Spark 数据帧随机拆分 [英] Spark Data Frame Random Splitting

问题描述

我有一个 spark 数据框，我想按比例 0.60、0.20、0.20 将其分为训练、验证和测试.

I have a spark data frame which I want to divide into train, validation and test in the ratio 0.60, 0.20,0.20.

我使用了以下代码:

def data_split(x):
    global data_map_var
    d_map = data_map_var.value
    data_row = x.asDict()
    import random
    rand = random.uniform(0.0,1.0)
    ret_list = ()
    if rand <= 0.6:
        ret_list = (data_row['TRANS'] , d_map[data_row['ITEM']] , data_row['Ratings'] , 'train')
    elif rand <=0.8:
        ret_list = (data_row['TRANS'] , d_map[data_row['ITEM']] , data_row['Ratings'] , 'test')
    else:
        ret_list = (data_row['TRANS'] , d_map[data_row['ITEM']] , data_row['Ratings'] , 'validation')
    return ret_list
​
​
split_sdf = ratings_sdf.map(data_split)
train_sdf = split_sdf.filter(lambda x : x[-1] == 'train').map(lambda x :(x[0],x[1],x[2]))
test_sdf = split_sdf.filter(lambda x : x[-1] == 'test').map(lambda x :(x[0],x[1],x[2]))
validation_sdf = split_sdf.filter(lambda x : x[-1] == 'validation').map(lambda x :(x[0],x[1],x[2]))
​
print "Total Records in Original Ratings RDD is {}".format(split_sdf.count())
​
print "Total Records in training data RDD is {}".format(train_sdf.count())
​
print "Total Records in validation data RDD is {}".format(validation_sdf.count())
​
print "Total Records in test data RDD is {}".format(test_sdf.count())
​
​
#help(ratings_sdf)
Total Records in Original Ratings RDD is 300001
Total Records in training data RDD is 180321
Total Records in validation data RDD is 59763
Total Records in test data RDD is 59837

我的原始数据框是 ratings_sdf，我用它来传递执行拆分的映射器函数.

My original data frame is ratings_sdf which I use to pass a mapper function which does the splitting.

如果您检查训练的总和，验证和测试的总和不等于拆分(原始评分)计数.这些数字在每次运行代码时都会发生变化.

If you check the total sum of train, validation and test does not sum to split (original ratings) count. And these numbers change at every run of the code.

剩余的记录去哪里了，为什么总和不相等?

Where is the remaining records going and why the sum is not equal?

推荐答案

TL;DR 如果要拆分 DataFrame 使用 randomSplit 方法:

TL;DR If you want to split DataFrame use randomSplit method:

ratings_sdf.randomSplit([0.6, 0.2, 0.2])

您的代码在多个层面上都是错误的，但有两个基本问题使其无法修复:

Your code is just wrong on multiple levels but there are two fundamental problems that make it broken beyond repair:

• Spark 转换可以被评估任意次数，并且您使用的函数应该是引用透明的并且没有副作用.您的代码多次评估 split_sdf 并且您使用有状态的 RNG data_split 所以每次结果都不同.

• Spark transformations can be evaluated arbitrary number of times and functions you use should be referentially transparent and side effect free. Your code evaluates split_sdf multiple times and you use stateful RNG data_split so each time results are different.

这会导致您描述的行为，其中每个孩子看到父 RDD 的不同状态.

This results in a behavior you describe where each child sees different state of the parent RDD.

您没有正确初始化 RNG，因此您获得的随机值不是独立的.

You don't properly initialize RNG and in consequence random values you get are not independent.

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