我有一个 Pandas DataFrame,其外观大致如下:

cli_id | X1 | X2 | X3 | ... | Xn |  Y  |
----------------------------------------
123    | 1  | A  | XX | ... | 4  | 0.1 |
456    | 2  | B  | XY | ... | 5  | 0.2 |
789    | 1  | B  | XY | ... | 5  | 0.3 |
101    | 2  | A  | XX | ... | 4  | 0.1 |
...

我有客户端ID,很少有分类属性,Y是事件的概率,其值从0到1乘以0.1。

我需要在每组(所以10折)Y大小为200 的Y中进行分层抽样

在分为训练/测试时,我经常使用它来抽取分层样本:
def stratifiedSplit(X,y,size):
    sss = StratifiedShuffleSplit(y, n_iter=1, test_size=size, random_state=0)

    for train_index, test_index in sss:
        X_train, X_test = X.iloc[train_index], X.iloc[test_index]
        y_train, y_test = y.iloc[train_index], y.iloc[test_index]

    return X_train, X_test, y_train, y_test

但是在这种情况下,我不知道如何修改它。

最佳答案

我不确定您是否是这个意思:

strats = []
for k in range(11):
    y_val = k*0.1
    dummy_df = your_df[your_df['Y'] == y_val]
    stats.append( dummy_df.sample(200) )

这将使虚拟数据帧仅包含所需的Y值,然后获取200个样本。

确定,因此您需要不同的块以具有相同的结构。我想这有点难,这是我的处理方式:

首先,我将得到X1的直方图:
hist, edges = np.histogram(your_df['X1'], bins=np.linespace(min_x, max_x, nbins))

我们现在有了一个带有nbins bins的直方图。

现在的策略是根据X1的值绘制一定数量的行。我们将从具有更多观察结果的容器中获取更多信息,而从具有更少观察值的容器中获取更少信息,从而保留X的结构。

特别是,每个垃圾箱的相对贡献应为:
rel = [float(i) / sum(hist) for i in hist]

这将类似于[0.1, 0.2, 0.1, 0.3, 0.3]
如果需要200个样本,则需要绘制:
draws_in_bin = [int(i*200) for i in rel]

现在我们知道从每个箱中抽取多少个观测值:
strats = []
for k in range(11):
        y_val = k*0.1

        #get a dataframe for every value of Y
        dummy_df = your_df[your_df['Y'] == y_val]

        bin_strat = []
        for left_edge, right_edge, n_draws in zip(edges[:-1], edges[1:], draws_in_bin):

             bin_df = dummy_df[ (dummy_df['X1']> left_edge)
                              & (dummy_df['X1']< right_edge) ]

             bin_strat.append(bin_df.sample(n_draws))
             # this takes the right number of draws out
             # of the X1 bin where we currently are
             # Note that every element of bin_strat is a dataframe
             # with a number of entries that corresponds to the
             # structure of draws_in_bin
        #
        #concatenate the dataframes for every bin and append to the list
        strats.append( pd.concat(bin_strat) )

关于python - Pandas 的分层 sample ,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/41035187/

10-10 11:08