diff --git a/duplicate_checker.py b/duplicate_checker.py index ee9ea125..8a8ac6f5 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,5 @@ import re +import logging import pandas as pd import recordlinkage from rapidfuzz import fuzz @@ -14,14 +15,15 @@ WEIGHTS = { 'city': 0.1, } +# --- Logging Setup --- +logging.basicConfig( + level=logging.DEBUG, + format='%(asctime)s - %(levelname)-8s - %(message)s' +) +logger = logging.getLogger(__name__) + # --- Hilfsfunktionen --- def normalize_company_name(name: str) -> str: - """ - Vereinfacht Firmennamen: - - Unicode-safe Kleinschreibung - - Umlaute in ae/oe/ue, ß in ss - - Entfernen von Rechtsformen/Stop-Wörtern - """ s = str(name).casefold() for src, dst in [('ä','ae'), ('ö','oe'), ('ü','ue'), ('ß','ss')]: s = s.replace(src, dst) @@ -32,7 +34,6 @@ def normalize_company_name(name: str) -> str: def normalize_domain(url: str) -> str: - """Extrahiere Root-Domain, entferne Protokoll und www-Präfix""" s = str(url).casefold().strip() s = re.sub(r'^https?://', '', s) s = s.split('/')[0] @@ -42,77 +43,86 @@ def normalize_domain(url: str) -> str: def main(): - # Google Sheets laden + logger.info("Starte Duplikat-Check mit ausführlichem Logging...") sheet_handler = GoogleSheetHandler() - crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME) + 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: - print("Fehler: Leere Daten in einem der Tabs. Abbruch.") + logger.error("Leere Daten in CRM oder Matching Tab. Abbruch.") return # Normalisierung - for df in (crm_df, match_df): - df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name) + for df, name 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()) + 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()}") # Blocking per Domain indexer = recordlinkage.Index() indexer.block('norm_domain') candidate_pairs = indexer.index(crm_df, match_df) + logger.info(f"Blocking abgeschlossen: {len(candidate_pairs)} Kandidatenpaare gefunden") - # Vergleichsregeln definieren + # Vergleichsregeln 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()}" ) - # Gewichte und Score + # Score berechnen features['score'] = ( WEIGHTS['domain'] * features['domain'] + WEIGHTS['name'] * features['name_sim'] + WEIGHTS['city'] * features['city'] ) + logger.info("Scores berechnet") - # Bestes Match pro neuer Zeile - matches = features.reset_index() - best = matches.sort_values(['level_1','score'], ascending=[True, False]) \ - .drop_duplicates('level_1') - best = best[best['score'] >= SCORE_THRESHOLD] \ - .rename(columns={'level_0':'crm_idx','level_1':'match_idx'}) + # Best Match pro neuer Zeile mit Logging der Kandidaten + 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}") + else: + results.append((None, match_idx, top['score'])) + logger.info(f"Zeile {match_idx}: Kein ausreichender Score (top {top['score']:.2f})") - # Merges - crm_df = crm_df.reset_index() + # Ausgabe DataFrame + crm_df = crm_df.reset_index() match_df = match_df.reset_index() - merged = (best - .merge(crm_df, left_on='crm_idx', right_on='index') - .merge(match_df, left_on='match_idx', right_on='index', suffixes=('_CRM','_NEW')) - ) - - # Ausgabe aufbauen output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy() - output['Matched CRM Name'] = '' + output['Matched CRM Name'] = '' output['Matched CRM Website'] = '' - output['Matched CRM Ort'] = '' - output['Matched CRM Land'] = '' - output['Score'] = 0.0 + output['Matched CRM Ort'] = '' + output['Matched CRM Land'] = '' + output['Score'] = 0.0 - for _, row in merged.iterrows(): - i = int(row['match_idx']) - output.at[i, 'Matched CRM Name'] = row['CRM Name_CRM'] - output.at[i, 'Matched CRM Website'] = row['CRM Website_CRM'] - output.at[i, 'Matched CRM Ort'] = row['CRM Ort_CRM'] - output.at[i, 'Matched CRM Land'] = row['CRM Land_CRM'] - output.at[i, 'Score'] = row['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] + 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'] + output.at[match_idx, 'Matched CRM Land'] = row_crm['CRM Land'] + output.at[match_idx, 'Score'] = round(score, 3) # Zurückschreiben ins Google Sheet data = [output.columns.tolist()] + output.values.tolist() success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data) if success: - print(f"Erfolgreich: {len(best)} Matches mit Score ≥ {SCORE_THRESHOLD}") + logger.info("Erfolgreich geschrieben ins Google Sheet") else: - print("Fehler beim Schreiben ins Google Sheet.") + logger.error("Fehler beim Schreiben ins Google Sheet.") if __name__ == '__main__': main()