Pemangkasan hasil Apriori dasar

Analisis Market Basket dengan Python

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

Apriori dan aturan asosiasi

Apriori memangkas itemset.
- Menerapkan ambang dukungan minimum.
- Versi modifikasi bisa memangkas berdasar jumlah item.
- Tidak memberi tahu tentang aturan asosiasi.
Aturan asosiasi.
- Jauh lebih banyak aturan daripada itemset.
- {Tas, Kotak}: Tas -> Kotak ATAU Kotak -> Tas.

Cara menghitung aturan asosiasi

Menghitung aturan dari hasil Apriori.
- Sulit dienumerasi untuk n dan k besar.
- Bisa membatalkan pemangkasan itemset oleh Apriori.

Mengurangi jumlah aturan asosiasi.
- Modul mlxtend menyediakan cara memangkas aturan.
- association_rules() menerima item sering, metrik, dan ambang.

Cara menghitung aturan asosiasi

# Import Apriori algorithm
from mlxtend.frequent_patterns import apriori, association_rules

# Load one-hot encoded novelty gifts data
onehot = pd.read_csv('datasets/online_retail_onehot.csv')

# Apply Apriori algorithm
frequent_itemsets = apriori(onehot, 
                            use_colnames=True, 
                            min_support=0.0001)

# Compute association rules
rules = association_rules(frequent_itemsets,
                          metric = "support", 
                          min_threshold = 0.0)

Pentingnya pemangkasan

# Print the rules.
print(rules)

                               antecedents  ... conviction
0      (CARDHOLDER GINGHAM CHRISTMAS TREE)  ...      inf
...
79505      (SET OF 3 HEART COOKIE CUTTERS)  ... 1.998496

# Print the frequent itemsets.
print(frequent_itemsets)

       support                                           itemsets
0     0.000752                   ( 50'S CHRISTMAS GIFT BAG LARGE)
...
4707  0.000752                  (PIZZA PLATE IN BOX, CHRISTMAS ...

Pentingnya pemangkasan

# Compute association rules
rules = association_rules(frequent_itemsets,
                          metric = "support", 
                          min_threshold = 0.001)

# Print the rules.
print(rules)

                   antecedents      conviction  
0  (BIRTHDAY CARD, RETRO SPOT)  ...  2.977444 
1    (JUMBO BAG RED RETROSPOT)  ...  1.247180

Menjelajah himpunan aturan

print(rules.columns)

Index(['antecedents', 'consequents', 'antecedent support',
       'consequent support', 'support', 'confidence', 'lift', 'leverage',
       'conviction'],
      dtype='object')

print(rules[['antecedents','consequents']])

                   antecedents                  consequents
0    (JUMBO BAG RED RETROSPOT)  (BIRTHDAY CARD, RETRO SPOT)
1  (BIRTHDAY CARD, RETRO SPOT)    (JUMBO BAG RED RETROSPOT)

Pemangkasan dengan metrik lain

# Compute association rules
rules = association_rules(frequent_itemsets,
                          metric = "antecedent support", 
                          min_threshold = 0.002)

# Print the number of rules.
print(len(rules))

Ayo berlatih!

Analisis Market Basket dengan Python