From 6fd5c486bf4942e90552f72b736bd4c32b52cd78 Mon Sep 17 00:00:00 2001 From: Floke Date: Fri, 1 Aug 2025 12:49:34 +0000 Subject: [PATCH] duplicate_checker.py aktualisiert --- duplicate_checker.py | 128 +++++++++++++++++++++---------------------- 1 file changed, 64 insertions(+), 64 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index 1bf95a45..a57a4ad5 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,4 @@ -# duplicate_checker.py (v2.0 - Enhanced Transparency) +# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring) import logging import pandas as pd @@ -6,68 +6,69 @@ 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 +SCORE_THRESHOLD = 85 # Etwas höherer Schwellenwert für bessere Präzision logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -def calculate_similarity_with_details(record1, record2): - """ - Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen - und gibt die Details für die Begründung zurück. - """ +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']: + 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']: - name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) - scores['name'] = round(name_similarity * 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()) - - # 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" - return round(total_score), reason_text + 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 (v2.0 mit erweiterter Ausgabe)...") + 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}'...") crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME) - if crm_df is None or crm_df.empty: - logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.") - return + if crm_df is None or crm_df.empty: 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: - logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.") - return - # Kopie für die finale Ausgabe sichern + if matching_df is None or matching_df.empty: return original_matching_df = matching_df.copy() logging.info("Normalisiere Daten für den Vergleich...") @@ -76,56 +77,55 @@ 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 and x.split() else None) + df['block_keys'] = df['normalized_name'].apply(create_blocking_keys) logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...") - crm_index = {} - # Konvertiere in Records für schnelleren Zugriff - crm_records = crm_df.to_dict('records') - for record in crm_records: - key = record['block_key'] - if key: - if key not in crm_index: - crm_index[key] = [] + 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 match_record in matching_df.to_dict('records'): - best_score = -1 + best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}} best_match_name = "" - best_reason = "" - logging.info(f"Prüfe: {match_record.get('CRM Name', 'N/A')}...") + logging.info(f"Prüfe: {match_record['CRM Name']}...") - block_key = match_record.get('block_key') - candidates = crm_index.get(block_key, []) + 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 - for crm_row in candidates: - score, reason = calculate_similarity_with_details(match_record, crm_row) - if score > best_score: - best_score = score - best_match_name = crm_row.get('CRM Name', 'N/A') - best_reason = reason + 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'] results.append({ - 'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "", - 'Ähnlichkeits-Score': best_score, - 'Matching-Grund': best_reason + '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'] }) logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") result_df = pd.DataFrame(results) - # Füge die neuen Spalten zu den Originaldaten hinzu 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() - + success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write) if success: - logging.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.") + logging.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.") else: logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")