duplicate_checker.py aktualisiert

duplicate_checker.py

@@ -7,125 +7,120 @@ from thefuzz import fuzz
 from helpers import normalize_company_name, simple_normalize_url
 from google_sheet_handler import GoogleSheetHandler
 
-# duplicate_checker.py v2.1 (mit erweitertem Logging und Datum im Dateinamen)
+# duplicate_checker.py v2.2 (Domain-Fallback & Name-Partial-Bonus für bessere Matches)
-# Version: 2025-08-06_12-33
+# Version: 2025-08-06_15-20
 # --- Konfiguration ---
 CRM_SHEET_NAME      = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
-SCORE_THRESHOLD = 80  # Score ab hier gilt als Match
+SCORE_THRESHOLD     = 80
 LOG_DIR             = "Log"
 
-# --- Logging Setup mit Datum im Dateinamen ---
+# --- Logging Setup ---
 if not os.path.exists(LOG_DIR):
     os.makedirs(LOG_DIR, exist_ok=True)
 now = datetime.now().strftime('%Y-%m-%d_%H-%M')
 log_path = os.path.join(LOG_DIR, f"{now}_Duplicate.log")
-
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
-
+# Console
-# Console-Handler (INFO+)
 ch = logging.StreamHandler()
 ch.setLevel(logging.INFO)
 ch.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s"))
 logger.addHandler(ch)
-
+# File
-# File-Handler (DEBUG+)
 fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
 fh.setLevel(logging.DEBUG)
 fh.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(name)s - %(message)s"))
 logger.addHandler(fh)
-
 logger.info(f"Logging in Datei: {log_path}")
+logger.info("Version: duplicate_checker.py v2.2 (Domain-Fallback & Partial-Bonus) | Build: 2025-08-06_15-20")
 
 
 def calculate_similarity(record1, record2):
-    """Berechnet gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
+    """Gewichteter Score mit Domain-Fallback und Partial-Name-Bonus."""
     total = 0
-    # Domain exact match über normalisierte Domain
+    # Domain-Exact
     dom1 = record1.get('normalized_domain','')
     dom2 = record2.get('normalized_domain','')
     if dom1 and dom1 == dom2:
         total += 100
-    # Name fuzzy (Token-Set Ratio)
+    # Domain-Fallback: Substring
+    elif dom1 and dom2 and (dom1 in dom2 or dom2 in dom1):
+        total += 50
+    # Name-Fuzzy Token-Set
     name1 = record1.get('normalized_name','')
     name2 = record2.get('normalized_name','')
     if name1 and name2:
-        name_score = fuzz.token_set_ratio(name1, name2)
+        ts = fuzz.token_set_ratio(name1, name2)
-        total += name_score * 0.7
+        total += ts * 0.7
-    # Ort+Land exact
+        # Partial match bonus für kurze/abweichende Namen
+        pr = fuzz.partial_ratio(name1, name2)
+        if pr >= 85:
+            total += 20
+    # Ort+Land exakt
     if record1.get('CRM Ort') == record2.get('CRM Ort') and record1.get('CRM Land') == record2.get('CRM Land'):
         total += 20
     return round(total)
 
 
 def main():
-    logger.info("Starte Duplikats-Check (v2.0 mit Kern-Syntax nach Entwurf)")
+    logger.info("Starte Duplikats-Check v2.2 mit Domain-Fallback & Partial-Bonus")
     try:
         sheet = GoogleSheetHandler()
         logger.info("GoogleSheetHandler initialisiert")
     except Exception as e:
-        logger.critical(f"FEHLER beim Init GoogleSheetHandler: {e}")
+        logger.critical(f"FEHLER Init GoogleSheetHandler: {e}")
         sys.exit(1)
 
-    # Daten einlesen
     crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
     match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
     if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
-        logger.critical("CRM- oder Matching-Daten fehlen. Abbruch.")
+        logger.critical("Daten fehlen. Abbruch.")
         return
-    logger.info(f"{len(crm_df)} CRM-Datensätze, {len(match_df)} Matching-Datensätze geladen")
+    logger.info(f"{len(crm_df)} CRM-Zeilen, {len(match_df)} Matching-Zeilen geladen")
 
-    # Normalisierung und Blocking-Key
+    # Norm & Block-Key
     for df, label in [(crm_df,'CRM'),(match_df,'Matching')]:
         df['normalized_name']   = df['CRM Name'].astype(str).apply(normalize_company_name)
         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_key']         = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
-        logger.debug(f"{label}-Beispiel nach Normalisierung: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
+        logger.debug(f"{label}-Sample: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
 
-    # Blocking-Index
+    # Build index
     crm_index = {}
     for idx, row in crm_df.iterrows():
         key = row['block_key']
         if key:
             crm_index.setdefault(key, []).append(row)
-    logger.info(f"Blocking-Index erstellt: {len(crm_index)} Keys")
+    logger.info(f"Blocking-Index mit {len(crm_index)} Keys erstellt")
 
-    # Matching mit Log relevanter Kandidaten
     results = []
     total = len(match_df)
     for i, mrow in match_df.iterrows():
         key = mrow['block_key']
-        candidates = crm_index.get(key, [])
+        cands = crm_index.get(key, [])
-        logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(candidates)} Kandidaten")
+        logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(cands)} Kandidaten")
-        if not candidates:
+        if not cands:
-            results.append({'Match': '', 'Score': 0})
+            results.append({'Match':'','Score':0}); continue
-            continue
+        scored = [(crow['CRM Name'], calculate_similarity(mrow,crow)) for crow in cands]
-        # Scores sammeln
-        scored = [(crow['CRM Name'], calculate_similarity(mrow, crow)) for crow in candidates]
-        # Top 3 relevante Kandidaten loggen
         top3 = sorted(scored, key=lambda x:x[1], reverse=True)[:3]
-        logger.debug(f" Top 3 Kandidaten: {top3}")
+        logger.debug(f" Top3: {top3}")
-        # Besten Treffer wählen
+        best, score = max(scored, key=lambda x:x[1])
-        best_name, best_score = max(scored, key=lambda x: x[1])
+        if score >= SCORE_THRESHOLD:
-        if best_score >= SCORE_THRESHOLD:
+            results.append({'Match':best,'Score':score})
-            results.append({'Match': best_name, 'Score': best_score})
+            logger.info(f" --> Match: '{best}' ({score})")
-            logger.info(f" --> Match: '{best_name}' mit Score {best_score}")
         else:
-            results.append({'Match': '', 'Score': best_score})
+            results.append({'Match':'','Score':score})
-            logger.info(f" --> Kein Match (höchster Score {best_score})")
+            logger.info(f" --> Kein Match (Score {score})")
 
-    # Ergebnisse zurück ins Sheet
     out = pd.DataFrame(results)
     output = pd.concat([match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].reset_index(drop=True), out], axis=1)
     data = [output.columns.tolist()] + output.values.tolist()
     ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data)
-    if ok:
+    if ok: logger.info("Ergebnisse geschrieben")
-        logger.info("Ergebnisse erfolgreich geschrieben")
+    else: logger.error("Fehler beim Schreiben ins Sheet")
-    else:
-        logger.error("Fehler beim Schreiben ins Google Sheet")
 
 if __name__=='__main__':
     main()