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Depreciation of np.float causes incompatible numpy version #222

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tryingsomestuff opened this issue Jun 20, 2023 · 7 comments
Open

Depreciation of np.float causes incompatible numpy version #222

tryingsomestuff opened this issue Jun 20, 2023 · 7 comments

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@tryingsomestuff
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  • Chess Tuning Tools version: chess-tuning-tools 0.9.3
  • Python version: 3.9.17
  • Operating System: ubuntu 22.04

Description

Depreciation of np.float causes incompatible numpy version
Version installed is

numpy 1.25.0

numpy version shall be <=1.19 with the given scikit-learn 0.23.2

Or am i missing something ?
@tryingsomestuff
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tryingsomestuff commented Jun 20, 2023 •
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Even with a pre-install numpy 1.19.1 it does work

vivien@serv3:~/Minic/tune$ pip3 install numpy==1.19.1
Collecting numpy==1.19.1
  Downloading numpy-1.19.1.zip (7.3 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.3/7.3 MB 15.6 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Building wheels for collected packages: numpy
  Building wheel for numpy (pyproject.toml) ... done
  Created wheel for numpy: filename=numpy-1.19.1-cp39-cp39-linux_x86_64.whl size=14035527 sha256=7e05409e624b838e8f45c3f9c4a1a5e907b983c7a0b58c08d979894335d942da
  Stored in directory: /home/vivien/.cache/pip/wheels/e0/85/4b/385d1720e1dd8bee99bac183aac3454e88aba9fc3146ccfc27
Successfully built numpy
Installing collected packages: numpy
Successfully installed numpy-1.19.1


