No module named "hpsklearn.estimator" about hyperopt-sklearn HOT 15 CLOSED

I see, I get this error too when I'm installing in Jupyter. Looking at the package it installed, it only downloads the __init__ file.

I apologize for this oversight and aim to fix the issue and come with a solution within 48 hours.

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Hey there!

The pipy package might not be up to date. Could you try installing it like it's mentioned in the Readme?
pip install git+https://github.com/hyperopt/hyperopt-sklearn

Let me know if you run into any issues!

from hyperopt-sklearn.

Thank you for the fast response! I already tried that and the result remains the same even after restarting the runtime.

Collecting git+https://github.com/hyperopt/hyperopt-sklearn
Cloning https://github.com/hyperopt/hyperopt-sklearn to /tmp/pip-req-build-l8id_vgr
Running command git clone -q https://github.com/hyperopt/hyperopt-sklearn /tmp/pip-req-build-l8id_vgr
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing wheel metadata ... done
Requirement already satisfied: scipy>=1.7.1 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (1.7.3)
Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (1.21.6)
Requirement already satisfied: hyperopt>=0.2.5 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (0.2.7)
Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (1.0.2)
Requirement already satisfied: py4j in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (0.10.9.5)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (4.64.0)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (1.15.0)
Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (2.6.3)
Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (2.0.0)
Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (0.16.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0->hpsklearn==1.0.3) (3.1.0)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0->hpsklearn==1.0.3) (1.1.0)

ModuleNotFoundError Traceback (most recent call last)
in ()
2 #import hpsklearn
3 get_ipython().system('pip install git+https://github.com/hyperopt/hyperopt-sklearn')
----> 4 from hpsklearn import HyperoptEstimator
5 from hpsklearn import any_classifier
6 from hpsklearn import any_preprocessing

/usr/local/lib/python3.7/dist-packages/hpsklearn/init.py in ()
----> 1 from .estimator import hyperopt_estimator as HyperoptEstimator
2 from .components import * # noqa
3 from .components.multiclass import
4 one_vs_rest_classifier,
5 one_vs_one_classifier, \

ModuleNotFoundError: No module named 'hpsklearn.estimator'

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I have the exact same error, nothing seems to fix it.

from hyperopt-sklearn.

I have the same issue. Iam working with Google colab and after the end of the installation i have the following message:
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.

After from hpsklearn import HyperoptEstimator

ModuleNotFoundError: No module named 'hpsklearn.estimator'

Thanks in advance.

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@LarissaHolm @PhilippEberl @Bouchenemehdi24
Thank you for raising this issue. I determined what the mistake was and I suggested a fix in #186

When it gets merged (maximum 24 hours) I will close this issue.

If you require to use hpsklearn right now, please use pip install git+https://github.com/mandjevant/hyperopt-sklearn.

This has the fix and should work now.

Once again my apologies.

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Hello @mandjevant
Thank you very much!
I just tried the way you suggested, but am still getting the same error message in Colab:

!pip install git+https://github.com/mandjevant/hyperopt-sklearn
from hpsklearn import HyperoptEstimator
from hpsklearn import any_classifier
from hpsklearn import any_preprocessing
from hyperopt import tpe

Collecting git+https://github.com/mandjevant/hyperopt-sklearn
Cloning https://github.com/mandjevant/hyperopt-sklearn to /tmp/pip-req-build-csk9l733
Running command git clone -q https://github.com/mandjevant/hyperopt-sklearn /tmp/pip-req-build-csk9l733
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing wheel metadata ... done
Requirement already satisfied: scipy>=1.7.1 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (1.7.3)
Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (1.21.6)
Requirement already satisfied: hyperopt>=0.2.5 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (0.2.7)
Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.7/dist-packages (from hpsklearn==1.0.3) (1.0.2)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (1.15.0)
Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (0.16.0)
Requirement already satisfied: py4j in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (0.10.9.5)
Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (2.6.3)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (4.64.0)
Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.2.5->hpsklearn==1.0.3) (1.3.0)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0->hpsklearn==1.0.3) (1.1.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0->hpsklearn==1.0.3) (3.1.0)

ModuleNotFoundError Traceback (most recent call last)
in ()
1 get_ipython().system('pip install git+https://github.com/mandjevant/hyperopt-sklearn')
----> 2 from hpsklearn import HyperoptEstimator
3 from hpsklearn import any_classifier
4 from hpsklearn import any_preprocessing
5 from hyperopt import tpe

/usr/local/lib/python3.7/dist-packages/hpsklearn/init.py in ()
----> 1 from .estimator import hyperopt_estimator as HyperoptEstimator
2 from .components import * # noqa
3 from .components.multiclass import
4 one_vs_rest_classifier,
5 one_vs_one_classifier, \

ModuleNotFoundError: No module named 'hpsklearn.estimator'

Is it a mistake I'm making or should I wait until the merge is complete?

