使用Orange提取关联规则时的问题？

Question

我有一个尺寸为(878049, 6)的数据集。

它看起来像这样：

我想提取与其他列链接类别列关联规则。因此，从文档我试图用Orange-Associate如下：

In: 

import Orange 
data = Orange.data.Table("data.csv") 

In: 

data.domain.attributes 

Out: 

    (DiscreteVariable('Category', values=['ARSON', 'ASSAULT', 'BAD CHECKS', 'BRIBERY', 'BURGLARY', ...]), 
DiscreteVariable('Descript', values=['ABANDONMENT OF CHILD', 'ABORTION', 'ACCESS CARD INFORMATION, PUBLICATION OF', 'ACCESS CARD INFORMATION, THEFT OF', 'ACCIDENTAL BURNS', ...]), 
DiscreteVariable('DayOfWeek', values=['Friday', 'Monday', 'Saturday', 'Sunday', 'Thursday', ...]), 
DiscreteVariable('PdDistrict', values=['BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION', 'NORTHERN', ...]), 
DiscreteVariable('Resolution', values=['ARREST, BOOKED', 'ARREST, CITED', 'CLEARED-CONTACT JUVENILE FOR MORE INFO', 'COMPLAINANT REFUSES TO PROSECUTE', 'DISTRICT ATTORNEY REFUSES TO PROSECUTE', ...])) 

In: 

from orangecontrib.associate.fpgrowth import * 

X, mapping = OneHot.encode(data, include_class=True) 

X 

Out: 
array([[False, False, False, ..., False, False, False], 
     [False, False, False, ..., False, False, False], 
     [False, False, False, ..., False, False, False], 
     ..., 
     [False, False, False, ..., False, False, False], 
     [False, False, False, ..., False, False, False], 
     [False, False, False, ..., False, False, False]], dtype=bool) 

In: 

sorted(mapping.items()) 

Out: 

[(0, (0, 0)), 
(1, (0, 1)), 
(2, (0, 2)), 
(3, (0, 3)), 
(4, (0, 4)), 
(5, (0, 5)), 
(6, (0, 6)), 
(7, (0, 7)), 
.... 
(950, (4, 15)), 
(951, (4, 16))] 

然后：

In: 

itemsets = dict(frequent_itemsets(X, .4)) 

len(itemsets) 

Out: 

1 

In: 

class_items = {item 

       for item, var, _ in OneHot.decode(mapping, data, mapping) 

       if var is data.domain.class_var} 
In: 
sorted(class_items) 

Out: 

[] 

我认为，问题是我没有正确的Orange table屈服。因此，我应该如何使用橙色加载数据集才能生成关联规则？

更新

通过@ K3 --- RNC答案，我想这：

itemsets = dict(frequent_itemsets(X, .1)) 

print (len(itemsets)) 

print(itemsets) 

for itemset, _support in itemsets: 

    print(' '.join('{}={}'.format(var.name, val) 

        for _, var, val in OneHot.decode(itemset, data, mapping))) 

18 
{frozenset({935}): 206403, frozenset({20}): 92304, frozenset({928}): 119908, frozenset({924}): 129211, frozenset({946}): 526790, frozenset({921}): 116707, frozenset({946, 932}): 93924, frozenset({919}): 121584, frozenset({932}): 157182, frozenset({21}): 126182, frozenset({922}): 125038, frozenset({16}): 174900, frozenset({929}): 105296, frozenset({918}): 133734, frozenset({16, 946}): 156586, frozenset({925}): 89431, frozenset({923}): 124965, frozenset({920}): 126810} 

--------------------------------------------------------------------------- 
ValueError        Traceback (most recent call last) 
<ipython-input-83-83a24c082126> in <module>() 
     2 print (len(itemsets)) 
     3 print(itemsets) 
----> 4 for itemset, _support in itemsets: 
     5  print(' '.join('{}={}'.format(var.name, val) 
     6     for _, var, val in OneHot.decode(itemset, data, mapping))) 

ValueError: not enough values to unpack (expected 2, got 1) 

不过，我还是有同样的问题...我不能提取关联规则。

Answer 1

您试图在您的数据域中没有任何类变量的情况下引发分类规则。如果您打印data.domain，您会看到您只有常规属性和元数据。

[Category, DayOfWeek, PdDistrict, Resolution] {Descript, Address} 

要解决此问题，您需要将其中一个属性设置为类变量。

new_domain = Orange.data.Domain(list(data.domain.attributes[1:]), 
      data.domain.attributes[0], 
      metas=data.domain.metas) 

这将设置'Category'属性作为类变量。当然，你可以通过上面的例子设置自己的类变量。如果现在打印NEW_DOMAIN，你会看到这样的事情：

[DayOfWeek, PdDistrict, Resolution | Category] {Descript, Address} 

Answer 2

你可以看到你的发现项集包含有：

# Minimum 20% support. Decrease for more results 
itemsets = dict(frequent_itemsets(X, .2)) 

for itemset, _support in itemsets.items(): 
    print(' '.join('{}={}'.format(var.name, val) 
        for _, var, val in OneHot.decode(itemset, data, mapping))) 

会打印：

Category=ASSAULT DayOfWeek=Friday ... 

或任何具有40％支持的项目组是。