blob.py

def classification_report(y_test, y_pred):
    # calculate precision, recall, f1-score
    # TODO:
    cm = confusion_matrix(y_test,y_pred)
    precision = cm[1,1]/(cm[1,1] + cm[0,1])
    recall = cm[1,1]/(cm[1,1] + cm[1,0])
    f1 = 2*(precision * recall)/(precision + recall)
    acc = (cm[1,1] + cm[0,0]) / np.sum(cm.flatten())
    # end TODO
    return(precision,recall,f1,acc)

def confusion_matrix(y_test, y_pred):
    # return the 2x2 matrix
    # TODO:
    # https://stackoverflow.com/questions/68157408/using-numpy-to-test-for-false-positives-and-false-negatives
    result = np.array([[0, 0], [0, 0]])
    result[1,1] = np.sum(np.logical_and(y_pred == 1, y_test == 1))
    result[0,0] = np.sum(np.logical_and(y_pred == 0, y_test == 0))
    result[0,1] = np.sum(np.logical_and(y_pred == 1, y_test == 0))
    result[1,0] = np.sum(np.logical_and(y_pred == 0, y_test == 1))
    # end TODO
    return result