duplicate_checker.py aktualisiert

duplicate_checker.py

@@ -6,15 +6,15 @@ from thefuzz import fuzz
 from helpers import normalize_company_name, simple_normalize_url
 from google_sheet_handler import GoogleSheetHandler
 
-# duplicate_checker.py v2.8 (Match-Komponenten im Log)
+# duplicate_checker.py v2.9 (Bulletproof Name-Partial/SORT/SET + Bonus)
-# Version: 2025-08-06_17-50
+# Version: 2025-08-06_18-10
 
 # --- Konfiguration ---
 CRM_SHEET_NAME      = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
-SCORE_THRESHOLD     = 80
+SCORE_THRESHOLD     = 80   # Score-Schwelle
 LOG_DIR             = "Log"
-LOG_FILE            = "duplicate_check_v2.8.log"
+LOG_FILE            = "duplicate_check_v2.9.txt"
 
 # --- Logging Setup ---
 if not os.path.exists(LOG_DIR):
@@ -22,46 +22,60 @@ if not os.path.exists(LOG_DIR):
 log_path = os.path.join(LOG_DIR, LOG_FILE)
 root = logging.getLogger()
 root.setLevel(logging.DEBUG)
-# Remove old handlers
+# Remove existing handlers
 for h in list(root.handlers):
     root.removeHandler(h)
 formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
-# Console
+# Console handler (INFO+)
 ch = logging.StreamHandler(sys.stdout)
 ch.setLevel(logging.INFO)
 ch.setFormatter(formatter)
 root.addHandler(ch)
-# File
+# File handler (DEBUG+)
 fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
 fh.setLevel(logging.DEBUG)
 fh.setFormatter(formatter)
 root.addHandler(fh)
 logger = logging.getLogger(__name__)
 logger.info(f"Logging to console and file: {log_path}")
-logger.info("Starting duplicate_checker.py v2.8 | Version: 2025-08-06_17-50")
+logger.info("Starting duplicate_checker.py v2.9 | Version: 2025-08-06_18-10")
 
+# --- Ähnlichkeitsberechnung ---
+def calculate_similarity(record1, record2):
+    """Berechnet Score-Komponenten: Domain, Name (SET,PARTIAL,SORT), Ort und Bonus."""
+    # Domain exact match
+    dom1 = record1.get('normalized_domain', '')
+    dom2 = record2.get('normalized_domain', '')
+    domain_flag = 1 if dom1 and dom1 == dom2 else 0
 
-def calculate_similarity_components(r1, r2):
+    # Location exact match
-    """Gibt einzelne Komponenten und Gesamt-Score zurück."""
+    loc_flag = 1 if (record1.get('CRM Ort') == record2.get('CRM Ort') and
-    # Domain
+                     record1.get('CRM Land') == record2.get('CRM Land')) else 0
-    dom1 = r1.get('normalized_domain', '')
-    dom2 = r2.get('normalized_domain', '')
-    domain_match = 1 if dom1 and dom1 == dom2 else 0
-    # Name
-    name1 = r1.get('normalized_name', '')
-    name2 = r2.get('normalized_name', '')
-    name_score = fuzz.token_set_ratio(name1, name2) if name1 and name2 else 0
-    # Ort+Land
-    loc_match = 1 if (r1.get('CRM Ort') == r2.get('CRM Ort') and r1.get('CRM Land') == r2.get('CRM Land')) else 0
-    # Gewichte
-    total = domain_match * 100 + name_score * 0.7 + loc_match * 20
-    return round(total), domain_match, round(name_score,1), loc_match
 
+    # Name scores
+    n1 = record1.get('normalized_name', '')
+    n2 = record2.get('normalized_name', '')
+    if n1 and n2:
+        ts = fuzz.token_set_ratio(n1, n2)
+        pr = fuzz.partial_ratio(n1, n2)
+        ss = fuzz.token_sort_ratio(n1, n2)
+        name_score = max(ts, pr, ss)
+    else:
+        name_score = 0
 
+    # Bonus für reine Name-Matches
+    bonus_flag = 1 if domain_flag == 0 and loc_flag == 0 and name_score >= 85 else 0
+
+    # Gesamtscore
+    total = domain_flag * 100 + name_score * 1.0 + loc_flag * 20 + bonus_flag * 20
+    return round(total), domain_flag, name_score, loc_flag, bonus_flag
+
+# --- Hauptfunktion ---
 def main():
-    logger.info("Starte Duplikats-Check v2.8 (Match-Komponenten im Log)")
+    logger.info("Starte Duplikats-Check v2.9 (Bulletproof)")
+    # GoogleSheetHandler init
     try:
-        sheet_handler = GoogleSheetHandler()
+        sheet = GoogleSheetHandler()
         logger.info("GoogleSheetHandler initialisiert")
     except Exception as e:
         logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
@@ -69,18 +83,15 @@ def main():
 
