duplicate_checker.py aktualisiert
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@@ -1,10 +1,4 @@
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# duplicate_checker_v5.1.py
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# Build timestamp is injected into logfile name.
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# --- FEATURES v5.1 ---
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# - NEU: Robusteres, mehrstufiges Blocking, um sicherzustellen, dass relevante Kandidaten gefunden werden.
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# - Nutzt jetzt die Top-3 seltensten Tokens, wenn die primäre Suche zu wenige Ergebnisse liefert.
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# - Lädt den CRM-Datensatz aus einer lokalen .pkl-Datei, um Konsistenz zwischen Training und Anwendung zu garantieren.
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# duplicate_checker_v6.0.py
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import os
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import sys
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@@ -13,9 +7,9 @@ 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 numpy as np
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import joblib
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import xgboost as xgb
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import treelite_runtime
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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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@@ -24,35 +18,18 @@ 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 v5.1 ---
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CRM_SHEET_NAME = "CRM_Accounts" # Nur noch für den Fallback, falls .pkl fehlt
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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_v5.1.txt"
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MODEL_FILE = 'xgb_model.json'
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TERM_WEIGHTS_FILE = 'term_weights.joblib'
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CRM_DATA_FILE = 'crm_for_prediction.pkl' # WICHTIG
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LOG_DIR = "Log"
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MODEL_FILE = 'xgb_model.json' # Wird nicht mehr direkt genutzt, aber als Referenz behalten
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TERM_WEIGHTS_FILE = 'term_weights.joblib'
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CRM_DATA_FILE = 'crm_for_prediction.pkl'
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TREELITE_MODEL_FILE = 'xgb_model.treelite'
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PREDICTION_THRESHOLD = 0.5
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PREFILTER_MIN_PARTIAL = 70
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PREFILTER_LIMIT = 50
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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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now = datetime.now().strftime('%Y-%m-%d_%H-%M')
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LOG_FILE = f"{now}_duplicate_check_v6.0.txt"
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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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@@ -68,20 +45,27 @@ 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 v5.1 | 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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# --- Hilfsfunktionen ---
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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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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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@@ -98,9 +82,7 @@ def get_rarest_tokens(norm_name: str, term_weights: dict, count=3):
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if not toks: return []
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return sorted(list(toks), key=lambda t: term_weights.get(t, 0), reverse=True)[:count]
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# --- Feature Engineering Funktion ---
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def create_features(mrec: dict, crec: dict, term_weights: dict):
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# ... (Diese Funktion bleibt exakt identisch wie in der letzten Version)
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def create_features(mrec: dict, crec: dict, term_weights: dict, feature_names: list):
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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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@@ -126,10 +108,10 @@ def create_features(mrec: dict, crec: dict, term_weights: dict):
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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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# Stelle sicher, dass das Array die gleiche Reihenfolge hat wie beim Training
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return [features.get(name, 0) for name in feature_names]
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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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@@ -143,17 +125,15 @@ def build_indexes(crm_df: pd.DataFrame):
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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 v5.1 (ML Model with Robust Blocking)")
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logger.info(f"Starting duplicate_checker.py v6.0 | Build: {now}")
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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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predictor = treelite_runtime.Predictor(TREELITE_MODEL_FILE, nthread=4)
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term_weights = joblib.load(TERM_WEIGHTS_FILE)
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crm_df = pd.read_pickle(CRM_DATA_FILE)
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logger.info("ML-Modell, Wortgewichte und lokaler CRM-Datensatz erfolgreich geladen.")
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logger.info("Treelite-Modell, Gewichte und lokaler CRM-Datensatz erfolgreich geladen.")
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except Exception as e:
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logger.critical(f"Konnte Modelldateien/CRM-Daten nicht laden. Fehler: {e}")
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update_status(job_id, "Fehlgeschlagen", f"Modelldateien/CRM-Daten nicht gefunden: {e}")
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sys.exit(1)
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try:
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@@ -161,13 +141,9 @@ def main(job_id=None):
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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 Matching-Daten aus Google Sheets: {e}")
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update_status(job_id, "Fehlgeschlagen", f"Fehler beim Matching-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 match_df is None or match_df.empty:
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logger.critical("Leere Daten im Matching-Sheet. Abbruch.")
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return
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logger.info(f"{len(crm_df)} CRM-Datensätze (lokal) | {total} Matching-Datensätze")
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match_df['normalized_name'] = match_df['CRM Name'].astype(str).apply(normalize_company_name)
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@@ -189,27 +165,20 @@ def main(job_id=None):
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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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# --- NEU: Robusteres, mehrstufiges Blocking ---
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candidate_indices = set()
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# Stufe 1: Präzises Blocking
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if mrow.get('normalized_domain'):
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# Hier verwenden wir direkt die Records, da der Index-Aufbau komplexer wäre
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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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# Finde den Index des Records
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indices = crm_df.index[crm_df['normalized_name'] == c['normalized_name']].tolist()
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if indices: candidate_indices.add(indices[0])
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except Exception: continue
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# Stufe 2: Großzügiges Token-Blocking
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if len(candidate_indices) < 5:
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top_tokens = get_rarest_tokens(mrow.get('normalized_name',''), term_weights, count=3)
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for token in top_tokens:
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candidate_indices.update(token_index.get(token, []))
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# Stufe 3: Fallback-Prefilter
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if len(candidate_indices) < 5:
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clean1, _ = clean_name_for_scoring(mrow.get('normalized_name',''))
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pf = sorted([(fuzz.partial_ratio(clean1, 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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@@ -220,15 +189,12 @@ def main(job_id=None):
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results.append({'Match':'', 'Score':0, 'Match_Grund':'keine Kandidaten'})
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continue
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# Feature Engineering und Prediction
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feature_list = [create_features(mrow, cr, term_weights) for cr in candidates]
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feature_df = pd.DataFrame(feature_list)
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feature_df = feature_df[model.feature_names_in_]
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feature_list = [create_features(mrow, cr, term_weights, predictor.feature_names) for cr in candidates]
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probabilities = model.predict_proba(feature_df)[:, 1]
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dmatrix = treelite_runtime.DMatrix(np.array(feature_list, dtype='float32'))
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probabilities = predictor.predict(dmatrix)[:, 1]
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scored_candidates = sorted([{'name': candidates[i].get('CRM Name', ''), 'score': prob} for i, prob in enumerate(probabilities)], 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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if best_match and best_match['score'] >= PREDICTION_THRESHOLD:
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@@ -256,7 +222,7 @@ def main(job_id=None):
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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 v5.1 (ML Model)")
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parser = argparse.ArgumentParser(description="Duplicate Checker v6.0 (Treelite 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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main(job_id=args.job_id)
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