duplicate_checker.py aktualisiert

duplicate_checker.py

@@ -1,4 +1,4 @@
-# duplicate_checker.py (v2.6 - Final Optimized Brute-Force)
+# duplicate_checker.py (v2.0 - Enhanced Transparency)
 
 import logging
 import pandas as pd
@@ -6,53 +6,68 @@ from thefuzz import fuzz
 from config import Config
 from helpers import normalize_company_name, simple_normalize_url
 from google_sheet_handler import GoogleSheetHandler
-import time
 
 # --- Konfiguration ---
 CRM_SHEET_NAME = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
-SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt
+SCORE_THRESHOLD = 80
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-def calculate_similarity_details(record1, record2):
+def calculate_similarity_with_details(record1, record2):
-    """Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
+    """
+    Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen
+    und gibt die Details für die Begründung zurück.
+    """
     scores = {'name': 0, 'location': 0, 'domain': 0}
     
-    # Domain-Match (höchste Priorität)
+    # 1. Website-Domain (stärkstes Signal)
-    if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
+    if record1['normalized_domain'] and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2['normalized_domain']:
         scores['domain'] = 100
     
-    # Namensähnlichkeit (hohe Gewichtung)
+    # 2. Firmenname (Fuzzy-Signal)
-    if record1.get('normalized_name') and record2.get('normalized_name'):
+    if record1['normalized_name'] and record2['normalized_name']:
-        # token_set_ratio ist gut bei unterschiedlicher Wortreihenfolge und Zusatzwörtern
+        name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
-        scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
+        scores['name'] = round(name_similarity * 0.7)
 
-    # Standort-Bonus
+    # 3. Standort (Bestätigungs-Signal)
-    if record1.get('CRM Ort') and record1['CRM Ort'] == record2.get('CRM Ort'):
+    if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
-        if record1.get('CRM Land') and record1['CRM Land'] == record2.get('CRM Land'):
+        if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
             scores['location'] = 20
-
+            
     total_score = sum(scores.values())
-    return {'total': total_score, 'details': scores}
+    
+    # Erstelle den Begründungstext
+    reasons = []
+    if scores['domain'] > 0: reasons.append(f"Domain({scores['domain']})")
+    if scores['name'] > 0: reasons.append(f"Name({scores['name']})")
+    if scores['location'] > 0: reasons.append(f"Ort({scores['location']})")
+    reason_text = " + ".join(reasons) if reasons else "Keine Übereinstimmung"
+
+    return round(total_score), reason_text
 
 def main():
-    start_time = time.time()
+    """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
-    logging.info("Starte den Duplikats-Check (v2.6 - Final Optimized Brute-Force)...")
+    logging.info("Starte den Duplikats-Check (v2.0 mit erweiterter Ausgabe)...")
     
     try:
         sheet_handler = GoogleSheetHandler()
     except Exception as e:
-        logging.critical(f"FEHLER bei Initialisierung: {e}")
+        logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
         return
 
     logging.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
+    if crm_df is None or crm_df.empty:
+        logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.")
+        return
 
     logging.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
+    if matching_df is None or matching_df.empty:
+        logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
+        return
+    # Kopie für die finale Ausgabe sichern
     original_matching_df = matching_df.copy()
 
     logging.info("Normalisiere Daten für den Vergleich...")
@@ -61,49 +76,58 @@ 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_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x and x.split() else None)
 
+    logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
+    crm_index = {}
+    # Konvertiere in Records für schnelleren Zugriff
     crm_records = crm_df.to_dict('records')
-    matching_records = matching_df.to_dict('records')
+    for record in crm_records:
-    
+        key = record['block_key']
-    logging.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...")
+        if key:
+            if key not in crm_index:
+                crm_index[key] = []
+            crm_index[key].append(record)
+
+    logging.info("Starte Matching-Prozess...")
     results = []
     
-    for i, match_record in enumerate(matching_records):
+    for match_record in matching_df.to_dict('records'):
-        best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
+        best_score = -1
         best_match_name = ""
+        best_reason = ""
         
-        logging.info(f"Prüfe {i + 1}/{len(matching_records)}: {match_record.get('CRM Name', 'N/A')}...")
+        logging.info(f"Prüfe: {match_record.get('CRM Name', 'N/A')}...")
+
+        block_key = match_record.get('block_key')
+        candidates = crm_index.get(block_key, [])
         
-        # Brute-Force-Vergleich: Jede Zeile wird mit jeder CRM-Zeile verglichen
+        for crm_row in candidates:
-        for crm_record in crm_records:
+            score, reason = calculate_similarity_with_details(match_record, crm_row)
-            score_info = calculate_similarity_details(match_record, crm_record)
+            if score > best_score:
-            if score_info['total'] > best_score_info['total']:
+                best_score = score
-                best_score_info = score_info
+                best_match_name = crm_row.get('CRM Name', 'N/A')
-                best_match_name = crm_record.get('CRM Name', 'N/A')
+                best_reason = reason
         
         results.append({
-            'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
+            'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "",
-            'Score (Gesamt)': best_score_info['total'],
+            'Ähnlichkeits-Score': best_score,
-            'Score (Name)': best_score_info['details']['name'],
+            'Matching-Grund': best_reason
-            'Bonus (Standort)': best_score_info['details']['location'],
-            'Bonus (Domain)': best_score_info['details']['domain']
         })
 
     logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
     result_df = pd.DataFrame(results)
     
+    # Füge die neuen Spalten zu den Originaldaten hinzu
     output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
-    
+
     data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
-    
+
     success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
     if success:
-        logging.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.")
+        logging.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
     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()