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best_params_pro.py
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best_params_pro.py
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PARAMS_CPH = {'alpha': 0.1,
'ties': 'breslow',
'n_iter': 100,
'tol': 0.1}
PARAMS_CPH_RIDGE = {'alpha': 0.5,
'ties': 'breslow',
'n_iter': 100,
'tol': 1e-9}
PARAMS_CPH_LASSO = {'l1_ratio': 1.0,
'alpha_min_ratio': 0.5,
'fit_baseline_model': True,
'normalize': False,
'tol': 1e-7,
'max_iter': 100000}
PARAMS_CPH_ELASTIC = {'l1_ratio': 0.5,
'alpha_min_ratio': 0.1,
'fit_baseline_model': True,
'normalize': False,
'tol': 1e-7,
'max_iter': 100000}
PARAMS_RSF = {
'n_estimators': 100,
'max_depth' : 7,
'min_samples_split': 10,
'min_samples_leaf': 2,
'max_features': None,
'random_state': 0
}
PARAMS_GRADBOOST = {'n_estimators': 400,
'learning_rate': 0.1,
'max_depth': 5,
'loss': 'coxph',
'min_samples_split': 5,
'min_samples_leaf': 4,
'max_features': None,
'dropout_rate': 0.0,
'subsample': 1.0,
'random_state': 0}
PARAMS_GRADBOOST_DART = {'n_estimators': 100,
'learning_rate': 0.1,
'max_depth': 3,
'loss': 'coxph',
'min_samples_split': 2,
'min_samples_leaf': 1,
'max_features': None,
'dropout_rate': 0.2,
'subsample': 1.0,
'random_state': 0}
PARAMS_SVM = {'alpha': 1,
'rank_ratio': 0.8,
'max_iter': 40,
'optimizer': 'avltree'}
PARAMS_DEEPSURV = {'batch_size' : 16,
'learning_rate' : 0.01,
'iters': 50}
PARAMS_DSM = {'batch_size' : 32,
'learning_rate' : 0.01,
'iters': 100}
PARAMS_WEIBULL = {'alpha': 0.2,
'penalizer': 0.02,
'l1_ratio': 0.0,
'fit_intercept': True}