LIBSVM easy.py (Process script) comments

Given the importance of the script, it is necessary to make a comprehensive comment on the script so that the LIBSVM can be used flexibly.

#!/usr/bin/env python#This method of setting the Python path is more scientificImportSYSImportOS fromSubprocessImport*#too few input parameters will prompt program usageifLen (SYS.ARGV) <= 1:    Print('Usage: {0} training_file [Testing_file]'. Format (sys.argv[0]))RaiseSystemexit#SVM, grid, and gnuplot executable filesIs_win32= (Sys.platform = ='Win32')if  notIs_win32:#program path configuration under Linux systemSvmscale_exe =".. /svm-scale"Svmtrain_exe=".. /svm-train"Svmpredict_exe=".. /svm-predict"grid_py="./grid.py"Gnuplot_exe="/usr/bin/gnuplot"   #you need to modify the secondary path, gnuplot the path to the executable, not the folder pathElse:    #Program path configuration under Windows systemSvmscale_exe = R".. \windows\svm-scale.exe"Svmtrain_exe= R".. \windows\svm-train.exe"Svmpredict_exe= R".. \windows\svm-predict.exe"Gnuplot_exe= R"C:\gnuplot\bin\gnuplot.exe"grid_py= R". \grid.py"assertOs.path.exists (Svmscale_exe),"Svm-scale executable not found"assertOs.path.exists (Svmtrain_exe),"Svm-train executable not found"assertOs.path.exists (Svmpredict_exe),"svm-predict executable not found"assertOs.path.exists (Gnuplot_exe),"gnuplot executable not found"assertOs.path.exists (Grid_py),"grid.py not found"#Create a training dataset related file: ". Scale", ". Model", ". Range" three filesTrain_pathname = sys.argv[1]assertOs.path.exists (Train_pathname),"training File not found"file_name= Os.path.split (train_pathname) [1]scaled_file= file_name +". scale"Model_file= file_name +". Model"Range_file= file_name +". Range"#Create a test dataset related file: ". Scale", ". Predict" two filesifLen (SYS.ARGV) > 2: Test_pathname= Sys.argv[2] file_name= Os.path.split (test_pathname) [1]    assertOs.path.exists (Test_pathname),"testing file not found"Scaled_test_file= file_name +". scale"Predict_test_file= file_name +". Predict"#process Command One: Svm-scale scaling, training set scaling, parameters are as follows:cmd ='{0}-S "{1}" "{2}" > "{3}"'. Format (Svmscale_exe, Range_file, Train_pathname, Scaled_file)Print('Scaling Training Data ...') Popen (cmd, Shell= True, stdout =PIPE). Communicate ()#process command Two: parameter optimization, using grid.py script, cross-validation, the parameters are as follows:cmd ='{0}-svmtrain "{1}"-gnuplot "{2} " "{3}"'. Format (grid_py, Svmtrain_exe, Gnuplot_exe, Scaled_file)Print('Cross validation ...') F= Popen (cmd, Shell = True, stdout =PIPE). Stdoutline="' whileTrue:last_line= Line Line=F.readline ()if  notLine Breakc,g,rate=map (Float,last_line.split ())#output optimal parameter c,gPrint('Best c={0}, G={1} CV rate={2}'. Format (c,g,rate))#process Command Three: Svm-train training, the parameters are set as followscmd ='{0}-c {1}-G {2} ' {3} ' ' {4} ''. Format (svmtrain_exe,c,g,scaled_file,model_file)Print('Training ...') Popen (cmd, Shell= True, stdout =PIPE). Communicate ()Print('Output model: {0}'. Format (model_file))ifLen (SYS.ARGV) > 2:        #process command four: Svm-scale scaling, test data scaling, parameter settings are as follows:cmd ='{0}-R "{1}" "{2}" > "{3}"'. Format (Svmscale_exe, Range_file, Test_pathname, Scaled_test_file)Print('Scaling testing Data ...') Popen (cmd, Shell= True, stdout =PIPE). Communicate ()#process Command Five: svm-predict prediction, the parameters are set as follows:cmd ='{0} "{1} " "{2}" "{3}"'. Format (Svmpredict_exe, Scaled_test_file, Model_file, Predict_test_file)Print('Testing ...') Popen (cmd, Shell=True). Communicate ()Print('Output prediction: {0}'. Format (Predict_test_file))

LIBSVM easy.py (Process script) comments