You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
276 lines
9.9 KiB
Python
276 lines
9.9 KiB
Python
#!/usr/bin/env python
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import unicode_literals
|
|
from __future__ import print_function
|
|
from __future__ import division
|
|
|
|
from builtins import str, bytes, dict, int
|
|
from builtins import object, range
|
|
from builtins import map, zip, filter
|
|
|
|
import os
|
|
import sys
|
|
from .libsvm import *
|
|
from .libsvm import __all__ as svm_all
|
|
|
|
__all__ = ['evaluations', 'svm_load_model', 'svm_predict', 'svm_read_problem',
|
|
'svm_save_model', 'svm_train'] + svm_all
|
|
|
|
sys.path = [os.path.dirname(os.path.abspath(__file__))] + sys.path
|
|
|
|
|
|
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 += [float(label)]
|
|
prob_x += [xi]
|
|
return (prob_y, prob_x)
|
|
|
|
|
|
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(model_file_name.encode())
|
|
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(model_file_name.encode(), model)
|
|
|
|
|
|
def evaluations(ty, pv):
|
|
"""
|
|
evaluations(ty, pv) -> (ACC, MSE, SCC)
|
|
|
|
Calculate accuracy, mean squared error and squared correlation coefficient
|
|
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
|
|
sumv = sumy = sumvv = sumyy = sumvy = 0
|
|
for v, y in zip(pv, ty):
|
|
if y == v:
|
|
total_correct += 1
|
|
total_error += (v - y) * (v - y)
|
|
sumv += v
|
|
sumy += y
|
|
sumvv += v * v
|
|
sumyy += y * y
|
|
sumvy += v * y
|
|
l = len(ty)
|
|
ACC = 100.0 * total_correct / l
|
|
MSE = total_error / l
|
|
try:
|
|
SCC = ((l * sumvy - sumv * sumy) * (l * sumvy - sumv * sumy)) / ((l * sumvv - sumv * sumv) * (l * sumyy - sumy * sumy))
|
|
except:
|
|
SCC = float('nan')
|
|
return (ACC, MSE, SCC)
|
|
|
|
|
|
def svm_train(arg1, arg2=None, arg3=None):
|
|
"""
|
|
svm_train(y, x [, options]) -> model | ACC | MSE
|
|
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)):
|
|
assert isinstance(arg2, (list, tuple))
|
|
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 is None or param is None:
|
|
raise TypeError("Wrong types for the arguments")
|
|
|
|
if param.kernel_type == PRECOMPUTED:
|
|
for xi in prob.x_space:
|
|
idx, val = xi[0].index, xi[0].value
|
|
if xi[0].index != 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)
|
|
|
|
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)
|
|
|
|
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 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 xi in x:
|
|
xi, idx = gen_svm_nodearray(xi, 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 xi in x:
|
|
xi, idx = gen_svm_nodearray(xi, 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]
|
|
|
|
ACC, MSE, SCC = evaluations(y, pred_labels)
|
|
l = len(y)
|
|
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(l * ACC / 100), l))
|
|
|
|
return pred_labels, (ACC, MSE, SCC), pred_values
|