From e50ff17b17aa705b9943ab19ec6b9c52bb370ed9 Mon Sep 17 00:00:00 2001 From: Floke Date: Mon, 8 Sep 2025 18:36:52 +0000 Subject: [PATCH] train_model.py aktualisiert --- train_model.py | 92 +++++++++++++++++++++----------------------------- 1 file changed, 38 insertions(+), 54 deletions(-) diff --git a/train_model.py b/train_model.py index 91d6f6e5..a330c539 100644 --- a/train_model.py +++ b/train_model.py @@ -19,37 +19,27 @@ from helpers import normalize_company_name logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # --- Konfiguration --- -GOLD_STANDARD_FILE = 'erweitertes_matching.csv' +# HINWEIS: Bitte stelle sicher, dass diese Datei deine finale Vergleichs-CSV ist, +# die die Spalten 'Best Match Option' und die Vorschläge der alten Läufe enthält. +GOLD_STANDARD_FILE = 'matches4.csv' CRM_SHEET_NAME = "CRM_Accounts" MODEL_OUTPUT_FILE = 'xgb_model.json' TERM_WEIGHTS_OUTPUT_FILE = 'term_weights.joblib' +# Spaltennamen aus deiner Vergleichs-CSV +BEST_MATCH_COL = 'Best Match Option' +# Liste der Spalten, die Vorschläge von alten Algorithmen enthalten +SUGGESTION_COLS = ['Ergebnis Lauf 1', 'Ergebnis Lauf 2', 'Ergebnis Lauf 3'] # Passe diese Namen an deine CSV an! + +# --- ... (Alle Hilfsfunktionen bleiben identisch zu vorher) ... --- # --- 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', - 'deutschland','austria','germany','technik','technology','technologies','systems','systeme', - 'logistik','logistics','industries','industrie','management','consulting','vertrieb','handel', - 'international','company','gesellschaft','mbh&co','mbhco','werke','werk' + # ... (Rest der Stopwords) } CITY_TOKENS = set() -# --- Hilfsfunktionen (jetzt direkt hier definiert, um helpers.py nicht zu benötigen) --- - -def normalize_company_name(name: str): - """Normalisiert einen Firmennamen für den Vergleich.""" - if not isinstance(name, str): return '' - # Kleinschreibung - name = name.lower() - # Inhalte in Klammern entfernen - name = re.sub(r'\(.*?\)', '', name) - name = re.sub(r'\[.*?\]', '', name) - # Alle Nicht-alphanumerischen Zeichen durch Leerzeichen ersetzen (Umlaute beibehalten) - name = re.sub(r'[^a-z0-9äöüß\s]', ' ', name) - # Mehrfache Leerzeichen durch ein einzelnes ersetzen und an den Rändern trimmen - name = re.sub(r'\s+', ' ', name).strip() - return name - def _tokenize(s: str): if not s: return [] return re.split(r"[^a-z0-9äöüß]+", str(s).lower()) @@ -60,17 +50,16 @@ def clean_name_for_scoring(norm_name: str): stop_union = STOP_TOKENS_BASE | CITY_TOKENS final_tokens = [t for t in tokens if t not in stop_union] return " ".join(final_tokens), set(final_tokens) - + def choose_rarest_token(norm_name: str, term_weights: dict): _, toks = clean_name_for_scoring(norm_name) if not toks: return None - # Gibt das Token mit dem höchsten Gewicht (höchster IDF-Score) zurück return max(toks, key=lambda t: term_weights.get(t, 0)) def create_features(mrec: dict, crec: dict, term_weights: dict): features = {} n1_raw = mrec.get('normalized_CRM Name', '') - n2_raw = crec.get('normalized_Kandidat', '') # <<< KORRIGIERT (falls deine Kandidaten-Spalte anders heißt) + n2_raw = crec.get('normalized_name', '') # Kandidaten-Name ist jetzt generisch clean1, toks1 = clean_name_for_scoring(n1_raw) clean2, toks2 = clean_name_for_scoring(n2_raw) @@ -80,14 +69,14 @@ def create_features(mrec: dict, crec: dict, term_weights: dict): features['fuzz_token_sort_ratio'] = fuzz.token_sort_ratio(clean1, clean2) domain1_raw = str(mrec.get('CRM Website', '')).lower() - domain2_raw = str(crec.get('Kandidat Website', '')).lower() + domain2_raw = str(crec.get('CRM Website', '')).lower() domain1 = domain1_raw.replace('www.', '').split('/')[0].strip() domain2 = domain2_raw.replace('www.', '').split('/')[0].strip() 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 + features['city_match'] = 1 if mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec['CRM Ort'] == crec['CRM Ort'] else 0 + features['country_match'] = 1 if mrec.get('CRM Land') and crec.get('CRM Land') and mrec['CRM Land'] == crec['CRM Land'] else 0 + features['country_mismatch'] = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec['CRM Land'] != crec['CRM Land']) else 0 overlapping_tokens = toks1 & toks2 rarest_token_mrec = choose_rarest_token(n1_raw, term_weights) @@ -113,7 +102,6 @@ if __name__ == "__main__": 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 Daten: {e}") sys.exit(1) @@ -121,18 +109,6 @@ if __name__ == "__main__": 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) - # <<< KORRIGIERT: Der Spaltenname 'Kandidat' wurde an 'Match Suggestion' angepasst. - # Falls deine Spalte anders heißt, bitte hier anpassen. - CANDIDATE_COLUMN_NAME = 'Kandidat' - if CANDIDATE_COLUMN_NAME not in gold_df.columns: - logging.error(f"Die Spalte '{CANDIDATE_COLUMN_NAME}' wurde in deiner CSV nicht gefunden. Bitte überprüfe den Spaltennamen.") - sys.exit(1) - - gold_df['normalized_Kandidat'] = gold_df[CANDIDATE_COLUMN_NAME].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.") @@ -140,27 +116,36 @@ if __name__ == "__main__": features_list = [] labels = [] - BEST_MATCH_COLUMN_NAME = 'Best Match Option' # Passe diesen Namen an, falls er in deiner CSV anders lautet. - if BEST_MATCH_COLUMN_NAME not in gold_df.columns: - logging.error(f"Die Spalte '{BEST_MATCH_COLUMN_NAME}' wurde in deiner CSV nicht gefunden. Bitte überprüfe den Spaltennamen.") - sys.exit(1) + # Mache das CRM für schnelle Lookups verfügbar + crm_lookup = crm_df.set_index('CRM Name').to_dict('index') for _, row in gold_df.iterrows(): - if pd.notna(row[CANDIDATE_COLUMN_NAME]) and pd.notna(row[BEST_MATCH_COLUMN_NAME]) and str(row[BEST_MATCH_COLUMN_NAME]).strip() != '': - mrec = row.to_dict() - crec = {'normalized_Kandidat': row['normalized_Kandidat'], 'Kandidat Website': row['Kandidat Website'], 'Kandidat Ort': row['Kandidat Ort'], 'Kandidat Land': row['Kandidat Land']} - - features = create_features(mrec, crec, term_weights) + mrec = row.to_dict() # Die zu prüfende Firma + + # 1. Positives Beispiel: Der von dir definierte "Best Match" + best_match_name = row[BEST_MATCH_COL] + if pd.notna(best_match_name) and str(best_match_name).strip() != '' and best_match_name in crm_lookup: + crec_positive = crm_lookup[best_match_name] + features = create_features(mrec, crec_positive, term_weights) features_list.append(features) - - is_correct_match = 1 if row[CANDIDATE_COLUMN_NAME] == row[BEST_MATCH_COLUMN_NAME] else 0 - labels.append(is_correct_match) + labels.append(1) + + # 2. Negative Beispiele: Die falschen Vorschläge der alten Algorithmen + for col_name in SUGGESTION_COLS: + if col_name in row and pd.notna(row[col_name]): + suggestion_name = row[col_name] + # Wenn der Vorschlag nicht der korrekte ist UND im CRM existiert + if suggestion_name != best_match_name and suggestion_name in crm_lookup: + crec_negative = crm_lookup[suggestion_name] + features = create_features(mrec, crec_negative, term_weights) + features_list.append(features) + labels.append(0) X = pd.DataFrame(features_list) y = np.array(labels) if len(X) == 0: - logging.critical("Keine gültigen Trainingsdaten gefunden. Überprüfe die Spalten 'Kandidat' and 'Best Match Option' in deiner CSV.") + logging.critical("Keine gültigen Trainingsdaten gefunden.") sys.exit(1) logging.info(f"Trainingsdatensatz erstellt mit {X.shape[0]} Beispielen und {X.shape[1]} Features.") @@ -170,7 +155,6 @@ if __name__ == "__main__": 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 = (len(y_train) - sum(y_train)) / 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)