duplicate_checker.py aktualisiert

duplicate_checker.py

@@ -1,4 +1,4 @@
-# duplicate_checker.py (v2.0 - mit Blocking-Strategie)
+# duplicate_checker.py (v2.5 - Final Hybrid Approach)
 
 import logging
 import pandas as pd
@@ -6,48 +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
+from collections import defaultdict
+import time
 
 # --- Konfiguration ---
 CRM_SHEET_NAME = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
-SCORE_THRESHOLD = 80
+SCORE_THRESHOLD = 85 # Zeigt nur Treffer an, die diesen Score erreichen oder übertreffen
+
+# Erweiterte Liste von generischen Wörtern, die für das Blocking ignoriert werden
+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'
+}
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-def calculate_similarity(record1, record2):
+def calculate_similarity_details(record1, record2):
-    """Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
+    """Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
-    total_score = 0
+    scores = {'name': 0, 'location': 0, 'domain': 0}
-    if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
+    
-        total_score += 100
+    if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
-    if record1['normalized_name'] and record2['normalized_name']:
+        scores['domain'] = 100
-        name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
+    
-        total_score += name_similarity * 0.7
+    if record1.get('normalized_name') and record2.get('normalized_name'):
-    if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
+        # Wir verwenden token_sort_ratio für eine gute Balance zwischen Wortreihenfolge und Inhalt
-        if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
+        scores['name'] = round(fuzz.token_sort_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
-            total_score += 20
+
-    return round(total_score)
+    if record1.get('CRM Ort') and record1['CRM Ort'] == record2.get('CRM Ort'):
+        if record1.get('CRM Land') and record1['CRM Land'] == record2.get('CRM Land'):
+            scores['location'] = 20
+
+    total_score = sum(scores.values())
+    return {'total': total_score, 'details': scores}
+
+def create_blocking_keys(name):
+    """Erstellt Blocking Keys aus allen signifikanten Wörtern eines Namens."""
+    if not name:
+        return []
+    # Filtere Stop-Wörter und sehr kurze Wörter (z.B. '&') aus der Wortliste
+    significant_words = {word for word in name.split() if word not in BLOCKING_STOP_WORDS and len(word) > 2}
+    return list(significant_words)
 
 def main():
-    """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
+    start_time = time.time()
-    logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...")
+    logging.info("Starte den Duplikats-Check (v2.5 - Final Hybrid Approach)...")
     
+    # ... (Initialisierung und Laden der Daten bleibt gleich) ...
     try:
         sheet_handler = GoogleSheetHandler()
     except Exception as e:
-        logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
+        logging.critical(f"FEHLER bei Initialisierung: {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:
+    if crm_df is None or crm_df.empty: return
-        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:
+    if matching_df is None or matching_df.empty: return
-        logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
+    original_matching_df = matching_df.copy()
-        return
 
     logging.info("Normalisiere Daten für den Vergleich...")
     for df in [crm_df, matching_df]:
@@ -55,60 +75,59 @@ 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()
-        # Blocking Key: Das erste Wort des normalisierten Namens
+        df['block_keys'] = df['normalized_name'].apply(create_blocking_keys)
-        df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
 
-    # --- NEUE, SCHNELLE BLOCKING-STRATEGIE ---
     logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
-    crm_index = {}
+    crm_index = defaultdict(list)
-    for index, row in crm_df.iterrows():
+    crm_records = crm_df.to_dict('records')
-        key = row['block_key']
+    for record in crm_records:
-        if key:
+        for key in record['block_keys']:
-            if key not in crm_index:
+            crm_index[key].append(record)
-                crm_index[key] = []
-            crm_index[key].append(row)
     
     logging.info("Starte Matching-Prozess...")
     results = []
-    total_matches = len(matching_df)
     
-    for index, match_row in matching_df.iterrows():
+    for match_record in matching_df.to_dict('records'):
-        best_score = 0
+        best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
         best_match_name = ""
         
-        logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...")
+        logging.info(f"Prüfe: {match_record['CRM Name']}...")
 
-        # Finde den Block von Kandidaten
+        candidate_pool = {}
-        block_key = match_row['block_key']
+        for key in match_record['block_keys']:
-        candidates = crm_index.get(block_key, [])
+            for crm_record in crm_index.get(key, []):
+                candidate_pool[crm_record['CRM Name']] = crm_record
         
-        # Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch
+        # Brute-Force-Vergleich innerhalb des intelligenten Blocks
-        for crm_row in candidates:
+        for crm_record in candidate_pool.values():
-            score = calculate_similarity(match_row, 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['CRM Name']
+                best_match_name = crm_record['CRM Name']
         
-        if best_score >= SCORE_THRESHOLD:
+        results.append({
-            results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score})
+            'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
-        else:
+            'Score (Gesamt)': best_score_info['total'],
-            # Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen
+            'Score (Name)': best_score_info['details']['name'],
-            results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score})
+            '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)
     
-    # Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen
+    output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
-    output_df = matching_df[['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land']].copy()
-    output_df = pd.concat([output_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 das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
+        logging.info(f"Ergebnisse erfolgreich in '{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()