using BayesSearchCV AttributeError module 'numpy' has no attribute 'int'. np.int was a deprecated alias for the builtin int. To avoid this error in existing code, use int by itself. Doing this will not modify any behavior and is safe. When replacing np.int, you may wish to use e.g. np.int64 or np.int32 to specify the precision. If you wish to review your current use, check the release note link for additional information. about scikit-optimize

[RNarayan73]

A workaround that worked for me is to put these lines before fitting the estimator:

import numpy as np
np.int = int

[zeron-G]

Got same problem with the following code:

opt = BayesSearchCV(
    SVR(),
    {
        'C': (1e-6, 1e+6, 'log-uniform'),
        'gamma': (1e-6, 1e+1, 'log-uniform'),
        'degree': (1, 8),
        'kernel': ['linear', 'poly', 'rbf'],
    },
    n_iter=10,
    cv=5
)

opt.fit(trainX, trainY)  # RAISE AN ERROR AT THIS LINE

print("validation Score: ", opt.best_score_)
print("test score: ", opt.score(testX, testY))

AttributeError                            Traceback (most recent call last)
[~\AppData\Local\Temp/ipykernel_7760/3848764104.py](https://file+.vscode-resource.vscode-cdn.net/e%3A/Programming/pythonAI/sklearn/~/AppData/Local/Temp/ipykernel_7760/3848764104.py) in <module>
     14 )
     15 
---> 16 opt.fit(trainX, trainY)
     17 
     18 print("validation Score: ", opt.best_score_)

[d:\Python\lib\site-packages\skopt\searchcv.py](file:///D:/Python/lib/site-packages/skopt/searchcv.py) in fit(self, X, y, groups, callback, **fit_params)
    464             self.optimizer_kwargs_ = dict(self.optimizer_kwargs)
    465 
--> 466         super().fit(X=X, y=y, groups=groups, **fit_params)
    467 
    468         # BaseSearchCV never ranked train scores,

[d:\Python\lib\site-packages\sklearn\base.py](file:///D:/Python/lib/site-packages/sklearn/base.py) in wrapper(estimator, *args, **kwargs)
   1150                 )
   1151             ):
-> 1152                 return fit_method(estimator, *args, **kwargs)
   1153 
   1154         return wrapper

[d:\Python\lib\site-packages\sklearn\model_selection\_search.py](file:///D:/Python/lib/site-packages/sklearn/model_selection/_search.py) in fit(self, X, y, groups, **fit_params)
    896                 return results
...

AttributeError: module 'numpy' has no attribute 'int'.
`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

I guess downgrade numpy to < 1.20 will fix this problem?

that is because of BayesSearchCV.fit() function uses the np.int, you can go to skopt's library position and edit the 'the site-packages\skopt\space\transformers.py' by replacing all np.int with int it will fix
I pass the error by this method

[sushi3085]

Got same problem with the following code:

opt = BayesSearchCV(
    SVR(),
    {
        'C': (1e-6, 1e+6, 'log-uniform'),
        'gamma': (1e-6, 1e+1, 'log-uniform'),
        'degree': (1, 8),
        'kernel': ['linear', 'poly', 'rbf'],
    },
    n_iter=10,
    cv=5
)

opt.fit(trainX, trainY)  # RAISE AN ERROR AT THIS LINE

print("validation Score: ", opt.best_score_)
print("test score: ", opt.score(testX, testY))

AttributeError                            Traceback (most recent call last)
[~\AppData\Local\Temp/ipykernel_7760/3848764104.py](https://file+.vscode-resource.vscode-cdn.net/e%3A/Programming/pythonAI/sklearn/~/AppData/Local/Temp/ipykernel_7760/3848764104.py) in <module>
     14 )
     15 
---> 16 opt.fit(trainX, trainY)
     17 
     18 print("validation Score: ", opt.best_score_)

[d:\Python\lib\site-packages\skopt\searchcv.py](file:///D:/Python/lib/site-packages/skopt/searchcv.py) in fit(self, X, y, groups, callback, **fit_params)
    464             self.optimizer_kwargs_ = dict(self.optimizer_kwargs)
    465 
--> 466         super().fit(X=X, y=y, groups=groups, **fit_params)
    467 
    468         # BaseSearchCV never ranked train scores,

[d:\Python\lib\site-packages\sklearn\base.py](file:///D:/Python/lib/site-packages/sklearn/base.py) in wrapper(estimator, *args, **kwargs)
   1150                 )
   1151             ):
-> 1152                 return fit_method(estimator, *args, **kwargs)
   1153 
   1154         return wrapper

[d:\Python\lib\site-packages\sklearn\model_selection\_search.py](file:///D:/Python/lib/site-packages/sklearn/model_selection/_search.py) in fit(self, X, y, groups, **fit_params)
    896                 return results
...

AttributeError: module 'numpy' has no attribute 'int'.
`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

I guess downgrade numpy to < 1.20 will fix this problem?

[FlorinAndrei]

Another example that fails the same way.

Are there any plans to fix this bug? It's pretty old.

import numpy as np
import matplotlib.pyplot as plt
from warnings import filterwarnings
from skopt.plots import plot_convergence
from skopt import gp_minimize

filterwarnings('ignore')  # suppress some of the warnings from B.O.

np.random.seed(42)


# same "bumpy" function as in simulated annealing, just written differently
# assumes xy is a list or array-like with xy = [x, y]
def bumpy(xy):
    x = xy[0]
    y = xy[1]
    obj = (
        0.2
        + x**2
        + y**2
        - 0.1 * np.cos(6 * np.pi * x)
        - 0.1 * np.cos(6 * np.pi * y)
    )
    return obj


# call the optimization.
res = gp_minimize(
    bumpy,  # the function to minimize
    [(-1, 1), (-1, 1)],  # the bounds on each dimension of x
    acq_func="EI",  # the acquisition function
    n_calls=20,  # the number of evaluations of the objective function
    n_random_starts=5,  # the number of random initialization points
    random_state=42,
)  # the random seed

print(
    f'The minimum value of f(x) is {res.fun:0.4f} and occurs at x={res.x[0]:0.4f}, y={res.x[1]:0.4f}'
)
print(
    f'Recall that the objective function may include noise, so the optimized function value may not be exact.'
)

fig = plt.figure(
    figsize=(10, 6)
)  # we initialize the plot so we can control dimensions
ax = fig.add_axes
plot_convergence(res, ax)