Add Logging

duplicate_checker.py

@@ -1,4 +1,4 @@
-# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring)
+# duplicate_checker.py (v2.7 - Maximum Logging)
 
 import logging
 import pandas as pd
@@ -7,13 +7,23 @@ 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 # Etwas höherer Schwellenwert für bessere Präzision
+SCORE_THRESHOLD = 85
 
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# WICHTIG: Logging auf DEBUG-Level setzen, um alles zu sehen
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-8s - %(name)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+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."""
@@ -22,7 +32,6 @@ def calculate_similarity_details(record1, record2):
     if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
         scores['domain'] = 100
     
-    # Höhere Gewichtung für den Namen, da die Website oft fehlt
     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)
 
@@ -34,44 +43,32 @@ def calculate_similarity_details(record1, record2):
     return {'total': total_score, 'details': scores}
 
 def create_blocking_keys(name):
-    """Erstellt mehrere Blocking Keys für einen Namen, um die Sensitivität zu erhöhen."""
+    """Erstellt Blocking Keys aus allen signifikanten Wörtern eines Namens."""
     if not name:
         return []
-    
-    words = name.split()
-    keys = set()
-    
-    # 1. Erstes Wort
-    if len(words) > 0:
-        keys.add(words[0])
-    # 2. Zweites Wort (falls vorhanden)
-    if len(words) > 1:
-        keys.add(words[1])
-    # 3. Erste 4 Buchstaben des ersten Wortes
-    if len(words) > 0 and len(words[0]) >= 4:
-        keys.add(words[0][:4])
-        
-    return list(keys)
+    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():
-    logging.info("Starte den Duplikats-Check (v2.2 mit Multi-Key Blocking)...")
+    start_time = time.time()
+    logger.info("Starte den Duplikats-Check (v2.7 - Maximum Logging)...")
     
     try:
         sheet_handler = GoogleSheetHandler()
     except Exception as e:
-        logging.critical(f"FEHLER bei Initialisierung: {e}")
+        logger.critical(f"FEHLER bei Initialisierung: {e}")
         return
 
-    logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
+    logger.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
     crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
     if crm_df is None or crm_df.empty: return
 
-    logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
+    logger.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
     matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
     if matching_df is None or matching_df.empty: return
     original_matching_df = matching_df.copy()
 
-    logging.info("Normalisiere Daten für den Vergleich...")
+    logger.info("Normalisiere Daten für den Vergleich...")
     for df in [crm_df, matching_df]:
         df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
         df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
@@ -79,35 +76,53 @@ def main():
         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...")
+    logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
     crm_index = defaultdict(list)
-    for record in crm_df.to_dict('records'):
+    crm_records = crm_df.to_dict('records')
+    for record in crm_records:
         for key in record['block_keys']:
             crm_index[key].append(record)
     
-    logging.info("Starte Matching-Prozess...")
+    logger.info("Starte Matching-Prozess...")
     results = []
     
     for match_record in matching_df.to_dict('records'):
-        best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
+        best_score_info = {'total': -1, 'details': {'name': 0, 'location': 0, 'domain': 0}}
         best_match_name = ""
         
-        logging.info(f"Prüfe: {match_record['CRM Name']}...")
+        logger.info(f"--- Prüfe: '{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']:
-            for crm_record in crm_index.get(key, []):
-                candidate_pool[crm_record['CRM Name']] = crm_record
+            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:
-            logging.debug("  -> Keine Kandidaten im Index gefunden.")
+            logger.debug("  -> Keine Kandidaten im Index gefunden. Überspringe Vergleich.")
+            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)
+            
+            # Logge jeden einzelnen Vergleich, der einen Score > 0 hat
+            if score_info['total'] > 0:
+                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['CRM Name']
-        
+                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']}")
+
         results.append({
             'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
             'Score (Gesamt)': best_score_info['total'],
@@ -129,5 +144,8 @@ 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()