diff --git a/duplicate_checker.py b/duplicate_checker.py index a57a4ad5..cf81b6b1 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,4 @@ -# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring) +# duplicate_checker.py (v1.1 - Lauf 1 Logik + Match-Grund) import logging import pandas as pd @@ -6,55 +6,50 @@ 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 = 85 # Etwas höherer Schwellenwert für bessere Präzision +SCORE_THRESHOLD = 80 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt 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.""" +def calculate_similarity_with_details(record1, record2): + """ + Berechnet einen gewichteten Ähnlichkeits-Score und gibt den Score und den Grund zurück. + Dies ist die originale Scoring-Logik von Lauf 1. + """ scores = {'name': 0, 'location': 0, 'domain': 0} + # Domain-Match (100 Punkte) if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'): scores['domain'] = 100 - # Höhere Gewichtung für den Namen, da die Website oft fehlt + # Namensähnlichkeit (70% Gewichtung) 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) + name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) + scores['name'] = round(name_similarity * 0.7) + # 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 - + total_score = sum(scores.values()) - return {'total': total_score, 'details': scores} + + # Erstelle den Begründungstext + reasons = [] + if scores['domain'] > 0: reasons.append(f"Domain({scores['domain']})") + if scores['name'] > 0: reasons.append(f"Name({scores['name']})") + if scores['location'] > 0: reasons.append(f"Ort({scores['location']})") + reason_text = " + ".join(reasons) if reasons else "Keine Übereinstimmung" -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) + return round(total_score), reason_text def main(): - logging.info("Starte den Duplikats-Check (v2.2 mit Multi-Key Blocking)...") + start_time = time.time() + logging.info("Starte den Duplikats-Check (v1.1 - Brute-Force mit Match-Grund)...") try: sheet_handler = GoogleSheetHandler() @@ -77,43 +72,32 @@ 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) - logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...") - crm_index = defaultdict(list) - for record in crm_df.to_dict('records'): - for key in record['block_keys']: - crm_index[key].append(record) + crm_records = crm_df.to_dict('records') + matching_records = matching_df.to_dict('records') - logging.info("Starte Matching-Prozess...") + logging.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'): - best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}} + for i, match_record in enumerate(matching_records): + best_score = -1 best_match_name = "" + best_reason = "" - logging.info(f"Prüfe: {match_record['CRM Name']}...") - - candidate_pool = {} - for key in match_record['block_keys']: - for crm_record in crm_index.get(key, []): - candidate_pool[crm_record['CRM Name']] = crm_record + logging.info(f"Prüfe {i + 1}/{len(matching_records)}: {match_record.get('CRM Name', 'N/A')}...") - 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_record['CRM Name'] + # Brute-Force-Vergleich: Jede Zeile wird mit jeder CRM-Zeile verglichen + for crm_record in crm_records: + score, reason = calculate_similarity_with_details(match_record, crm_record) + if score > best_score: + best_score = score + best_match_name = crm_record.get('CRM Name', 'N/A') + best_reason = reason 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'] + 'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "", + 'Ähnlichkeits-Score': best_score, + 'Matching-Grund': best_reason }) logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") @@ -129,5 +113,8 @@ 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