vivien@serv3:~/Minic/tune$ pip3 install chess-tuning-tools
Collecting chess-tuning-tools
  Using cached chess_tuning_tools-0.9.3-py3-none-any.whl (48 kB)
Collecting Click<=8.0.4,>=7.1.2 (from chess-tuning-tools)
  Using cached click-8.0.4-py3-none-any.whl (97 kB)
Collecting atomicwrites>=1.4.0 (from chess-tuning-tools)
  Using cached atomicwrites-1.4.1-py2.py3-none-any.whl
Collecting bask<1.0.0,>=0.9.3 (from chess-tuning-tools)
  Using cached bask-0.10.8-py3-none-any.whl (32 kB)
Collecting dill>=0.3.4 (from chess-tuning-tools)
  Using cached dill-0.3.6-py3-none-any.whl (110 kB)
Requirement already satisfied: numpy>=1.19.1 in /home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages (from chess-tuning-tools) (1.19.1)
Collecting scikit-learn<0.24,>=0.22 (from chess-tuning-tools)
  Using cached scikit_learn-0.23.2-cp39-cp39-linux_x86_64.whl
Collecting scikit-optimize<0.9,>=0.8 (from chess-tuning-tools)
  Using cached scikit_optimize-0.8.1-py2.py3-none-any.whl (101 kB)
Collecting scipy>=1.5.2 (from chess-tuning-tools)
  Using cached scipy-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.5 MB)
Collecting arviz>=0.10.0 (from bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached arviz-0.15.1-py3-none-any.whl (1.6 MB)
Collecting emcee>=3.0.2 (from bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached emcee-3.1.4-py2.py3-none-any.whl (46 kB)
Collecting matplotlib>=3.3.0 (from bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached matplotlib-3.7.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)
Collecting tqdm>=4.48.2 (from bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached tqdm-4.65.0-py3-none-any.whl (77 kB)
Collecting joblib>=0.11 (from scikit-learn<0.24,>=0.22->chess-tuning-tools)
  Using cached joblib-1.2.0-py3-none-any.whl (297 kB)
Collecting threadpoolctl>=2.0.0 (from scikit-learn<0.24,>=0.22->chess-tuning-tools)
  Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
Collecting pyaml>=16.9 (from scikit-optimize<0.9,>=0.8->chess-tuning-tools)
  Using cached pyaml-23.5.9-py3-none-any.whl (17 kB)
Collecting numpy>=1.19.1 (from chess-tuning-tools)
  Using cached numpy-1.25.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.7 MB)
Requirement already satisfied: setuptools>=60.0.0 in /home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages (from arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools) (68.0.0)
Collecting packaging (from arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached packaging-23.1-py3-none-any.whl (48 kB)
Collecting pandas>=1.3.0 (from arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached pandas-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.4 MB)
Collecting xarray>=0.21.0 (from arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached xarray-2023.5.0-py3-none-any.whl (994 kB)
Collecting h5netcdf>=1.0.2 (from arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached h5netcdf-1.2.0-py3-none-any.whl (43 kB)
Collecting typing-extensions>=4.1.0 (from arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached typing_extensions-4.6.3-py3-none-any.whl (31 kB)
Collecting xarray-einstats>=0.3 (from arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached xarray_einstats-0.5.1-py3-none-any.whl (28 kB)
Collecting contourpy>=1.0.1 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached contourpy-1.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (300 kB)
Collecting cycler>=0.10 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)
Collecting fonttools>=4.22.0 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached fonttools-4.40.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.4 MB)
Collecting kiwisolver>=1.0.1 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)
Collecting pillow>=6.2.0 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached Pillow-9.5.0-cp39-cp39-manylinux_2_28_x86_64.whl (3.4 MB)
Collecting pyparsing>=2.3.1 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached pyparsing-3.1.0-py3-none-any.whl (102 kB)
Collecting python-dateutil>=2.7 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
Collecting importlib-resources>=3.2.0 (from matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached importlib_resources-5.12.0-py3-none-any.whl (36 kB)
Collecting PyYAML (from pyaml>=16.9->scikit-optimize<0.9,>=0.8->chess-tuning-tools)
  Using cached PyYAML-6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (661 kB)
Collecting h5py (from h5netcdf>=1.0.2->arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached h5py-3.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)
Collecting zipp>=3.1.0 (from importlib-resources>=3.2.0->matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached zipp-3.15.0-py3-none-any.whl (6.8 kB)
Collecting pytz>=2020.1 (from pandas>=1.3.0->arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached pytz-2023.3-py2.py3-none-any.whl (502 kB)
Collecting tzdata>=2022.1 (from pandas>=1.3.0->arviz>=0.10.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached tzdata-2023.3-py2.py3-none-any.whl (341 kB)
Collecting six>=1.5 (from python-dateutil>=2.7->matplotlib>=3.3.0->bask<1.0.0,>=0.9.3->chess-tuning-tools)
  Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Installing collected packages: pytz, zipp, tzdata, typing-extensions, tqdm, threadpoolctl, six, PyYAML, pyparsing, pillow, packaging, numpy, kiwisolver, joblib, fonttools, dill, cycler, Click, atomicwrites, scipy, python-dateutil, pyaml, importlib-resources, h5py, emcee, contourpy, scikit-learn, pandas, matplotlib, h5netcdf, xarray, scikit-optimize, xarray-einstats, arviz, bask, chess-tuning-tools
  Attempting uninstall: numpy
    Found existing installation: numpy 1.19.1
    Uninstalling numpy-1.19.1:
      Successfully uninstalled numpy-1.19.1
Successfully installed Click-8.0.4 PyYAML-6.0 arviz-0.15.1 atomicwrites-1.4.1 bask-0.10.8 chess-tuning-tools-0.9.3 contourpy-1.1.0 cycler-0.11.0 dill-0.3.6 emcee-3.1.4 fonttools-4.40.0 h5netcdf-1.2.0 h5py-3.9.0 importlib-resources-5.12.0 joblib-1.2.0 kiwisolver-1.4.4 matplotlib-3.7.1 numpy-1.25.0 packaging-23.1 pandas-2.0.2 pillow-9.5.0 pyaml-23.5.9 pyparsing-3.1.0 python-dateutil-2.8.2 pytz-2023.3 scikit-learn-0.23.2 scikit-optimize-0.8.1 scipy-1.10.1 six-1.16.0 threadpoolctl-3.1.0 tqdm-4.65.0 typing-extensions-4.6.3 tzdata-2023.3 xarray-2023.5.0 xarray-einstats-0.5.1 zipp-3.15.0

