def assert_params_ok(params_arg): Gopt.assert_params_ok(params_arg) # vw params assert "loss_function" in params_arg assert "passes" in params_arg assert "l1" in params_arg assert "l2" in params_arg assert "decay_learning_rate" in params_arg assert "learning_rate" in params_arg
def assert_params_ok(params_arg): Gopt.assert_params_ok(params_arg) # xgb params assert 'eval_metric' in params_arg assert 'booster' in params_arg assert 'objective' in params_arg assert 'eta' in params_arg assert 'num_round' in params_arg assert 'silent' in params_arg assert 'seed' in params_arg assert 'num_boost_round' in params_arg
def assert_params_ok(params_arg): Gopt.assert_params_ok(params_arg) # keras params assert 'loss_function' in params_arg assert 'verbose' in params_arg assert 'batch_norm' in params_arg assert 'hidden_units' in params_arg assert 'hidden_layers' in params_arg assert 'input_dropout' in params_arg assert 'hidden_dropout' in params_arg assert 'hidden_activation' in params_arg assert 'batch_size' in params_arg assert 'nb_epoch' in params_arg
def assert_params_ok(params_arg): Gopt.assert_params_ok(params_arg) assert 'type' in params_arg if params_arg['type'] == 'random_forest': # RF params assert 'n_estimators' in params_arg assert 'max_features' in params_arg assert 'random_state' in params_arg assert 'max_depth' in params_arg assert 'n_jobs' in params_arg if params_arg['type'] == 'logistic_regression': # LogReg params assert 'C' in params_arg
def assert_params_ok(params_arg): Gopt.assert_params_ok(params_arg) assert "type" in params_arg if params_arg["type"] == "random_forest": # RF params assert "n_estimators" in params_arg assert "max_features" in params_arg assert "random_state" in params_arg assert "max_depth" in params_arg assert "n_jobs" in params_arg if params_arg["type"] == "logistic_regression": # LogReg params assert "C" in params_arg