     # Daten laden
     logger.info(f"Lade CRM-Daten aus '{CRM_SHEET_NAME}'...")
-    crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
+    crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
-    if crm_df is None or crm_df.empty:
+    logger.info(f"{0 if crm_df is None else len(crm_df)} CRM-Datensätze geladen")
-        logger.critical("CRM-Tab leer. Abbruch.")
-        return
-    logger.info(f"{len(crm_df)} CRM-Datensätze geladen")
-
     logger.info(f"Lade Matching-Daten aus '{MATCHING_SHEET_NAME}'...")
-    match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
+    match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
-    if match_df is None or match_df.empty:
+    logger.info(f"{0 if match_df is None else len(match_df)} Matching-Datensätze geladen")
-        logger.critical("Matching-Tab leer. Abbruch.")
+
+    if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
+        logger.critical("Leere Daten in einem der Sheets. Abbruch.")
         return
-    logger.info(f"{len(match_df)} Matching-Datensätze geladen")
 
     # Normalisierung & Blocking-Key
     for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
@@ -91,50 +102,49 @@ 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 blocking index
+    # Blocking-Index erzeugen
-    logger.info("Erstelle Blocking-Index...")
     crm_index = {}
-    for idx, row in crm_df.iterrows():
+    for _, row in crm_df.iterrows():
         key = row['block_key']
         if key:
             crm_index.setdefault(key, []).append(row)
     logger.info(f"Blocking-Index mit {len(crm_index)} Keys erstellt")
 
     # Matching
-    logger.info("Starte Matching...")
     results = []
     total = len(match_df)
+    logger.info("Starte Matching-Prozess...")
     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']}' -> {len(candidates)} Kandidaten")
+        logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' -> {len(cands)} Kandidaten")
-        if not candidates:
+        if not cands:
-            results.append({'Potenzieller Treffer im CRM':'', 'Ähnlichkeits-Score':0})
+            results.append({'Match':'', 'Score':0})
             continue
-        scored = []
-        for crow in candidates:
-            score, dm, ns, lm = calculate_similarity_components(mrow, crow)
-            scored.append((crow['CRM Name'], score, dm, ns, lm))
-        top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3]
-        # Log Top3 mit Komponenten
-        for name, sc, dm, ns, lm in top3:
-            logger.debug(f"  Kandidat: {name}, Score={sc}, Domain={dm}, Name={ns}, Ort={lm}")
-        best_name, best_score, best_dm, best_ns, best_lm = 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}' Score={best_score} (Dom={best_dm}, Name={best_ns}, Ort={best_lm})")
-        else:
-            results.append({'Potenzieller Treffer im CRM':'', 'Ähnlichkeits-Score':best_score})
-            logger.info(f" --> Kein Match (Score={best_score}, Dom={best_dm}, Name={best_ns}, Ort={best_lm})")
 
-    # Write back
+        scored = []
-    logger.info("Schreibe Ergebnisse zurück ins Sheet...")
+        for crow in cands:
-    out_df = pd.DataFrame(results)
+            sc, dm, ns, lm, bf = calculate_similarity(mrow, crow)
+            scored.append((crow['CRM Name'], sc, dm, ns, lm, bf))
+        # Top 3 loggen
+        for name, sc, dm, ns, lm, bf in sorted(scored, key=lambda x: x[1], reverse=True)[:3]:
+            logger.debug(f"  Kandidat: {name}, Score={sc}, Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}")
+
+        best_name, best_score, dm, ns, lm, bf = max(scored, key=lambda x: x[1])
+        if best_score >= SCORE_THRESHOLD:
+            results.append({'Match':best_name, 'Score':best_score})
+            logger.info(f" --> Match: '{best_name}' ({best_score}) [Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}]")
+        else:
+            results.append({'Match':'', 'Score':best_score})
+            logger.info(f" --> Kein Match (Score={best_score}) [Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}]")
+
+    # Ergebnisse zurückschreiben
+    logger.info("Schreibe Ergebnisse ins Sheet...")
+    out = pd.DataFrame(results)
     output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
-    output = pd.concat([output.reset_index(drop=True), out_df], axis=1)
+    output = pd.concat([output.reset_index(drop=True), out], axis=1)
     data = [output.columns.tolist()] + output.values.tolist()
-    success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
+    if sheet.clear_and_write_data(MATCHING_SHEET_NAME, data):
-    if success:
         logger.info("Ergebnisse erfolgreich geschrieben")
     else:
         logger.error("Fehler beim Schreiben ins Google Sheet")