python-liblinear 1.8+dfsg-1 / usr / share / pyshared / liblinearutil.py

#!/usr/bin/env python

from liblinear 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 (primal)
			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
			7 -- L2-regularized logistic regression (dual)
		-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, 4, and 7
				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