144 lines
5.8 KiB
Python
144 lines
5.8 KiB
Python
import os
|
|
import sys
|
|
import logging
|
|
import pandas as pd
|
|
from thefuzz import fuzz
|
|
from helpers import normalize_company_name, simple_normalize_url
|
|
from google_sheet_handler import GoogleSheetHandler
|
|
|
|
# duplicate_checker.py v2.8 (Match-Komponenten im Log)
|
|
# Version: 2025-08-06_17-50
|
|
|
|
# --- Konfiguration ---
|
|
CRM_SHEET_NAME = "CRM_Accounts"
|
|
MATCHING_SHEET_NAME = "Matching_Accounts"
|
|
SCORE_THRESHOLD = 80
|
|
LOG_DIR = "Log"
|
|
LOG_FILE = "duplicate_check_v2.8.log"
|
|
|
|
# --- Logging Setup ---
|
|
if not os.path.exists(LOG_DIR):
|
|
os.makedirs(LOG_DIR, exist_ok=True)
|
|
log_path = os.path.join(LOG_DIR, LOG_FILE)
|
|
root = logging.getLogger()
|
|
root.setLevel(logging.DEBUG)
|
|
# Remove old handlers
|
|
for h in list(root.handlers):
|
|
root.removeHandler(h)
|
|
formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
|
|
# Console
|
|
ch = logging.StreamHandler(sys.stdout)
|
|
ch.setLevel(logging.INFO)
|
|
ch.setFormatter(formatter)
|
|
root.addHandler(ch)
|
|
# File
|
|
fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
|
|
fh.setLevel(logging.DEBUG)
|
|
fh.setFormatter(formatter)
|
|
root.addHandler(fh)
|
|
logger = logging.getLogger(__name__)
|
|
logger.info(f"Logging to console and file: {log_path}")
|
|
logger.info("Starting duplicate_checker.py v2.8 | Version: 2025-08-06_17-50")
|
|
|
|
|
|
def calculate_similarity_components(r1, r2):
|
|
"""Gibt einzelne Komponenten und Gesamt-Score zurück."""
|
|
# Domain
|
|
dom1 = r1.get('normalized_domain', '')
|
|
dom2 = r2.get('normalized_domain', '')
|
|
domain_match = 1 if dom1 and dom1 == dom2 else 0
|
|
# Name
|
|
name1 = r1.get('normalized_name', '')
|
|
name2 = r2.get('normalized_name', '')
|
|
name_score = fuzz.token_set_ratio(name1, name2) if name1 and name2 else 0
|
|
# Ort+Land
|
|
loc_match = 1 if (r1.get('CRM Ort') == r2.get('CRM Ort') and r1.get('CRM Land') == r2.get('CRM Land')) else 0
|
|
# Gewichte
|
|
total = domain_match * 100 + name_score * 0.7 + loc_match * 20
|
|
return round(total), domain_match, round(name_score,1), loc_match
|
|
|
|
|
|
def main():
|
|
logger.info("Starte Duplikats-Check v2.8 (Match-Komponenten im Log)")
|
|
try:
|
|
sheet_handler = GoogleSheetHandler()
|
|
logger.info("GoogleSheetHandler initialisiert")
|
|
except Exception as e:
|
|
logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
|
|
sys.exit(1)
|
|
|
|
# Daten laden
|
|
logger.info(f"Lade CRM-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:
|
|
logger.critical("CRM-Tab leer. Abbruch.")
|
|
return
|
|
logger.info(f"{len(crm_df)} CRM-Datensätze geladen")
|
|
|
|
logger.info(f"Lade Matching-Daten aus '{MATCHING_SHEET_NAME}'...")
|
|
match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
|
|
if match_df is None or match_df.empty:
|
|
logger.critical("Matching-Tab leer. Abbruch.")
|
|
return
|
|
logger.info(f"{len(match_df)} Matching-Datensätze geladen")
|
|
|
|
# Normalisierung & Blocking-Key
|
|
for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
|
|
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_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
|
|
logger.debug(f"{label}-Sample: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
|
|
|
|
# Build blocking index
|
|
logger.info("Erstelle Blocking-Index...")
|
|
crm_index = {}
|
|
for idx, row in crm_df.iterrows():
|
|
key = row['block_key']
|
|
if key:
|
|
crm_index.setdefault(key, []).append(row)
|
|
logger.info(f"Blocking-Index mit {len(crm_index)} Keys erstellt")
|
|
|
|
# Matching
|
|
logger.info("Starte Matching...")
|
|
results = []
|
|
total = len(match_df)
|
|
for i, mrow in match_df.iterrows():
|
|
key = mrow['block_key']
|
|
candidates = crm_index.get(key, [])
|
|
logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' -> {len(candidates)} Kandidaten")
|
|
if not candidates:
|
|
results.append({'Potenzieller Treffer im CRM':'', 'Ähnlichkeits-Score':0})
|
|
continue
|
|
scored = []
|
|
for crow in candidates:
|
|
score, dm, ns, lm = calculate_similarity_components(mrow, crow)
|
|
scored.append((crow['CRM Name'], score, dm, ns, lm))
|
|
top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3]
|
|
# Log Top3 mit Komponenten
|
|
for name, sc, dm, ns, lm in top3:
|
|
logger.debug(f" Kandidat: {name}, Score={sc}, Domain={dm}, Name={ns}, Ort={lm}")
|
|
best_name, best_score, best_dm, best_ns, best_lm = max(scored, key=lambda x: x[1])
|
|
if best_score >= SCORE_THRESHOLD:
|
|
results.append({'Potenzieller Treffer im CRM':best_name, 'Ähnlichkeits-Score':best_score})
|
|
logger.info(f" --> Match: '{best_name}' Score={best_score} (Dom={best_dm}, Name={best_ns}, Ort={best_lm})")
|
|
else:
|
|
results.append({'Potenzieller Treffer im CRM':'', 'Ähnlichkeits-Score':best_score})
|
|
logger.info(f" --> Kein Match (Score={best_score}, Dom={best_dm}, Name={best_ns}, Ort={best_lm})")
|
|
|
|
# Write back
|
|
logger.info("Schreibe Ergebnisse zurück ins Sheet...")
|
|
out_df = pd.DataFrame(results)
|
|
output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
|
|
output = pd.concat([output.reset_index(drop=True), out_df], axis=1)
|
|
data = [output.columns.tolist()] + output.values.tolist()
|
|
success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
|
|
if success:
|
|
logger.info("Ergebnisse erfolgreich geschrieben")
|
|
else:
|
|
logger.error("Fehler beim Schreiben ins Google Sheet")
|
|
|
|
if __name__ == '__main__':
|
|
main()
|