Floke/Brancheneinstufung2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

train_model.py aktualisiert

Browse Source
This commit is contained in:
Floke
2025-09-08 12:29:47 +00:00
parent a3a5bb049f
commit 1b29cfb6a3
1 changed files with 35 additions and 59 deletions
Download Patch File Download Diff File Expand all files Collapse all files

94
train_model.py
View File

@@ -9,13 +9,18 @@ from sklearn.metrics import accuracy_score, classification_report
from thefuzz import fuzz
from collections import Counter
import logging
import sys
# 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')
# --- Konfiguration ---
GOLD_STANDARD_FILE = 'erweitertes_matching.csv'
CRM_ACCOUNTS_FILE = 'CRM_Accounts.csv' # Annahme: Du hast einen Export des CRM Sheets als CSV
CRM_SHEET_NAME = "CRM_Accounts"
MODEL_OUTPUT_FILE = 'xgb_model.json'
TERM_WEIGHTS_OUTPUT_FILE = 'term_weights.joblib'
@@ -29,20 +34,11 @@ STOP_TOKENS_BASE = {
}
CITY_TOKENS = set()
# --- Hilfsfunktionen (aus dem Original-Skript übernommen) ---
# --- Hilfsfunktionen ---
def _tokenize(s: str):
if not s: return []
return re.split(r"[^a-z0-9äöüß]+", str(s).lower())
def normalize_company_name(name: str):
if not isinstance(name, str): return ''
name = name.lower()
name = re.sub(r'\(.*?\)', '', name)
name = re.sub(r'\[.*?\]', '', name)
name = re.sub(r'[^a-z0-9äöüß\s]', ' ', name)
name = re.sub(r'\s+', ' ', name).strip()
return name
def clean_name_for_scoring(norm_name: str):
if not norm_name: return "", set()
tokens = [t for t in _tokenize(norm_name) if len(t) >= 3]
@@ -67,9 +63,13 @@ def create_features(mrec: dict, crec: dict, term_weights: dict):
features['fuzz_token_set_ratio'] = fuzz.token_set_ratio(clean1, clean2)
features['fuzz_token_sort_ratio'] = fuzz.token_sort_ratio(clean1, clean2)
features['domain_match'] = 1 if mrec.get('CRM Website') and str(mrec.get('CRM Website')).strip() != '' and mrec.get('CRM Website') == crec.get('Kandidat Website') else 0
features['city_match'] = 1 if mrec.get('CRM Ort') and str(mrec.get('CRM Ort')).strip() != '' and mrec.get('CRM Ort') == crec.get('Kandidat Ort') else 0
features['country_match'] = 1 if mrec.get('CRM Land') and str(mrec.get('CRM Land')).strip() != '' and mrec.get('CRM Land') == crec.get('Kandidat Land') else 0
# Normalisiere Domains für den Vergleich
domain1 = str(mrec.get('CRM Website', '')).lower().replace('www.', '').split('/')[0]
domain2 = str(crec.get('Kandidat Website', '')).lower().replace('www.', '').split('/')[0]
features['domain_match'] = 1 if domain1 and domain1 == domain2 else 0
features['city_match'] = 1 if mrec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('Kandidat Ort') else 0
features['country_match'] = 1 if mrec.get('CRM Land') and mrec.get('CRM Land') == crec.get('Kandidat Land') else 0
features['country_mismatch'] = 1 if (mrec.get('CRM Land') and crec.get('Kandidat Land') and mrec.get('CRM Land') != crec.get('Kandidat Land')) else 0
overlapping_tokens = toks1 & toks2
@@ -88,63 +88,42 @@ def create_features(mrec: dict, crec: dict, term_weights: dict):
if __name__ == "__main__":
logging.info("Starte Trainingsprozess für Duplikats-Checker v5.0")
# 1. Daten laden
try:
gold_df = pd.read_csv(GOLD_STANDARD_FILE, sep=';')
# Lade CRM Daten, um Gewichte zu berechnen.
# Idealerweise wäre dies ein aktueller Export aus dem Google Sheet.
# Für die Simulation nehmen wir die Daten aus dem Gold-Standard.
# Besser: Lade hier alle 22.000 CRM Accounts.
# Annahme: Du hast einen Export als CRM_Accounts.csv im Ordner
try:
crm_df = pd.read_csv(CRM_ACCOUNTS_FILE, sep=',') # Passe Trennzeichen ggf. an
logging.info(f"{len(crm_df)} CRM Accounts geladen für die Gewichtsberechnung.")
except FileNotFoundError:
logging.warning(f"'{CRM_ACCOUNTS_FILE}' nicht gefunden. Verwende Daten aus '{GOLD_STANDARD_FILE}' für Gewichte.")
crm_df = gold_df.rename(columns={'Kandidat': 'CRM Name'})
