train_model.py aktualisiert

train_model.py

@@ -4,43 +4,33 @@ import re
 import math
 import joblib
 import xgboost as xgb
-import treelite
-import treelite_runtime
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score, classification_report
 from thefuzz import fuzz
 from collections import Counter
 import logging
 import sys
-import os
 
-# Importiere deine bestehenden Helfer
 from google_sheet_handler import GoogleSheetHandler
 from helpers import normalize_company_name
 
-# Logging Setup
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler(sys.stdout)])
 log = logging.getLogger()
 
-# --- Konfiguration ---
 GOLD_STANDARD_FILE = 'erweitertes_matching.csv' 
 CRM_SHEET_NAME = "CRM_Accounts"
 MODEL_OUTPUT_FILE = 'xgb_model.json'
 TERM_WEIGHTS_OUTPUT_FILE = 'term_weights.joblib'
 CRM_PREDICTION_FILE = 'crm_for_prediction.pkl'
-TREELITE_MODEL_FILE = 'xgb_model.treelite'
-
 BEST_MATCH_COL = 'Best Match Option'
 SUGGESTION_COLS = ['V2_Match_Suggestion', 'V3_Match_Suggestion', 'V4_Match_Suggestion']
 
-# --- Stop-/City-Tokens ---
 STOP_TOKENS_BASE = {
     'gmbh','mbh','ag','kg','ug','ohg','se','co','kgaa','inc','llc','ltd','sarl', 'b.v', 'bv',
     'holding','gruppe','group','international','solutions','solution','service','services',
 }
 CITY_TOKENS = set()
 
-# --- Hilfsfunktionen ---
 def _tokenize(s: str):
     if not s: return []
     return re.split(r"[^a-z0-9äöüß]+", str(s).lower())
@@ -91,35 +81,22 @@ def create_features(mrec: dict, crec: dict, term_weights: dict):
 
     return features
 
-# --- Haupt-Trainingsskript ---
 if __name__ == "__main__":
     log.info("Starte Trainingsprozess für Duplikats-Checker v5.0")
     try:
-        log.info(f"Versuche, Gold-Standard-Datei zu laden: '{GOLD_STANDARD_FILE}'")
-        if not os.path.exists(GOLD_STANDARD_FILE):
-            log.critical(f"FEHLER: Die Datei '{GOLD_STANDARD_FILE}' wurde nicht gefunden.")
-            sys.exit(1)
         gold_df = pd.read_csv(GOLD_STANDARD_FILE, sep=';', encoding='utf-8')
-        log.info(f"{len(gold_df)} Zeilen aus '{GOLD_STANDARD_FILE}' geladen.")
-        
-        log.info("Verbinde mit Google Sheets, um CRM-Daten zu laden...")
         sheet_handler = GoogleSheetHandler()
         crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
-        log.info(f"{len(crm_df)} CRM Accounts aus Google Sheets geladen.")
     except Exception as e:
         log.critical(f"Fehler beim Laden der Daten: {e}")
         sys.exit(1)
 
-    log.info("Entferne Duplikate aus CRM-Daten für das Training...")
     crm_df.drop_duplicates(subset=['CRM Name'], keep='first', inplace=True)
-    log.info(f"CRM-Daten auf {len(crm_df)} eindeutige Firmennamen reduziert.")
     crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
     gold_df['normalized_CRM Name'] = gold_df['CRM Name'].astype(str).apply(normalize_company_name)
     
-    log.info("Berechne Wortgewichte (TF-IDF)...")
     term_weights = {token: math.log(len(crm_df) / (count + 1)) for token, count in Counter(t for n in crm_df['normalized_name'] for t in set(clean_name_for_scoring(n)[1])).items()}
     
-    log.info("Erstelle Features für den Trainingsdatensatz...")
     features_list, labels = [], []
     crm_lookup = crm_df.set_index('CRM Name').to_dict('index')
     
@@ -140,29 +117,16 @@ if __name__ == "__main__":
     X, y = pd.DataFrame(features_list), np.array(labels)
     log.info(f"Trainingsdatensatz erstellt mit {X.shape[0]} Beispielen. Klassenverteilung: {Counter(y)}")
     
-    log.info("Trainiere das XGBoost-Modell...")
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
     scale_pos_weight = sum(y_train == 0) / sum(y_train) if sum(y_train) > 0 else 1
     model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', scale_pos_weight=scale_pos_weight)
     model.fit(X_train, y_train)
     log.info("Modell erfolgreich trainiert.")
-
-    log.info("Validiere Modell auf Testdaten...")
+    
     y_pred = model.predict(X_test)
     log.info(f"\n--- Validierungsergebnis ---\nGenauigkeit: {accuracy_score(y_test, y_pred):.2%}\n" + classification_report(y_test, y_pred, zero_division=0))
     
-    try:
-        model.save_model(MODEL_OUTPUT_FILE)
-        log.info(f"Modell in '{MODEL_OUTPUT_FILE}' gespeichert.")
-        
-        # KORREKTUR: Wir nutzen den internen Booster für Treelite
-        treelite_model = treelite.Model.from_xgboost(model.get_booster())
-        treelite_model.export_lib(toolchain='gcc', libpath=TREELITE_MODEL_FILE, params={'parallel_comp': 4}, verbose=True)
-        log.info(f"Leichtgewichtiges Modell in '{TREELITE_MODEL_FILE}' gespeichert.")
-        
-        joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE)
-        log.info(f"Wortgewichte in '{TERM_WEIGHTS_OUTPUT_FILE}' gespeichert.")
-        crm_df.to_pickle(CRM_PREDICTION_FILE)
-        log.info(f"CRM-Daten in '{CRM_PREDICTION_FILE}' gespeichert.")
-    except Exception as e:
-        log.critical(f"FEHLER BEIM SPEICHERN DER DATEIEN: {e}")
+    model.save_model(MODEL_OUTPUT_FILE)
+    joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE)
+    crm_df.to_pickle(CRM_PREDICTION_FILE)
+    log.info("Alle Modelldateien erfolgreich erstellt.")