371 lines
17 KiB
Python
371 lines
17 KiB
Python
import os
|
|
import sys
|
|
import re
|
|
import logging
|
|
import pandas as pd
|
|
from datetime import datetime
|
|
from collections import Counter
|
|
from thefuzz import fuzz
|
|
from helpers import normalize_company_name, simple_normalize_url, serp_website_lookup
|
|
from config import Config
|
|
from google_sheet_handler import GoogleSheetHandler
|
|
|
|
# duplicate_checker.py v2.14 (Quality-first + SERP nur falls B/E leer: Domain-Gate, Location-Penalties, Smart Blocking, Serp-Trust, Metrics)
|
|
# Version-Build: dynamic timestamp below
|
|
|
|
# --- Konfiguration ---
|
|
CRM_SHEET_NAME = "CRM_Accounts"
|
|
MATCHING_SHEET_NAME = "Matching_Accounts"
|
|
SCORE_THRESHOLD = 80 # Schwellwert fürs Auto-Match
|
|
MIN_NAME_FOR_DOMAIN = 70 # Domain-Match gilt nur, wenn Name >= 70 ODER Ort matcht
|
|
CITY_MISMATCH_PENALTY = 30
|
|
COUNTRY_MISMATCH_PENALTY = 40
|
|
PREFILTER_MIN_PARTIAL = 60 # Vorfilter über gesamte CRM-Liste bei fehlenden Kandidaten
|
|
PREFILTER_LIMIT = 50 # Max. Kandidaten aus Vorfilter
|
|
LOG_DIR = "Log"
|
|
now = datetime.now().strftime('%Y-%m-%d_%H-%M')
|
|
LOG_FILE = f"{now}_duplicate_check_v2.14.txt"
|
|
|
|
# --- 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)
|
|
for h in list(root.handlers):
|
|
root.removeHandler(h)
|
|
formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
|
|
ch = logging.StreamHandler(sys.stdout)
|
|
ch.setLevel(logging.INFO)
|
|
ch.setFormatter(formatter)
|
|
root.addHandler(ch)
|
|
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(f"Starting duplicate_checker.py v2.14 | Build: {now}")
|
|
|
|
# --- SerpAPI Key laden ---
|
|
try:
|
|
Config.load_api_keys()
|
|
serp_key = Config.API_KEYS.get('serpapi')
|
|
if not serp_key:
|
|
logger.warning("SerpAPI Key nicht gefunden; Serp-Fallback deaktiviert.")
|
|
except Exception as e:
|
|
logger.warning(f"Fehler beim Laden API-Keys: {e}")
|
|
serp_key = None
|
|
|
|
STOP_TOKENS = {
|
|
'gmbh','mbh','ag','kg','ug','ohg','se','co','kg','kgaa','inc','llc','ltd','sarl',
|
|
'holding','gruppe','group','international','solutions','solution','service','services',
|
|
'deutschland','austria','germany','technik','technology','technologies','systems','systeme',
|
|
'logistik','logistics','industries','industrie','management','consulting','vertrieb','handel'
|
|
}
|
|
|
|
# --- Utilitys ---
|
|
def split_tokens(name: str):
|
|
if not name:
|
|
return []
|
|
return [t for t in str(name).split() if len(t) >= 3 and t not in STOP_TOKENS]
|
|
|
|
def assess_serp_trust(company_name: str, url: str) -> str:
|
|
"""Einfache Vertrauensstufe für recherchierte URL: hoch/mittel/niedrig."""