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You should probably manually remove the current hpsklearn directory and it's dist- directory. @LarissaHolm
On windows it's in C:/Users/<Username>/Anaconda3/Envs/<EnvName>/Lib/site-packages/hpsklearn

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Hey @LarissaHolm I just noticed I misread Colab as Conda.

I do think you must uninstall the previously installed hpsklearn module.
I have just tested running !pip install git+https://github.com/mandjevant/hyperopt-sklearn on a clean google colab notebook and it runs without problems.

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@mandjevant Thanks for the quick fix, though now I get a new error

      3 import matplotlib.pyplot as plt
      5 from hyperopt import tpe
----> 6 from hpsklearn import HyperoptEstimator, k_neighbors_regressor, linear_svr
      7 from sklearn.model_selection import cross_val_score, train_test_split

File /opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/hpsklearn/__init__.py:1, in <module>
----> 1 from .estimator import hyperopt_estimator as HyperoptEstimator
      2 from .components import *  # noqa
      3 from .components.multiclass import \
      4     one_vs_rest_classifier, \
      5     one_vs_one_classifier, \
      6     output_code_classifier

File /opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/hpsklearn/estimator/__init__.py:1, in <module>
----> 1 from .estimator import hyperopt_estimator

File /opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/hpsklearn/estimator/estimator.py:14, in <module>
     12 import scipy.sparse
     13 import numpy as np
---> 14 import numpy.typing as npt
     15 import pathlib
     16 import inspect

File /opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/numpy/typing/__init__.py:324, in <module>
    311 from ._scalars import (
    312     _CharLike_co,
    313     _BoolLike_co,
   (...)
    321     _VoidLike_co,
    322 )
    323 from ._shape import _Shape, _ShapeLike
--> 324 from ._dtype_like import (
    325     DTypeLike as DTypeLike,
    326     _SupportsDType,
    327     _VoidDTypeLike,
    328     _DTypeLikeBool,
    329     _DTypeLikeUInt,
    330     _DTypeLikeInt,
    331     _DTypeLikeFloat,
    332     _DTypeLikeComplex,
    333     _DTypeLikeTD64,
    334     _DTypeLikeDT64,
    335     _DTypeLikeObject,
    336     _DTypeLikeVoid,
    337     _DTypeLikeStr,
    338     _DTypeLikeBytes,
    339     _DTypeLikeComplex_co,
    340 )
    341 from ._array_like import (
    342     ArrayLike as ArrayLike,
    343     _ArrayLike,
   (...)
    358     _ArrayLikeBytes_co,
    359 )
    360 from ._generic_alias import (
    361     NDArray as NDArray,
    362     _DType,
    363     _GenericAlias,
    364 )

File /opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/numpy/typing/_dtype_like.py:16, in <module>
     13 import numpy as np
     15 from ._shape import _ShapeLike
---> 16 from ._generic_alias import _DType as DType
     18 from ._char_codes import (
     19     _BoolCodes,
     20     _UInt8Codes,
   (...)
     57     _ObjectCodes,
     58 )
     60 _DTypeLikeNested = Any  # TODO: wait for support for recursive types

File /opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/numpy/typing/_generic_alias.py:211, in <module>
    208 ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True)
    210 if TYPE_CHECKING or sys.version_info >= (3, 9):
--> 211     _DType = np.dtype[ScalarType]
    212     NDArray = np.ndarray[Any, np.dtype[ScalarType]]
    213 else:

TypeError: 'type' object is not subscriptable

I uninstalled the old version a did a reinstall with the new one, but maybe I am doing something wrong.

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Hey @PhilippEberl I'm quite certain you should update numpy to fix this issue.
Could you let me know what your current numpy version is?

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@mandjevant I run on numpy v1.22.1

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@mandjevant I run on numpy v1.22.1

I personally do not experience any problems with hpsklearn or numpy in v1.22.1 or other versions.
I am confused as to why you get this error. I'd like to help you fix it.

Could you try removing your entire environment, then starting with a clean environment and see if you still get any issues?

If you do have the same issue, could you give me

• The pip list
• And your OS and current version

With this information I can try and replicate your computer 1:1 to see if the issue is perhaps platform-specific.

Thank you and good luck!

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@mandjevant I run on numpy v1.22.1

I personally do not experience any problems with hpsklearn or numpy in v1.22.1 or other versions. I am confused as to why you get this error. I'd like to help you fix it.

Could you try removing your entire environment, then starting with a clean environment and see if you still get any issues?

If you do have the same issue, could you give me

• The pip list
• And your OS and current version

With this information I can try and replicate your computer 1:1 to see if the issue is perhaps platform-specific.

Thank you and good luck!

I reinstalled all required packages, including hpsklearn and now the issue is resovled. All notebooks run normally.

Thanks!

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Glad to hear this, thanks for letting me know @PhilippEberl

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