109 lines
4.8 KiB
Python
109 lines
4.8 KiB
Python
# duplicate_checker.py (v2.6 - Final Optimized Brute-Force)
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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 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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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def calculate_similarity_details(record1, record2):
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"""Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
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scores = {'name': 0, 'location': 0, 'domain': 0}
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# Domain-Match (höchste Priorität)
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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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scores['domain'] = 100
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# Namensähnlichkeit (hohe Gewichtung)
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if record1.get('normalized_name') and record2.get('normalized_name'):
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# token_set_ratio ist gut bei unterschiedlicher Wortreihenfolge und Zusatzwörtern
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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
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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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total_score = sum(scores.values())
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return {'total': total_score, 'details': scores}
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def main():
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start_time = time.time()
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logging.info("Starte den Duplikats-Check (v2.6 - Final Optimized Brute-Force)...")
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try:
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sheet_handler = GoogleSheetHandler()
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except Exception as e:
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logging.critical(f"FEHLER bei Initialisierung: {e}")
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return
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logging.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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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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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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crm_records = crm_df.to_dict('records')
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matching_records = matching_df.to_dict('records')
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logging.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...")
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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': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
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best_match_name = ""
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logging.info(f"Prüfe {i + 1}/{len(matching_records)}: {match_record.get('CRM Name', 'N/A')}...")
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# Brute-Force-Vergleich: Jede Zeile wird mit jeder CRM-Zeile verglichen
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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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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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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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})
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logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
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result_df = pd.DataFrame(results)
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output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
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data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
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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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logging.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.")
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else:
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logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
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end_time = time.time()
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logging.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
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if __name__ == "__main__":
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main() |