#!/usr/bin/env python import os, sys sys.path = [os.path.dirname(os.path.abspath(__file__))] + sys.path from .svm import * from .svm import __all__ as svm_all from .svm import scipy, sparse from .commonutil import * from .commonutil import __all__ as common_all if sys.version_info[0] < 3: range = xrange from itertools import izip as zip _cstr = lambda s: s.encode("utf-8") if isinstance(s,unicode) else str(s) else: _cstr = lambda s: bytes(s, "utf-8") __all__ = ['svm_load_model', 'svm_predict', 'svm_save_model', 'svm_train'] + svm_all + common_all 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(_cstr(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(_cstr(model_file_name), model) def svm_train(arg1, arg2=None, arg3=None): """ svm_train(y, x [, options]) -> model | ACC | MSE y: a list/tuple/ndarray of l true labels (type must be int/double). x: 1. a list/tuple of l training instances. Feature vector of each training instance is a list/tuple or dictionary. 2. an l * n numpy ndarray or scipy spmatrix (n: number of features). 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) """ prob, param = None, None if isinstance(arg1, (list, tuple)) or (scipy and isinstance(arg1, scipy.ndarray)): assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (scipy.ndarray, sparse.spmatrix))) y, x, options = arg1, arg2, arg3 param = svm_parameter(options) prob = svm_problem(y, x, isKernel=(param.kernel_type == PRECOMPUTED)) 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 i in range(prob.l): xi = prob.x[i] idx, val = xi[0].index, xi[0].value if idx != 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) y: a list/tuple/ndarray of l true labels (type must be int/double). It is used for calculating the accuracy. Use [] if true labels are unavailable. x: 1. a list/tuple of l training instances. Feature vector of each training instance is a list/tuple or dictionary. 2. an l * n numpy ndarray or scipy spmatrix (n: number of features). 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. """ def info(s): print(s) if scipy and isinstance(x, scipy.ndarray): x = scipy.ascontiguousarray(x) # enforce row-major elif sparse and isinstance(x, sparse.spmatrix): x = x.tocsr() elif not isinstance(x, (list, tuple)): raise TypeError("type of x: {0} is not supported!".format(type(x))) if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.ndarray))): raise TypeError("type of y: {0} is not supported!".format(type(y))) predict_probability = 0 argv = options.split() i = 0 while i < len(argv): if argv[i] == '-b': i += 1 predict_probability = int(argv[i]) elif argv[i] == '-q': info = print_null 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 scipy and isinstance(x, sparse.spmatrix): nr_instance = x.shape[0] else: nr_instance = len(x) if predict_probability: if not is_prob_model: raise ValueError("Model does not support probabiliy estimates") if svm_type in [NU_SVR, EPSILON_SVR]: info("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 i in range(nr_instance): if scipy and isinstance(x, sparse.spmatrix): indslice = slice(x.indptr[i], x.indptr[i+1]) xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED)) else: xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED)) 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: info("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 i in range(nr_instance): if scipy and isinstance(x, sparse.spmatrix): indslice = slice(x.indptr[i], x.indptr[i+1]) xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED)) else: xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED)) label = libsvm.svm_predict_values(m, xi, dec_values) if(nr_class == 1): values = [1] else: values = dec_values[:nr_classifier] pred_labels += [label] pred_values += [values] if len(y) == 0: y = [0] * nr_instance ACC, MSE, SCC = evaluations(y, pred_labels) if svm_type in [EPSILON_SVR, NU_SVR]: info("Mean squared error = %g (regression)" % MSE) info("Squared correlation coefficient = %g (regression)" % SCC) else: info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(round(nr_instance*ACC/100)), nr_instance)) return pred_labels, (ACC, MSE, SCC), pred_values