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Merge pull request #29 from TEAM-7-SAD/new
SPD-81 - feat: add the apriori algo and its data visualization on the…
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| import warnings | ||
| import mysql.connector | ||
| import pandas as pd | ||
| from mlxtend.frequent_patterns import apriori, association_rules | ||
| import datetime | ||
|
|
||
| # Suppress specific warnings | ||
| warnings.filterwarnings('ignore', category=DeprecationWarning) | ||
|
|
||
| # Database connection details | ||
| db_config = { | ||
| 'user': 'root', | ||
| 'password': '', | ||
| 'host': 'localhost', | ||
| 'database': 'sweet_avenue_db' | ||
| } | ||
|
|
||
| # Connect to the database | ||
| cnx = mysql.connector.connect(**db_config) | ||
| cursor = cnx.cursor() | ||
|
|
||
| # Create the table if it doesn't exist | ||
| create_table_query = """ | ||
| CREATE TABLE IF NOT EXISTS frequent_items ( | ||
| id INT AUTO_INCREMENT PRIMARY KEY, | ||
| antecedent VARCHAR(255), | ||
| consequent VARCHAR(255), | ||
| support FLOAT, | ||
| confidence FLOAT, | ||
| lift FLOAT, | ||
| conviction FLOAT | ||
| ) | ||
| """ | ||
| cursor.execute(create_table_query) | ||
|
|
||
| # Fetch transaction data for today | ||
| today = datetime.datetime.now().strftime('2024-05-01') # Corrected the date format | ||
| query = """ | ||
| SELECT ip.transaction_id, d.name AS item_name | ||
| FROM items_purchased ip | ||
| JOIN drink_item d ON ip.item_id = d.id | ||
| JOIN transaction t ON ip.transaction_id = t.id | ||
| WHERE DATE(t.timestamp) = %s | ||
| UNION | ||
| SELECT ip.transaction_id, f.name AS item_name | ||
| FROM items_purchased ip | ||
| JOIN food_item f ON ip.item_id = f.id | ||
| JOIN transaction t ON ip.transaction_id = t.id | ||
| WHERE DATE(t.timestamp) = %s | ||
| """ | ||
| cursor.execute(query, (today, today)) | ||
| rows = cursor.fetchall() | ||
|
|
||
| # Convert data to a DataFrame | ||
| df = pd.DataFrame(rows, columns=['transaction_id', 'item_name']) | ||
|
|
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| # Create the basket format needed for Apriori | ||
| basket = df.pivot_table(index='transaction_id', columns='item_name', aggfunc=len, fill_value=0) | ||
|
|
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| # Debug: Print the basket DataFrame | ||
| print("Basket DataFrame:") | ||
| print(basket.head()) | ||
|
|
||
| # Run Apriori algorithm with lower min_support | ||
| freq_items = apriori(basket, min_support=0.5, use_colnames=True) | ||
|
|
||
| # Debug: Print the frequent itemsets DataFrame | ||
| print("Frequent Itemsets DataFrame:") | ||
| print(freq_items.head()) | ||
|
|
||
| # Check if freq_items is empty | ||
| if freq_items.empty: | ||
| print("No frequent itemsets found. Adjust the min_support value or check the data.") | ||
| else: | ||
| # Run association rules | ||
| rules = association_rules(freq_items, metric="conviction", min_threshold=0.01) | ||
|
|
||
| # Filter out rules with inf values for conviction | ||
| rules = rules.replace([float('inf'), -float('inf')], float('nan')).dropna(subset=['conviction']) | ||
| rules = rules.sort_values('conviction', ascending=False) | ||
|
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||
| # Debug: Print the rules DataFrame | ||
| print("Association Rules DataFrame:") | ||
| print(rules.head()) | ||
|
|
||
| # Insert results into the database | ||
| cursor.execute("TRUNCATE TABLE frequent_items") | ||
| insert_query = "INSERT INTO frequent_items (antecedent, consequent, support, confidence, lift, conviction) VALUES (%s, %s, %s, %s, %s, %s)" | ||
| for _, row in rules.iterrows(): | ||
| antecedents = ', '.join(list(row['antecedents'])) | ||
| consequents = ', '.join(list(row['consequents'])) | ||
| cursor.execute(insert_query, (antecedents, consequents, row['support'], row['confidence'], row['lift'], row['conviction'])) | ||
|
|
||
| # Commit the transaction | ||
| cnx.commit() | ||
|
|
||
| # Close the cursor and connection | ||
| cursor.close() | ||
| cnx.close() |
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