diff --git a/duplicate_checker.py b/duplicate_checker.py index 8f63128d..c88a99dc 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,106 +1,155 @@ -# duplicate_checker.py (v3.0 - Back to Basics: Optimized Brute-Force) +# duplicate_checker.py (v2.0 + Transparenz) import logging import pandas as pd from thefuzz import fuzz from config import Config -from helpers import normalize_company_name, simple_normalize_url +from helpers import normalize_company_name, simple_normalize_url, create_log_filename from google_sheet_handler import GoogleSheetHandler import time # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt +SCORE_THRESHOLD = 80 + +# --- VOLLSTÄNDIGES LOGGING SETUP --- +LOG_LEVEL = logging.DEBUG if Config.DEBUG else logging.INFO +LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s' + +root_logger = logging.getLogger() +root_logger.setLevel(LOG_LEVEL) + +# Handler nur hinzufügen, wenn noch keine konfiguriert sind, um Dopplung zu vermeiden +if not root_logger.handlers: + stream_handler = logging.StreamHandler() + stream_handler.setFormatter(logging.Formatter(LOG_FORMAT)) + root_logger.addHandler(stream_handler) + + log_file_path = create_log_filename("duplicate_check_v2_final") + 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) +else: + log_file_path = next((h.baseFilename for h in root_logger.handlers if isinstance(h, logging.FileHandler)), None) -# WICHTIG: Logging Setup für detaillierte Ausgaben -logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-8s - %(name)s - %(message)s') logger = logging.getLogger(__name__) - -def calculate_similarity_details(record1, record2): +def calculate_similarity_with_details(record1, record2): """ - Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück. + Berechnet einen gewichteten Ähnlichkeits-Score und gibt den Score und den Grund zurück. + Basierend auf der v2.0 Scoring-Logik. """ 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'): + domain1 = record1.get('normalized_domain') + domain2 = record2.get('normalized_domain') + if domain1 and domain1 != 'k.a.' and domain1 == domain2: 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 + name1 = record1.get('normalized_name') + name2 = record2.get('normalized_name') + if name1 and name2: + name_similarity = fuzz.token_set_ratio(name1, name2) + scores['name'] = round(name_similarity * 0.7) + ort1 = record1.get('CRM Ort') + ort2 = record2.get('CRM Ort') + land1 = record1.get('CRM Land') + land2 = record2.get('CRM Land') + if ort1 and ort1 == ort2 and land1 and land1 == land2: + scores['location'] = 20 + total_score = sum(scores.values()) - return {'total': total_score, 'details': scores} + + 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 def main(): + """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten.""" start_time = time.time() - logger.info("Starte den Duplikats-Check (v3.0 - Back to Basics)...") + logger.info("Starte den Duplikats-Check (v2.0 mit Blocking und Maximum Logging)...") + logger.info(f"Logdatei: {log_file_path}") - # ... (Initialisierung und Laden der Daten bleibt gleich) ... try: sheet_handler = GoogleSheetHandler() except Exception as e: - logger.critical(f"FEHLER bei Initialisierung: {e}") + logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}") return - logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...") + logger.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 - logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...") + logger.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() - logging.info("Normalisiere Daten für den Vergleich...") + logger.info("Normalisiere Daten für den Vergleich...") for df in [crm_df, matching_df]: df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name) 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) + logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...") + crm_index = {} crm_records = crm_df.to_dict('records') - matching_records = matching_df.to_dict('records') - - logger.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...") + for record in crm_records: + key = record['block_key'] + if key: + if key not in crm_index: + crm_index[key] = [] + crm_index[key].append(record) + + logger.info("Starte Matching-Prozess...") results = [] - for i, match_record in enumerate(matching_records): - best_score_info = {'total': -1, 'details': {'name': 0, 'location': 0, 'domain': 0}} + for match_record in matching_df.to_dict('records'): + best_score = -1 best_match_name = "" + best_reason = "" - logger.info(f"--- Prüfe {i + 1}/{len(matching_records)}: '{match_record.get('CRM Name', 'N/A')}' ---") - - # BRUTE-FORCE: Vergleiche mit jedem einzelnen CRM-Eintrag - for crm_record in crm_records: - score_info = calculate_similarity_details(match_record, crm_record) - - # 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']})") + 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')}', Key: '{match_record.get('block_key')}']") - if score_info['total'] > best_score_info['total']: - best_score_info = score_info - best_match_name = crm_record.get('CRM Name', 'N/A') + block_key = match_record.get('block_key') + candidates = crm_index.get(block_key, []) - logger.info(f" --> Bester Treffer: '{best_match_name}' mit Score {best_score_info['total']}") + if not candidates: + logger.debug(" -> Keine Kandidaten im Index gefunden. Überspringe Vergleich.") + results.append({ + 'Potenzieller Treffer im CRM': "", 'Ähnlichkeits-Score': 0, 'Matching-Grund': "Keine Kandidaten" + }) + continue + + logger.debug(f" -> Vergleiche mit {len(candidates)} Kandidaten aus Block '{block_key}'.") + + for crm_row in candidates: + score, reason = calculate_similarity_with_details(match_record, crm_row) + + if score > 0: + logger.debug(f" - Kandidat: '{crm_row.get('CRM Name', 'N/A')}' -> Score: {score} (Grund: {reason})") + + if score > best_score: + best_score = score + best_match_name = crm_row.get('CRM Name', 'N/A') + best_reason = reason + + logger.info(f" --> Bester Treffer: '{best_match_name}' mit Score {best_score} (Grund: {best_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...") @@ -112,12 +161,13 @@ def main(): success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write) if success: - logger.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.") + logger.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.") else: logger.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.") end_time = time.time() logger.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.") + logger.info(f"===== Skript beendet =====") if __name__ == "__main__": main() \ No newline at end of file