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duplicate_checker.py aktualisiert

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Floke
2025-08-18 08:39:27 +00:00
parent edd0b14f6c
commit 7d76a38cae
1 changed files with 57 additions and 31 deletions
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duplicate_checker.py
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@@ -1,6 +1,8 @@
import os
import sys
import re
import argparse
import json
import logging
import pandas as pd
from datetime import datetime
@@ -10,6 +12,19 @@ from helpers import normalize_company_name, simple_normalize_url, serp_website_l
from config import Config
from google_sheet_handler import GoogleSheetHandler
STATUS_DIR = "job_status"
def update_status(job_id, status, progress_message):
"""Schreibt den aktuellen Status in eine JSON-Datei."""
if not job_id: return
status_file = os.path.join(STATUS_DIR, f"{job_id}.json")
try:
with open(status_file, 'w') as f:
json.dump({"status": status, "progress": progress_message}, f)
except Exception as e:
# Logge den Fehler, aber lasse das Hauptskript nicht abstürzen
logging.error(f"Konnte Statusdatei für Job {job_id} nicht schreiben: {e}")
# duplicate_checker.py v2.15
# Quality-first ++: Domain-Gate, Location-Penalties, Smart Blocking (IDF-light),
# Serp-Trust, Weak-Threshold, City-Bias-Guard, Prefilter tightened, Metrics
@@ -209,25 +224,30 @@ def choose_rarest_token(norm_name: str, token_freq: Counter):
return lst[0] if lst else None
# --- Hauptfunktion ---
def main():
def main(job_id=None):
logger.info("Starte Duplikats-Check v2.15 (Quality-first++)")
update_status(job_id, "Läuft", "Initialisiere GoogleSheetHandler...")
try:
sheet = GoogleSheetHandler()
logger.info("GoogleSheetHandler initialisiert")
except Exception as e:
logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
update_status(job_id, "Fehlgeschlagen", f"Init GoogleSheetHandler fehlgeschlagen: {e}")
sys.exit(1)
# Daten laden
update_status(job_id, "Läuft", "Lade CRM- und Matching-Daten...")
crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
logger.info(f"{0 if crm_df is None else len(crm_df)} CRM-Datensätze | {0 if match_df is None else len(match_df)} Matching-Datensätze")
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.")
update_status(job_id, "Fehlgeschlagen", "Leere Daten in einem der Sheets.")
return
# SerpAPI nur für Matching (B und E leer)
if serp_key:
# Annahme: serp_key wird global geladen, z.B. durch Config.load_api_keys()
if Config.API_KEYS.get('serpapi'):
if 'Gefundene Website' not in match_df.columns:
match_df['Gefundene Website'] = ''
b_empty = match_df['CRM Website'].fillna('').astype(str).str.strip().str.lower().isin(['','k.a.','k.a','n/a','na'])
@@ -235,6 +255,7 @@ def main():
empty_mask = b_empty & e_empty
empty_count = int(empty_mask.sum())
if empty_count > 0:
update_status(job_id, "Läuft", f"Suche Websites für {empty_count} Firmen via SerpAPI...")
logger.info(f"Serp-Fallback für Matching: {empty_count} Firmen ohne URL in B/E")
found_cnt = 0
trust_stats = Counter()
@@ -260,14 +281,16 @@ def main():
logger.info("Serp-Fallback übersprungen: B oder E bereits befüllt (keine fehlenden Matching-URLs)")
# Normalisierung CRM
update_status(job_id, "Läuft", "Normalisiere CRM-Daten...")
crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
crm_df['normalized_domain'] = crm_df['CRM Website'].astype(str).apply(simple_normalize_url)
crm_df['CRM Ort'] = crm_df['CRM Ort'].astype(str).str.lower().str.strip()
crm_df['CRM Land'] = crm_df['CRM Land'].astype(str).str.lower().str.strip()
crm_df['block_key'] = crm_df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
crm_df['domain_use_flag'] = 1 # CRM-Domain gilt als vertrauenswürdig
crm_df['domain_use_flag'] = 1
# Normalisierung Matching
update_status(job_id, "Läuft", "Normalisiere Matching-Daten...")
