AttributeError: can't set attribute about sklearn-deap HOT 10 OPEN

I am using the same package on Linux (Fedora) and receiving the same error. This is occurring when simply running the example test code:

import sklearn.datasets
import numpy as np
import random

data = sklearn.datasets.load_digits()
X = data["data"]
y = data["target"]

from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold

paramgrid = {"kernel": ["rbf"],
             "C"     : np.logspace(-9, 9, num=25, base=10),
             "gamma" : np.logspace(-9, 9, num=25, base=10)}

random.seed(1)

from evolutionary_search import EvolutionaryAlgorithmSearchCV
cv = EvolutionaryAlgorithmSearchCV(estimator=SVC(),
                                   params=paramgrid,
                                   scoring="accuracy",
                                   cv=StratifiedKFold(n_splits=4),
                                   verbose=1,
                                   population_size=50,
                                   gene_mutation_prob=0.10,
                                   gene_crossover_prob=0.5,
                                   tournament_size=3,
                                   generations_number=5,
                                   n_jobs=4)

Traceback (most recent call last):

  File "<ipython-input-1-d9e256e00ecd>", line 29, in <module>
    n_jobs=4)

  File "/home/mathewsa/.local/lib/python2.7/site-packages/evolutionary_search/cv.py", line 301, in __init__
    self.best_score_ = None

AttributeError: can't set attribute

And the pip freeze:

[mathewsa@desktop ~]$ python -m pip freeze
Babel==1.3
Bottleneck==0.6.0
chardet==2.2.1
cssselect==0.9.1
cycler==0.10.0
Cython==0.23.4
deap==1.2.2
decorator==4.0.10
docutils==0.12
emcee==2.2.1
empy==3.3.2
eqtools==1.3.1
fail2ban==0.9.3
funcsigs==1.0.2
gps==3.15
gptools==0.2.3
h5-data==0.2.0
h5py==2.5.0
hgapi==1.7.2
husl==4.0.3
idl-python==2.0
iniparse==0.4
IPy==0.81
ipython==3.2.1
Jinja2==2.8
jsonschema==2.4.0
kitchen==1.2.1
lxml==3.4.4
MarkupSafe==0.23
matplotlib==1.5.1
mdsconnector==1.0
mdsplus-alpha==7.46.1
mercurial==3.5.2
mistune==0.6
mock==2.0.0
mpmath==1.0.0
netCDF4==1.1.6
nose==1.3.7
Numeric==24.2
numexpr==2.4.6
numpy==1.13.1
pandas==0.17.1
path.py==5.2
pbr==1.10.0
pexpect==4.0.1
Pillow==3.0.0
plumbum==1.6.3
policycoreutils-default-encoding==0.1
profiletools==1.0.0
ptyprocess==0.5.1
pwquality==1.3.0
pyandoc==0.0.1
pycurl==7.19.5.1
pyflakes==1.0.0
Pygments==2.1.3
pygobject==3.18.2
pygpgme==0.3
pyinotify==0.9.6
pyliblzma==0.5.3
pymssql==2.1.3
PyOpenGL==3.1.0
PyPAM==0.5.0
pyparsing==2.1.5
PyPDF2==1.26.0
python-dateutil==2.5.3
python-Levenshtein==0.10.1
python-systemd==231
pytz==2016.6.1
pyxattr==0.5.3
pyxdg==0.25
pyzmq==14.7.0
rope==0.10.2
rpm-python==4.13.0rc1
rpyc==3.4.4
scdate==1.10.9
scikit-learn==0.18.1
scipy==1.1.0
seaborn==0.5.1
seobject==0.1
sepolicy==1.1
simplegeneric==0.8.1
simplejson==3.5.3
six==1.10.0
sklearn==0.0
sklearn-deap==0.2.2
slip==0.6.4
Sphinx==1.2.3
spyder==2.3.8
SSSDConfig==1.14.2
tables==3.2.2
tornado==4.2.1
translate-toolkit==1.9.0
triangle-plot==0.3.0
urlgrabber==3.10.1
vboxapi==1.0
virtualenv==15.0.2
vobject==0.8.1rc0
wxPython==3.0.2.0
wxPython-common==3.0.2.0
yum-metadata-parser==1.1.4
[mathewsa@desktop ~]$ python -m pip freeze --user
deap==1.2.2
scipy==1.1.0
sklearn-deap==0.2.2

from sklearn-deap.

