最も簡単な指標

Python で学ぶマーケットバスケット分析

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

指標と枝刈り

「指標」はルールの性能指標です。
- {humor} $\rightarrow$ {poetry}
  - 0.81
- {fiction} $\rightarrow$ {travel}
  - 0.23
「枝刈り」は指標でルールを捨てることです。
- 残す: {humor} $\rightarrow$ {poetry}
- 捨てる: {fiction} $\rightarrow$ {travel}

最も簡単な指標

「サポート」は、アイテム集合を含む取引の割合です。

$$\frac{\text{items を含む取引数}}{\text{取引総数}}$$

$$\frac{\text{milk を含む取引数}}{\text{取引総数}}$$

language のサポート

TID	Transaction
0	travel, humor, fiction
1	humor, language
2	humor, biography, cooking
3	cooking, language
4	travel

{language} のサポート = 2 / 10 = 0.2

TID	Transaction
5	poetry, health, travel, history
6	humor
7	travel
8	poetry, fiction, humor
9	fiction, biography

{Humor} $\rightarrow$ {Language} のサポート

TID	Transaction
0	travel,humor,fiction
1	humor,language
2	humor,biography,cooking
3	cooking,language
4	travel

{language} $\rightarrow$ {humor} のサポート = 0.1

TID	Transaction
5	poetry,health,travel,history
6	humor
7	travel
8	poetry,fiction,humor
9	fiction,biography

データの準備

print(transactions)

[['travel', 'humor', 'fiction'],
...
['fiction', 'biography']]

from mlxtend.preprocessing import TransactionEncoder

# Instantiate transaction encoder
encoder = TransactionEncoder().fit(transactions)

データの準備

# One-hot encode itemsets by applying fit and transform
onehot = encoder.transform(transactions)

# Convert one-hot encoded data to DataFrame
onehot = pd.DataFrame(onehot, columns = encoder.columns_)
print(onehot)

   biography  cooking  ...  poetry  travel
0  False      False   ...   False    True
...
9  True       False   ...   False    False

単一アイテムのサポートの計算

print(onehot.mean())

biography    0.2
cooking      0.2
fiction      0.3
health       0.1
history      0.1
humor        0.5
language     0.2
poetry       0.2
travel       0.4
dtype: float64

複数アイテムのサポートの計算

import numpy as np

# Define itemset that contains fiction and poetry
onehot['fiction+poetry'] = np.logical_and(onehot['fiction'],onehot['poetry'])

print(onehot.mean())

biography         0.2
cooking           0.2
...               ...
travel            0.4
fiction+poetry    0.1
dtype: float64

Vamos praticar!

Python で学ぶマーケットバスケット分析