你好，我已经对数据集进行了降采样，现在需要帮助来增加权重或为降采样的样本添加权重。请看下面的代码

#Separating majority and minority classesdf_majority = data[data.Collected_ind == 1]df_minority =  data[data.Collected_ind == 0]# Downsample majority classdf_majority_downsampled = resample(df_majority,                                  replace=False,    # sample without replacement                                 n_samples=152664,     # to match minority class                                 random_state=1) # reproducible results# Combining minority class with downsampled majority classdf_downsampled = pd.concat([df_majority_downsampled, df_minority])# Display new class countsdf_downsampled.Collected_ind.value_counts()df_downsampled['Collected_ind'].value_counts()df_downsampled['Collected_ind'].value_counts(normalize=True)#Randomly shuffle the rows.df_downsampled = df_downsampled.sample(frac=1)df_downsampled.to_csv("Sampled_Data.csv", index=False)#Generate a train and test dataset train = df_downsampled.sample(frac=0.8)test = df_downsampled.drop(train.index)train.to_csv("trainNew.csv", index=False)test.to_csv("testNew.csv", index=False)   

回答：

你的问题实际上帮助我解答了自己的问题，因为我正在寻找这种语法。既然我已经在这里了，我就给你展示一下我在做什么。我不知道你的权重定义是否与我的一样，但我们使用的是：

class_weight = (original_class_count/original_row_count) / (new_class_count/new_row_count)

所以，为了重新格式化你的代码，我会用len(df_minority)替换n_samples，然后通过动态使用各个数据框的长度，将上述公式作为一列添加到你的数据框中。

也许像这样

df_downsampled['weight']=np.where(df_downsampled['Collected_Ind']==1,(len(df_majority) / len(data) ) / ( len(df_minority) / len(df_minority) *2),(len(df_minority) / len(data) ) / ( len(df_minority) / len(df_minority) *2))