duplicate_checker.py aktualisiert

duplicate_checker.py

@@ -1,64 +1,39 @@
-# duplicate_checker.py (v2.8 - Vollständiges Logging & Maximum Debugging)
+# duplicate_checker.py (v3.0 - Back to Basics: Optimized Brute-Force)
 
 import logging
 import pandas as pd
 from thefuzz import fuzz
 from config import Config
-from helpers import normalize_company_name, simple_normalize_url, create_log_filename
+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
+SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt
 
-# --- WICHTIG: VOLLSTÄNDIGES LOGGING SETUP ---
+# WICHTIG: Logging Setup für detaillierte Ausgaben
-LOG_LEVEL = logging.DEBUG if Config.DEBUG else logging.INFO
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-8s - %(name)s - %(message)s')
-LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s'
+logger = logging.getLogger(__name__)
 
-# Root-Logger konfigurieren
-root_logger = logging.getLogger()
-root_logger.setLevel(LOG_LEVEL)
-
-# Bestehende Handler entfernen, um Dopplung zu vermeiden
-for handler in root_logger.handlers[:]:
-    root_logger.removeHandler(handler)
-
-# Konsole-Handler hinzufügen
-stream_handler = logging.StreamHandler()
-stream_handler.setFormatter(logging.Formatter(LOG_FORMAT))
-root_logger.addHandler(stream_handler)
-
-# File-Handler hinzufügen
-log_file_path = create_log_filename("duplicate_check")
-if log_file_path:
-    file_handler = logging.FileHandler(log_file_path, mode='a', encoding='utf-8')
-    file_handler.setFormatter(logging.Formatter(LOG_FORMAT))
-    root_logger.addHandler(file_handler)
-
-logger = logging.getLogger(__name__) # Logger für dieses Modul holen
-
-# --- Der eigentliche Code beginnt hier ---
-
-BLOCKING_STOP_WORDS = {
-    'gmbh', 'ag', 'co', 'kg', 'se', 'holding', 'gruppe', 'industries', 'systems', 
-    'technik', 'service', 'services', 'solutions', 'management', 'international', 'und', 
-    'germany', 'deutschland', 'gbr', 'mbh', 'company', 'limited', 'logistics', 
-    'construction', 'products', 'group', 'b-v'
-}
 
 def calculate_similarity_details(record1, record2):
-    """Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
+    """
+    Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück.
+    """
     scores = {'name': 0, 'location': 0, 'domain': 0}
     
+    # Domain-Match (höchste Priorität, 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 (hohe 85% Gewichtung)
     if record1.get('normalized_name') and record2.get('normalized_name'):
+        # token_set_ratio ist robust gegen zusätzliche Wörter wie "Holding" oder "Gruppe"
         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
@@ -66,18 +41,12 @@ def calculate_similarity_details(record1, record2):
     total_score = sum(scores.values())
     return {'total': total_score, 'details': scores}
 
-def create_blocking_keys(name):
-    """Erstellt Blocking Keys aus allen signifikanten Wörtern eines Namens."""
-    if not name:
-        return []
-    significant_words = {word for word in name.split() if word not in BLOCKING_STOP_WORDS and len(word) >= 3}
-    return list(significant_words)
 
 def main():
     start_time = time.time()
-    logger.info(f"===== Skript gestartet: Modus 'duplicate_check' v2.8 =====")
+    logger.info("Starte den Duplikats-Check (v3.0 - Back to Basics)...")
-    logger.info(f"Logdatei: {log_file_path}")
     
+    # ... (Initialisierung und Laden der Daten bleibt gleich) ...
     try:
         sheet_handler = GoogleSheetHandler()
     except Exception as e:
@@ -99,54 +68,33 @@ 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)
 
-    logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
-    crm_index = defaultdict(list)
     crm_records = crm_df.to_dict('records')
-    for record in crm_records:
+    matching_records = matching_df.to_dict('records')
-        for key in record['block_keys']:
-            crm_index[key].append(record)
     
-    logger.info("Starte Matching-Prozess...")
+    logger.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': -1, 'details': {'name': 0, 'location': 0, 'domain': 0}}
         best_match_name = ""
         
-        logger.info(f"--- Prüfe: '{match_record.get('CRM Name', 'N/A')}' ---")
+        logger.info(f"--- Prüfe {i + 1}/{len(matching_records)}: '{match_record.get('CRM Name', 'N/A')}' ---")
-        logger.debug(f"  [Normalisiert: '{match_record.get('normalized_name')}', Domain: '{match_record.get('normalized_domain')}', Keys: {match_record.get('block_keys')}]")
-
-        candidate_pool = {}
-        for key in match_record['block_keys']:
-            candidates_from_key = crm_index.get(key, [])
-            if candidates_from_key:
-                logger.debug(f"  -> Block-Key '{key}' gefunden. {len(candidates_from_key)} Kandidaten hinzugefügt.")
-                for crm_record in candidates_from_key:
-                    candidate_pool[crm_record['CRM Name']] = crm_record
         
-        if not candidate_pool:
+        # BRUTE-FORCE: Vergleiche mit jedem einzelnen CRM-Eintrag
-            logger.debug("  -> Keine Kandidaten im Index gefunden. Überspringe Vergleich.")
+        for crm_record in crm_records:
-            results.append({
-                'Potenzieller Treffer im CRM': "", 'Score (Gesamt)': 0, 'Score (Name)': 0,
-                'Bonus (Standort)': 0, 'Bonus (Domain)': 0
-            })
-            continue
-        
-        logger.debug(f"  -> Vergleiche mit insgesamt {len(candidate_pool)} einzigartigen Kandidaten.")
-        
-        for crm_record in candidate_pool.values():
             score_info = calculate_similarity_details(match_record, crm_record)
             
-            if score_info['total'] > 50: # Logge nur Vergleiche mit einem minimalen Score, um das Log nicht zu überfluten
+            # Logge jeden interessanten Vergleich (Score > 60)
-                logger.debug(f"    - Kandidat: '{crm_record.get('CRM Name', 'N/A')}' -> Score: {score_info['total']} (Details: {score_info['details']})")
+            if score_info['total'] > 60:
+                logger.debug(f"  - Kandidat: '{crm_record.get('CRM Name', 'N/A')}' -> Score: {score_info['total']} (Details: {score_info['details']})")
 
             if score_info['total'] > best_score_info['total']:
                 best_score_info = score_info
                 best_match_name = crm_record.get('CRM Name', 'N/A')
-                logger.info(f"  --> Neuer bester Treffer: '{best_match_name}' mit Score {best_score_info['total']}")
+        
-
+        logger.info(f"  --> Bester Treffer: '{best_match_name}' mit Score {best_score_info['total']}")
+        
         results.append({
             'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
             'Score (Gesamt)': best_score_info['total'],
@@ -170,8 +118,6 @@ def main():
 
     end_time = time.time()
     logger.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
-    logger.info(f"===== Skript beendet: Modus 'duplicate_check' =====")
-
 
 if __name__ == "__main__":
     main()