Floke
c356203b52
- Domain-Gate: Domain (100) zählt nur, wenn Name >= MIN_NAME_FOR_DOMAIN (70) ODER (City & Country) matchen
- Location-Penalties: City-Mismatch -30, Country-Mismatch -40 (nur wenn Felder befüllt)
- Name-Scoring: max(token_set, partial, token_sort) auf bereinigten Tokens (Stopwörter + City-Tokens)
- City-Bias-Guard: City-only Overlap -> Name-Score Cap auf 70
- Rare-Token-Check (IDF-light): Name-only-Matches brauchen seltenen Token-Overlap
- Weak-Threshold: 95, wenn weder Domain_used noch (City & Country) matchen
- Smart Blocking: Domain-Index -> seltenster Token -> Prefilter (partial>=70, Top 30, nur wenn seltenster Token im Kandidaten vorkommt)
- SerpAPI: nur für Matching-Accounts und nur wenn B/E leer; schreibt „Gefundene Website“ + „Serp Vertrauen“, Domain-100 nur bei Vertrauen=hoch
- Output: neue Spalten „Gefundene Website“, „Serp Vertrauen“, „Match“, „Score“, „Match_Grund“
- Writeback: SAFE (alle Originalspalten), interne Felder werden gedroppt + CSV-Backup
- Logging: Log/{timestamp}_duplicate_check_v2.15.txt, Summary-Metriken am Ende
422 lines
19 KiB
Python
422 lines
19 KiB
Python
import os
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import sys
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import re
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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 collections import Counter
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from thefuzz import fuzz
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from helpers import normalize_company_name, simple_normalize_url, serp_website_lookup
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from config import Config
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from google_sheet_handler import GoogleSheetHandler
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# duplicate_checker.py v2.15
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# Quality-first ++: Domain-Gate, Location-Penalties, Smart Blocking (IDF-light),
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# Serp-Trust, Weak-Threshold, City-Bias-Guard, Prefilter tightened, Metrics
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# Build timestamp is injected into logfile name.
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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 # Standard-Schwelle
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SCORE_THRESHOLD_WEAK= 95 # Schwelle, wenn weder Domain noch (City&Country) matchen
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MIN_NAME_FOR_DOMAIN = 70 # Domain-Score nur, wenn Name >= 70 ODER Ort+Land matchen
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CITY_MISMATCH_PENALTY = 30
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COUNTRY_MISMATCH_PENALTY = 40
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PREFILTER_MIN_PARTIAL = 70 # (vorher 60)
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PREFILTER_LIMIT = 30 # (vorher 50)
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LOG_DIR = "Log"
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now = datetime.now().strftime('%Y-%m-%d_%H-%M')
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LOG_FILE = f"{now}_duplicate_check_v2.15.txt"
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# --- Logging Setup ---
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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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log_path = os.path.join(LOG_DIR, LOG_FILE)
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root = logging.getLogger()
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root.setLevel(logging.DEBUG)
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for h in list(root.handlers):
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root.removeHandler(h)
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formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
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ch = logging.StreamHandler(sys.stdout)
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ch.setLevel(logging.INFO)
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ch.setFormatter(formatter)
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root.addHandler(ch)
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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(formatter)
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root.addHandler(fh)
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logger = logging.getLogger(__name__)
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logger.info(f"Logging to console and file: {log_path}")
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logger.info(f"Starting duplicate_checker.py v2.15 | Build: {now}")
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# --- SerpAPI Key laden ---
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try:
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Config.load_api_keys()
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serp_key = Config.API_KEYS.get('serpapi')
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if not serp_key:
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logger.warning("SerpAPI Key nicht gefunden; Serp-Fallback deaktiviert.")
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except Exception as e:
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logger.warning(f"Fehler beim Laden API-Keys: {e}")
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serp_key = None
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# --- Stop-/City-Tokens ---
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STOP_TOKENS_BASE = {
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'gmbh','mbh','ag','kg','ug','ohg','se','co','kgaa','inc','llc','ltd','sarl',
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'holding','gruppe','group','international','solutions','solution','service','services',
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'deutschland','austria','germany','technik','technology','technologies','systems','systeme',
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'logistik','logistics','industries','industrie','management','consulting','vertrieb','handel',
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'international','company','gesellschaft','mbh&co','mbhco','werke','werk','renkhoff','sonnenschutztechnik'
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}
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CITY_TOKENS = set() # dynamisch befüllt nach Datennormalisierung
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# --- Utilities ---
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def _tokenize(s: str):
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if not s:
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return []
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return re.split(r"[^a-z0-9]+", str(s).lower())
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def split_tokens(name: str):
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"""Tokens für Indexing/Scoring (Basis-Stop + dynamische City-Tokens)."""
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if not name:
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return []
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tokens = [t for t in _tokenize(name) if len(t) >= 3]
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stop_union = STOP_TOKENS_BASE | CITY_TOKENS
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return [t for t in tokens if t not in stop_union]
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def clean_name_for_scoring(norm_name: str):
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"""Entfernt Stop- & City-Tokens. Leerer Output => kein sinnvoller Namevergleich."""
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toks = split_tokens(norm_name)
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return " ".join(toks), set(toks)
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def assess_serp_trust(company_name: str, url: str) -> str:
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"""Vertrauen 'hoch/mittel/niedrig' anhand Token-Vorkommen in Domain."""
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if not url:
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return 'n/a'
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host = simple_normalize_url(url) or ''
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host = host.replace('www.', '')
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name_toks = [t for t in split_tokens(normalize_company_name(company_name)) if len(t) >= 3]
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if any(t in host for t in name_toks if len(t) >= 4):
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return 'hoch'
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if any(t in host for t in name_toks if len(t) == 3):
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return 'mittel'
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return 'niedrig'
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# --- Similarity ---
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def calculate_similarity(mrec: dict, crec: dict, token_freq: Counter):
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# Domain (mit Gate)
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dom1 = mrec.get('normalized_domain','')
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dom2 = crec.get('normalized_domain','')
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m_domain_use = mrec.get('domain_use_flag', 0)
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domain_flag_raw = 1 if (m_domain_use == 1 and dom1 and dom1 == dom2) else 0
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# Location flags
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city_match = 1 if (mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('CRM Ort')) else 0
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country_match = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec.get('CRM Land') == crec.get('CRM Land')) else 0
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# Name (nur sinnvolle Tokens)
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n1 = mrec.get('normalized_name','')
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n2 = crec.get('normalized_name','')
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clean1, toks1 = clean_name_for_scoring(n1)
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clean2, toks2 = clean_name_for_scoring(n2)
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# Overlaps
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overlap_clean = toks1 & toks2
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# city-only overlap check (wenn nach Clean nichts übrig, aber Roh-Overlap evtl. Städte; wir cappen Score)
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raw_overlap = set(_tokenize(n1)) & set(_tokenize(n2))
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city_only_overlap = (not overlap_clean) and any(t in CITY_TOKENS for t in raw_overlap)
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# Name-Score
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if clean1 and clean2:
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ts = fuzz.token_set_ratio(clean1, clean2)
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pr = fuzz.partial_ratio(clean1, clean2)
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ss = fuzz.token_sort_ratio(clean1, clean2)
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name_score = max(ts, pr, ss)
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else:
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name_score = 0
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if city_only_overlap and name_score > 70:
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name_score = 70 # cap
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# Rare-token-overlap (IDF-light): benutze seltensten Token aus mrec
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rtoks_sorted = sorted(list(toks1), key=lambda t: (token_freq.get(t, 10**9), -len(t)))
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rare_token = rtoks_sorted[0] if rtoks_sorted else None
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rare_overlap = 1 if (rare_token and rare_token in toks2) else 0
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# Domain Gate
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domain_gate_ok = (name_score >= MIN_NAME_FOR_DOMAIN) or (city_match and country_match)
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domain_used = 1 if (domain_flag_raw and domain_gate_ok) else 0
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# Basisscore
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total = domain_used*100 + name_score*1.0 + (1 if (city_match and country_match) else 0)*20
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# Penalties
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penalties = 0
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if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match:
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penalties += COUNTRY_MISMATCH_PENALTY
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if mrec.get('CRM Ort') and crec.get('CRM Ort') and not city_match:
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penalties += CITY_MISMATCH_PENALTY
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total -= penalties
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# Bonus für starke Name-only Fälle
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name_bonus = 1 if (domain_used == 0 and not (city_match and country_match) and name_score >= 85 and rare_overlap==1) else 0
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if name_bonus:
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total += 20
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comp = {
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'domain_raw': domain_flag_raw,
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'domain_used': domain_used,
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'domain_gate_ok': int(domain_gate_ok),
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'name': round(name_score,1),
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'city_match': city_match,
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'country_match': country_match,
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'penalties': penalties,
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'name_bonus': name_bonus,
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'rare_overlap': rare_overlap,
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'city_only_overlap': int(city_only_overlap)
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}
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return round(total), comp
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# --- Indexe ---
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def build_indexes(crm_df: pd.DataFrame):
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records = list(crm_df.to_dict('records'))
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# Domain-Index
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domain_index = {}
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for r in records:
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d = r.get('normalized_domain')
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if d:
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domain_index.setdefault(d, []).append(r)
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# Token-Frequenzen (auf gereinigten Tokens)
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token_freq = Counter()
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for r in records:
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_, toks = clean_name_for_scoring(r.get('normalized_name',''))
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for t in set(toks):
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token_freq[t] += 1
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# Token-Index
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token_index = {}
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for r in records:
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_, toks = clean_name_for_scoring(r.get('normalized_name',''))
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for t in set(toks):
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token_index.setdefault(t, []).append(r)
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return records, domain_index, token_freq, token_index
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def choose_rarest_token(norm_name: str, token_freq: Counter):
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_, toks = clean_name_for_scoring(norm_name)
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if not toks:
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return None
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lst = sorted(list(toks), key=lambda x: (token_freq.get(x, 10**9), -len(x)))
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return lst[0] if lst else None
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# --- Hauptfunktion ---
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def main():
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logger.info("Starte Duplikats-Check v2.15 (Quality-first++)")
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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"Init GoogleSheetHandler fehlgeschlagen: {e}")
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sys.exit(1)
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# Daten laden
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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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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")
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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("Leere Daten in einem der Sheets. Abbruch.")
