Question

我试图像下面的代码那样解决数据，但是我没有使用groupy和udf弄清楚它，而且发现udf无法返回数据帧。

是否有任何方法可以通过spark或其他方法来处理不平衡数据

ratio = 3
def balance_classes(grp):
    picked = grp.loc[grp.editorsSelection == True]
    n = round(picked.shape[0]*ratio)
    if n:        
        try:
            not_picked = grp.loc[grp.editorsSelection == False].sample(n)
        except: # In case, fewer than n comments with `editorsSelection == False`
            not_picked = grp.loc[grp.editorsSelection == False]
        balanced_grp = pd.concat([picked, not_picked])
        return balanced_grp
    else: # If no editor's pick for an article, dicard all comments from that article
        return None 

comments = comments.groupby('articleID').apply(balance_classes).reset_index(drop=True)

Answer 1

我通常使用这种逻辑来欠采样：

def resample(base_features,ratio,class_field,base_class):
    pos = base_features.filter(col(class_field)==base_class)
    neg = base_features.filter(col(class_field)!=base_class)
    total_pos = pos.count()
    total_neg = neg.count()
    fraction=float(total_pos*ratio)/float(total_neg)
    sampled = neg.sample(False,fraction)
    return sampled.union(pos)

base_feature是具有这些功能的Spark数据框。 ratio是期望的正负比率。class_field是保存类的列的名称，base_class是类的ID

如何使用pyspark对多数类进行低采样

1 个答案: