print("\nPredicting a 1v1 classifier for funny :\n")
svm_funny_1v1 = OneVsOneClassifier(LinearSVC(random_state=0)).fit(
X, funny).predict(X)
print(svm_funny_1v1)
print("\nPredicting a 1vrest classifier for funny :\n")
svm_funny_1vr = OneVsRestClassifier(LinearSVC(random_state=0)).fit(
X, funny).predict(X)
print(svm_funny_1vr)
print("\nGetting results for 1v1 classifier using decision_function :\n")
funny_train = X[0:600]
funny_test = X[601:949]
funny_train_labels = funny[0:600]
funny_true = funny[601:949]
funny_pred = OneVsOneClassifier(LinearSVC(random_state=0)).fit(
funny_train, funny_train_labels).decision_function(funny_test)
funny_pred = funny_pred.astype(int)
funny_pred[funny_pred < 0] = 0
funny_pred[funny_pred > 1] = 1
funny_classes = ['class funny', 'class not_funny']
print("\nClassification report for funny 1v1 :\n")
print(classification_report(funny_true, funny_pred,
target_names=funny_classes))
print("\nAccuracy report for funny 1v1 :\n")
print(accuracy_score(funny_true, funny_pred))
print("\nGetting results for 1vrest classifier using decision_function :\n")
funny_train = X[0:600]
funny_test = X[601:949]
funny_train_labels = funny[0:600]
funny_true = funny[601:949]
funny_pred = OneVsRestClassifier(LinearSVC(random_state=0)).fit(
funny_train, funny_train_labels).decision_function(funny_test)
