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from functools import partial


def hyperopt_clt(params, data_x, data_y):
    global best
    model_clt = RandomForestClassifier(**params)
    acc = cross_val_score(model_clt, data_x, data_y).mean()
    if acc > best:
        best = acc
        print('new best:', best, params)
    return {'loss': 1 - acc, 'status': STATUS_OK}


space_clf = {
    'max_depth': hp.choice('max_depth', range(1, 51)),
    'n_estimators': hp.choice('n_estimators', range(1, 21))
}

best = 0
trials_clt = Trials()
hyperopt_clt_partial = partial(hyperopt_clt, data_x = data_x, data_y = data_y)
best_clt = fmin(hyperopt_clt_partial, space = space_clf, algo = tpe.suggest, max_evals = 100, trials = trials_clt)
print(best, '----------------')
print(best_clt, '------------')`

new best:
0.7955077744291005
{'max_depth': 47, 'n_estimators': 12}
new best:
0.8071266849594411
{'max_depth': 5, 'n_estimators': 20}
100%|██████████| 100/100 [00:30<00:00,  3.29trial/s, best loss: 0.19287331504055893]
0.8071266849594411 ----------------
{'max_depth': 4, 'n_estimators': 19} ------------