CORA basic word embedding SVC model

cora_word_emb_svc.py

def modeler(k_folds=5, model='linear', show_matrix=True):

    acc_scores = []

    df = create_df()
    X, y = create_X_y(df)

    for i in range(0, k_folds):
        
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
        clf = svm.SVC(kernel='linear', class_weight='balanced')
        clf.fit(X_train, y_train)
        pred = clf.predict(X_test)

        acc = accuracy_score(pred, y_test)
        acc_scores.append(acc)        
        
    print('Accuracy scores: ', acc_scores)
    print('Mean accuracy: ', np.mean(acc_scores))
    
    if show_matrix:
        matrix = plot_confusion_matrix(clf, X_test, y_test, cmap=plt.cm.Blues, normalize='true')
        plt.show(matrix)
        plt.show()
        
    return