qloguniform search space setting issue in Hyperopt

I am working on using hyperopt to tune my ML model but having troubles in using the qloguniform as the search space. I am giving the example from official wiki and changed the search space.

import pickle
import time
#utf8
import pandas as pd
import numpy as np
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials

def objective(x):
    return {
        'loss': x ** 2,
        'status': STATUS_OK,
        # -- store other results like this
        'eval_time': time.time(),
        'other_stuff': {'type': None, 'value': [0, 1, 2]},
        # -- attachments are handled differently
        'attachments':
            {'time_module': pickle.dumps(time.time)}
        }
trials = Trials()
best = fmin(objective,
    space=hp.qloguniform('x', np.log(0.001), np.log(0.1), np.log(0.001)),
    algo=tpe.suggest,
    max_evals=100,
    trials=trials)
pd.DataFrame(trials.trials)

But getting the following error.

ValueError: ('negative arg to lognormal_cdf', array([-3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764, -3.45387764]))

I have tried without log transform as below but the output values turns out to be log transformation (ex- 1.017,1.0008,1.02456), which is wrong. It is consistent with the documentation.

hp.qloguniform('x', 0.001,0.1, 0.001)

Thanks

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