def train_liblinear_classifier_core(trainXy,
classifier_type = "liblinear",
trace_normalize=False,
**kwargs
):
""" Classifier training using SVMs
Input:
train_features = training features (both positive and negative)
train_labels = corresponding label vector
svm_eps = eps of svm
svm_C = C parameter of svm
classifier_type = liblinear or libsvm"""
#do normalization
(train_features, train_labels), train_mean, train_std, trace = normalize([trainXy],
trace_normalize=trace_normalize)
if classifier_type == 'liblinear':
clf = sklearn_svm.LinearSVC(**kwargs)
if classifier_type == 'libSVM':
clf = sklearn_svm.SVC(**kwargs)
elif classifier_type == 'LRL':
clf = LogisticRegression(**kwargs)
elif classifier_type == 'MCC':
clf = CorrelationClassifier(**kwargs)
elif classifier_type.startswith('svm.'):
ct = classifier_type.split('.')[-1]
clf = getattr(sklearn_svm,ct)(**kwargs)
elif classifier_type.startswith('linear_model.'):
ct = classifier_type.split('.')[-1]
clf = getattr(sklearn_linear_model,ct)(**kwargs)
clf.fit(train_features, train_labels)
return clf, train_mean, train_std, trace
def classifier_train(train_features,
train_labels,
test_features,
svm_eps = 1e-5,
svm_C = 10**4,
classifier_type = "liblinear",
multi_class=False,
sphere = True
):
""" Classifier training using SVMs
Input:
train_features = training features (both positive and negative)
train_labels = corresponding label vector
svm_eps = eps of svm
svm_C = C parameter of svm
classifier_type = liblinear or libsvm"""
#sphering
if sphere:
train_features, test_features = __sphere(train_features, test_features)
if classifier_type == 'liblinear':
clf = svm.LinearSVC(eps = svm_eps, C = svm_C,multi_class=multi_class)
if classifier_type == 'libSVM':
clf = svm.SVC(eps = svm_eps, C = svm_C, probability = True)
elif classifier_type == 'LRL1':
clf = LogisticRegression(C=svm_C, penalty = 'l1')
elif classifier_type == 'LRL2':
clf = LogisticRegression(C=svm_C, penalty = 'l1')
clf.fit(train_features, train_labels)
return clf
def classifier_train(train_features,
train_labels,
test_features,
classifier_type = "liblinear",
sphere = True,
**kwargs
):
""" Classifier training using SVMs
Input:
train_features = training features (both positive and negative)
train_labels = corresponding label vector
svm_eps = eps of svm
svm_C = C parameter of svm
classifier_type = liblinear or libsvm"""
#sphering
if sphere:
train_features, test_features,fmean,fstd = __sphere(train_features, test_features)
else:
fmean = None
fstd = None
if classifier_type == 'liblinear':
clf = svm.LinearSVC(**kwargs)
if classifier_type == 'libSVM':
clf = svm.SVC(**kwargs)
elif classifier_type == 'LRL':
clf = LogisticRegression(**kwargs)
elif classifier_type == 'MCC':
clf = CorrelationClassifier(**kwargs)
clf.fit(train_features, train_labels)
return clf,fmean, fstd, sphere
def train_liblinear_classifier_core(trainXy,
classifier_type="liblinear",
trace_normalize=False,
**kwargs):
""" Classifier training using SVMs
Input:
train_features = training features (both positive and negative)
train_labels = corresponding label vector
svm_eps = eps of svm
svm_C = C parameter of svm
classifier_type = liblinear or libsvm"""
#do normalization
(train_features, train_labels), train_mean, train_std, trace = normalize(
[trainXy], trace_normalize=trace_normalize)
if classifier_type == 'liblinear':
clf = sklearn_svm.LinearSVC(**kwargs)
if classifier_type == 'libSVM':
clf = sklearn_svm.SVC(**kwargs)
elif classifier_type == 'LRL':
clf = LogisticRegression(**kwargs)
elif classifier_type == 'MCC':
clf = CorrelationClassifier(**kwargs)
elif classifier_type.startswith('svm.'):
ct = classifier_type.split('.')[-1]
clf = getattr(sklearn_svm, ct)(**kwargs)
elif classifier_type.startswith('linear_model.'):
ct = classifier_type.split('.')[-1]
clf = getattr(sklearn_linear_model, ct)(**kwargs)
clf.fit(train_features, train_labels)
return clf, train_mean, train_std, trace
def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=None):
min_c = l1_min_c(X, y, loss, fit_intercept, intercept_scaling)
clf = {
('log', False): LogisticRegression(penalty='l1'),
('log', True): SparseLogRegression(penalty='l1'),
('l2', False): LinearSVC(loss='l2', penalty='l1', dual=False),
('l2', True): SparseSVC(loss='l2', penalty='l1', dual=False),
}[loss, sp.issparse(X)]
clf.fit_intercept = fit_intercept
clf.intercept_scaling = intercept_scaling
clf.C = min_c
clf.fit(X, y)
assert (np.asanyarray(clf.coef_) == 0).all()
assert (np.asanyarray(clf.intercept_) == 0).all()
clf.C = min_c * 1.01
clf.fit(X, y)
assert (np.asanyarray(clf.coef_) != 0).any() or \
(np.asanyarray(clf.intercept_) != 0).any()