|
|
if not url:
|
|
return 'n/a'
|
|
host = simple_normalize_url(url) or ''
|
|
host = host.replace('www.', '')
|
|
tokens = [t for t in split_tokens(normalize_company_name(company_name)) if len(t) >= 4]
|
|
if any(t in host for t in tokens):
|
|
return 'hoch'
|
|
tokens3 = [t for t in split_tokens(normalize_company_name(company_name)) if len(t) == 3]
|
|
if any(t in host for t in tokens3):
|
|
return 'mittel'
|
|
return 'niedrig'
|
|
|
|
# --- Ähnlichkeitsberechnung ---
|
|
def calculate_similarity(mrec: dict, crec: dict):
|
|
# Domain-Komponente (mit Gate)
|
|
dom1 = mrec.get('normalized_domain','')
|
|
dom2 = crec.get('normalized_domain','')
|
|
m_domain_use = mrec.get('domain_use_flag', 0) # 1 nur wenn original URL oder Serp-Vertrauen hoch
|
|
domain_flag_raw = 1 if (m_domain_use == 1 and dom1 and dom1 == dom2) else 0
|
|
|
|
# Location
|
|
city_match = 1 if (mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('CRM Ort')) else 0
|
|
country_match = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec.get('CRM Land') == crec.get('CRM Land')) else 0
|
|
|
|
# Name
|
|
n1, n2 = mrec.get('normalized_name',''), crec.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
|
|
|
|
# Domain-Gate: Domain zählt nur, wenn Name >= MIN_NAME_FOR_DOMAIN ODER Ort+Land passt
|
|
domain_gate_ok = (name_score >= MIN_NAME_FOR_DOMAIN) or (city_match and country_match)
|
|
domain_flag = 1 if (domain_flag_raw and domain_gate_ok) else 0
|
|
|
|
# Basisscore
|
|
total = domain_flag*100 + name_score*1.0 + (1 if (city_match and country_match) else 0)*20
|
|
|
|
# Penalties bei Mismatch (nur anwenden, wenn entsprechende Felder befüllt und kein voller Location-Match)
|
|
penalties = 0
|
|
if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match:
|
|
penalties += COUNTRY_MISMATCH_PENALTY
|
|
if mrec.get('CRM Ort') and crec.get('CRM Ort') and not city_match:
|
|
penalties += CITY_MISMATCH_PENALTY
|
|
|
|
total -= penalties
|
|
|
|
# Bonus für reine Name-Matches (keine Domain, kein Ort) wenn stark
|
|
bonus_flag = 1 if (domain_flag == 0 and not (city_match and country_match) and name_score >= 85) else 0
|
|
if bonus_flag:
|
|
total += 20
|
|
|
|
return (
|
|
round(total),
|
|
{
|
|
'domain_raw': domain_flag_raw,
|
|
'domain_used': domain_flag,
|
|
'domain_gate_ok': int(domain_gate_ok),
|
|
'name': round(name_score,1),
|
|
'city_match': city_match,
|
|
'country_match': country_match,
|
|
'penalties': penalties,
|
|
'name_bonus': bonus_flag
|
|
}
|
|
)
|
|
|
|
# --- Blocking vorbereiten ---
|
|
def build_indexes(crm_df: pd.DataFrame):
|
|
records = list(crm_df.to_dict('records'))
|
|
# Domain-Index
|
|
domain_index = {}
|
|
for r in records:
|
|
d = r.get('normalized_domain')
|
|
if d:
|
|
domain_index.setdefault(d, []).append(r)
|
|
# Token-Frequenzen
|
|
token_freq = Counter()
|
|
for r in records:
|
|
for t in set(split_tokens(r.get('normalized_name',''))):
|
|
token_freq[t] += 1
|
|
# Token-Index (nur sinnvolle Tokens)
|
|
token_index = {}
|
|
for r in records:
|
|
toks = [t for t in set(split_tokens(r.get('normalized_name',''))) if token_freq[t] > 0]
|
|
for t in toks:
|
|
token_index.setdefault(t, []).append(r)
|
|
return records, domain_index, token_freq, token_index
|
|
|
|
|
|
def choose_rarest_token(norm_name: str, token_freq: Counter):
|
|
toks = [t for t in split_tokens(norm_name) if len(t) >= 4 and token_freq.get(t, 0) > 0]
|
|
if not toks:
|
|
return None
|
|
# Rarest (kleinste Frequenz), zweitkriterium längster Token
|
|
toks.sort(key=lambda x: (token_freq.get(x, 0), -len(x)))
|
|
return toks[0]
|
|
|
|
# --- Hauptfunktion ---
|
|
def main():
|
|
logger.info("Starte Duplikats-Check v2.14 (Quality-first)")
|
|
try:
|
|
sheet = GoogleSheetHandler()
|
|
logger.info("GoogleSheetHandler initialisiert")
|
|
except Exception as e:
|
|
logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
|
|
sys.exit(1)
|
|
|
|
# Daten laden
|
|
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.")
