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libsvm-3.36/python/libsvm/svmutil.py
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libsvm-3.36/python/libsvm/svmutil.py
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import os, sys
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from .svm import *
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from .svm import __all__ as svm_all
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from .commonutil import *
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from .commonutil import __all__ as common_all
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try:
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import numpy as np
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import scipy
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from scipy import sparse
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except:
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scipy = None
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if sys.version_info[0] < 3:
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range = xrange
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from itertools import izip as zip
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_cstr = lambda s: s.encode("utf-8") if isinstance(s,unicode) else str(s)
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else:
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_cstr = lambda s: bytes(s, "utf-8")
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__all__ = ['svm_load_model', 'svm_predict', 'svm_save_model', 'svm_train'] + svm_all + common_all
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def svm_load_model(model_file_name):
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"""
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svm_load_model(model_file_name) -> model
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Load a LIBSVM model from model_file_name and return.
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"""
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model = libsvm.svm_load_model(_cstr(model_file_name))
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if not model:
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print("can't open model file %s" % model_file_name)
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return None
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model = toPyModel(model)
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return model
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def svm_save_model(model_file_name, model):
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"""
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svm_save_model(model_file_name, model) -> None
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Save a LIBSVM model to the file model_file_name.
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"""
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libsvm.svm_save_model(_cstr(model_file_name), model)
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def svm_train(arg1, arg2=None, arg3=None):
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"""
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svm_train(y, x [, options]) -> model | ACC | MSE
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y: a list/tuple/ndarray of l true labels (type must be int/double).
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x: 1. a list/tuple of l training instances. Feature vector of
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each training instance is a list/tuple or dictionary.
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2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
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svm_train(prob [, options]) -> model | ACC | MSE
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svm_train(prob, param) -> model | ACC| MSE
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Train an SVM model from data (y, x) or an svm_problem prob using
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'options' or an svm_parameter param.
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If '-v' is specified in 'options' (i.e., cross validation)
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either accuracy (ACC) or mean-squared error (MSE) is returned.
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options:
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-s svm_type : set type of SVM (default 0)
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0 -- C-SVC (multi-class classification)
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1 -- nu-SVC (multi-class classification)
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2 -- one-class SVM
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3 -- epsilon-SVR (regression)
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4 -- nu-SVR (regression)
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-t kernel_type : set type of kernel function (default 2)
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0 -- linear: u'*v
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1 -- polynomial: (gamma*u'*v + coef0)^degree
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2 -- radial basis function: exp(-gamma*|u-v|^2)
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3 -- sigmoid: tanh(gamma*u'*v + coef0)
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4 -- precomputed kernel (kernel values in training_set_file)
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-d degree : set degree in kernel function (default 3)
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-g gamma : set gamma in kernel function (default 1/num_features)
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-r coef0 : set coef0 in kernel function (default 0)
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-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
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-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
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-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
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-m cachesize : set cache memory size in MB (default 100)
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-e epsilon : set tolerance of termination criterion (default 0.001)
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-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
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-b probability_estimates : whether to train a model for probability estimates, 0 or 1 (default 0)
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-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
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-v n: n-fold cross validation mode
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-q : quiet mode (no outputs)
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"""
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prob, param = None, None
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if isinstance(arg1, (list, tuple)) or (scipy and isinstance(arg1, np.ndarray)):
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assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (np.ndarray, sparse.spmatrix)))
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y, x, options = arg1, arg2, arg3
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param = svm_parameter(options)
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prob = svm_problem(y, x, isKernel=(param.kernel_type == kernel_names.PRECOMPUTED))
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elif isinstance(arg1, svm_problem):
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prob = arg1
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if isinstance(arg2, svm_parameter):
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param = arg2
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else:
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param = svm_parameter(arg2)
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if prob == None or param == None:
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raise TypeError("Wrong types for the arguments")
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if param.kernel_type == kernel_names.PRECOMPUTED:
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for i in range(prob.l):
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xi = prob.x[i]
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idx, val = xi[0].index, xi[0].value
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if idx != 0:
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raise ValueError('Wrong input format: first column must be 0:sample_serial_number')
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if val <= 0 or val > prob.n:
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raise ValueError('Wrong input format: sample_serial_number out of range')
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if param.gamma == 0 and prob.n > 0:
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param.gamma = 1.0 / prob.n
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libsvm.svm_set_print_string_function(param.print_func)
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err_msg = libsvm.svm_check_parameter(prob, param)
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if err_msg:
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raise ValueError('Error: %s' % err_msg)
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if param.cross_validation:
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l, nr_fold = prob.l, param.nr_fold
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target = (c_double * l)()
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libsvm.svm_cross_validation(prob, param, nr_fold, target)
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ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
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if param.svm_type in [svm_forms.EPSILON_SVR, svm_forms.NU_SVR]:
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print("Cross Validation Mean squared error = %g" % MSE)
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print("Cross Validation Squared correlation coefficient = %g" % SCC)
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return MSE
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else:
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print("Cross Validation Accuracy = %g%%" % ACC)
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return ACC
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else:
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m = libsvm.svm_train(prob, param)
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m = toPyModel(m)
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# If prob is destroyed, data including SVs pointed by m can remain.
