diff --git a/duplicate_checker.py b/duplicate_checker.py index 015a4f26..62824980 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -2,60 +2,69 @@ import os import sys import logging import pandas as pd +import tldextract from datetime import datetime from thefuzz import fuzz from helpers import normalize_company_name, simple_normalize_url from google_sheet_handler import GoogleSheetHandler -# duplicate_checker.py v2.3 (Rückkehr zum v2.0-Scoring, erweitert mit Logging) -# Version: 2025-08-06_16-00 +# duplicate_checker.py v2.4 (root-domain match via tldextract) +# Version: 2025-08-06_16-30 # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 80 # v2.0 Schwelle (0–190 Skala) +SCORE_THRESHOLD = 80 # Schwelle für automatisches Match LOG_DIR = "Log" # --- Logging Setup mit Datum im Dateinamen --- if not os.path.exists(LOG_DIR): os.makedirs(LOG_DIR, exist_ok=True) now = datetime.now().strftime('%Y-%m-%d_%H-%M') -log_path = os.path.join(LOG_DIR, f"{now}_Duplicate_v2.3.log") +log_path = os.path.join(LOG_DIR, f"{now}_Duplicate_v2.4.log") + logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) -# Console Handler (INFO+) + +# Console-Handler (INFO+) ch = logging.StreamHandler() ch.setLevel(logging.INFO) ch.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")) logger.addHandler(ch) -# File Handler (DEBUG+) + +# File-Handler (DEBUG+) fh = logging.FileHandler(log_path, mode='a', encoding='utf-8') fh.setLevel(logging.DEBUG) fh.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(name)s - %(message)s")) logger.addHandler(fh) + logger.info(f"Logging in Datei: {log_path}") -logger.info("Version: duplicate_checker.py v2.3 (v2.0-Scoring mit Logging) | Build: 2025-08-06_16-00") +logger.info("Version: duplicate_checker.py v2.4 (root-domain match via tldextract) | Build: 2025-08-06_16-30") def calculate_similarity(record1, record2): - """Berechnet den v2.0-Score: Domain=100, Name*0.7, Ort+Land=20.""" + """Berechnet v2.0-Score mit root-domain match.""" total_score = 0 - # Domain(exakt) - if record1.get('normalized_domain') and record1['normalized_domain'] == record2.get('normalized_domain'): + # Domain root only + url1 = record1.get('CRM Website', '') + url2 = record2.get('CRM Website', '') + dom1 = tldextract.extract(url1).domain + dom2 = tldextract.extract(url2).domain + if dom1 and dom1 == dom2: total_score += 100 # Name fuzzy - name1 = record1.get('normalized_name','') - name2 = record2.get('normalized_name','') + name1 = record1.get('normalized_name', '') + name2 = record2.get('normalized_name', '') if name1 and name2: sim = fuzz.token_set_ratio(name1, name2) total_score += sim * 0.7 - # Ort+Land exakt + # Ort+Land exact if record1.get('CRM Ort') == record2.get('CRM Ort') and record1.get('CRM Land') == record2.get('CRM Land'): total_score += 20 return round(total_score) def main(): - logger.info("Starte Duplikats-Check v2.3 (v2.0-Scoring mit Logging)") + logger.info("Starte Duplikats-Check v2.4 (root-domain match)") try: sheet = GoogleSheetHandler() logger.info("GoogleSheetHandler initialisiert") @@ -72,15 +81,15 @@ def main(): logger.info(f"{len(crm_df)} CRM- und {len(match_df)} Matching-Zeilen geladen") # Normalisierung & Blocking-Key - for df, label in [(crm_df,'CRM'), (match_df,'Matching')]: - df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name) + for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]: + 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 else None) + 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 else None) logger.debug(f"{label}-Sample: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}") - # Build Blocking-Index + # Blocking-Index bauen crm_index = {} for idx, row in crm_df.iterrows(): key = row['block_key'] @@ -88,32 +97,31 @@ def main(): crm_index.setdefault(key, []).append(row) logger.info(f"Blocking-Index mit {len(crm_index)} Keys erstellt") - # Matching mit relevanten Kandidaten im Log + # Matching mit Top-3-Log results = [] total = len(match_df) for i, mrow in match_df.iterrows(): key = mrow['block_key'] - candidates = crm_index.get(key, []) - logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(candidates)} Kandidaten") - if not candidates: - results.append({'Potenzieller Treffer im CRM':'','Ähnlichkeits-Score':0}) + cands = crm_index.get(key, []) + logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(cands)} Kandidaten") + if not cands: + results.append({'Potenzieller Treffer im CRM': '', 'Ähnlichkeits-Score': 0}) continue - # Score for each candidate - scored = [(crow['CRM Name'], calculate_similarity(mrow,crow)) for crow in candidates] - # Top 3 candidates logged + # Score für Kandidaten + scored = [(crow['CRM Name'], calculate_similarity(mrow, crow)) for crow in cands] top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3] logger.debug(f" Top3 Kandidaten: {top3}") best_name, best_score = max(scored, key=lambda x: x[1]) if best_score >= SCORE_THRESHOLD: - results.append({'Potenzieller Treffer im CRM':best_name,'Ähnlichkeits-Score':best_score}) + results.append({'Potenzieller Treffer im CRM': best_name, 'Ähnlichkeits-Score': best_score}) logger.info(f" --> Match: '{best_name}' mit Score {best_score}") else: - results.append({'Potenzieller Treffer im CRM':'','Ähnlichkeits-Score':best_score}) + results.append({'Potenzieller Treffer im CRM': '', 'Ähnlichkeits-Score': best_score}) logger.info(f" --> Kein Match (höchster Score {best_score})") - # Write results back - out = pd.DataFrame(results) - output = pd.concat([match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].reset_index(drop=True), out], axis=1) + # Ergebnisse zurückschreiben + out_df = pd.DataFrame(results) + output = pd.concat([match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].reset_index(drop=True), out_df], axis=1) data = [output.columns.tolist()] + output.values.tolist() ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data) if ok: @@ -121,5 +129,5 @@ def main(): else: logger.error("Fehler beim Schreiben ins Google Sheet") -if __name__=='__main__': +if __name__ == '__main__': main()