From aeda711dbde5112ae862be56f763c3d61f7dbe94 Mon Sep 17 00:00:00 2001 From: Floke Date: Fri, 1 Aug 2025 11:35:37 +0000 Subject: [PATCH] duplicate_checker.py aktualisiert --- duplicate_checker.py | 85 +++++++++++++++++++++++++++----------------- 1 file changed, 53 insertions(+), 32 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index 045632aa..e5425c20 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,4 @@ -# duplicate_checker.py (v2.1 - mit Match-Basis-Anzeige) +# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring) import logging import pandas as pd @@ -6,44 +6,60 @@ 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 # --- 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 = 85 # Etwas höherer Schwellenwert für bessere Präzision 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. - """ + """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']: + if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'): scores['domain'] = 100 - # 2. Firmenname (Fuzzy-Signal) - if record1['normalized_name'] and record2['normalized_name']: - scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.7) + # Höhere Gewichtung für den Namen, da die Website oft fehlt + if record1.get('normalized_name') and record2.get('normalized_name'): + scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85) - # 3. Standort (Bestätigungs-Signal) - if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']: - if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']: + 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 mehrere Blocking Keys für einen Namen, um die Sensitivität zu erhöhen.""" + if not name: + return [] + + words = name.split() + keys = set() + + # 1. Erstes Wort + if len(words) > 0: + keys.add(words[0]) + # 2. Zweites Wort (falls vorhanden) + if len(words) > 1: + keys.add(words[1]) + # 3. Erste 4 Buchstaben des ersten Wortes + if len(words) > 0 and len(words[0]) >= 4: + keys.add(words[0][:4]) + + return list(keys) + 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 (v2.2 mit Multi-Key Blocking)...") 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}'...") @@ -53,6 +69,8 @@ def main(): 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 + # Speichere eine saubere Kopie der Originaldaten für die Ausgabe + original_matching_df = matching_df.copy() logging.info("Normalisiere Daten für den Vergleich...") for df in [crm_df, matching_df]: @@ -60,28 +78,38 @@ 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 else None) + df['block_keys'] = df['normalized_name'].apply(create_blocking_keys) 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 = defaultdict(list) + for record in crm_df.to_dict('records'): + for key in record['block_keys']: + crm_index[key].append(record) logging.info("Starte Matching-Prozess...") results = [] - for index, match_row in matching_df.iterrows(): + for match_record in matching_df.to_dict('records'): best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}} best_match_name = "" - logging.info(f"Prüfe {index + 1}/{len(matching_df)}: {match_row['CRM Name']}...") + logging.info(f"Prüfe: {match_record['CRM Name']}...") - block_key = match_row['block_key'] - candidates = crm_index.get(block_key, []) + # Sammle alle einzigartigen Kandidaten aus den Blöcken + candidate_pool = {} # Verwende ein Dict, um Duplikate zu vermeiden + for key in match_record['block_keys']: + for crm_record in crm_index.get(key, []): + # Verwende den CRM Namen als eindeutigen Schlüssel für den Pool + candidate_pool[crm_record['CRM Name']] = crm_record - for crm_row in candidates: - score_info = calculate_similarity_details(match_row, crm_row) + if not candidate_pool: + logging.debug(" -> Keine Kandidaten im Index gefunden.") + + for crm_record in candidate_pool.values(): + score_info = calculate_similarity_details(match_record, crm_record) if score_info['total'] > best_score_info['total']: best_score_info = score_info - best_match_name = crm_row['CRM Name'] + best_match_name = crm_record['CRM Name'] results.append({ 'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "", @@ -94,15 +122,8 @@ def main(): logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") result_df = pd.DataFrame(results) - # KORRIGIERTE LOGIK: Hole die Originaldaten aus dem DataFrame, bevor er normalisiert wurde. - # `matching_df` enthält hier bereits die normalisierten Spalten, die wir nicht wollen. - # Wir laden die Originaldaten neu oder verwenden eine Kopie. Der einfachste Weg: - original_matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME) - - # Füge die Ergebnisse zu den Originaldaten hinzu output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1) - # Konvertiere DataFrame in Liste von Listen für den Upload 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)