def supperclassify(train_set, train_label, test_set, test_label):
'''Different methods'''
train_voted = voting(train_set)
aux = train_voted == train_label
correct = sum(aux.astype(int))
_accuracy = (correct * 100) / len(train_label)
_precision, _recall, _f1score, _support = ut.get_measures_for_each_class(
train_label, train_voted)
print 'Estimator VOTING'
print 'Average Accuracy:\t', _accuracy
print 'Average Precision:\t', _precision
print 'Average Recall:\t', _recall
print 'Average F1 Measure:\t', _f1score
print '\n'
lambdas = weighted_voting_getlambdas(train_set, train_label)
results = weighted_voting(test_set, lambdas)
aux = results == test_label
correct = sum(aux.astype(int))
_accuracy = (correct * 100) / len(test_label)
_precision, _recall, _f1score, _support = ut.get_measures_for_each_class(
test_label, results)
print 'Estimator W_VOTING'
print 'Average Accuracy:\t', _accuracy
print 'Average Precision:\t', _precision
print 'Average Recall:\t', _recall
print 'Average F1 Measure:\t', _f1score
rf = clf.classifier_randomForest(train_set, train_label)
results = clf.evaluateResults(rf,
test_set,
test_label,
estimator_name='RF')
lr = clf.logistic_regression(train_set, train_label)
results = clf.evaluateResults(lr,
test_set,
test_label,
estimator_name='LR')
svm = clf.classifier_svm(train_set, train_label)
results = clf.evaluateResults(svm,
test_set,
test_label,
estimator_name='SVM')
rbf = clf.rbf_classifier(train_set, train_label)
results = clf.evaluateResults(rbf,
test_set,
test_label,
estimator_name='RBF')
def supperclassify(train_set, train_label, test_set, test_label):
'''Different methods'''
train_voted = voting(train_set)
aux = train_voted == train_label
correct = sum(aux.astype(int))
_accuracy = (correct * 100) / len(train_label)
_precision, _recall, _f1score, _support = ut.get_measures_for_each_class(train_label, train_voted)
print 'Estimator VOTING'
print 'Average Accuracy:\t', _accuracy
print 'Average Precision:\t', _precision
print 'Average Recall:\t', _recall
print 'Average F1 Measure:\t', _f1score
print '\n'
lambdas = weighted_voting_getlambdas(train_set, train_label)
results = weighted_voting(test_set, lambdas)
aux = results == test_label
correct = sum(aux.astype(int))
_accuracy = (correct * 100) / len(test_label)
_precision, _recall, _f1score, _support = ut.get_measures_for_each_class(test_label, results)
print 'Estimator W_VOTING'
print 'Average Accuracy:\t', _accuracy
print 'Average Precision:\t', _precision
print 'Average Recall:\t', _recall
print 'Average F1 Measure:\t', _f1score
rf = clf.classifier_randomForest(train_set, train_label)
results = clf.evaluateResults(rf, test_set, test_label, estimator_name='RF')
lr = clf.logistic_regression(train_set, train_label)
results = clf.evaluateResults(lr, test_set, test_label, estimator_name='LR')
svm = clf.classifier_svm(train_set, train_label)
results = clf.evaluateResults(svm, test_set, test_label, estimator_name='SVM')
rbf = clf.rbf_classifier(train_set, train_label)
results = clf.evaluateResults(rbf, test_set, test_label, estimator_name='RBF')
# import pdb; pdb.set_trace()
def evaluateResults(estimator, test_set, test_labels, estimator_name='Unknown', file_name=''):
result = estimator.predict(test_set)
# print result
aux = result == test_labels
correct = sum(aux.astype(int))
_accuracy = (correct * 100) / len(test_set)
cm = ut.get_confusion_matrix(test_labels, result, estimator_name, file_name=file_name)
print '\n'
_precision, _recall, _f1score, _support = ut.get_measures_for_each_class(test_labels, result)
print 'Estimator ', estimator_name
print 'Average Accuracy:\t', _accuracy
print 'Average Precision:\t', _precision
print 'Average Recall:\t', _recall
print 'Average F1 Measure:\t', _f1score
print '\n'
return result, _accuracy, _precision, _recall, _f1score
