duplicate_checker.py aktualisiert
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@@ -1,106 +1,155 @@
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# duplicate_checker.py (v3.0 - Back to Basics: Optimized Brute-Force)
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# duplicate_checker.py (v2.0 + Transparenz)
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import logging
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import pandas as pd
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from thefuzz import fuzz
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from config import Config
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from helpers import normalize_company_name, simple_normalize_url
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from helpers import normalize_company_name, simple_normalize_url, create_log_filename
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from google_sheet_handler import GoogleSheetHandler
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import time
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# --- Konfiguration ---
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CRM_SHEET_NAME = "CRM_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt
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SCORE_THRESHOLD = 80
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# --- VOLLSTÄNDIGES LOGGING SETUP ---
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LOG_LEVEL = logging.DEBUG if Config.DEBUG else logging.INFO
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LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s'
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root_logger = logging.getLogger()
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root_logger.setLevel(LOG_LEVEL)
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# Handler nur hinzufügen, wenn noch keine konfiguriert sind, um Dopplung zu vermeiden
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if not root_logger.handlers:
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stream_handler = logging.StreamHandler()
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stream_handler.setFormatter(logging.Formatter(LOG_FORMAT))
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root_logger.addHandler(stream_handler)
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log_file_path = create_log_filename("duplicate_check_v2_final")
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if log_file_path:
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file_handler = logging.FileHandler(log_file_path, mode='a', encoding='utf-8')
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file_handler.setFormatter(logging.Formatter(LOG_FORMAT))
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root_logger.addHandler(file_handler)
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else:
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log_file_path = next((h.baseFilename for h in root_logger.handlers if isinstance(h, logging.FileHandler)), None)
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# WICHTIG: Logging Setup für detaillierte Ausgaben
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logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-8s - %(name)s - %(message)s')
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logger = logging.getLogger(__name__)
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def calculate_similarity_details(record1, record2):
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def calculate_similarity_with_details(record1, record2):
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"""
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Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück.
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Berechnet einen gewichteten Ähnlichkeits-Score und gibt den Score und den Grund zurück.
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Basierend auf der v2.0 Scoring-Logik.
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"""
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scores = {'name': 0, 'location': 0, 'domain': 0}
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# Domain-Match (höchste Priorität, 100 Punkte)
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if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
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domain1 = record1.get('normalized_domain')
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domain2 = record2.get('normalized_domain')
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if domain1 and domain1 != 'k.a.' and domain1 == domain2:
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scores['domain'] = 100
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# Namensähnlichkeit (hohe 85% Gewichtung)
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if record1.get('normalized_name') and record2.get('normalized_name'):
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# token_set_ratio ist robust gegen zusätzliche Wörter wie "Holding" oder "Gruppe"
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scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)
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# Standort-Bonus (20 Punkte)
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if record1.get('CRM Ort') and record1['CRM Ort'] == record2.get('CRM Ort'):
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if record1.get('CRM Land') and record1['CRM Land'] == record2.get('CRM Land'):
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scores['location'] = 20
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name1 = record1.get('normalized_name')
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name2 = record2.get('normalized_name')
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if name1 and name2:
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name_similarity = fuzz.token_set_ratio(name1, name2)
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scores['name'] = round(name_similarity * 0.7)
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ort1 = record1.get('CRM Ort')
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ort2 = record2.get('CRM Ort')
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land1 = record1.get('CRM Land')
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land2 = record2.get('CRM Land')
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if ort1 and ort1 == ort2 and land1 and land1 == land2:
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scores['location'] = 20
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total_score = sum(scores.values())
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return {'total': total_score, 'details': scores}
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reasons = []
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if scores['domain'] > 0: reasons.append(f"Domain({scores['domain']})")
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if scores['name'] > 0: reasons.append(f"Name({scores['name']})")
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if scores['location'] > 0: reasons.append(f"Ort({scores['location']})")
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reason_text = " + ".join(reasons) if reasons else "Keine Übereinstimmung"
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return round(total_score), reason_text
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def main():
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"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
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start_time = time.time()
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logger.info("Starte den Duplikats-Check (v3.0 - Back to Basics)...")
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logger.info("Starte den Duplikats-Check (v2.0 mit Blocking und Maximum Logging)...")
