From a10caa5ad038c3b7c96d1e6609b6b435ec14de19 Mon Sep 17 00:00:00 2001 From: Floke Date: Fri, 1 Aug 2025 13:17:27 +0000 Subject: [PATCH] revoce --- duplicate_checker.py | 116 ++++++++++++++++++++----------------------- 1 file changed, 55 insertions(+), 61 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index cf81b6b1..51ff913c 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,4 @@ -# duplicate_checker.py (v1.1 - Lauf 1 Logik + Match-Grund) +# duplicate_checker.py (v2.0 - mit Blocking-Strategie) import logging import pandas as pd @@ -6,65 +6,48 @@ from thefuzz import fuzz from config import Config from helpers import normalize_company_name, simple_normalize_url from google_sheet_handler import GoogleSheetHandler -import time # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 80 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt +SCORE_THRESHOLD = 80 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -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 - - # Namensähnlichkeit (70% Gewichtung) - if record1.get('normalized_name') and record2.get('normalized_name'): +def calculate_similarity(record1, record2): + """Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen.""" + total_score = 0 + if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']: + total_score += 100 + 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) - - # 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()) - - # 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 += name_similarity * 0.7 + if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']: + if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']: + total_score += 20 + return round(total_score) def main(): - start_time = time.time() - logging.info("Starte den Duplikats-Check (v1.1 - Brute-Force mit Match-Grund)...") + """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten.""" + logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...") try: sheet_handler = GoogleSheetHandler() except Exception as e: - logging.critical(f"FEHLER bei Initialisierung: {e}") + logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {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: return + if crm_df is None or crm_df.empty: + logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.") + 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: return - original_matching_df = matching_df.copy() + if matching_df is None or matching_df.empty: + logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.") + return logging.info("Normalisiere Daten für den Vergleich...") for df in [crm_df, matching_df]: @@ -72,49 +55,60 @@ 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() + # Blocking Key: Das erste Wort des normalisierten Namens + df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None) - crm_records = crm_df.to_dict('records') - matching_records = matching_df.to_dict('records') + # --- NEUE, SCHNELLE BLOCKING-STRATEGIE --- + logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...") + crm_index = {} + for index, row in crm_df.iterrows(): + key = row['block_key'] + if key: + if key not in crm_index: + crm_index[key] = [] + crm_index[key].append(row) - logging.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...") + logging.info("Starte Matching-Prozess...") results = [] + total_matches = len(matching_df) - for i, match_record in enumerate(matching_records): - best_score = -1 + for index, match_row in matching_df.iterrows(): + best_score = 0 best_match_name = "" - best_reason = "" - logging.info(f"Prüfe {i + 1}/{len(matching_records)}: {match_record.get('CRM Name', 'N/A')}...") + logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...") + + # Finde den Block von Kandidaten + block_key = match_row['block_key'] + candidates = crm_index.get(block_key, []) - # 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) + # Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch + for crm_row in candidates: + score = calculate_similarity(match_row, crm_row) if score > best_score: best_score = score - best_match_name = crm_record.get('CRM Name', 'N/A') - best_reason = reason + best_match_name = crm_row['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 - }) + if best_score >= SCORE_THRESHOLD: + results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score}) + else: + # Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen + results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score}) logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") result_df = pd.DataFrame(results) - output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1) + # Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen + output_df = matching_df[['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land']].copy() + output_df = pd.concat([output_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 '{MATCHING_SHEET_NAME}' geschrieben.") + logging.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.") 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