duplicate_checker.py aktualisiert

duplicate_checker.py

@@ -1,4 +1,4 @@
-# duplicate_checker.py (v2.1 - mit Match-Basis-Anzeige)
+# duplicate_checker.py (v2.0 - mit Blocking-Strategie)
 
 import logging
 import pandas as pd
@@ -10,35 +10,26 @@ from google_sheet_handler import GoogleSheetHandler
 # --- Konfiguration ---
 CRM_SHEET_NAME = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
-SCORE_THRESHOLD = 80 # Wird jetzt nur noch zur Hervorhebung genutzt, angezeigt werden alle
+SCORE_THRESHOLD = 80
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-def calculate_similarity_details(record1, record2):
-    """
-    Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück.
-    """
-    scores = {'name': 0, 'location': 0, 'domain': 0}
-    
-    # 1. Website-Domain (stärkstes Signal)
-    if record1['normalized_domain'] and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2['normalized_domain']:
-        scores['domain'] = 100
-    
-    # 2. Firmenname (Fuzzy-Signal)
+def calculate_similarity(record1, record2):
+    """Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
+    total_score = 0
+    if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
+        total_score += 100
     if record1['normalized_name'] and record2['normalized_name']:
-        scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.7)
-
-    # 3. Standort (Bestätigungs-Signal)
+        name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
+        total_score += name_similarity * 0.7
     if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
         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}
+            total_score += 20
+    return round(total_score)
 
 def main():
     """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
-    logging.info("Starte den Duplikats-Check (v2.1 mit Match-Basis)...")
+    logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...")
     
     try:
         sheet_handler = GoogleSheetHandler()
@@ -48,11 +39,15 @@ def main():
 
     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
 
     logging.info("Normalisiere Daten für den Vergleich...")
     for df in [crm_df, matching_df]:
@@ -60,49 +55,58 @@ 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()
+        # Blocking Key: Das erste Wort des normalisierten Namens
         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_df.groupby('block_key').apply(lambda x: x.to_dict('records')).to_dict()
+    crm_index = {}
+    for index, row in crm_df.iterrows():
+        key = row['block_key']
+        if key:
+            if key not in crm_index:
+                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():
-        best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
+        best_score = 0
         best_match_name = ""
         
-        logging.info(f"Prüfe {index + 1}/{len(matching_df)}: {match_row['CRM Name']}...")
+        logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...")
 
+        # Finde den Block von Kandidaten
         block_key = match_row['block_key']
         candidates = crm_index.get(block_key, [])
         
+        # Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch
         for crm_row in candidates:
-            score_info = calculate_similarity_details(match_row, crm_row)
-            if score_info['total'] > best_score_info['total']:
-                best_score_info = score_info
+            score = calculate_similarity(match_row, crm_row)
+            if score > best_score:
+                best_score = score
                 best_match_name = crm_row['CRM Name']
         
-        results.append({
-            'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
-            'Score (Gesamt)': best_score_info['total'],
-            'Score (Name)': best_score_info['details']['name'],
-            'Bonus (Standort)': best_score_info['details']['location'],
-            'Bonus (Domain)': best_score_info['details']['domain']
-        })
+        if best_score >= SCORE_THRESHOLD:
+            results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score})
+        else:
+            # Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen
+            results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score})
 
     logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
     result_df = pd.DataFrame(results)
     
-    # Originale Spalten aus matching_df für die Ausgabe nehmen
-    original_cols = [col for col in ['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land'] if col in matching_df.columns]
-    output_df = pd.concat([matching_df[original_cols].reset_index(drop=True), result_df], axis=1)
+    # Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen
+    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 '{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.")