From bc9591a4099b7cd115a67957aeeb9f8fdcd253b8 Mon Sep 17 00:00:00 2001 From: Floke Date: Mon, 4 Aug 2025 05:35:07 +0000 Subject: [PATCH] Add Logging --- duplicate_checker.py | 165 ++++++++++++++++++++++++++----------------- 1 file changed, 99 insertions(+), 66 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index a57a4ad5..7bc38e25 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,119 +1,148 @@ -# duplicate_checker.py (v2.2 - Multi-Key Blocking & optimiertes Scoring) +# duplicate_checker.py (v2.0 - Maximum Logging) 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 -from collections import defaultdict +import time # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 85 # Etwas höherer Schwellenwert für bessere Präzision +SCORE_THRESHOLD = 80 -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +# --- VOLLSTÄNDIGES LOGGING SETUP --- +LOG_LEVEL = logging.DEBUG if Config.DEBUG else logging.INFO +LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s' -def calculate_similarity_details(record1, record2): - """Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück.""" +root_logger = logging.getLogger() +root_logger.setLevel(LOG_LEVEL) + +for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + +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_0") +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) + +logger = logging.getLogger(__name__) + +def calculate_similarity_with_details(record1, record2): + """ + Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen + und gibt die Details für die Begründung zurück. + """ scores = {'name': 0, 'location': 0, 'domain': 0} - if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'): + if record1['normalized_domain'] and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2['normalized_domain']: scores['domain'] = 100 - # Höhere Gewichtung für den Namen, da die Website oft fehlt - if record1.get('normalized_name') and record2.get('normalized_name'): - scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85) + if record1['normalized_name'] and record2['normalized_name']: + name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) + scores['name'] = round(name_similarity * 0.7) - 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'): + if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']: + if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']: 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" -def create_blocking_keys(name): - """Erstellt mehrere Blocking Keys für einen Namen, um die Sensitivität zu erhöhen.""" - if not name: - return [] - - words = name.split() - keys = set() - - # 1. Erstes Wort - if len(words) > 0: - keys.add(words[0]) - # 2. Zweites Wort (falls vorhanden) - if len(words) > 1: - keys.add(words[1]) - # 3. Erste 4 Buchstaben des ersten Wortes - if len(words) > 0 and len(words[0]) >= 4: - keys.add(words[0][:4]) - - return list(keys) + return round(total_score), reason_text def main(): - logging.info("Starte den Duplikats-Check (v2.2 mit Multi-Key Blocking)...") + """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten.""" + start_time = time.time() + logger.info("Starte den Duplikats-Check (v2.0 mit Blocking und Maximum Logging)...") + logger.info(f"Logdatei: {log_file_path}") try: sheet_handler = GoogleSheetHandler() except Exception as e: - logging.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 + 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 + 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_keys'] = df['normalized_name'].apply(create_blocking_keys) + df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x and x.split() else None) - logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...") - crm_index = defaultdict(list) - for record in crm_df.to_dict('records'): - for key in record['block_keys']: + logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...") + crm_index = {} + crm_records = crm_df.to_dict('records') + 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) - - logging.info("Starte Matching-Prozess...") + + logger.info("Starte Matching-Prozess...") results = [] for match_record in matching_df.to_dict('records'): - best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}} + best_score = -1 best_match_name = "" + best_reason = "" - logging.info(f"Prüfe: {match_record['CRM Name']}...") + 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')}']") - candidate_pool = {} - for key in match_record['block_keys']: - for crm_record in crm_index.get(key, []): - candidate_pool[crm_record['CRM Name']] = crm_record + block_key = match_record.get('block_key') + candidates = crm_index.get(block_key, []) - if not candidate_pool: - logging.debug(" -> Keine Kandidaten im Index gefunden.") + 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_record in candidate_pool.values(): - score_info = calculate_similarity_details(match_record, crm_record) - if score_info['total'] > best_score_info['total']: - best_score_info = score_info - best_match_name = crm_record['CRM Name'] + for crm_row in candidates: + score, reason = calculate_similarity_with_details(match_record, crm_row) + + if score > 0: # Logge jeden Vergleich, der einen Score > 0 hat + 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" --> Neuer bester Treffer: '{best_match_name}' mit Score {best_score}") 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...") @@ -129,5 +158,9 @@ def main(): else: logging.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