match_df['Gefundene Website'] = match_df.get('Gefundene Website', pd.Series(index=match_df.index, dtype=object))
match_df['Serp Vertrauen'] = match_df.get('Serp Vertrauen', pd.Series(index=match_df.index, dtype=object))
match_df['Effektive Website'] = match_df['CRM Website'].fillna('').astype(str).str.strip()
@@ -280,27 +303,25 @@ def main():
match_df['CRM Land'] = match_df['CRM Land'].astype(str).str.lower().str.strip()
match_df['block_key'] = match_df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
# Domain-Vertrauen/Use-Flag
def _domain_use(row):
if str(row.get('CRM Website','')).strip():
return 1
if str(row.get('CRM Website','')).strip(): return 1
trust = str(row.get('Serp Vertrauen','')).lower()
return 1 if trust == 'hoch' else 0
match_df['domain_use_flag'] = match_df.apply(_domain_use, axis=1)
# City-Tokens dynamisch bauen (nach Normalisierung von Ort)
# City-Tokens dynamisch bauen
def build_city_tokens(crm_df, match_df):
cities = set()
for s in pd.concat([crm_df['CRM Ort'], match_df['CRM Ort']], ignore_index=True).dropna().unique():
for t in _tokenize(s):
if len(t) >= 3:
cities.add(t)
if len(t) >= 3: cities.add(t)
return cities
global CITY_TOKENS
CITY_TOKENS = build_city_tokens(crm_df, match_df)
logger.info(f"City tokens gesammelt: {len(CITY_TOKENS)}")
# Blocking-Indizes (nachdem CITY_TOKENS gesetzt wurde)
# Blocking-Indizes
update_status(job_id, "Läuft", "Erstelle Blocking-Indizes...")
crm_records, domain_index, token_freq, token_index = build_indexes(crm_df)
logger.info(f"Blocking: Domains={len(domain_index)} | TokenKeys={len(token_index)}")
@@ -309,12 +330,14 @@ def main():
metrics = Counter()
total = len(match_df)
logger.info("Starte Matching-Prozess…")
processed = 0
for idx, mrow in match_df.to_dict('index').items():
processed += 1
name_disp = mrow.get('CRM Name','')
# Kandidatenwahl
processed = idx + 1 # Annahme, dass der Index 0-basiert ist
progress_message = f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}'"
logger.info(progress_message)
if processed % 5 == 0: # Status alle 5 Zeilen aktualisieren
update_status(job_id, "Läuft", progress_message)
candidates = []
used_block = ''
if mrow.get('normalized_domain') and mrow.get('domain_use_flag') == 1:
@@ -326,7 +349,6 @@ def main():
candidates = token_index.get(rtok, [])
used_block = f"token:{rtok}"
if not candidates:
# Prefilter über gesamte CRM-Liste (strenger + limitierter; erfordert Rarest-Token-Overlap)
pf = []
n1 = mrow.get('normalized_name','')
rtok = choose_rarest_token(n1, token_freq)
@@ -335,10 +357,8 @@ def main():
for r in crm_records:
n2 = r.get('normalized_name','')
clean2, toks2 = clean_name_for_scoring(n2)
if not clean2:
continue
if rtok and rtok not in toks2:
continue
if not clean2: continue
if rtok and rtok not in toks2: continue
pr = fuzz.partial_ratio(clean1, clean2)
if pr >= PREFILTER_MIN_PARTIAL:
pf.append((pr, r))
@@ -346,7 +366,7 @@ def main():
candidates = [r for _, r in pf[:PREFILTER_LIMIT]]
used_block = f"prefilter:{PREFILTER_MIN_PARTIAL}/{len(pf)}"
logger.info(f"Prüfe {processed}/{total}: '{name_disp}' -> {len(candidates)} Kandidaten (Block={used_block})")
logger.info(f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}' -> {len(candidates)} Kandidaten (Block={used_block})")
if not candidates:
results.append({'Match':'', 'Score':0, 'Match_Grund':'keine Kandidaten'})
continue
@@ -357,13 +377,11 @@ def main():
scored.append((cr.get('CRM Name',''), score, comp))
scored.sort(key=lambda x: x[1], reverse=True)
