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, 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
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
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, 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
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