tune local -c simple_tune.json 
Traceback (most recent call last):
  File "/home/vivien/.pyenv/versions/3.9.17/bin/tune", line 5, in <module>
    from tune.cli import cli
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/tune/__init__.py", line 11, in <module>
    from tune.io import InitStrings, load_tuning_config, parse_ranges
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/tune/io.py", line 8, in <module>
    import skopt.space as skspace
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/skopt/__init__.py", line 44, in <module>
    from . import callbacks
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/skopt/callbacks.py", line 17, in <module>
    from skopt.utils import dump
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/skopt/utils.py", line 8, in <module>
    from sklearn.ensemble import GradientBoostingRegressor
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/ensemble/__init__.py", line 8, in <module>
    from ._forest import RandomForestClassifier
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/ensemble/_forest.py", line 56, in <module>
    from ..tree import (DecisionTreeClassifier, DecisionTreeRegressor,
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/tree/__init__.py", line 6, in <module>
    from ._classes import BaseDecisionTree
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/tree/_classes.py", line 41, in <module>
    from ._criterion import Criterion
  File "sklearn/tree/_criterion.pyx", line 1, in init sklearn.tree._criterion
  File "sklearn/tree/_splitter.pyx", line 1, in init sklearn.tree._splitter
  File "sklearn/tree/_tree.pyx", line 1, in init sklearn.tree._tree
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/neighbors/__init__.py", line 17, in <module>
    from ._nca import NeighborhoodComponentsAnalysis
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/neighbors/_nca.py", line 22, in <module>
    from ..decomposition import PCA
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/decomposition/__init__.py", line 17, in <module>
    from .dict_learning import dict_learning
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/decomposition/dict_learning.py", line 5, in <module>
    from . import _dict_learning  # type: ignore
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/decomposition/_dict_learning.py", line 21, in <module>
    from ..linear_model import Lasso, orthogonal_mp_gram, LassoLars, Lars
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/linear_model/__init__.py", line 11, in <module>
    from ._least_angle import (Lars, LassoLars, lars_path, lars_path_gram, LarsCV,
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/sklearn/linear_model/_least_angle.py", line 34, in <module>
    method='lar', copy_X=True, eps=np.finfo(np.float).eps,
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/numpy/__init__.py", line 313, in __getattr__
    raise AttributeError(__former_attrs__[attr])
AttributeError: module 'numpy' has no attribute 'float'.
`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
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

why is it doing this ?

Collecting numpy>=1.19.1 (from chess-tuning-tools)
  Using cached numpy-1.25.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.7 MB)

1.25 is not even in the cache !

vivien@serv3:~/Minic/tune$ pip3 cache list
Cache contents:

 - atomicwrites-1.4.1-py2.py3-none-any.whl (6.9 kB)
 - lit-16.0.3-py3-none-any.whl (88 kB)
 - numpy-1.19.0-cp39-cp39-linux_x86_64.whl (14.0 MB)
 - numpy-1.19.1-cp39-cp39-linux_x86_64.whl (14.0 MB)
 - scikit_learn-0.23.2-cp39-cp39-linux_x86_64.whl (25.2 MB)

sorry python is hell to me :'(

@vshcherbyna
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Igel has the same problem as Minic ;)

@kiudee
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kiudee commented Jul 19, 2023 •
edited
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Indeed it looks like scikit-learn is incompatible with newer versions of numpy. Working on a patch that fixes the dependency specification from our side. It turned out that the old version of scikit-learn no longer builds on python 3.7 and 3.9, which is why I had to restrict chess-tuning-tools to 3.8 for now.
As soon as the upstream bayes-skopt is compatible with a newer scikit-learn, I will update this again.

@tryingsomestuff
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Thanks, almost good now, still I get

2023-07-24 08:54:38,865 INFO     Computing current optimum was not successful. This can happen in rare cases and running the tuner again usually works.
Traceback (most recent call last):
  File "/home/vivien/.pyenv/versions/3.9.17/bin/tune", line 8, in <module>
    sys.exit(cli())
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/click/core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/click/core.py", line 1078, in main
    rv = self.invoke(ctx)
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/click/core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/click/core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/click/core.py", line 783, in invoke
    return __callback(*args, **kwargs)
  File "/home/vivien/.pyenv/versions/3.9.17/lib/python3.9/site-packages/tune/cli.py", line 454, in local
    iterations=np.array(performance)[:, 0],
IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed

@vshcherbyna
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vshcherbyna commented Jul 24, 2023 via email

@kiudee
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kiudee commented Jul 24, 2023 via email •
edited
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@tryingsomestuff
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On this box I have cutechess-cli 1.2.0, it is enough ?

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@vshcherbyna @kiudee @tryingsomestuff