# Log Top5
for cand_name, sc, comp in scored[:5]:
logger.debug(f" Kandidat: {cand_name} | Score={sc} | Comp={comp}")
best_name, best_score, best_comp = scored[0]
# Akzeptanzlogik (Weak-Threshold + Guard)
weak = (best_comp.get('domain_used') == 0 and not (best_comp.get('city_match') and best_comp.get('country_match')))
applied_threshold = SCORE_THRESHOLD_WEAK if weak else SCORE_THRESHOLD
weak_guard_fail = (weak and best_comp.get('rare_overlap') == 0)
@@ -371,10 +389,8 @@ def main():
if not weak_guard_fail and best_score >= applied_threshold:
results.append({'Match': best_name, 'Score': best_score, 'Match_Grund': str(best_comp)})
metrics['matches_total'] += 1
if best_comp.get('domain_used') == 1:
metrics['matches_domain'] += 1
if best_comp.get('city_match') and best_comp.get('country_match'):
metrics['matches_with_loc'] += 1
if best_comp.get('domain_used') == 1: metrics['matches_domain'] += 1
if best_comp.get('city_match') and best_comp.get('country_match'): metrics['matches_with_loc'] += 1
if best_comp.get('domain_used') == 0 and best_comp.get('name') >= 85 and not (best_comp.get('city_match') and best_comp.get('country_match')):
metrics['matches_name_only'] += 1
logger.info(f" --> Match: '{best_name}' ({best_score}) {best_comp} | TH={applied_threshold}{' weak' if weak else ''}")
@@ -383,8 +399,8 @@ def main():
results.append({'Match':'', 'Score': best_score, 'Match_Grund': f"{best_comp} | {reason} TH={applied_threshold}"})
logger.info(f" --> Kein Match (Score={best_score}) {best_comp} | {reason} TH={applied_threshold}")
# Ergebnisse zurückschreiben (SAFE)
logger.info("Schreibe Ergebnisse ins Sheet (SAFE in-place, keine Spaltenverluste)…")
# Ergebnisse zurückschreiben
update_status(job_id, "Läuft", "Schreibe Ergebnisse zurück ins Sheet...")
res_df = pd.DataFrame(results, index=match_df.index)
write_df = match_df.copy()
write_df['Match'] = res_df['Match']
@@ -396,7 +412,8 @@ def main():
if c in write_df.columns:
write_df.drop(columns=[c], inplace=True)
backup_path = os.path.join(LOG_DIR, f"{now}_backup_{MATCHING_SHEET_NAME}.csv")
now = datetime.now().strftime("%Y-%m-%d_%H-%M")
backup_path = os.path.join(Config.LOG_DIR, f"{now}_backup_{MATCHING_SHEET_NAME}.csv")
try:
write_df.to_csv(backup_path, index=False, encoding='utf-8')
logger.info(f"Lokales Backup geschrieben: {backup_path}")
@@ -407,8 +424,10 @@ def main():
ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data)
if ok:
logger.info("Ergebnisse erfolgreich geschrieben")
update_status(job_id, "Abgeschlossen", f"{total} Accounts erfolgreich geprüft.")
else:
logger.error("Fehler beim Schreiben ins Google Sheet")
update_status(job_id, "Fehlgeschlagen", "Fehler beim Schreiben ins Google Sheet.")
# Summary
serp_counts = Counter((str(x).lower() for x in write_df.get('Serp Vertrauen', [])))
@@ -418,4 +437,11 @@ def main():
logger.info(f"Config: TH={SCORE_THRESHOLD}, TH_WEAK={SCORE_THRESHOLD_WEAK}, MIN_NAME_FOR_DOMAIN={MIN_NAME_FOR_DOMAIN}, Penalties(city={CITY_MISMATCH_PENALTY},country={COUNTRY_MISMATCH_PENALTY}), Prefilter(partial>={PREFILTER_MIN_PARTIAL}, limit={PREFILTER_LIMIT})")
if __name__=='__main__':
main()
# Hinzufügen des Argument-Parsers für die Job-ID
parser = argparse.ArgumentParser()
parser.add_argument("--job-id", type=str, help="Eindeutige ID für den Job-Status.")
# Fügen Sie hier weitere Argumente hinzu, die Ihr Skript benötigt
args = parser.parse_args()
# Rufen Sie die main-Funktion mit der Job-ID auf
main(job_id=args.job_id)