diff --git a/duplicate_checker.py b/duplicate_checker.py index 100ed636..948d5a9f 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,4 @@ -# duplicate_checker.py (v2.7 - Maximum Logging) +# duplicate_checker.py (v2.3 - Intelligent Blocking) import logging import pandas as pd @@ -12,19 +12,16 @@ import time # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 85 - -# WICHTIG: Logging auf DEBUG-Level setzen, um alles zu sehen -logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-8s - %(name)s - %(message)s') -logger = logging.getLogger(__name__) +SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht angezeigt +# NEU: 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', 'b-v' + 'technik', 'service', 'services', 'solutions', 'management', 'international' } +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} @@ -43,32 +40,43 @@ def calculate_similarity_details(record1, record2): return {'total': total_score, 'details': scores} def create_blocking_keys(name): - """Erstellt Blocking Keys aus allen signifikanten Wörtern eines Namens.""" + """Erstellt mehrere Blocking Keys aus den signifikanten Wörtern eines Namens.""" if not name: return [] - significant_words = {word for word in name.split() if word not in BLOCKING_STOP_WORDS and len(word) >= 3} - return list(significant_words) + + # Filtere Stop-Wörter aus der Wortliste + significant_words = [word for word in name.split() if word not in BLOCKING_STOP_WORDS] + keys = set() + + # 1. Erstes signifikantes Wort + if len(significant_words) > 0: + keys.add(significant_words[0]) + # 2. Zweites signifikantes Wort (falls vorhanden) + if len(significant_words) > 1: + keys.add(significant_words[1]) + + return list(keys) def main(): - start_time = time.time() - logger.info("Starte den Duplikats-Check (v2.7 - Maximum Logging)...") + logging.info("Starte den Duplikats-Check (v2.3 mit Intelligent Blocking)...") + # ... (Initialisierung des GoogleSheetHandler bleibt gleich) ... try: sheet_handler = GoogleSheetHandler() except Exception as e: - logger.critical(f"FEHLER bei Initialisierung: {e}") + logging.critical(f"FEHLER bei Initialisierung: {e}") return - logger.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...") + 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 - logger.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...") + 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 original_matching_df = matching_df.copy() - logger.info("Normalisiere Daten für den Vergleich...") + logging.info("Normalisiere Daten für den Vergleich...") for df in [crm_df, matching_df]: df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name) df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url) @@ -76,53 +84,33 @@ def main(): df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip() df['block_keys'] = df['normalized_name'].apply(create_blocking_keys) - logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...") + logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...") crm_index = defaultdict(list) crm_records = crm_df.to_dict('records') for record in crm_records: for key in record['block_keys']: crm_index[key].append(record) - logger.info("Starte Matching-Prozess...") + logging.info("Starte Matching-Prozess...") results = [] for match_record in matching_df.to_dict('records'): - best_score_info = {'total': -1, 'details': {'name': 0, 'location': 0, 'domain': 0}} + best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}} best_match_name = "" - logger.info(f"--- Prüfe: '{match_record.get('CRM Name', 'N/A')}' ---") - logger.debug(f" [Normalisiert: '{match_record.get('normalized_name')}', Domain: '{match_record.get('normalized_domain')}', Keys: {match_record.get('block_keys')}]") + logging.info(f"Prüfe: {match_record['CRM Name']}...") candidate_pool = {} for key in match_record['block_keys']: - candidates_from_key = crm_index.get(key, []) - if candidates_from_key: - logger.debug(f" -> Block-Key '{key}' gefunden. {len(candidates_from_key)} Kandidaten hinzugefügt.") - for crm_record in candidates_from_key: - candidate_pool[crm_record['CRM Name']] = crm_record - - if not candidate_pool: - logger.debug(" -> Keine Kandidaten im Index gefunden. Überspringe Vergleich.") - results.append({ - 'Potenzieller Treffer im CRM': "", 'Score (Gesamt)': 0, 'Score (Name)': 0, - 'Bonus (Standort)': 0, 'Bonus (Domain)': 0 - }) - continue - - logger.debug(f" -> Vergleiche mit insgesamt {len(candidate_pool)} einzigartigen Kandidaten.") + for crm_record in crm_index.get(key, []): + candidate_pool[crm_record['CRM Name']] = crm_record for crm_record in candidate_pool.values(): score_info = calculate_similarity_details(match_record, crm_record) - - # Logge jeden einzelnen Vergleich, der einen Score > 0 hat - if score_info['total'] > 0: - logger.debug(f" - Kandidat: '{crm_record.get('CRM Name', 'N/A')}' -> Score: {score_info['total']} (Details: {score_info['details']})") - if score_info['total'] > best_score_info['total']: best_score_info = score_info - best_match_name = crm_record.get('CRM Name', 'N/A') - logger.info(f" --> Neuer bester Treffer: '{best_match_name}' mit Score {best_score_info['total']}") - + best_match_name = crm_record['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'], @@ -134,6 +122,7 @@ def main(): logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") result_df = pd.DataFrame(results) + # Originalspalten aus der Kopie nehmen, um saubere Ausgabe zu garantieren 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() @@ -144,8 +133,5 @@ def main(): 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() \ No newline at end of file