revoce

duplicate_checker.py

@@ -1,4 +1,4 @@
-# duplicate_checker.py (v2.3 - Intelligent Blocking)
+# duplicate_checker.py (v2.4 - Optimized Brute-Force)
 
 import logging
 import pandas as pd
@@ -6,18 +6,12 @@ from thefuzz import fuzz
 from config import Config
 from helpers import normalize_company_name, simple_normalize_url
 from google_sheet_handler import GoogleSheetHandler
-from collections import defaultdict
+import time
 
 # --- Konfiguration ---
 CRM_SHEET_NAME = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
-SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht angezeigt
-
-# NEU: Liste von generischen Wörtern, die für das Blocking ignoriert werden
-BLOCKING_STOP_WORDS = {
-    'gmbh', 'ag', 'co', 'kg', 'se', 'holding', 'gruppe', 'industries', 'systems', 
-    'technik', 'service', 'services', 'solutions', 'management', 'international'
-}
+SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
@@ -25,12 +19,15 @@ def calculate_similarity_details(record1, record2):
     """Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
     scores = {'name': 0, 'location': 0, 'domain': 0}
     
+    # Domain-Match gibt 100 Punkte
     if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
         scores['domain'] = 100
     
+    # Namensähnlichkeit (85% Gewichtung)
     if record1.get('normalized_name') and record2.get('normalized_name'):
         scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
 
+    # Standort-Bonus (20 Punkte)
     if record1.get('CRM Ort') and record1['CRM Ort'] == record2.get('CRM Ort'):
         if record1.get('CRM Land') and record1['CRM Land'] == record2.get('CRM Land'):
             scores['location'] = 20
@@ -38,28 +35,10 @@ def calculate_similarity_details(record1, record2):
     total_score = sum(scores.values())
     return {'total': total_score, 'details': scores}
 
-def create_blocking_keys(name):
-    """Erstellt mehrere Blocking Keys aus den signifikanten Wörtern eines Namens."""
-    if not name:
-        return []
-    
-    # Filtere Stop-Wörter aus der Wortliste
-    significant_words = [word for word in name.split() if word not in BLOCKING_STOP_WORDS]
-    keys = set()
-    
-    # 1. Erstes signifikantes Wort
-    if len(significant_words) > 0:
-        keys.add(significant_words[0])
-    # 2. Zweites signifikantes Wort (falls vorhanden)
-    if len(significant_words) > 1:
-        keys.add(significant_words[1])
-        
-    return list(keys)
-
 def main():
-    logging.info("Starte den Duplikats-Check (v2.3 mit Intelligent Blocking)...")
+    start_time = time.time()
+    logging.info("Starte den Duplikats-Check (v2.4 - Optimized Brute-Force)...")
     
-    # ... (Initialisierung des GoogleSheetHandler bleibt gleich) ...
     try:
         sheet_handler = GoogleSheetHandler()
     except Exception as e:
@@ -81,29 +60,22 @@ def main():
         df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
         df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
         df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
-        df['block_keys'] = df['normalized_name'].apply(create_blocking_keys)
 
-    logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
-    crm_index = defaultdict(list)
-    for record in crm_df.to_dict('records'):
-        for key in record['block_keys']:
-            crm_index[key].append(record)
+    # Konvertiere DataFrames in Listen von Dictionaries für schnelleren Zugriff in der Schleife
+    crm_records = crm_df.to_dict('records')
+    matching_records = matching_df.to_dict('records')
     
-    logging.info("Starte Matching-Prozess...")
+    logging.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...")
     results = []
     
-    for match_record in matching_df.to_dict('records'):
+    for i, match_record in enumerate(matching_records):
         best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
         best_match_name = ""
         
-        logging.info(f"Prüfe: {match_record['CRM Name']}...")
-
-        candidate_pool = {}
-        for key in match_record['block_keys']:
-            for crm_record in crm_index.get(key, []):
-                candidate_pool[crm_record['CRM Name']] = crm_record
+        logging.info(f"Prüfe {i + 1}/{len(matching_records)}: {match_record['CRM Name']}...")
         
-        for crm_record in candidate_pool.values():
+        # Der Brute-Force-Ansatz: Vergleiche mit jedem einzelnen CRM-Eintrag
+        for crm_record in crm_records:
             score_info = calculate_similarity_details(match_record, crm_record)
             if score_info['total'] > best_score_info['total']:
                 best_score_info = score_info
@@ -120,7 +92,6 @@ def main():
     logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
     result_df = pd.DataFrame(results)
     
-    # Originalspalten aus der Kopie nehmen, um saubere Ausgabe zu garantieren
     output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
     
     data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
@@ -131,5 +102,8 @@ def main():
     else:
         logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
 
+    end_time = time.time()
+    logging.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
+
 if __name__ == "__main__":
     main()