svmutil.py

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#!/usr/bin/env python

import pdb
from svm import *

def svm_read_problem(data_file_name):
        """
        svm_read_problem(data_file_name) -> [y, x]

        Read LIBSVM-format data from data_file_name and return labels y
        and data instances x.
        """
        prob_y = []
        prob_x = []
        for line in open(data_file_name):
                line = line.split(None, 1)
                # In case an instance with all zero features
                if len(line) == 1: line += ['']
                label, features = line
                xi = {}
                for e in features.split():
                        ind, val = e.split(":")
                        xi[int(ind)] = float(val)
                prob_y += [float(label)]
                prob_x += [xi]
        return (prob_y, prob_x)

def svm_load_model(model_file_name):
        """
        svm_load_model(model_file_name) -> model
        
        Load a LIBSVM model from model_file_name and return.
        """
        model = libsvm.svm_load_model(model_file_name)
        if not model: 
                print("can't open model file %s" % model_file_name)
                return None
        model = toPyModel(model)
        return model

def svm_save_model(model_file_name, model):
        """
        svm_save_model(model_file_name, model) -> None

        Save a LIBSVM model to the file model_file_name.
        """
        libsvm.svm_save_model(model_file_name, model)

def evaluations(ty, pv):
        """
        evaluations(ty, pv) -> (ACC, MSE, SCC)

        Calculate accuracy, mean squared error and squared correlation coefficient
        using the true values (ty) and predicted values (pv).
        """
        if len(ty) != len(pv):
                raise ValueError("len(ty) must equal to len(pv)")
        total_correct = total_error = 0
        sumv = sumy = sumvv = sumyy = sumvy = 0
        for v, y in zip(pv, ty):
                if y == v: 
                        total_correct += 1
                total_error += (v-y)*(v-y)
                sumv += v
                sumy += y
                sumvv += v*v
                sumyy += y*y
                sumvy += v*y 
        l = len(ty)
        ACC = 100.0*total_correct/l
        MSE = total_error/l
        try:
                SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
        except:
                SCC = float('nan')
        return (ACC, MSE, SCC)

def svm_train(arg1, arg2=None, arg3=None):
        """
        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
                1 -- nu-SVC
                2 -- one-class SVM
                3 -- epsilon-SVR
                4 -- nu-SVR
            -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)
        """
        prob, param = None, None
        #pdb.set_trace()
        if isinstance(arg1, (list, tuple)):
                assert isinstance(arg2, (list, tuple))
                y, x, options = arg1, arg2, arg3
                prob = svm_problem(y, x)
                param = svm_parameter(options)
        elif isinstance(arg1, svm_problem):
                prob = arg1
                if isinstance(arg2, svm_parameter):
                        param = arg2
                else:
                        param = svm_parameter(arg2)
        if prob == None or param == None:
                raise TypeError("Wrong types for the arguments")

        if param.kernel_type == PRECOMPUTED:
                for xi in prob.x_space:
                        idx, val = xi[0].index, xi[0].value
                        if xi[0].index != 0:
                                raise ValueError('Wrong input format: first column must be 0:sample_serial_number')
                        if val <= 0 or val > prob.n:
                                raise ValueError('Wrong input format: sample_serial_number out of range')

        if param.gamma == 0 and prob.n > 0: 
                param.gamma = 1.0 / prob.n
        libsvm.svm_set_print_string_function(param.print_func)
        err_msg = libsvm.svm_check_parameter(prob, param)
        if err_msg:
                raise ValueError('Error: %s' % err_msg)

        if param.cross_validation:
                l, nr_fold = prob.l, param.nr_fold
                target = (c_double * l)()
                libsvm.svm_cross_validation(prob, param, nr_fold, target)       
                ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
                if param.svm_type in [EPSILON_SVR, NU_SVR]:
                        print("Cross Validation Mean squared error = %g" % MSE)
                        print("Cross Validation Squared correlation coefficient = %g" % SCC)
                        return MSE
                else:
                        print("Cross Validation Accuracy = %g%%" % ACC)
                        return ACC
        else:
                m = libsvm.svm_train(prob, param)
                m = toPyModel(m)

                # If prob is destroyed, data including SVs pointed by m can remain.
                m.x_space = prob.x_space
                return m

def svm_predict(y, x, m, options=""):
        """
        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.

        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.
        """
        predict_probability = 0
        argv = options.split()
        i = 0
        while i < len(argv):
                if argv[i] == '-b':
                        i += 1
                        predict_probability = int(argv[i])
                else:
                        raise ValueError("Wrong options")
                i+=1

        svm_type = m.get_svm_type()
        is_prob_model = m.is_probability_model()
        nr_class = m.get_nr_class()
        pred_labels = []
        pred_values = []

        if predict_probability:
                if not is_prob_model:
                        raise ValueError("Model does not support probabiliy estimates")

                if svm_type in [NU_SVR, EPSILON_SVR]:
                        print("Prob. model for test data: target value = predicted value + z,\n"
                        "z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g" % m.get_svr_probability());
                        nr_class = 0

                prob_estimates = (c_double * nr_class)()
                for xi in x:
                        xi, idx = gen_svm_nodearray(xi)
                        label = libsvm.svm_predict_probability(m, xi, prob_estimates)
                        values = prob_estimates[:nr_class]
                        pred_labels += [label]
                        pred_values += [values]
        else:
                if is_prob_model:
                        print("Model supports probability estimates, but disabled in predicton.")
                if svm_type in (ONE_CLASS, EPSILON_SVR, NU_SVC):
                        nr_classifier = 1
                else:
                        nr_classifier = nr_class*(nr_class-1)//2
                dec_values = (c_double * nr_classifier)()
                for xi in x:
                        #pdb.set_trace()
                        xi, idx = gen_svm_nodearray(xi)
                        label = libsvm.svm_predict_values(m, xi, dec_values)
                        values = dec_values[:nr_classifier]
                        pred_labels += [label]
                        pred_values += [values]

        ACC, MSE, SCC = evaluations(y, pred_labels)
        l = len(y)
        if svm_type in [EPSILON_SVR, NU_SVR]:
                print("Mean squared error = %g (regression)" % MSE)
                print("Squared correlation coefficient = %g (regression)" % SCC)
        else:
                print("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(l*ACC/100), l))

        return pred_labels, (ACC, MSE, SCC), pred_values