172 lines
7.8 KiB
Python
172 lines
7.8 KiB
Python
import pandas as pd
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import numpy as np
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import re
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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 sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, classification_report
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from thefuzz import fuzz
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from collections import Counter
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import logging
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import sys
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# Importiere deine bestehenden Helfer
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from google_sheet_handler import GoogleSheetHandler
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from helpers import normalize_company_name
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# Logging Setup
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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# --- Konfiguration ---
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# HINWEIS: Bitte stelle sicher, dass diese Datei deine finale Vergleichs-CSV ist,
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# die die Spalten 'Best Match Option' und die Vorschläge der alten Läufe enthält.
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GOLD_STANDARD_FILE = 'matches4.csv'
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CRM_SHEET_NAME = "CRM_Accounts"
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MODEL_OUTPUT_FILE = 'xgb_model.json'
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TERM_WEIGHTS_OUTPUT_FILE = 'term_weights.joblib'
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# Spaltennamen aus deiner Vergleichs-CSV
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BEST_MATCH_COL = 'Best Match Option'
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# Liste der Spalten, die Vorschläge von alten Algorithmen enthalten
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SUGGESTION_COLS = ['Ergebnis Lauf 1', 'Ergebnis Lauf 2', 'Ergebnis Lauf 3'] # Passe diese Namen an deine CSV an!
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# --- ... (Alle Hilfsfunktionen bleiben identisch zu vorher) ... ---
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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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# ... (Rest der Stopwords)
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}
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CITY_TOKENS = set()
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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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def create_features(mrec: dict, crec: dict, term_weights: dict):
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features = {}
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n1_raw = mrec.get('normalized_CRM Name', '')
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n2_raw = crec.get('normalized_name', '') # Kandidaten-Name ist jetzt generisch
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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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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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domain1_raw = str(mrec.get('CRM Website', '')).lower()
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domain2_raw = str(crec.get('CRM Website', '')).lower()
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domain1 = domain1_raw.replace('www.', '').split('/')[0].strip()
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domain2 = domain2_raw.replace('www.', '').split('/')[0].strip()
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features['domain_match'] = 1 if domain1 and domain1 == domain2 else 0
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features['city_match'] = 1 if mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec['CRM Ort'] == crec['CRM Ort'] else 0
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features['country_match'] = 1 if mrec.get('CRM Land') and crec.get('CRM Land') and mrec['CRM Land'] == crec['CRM Land'] else 0
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features['country_mismatch'] = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec['CRM Land'] != crec['CRM Land']) else 0
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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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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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# --- Haupt-Trainingsskript ---
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if __name__ == "__main__":
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logging.info("Starte Trainingsprozess für Duplikats-Checker v5.0")
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try:
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gold_df = pd.read_csv(GOLD_STANDARD_FILE, sep=';', encoding='utf-8')
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logging.info(f"{len(gold_df)} Zeilen aus Gold-Standard-Datei '{GOLD_STANDARD_FILE}' geladen.")
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logging.info("Verbinde mit Google Sheets, um CRM-Daten zu laden...")
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sheet_handler = GoogleSheetHandler()
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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logging.info(f"{len(crm_df)} CRM Accounts aus Google Sheets geladen.")
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except Exception as e:
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logging.critical(f"Fehler beim Laden der Daten: {e}")
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sys.exit(1)
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crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
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gold_df['normalized_CRM Name'] = gold_df['CRM Name'].astype(str).apply(normalize_company_name)
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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()}
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logging.info(f"{len(term_weights)} Wortgewichte berechnet.")
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logging.info("Erstelle Features für den Trainingsdatensatz...")
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features_list = []
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labels = []
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# Mache das CRM für schnelle Lookups verfügbar
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crm_lookup = crm_df.set_index('CRM Name').to_dict('index')
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for _, row in gold_df.iterrows():
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mrec = row.to_dict() # Die zu prüfende Firma
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# 1. Positives Beispiel: Der von dir definierte "Best Match"
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best_match_name = row[BEST_MATCH_COL]
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if pd.notna(best_match_name) and str(best_match_name).strip() != '' and best_match_name in crm_lookup:
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crec_positive = crm_lookup[best_match_name]
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features = create_features(mrec, crec_positive, term_weights)
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features_list.append(features)
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labels.append(1)
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# 2. Negative Beispiele: Die falschen Vorschläge der alten Algorithmen
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for col_name in SUGGESTION_COLS:
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if col_name in row and pd.notna(row[col_name]):
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suggestion_name = row[col_name]
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# Wenn der Vorschlag nicht der korrekte ist UND im CRM existiert
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if suggestion_name != best_match_name and suggestion_name in crm_lookup:
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crec_negative = crm_lookup[suggestion_name]
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features = create_features(mrec, crec_negative, term_weights)
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features_list.append(features)
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labels.append(0)
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X = pd.DataFrame(features_list)
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y = np.array(labels)
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if len(X) == 0:
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logging.critical("Keine gültigen Trainingsdaten gefunden.")
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sys.exit(1)
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logging.info(f"Trainingsdatensatz erstellt mit {X.shape[0]} Beispielen und {X.shape[1]} Features.")
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logging.info(f"Verteilung der Klassen: {Counter(y)}")
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logging.info("Trainiere das XGBoost-Modell...")
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
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scale_pos_weight = (len(y_train) - sum(y_train)) / sum(y_train) if sum(y_train) > 0 else 1
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model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', scale_pos_weight=scale_pos_weight)
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model.fit(X_train, y_train)
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logging.info("Modell erfolgreich trainiert.")
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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logging.info(f"\n--- Validierungsergebnis ---")
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logging.info(f"Genauigkeit auf Testdaten: {accuracy:.2%}")
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logging.info("Detaillierter Report:")
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logging.info("\n" + classification_report(y_test, y_pred, zero_division=0))
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model.save_model(MODEL_OUTPUT_FILE)
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joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE)
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logging.info(f"Modell in '{MODEL_OUTPUT_FILE}' und Gewichte in '{TERM_WEIGHTS_OUTPUT_FILE}' erfolgreich gespeichert.") |