def classifier_multiclass_ecoc_discriminant (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,lawidth=2.1,C=1,epsilon=1e-5):
from modshogun import RealFeatures, MulticlassLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun import ECOCStrategy, ECOCDiscriminantEncoder, ECOCHDDecoder
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = LibLinear(L2R_L2LOSS_SVC)
classifier.set_epsilon(epsilon)
classifier.set_bias_enabled(True)
encoder = ECOCDiscriminantEncoder()
encoder.set_features(feats_train)
encoder.set_labels(labels)
encoder.set_sffs_iterations(50)
strategy = ECOCStrategy(encoder, ECOCHDDecoder())
classifier = LinearMulticlassMachine(strategy, feats_train, classifier, labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from modshogun import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def classifier_multiclass_ecoc_discriminant(
fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
label_test_multiclass=label_testdat,
lawidth=2.1,
C=1,
epsilon=1e-5):
from modshogun import RealFeatures, MulticlassLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun import ECOCStrategy, ECOCDiscriminantEncoder, ECOCHDDecoder
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = LibLinear(L2R_L2LOSS_SVC)
classifier.set_epsilon(epsilon)
classifier.set_bias_enabled(True)
encoder = ECOCDiscriminantEncoder()
encoder.set_features(feats_train)
encoder.set_labels(labels)
encoder.set_sffs_iterations(50)
strategy = ECOCStrategy(encoder, ECOCHDDecoder())
classifier = LinearMulticlassMachine(strategy, feats_train, classifier,
labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from modshogun import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def classifier_liblinear_modular (train_fname, test_fname, label_fname, C, epsilon): from modshogun import RealFeatures, SparseRealFeatures, BinaryLabels from modshogun import LibLinear, L2R_L2LOSS_SVC_DUAL from modshogun import Math_init_random, CSVFile Math_init_random(17) feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) labels=BinaryLabels(CSVFile(label_fname)) svm=LibLinear(C, feats_train, labels) svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) svm.set_epsilon(epsilon) svm.set_bias_enabled(True) svm.train() predictions = svm.apply(feats_test) return predictions, svm, predictions.get_labels()
def main():
fright=open(r'circle counterclockwise/train_cc_10_dim.txt','r')
fleft=open(r'letters/Z/train_Z_10_dim.txt','r')
data_right=np.load(fright)
data_left=np.load(fleft)
lenr=len(data_right[0])
lenl=len(data_left[0])
dim=10
endr=int(0.9*lenr)
endl=int(0.9*lenl)
data_right_train=data_right[:,0:endr]
data_left_train=data_left[:,0:endl]
data_right_test=data_right[:,endr:]
data_left_test=data_left[:,endl:]
len_right_train=len(data_right_train[0])
len_left_train=len(data_left_train[0])
len_right_test=len(data_right_test[0])
len_left_test=len(data_left_test[0])
label_right_train=np.ones(len_right_train)
label_right_test=np.ones(len_right_test)
label_left_train=-1*np.ones(len_left_train)
label_left_test=-1*np.ones(len_left_test)
C=10
train_dataset=np.hstack((data_right_train,data_left_train))
normalization_mean=[]
normalization_std=[]
for i in range(0,dim):
mean1=np.mean(train_dataset[i])
std1=np.std(train_dataset[i])
train_dataset[i]=(train_dataset[i]-mean1)/std1
normalization_mean.append(mean1)
normalization_std.append(std1)
feats_train=RealFeatures(train_dataset)
f,axes=subplots(2,2)
fig = figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data_right_train[0],data_right_train[1],data_right_train[2],c='r')
ax.scatter(data_left_train[0],data_left_train[1],data_left_train[2],c='b')
ax.set_xlabel('STD X')
ax.set_ylabel('STD Y')
ax.set_zlabel('STD Z')
ax.set_title('3D PLOT OF STD')
axes[0,0].plot(data_right_train[0],data_right_train[1],'*')
axes[0,0].plot(data_left_train[0],data_left_train[1],'x')
axes[0,0].set_xlabel('STDX')
axes[0,0].set_ylabel('std y')
axes[0,0].set_title('STDX VS stdy')
axes[0,1].plot(data_right_train[1],data_right_train[2],'*')
axes[0,1].plot(data_left_train[1],data_left_train[2],'x')
axes[0,1].set_xlabel('std Y')
axes[0,1].set_ylabel('std Z')
axes[0,1].set_title('stdY VS stdZ')
axes[1,0].plot(data_right_train[0],data_right_train[2],'*')
axes[1,0].plot(data_left_train[0],data_left_train[2],'x')
axes[1,0].set_xlabel('std X')
axes[1,0].set_ylabel('std z')
axes[1,0].set_title('std X VS stdz')
show()
train_labels=np.hstack((label_right_train,label_left_train))
test_dataset=np.hstack((data_right_test,data_left_test))
normalization_file=open('normalization_parameters_cc_vs_letters.txt','w')
norm_params=[normalization_mean,normalization_std]
norm_params=np.asarray(norm_params)
np.save(normalization_file,norm_params)
for i in range(0,dim):
test_dataset[i]=(test_dataset[i]-normalization_mean[i])/normalization_std[i]
labels_train=BinaryLabels(train_labels)
print 'the length of test_dataset is ',len(test_dataset[0])
feats_test=RealFeatures(test_dataset)
test_labels=np.hstack((label_right_test,label_left_test))
print 'the length is test_labels is ',len(test_labels)
labels_test=BinaryLabels(test_labels)
svm=LibLinear(C,feats_train,labels_train)
epsilon=1e-3
svm.set_epsilon(epsilon)
svm.train()
predictions=svm.apply(feats_test)
predictions_on_train=svm.apply(feats_train)
evaluator1=ROCEvaluation()
evaluator2=AccuracyMeasure()
evaluator1.evaluate(predictions_on_train,labels_train)
evaluator2.evaluate(predictions,labels_test)
file_name="z_vs_cc/accuracy="+ str(evaluator2.get_accuracy()) + " liblinear_cc_vs_Z_svm_classifier_with_C_10_and_normalized.h5"
f3=SerializableHdf5File(file_name, "w")
svm.save_serializable(f3)
p_test=predictions.get_labels()
for i in range(0,len(test_labels)):
print 'predicted : ',p_test[i] ,' and actual ',test_labels[i]
print 'the Area under the curve is ',evaluator1.get_auROC()
print 'the accuracy is ',evaluator2.get_accuracy()*100
evaluator1.evaluate(predictions,labels_test)
print 'the auc for test set is ',evaluator1.get_auROC()
#!/usr/bin/env python2.7 # # This software is distributed under BSD 3-clause license (see LICENSE file). # # Copyright (C) 2014 Thoralf Klein # from modshogun import RealFeatures, BinaryLabels, LibLinear from numpy import random, mean X_train = RealFeatures(random.randn(30, 100)) Y_train = BinaryLabels(random.randn(X_train.get_num_vectors())) svm = LibLinear(1.0, X_train, Y_train) svm.train() Y_pred = svm.apply_binary(X_train) Y_train.get_labels() == Y_pred.get_labels() print "accuracy:", mean(Y_train.get_labels() == Y_pred.get_labels())