|
|
return
|
|
|
|
# SerpAPI nur für Matching (fehlende URLs in B/E) → in 'Gefundene Website' speichern
|
|
if serp_key:
|
|
# Stelle sicher, dass Spalte E existiert
|
|
if 'Gefundene Website' not in match_df.columns:
|
|
match_df['Gefundene Website'] = ''
|
|
# B/E beide leer? Dann erst suchen. Alles andere: überspringen.
|
|
b_empty = match_df['CRM Website'].fillna('').astype(str).str.strip().str.lower().isin(['','k.a.','k.a','n/a','na'])
|
|
e_empty = match_df['Gefundene Website'].fillna('').astype(str).str.strip().str.lower().isin(['','k.a.','k.a','n/a','na'])
|
|
empty_mask = b_empty & e_empty
|
|
empty_count = int(empty_mask.sum())
|
|
if empty_count > 0:
|
|
logger.info(f"Serp-Fallback für Matching: {empty_count} Firmen ohne URL in B/E")
|
|
found_cnt = 0
|
|
trust_stats = Counter()
|
|
for idx, row in match_df[empty_mask].iterrows():
|
|
company = row['CRM Name']
|
|
try:
|
|
url = serp_website_lookup(company)
|
|
if url and 'k.A.' not in url:
|
|
if not str(url).startswith(('http://','https://')):
|
|
url = 'https://' + str(url).lstrip()
|
|
trust = assess_serp_trust(company, url)
|
|
match_df.at[idx, 'Gefundene Website'] = url
|
|
match_df.at[idx, 'Serp Vertrauen'] = trust
|
|
trust_stats[trust] += 1
|
|
logger.info(f" ✓ URL gefunden: '{company}' -> {url} (Vertrauen: {trust})")
|
|
found_cnt += 1
|
|
else:
|
|
logger.debug(f" ✗ Keine eindeutige URL: '{company}' -> {url}")
|
|
except Exception as e:
|
|
logger.warning(f" ! Serp-Fehler für '{company}': {e}")
|
|
logger.info(f"Serp-Fallback beendet: {found_cnt}/{empty_count} URLs ergänzt | Trust: {dict(trust_stats)}")
|
|
else:
|
|
logger.info("Serp-Fallback übersprungen: B oder E bereits befüllt (keine fehlenden Matching-URLs)")
|
|
|
|
# Normalisierung CRM
|
|
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 immer als vertrauenswürdig
|
|
|
|
# Normalisierung Matching (Effektive Website: Original oder Gefundene, aber Domain nur nutzen bei Vertrauen=hoch)
|
|
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()
|
|
mask_eff = match_df['Effektive Website'] == ''
|
|
match_df.loc[mask_eff, 'Effektive Website'] = match_df['Gefundene Website'].fillna('').astype(str).str.strip()
|
|
|
|
match_df['normalized_name'] = match_df['CRM Name'].astype(str).apply(normalize_company_name)
|
|
match_df['normalized_domain'] = match_df['Effektive Website'].astype(str).apply(simple_normalize_url)
|
|
match_df['CRM Ort'] = match_df['CRM Ort'].astype(str).str.lower().str.strip()
|
|
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
|
|
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)
|
|
|
|
# Debug-Sample
|
|
logger.debug(f"CRM-Sample: {crm_df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
|
|
logger.debug(f"Matching-Sample: {match_df.iloc[0][['normalized_name','normalized_domain','block_key','Effektive Website','Gefundene Website','Serp Vertrauen','domain_use_flag']].to_dict()}")
|
|
|
|
# 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)}")
|
|
|
|
# Matching
|
|
results = []
|
|
metrics = Counter()
|
|
total = len(match_df)
|
|
logger.info("Starte Matching-Prozess…")
|
|
processed = 0
|
|
|
|
# iterate safely with index
|
