target = self._cache[_hashable(x)] KeyError

Hello,

I was trying to use the optimizer to find the best hyperparameters for the XGboostRegressor, however, I have encountered the following error and, I am not sure how to deal with it.

Traceback (most recent call last):
  File "C:\Users\User\Anaconda3\envs\datascience\lib\site-packages\bayes_opt\target_space.py", line 191, in probe
    target = self._cache[_hashable(x)]
KeyError: (0.449816047538945, 0.09507143064099162, 0.14907884894416698, 4.79597545259111, 1.7340279606636548, 1655.9945203362026, 0.05808361216819946, 0.8661761457749352, 0.8005575058716043)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Program Files\JetBrains\PyCharm Community Edition with Anaconda plugin 2019.1.3\plugins\python-ce\helpers\pydev\pydevd.py", line 1434, in _exec
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "C:\Program Files\JetBrains\PyCharm Community Edition with Anaconda plugin 2019.1.3\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/Users/User/PycharmProjects/BayesOpt/asd.py", line 64, in <module>
    optimize_xgboost(data=X_arr, targets=y_arr)
  File "C:/Users/User/PycharmProjects/BayesOpt/asd.py", line 61, in optimize_xgboost
    optimizer.maximize(n_iter=10)
  File "C:\Users\User\Anaconda3\envs\datascience\lib\site-packages\bayes_opt\bayesian_optimization.py", line 185, in maximize
    self.probe(x_probe, lazy=False)
  File "C:\Users\User\Anaconda3\envs\datascience\lib\site-packages\bayes_opt\bayesian_optimization.py", line 116, in probe
    self._space.probe(params)
  File "C:\Users\User\Anaconda3\envs\datascience\lib\site-packages\bayes_opt\target_space.py", line 195, in probe
    self.register(x, target)
  File "C:\Users\User\Anaconda3\envs\datascience\lib\site-packages\bayes_opt\target_space.py", line 167, in register
    self._target = np.concatenate([self._target, [target]])
  File "<__array_function__ internals>", line 5, in concatenate
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 1 dimension(s) and the array at index 1 has 2 dimension(s)

Here is the code I have used. I would be very greatful for any kind of help.

from bayes_opt import BayesianOptimization

from xgboost import XGBRegressor
from sklearn.model_selection import cross_val_score

import pandas as pd

SEED = 42

X = pd.read_pickle('X.pkl')
y = pd.read_pickle('y.pkl')

X_arr = X.to_numpy()
y_arr = y.to_numpy()

def xgboost_cv(colsample_bytree, gamma, learning_rate, max_depth, min_child_weight,
               n_estimators, subsample, reg_alpha, reg_lambda, data, targets):
    estimator = XGBRegressor(random_state=SEED, n_jobs=-1, colsample_bytree=colsample_bytree, gamma=gamma,
                             learning_rate=learning_rate, max_depth=max_depth, min_child_weight=min_child_weight,
                             n_estimators=n_estimators, subsample=subsample, reg_alpha=reg_alpha, reg_lambda=reg_lambda)
    cval = cross_val_score(estimator, data, targets, scoring='neg_mean_squared_error', cv=4)
    return -cval


def optimize_xgboost(data, targets):
    def xgboost_crossval(colsample_bytree, gamma, learning_rate, max_depth, min_child_weight,
                         n_estimators, subsample, reg_alpha, reg_lambda):
        return xgboost_cv(
            colsample_bytree=colsample_bytree,
            gamma=gamma,
            learning_rate=learning_rate,
            max_depth=int(max_depth),
            min_child_weight=min_child_weight,
            n_estimators=int(n_estimators),
            subsample=subsample,
            reg_alpha=reg_alpha,
            reg_lambda=reg_lambda,
            data=data,
            targets=targets,
        )

    optimizer = BayesianOptimization(
        f=xgboost_crossval,
        pbounds={
            'colsample_bytree': (0.3, 0.7),
            'gamma': (0, 0.1),
            'learning_rate': (0.01, 0.2),
            'max_depth': (3, 6),
            'min_child_weight': (1.5, 3),
            'n_estimators': (1500, 2500),
            'subsample': (0.5, 1),
            'reg_alpha': (0, 1),
            'reg_lambda': (0, 1),
        },
        random_state=SEED,
        verbose=2
    )

    optimizer.maximize(n_iter=10)
    print(f'Final result: {optimizer.max}')

optimize_xgboost(data=X_arr, targets=y_arr)

[JiHong1235676]

Thank you very much for your reply. I have uploaded my question on GitHub, and I hope it can be solved. https://github.com/fmfn/BayesianOptimization/issues/352

[Qianyic]

Almost same error occurs in my program. TAT