From 7c9ee2f79943d02dd54e4bb7530b188ad0b20645 Mon Sep 17 00:00:00 2001 From: Floke Date: Sun, 3 Aug 2025 08:31:02 +0000 Subject: [PATCH] duplicate_checker.py aktualisiert --- duplicate_checker.py | 108 +++++++++++-------------------------------- 1 file changed, 27 insertions(+), 81 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index d7fb1ee1..8eca0e39 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,64 +1,39 @@ -# duplicate_checker.py (v2.8 - Vollständiges Logging & Maximum Debugging) +# duplicate_checker.py (v3.0 - Back to Basics: Optimized Brute-Force) import logging import pandas as pd from thefuzz import fuzz from config import Config -from helpers import normalize_company_name, simple_normalize_url, create_log_filename +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 = 85 +SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt -# --- WICHTIG: VOLLSTÄNDIGES LOGGING SETUP --- -LOG_LEVEL = logging.DEBUG if Config.DEBUG else logging.INFO -LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s' +# WICHTIG: Logging Setup für detaillierte Ausgaben +logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-8s - %(name)s - %(message)s') +logger = logging.getLogger(__name__) -# Root-Logger konfigurieren -root_logger = logging.getLogger() -root_logger.setLevel(LOG_LEVEL) - -# Bestehende Handler entfernen, um Dopplung zu vermeiden -for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - -# Konsole-Handler hinzufügen -stream_handler = logging.StreamHandler() -stream_handler.setFormatter(logging.Formatter(LOG_FORMAT)) -root_logger.addHandler(stream_handler) - -# File-Handler hinzufügen -log_file_path = create_log_filename("duplicate_check") -if log_file_path: - file_handler = logging.FileHandler(log_file_path, mode='a', encoding='utf-8') - file_handler.setFormatter(logging.Formatter(LOG_FORMAT)) - root_logger.addHandler(file_handler) - -logger = logging.getLogger(__name__) # Logger für dieses Modul holen - -# --- Der eigentliche Code beginnt hier --- - -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' -} 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} + # Domain-Match (höchste Priorität, 100 Punkte) if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'): scores['domain'] = 100 + # Namensähnlichkeit (hohe 85% Gewichtung) if record1.get('normalized_name') and record2.get('normalized_name'): + # token_set_ratio ist robust gegen zusätzliche Wörter wie "Holding" oder "Gruppe" scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85) + # Standort-Bonus (20 Punkte) 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 @@ -66,18 +41,12 @@ def calculate_similarity_details(record1, record2): 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 [] - significant_words = {word for word in name.split() if word not in BLOCKING_STOP_WORDS and len(word) >= 3} - return list(significant_words) def main(): start_time = time.time() - logger.info(f"===== Skript gestartet: Modus 'duplicate_check' v2.8 =====") - logger.info(f"Logdatei: {log_file_path}") + logger.info("Starte den Duplikats-Check (v3.0 - Back to Basics)...") + # ... (Initialisierung und Laden der Daten bleibt gleich) ... try: sheet_handler = GoogleSheetHandler() except Exception as e: @@ -99,54 +68,33 @@ 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_keys'] = df['normalized_name'].apply(create_blocking_keys) - logger.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) + matching_records = matching_df.to_dict('records') - logger.info("Starte Matching-Prozess...") + logger.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...") results = [] - for match_record in matching_df.to_dict('records'): + for i, match_record in enumerate(matching_records): best_score_info = {'total': -1, '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')}]") - - 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 + logger.info(f"--- Prüfe {i + 1}/{len(matching_records)}: '{match_record.get('CRM Name', 'N/A')}' ---") - 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 candidate_pool.values(): + # BRUTE-FORCE: Vergleiche mit jedem einzelnen CRM-Eintrag + for crm_record in crm_records: score_info = calculate_similarity_details(match_record, crm_record) - if score_info['total'] > 50: # Logge nur Vergleiche mit einem minimalen Score, um das Log nicht zu überfluten - logger.debug(f" - Kandidat: '{crm_record.get('CRM Name', 'N/A')}' -> Score: {score_info['total']} (Details: {score_info['details']})") + # Logge jeden interessanten Vergleich (Score > 60) + if score_info['total'] > 60: + 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']}") - + + logger.info(f" --> Bester Treffer: '{best_match_name}' mit Score {best_score_info['total']}") + results.append({ 'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "", 'Score (Gesamt)': best_score_info['total'], @@ -170,8 +118,6 @@ def main(): end_time = time.time() logger.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.") - logger.info(f"===== Skript beendet: Modus 'duplicate_check' =====") - if __name__ == "__main__": main() \ No newline at end of file