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Ricardo Montañana Gómez
2025-06-22 00:31:33 +02:00
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commit 4bdbcad256
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import os, sys
from .liblinear import *
from .liblinear import __all__ as liblinear_all
from .commonutil import *
from .commonutil import __all__ as common_all
from ctypes import c_double
try:
import numpy as np
import scipy
from scipy import sparse
except:
scipy = None
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__ = ['load_model', 'save_model', 'train', 'predict'] + liblinear_all + common_all
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(_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 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(_cstr(model_file_name), model)
def train(arg1, arg2=None, arg3=None):
"""
train(y, x [, options]) -> model | ACC
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).
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)
either accuracy (ACC) or mean-squared error (MSE) is returned.
options:
-s type : set type of solver (default 1)
for multi-class classification
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 -- 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)
for regression
11 -- L2-regularized L2-loss support vector regression (primal)
12 -- L2-regularized L2-loss support vector regression (dual)
13 -- L2-regularized L1-loss support vector regression (dual)
for outlier detection
21 -- one-class support vector machine (dual)
-c cost : set the parameter C (default 1)
-p epsilon : set the epsilon in loss function of SVR (default 0.1)
-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 11
|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)
-s 1, 3, 4, 7, and 21
Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)
-s 5 and 6
|f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf,
where f is the primal function (default 0.01)
-s 12 and 13
|f'(alpha)|_1 <= eps |f'(alpha0)|,
where f is the dual function (default 0.1)
-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
-R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is
(for -s 0, 2, 5, 6, 11)"
-wi weight: weights adjust the parameter C of different classes (see README for details)
-v n: n-fold cross validation mode
-C : find parameters (C for -s 0, 2 and C, p for -s 11)
-q : quiet mode (no outputs)
"""
prob, param = None, None
if isinstance(arg1, (list, tuple)) or (scipy and isinstance(arg1, np.ndarray)):
assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (np.ndarray, sparse.spmatrix)))
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.flag_find_parameters:
nr_fold = param.nr_fold
best_C = c_double()
best_p = c_double()
best_score = c_double()
if param.flag_C_specified:
start_C = param.C
else:
start_C = -1.0
if param.flag_p_specified:
start_p = param.p
else:
start_p = -1.0
liblinear.find_parameters(prob, param, nr_fold, start_C, start_p, best_C, best_p, best_score)
if param.solver_type in [solver_names.L2R_LR, solver_names.L2R_L2LOSS_SVC]:
print("Best C = %g CV accuracy = %g%%\n"% (best_C.value, 100.0*best_score.value))
elif param.solver_type in [solver_names.L2R_L2LOSS_SVR]:
print("Best C = %g Best p = %g CV MSE = %g\n"% (best_C.value, best_p.value, best_score.value))
return best_C.value,best_p.value,best_score.value
elif param.flag_cross_validation:
l, nr_fold = prob.l, param.nr_fold
target = (c_double * l)()
liblinear.cross_validation(prob, param, nr_fold, target)
ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
if param.solver_type in [solver_names.L2R_L2LOSS_SVR, solver_names.L2R_L2LOSS_SVR_DUAL, solver_names.L2R_L1LOSS_SVR_DUAL]:
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 = liblinear.train(prob, param)
m = toPyModel(m)
return m
def predict(y, x, m, options=""):
"""
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 output probability estimates, 0 or 1 (default 0); currently for logistic regression only
-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 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.
"""
def info(s):
print(s)
if scipy and isinstance(x, np.ndarray):
x = np.ascontiguousarray(x) # enforce row-major
elif scipy 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, np.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
solver_type = m.param.solver_type
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 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 TypeError('probability output is only supported for logistic regression')
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_feature_nodearray((x.indices[indslice], x.data[indslice]), feature_max=nr_feature)
else:
xi, idx = gen_feature_nodearray(x[i], 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 i in range(nr_instance):
if scipy and isinstance(x, sparse.spmatrix):
indslice = slice(x.indptr[i], x.indptr[i+1])
xi, idx = gen_feature_nodearray((x.indices[indslice], x.data[indslice]), feature_max=nr_feature)
else:
xi, idx = gen_feature_nodearray(x[i], 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] * nr_instance
ACC, MSE, SCC = evaluations(y, pred_labels)
if m.is_regression_model():
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