#!/usr/bin/env python from liblinear.linear 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 += [int(label)] prob_x += [xi] return (prob_y, prob_x) def load_model(model_file_name): """ load_model(model_file_name) -> model Load a LIBLINEAR model from model_file_name and return. """ model = liblinear.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 save_model(model_file_name, model): """ save_model(model_file_name, model) -> None Save a LIBLINEAR model to the file model_file_name. """ liblinear.save_model(model_file_name, model) def evaluations(ty, pv): """ evaluations(ty, pv) -> ACC Calculate accuracy 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 for v, y in zip(pv, ty): if y == v: total_correct += 1 l = len(ty) ACC = 100.0*total_correct/l return ACC def train(arg1, arg2=None, arg3=None): """ train(y, x [, 'options']) -> model | ACC train(prob, [, 'options']) -> model | ACC train(prob, param) -> model | ACC Train a model from data (y, x) or a problem prob using 'options' or a parameter param. If '-v' is specified in 'options' (i.e., cross validation) accuracy (ACC) is returned. 'options': -s type : set type of solver (default 1) 0 -- L2-regularized logistic regression 1 -- L2-regularized L2-loss support vector classification (dual) 2 -- L2-regularized L2-loss support vector classification (primal) 3 -- L2-regularized L1-loss support vector classification (dual) 4 -- multi-class support vector classification by Crammer and Singer 5 -- L1-regularized L2-loss support vector classification 6 -- L1-regularized logistic regression -c cost : set the parameter C (default 1) -e epsilon : set tolerance of termination criterion -s 0 and 2 |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2, where f is the primal function, (default 0.01) -s 1, 3, and 4 Dual maximal violation <= eps; similar to liblinear (default 0.1) -s 5 and 6 |f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal function (default 0.01) -B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1) -wi weight: weights adjust the parameter C of different classes (see README for details) -v n: n-fold cross validation mode -q : quiet mode (no outputs) """ prob, param = None, None if isinstance(arg1, (list, tuple)): assert isinstance(arg2, (list, tuple)) y, x, options = arg1, arg2, arg3 prob = problem(y, x) param = parameter(options) elif isinstance(arg1, problem): prob = arg1 if isinstance(arg2, parameter): param = arg2 else : param = parameter(arg2) if prob == None or param == None : raise TypeError("Wrong types for the arguments") prob.set_bias(param.bias) liblinear.set_print_string_function(param.print_func) err_msg = liblinear.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_int * l)() liblinear.cross_validation(prob, param, nr_fold, target) ACC = evaluations(prob.y[:l], target[:l]) print("Cross Validation Accuracy = %g%%" % ACC) return ACC else : m = liblinear.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 predict(y, x, m, options=""): """ 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); The return tuple contains p_labels: a list of predicted labels p_acc: testing accuracy. 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 binary-class SVMs. if k = 2 and solver is not MCSVM_CS, only one decision value is returned. 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 nr_class = m.get_nr_class() nr_feature = m.get_nr_feature() is_prob_model = m.is_probability_model() bias = m.bias if bias >= 0: biasterm = feature_node(nr_feature+1, bias) else: biasterm = feature_node(-1, bias) pred_labels = [] pred_values = [] if predict_probability: if not is_prob_model: raise TypeError('probability output is only supported for logistic regression') prob_estimates = (c_double * nr_class)() for xi in x: xi, idx = gen_feature_nodearray(xi, feature_max=nr_feature) xi[-2] = biasterm label = liblinear.predict_probability(m, xi, prob_estimates) values = prob_estimates[:nr_class] pred_labels += [label] pred_values += [values] else: if nr_class <= 2: nr_classifier = 1 else: nr_classifier = nr_class dec_values = (c_double * nr_classifier)() for xi in x: xi, idx = gen_feature_nodearray(xi, feature_max=nr_feature) xi[-2] = biasterm label = liblinear.predict_values(m, xi, dec_values) values = dec_values[:nr_classifier] pred_labels += [label] pred_values += [values] if len(y) == 0: y = [0] * len(x) ACC = evaluations(y, pred_labels) l = len(y) print("Accuracy = %g%% (%d/%d)" % (ACC, int(l*ACC//100), l)) return pred_labels, ACC, pred_values