duplicate_checker.py aktualisiert

duplicate_checker.py

@@ -7,64 +7,55 @@ from thefuzz import fuzz
 from helpers import normalize_company_name, simple_normalize_url
 from google_sheet_handler import GoogleSheetHandler
 
-# duplicate_checker.py v2.2 (Domain-Fallback & Name-Partial-Bonus für bessere Matches)
+# duplicate_checker.py v2.3 (Rückkehr zum v2.0-Scoring, erweitert mit Logging)
-# Version: 2025-08-06_15-20
+# Version: 2025-08-06_16-00
 # --- Konfiguration ---
 CRM_SHEET_NAME      = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
-SCORE_THRESHOLD     = 80
+SCORE_THRESHOLD     = 80   # v2.0 Schwelle (0–190 Skala)
 LOG_DIR             = "Log"
 
-# --- Logging Setup ---
+# --- Logging Setup mit Datum im Dateinamen ---
 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")
+log_path = os.path.join(LOG_DIR, f"{now}_Duplicate_v2.3.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")
+logger.info("Version: duplicate_checker.py v2.3 (v2.0-Scoring mit Logging) | Build: 2025-08-06_16-00")
 
 
 def calculate_similarity(record1, record2):
-    """Gewichteter Score mit Domain-Fallback und Partial-Name-Bonus."""
+    """Berechnet den v2.0-Score: Domain=100, Name*0.7, Ort+Land=20."""
-    total = 0
+    total_score = 0
-    # Domain-Exact
+    # Domain(exakt)
-    dom1 = record1.get('normalized_domain','')
+    if record1.get('normalized_domain') and record1['normalized_domain'] == record2.get('normalized_domain'):
-    dom2 = record2.get('normalized_domain','')
+        total_score += 100
-    if dom1 and dom1 == dom2:
+    # Name fuzzy
-        total += 100
-    # 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:
-        ts = fuzz.token_set_ratio(name1, name2)
+        sim = fuzz.token_set_ratio(name1, name2)
-        total += ts * 0.7
+        total_score += sim * 0.7
-        # 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
+        total_score += 20
-    return round(total)
+    return round(total_score)
 
 
 def main():
-    logger.info("Starte Duplikats-Check v2.2 mit Domain-Fallback & Partial-Bonus")
+    logger.info("Starte Duplikats-Check v2.3 (v2.0-Scoring mit Logging)")
     try:
         sheet = GoogleSheetHandler()
         logger.info("GoogleSheetHandler initialisiert")
@@ -72,15 +63,16 @@ def main():
         logger.critical(f"FEHLER Init GoogleSheetHandler: {e}")
         sys.exit(1)
 
+    # Daten laden
     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("Daten fehlen. Abbruch.")
         return
-    logger.info(f"{len(crm_df)} CRM-Zeilen, {len(match_df)} Matching-Zeilen geladen")
+    logger.info(f"{len(crm_df)} CRM- und {len(match_df)} Matching-Zeilen geladen")
 
-    # Norm & Block-Key
+    # Normalisierung & Blocking-Key
-    for df, label in [(crm_df,'CRM'),(match_df,'Matching')]:
+    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()
@@ -88,7 +80,7 @@ def main():
         df['block_key']         = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
         logger.debug(f"{label}-Sample: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
 
-    # Build index
+    # Build Blocking-Index
     crm_index = {}
     for idx, row in crm_df.iterrows():
         key = row['block_key']
@@ -96,31 +88,38 @@ def main():
             crm_index.setdefault(key, []).append(row)
     logger.info(f"Blocking-Index mit {len(crm_index)} Keys erstellt")
 
+    # Matching mit relevanten Kandidaten im Log
     results = []
     total = len(match_df)
     for i, mrow in match_df.iterrows():
         key = mrow['block_key']
-        cands = crm_index.get(key, [])
+        candidates = crm_index.get(key, [])
-        logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(cands)} Kandidaten")
+        logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(candidates)} Kandidaten")
-        if not cands:
+        if not candidates:
-            results.append({'Match':'','Score':0}); continue
+            results.append({'Potenzieller Treffer im CRM':'','Ähnlichkeits-Score':0})
-        scored = [(crow['CRM Name'], calculate_similarity(mrow,crow)) for crow in cands]
+            continue
-        top3 = sorted(scored, key=lambda x:x[1], reverse=True)[:3]
+        # Score for each candidate
-        logger.debug(f" Top3: {top3}")
+        scored = [(crow['CRM Name'], calculate_similarity(mrow,crow)) for crow in candidates]
-        best, score = max(scored, key=lambda x:x[1])
+        # Top 3 candidates logged
-        if score >= SCORE_THRESHOLD:
+        top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3]
-            results.append({'Match':best,'Score':score})
+        logger.debug(f" Top3 Kandidaten: {top3}")
-            logger.info(f" --> Match: '{best}' ({score})")
+        best_name, best_score = max(scored, key=lambda x: x[1])
+        if best_score >= SCORE_THRESHOLD:
+            results.append({'Potenzieller Treffer im CRM':best_name,'Ähnlichkeits-Score':best_score})
+            logger.info(f" --> Match: '{best_name}' mit Score {best_score}")
         else:
-            results.append({'Match':'','Score':score})
+            results.append({'Potenzieller Treffer im CRM':'','Ähnlichkeits-Score':best_score})
-            logger.info(f" --> Kein Match (Score {score})")
+            logger.info(f" --> Kein Match (höchster Score {best_score})")
 
+    # Write results back
     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: logger.info("Ergebnisse geschrieben")
+    if ok:
-    else: logger.error("Fehler beim Schreiben ins Sheet")
+        logger.info("Ergebnisse erfolgreich geschrieben")
+    else:
+        logger.error("Fehler beim Schreiben ins Google Sheet")
 
 if __name__=='__main__':
     main()