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m.x_space = prob.x_space
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return m
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def svm_predict(y, x, m, options=""):
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"""
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svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)
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y: a list/tuple/ndarray of l true labels (type must be int/double).
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It is used for calculating the accuracy. Use [] if true labels are
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unavailable.
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x: 1. a list/tuple of l training instances. Feature vector of
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each training instance is a list/tuple or dictionary.
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2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
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Predict data (y, x) with the SVM model m.
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options:
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-b probability_estimates: whether to predict probability estimates,
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0 or 1 (default 0).
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-q : quiet mode (no outputs).
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The return tuple contains
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p_labels: a list of predicted labels
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p_acc: a tuple including accuracy (for classification), mean-squared
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error, and squared correlation coefficient (for regression).
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p_vals: a list of decision values or probability estimates (if '-b 1'
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is specified). If k is the number of classes, for decision values,
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each element includes results of predicting k(k-1)/2 binary-class
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SVMs. For probabilities, each element contains k values indicating
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the probability that the testing instance is in each class.
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Note that the order of classes here is the same as 'model.label'
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field in the model structure.
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"""
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def info(s):
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print(s)
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if scipy and isinstance(x, np.ndarray):
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x = np.ascontiguousarray(x) # enforce row-major
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elif sparse and isinstance(x, sparse.spmatrix):
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x = x.tocsr()
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elif not isinstance(x, (list, tuple)):
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raise TypeError("type of x: {0} is not supported!".format(type(x)))
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if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, np.ndarray))):
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raise TypeError("type of y: {0} is not supported!".format(type(y)))
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predict_probability = 0
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argv = options.split()
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i = 0
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while i < len(argv):
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if argv[i] == '-b':
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i += 1
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predict_probability = int(argv[i])
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elif argv[i] == '-q':
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info = print_null
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else:
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raise ValueError("Wrong options")
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i+=1
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svm_type = m.get_svm_type()
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is_prob_model = m.is_probability_model()
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nr_class = m.get_nr_class()
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pred_labels = []
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pred_values = []
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if scipy and isinstance(x, sparse.spmatrix):
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nr_instance = x.shape[0]
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else:
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nr_instance = len(x)
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if predict_probability:
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if not is_prob_model:
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raise ValueError("Model does not support probabiliy estimates")
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if svm_type in [svm_forms.NU_SVR, svm_forms.EPSILON_SVR]:
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info("Prob. model for test data: target value = predicted value + z,\n"
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"z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g" % m.get_svr_probability());
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nr_class = 0
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prob_estimates = (c_double * nr_class)()
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for i in range(nr_instance):
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if scipy and isinstance(x, sparse.spmatrix):
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indslice = slice(x.indptr[i], x.indptr[i+1])
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xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED))
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else:
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xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED))
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label = libsvm.svm_predict_probability(m, xi, prob_estimates)
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values = prob_estimates[:nr_class]
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pred_labels += [label]
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pred_values += [values]
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else:
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if is_prob_model:
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info("Model supports probability estimates, but disabled in predicton.")
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if svm_type in [svm_forms.ONE_CLASS, svm_forms.EPSILON_SVR, svm_forms.NU_SVC]:
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nr_classifier = 1
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else:
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nr_classifier = nr_class*(nr_class-1)//2
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dec_values = (c_double * nr_classifier)()
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for i in range(nr_instance):
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if scipy and isinstance(x, sparse.spmatrix):
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indslice = slice(x.indptr[i], x.indptr[i+1])
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xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED))
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else:
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xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == kernel_names.PRECOMPUTED))
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label = libsvm.svm_predict_values(m, xi, dec_values)
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if(nr_class == 1):
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values = [1]
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else:
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values = dec_values[:nr_classifier]
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pred_labels += [label]
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pred_values += [values]
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if len(y) == 0:
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y = [0] * nr_instance
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ACC, MSE, SCC = evaluations(y, pred_labels)
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if svm_type in [svm_forms.EPSILON_SVR, svm_forms.NU_SVR]:
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info("Mean squared error = %g (regression)" % MSE)
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info("Squared correlation coefficient = %g (regression)" % SCC)
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else:
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info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(round(nr_instance*ACC/100)), nr_instance))
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return pred_labels, (ACC, MSE, SCC), pred_values
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