Created
April 21, 2021 21:14
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Hyperopt search parameters dictionary
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#integer and string parameters, used with hp.choice() | |
bootstrap_type = [{'bootstrap_type':'Poisson'}, | |
{'bootstrap_type':'Bayesian', | |
'bagging_temperature' : hp.loguniform('bagging_temperature', np.log(1), np.log(50))}, | |
{'bootstrap_type':'Bernoulli'}] | |
LEB = ['No', 'AnyImprovement'] #remove 'Armijo' if not using GPU | |
grow_policy = [ | |
{'grow_policy':'SymmetricTree'}, | |
# {'grow_policy':'Depthwise'}, | |
{'grow_policy':'Lossguide', | |
'max_leaves': hp.quniform('max_leaves', 2, 32, 1)}] | |
ensemble_params = { | |
"lgb" : { | |
"num_leaves": scope.int(hp.quniform("num_leaves", 31, 200, 1)), | |
"max_depth": scope.int(hp.quniform("max_depth", 10, 24, 1)), | |
'learning_rate': hp.uniform('learning_rate', 0.01, 0.3), | |
'min_split_gain': hp.uniform('min_split_gain', 0, 1.0), | |
'min_child_samples': scope.int(hp.quniform("min_child_samples", 2, 700, 1)), | |
"subsample": hp.uniform("subsample", 0.2, 1.0), | |
"colsample_bytree": hp.uniform("colsample_bytree", 0.5, 1.0), | |
'reg_alpha': hp.uniform('reg_alpha', 1e-5, 1.0), | |
'reg_lambda': hp.uniform('reg_lambda', 0, 50), | |
'metric': 'auc', | |
'n_jobs': -1, | |
'n_estimators': 2000}, | |
'xgb': { | |
'max_depth': scope.int(hp.quniform('xgb.max_depth', 10, 24, 1)), | |
'learning_rate': hp.uniform('xgb.learning_rate', 0.01, 0.3), | |
'gamma': hp.uniform('xgb.gamma', 1, 10), | |
'min_child_weight': scope.int(hp.quniform('xgb.min_child_weight', 2, 700, 1)), | |
'n_estimators': 2000, | |
'colsample_bytree': hp.uniform('xgb.colsample_bytree', 0.5, 0.9), | |
'subsample': hp.uniform('xgb.subsample', 0.5, 1.0), | |
'reg_lambda': hp.uniform('xgb.reg_lambda', 0, 100), | |
'reg_alpha': hp.uniform('xgb.reg_alpha', 1e-5, 0.5), | |
'objective': 'binary:logistic', | |
'tree_method': 'hist', | |
'eval_metric': 'auc', | |
'n_jobs': -1}, | |
'cat': { | |
'depth': hp.quniform("cat.depth", 2, 16, 1), | |
'learning_rate': hp.uniform('cat.learning_rate', 0.01, 0.3), | |
'l2_leaf_reg': hp.uniform('cat.l2_leaf_reg', 3, 8), | |
'max_bin' : hp.quniform('cat.max_bin', 1, 254, 1), | |
'min_data_in_leaf' : hp.quniform('cat.min_data_in_leaf', 2, 700, 1), | |
'random_strength' : hp.loguniform('cat.random_strength', np.log(0.005), np.log(5)), | |
'leaf_estimation_backtracking' : hp.choice('cat.leaf_estimation_backtracking', LEB), | |
'fold_len_multiplier' : hp.loguniform('cat.fold_len_multiplier', np.log(1.01), np.log(2.5)), | |
'eval_metric': 'AUC', | |
'n_estimators': 2000 | |
} | |
} |
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