('Test Error ='+ str (TESTERR))#Test Error = 0.0Print('learned classification GBT model:')Print(Model.todebugstring ())" "Treeensemblemodel classifier with Trees Tree 0:if (feature 434 <= 0.0) If (feature-<= 165.0) Pr    Edict: -1.0 Else (Feature > 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: 0.4768116880 884702 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 2       > 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 ModelModel.save (SC,"Mygradientboostingclassificationmodel") Samemodel= Gradientboostedtreesmodel.load (SC,"Mygradientboostingclassificationmodel")PrintSamemodel.predict (Data.collect () [0].features)#0.0

Back to Catalog

"Spark Mllib crash Treasure" model 07 gradient Lift Tree "gradient-boosted Trees" (Python version)

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.