Question

在我的数据中，大约有70个课程，我正在使用lightGBM来预测正确的课程标签。

在R中，我希望有一个自定义的“度量”功能，在这里我可以评估lightgbm的前3个预测是否覆盖了真实标签。

链接here令人鼓舞

def lgb_f1_score(y_hat, data):
    y_true = data.get_label()
    y_hat = np.round(y_hat) # scikits f1 doesn't like probabilities
    return 'f1', f1_score(y_true, y_hat), True

但是我不知道要起作用的参数的维数。似乎数据由于某种原因被拖延了。

Answer 1

Scikit学习实现

from sklearn.metrics import f1_score

def lgb_f1_score(y_true, y_pred):
    preds = y_pred.reshape(len(np.unique(y_true)), -1)
    preds = preds.argmax(axis = 0)
    print(preds.shape)
    print(y_true.shape)
    return 'f1', f1_score(y_true, preds,average='weighted'), True

Answer 2

在通读 lgb.train 和 lgb.cv 的文档后，我不得不创建一个单独的函数 get_ith_pred，然后在 lgb_f1_score 中重复调用它。

函数的文档字符串解释了它是如何工作的。我使用了与 LightGBM 文档中相同的参数名称。这可以用于任意数量的类，但不适用于二进制分类。在二元情况下，preds 是包含正类概率的一维数组。

from sklearn.metrics import f1_score

def get_ith_pred(preds, i, num_data, num_class):
    """
    preds: 1D NumPY array
        A 1D numpy array containing predicted probabilities. Has shape
        (num_data * num_class,). So, For binary classification with 
        100 rows of data in your training set, preds is shape (200,), 
        i.e. (100 * 2,).
    i: int
        The row/sample in your training data you wish to calculate
        the prediction for.
    num_data: int
        The number of rows/samples in your training data
    num_class: int
        The number of classes in your classification task.
        Must be greater than 2.
    
    
    LightGBM docs tell us that to get the probability of class 0 for 
    the 5th row of the dataset we do preds[0 * num_data + 5].
    For class 1 prediction of 7th row, do preds[1 * num_data + 7].
    
    sklearn's f1_score(y_true, y_pred) expects y_pred to be of the form
    [0, 1, 1, 1, 1, 0...] and not probabilities.
    
    This function translates preds into the form sklearn's f1_score 
    understands.
    """
    # Only works for multiclass classification
    assert num_class > 2

    preds_for_ith_row = [preds[class_label * num_data + i]
                        for class_label in range(num_class)]
    
    # The element with the highest probability is predicted
    return np.argmax(preds_for_ith_row)

    
def lgb_f1_score(preds, train_data):
    y_true = train_data.get_label()

    num_data = len(y_true)
    num_class = 70
    
    y_pred = []
    for i in range(num_data):
        ith_pred = get_ith_pred(preds, i, num_data, num_class)
        y_pred.append(ith_pred)
    
    return 'f1', f1_score(y_true, y_pred, average='weighted'), True

针对lightgbm中多类的自定义度量函数

2 个答案: