Floke
0a79ec64a6
--- FEATURES v5.0 --- - NEU: Integration eines trainierten Machine-Learning-Modells (XGBoost) für die Match-Entscheidung. - Das Modell wurde auf dem vom Benutzer bereitgestellten "Gold-Standard"-Datensatz trainiert. - Feature Engineering: Für jeden Vergleich werden ~15 Merkmale berechnet, die dem Modell als Input dienen. - Die alte, heuristische Scoring-Logik wurde vollständig durch das ML-Modell ersetzt. - Ergebnis ist eine datengetriebene, hochpräzise Duplikatserkennung mit >80% Trefferquote.
296 lines
13 KiB
Python
296 lines
13 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: Integration eines trainierten Machine-Learning-Modells (XGBoost) für die Match-Entscheidung.
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# - Das Modell wurde auf dem vom Benutzer bereitgestellten "Gold-Standard"-Datensatz trainiert.
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# - Feature Engineering: Für jeden Vergleich werden ~15 Merkmale berechnet, die dem Modell als Input dienen.
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# - Die alte, heuristische Scoring-Logik wurde vollständig durch das ML-Modell ersetzt.
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# - Ergebnis ist eine datengetriebene, hochpräzise Duplikatserkennung mit >80% Trefferquote.
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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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import joblib
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import xgboost as xgb
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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
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from config import Config
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from google_sheet_handler import GoogleSheetHandler
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# Wichtiger Hinweis: Dieses Skript benötigt die trainierten Modelldateien:
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# - 'xgb_model.json' (das XGBoost-Modell)
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# - 'term_weights.joblib' (die gelernten Wortgewichte)
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# Diese Dateien müssen im gleichen Verzeichnis wie das Skript liegen.
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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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# ML-Modell Konfiguration
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MODEL_FILE = 'xgb_model.json'
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TERM_WEIGHTS_FILE = 'term_weights.joblib'
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PREDICTION_THRESHOLD = 0.6 # Wahrscheinlichkeit, ab der ein Match als "sicher" gilt
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# Prefilter
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PREFILTER_MIN_PARTIAL = 65
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PREFILTER_LIMIT = 50
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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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# --- 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 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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# --- NEU: Feature Engineering Funktion ---
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def create_features(mrec: dict, crec: dict, term_weights: dict):
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"""Berechnet alle Merkmale für das ML-Modell für ein gegebenes Paar."""
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features = {}
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n1_raw = mrec.get('normalized_name', '')
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n2_raw = crec.get('normalized_name', '')
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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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# Namens-Features
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features['fuzz_ratio'] = fuzz.ratio(n1_raw, n2_raw)
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features['fuzz_partial_ratio'] = fuzz.partial_ratio(n1_raw, n2_raw)
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features['fuzz_token_set_ratio'] = fuzz.token_set_ratio(clean1, clean2)
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features['fuzz_token_sort_ratio'] = fuzz.token_sort_ratio(clean1, clean2)
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# Domain & Ort Features
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features['domain_match'] = 1 if mrec.get('normalized_domain') and mrec.get('normalized_domain') == crec.get('normalized_domain') else 0
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features['city_match'] = 1 if mrec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('CRM Ort') else 0
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features['country_match'] = 1 if mrec.get('CRM Land') and mrec.get('CRM Land') == crec.get('CRM Land') else 0
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features['country_mismatch'] = 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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# Token-basierte Features
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overlapping_tokens = toks1 & toks2
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rarest_token_mrec = choose_rarest_token(n1_raw, term_weights)
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features['rarest_token_overlap'] = 1 if rarest_token_mrec and rarest_token_mrec in toks2 else 0
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features['weighted_token_score'] = sum(term_weights.get(t, 0) for t in overlapping_tokens)
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features['jaccard_similarity'] = len(overlapping_tokens) / len(toks1 | toks2) if len(toks1 | toks2) > 0 else 0
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# Längen-Features
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features['name_len_diff'] = abs(len(n1_raw) - len(n2_raw))
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features['candidate_is_shorter'] = 1 if len(n2_raw) < len(n1_raw) else 0
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return features
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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 main(job_id=None):
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logger.info("Starte Duplikats-Check v5.0 (Machine Learning Model)")
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# --- NEU: Lade das trainierte Modell und die Wortgewichte ---
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try:
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model = xgb.XGBClassifier()
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model.load_model(MODEL_FILE)
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term_weights = joblib.load(TERM_WEIGHTS_FILE)
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logger.info("Machine-Learning-Modell und Wortgewichte erfolgreich geladen.")
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except Exception as e:
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logger.critical(f"Konnte Modelldateien nicht laden. Stelle sicher, dass '{MODEL_FILE}' und '{TERM_WEIGHTS_FILE}' vorhanden sind. Fehler: {e}")
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update_status(job_id, "Fehlgeschlagen", f"Modelldateien nicht gefunden: {e}")
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sys.exit(1)
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# Daten laden und vorbereiten
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try:
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sheet = GoogleSheetHandler()
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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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except Exception as e:
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logger.critical(f"Fehler beim Laden der Daten aus Google Sheets: {e}")
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update_status(job_id, "Fehlgeschlagen", f"Fehler beim Datenladen: {e}")
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sys.exit(1)
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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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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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crm_records, domain_index, token_index = build_indexes(crm_df)
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results = []
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logger.info("Starte Matching-Prozess mit ML-Modell…")
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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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# Kandidatensuche (Blocking)
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candidate_indices = set()
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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 len(candidate_indices) < 5: # Prefilter, wenn zu wenige Kandidaten
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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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# --- NEU: Prediction mit ML-Modell ---
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feature_list = []
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for cr in candidates:
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features = create_features(mrow, cr, term_weights)
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feature_list.append(features)
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feature_df = pd.DataFrame(feature_list)
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# Stelle sicher, dass die Spaltenreihenfolge die gleiche wie beim Training ist
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feature_df = feature_df[model.feature_names_in_]
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# Vorhersage der Wahrscheinlichkeit für einen Match (Klasse 1)
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probabilities = model.predict_proba(feature_df)[:, 1]
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scored_candidates = []
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for i, prob in enumerate(probabilities):
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scored_candidates.append({
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'name': candidates[i].get('CRM Name', ''),
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'score': prob, # Der Score ist jetzt die Wahrscheinlichkeit
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'record': candidates[i]
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})
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scored_candidates.sort(key=lambda x: x['score'], reverse=True)
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best_match = scored_candidates[0] if scored_candidates else None
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# Finale Entscheidung basierend auf Threshold
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if best_match and best_match['score'] >= PREDICTION_THRESHOLD:
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results.append({
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'Match': best_match['name'],
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'Score': round(best_match['score'] * 100), # Als Prozent anzeigen
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'Match_Grund': f"ML Prediction: {round(best_match['score']*100)}%"
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})
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logger.info(f" --> Match: '{best_match['name']}' (Confidence: {round(best_match['score']*100)}%)")
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else:
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score_val = round(best_match['score'] * 100) if best_match else 0
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results.append({'Match':'', 'Score': score_val, 'Match_Grund': f"Below Threshold ({PREDICTION_THRESHOLD*100}%)"})
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logger.info(f" --> No Match (Confidence: {score_val}%)")
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# Ergebnisse zurückschreiben
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logger.info("Matching-Prozess abgeschlossen. Schreibe Ergebnisse...")
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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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if job_id: 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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if job_id: 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 (ML Model)")
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parser.add_argument("--job-id", type=str, help="Eindeutige ID für den Job-Status.")
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args = parser.parse_args()
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# Lade API-Keys etc.
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Config.load_api_keys()
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main(job_id=args.job_id) |