XGBoostError: b'value 1.16151 for Parameter colsample_bytree exceed bound [0,1]'

[daveliu-riviera]

import numpy as np

import xgboost as xgb

from scipy.stats import randint as sp_randint
from scipy.stats import uniform as sp_float


# XGBoost with Hyperband Hyperparameter Optimization
clf = xgb.XGBRegressor()

clf.set_params(**{"n_jobs": 4})

# Hyperparameter search boundaries
param_grid = {
              # Parameters for Tree Booster
              'eta': sp_float(0, 1),
              'gamma': sp_randint(0, 100),
              'max_depth': sp_randint(1, 3),
              'learning_rate': sp_float(.001, .005),
              'n_estimators': sp_randint(5000, 40000),

              'min_child_weight': sp_randint(0, 50),
              'max_delta_step': sp_randint(0, np.log(upper_limit)),
              'subsample': sp_float(0, 1),

              # Family of parameters for subsampling of columns
              'colsample_bytree': sp_float(0.2, 1),
              'colsample_bylevel': sp_float(0.2, 1),
              'colsample_bynode': sp_float(0.2, 1),

              # Regularization Params
              'lambda': sp_randint(1, 10),
              'alpha': sp_randint(0, 100),
              }

from civismlext.hyperband import HyperbandSearchCV
    tuned_model = HyperbandSearchCV(regressor,
                                    param_distributions=param_grid,
                                    cost_parameter_max={'n_estimators': 20000},
                                    cost_parameter_min={'n_estimators': 2000},
                                    n_jobs=4,
                                    cv=2)

Somehow I got an out-of-bounds error when I tried to set the range for colsample_by* as (0.2, 1), but when I changed it back to (0, 1) it worked.

Seems like it might be an async/distributed computing issue?