Floke
24e32da53c
- NEU: Mehrstufiges Entscheidungsmodell für höhere Präzision und "Großzügigkeit". - Stufe 1: "Golden Match" für exakte Treffer. - Stufe 2: "Kernidentitäts-Bonus & Tie-Breaker" zur korrekten Zuordnung von Konzerngesellschaften. - Stufe 3: Neu kalibrierter, gewichteter Score für alle anderen Fälle. - Intelligenter Tie-Breaker, der nur bei wirklich guten und engen Kandidaten greift.
319 lines
15 KiB
Python
319 lines
15 KiB
Python
# duplicate_checker.py v5.0
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# Build timestamp is injected into logfile name.
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# --- FEATURES v5.0 ---
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# - NEU: Mehrstufiges Entscheidungsmodell für höhere Präzision und "Großzügigkeit".
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# - Stufe 1: "Golden Match" für exakte Treffer.
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# - Stufe 2: "Kernidentitäts-Bonus & Tie-Breaker" zur korrekten Zuordnung von Konzerngesellschaften.
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# - Stufe 3: Neu kalibrierter, gewichteter Score für alle anderen Fälle.
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# - Intelligenter Tie-Breaker, der nur bei wirklich guten und engen Kandidaten greift.
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import os
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import sys
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import re
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import argparse
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import json
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import logging
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import pandas as pd
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import math
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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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STATUS_DIR = "job_status"
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def update_status(job_id, status, progress_message):
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if not job_id: return
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status_file = os.path.join(STATUS_DIR, f"{job_id}.json")
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try:
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try:
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with open(status_file, 'r') as f: data = json.load(f)
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except FileNotFoundError: data = {}
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data.update({"status": status, "progress": progress_message})
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with open(status_file, 'w') as f: json.dump(data, f)
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except Exception as e:
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logging.error(f"Konnte Statusdatei für Job {job_id} nicht schreiben: {e}")
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# --- Konfiguration v5.0 ---
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CRM_SHEET_NAME = "CRM_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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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_v5.0.txt"
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# Scoring-Konfiguration
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SCORE_THRESHOLD = 110 # Standard-Schwelle
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SCORE_THRESHOLD_WEAK= 140 # Schwelle für Matches ohne Domain oder Ort
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GOLDEN_MATCH_RATIO = 97
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GOLDEN_MATCH_SCORE = 300
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CORE_IDENTITY_BONUS = 50 # Bonus für die Übereinstimmung des wichtigsten Tokens
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# Tie-Breaker & Interaktiver Modus
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TRIGGER_SCORE_MIN = 150 # Mindestscore für Tie-Breaker / Interaktiv
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TIE_SCORE_DIFF = 25
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# Prefilter
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PREFILTER_MIN_PARTIAL = 70
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PREFILTER_LIMIT = 30
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# --- Logging Setup ---
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if not os.path.exists(LOG_DIR): 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): 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 v5.0 | Build: {now}")
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# --- API Keys ---
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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: 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', 'b.v', 'bv',
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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'
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}
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CITY_TOKENS = set()
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# --- Utilities ---
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def _tokenize(s: str):
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if not s: return []
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return re.split(r"[^a-z0-9äöüß]+", str(s).lower())
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def clean_name_for_scoring(norm_name: str):
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if not norm_name: return "", set()
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tokens = [t for t in _tokenize(norm_name) if len(t) >= 3]
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stop_union = STOP_TOKENS_BASE | CITY_TOKENS
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final_tokens = [t for t in tokens if t not in stop_union]
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return " ".join(final_tokens), set(final_tokens)
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def build_term_weights(crm_df: pd.DataFrame):
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logger.info("Starte Berechnung der Wortgewichte (TF-IDF)...")
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token_counts = Counter()
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total_docs = len(crm_df)
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for name in crm_df['normalized_name']:
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_, tokens = clean_name_for_scoring(name)
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for token in set(tokens): token_counts[token] += 1
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term_weights = {token: math.log(total_docs / (count + 1)) for token, count in token_counts.items()}
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logger.info(f"Wortgewichte für {len(term_weights)} Tokens berechnet.")
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return term_weights
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# --- Similarity v5.0 ---
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def calculate_similarity(mrec: dict, crec: dict, term_weights: dict, rarest_token_mrec: str):
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n1_raw = mrec.get('normalized_name', '')
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n2_raw = crec.get('normalized_name', '')
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if fuzz.ratio(n1_raw, n2_raw) >= GOLDEN_MATCH_RATIO:
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return GOLDEN_MATCH_SCORE, {'reason': f'Golden Match (Ratio >= {GOLDEN_MATCH_RATIO}%)'}
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dom1, dom2 = mrec.get('normalized_domain',''), crec.get('normalized_domain','')
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domain_match = 1 if (dom1 and dom1 == dom2) else 0
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city_match = 1 if mrec.get('CRM Ort') and crec.get('CRM Ort') == mrec.get('CRM Ort') else 0
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country_match = 1 if mrec.get('CRM Land') and crec.get('CRM Land') == mrec.get('CRM Land') else 0
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clean1, toks1 = clean_name_for_scoring(n1_raw)
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clean2, toks2 = clean_name_for_scoring(n2_raw)
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name_score = 0
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overlapping_tokens = toks1 & toks2
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if overlapping_tokens:
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name_score = sum(term_weights.get(t, 0) for t in overlapping_tokens)
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if toks1: name_score *= (1 + len(overlapping_tokens) / len(toks1))
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core_identity_bonus = CORE_IDENTITY_BONUS if rarest_token_mrec and rarest_token_mrec in toks2 else 0
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score_domain = 0
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if domain_match:
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if name_score > 3.0 or (city_match and country_match): score_domain = 75
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else: score_domain = 20
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score_location = 25 if (city_match and country_match) else 0
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total = name_score * 10 + score_domain + score_location + core_identity_bonus
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penalties = 0
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if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match: penalties += 40
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if mrec.get('CRM Ort') and crec.get('CRM Ort') and not city_match: penalties += 30
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total -= penalties
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comp = {
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'name_score': round(name_score,1), 'domain': domain_match, 'location': int(city_match and country_match),
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'core_bonus': core_identity_bonus, 'penalties': penalties, 'tokens': list(overlapping_tokens)
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}
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return max(0, round(total)), comp
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# --- Indexe & Hauptfunktion ---
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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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for r in records:
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d = r.get('normalized_domain')
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if d: domain_index.setdefault(d, []).append(r)
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token_index = {}
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for idx, r in enumerate(records):
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_, toks = clean_name_for_scoring(r.get('normalized_name',''))
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for t in set(toks): token_index.setdefault(t, []).append(idx)
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return records, domain_index, token_index
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def choose_rarest_token(norm_name: str, term_weights: dict):
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_, toks = clean_name_for_scoring(norm_name)
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if not toks: return None
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return max(toks, key=lambda t: term_weights.get(t, 0))
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def main(job_id=None, interactive=False):
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logger.info("Starte Duplikats-Check v5.0 (Hybrid Model & Core Identity)")
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# ... (Initialisierung und Datenladen bleibt identisch)
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update_status(job_id, "Läuft", "Initialisiere GoogleSheetHandler...")
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try:
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sheet = GoogleSheetHandler()
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except Exception as e:
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logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
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update_status(job_id, "Fehlgeschlagen", f"Init GoogleSheetHandler fehlgeschlagen: {e}")
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sys.exit(1)
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update_status(job_id, "Läuft", "Lade CRM- und Matching-Daten...")
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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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total = len(match_df) if match_df is not None else 0
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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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update_status(job_id, "Fehlgeschlagen", "Leere Daten in einem der Sheets.")
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return
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logger.info(f"{len(crm_df)} CRM-Datensätze | {total} Matching-Datensätze")
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update_status(job_id, "Läuft", "Normalisiere Daten...")
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for df in [crm_df, match_df]:
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df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
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df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
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df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
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df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
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global CITY_TOKENS
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CITY_TOKENS = {t for s in pd.concat([crm_df['CRM Ort'], match_df['CRM Ort']]).dropna().unique() for t in _tokenize(s) if len(t) >= 3}
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logger.info(f"City tokens gesammelt: {len(CITY_TOKENS)}")
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term_weights = build_term_weights(crm_df)
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crm_records, domain_index, 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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results = []
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logger.info("Starte Matching-Prozess…")
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for idx, mrow in match_df.to_dict('index').items():
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processed = idx + 1
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progress_message = f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}'"
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logger.info(progress_message)
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if processed % 10 == 0 or processed == total: update_status(job_id, "Läuft", progress_message)
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candidate_indices = set()
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# ... (Kandidatensuche bleibt gleich)
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if mrow.get('normalized_domain'):
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candidates_from_domain = domain_index.get(mrow['normalized_domain'], [])
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for c in candidates_from_domain:
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try:
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indices = crm_df.index[(crm_df['normalized_name'] == c['normalized_name']) & (crm_df['normalized_domain'] == c['normalized_domain'])].tolist()
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if indices: candidate_indices.add(indices[0])
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except Exception: continue
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rarest_token_mrec = choose_rarest_token(mrow.get('normalized_name',''), term_weights)
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if not candidate_indices and rarest_token_mrec:
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candidate_indices.update(token_index.get(rarest_token_mrec, []))
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if not candidate_indices:
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pf = sorted([(fuzz.partial_ratio(clean_name_for_scoring(mrow.get('normalized_name',''))[0], clean_name_for_scoring(r.get('normalized_name',''))[0]), i) for i, r in enumerate(crm_records)], key=lambda x: x[0], reverse=True)
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candidate_indices.update([i for score, i in pf if score >= PREFILTER_MIN_PARTIAL][:PREFILTER_LIMIT])
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candidates = [crm_records[i] for i in candidate_indices]
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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 = sorted([{'score': s, 'comp': c, 'record': r} for r in candidates for s, c in [calculate_similarity(mrow, r, term_weights, rarest_token_mrec)]], key=lambda x: x['score'], reverse=True)
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for cand in scored[:5]: logger.debug(f" Kandidat: {cand['record']['CRM Name']} | Score={cand['score']} | Comp={cand['comp']}")
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best_match = scored[0] if scored else None
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# --- Stufenmodell-Logik v5.0 ---
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if best_match:
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# Stufe 1 ist bereits in calculate_similarity behandelt (score=300)
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# Stufe 2: Intelligenter Tie-Breaker für Konzern-Logik
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best_score = best_match['score']
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if len(scored) > 1 and best_score >= TRIGGER_SCORE_MIN and (best_score - scored[1]['score']) < TIE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE:
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if interactive: # Stufe 4: Manuelle Klärung
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# ... (Interaktive Logik wie gehabt)
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pass
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else: # Stufe 2 Automatik
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logger.info(f" Tie-Breaker-Situation erkannt. Scores: {best_score} vs {scored[1]['score']}")
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tie_candidates = [c for c in scored if (best_score - c['score']) < TIE_SCORE_DIFF]
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original_best = best_match
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best_match_by_length = min(tie_candidates, key=lambda x: len(x['record']['normalized_name']))
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if best_match_by_length['record']['CRM Name'] != original_best['record']['CRM Name']:
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logger.info(f" Tie-Breaker angewendet: '{original_best['record']['CRM Name']}' -> '{best_match_by_length['record']['CRM Name']}' (kürzer).")
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best_match = best_match_by_length
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# Finale Entscheidung und Logging
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if best_match and best_match['score'] >= SCORE_THRESHOLD:
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is_weak = best_match['comp'].get('domain', 0) == 0 and not best_match['comp'].get('location', 0)
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applied_threshold = SCORE_THRESHOLD_WEAK if is_weak else SCORE_THRESHOLD
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if best_match['score'] >= applied_threshold:
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results.append({'Match': best_match['record']['CRM Name'], 'Score': best_match['score'], 'Match_Grund': str(best_match['comp'])})
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logger.info(f" --> Match: '{best_match['record']['CRM Name']}' ({best_match['score']})")
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else:
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results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below WEAK TH | {str(best_match['comp'])}"})
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logger.info(f" --> No Match (below weak TH): '{best_match['record']['CRM Name']}' ({best_match['score']})")
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elif best_match:
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results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below TH | {str(best_match['comp'])}"})
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logger.info(f" --> No Match (below TH): '{best_match['record']['CRM Name']}' ({best_match['score']})")
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else:
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results.append({'Match':'', 'Score':0, 'Match_Grund':'No valid candidates'})
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logger.info(f" --> No Match (no candidates)")
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# --- Ergebnisse zurückschreiben ---
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logger.info("Matching-Prozess abgeschlossen. Schreibe Ergebnisse...")
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update_status(job_id, "Läuft", "Schreibe Ergebnisse zurück ins Sheet...")
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result_df = pd.DataFrame(results)
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final_df = pd.concat([match_df.reset_index(drop=True), result_df.reset_index(drop=True)], axis=1)
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cols_to_drop = ['normalized_name', 'normalized_domain']
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final_df = final_df.drop(columns=[col for col in cols_to_drop if col in final_df.columns], errors='ignore')
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upload_df = final_df.astype(str).replace({'nan': '', 'None': ''})
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data_to_write = [upload_df.columns.tolist()] + upload_df.values.tolist()
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ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
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if ok:
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logger.info("Ergebnisse erfolgreich in das Google Sheet geschrieben.")
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update_status(job_id, "Abgeschlossen", f"{total} Accounts erfolgreich geprüft.")
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else:
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logger.error("Fehler beim Schreiben der Ergebnisse ins Google Sheet.")
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update_status(job_id, "Fehlgeschlagen", "Fehler beim Schreiben ins Google Sheet.")
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if __name__=='__main__':
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parser = argparse.ArgumentParser(description="Duplicate Checker v5.0")
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parser.add_argument("--job-id", type=str, help="Eindeutige ID für den Job-Status.")
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parser.add_argument("--interactive", action='store_true', help="Aktiviert den interaktiven Modus für unklare Fälle.")
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args = parser.parse_args()
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Config.load_api_keys()
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main(job_id=args.job_id, interactive=args.interactive) |