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logger.info(f"Logdatei: {log_file_path}")
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# ... (Initialisierung und Laden der Daten bleibt gleich) ...
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try:
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sheet_handler = GoogleSheetHandler()
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except Exception as e:
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logger.critical(f"FEHLER bei Initialisierung: {e}")
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logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
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return
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logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
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logger.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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if crm_df is None or crm_df.empty: return
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logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
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logger.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
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matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if matching_df is None or matching_df.empty: return
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original_matching_df = matching_df.copy()
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logging.info("Normalisiere Daten für den Vergleich...")
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logger.info("Normalisiere Daten für den Vergleich...")
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for df in [crm_df, matching_df]:
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df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
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df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
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df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
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df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
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df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x and x.split() else None)
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logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
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crm_index = {}
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crm_records = crm_df.to_dict('records')
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matching_records = matching_df.to_dict('records')
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logger.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...")
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for record in crm_records:
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key = record['block_key']
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if key:
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if key not in crm_index:
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crm_index[key] = []
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crm_index[key].append(record)
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logger.info("Starte Matching-Prozess...")
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results = []
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for i, match_record in enumerate(matching_records):
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best_score_info = {'total': -1, 'details': {'name': 0, 'location': 0, 'domain': 0}}
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for match_record in matching_df.to_dict('records'):
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best_score = -1
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best_match_name = ""
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best_reason = ""
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logger.info(f"--- Prüfe {i + 1}/{len(matching_records)}: '{match_record.get('CRM Name', 'N/A')}' ---")
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# BRUTE-FORCE: Vergleiche mit jedem einzelnen CRM-Eintrag
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for crm_record in crm_records:
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score_info = calculate_similarity_details(match_record, crm_record)
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# Logge jeden interessanten Vergleich (Score > 60)
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if score_info['total'] > 60:
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logger.debug(f" - Kandidat: '{crm_record.get('CRM Name', 'N/A')}' -> Score: {score_info['total']} (Details: {score_info['details']})")
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logger.info(f"--- Prüfe: '{match_record.get('CRM Name', 'N/A')}' ---")
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logger.debug(f" [Normalisiert: '{match_record.get('normalized_name')}', Domain: '{match_record.get('normalized_domain')}', Key: '{match_record.get('block_key')}']")
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if score_info['total'] > best_score_info['total']:
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best_score_info = score_info
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best_match_name = crm_record.get('CRM Name', 'N/A')
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block_key = match_record.get('block_key')
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candidates = crm_index.get(block_key, [])
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logger.info(f" --> Bester Treffer: '{best_match_name}' mit Score {best_score_info['total']}")
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if not candidates:
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logger.debug(" -> Keine Kandidaten im Index gefunden. Überspringe Vergleich.")
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results.append({
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'Potenzieller Treffer im CRM': "", 'Ähnlichkeits-Score': 0, 'Matching-Grund': "Keine Kandidaten"
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})
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continue
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logger.debug(f" -> Vergleiche mit {len(candidates)} Kandidaten aus Block '{block_key}'.")
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for crm_row in candidates:
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score, reason = calculate_similarity_with_details(match_record, crm_row)
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if score > 0:
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logger.debug(f" - Kandidat: '{crm_row.get('CRM Name', 'N/A')}' -> Score: {score} (Grund: {reason})")
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if score > best_score:
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best_score = score
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best_match_name = crm_row.get('CRM Name', 'N/A')
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best_reason = reason
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logger.info(f" --> Bester Treffer: '{best_match_name}' mit Score {best_score} (Grund: {best_reason})")
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results.append({
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'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
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'Score (Gesamt)': best_score_info['total'],
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'Score (Name)': best_score_info['details']['name'],
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'Bonus (Standort)': best_score_info['details']['location'],
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'Bonus (Domain)': best_score_info['details']['domain']
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'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "",
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'Ähnlichkeits-Score': best_score,
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'Matching-Grund': best_reason
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})
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logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
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@@ -112,12 +161,13 @@ def main():
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
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if success:
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logger.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.")
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logger.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
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else:
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logger.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
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end_time = time.time()
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logger.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
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logger.info(f"===== Skript beendet =====")
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if __name__ == "__main__":
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main()
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