LIBSVM Core library

svm

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

class libsvm.svm.svm_model

class libsvm.svm.svm_node

class libsvm.svm.svm_parameter(options=None)

class libsvm.svm.svm_problem(y, x, isKernel=None)

libsvm.svm.toPyModel(model_ptr) → svm_model

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)

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

libsvm.svmutil.read_svm(svm_file_name)

Read SVM model in PICKLE format

Parameters

svm_file_name – name of the file to read from

libsvm.svmutil.save_svm(svm_file_name, w, b)

Save SVM weights and biais in PICKLE format :return:

libsvm.svmutil.svm_load_model(model_file_name) → model

Load a LIBSVM model from model_file_name and return. :param model_file_name: file name to load from

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

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]

Read LIBSVM-format data from data_file_name and return labels y and data instances x. :param data_file_name: name of the file to load from

libsvm.svmutil.svm_save_model(model_file_name, model) → None

Save a LIBSVM model to the file model_file_name. :param model_file_name: file name to write to :param model: model to save

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

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)