我想将先验算法应用于零售数据集(来自零售商店的市场篮数据(。它的数据形式为：-
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
30 31 32
33 34 35
36 37 38 39 40 41 42 43 44 45 46
38 39 47 48
38 39 48 49 50 51 52 53 54 55 56 57 58
32 41 59 60 61 62
3 39 48
因此，为了使用Apriori算法，我需要将Python列表形式的数据放入Numpy数组中，如下所示：
Column Names as 0 1 2 3 4 5 6 7 8 9 10........
数据集为：
0 1 2 3 4 5 6 7 8 9 10 .........30 31 32 33 34 35....
1 1 1 1 1 1 1 1 1 1 1...........0 0 0 0 0 0...
0 0 0 0 0 0 0 0 0 0 0...........1 1 1 0 0 0..
and so on..
为此，我正在尝试使用事务编码器：-
dataset = pd.read_csv('retail.dat', header=None)
from mlxtend.preprocessing import TransactionEncoder
transactionEncoder = TransactionEncoder()
dataset = transactionEncoder.fit(dataset).transform(dataset)
dataset.astype('int')
print(dataset)
但是我收到错误：-
TypeError: 'int' object is not iterable
我还想将列名作为 0 1 2.... 附加到新形成的数据集，但print(transactionEncoder.columns_)没有给出有效的列。请告诉可能是什么问题以及在此数据集上应用事务编码器的正确方法是什么......
IIUC，您可以堆叠数据帧并尝试crosstab：
df = pd.read_csv('retail.dat', sep=' ', header=None)
new_df = df.stack().astype(int).reset_index(name='value')
pd.crosstab(new_df['level_0'], new_df['value'])
输出：
value 0 1 2 3 4 5 6 7 8 9 ... 53 54 55 56 57 58 ...
level_0 ...
0 1 1 1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1
6 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0
7 0 0 0 1 0 0 0 0 0 0 ... 0 0 0 0 0 0
你可以试试这个：
import pandas as pd
import numpy as np
from io import StringIO
from mlxtend.preprocessing import TransactionEncoder
inputstr = StringIO("""0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
30 31 32
33 34 35
36 37 38 39 40 41 42 43 44 45 46
38 39 47 48
38 39 48 49 50 51 52 53 54 55 56 57 58
32 41 59 60 61 62
3 39 48 """)
df = pd.read_csv(inputstr, header=None,sep='s+')
df_out = df.apply(lambda x: list(x.dropna().values), axis=1).tolist()
transactionEncoder = TransactionEncoder()
dataset = transactionEncoder.fit(df_out).transform(df_out)
dataset = dataset.astype('int')
print(dataset)
输出：
[[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1]
[0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
并转换为数据帧：
dataset_df = pd.DataFrame(dataset)
输出：
0 1 2 3 4 5 6 7 8 9 ... 53 54 55 56 57 58 59
0 1 1 1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 0
6 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 1
7 0 0 0 1 0 0 0 0 0 0 ... 0 0 0 0 0 0 0