From 9789b388362c45e4e53d9e8d0527ba3f6d96eacb Mon Sep 17 00:00:00 2001 From: Floke Date: Wed, 6 Aug 2025 05:59:05 +0000 Subject: [PATCH] duplicate_checker.py aktualisiert --- duplicate_checker.py | 67 +++++++++++++++++++++++++------------------- 1 file changed, 38 insertions(+), 29 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index 5b9adefd..6c17c6f4 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -10,13 +10,15 @@ from google_sheet_handler import GoogleSheetHandler # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 0.8 +# Threshold gesenkt und konfigurierbar im Code +SCORE_THRESHOLD = 0.75 WEIGHTS = { 'domain': 0.5, 'name': 0.4, 'city': 0.1, } -LOG_DIR = '/log' +# Relativer Log-Ordner +LOG_DIR = 'log' LOG_FILENAME = 'duplicate_check.log' # --- Logging Setup --- @@ -30,11 +32,13 @@ log_path = os.path.join(LOG_DIR, LOG_FILENAME) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(levelname)-8s - %(name)s - %(message)s') + # Console handler console_handler = logging.StreamHandler() console_handler.setLevel(logging.INFO) console_handler.setFormatter(formatter) logger.addHandler(console_handler) + # File handler try: file_handler = logging.FileHandler(log_path, mode='a', encoding='utf-8') @@ -55,6 +59,7 @@ def normalize_company_name(name: str) -> str: tokens = [t for t in s.split() if t and t not in stops] return ' '.join(tokens) + def normalize_domain(url: str) -> str: s = str(url).casefold().strip() s = re.sub(r'^https?://', '', s) @@ -65,8 +70,8 @@ def normalize_domain(url: str) -> str: def main(): - logger.info("Starte den Duplikats-Check (v2.0 mit korrekten Missing-Werten)...") - # Initialize GoogleSheetHandler + logger.info("Starte den Duplikats-Check mit Fallback-Blocking...") + # GoogleSheetHandler initialisieren try: sheet_handler = GoogleSheetHandler() logger.info("GoogleSheetHandler initialisiert") @@ -74,7 +79,7 @@ def main(): logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}") return - # Load data + # Daten laden 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: @@ -82,29 +87,35 @@ def main(): return logger.info(f"{len(crm_df)} CRM-Zeilen, {len(match_df)} Matching-Zeilen geladen") - # Normalize fields + # Normalisierung 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()) - # Replace empty strings with NaN so they aren't considered matches + df['name_token'] = df['norm_name'].apply(lambda x: x.split()[0] if x else np.nan) + # Leere Werte als NaN markieren df['norm_domain'].replace('', np.nan, inplace=True) df['city'].replace('', np.nan, inplace=True) - logger.debug(f"{label}-Daten normalisiert: Beispiel: {{'norm_name': df.iloc[0]['norm_name'], 'norm_domain': df.iloc[0]['norm_domain'], 'city': df.iloc[0]['city']}}") + logger.debug(f"{label}-Normalisierung: norm_domain={df.iloc[0]['norm_domain']}, name_token={df.iloc[0]['name_token']}") - # 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") + # Blocking: Domain und Name-Token + index_dom = recordlinkage.Index() + index_dom.block('norm_domain') + pairs_dom = index_dom.index(crm_df, match_df) + index_name = recordlinkage.Index() + index_name.block('name_token') + pairs_name = index_name.index(crm_df, match_df) + # Union der Kandidatenpaare + candidate_pairs = pairs_dom.append(pairs_name).drop_duplicates() + logger.info(f"Blocking abgeschlossen: Dom-Paare={len(pairs_dom)}, Name-Paare={len(pairs_name)}, Gesamt={len(candidate_pairs)}") # Vergleichsregeln definieren compare = recordlinkage.Compare() compare.exact('norm_domain', 'norm_domain', label='domain', missing_value=0) - compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim') - compare.exact('city', 'city', label='city', missing_value=0) + compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim') + compare.exact('city', 'city', label='city', missing_value=0) features = compare.compute(candidate_pairs, crm_df, match_df) - logger.debug(f"Features berechnet: {features.head()}\n...") + logger.debug(f"Features berechnet: {features.head()}...") # Score berechnen features['score'] = ( @@ -114,19 +125,17 @@ def main(): ) logger.info("Scores berechnet") - # Detailed per-match logging + # Per-Match Logging und Auswahl results = [] - crm_idx_map = crm_df.reset_index() + crm_map = crm_df.reset_index() for match_idx, group in features.reset_index().groupby('level_1'): logger.info(f"--- Prüfe Matching-Zeile {match_idx} ---") df_block = group.sort_values('score', ascending=False).copy() - # Enrich with CRM info - df_block['CRM Name'] = df_block['level_0'].map(crm_idx_map.set_index('index')['CRM Name']) - df_block['CRM Website'] = df_block['level_0'].map(crm_idx_map.set_index('index')['CRM Website']) - df_block['CRM Ort'] = df_block['level_0'].map(crm_idx_map.set_index('index')['CRM Ort']) - logger.debug("Kandidaten (Index, Score, Domain, Name_sim, City, CRM Name):") - for _, row in df_block.iterrows(): - logger.debug(f" [{int(row['level_0'])}] score={row['score']:.3f} dom={row['domain']} name={row['name_sim']:.3f} city={row['city']} => {row['CRM Name']}") + # CRM-Daten für Log + df_block['CRM Name'] = df_block['level_0'].map(crm_map.set_index('index')['CRM Name']) + # Log der Top-Kandidaten + for _, row in df_block.head(5).iterrows(): + logger.debug(f"Candidate [{int(row['level_0'])}]: score={row['score']:.3f}, name_sim={row['name_sim']:.3f}, dom={row['domain']}, city={row['city']} => {row['CRM Name']}") top = df_block.iloc[0] crm_idx = top['level_0'] if top['score'] >= SCORE_THRESHOLD else None if crm_idx is not None: @@ -135,13 +144,13 @@ def main(): logger.info(f" --> Kein Match (höchster Score {top['score']:.2f})") results.append((crm_idx, match_idx, top['score'])) - # Prepare output - match_idx_map = match_df.reset_index() - output = match_idx_map[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy() + # Ausgabe zusammenstellen + match_map = match_df.reset_index() + output = match_map[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy() 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: - crm_row = crm_idx_map[crm_idx_map['index']==crm_idx].iloc[0] + crm_row = crm_map[crm_map['index']==crm_idx].iloc[0] output.at[match_idx, 'Matched CRM Name'] = crm_row['CRM Name'] output.at[match_idx, 'Matched CRM Website'] = crm_row['CRM Website'] output.at[match_idx, 'Matched CRM Ort'] = crm_row['CRM Ort']