'n_estimators': 20000,
'max_depth': 5,
'max_delta_step': 5,
'colsample_bylevel': 0.9,
'colsample_bytree': 0.95,
'subsample': 0.8,
'gamma': 1.5,
'max_leaves': 10,
'min_child_weight': 50,
'reg_alpha': 0.6, # L1 regularization
'reg_lambda': 50, # L2 regularization
'seed': 42
}
model = ModelLoader(xgb.XGBClassifier, model_params, **xgboost_params)
fit_params = {'early_stopping_rounds': 2500, 'verbose': 1000}
predict_params = {}
results = model.run(data_loader,
roc_auc_score,
fit_params,
predict_params,
verbose=True)
if args.save:
current_file_path = os.path.abspath(__file__) # to save this .py file
model.save(data_loader, results, current_file_path, args.preds,
args.models)
## << Create and train model
"verbosity": 0,
"seed": 42
}
def fit(self, train, cv):
x_tr, y_tr = train
x_cv, y_cv = cv
trn_data = lgb.Dataset(x_tr, label=y_tr)
val_data = lgb.Dataset(x_cv, label=y_cv)
evals_result = {}
self.model = lgb.train(self.lgb_params,
trn_data,
100000,
valid_sets=[trn_data, val_data],
early_stopping_rounds=3000,
verbose_eval=1000,
evals_result=evals_result)
def predict(self, test):
return self.model.predict(test)
model = ModelLoader(LightGbmTrainer, model_params)
results = model.run(data_loader, roc_auc_score, {}, {}, verbose=True)
if args.save:
current_file_path = os.path.abspath(__file__) # to save this .py file
model.save(data_loader, results, current_file_path, args.preds,
args.models)
## << Create and train model