data = pd.read_csv(trainPath, header=0)

X = data.iloc[:, 5:17].values
y = data.iloc[:, 17:18].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

print(X_train.dtype, y_train.dtype)  # float64 int64

clf = svm.SVC(kernel='linear').fit(X_train, y_train.ravel())
print('done')

y_pred = clf.predict(X_test)

print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
print("Precision:", metrics.precision_score(y_test, y_pred))
print("Recall:", metrics.recall_score(y_test, y_pred))
tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
print(tn, fp, fn, tp)