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duplicate_checker.py aktualisiert

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Floke
2025-08-06 14:04:03 +00:00
parent 65c3f4dd85
commit b9bbab59bf
1 changed files with 74 additions and 64 deletions
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138
duplicate_checker.py
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@@ -6,15 +6,15 @@ 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
# duplicate_checker.py v2.9 (Bulletproof Name-Partial/SORT/SET + Bonus)
# Version: 2025-08-06_18-10
# --- Konfiguration ---
CRM_SHEET_NAME = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD = 80
SCORE_THRESHOLD = 80 # Score-Schwelle
LOG_DIR = "Log"
LOG_FILE = "duplicate_check_v2.8.log"
LOG_FILE = "duplicate_check_v2.9.txt"
# --- Logging Setup ---
if not os.path.exists(LOG_DIR):
@@ -22,46 +22,60 @@ if not os.path.exists(LOG_DIR):
log_path = os.path.join(LOG_DIR, LOG_FILE)
root = logging.getLogger()
root.setLevel(logging.DEBUG)
# Remove old handlers
# Remove existing handlers
for h in list(root.handlers):
root.removeHandler(h)
formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
# Console
# Console handler (INFO+)
ch = logging.StreamHandler(sys.stdout)
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
root.addHandler(ch)
# File
# File handler (DEBUG+)
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")
logger.info("Starting duplicate_checker.py v2.9 | Version: 2025-08-06_18-10")
# --- Ähnlichkeitsberechnung ---
def calculate_similarity(record1, record2):
"""Berechnet Score-Komponenten: Domain, Name (SET,PARTIAL,SORT), Ort und Bonus."""
# Domain exact match
dom1 = record1.get('normalized_domain', '')
dom2 = record2.get('normalized_domain', '')
domain_flag = 1 if dom1 and dom1 == dom2 else 0
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
# Location exact match
loc_flag = 1 if (record1.get('CRM Ort') == record2.get('CRM Ort') and
record1.get('CRM Land') == record2.get('CRM Land')) else 0
# Name scores
n1 = record1.get('normalized_name', '')
n2 = record2.get('normalized_name', '')
if n1 and n2:
ts = fuzz.token_set_ratio(n1, n2)
pr = fuzz.partial_ratio(n1, n2)
ss = fuzz.token_sort_ratio(n1, n2)
name_score = max(ts, pr, ss)
else:
name_score = 0
# Bonus für reine Name-Matches
bonus_flag = 1 if domain_flag == 0 and loc_flag == 0 and name_score >= 85 else 0
# Gesamtscore
total = domain_flag * 100 + name_score * 1.0 + loc_flag * 20 + bonus_flag * 20
return round(total), domain_flag, name_score, loc_flag, bonus_flag
# --- Hauptfunktion ---
def main():
logger.info("Starte Duplikats-Check v2.8 (Match-Komponenten im Log)")
logger.info("Starte Duplikats-Check v2.9 (Bulletproof)")
# GoogleSheetHandler init
try:
sheet_handler = GoogleSheetHandler()
sheet = GoogleSheetHandler()
logger.info("GoogleSheetHandler initialisiert")
except Exception as e:
logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
@@ -69,18 +83,15 @@ def main():
# 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")
crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
logger.info(f"{0 if crm_df is None else 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.")
match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
logger.info(f"{0 if match_df is None else len(match_df)} Matching-Datensätze geladen")
if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
logger.critical("Leere Daten in einem der Sheets. Abbruch.")
return
logger.info(f"{len(match_df)} Matching-Datensätze geladen")
# Normalisierung & Blocking-Key
for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
@@ -91,50 +102,49 @@ def main():
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...")
# Blocking-Index erzeugen
crm_index = {}
for idx, row in crm_df.iterrows():
for _, 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)
logger.info("Starte Matching-Prozess...")
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})
cands = crm_index.get(key, [])
logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' -> {len(cands)} Kandidaten")
if not cands:
results.append({'Match':'', '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)
scored = []
for crow in cands:
sc, dm, ns, lm, bf = calculate_similarity(mrow, crow)
scored.append((crow['CRM Name'], sc, dm, ns, lm, bf))
# Top 3 loggen
for name, sc, dm, ns, lm, bf in sorted(scored, key=lambda x: x[1], reverse=True)[:3]:
logger.debug(f" Kandidat: {name}, Score={sc}, Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}")
best_name, best_score, dm, ns, lm, bf = max(scored, key=lambda x: x[1])
if best_score >= SCORE_THRESHOLD:
results.append({'Match':best_name, 'Score':best_score})
logger.info(f" --> Match: '{best_name}' ({best_score}) [Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}]")
else:
results.append({'Match':'', 'Score':best_score})
logger.info(f" --> Kein Match (Score={best_score}) [Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}]")
# Ergebnisse zurückschreiben
logger.info("Schreibe Ergebnisse ins Sheet...")
out = 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)
output = pd.concat([output.reset_index(drop=True), out], axis=1)
data = [output.columns.tolist()] + output.values.tolist()
success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
if success:
if sheet.clear_and_write_data(MATCHING_SHEET_NAME, data):
logger.info("Ergebnisse erfolgreich geschrieben")
else:
logger.error("Fehler beim Schreiben ins Google Sheet")