|
for idx, mrow in match_df.to_dict('index').items():
|
|
name_disp = mrow.get('CRM Name','')
|
|
processed += 1
|
|
# Kandidatenwahl
|
|
candidates = []
|
|
used_block = ''
|
|
if mrow.get('normalized_domain') and mrow.get('domain_use_flag') == 1:
|
|
candidates = domain_index.get(mrow['normalized_domain'], [])
|
|
used_block = f"domain:{mrow['normalized_domain']}"
|
|
if not candidates:
|
|
rtok = choose_rarest_token(mrow.get('normalized_name',''), token_freq)
|
|
if rtok:
|
|
candidates = token_index.get(rtok, [])
|
|
used_block = f"token:{rtok}"
|
|
if not candidates:
|
|
# Prefilter über gesamte CRM-Liste
|
|
pf = []
|
|
n1 = mrow.get('normalized_name','')
|
|
for r in crm_records:
|
|
n2 = r.get('normalized_name','')
|
|
if not n1 or not n2:
|
|
continue
|
|
pr = fuzz.partial_ratio(n1, n2)
|
|
if pr >= PREFILTER_MIN_PARTIAL:
|
|
pf.append((pr, r))
|
|
pf.sort(key=lambda x: x[0], reverse=True)
|
|
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})")
|
|
if not candidates:
|
|
results.append({'Match':'', 'Score':0, 'Match_Grund':'keine Kandidaten'})
|
|
continue
|
|
|
|
scored = []
|
|
for cr in candidates:
|
|
score, comp = calculate_similarity(mrow, cr)
|
|
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]
|
|
|
|
# Metriken
|
|
if best_score >= SCORE_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') == 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}")
|
|
else:
|
|
results.append({'Match':'', 'Score': best_score, 'Match_Grund': str(best_comp)})
|
|
logger.info(f" --> Kein Match (Score={best_score}) {best_comp}")
|
|
|
|
# Ergebnisse zurückschreiben (SAFE: alle Originalspalten + neue, ohne interne Felder)
|
|
logger.info("Schreibe Ergebnisse ins Sheet (SAFE in-place, keine Spaltenverluste)…")
|
|
res_df = pd.DataFrame(results, index=match_df.index)
|
|
write_df = match_df.copy()
|
|
write_df['Match'] = res_df['Match']
|
|
write_df['Score'] = res_df['Score']
|
|
write_df['Match_Grund'] = res_df['Match_Grund']
|
|
|
|
drop_cols = ['normalized_name','normalized_domain','block_key','Effektive Website','domain_use_flag']
|
|
for c in drop_cols:
|
|
if c in write_df.columns:
|
|
write_df.drop(columns=[c], inplace=True)
|
|
|
|
# Backup
|
|
backup_path = os.path.join(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}")
|
|
except Exception as e:
|
|
logger.warning(f"Backup fehlgeschlagen: {e}")
|
|
|
|
data = [write_df.columns.tolist()] + write_df.fillna('').values.tolist()
|
|
ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data)
|
|
if ok:
|
|
logger.info("Ergebnisse erfolgreich geschrieben")
|
|
else:
|
|
logger.error("Fehler beim Schreiben ins Google Sheet")
|
|
|
|
# Abschluss-Metriken
|
|
serp_counts = Counter((str(x).lower() for x in write_df.get('Serp Vertrauen', [])))
|
|
logger.info("===== Summary =====")
|
|
logger.info(f"Matches total: {metrics['matches_total']} | mit Domain: {metrics['matches_domain']} | mit Ort: {metrics['matches_with_loc']} | nur Name: {metrics['matches_name_only']}")
|
|
logger.info(f"Serp Vertrauen: {dict(serp_counts)}")
|
|
logger.info(f"Config: TH={SCORE_THRESHOLD}, 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()
|