PrefixSpan 序列提取误区 [英] PrefixSpan sequence extraction misunderstanding

问题描述

我在代表窗口序列的列表中有一组大小为 3 的元组.我需要的是使用 pyspask 能够获得(给定元组的前两个部分)第三个.

I have a set of tuples of size three in a list that represent windowed sequences. What I need is using pyspask to be able to get (given the two first parts of the tuple) the third one.

所以我需要它根据频率创建三个元素的序列.

So I need it to create sequences of three elements based on their frequency.

这就是我正在做的:

data = [[['a','b','c'],['b','c','d'],['c','d','e'],['d','e','f'],['e','f','g'],['f','g','h'],['a','b','c'],['d','e','f'],['a','b','c'],['b','c','d'],['f','g','h'],['d','e','f'],['b','c','d']]]
rdd = spark.sparkContext.parallelize(data,2)
rdd.cache()
model = PrefixSpan.train( rdd, 0.2, 3)

print(sorted(model.freqSequences().take(100)))

虽然，我希望看到它们遵循字母表的序列和频率，但它们没有.

Although, I would expect to see the sequences and the frequencies o them to follow the alphabet, they don't.

我得到的序列如下:

FreqSequence(sequence=[[u'c'], [u'd'], [u'b']], freq=1)
FreqSequence(sequence=[[u'g'], [u'c'], [u'c']], freq=1)

没有出现在定义的那些中.显然，我构建特征的方式存在问题，或者我在此算法的目的和功能方面遗漏了一些东西..

which are not appearing in the defined ones. Obviously there is a problem in the way I have structure my features or I am missing something in the purpose and functionality of this algorithm..

谢谢！

推荐答案

首先让我们看看你的输入:

First let's look at your input:

rdd.count()

1

如您所见，您创建了一个只有一个序列的数据集.可以描述为:

As you can see you created a dataset with only one sequence. It can be described as:

<(abc)(bcd)(cde)(def)(efg)(fgh)(abc)(def)(abc)(bcd)(fgh)(def)(bcd)>

因此，鉴于输入，您获得的模式确实是正确的.例如

So patterns you get are indeed correct given the input. For example

FreqSequence(sequence=[[u'c'], [u'd'], [u'b']], freq=1)

对应于:

...(abc)(def)(abc)...

如果数据集的每个元素代表单独的序列数据，则可能具有以下形状:

If each element of the dataset represents individual sequence data could have the following shape:

rdd = sc.parallelize([
    [['a'], ['b'], ['c']], [['b'], ['c'], ['d']], [['c'], ['d'], ['e']],
    [['d'], ['e'], ['f']], [['e'], ['f'], ['g']], [['f'], ['g'], ['h']],
    [['a'], ['b'], ['c']], [['d'], ['e'], ['f']], [['a'], ['b'], ['c']],
    [['b'], ['c'], ['d']], [['f'], ['g'], ['h']], [['d'], ['e'], ['f']],
    [['b'], ['c'], ['d']]
])

rdd.count()

13

rdd.first()

[['a'], ['b'], ['c']]

哪里:

• 每个元素都是一个列表.
• 每个内部列表都代表给定位置的可能替代方案.

数据结构如下:

model = PrefixSpan.train(rdd, 0.2, 3)
model.freqSequences().top(5, key=lambda x: len(x.sequence))

[FreqSequence(sequence=[['d'], ['e'], ['f']], freq=3),
 FreqSequence(sequence=[['b'], ['c'], ['d']], freq=3),
 FreqSequence(sequence=[['a'], ['b'], ['c']], freq=3),
 FreqSequence(sequence=[['f'], ['g']], freq=3),
 FreqSequence(sequence=[['d'], ['f']], freq=3)]

model.freqSequences().top(5, key=lambda x: x.freq)

[FreqSequence(sequence=[['d']], freq=7),
 FreqSequence(sequence=[['c']], freq=7),
 FreqSequence(sequence=[['f']], freq=6),
 FreqSequence(sequence=[['b']], freq=6),
 FreqSequence(sequence=[['b'], ['c']], freq=6)]

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