132 lines
5.2 KiB
Python
132 lines
5.2 KiB
Python
import os
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import sys
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import logging
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import pandas as pd
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from datetime import datetime
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from thefuzz import fuzz
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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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# duplicate_checker.py v2.1 (mit erweitertem Logging und Datum im Dateinamen)
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# Version: 2025-08-06_12-33
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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 = 80 # Score ab hier gilt als Match
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LOG_DIR = "Log"
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# --- Logging Setup mit Datum im Dateinamen ---
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if not os.path.exists(LOG_DIR):
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os.makedirs(LOG_DIR, exist_ok=True)
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now = datetime.now().strftime('%Y-%m-%d_%H-%M')
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log_path = os.path.join(LOG_DIR, f"{now}_Duplicate.log")
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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# Console-Handler (INFO+)
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ch = logging.StreamHandler()
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ch.setLevel(logging.INFO)
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ch.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s"))
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logger.addHandler(ch)
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# File-Handler (DEBUG+)
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fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
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fh.setLevel(logging.DEBUG)
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fh.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(name)s - %(message)s"))
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logger.addHandler(fh)
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logger.info(f"Logging in Datei: {log_path}")
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def calculate_similarity(record1, record2):
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"""Berechnet gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
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total = 0
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# Domain exact match über normalisierte Domain
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dom1 = record1.get('normalized_domain', '')
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dom2 = record2.get('normalized_domain', '')
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if dom1 and dom1 == dom2:
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total += 100
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# Name fuzzy (Token-Set Ratio)
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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_score = fuzz.token_set_ratio(name1, name2)
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total += name_score * 0.7
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# Ort+Land exact
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if record1.get('CRM Ort') == record2.get('CRM Ort') and record1.get('CRM Land') == record2.get('CRM Land'):
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total += 20
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return round(total)
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def main():
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logger.info("Starte Duplikats-Check (v2.0 mit Kern-Syntax nach Entwurf)")
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try:
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sheet = GoogleSheetHandler()
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logger.info("GoogleSheetHandler initialisiert")
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except Exception as e:
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logger.critical(f"FEHLER beim Init GoogleSheetHandler: {e}")
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sys.exit(1)
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# Daten einlesen
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crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
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match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
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logger.critical("CRM- oder Matching-Daten fehlen. Abbruch.")
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return
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logger.info(f"{len(crm_df)} CRM-Datensätze, {len(match_df)} Matching-Datensätze geladen")
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# Normalisierung und Blocking-Key
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for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
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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 else None)
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logger.debug(f"{label}-Beispiel nach Normalisierung: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
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# Blocking-Index
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crm_index = {}
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for idx, row in crm_df.iterrows():
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key = row['block_key']
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if key:
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crm_index.setdefault(key, []).append(row)
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logger.info(f"Blocking-Index erstellt: {len(crm_index)} Keys")
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# Matching mit Log relevanter Kandidaten
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results = []
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total = len(match_df)
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for i, mrow in match_df.iterrows():
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key = mrow['block_key']
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candidates = crm_index.get(key, [])
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logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(candidates)} Kandidaten")
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if not candidates:
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results.append({'Match': '', 'Score': 0})
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continue
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# Scores sammeln
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scored = [(crow['CRM Name'], calculate_similarity(mrow, crow)) for crow in candidates]
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# Top 3 relevante Kandidaten loggen
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top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3]
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logger.debug(f" Top 3 Kandidaten: {top3}")
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# Besten Treffer wählen
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best_name, best_score = max(scored, key=lambda x: x[1])
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if best_score >= SCORE_THRESHOLD:
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results.append({'Match': best_name, 'Score': best_score})
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logger.info(f" --> Match: '{best_name}' mit Score {best_score}")
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else:
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results.append({'Match': '', 'Score': best_score})
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logger.info(f" --> Kein Match (höchster Score {best_score})")
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# Ergebnisse zurück ins Sheet
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out = pd.DataFrame(results)
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output = pd.concat([match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].reset_index(drop=True), out], axis=1)
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data = [output.columns.tolist()] + output.values.tolist()
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ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data)
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if ok:
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logger.info("Ergebnisse erfolgreich geschrieben")
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else:
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logger.error("Fehler beim Schreiben ins Google Sheet")
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if __name__ == '__main__':
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main()
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