LIBSVM Core library

svm

Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin All rights reserved.

libsvm.svm.toPyModel(model_ptr) → svm_model[source]

Convert a ctypes POINTER(svm_model) to a Python svm_model

svmutil

Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin All rights reserved.

libsvm.svmutil.evaluations(ty, pv) -> (ACC, MSE, SCC)[source]

Calculate accuracy, mean squared error and squared correlation coefficient using the true values (ty) and predicted values (pv).

libsvm.svmutil.read_svm(svmFileName)[source]

Read SVM model in PICKLE format

Parameters:svmFileNale – name of the file to read from
libsvm.svmutil.save_svm(svmFileName, w, b)[source]

Save SVM weights and biais in PICKLE format

Parameters:
  • svmFileName – name of the file to write
  • w – weight coefficients of the SVM to store
  • b – biais of the SVM to store
libsvm.svmutil.svm_load_model(model_file_name) → model[source]

Load a LIBSVM model from model_file_name and return.

libsvm.svmutil.svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)[source]

Predict data (y, x) with the SVM model m. options:

  • -b probability_estimates: whether to predict probability estimates,

    0 or 1 (default 0); for one-class SVM only 0 is supported.

  • -q : quiet mode (no outputs).

The return tuple contains

  • p_labels: a list of predicted labels
  • p_acc: a tuple including accuracy (for classification), mean-squared error, and squared correlation coefficient (for regression).
  • p_vals: a list of decision values or probability estimates (if ‘-b 1’ is specified). If k is the number of classes, for decision values, each element includes results of predicting k(k-1)/2 binary-class SVMs. For probabilities, each element contains k values indicating the probability that the testing instance is in each class.

Note

that the order of classes here is the same as ‘model.label’ field in the model structure.

libsvm.svmutil.svm_read_problem(data_file_name) → [y, x][source]

Read LIBSVM-format data from data_file_name and return labels y and data instances x.

libsvm.svmutil.svm_save_model(model_file_name, model) → None[source]

Save a LIBSVM model to the file model_file_name.

libsvm.svmutil.svm_train(y, x[, options]) → model | ACC | MSE[source]

svm_train(prob [, options]) -> model | ACC | MSE svm_train(prob, param) -> model | ACC| MSE

Train an SVM model from data (y, x) or an svm_problem prob using ‘options’ or an svm_parameter param. If ‘-v’ is specified in ‘options’ (i.e., cross validation) either accuracy (ACC) or mean-squared error (MSE) is returned. options:

  • -s svm_type : set type of SVM (default 0)

    • 0 – C-SVC (multi-class classification)
    • 1 – nu-SVC (multi-class classification)
    • 2 – one-class SVM
    • 3 – epsilon-SVR (regression)
    • 4 – nu-SVR (regression)
  • -t kernel_type : set type of kernel function (default 2)

    • 0 – linear: u’*v
    • 1 – polynomial: (gamma*u’*v + coef0)^degree
    • 2 – radial basis function: exp(-gamma*|u-v|^2)
    • 3 – sigmoid: tanh(gamma*u’*v + coef0)
    • 4 – precomputed kernel (kernel values in training_set_file)
  • -d degree : set degree in kernel function (default 3)

  • -g gamma : set gamma in kernel function (default 1/num_features)

  • -r coef0 : set coef0 in kernel function (default 0)

  • -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)

  • -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)

  • -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)

  • -m cachesize : set cache memory size in MB (default 100)

  • -e epsilon : set tolerance of termination criterion (default 0.001)

  • -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)

  • -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)

  • -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)

  • -v n: n-fold cross validation mode

  • -q : quiet mode (no outputs)

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