diff --git a/duplicate_checker.py b/duplicate_checker.py index 8a8ac6f5..6b275e98 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -16,10 +16,8 @@ WEIGHTS = { } # --- Logging Setup --- -logging.basicConfig( - level=logging.DEBUG, - format='%(asctime)s - %(levelname)-8s - %(message)s' -) +LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s' +logging.basicConfig(level=logging.DEBUG, format=LOG_FORMAT) logger = logging.getLogger(__name__) # --- Hilfsfunktionen --- @@ -43,20 +41,37 @@ def normalize_domain(url: str) -> str: def main(): - logger.info("Starte Duplikat-Check mit ausführlichem Logging...") - sheet_handler = GoogleSheetHandler() - crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME) - match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME) - if crm_df is None or crm_df.empty or match_df is None or match_df.empty: - logger.error("Leere Daten in CRM oder Matching Tab. Abbruch.") + logger.info("Starte den Duplikats-Check (v2.0 mit Blocking und Maximum Logging)...") + # GoogleSheetHandler initialisieren + try: + sheet_handler = GoogleSheetHandler() + logger.info("GoogleSheetHandler initialisiert") + except Exception as e: + logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}") return + # CRM-Daten laden + 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: + logger.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Abbruch.") + return + logger.info(f"{len(crm_df)} Zeilen aus '{CRM_SHEET_NAME}' geladen") + + # Matching-Daten laden + logger.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...") + match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME) + if match_df is None or match_df.empty: + logger.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Abbruch.") + return + logger.info(f"{len(match_df)} Zeilen aus '{MATCHING_SHEET_NAME}' geladen") + # Normalisierung - for df, name in [(crm_df, 'CRM'), (match_df, 'Matching')]: + for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]: df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name) df['norm_domain'] = df['CRM Website'].fillna('').apply(normalize_domain) df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip()) - logger.debug(f"{name}-Daten nach Normalisierung. Erste Zeile: {df.iloc[0].to_dict()}") + logger.debug(f"{label}-Daten nach Normalisierung. Erste Zeile: {df.iloc[0].to_dict()}") # Blocking per Domain indexer = recordlinkage.Index() @@ -64,13 +79,13 @@ def main(): candidate_pairs = indexer.index(crm_df, match_df) logger.info(f"Blocking abgeschlossen: {len(candidate_pairs)} Kandidatenpaare gefunden") - # Vergleichsregeln + # Vergleichsregeln definieren compare = recordlinkage.Compare() compare.exact('norm_domain', 'norm_domain', label='domain') compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim') compare.exact('city', 'city', label='city') features = compare.compute(candidate_pairs, crm_df, match_df) - logger.debug(f"Feature-DataFrame Vorschau:\n{features.head()}" ) + logger.debug(f"Feature-DataFrame Vorschau:\n{features.head()}") # Score berechnen features['score'] = ( @@ -80,36 +95,29 @@ def main(): ) logger.info("Scores berechnet") - # Best Match pro neuer Zeile mit Logging der Kandidaten + # Best Match pro neuer Zeile mit detailliertem Logging results = [] - crm_idx_col = [] - match_idx_col = [] for match_idx, group in features.reset_index().groupby('level_1'): - crm_idx_col.append(match_idx) - # sortiere Kandidaten nach Score - sorted_group = group.sort_values('score', ascending=False) - logger.debug(f"Matching-Index {match_idx}: untersuchte Kandidaten:\n{sorted_group[['level_0','score','domain','name_sim','city']]}" ) - top = sorted_group.iloc[0] - if top['score'] >= SCORE_THRESHOLD: - results.append((top['level_0'], match_idx, top['score'])) - logger.info(f"Zeile {match_idx}: Match mit CRM-Index {top['level_0']} Score {top['score']:.2f}") + logger.info(f"--- Prüfe: Zeile {match_idx} ---") + df_block = group.sort_values('score', ascending=False) + logger.debug(f" Kandidaten für Zeile {match_idx}:\n{df_block[['level_0','score','domain','name_sim','city']].to_string(index=False)}") + top = df_block.iloc[0] + crm_idx = top['level_0'] if top['score'] >= SCORE_THRESHOLD else None + if crm_idx is not None: + logger.info(f" --> Match: CRM-Index {crm_idx} mit Score {top['score']:.2f}") else: - results.append((None, match_idx, top['score'])) - logger.info(f"Zeile {match_idx}: Kein ausreichender Score (top {top['score']:.2f})") + logger.info(f" --> Kein Match (höchster Score {top['score']:.2f})") + results.append((crm_idx, match_idx, top['score'])) - # Ausgabe DataFrame + # Ausgabe DataFrame zusammenstellen crm_df = crm_df.reset_index() match_df = match_df.reset_index() output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy() - output['Matched CRM Name'] = '' - output['Matched CRM Website'] = '' - output['Matched CRM Ort'] = '' - output['Matched CRM Land'] = '' - output['Score'] = 0.0 + output[['Matched CRM Name','Matched CRM Website','Matched CRM Ort','Matched CRM Land','Score']] = '' for crm_idx, match_idx, score in results: if crm_idx is not None: - row_crm = crm_df.loc[crm_df['index'] == crm_idx].iloc[0] + row_crm = crm_df.loc[crm_df['index']==crm_idx].iloc[0] output.at[match_idx, 'Matched CRM Name'] = row_crm['CRM Name'] output.at[match_idx, 'Matched CRM Website'] = row_crm['CRM Website'] output.at[match_idx, 'Matched CRM Ort'] = row_crm['CRM Ort'] @@ -120,7 +128,7 @@ def main(): data = [output.columns.tolist()] + output.values.tolist() success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data) if success: - logger.info("Erfolgreich geschrieben ins Google Sheet") + logger.info(f"Erfolgreich geschrieben: {len([r for r in results if r[0] is not None])} Matches") else: logger.error("Fehler beim Schreiben ins Google Sheet.")