gold_df = pd.read_csv(GOLD_STANDARD_FILE, sep=';', encoding='utf-8')
logging.info(f"{len(gold_df)} Zeilen aus Gold-Standard-Datei '{GOLD_STANDARD_FILE}' geladen.")
logging.info("Verbinde mit Google Sheets, um CRM-Daten zu laden...")
sheet_handler = GoogleSheetHandler()
crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
logging.info(f"{len(crm_df)} CRM Accounts aus Google Sheets geladen.")
except Exception as e:
logging.critical(f"Fehler beim Laden der CSV-Dateien: {e}")
logging.critical(f"Fehler beim Laden der Daten: {e}")
sys.exit(1)
# 2. Daten normalisieren
for col in ['CRM Name', 'Kandidat']:
gold_df[f'normalized_{col}'] = gold_df[col].astype(str).apply(normalize_company_name)
for col in ['CRM Ort', 'Kandidat Ort', 'CRM Land', 'Kandidat Land']:
gold_df[col] = gold_df[col].astype(str).str.lower().str.strip()
crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
# 3. Term Weights (TF-IDF) auf dem gesamten CRM-Datensatz berechnen
gold_df['normalized_CRM Name'] = gold_df['CRM Name'].astype(str).apply(normalize_company_name)
gold_df['normalized_Kandidat'] = gold_df['Kandidat'].astype(str).apply(normalize_company_name)
for col in ['CRM Ort', 'Kandidat Ort', 'CRM Land', 'Kandidat Land']:
gold_df[col] = gold_df[col].astype(str).str.lower().str.strip().replace('nan', '')
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()}
logging.info(f"{len(term_weights)} Wortgewichte berechnet.")
# 4. Feature-Tabelle und Labels erstellen
logging.info("Erstelle Features für den Trainingsdatensatz...")
features_list = []
labels = []
for _, row in gold_df.iterrows():
mrec = {
'normalized_name': row['normalized_CRM Name'],
'CRM Website': row['CRM Website'],
'CRM Ort': row['CRM Ort'],
'CRM Land': row['CRM Land']
}
crec = {
'normalized_name': row['normalized_Kandidat'],
'Kandidat Website': row['Kandidat Website'],
'Kandidat Ort': row['Kandidat Ort'],
'Kandidat Land': row['Kandidat Land']
}
# Nur Zeilen mit einem Kandidaten verarbeiten
if pd.notna(row['Kandidat']):
# Nur Zeilen mit einem Kandidaten und einem Best Match verarbeiten
if pd.notna(row['Kandidat']) and pd.notna(row['Best Match Option']):
mrec = row.to_dict()
crec = {'Kandidat Website': row['Kandidat Website'], 'Kandidat Ort': row['Kandidat Ort'], 'Kandidat Land': row['Kandidat Land']}
features = create_features(mrec, crec, term_weights)
features_list.append(features)
# Label erstellen: 1 wenn der Kandidat dem Gold-Standard entspricht, sonst 0
is_correct_match = 1 if row['Kandidat'] == row.get('Best Match Option', '') else 0 # Angenommen Spalte G heißt jetzt so
is_correct_match = 1 if row['Kandidat'] == row['Best Match Option'] else 0
labels.append(is_correct_match)
X = pd.DataFrame(features_list)
@@ -153,24 +132,21 @@ if __name__ == "__main__":
logging.info(f"Trainingsdatensatz erstellt mit {X.shape[0]} Beispielen und {X.shape[1]} Features.")
logging.info(f"Verteilung der Klassen: {Counter(y)}")
# 5. Modell trainieren
logging.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)
model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')
model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', scale_pos_weight= (len(y) - sum(y)) / sum(y))
model.fit(X_train, y_train)
logging.info("Modell erfolgreich trainiert.")
# 6. Modell validieren
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
logging.info(f"\n--- Validierungsergebnis ---")
logging.info(f"Genauigkeit auf Testdaten: {accuracy:.2%}")
logging.info("Detaillierter Report:")
logging.info("\n" + classification_report(y_test, y_pred))
logging.info("\n" + classification_report(y_test, y_pred, zero_division=0))
# 7. Finales Modell und Gewichte speichern
model.save_model(MODEL_OUTPUT_FILE)
joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE)
logging.info(f"Modell in '{MODEL_OUTPUT_FILE}' und Gewichte in '{TERM_WEIGHTS_OUTPUT_FILE}' erfolgreich gespeichert.")