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return
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# SerpAPI nur für Matching (B und E leer)
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if serp_key:
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if 'Gefundene Website' not in match_df.columns:
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match_df['Gefundene Website'] = ''
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b_empty = match_df['CRM Website'].fillna('').astype(str).str.strip().str.lower().isin(['','k.a.','k.a','n/a','na'])
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e_empty = match_df['Gefundene Website'].fillna('').astype(str).str.strip().str.lower().isin(['','k.a.','k.a','n/a','na'])
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empty_mask = b_empty & e_empty
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empty_count = int(empty_mask.sum())
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if empty_count > 0:
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logger.info(f"Serp-Fallback für Matching: {empty_count} Firmen ohne URL in B/E")
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found_cnt = 0
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trust_stats = Counter()
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for idx, row in match_df[empty_mask].iterrows():
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company = row['CRM Name']
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try:
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url = serp_website_lookup(company)
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if url and 'k.A.' not in url:
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if not str(url).startswith(('http://','https://')):
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url = 'https://' + str(url).lstrip()
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trust = assess_serp_trust(company, url)
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match_df.at[idx, 'Gefundene Website'] = url
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match_df.at[idx, 'Serp Vertrauen'] = trust
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trust_stats[trust] += 1
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logger.info(f" ✓ URL gefunden: '{company}' -> {url} (Vertrauen: {trust})")
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found_cnt += 1
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else:
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logger.debug(f" ✗ Keine eindeutige URL: '{company}' -> {url}")
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except Exception as e:
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logger.warning(f" ! Serp-Fehler für '{company}': {e}")
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logger.info(f"Serp-Fallback beendet: {found_cnt}/{empty_count} URLs ergänzt | Trust: {dict(trust_stats)}")
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else:
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logger.info("Serp-Fallback übersprungen: B oder E bereits befüllt (keine fehlenden Matching-URLs)")
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# Normalisierung CRM
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crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
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crm_df['normalized_domain'] = crm_df['CRM Website'].astype(str).apply(simple_normalize_url)
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crm_df['CRM Ort'] = crm_df['CRM Ort'].astype(str).str.lower().str.strip()
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crm_df['CRM Land'] = crm_df['CRM Land'].astype(str).str.lower().str.strip()
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crm_df['block_key'] = crm_df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
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crm_df['domain_use_flag'] = 1 # CRM-Domain gilt als vertrauenswürdig
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# Normalisierung Matching
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match_df['Gefundene Website'] = match_df.get('Gefundene Website', pd.Series(index=match_df.index, dtype=object))
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match_df['Serp Vertrauen'] = match_df.get('Serp Vertrauen', pd.Series(index=match_df.index, dtype=object))
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match_df['Effektive Website'] = match_df['CRM Website'].fillna('').astype(str).str.strip()
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mask_eff = match_df['Effektive Website'] == ''
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match_df.loc[mask_eff, 'Effektive Website'] = match_df['Gefundene Website'].fillna('').astype(str).str.strip()
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match_df['normalized_name'] = match_df['CRM Name'].astype(str).apply(normalize_company_name)
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match_df['normalized_domain'] = match_df['Effektive Website'].astype(str).apply(simple_normalize_url)
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match_df['CRM Ort'] = match_df['CRM Ort'].astype(str).str.lower().str.strip()
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match_df['CRM Land'] = match_df['CRM Land'].astype(str).str.lower().str.strip()
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match_df['block_key'] = match_df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
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# Domain-Vertrauen/Use-Flag
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def _domain_use(row):
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if str(row.get('CRM Website','')).strip():
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return 1
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trust = str(row.get('Serp Vertrauen','')).lower()
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return 1 if trust == 'hoch' else 0
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match_df['domain_use_flag'] = match_df.apply(_domain_use, axis=1)
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# City-Tokens dynamisch bauen (nach Normalisierung von Ort)
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def build_city_tokens(crm_df, match_df):
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cities = set()
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for s in pd.concat([crm_df['CRM Ort'], match_df['CRM Ort']], ignore_index=True).dropna().unique():
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for t in _tokenize(s):
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if len(t) >= 3:
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cities.add(t)
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return cities
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global CITY_TOKENS
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CITY_TOKENS = build_city_tokens(crm_df, match_df)
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logger.info(f"City tokens gesammelt: {len(CITY_TOKENS)}")
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# Blocking-Indizes (nachdem CITY_TOKENS gesetzt wurde)
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crm_records, domain_index, token_freq, token_index = build_indexes(crm_df)
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logger.info(f"Blocking: Domains={len(domain_index)} | TokenKeys={len(token_index)}")
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# Matching
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results = []
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metrics = Counter()
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total = len(match_df)
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logger.info("Starte Matching-Prozess…")
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processed = 0
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for idx, mrow in match_df.to_dict('index').items():
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processed += 1
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name_disp = mrow.get('CRM Name','')
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# Kandidatenwahl
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candidates = []
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used_block = ''
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if mrow.get('normalized_domain') and mrow.get('domain_use_flag') == 1:
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candidates = domain_index.get(mrow['normalized_domain'], [])
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used_block = f"domain:{mrow['normalized_domain']}"
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if not candidates:
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rtok = choose_rarest_token(mrow.get('normalized_name',''), token_freq)
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if rtok:
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candidates = token_index.get(rtok, [])
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used_block = f"token:{rtok}"
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if not candidates:
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# Prefilter über gesamte CRM-Liste (strenger + limitierter; erfordert Rarest-Token-Overlap)
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pf = []
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n1 = mrow.get('normalized_name','')
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rtok = choose_rarest_token(n1, token_freq)
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clean1, toks1 = clean_name_for_scoring(n1)
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if clean1:
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for r in crm_records:
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n2 = r.get('normalized_name','')
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clean2, toks2 = clean_name_for_scoring(n2)
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if not clean2:
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continue
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if rtok and rtok not in toks2:
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continue
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pr = fuzz.partial_ratio(clean1, clean2)
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if pr >= PREFILTER_MIN_PARTIAL:
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pf.append((pr, r))
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pf.sort(key=lambda x: x[0], reverse=True)
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candidates = [r for _, r in pf[:PREFILTER_LIMIT]]
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used_block = f"prefilter:{PREFILTER_MIN_PARTIAL}/{len(pf)}"
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logger.info(f"Prüfe {processed}/{total}: '{name_disp}' -> {len(candidates)} Kandidaten (Block={used_block})")
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if not candidates:
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results.append({'Match':'', 'Score':0, 'Match_Grund':'keine Kandidaten'})
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continue
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scored = []
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for cr in candidates:
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score, comp = calculate_similarity(mrow, cr, token_freq)
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scored.append((cr.get('CRM Name',''), score, comp))
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scored.sort(key=lambda x: x[1], reverse=True)
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# Log Top5
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for cand_name, sc, comp in scored[:5]:
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logger.debug(f" Kandidat: {cand_name} | Score={sc} | Comp={comp}")
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best_name, best_score, best_comp = scored[0]
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# Akzeptanzlogik (Weak-Threshold + Guard)
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weak = (best_comp.get('domain_used') == 0 and not (best_comp.get('city_match') and best_comp.get('country_match')))
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applied_threshold = SCORE_THRESHOLD_WEAK if weak else SCORE_THRESHOLD
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weak_guard_fail = (weak and best_comp.get('rare_overlap') == 0)
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if not weak_guard_fail and best_score >= applied_threshold:
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results.append({'Match': best_name, 'Score': best_score, 'Match_Grund': str(best_comp)})
|
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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} | TH={applied_threshold}{' weak' if weak else ''}")
|
|
else:
|
|
reason = 'weak_guard_no_rare' if weak_guard_fail else 'below_threshold'
|
|
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)…")
|
|
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_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")
|
|
|
|
# Summary
|
|
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}, 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()
|