सबसे सरल मेट्रिक
Python में Market Basket Analysis
Isaiah Hull
Visiting Associate Professor of Finance, BI Norwegian Business School
मेट्रिक्स और प्रूनिंग
- मेट्रिक नियमों के प्रदर्शन का माप है.
- {humor} $\rightarrow$ {poetry}
- 0.81
- {fiction} $\rightarrow$ {travel}
- 0.23
- {humor} $\rightarrow$ {poetry}
- Pruning मतलब मेट्रिक से कमजोर नियम हटाना.
- रखें: {humor} $\rightarrow$ {poetry}
- हटाएँ: {fiction} $\rightarrow$ {travel}
सबसे सरल मेट्रिक
- support मेट्रिक उन transactions का हिस्सा मापता है जिनमें कोई itemset आता है.
$$\frac{\text{number of transactions with items(s)}}{\text{number of transactions}}$$
$$\frac{\text{number of transactions with milk}}{\text{total transactions}}$$
language का support
| TID | Transaction |
|---|---|
| 0 | travel, humor, fiction |
| 1 | humor, language |
| 2 | humor, biography, cooking |
| 3 | cooking, language |
| 4 | travel |
{language} का support = 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} का support
| TID | Transaction |
|---|---|
| 0 | travel,humor,fiction |
| 1 | humor,language |
| 2 | humor,biography,cooking |
| 3 | cooking,language |
| 4 | travel |
{language} $\rightarrow$ {humor} का SUPPORT = 0.1
| TID | Transaction |
|---|---|
| 5 | poetry,health,travel,history |
| 6 | humor |
| 7 | travel |
| 8 | poetry,fiction,humor |
| 9 | fiction,biography |
डेटा तैयार करना
print(transactions)
[['travel', 'humor', 'fiction'],
...
['fiction', 'biography']]
from mlxtend.preprocessing import TransactionEncoder
# Instantiate transaction encoder
encoder = TransactionEncoder().fit(transactions)
डेटा तैयार करना
# 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
एकल items के लिए support निकालना
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
एकाधिक items के लिए support निकालना
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
अभ्यास करते हैं!
Python में Market Basket Analysis