I couldn't reproduce this error. What version of the library are you using? Can you give me a pip freeze of your env?

from sklearn-deap.

Here the pip freeze:

alabaster==0.7.9
anaconda-clean==1.0
anaconda-client==1.6.3
anaconda-navigator==1.6.4
anaconda-project==0.6.0
appdirs==1.4.0
appnope==0.1.0
appscript==1.0.1
argcomplete==1.0.0
astroid==1.4.7
astropy==1.2.1
auto-sklearn==0.2.1
autopep8==1.3.3
Babel==2.3.4
backports.shutil-get-terminal-size==1.0.0
backports.weakref==1.0rc1
beautifulsoup4==4.5.1
bitarray==0.8.1
blaze==0.10.1
bleach==1.5.0
bokeh==0.12.2
boto==2.42.0
Bottleneck==1.1.0
cffi==1.7.0
chardet==2.3.0
chest==0.2.3
click==6.6
cloudpickle==0.2.1
clyent==1.2.2
colorama==0.3.7
conda==4.3.30
conda-build==2.0.2
configobj==5.0.6
ConfigSpace==0.3.10
contextlib2==0.5.3
cryptography==1.5
cycler==0.10.0
Cython==0.27.1
cytoolz==0.8.0
dask==0.15.0
datashape==0.5.2
deap==1.0.2
decorator==4.0.10
dill==0.2.5
docutils==0.12
dynd==0.7.3.dev1
et-xmlfile==1.0.1
f90wrap==0.1.4
fastcache==1.0.2
filelock==2.0.6
Flask==0.11.1
Flask-Cors==2.1.2
flatdict==1.2.0
future==0.16.0
gevent==1.1.2
greenlet==0.4.10
h5py==2.6.0
HeapDict==1.0.0
-e git+https://github.com/hyperopt/hyperopt-sklearn.git@e457746664fbfd5bb9f1536a1b71b178b09598a8#egg=hpsklearn
html5lib==0.9999999
hyperopt==0.1
idna==2.1
imagesize==0.7.1
ipykernel==4.5.0
ipython==5.1.0
ipython-genutils==0.1.0
ipywidgets==5.2.2
itsdangerous==0.24
jdcal==1.2
jedi==0.9.0
Jinja2==2.8
joblib==0.11
jsonpickle==0.9.4
jsonschema==2.5.1
jupyter==1.0.0
jupyter-client==4.4.0
jupyter-console==5.0.0
jupyter-core==4.2.0
Keras==2.0.5
Lasagne==0.1
lazy-object-proxy==1.2.1
liac-arff==2.1.1
llvmlite==0.13.0
locket==0.2.0
lockfile==0.12.2
lxml==3.6.4
Markdown==2.2.0
MarkupSafe==0.23
matplotlib==1.5.3
mistune==0.7.3
mock==2.0.0
mpmath==0.19
multipledispatch==0.4.8
nb-anacondacloud==1.2.0
nb-conda==2.0.0
nb-conda-kernels==2.0.0
nbconvert==4.2.0
nbformat==4.1.0
nbpresent==3.0.2
networkx==1.11
nltk==3.2.1
nose==1.3.7
notebook==4.2.3
numba==0.28.1
numexpr==2.6.1
numpy==1.13.3
numpydoc==0.6.0
odo==0.5.0
openpyxl==2.3.2
packaging==16.8
pandas==0.21.0
partd==0.3.6
path.py==0.0.0
pathlib2==2.1.0
patsy==0.4.1
pbr==1.10.0
pep8==1.7.0
pexpect==4.0.1
pickleshare==0.7.4
Pillow==3.3.1
pkginfo==1.3.2
ply==3.9
prompt-toolkit==1.0.3
protobuf==3.3.0
psutil==5.3.1
ptyprocess==0.5.1
py==1.4.31
pyasn1==0.1.9
pycodestyle==2.3.1
pycosat==0.6.1
pycparser==2.14
pycrypto==2.6.1
pycurl==7.43.0
pyflakes==1.3.0
pyFRF==0.34
Pygments==2.1.3
pylint==1.5.4
pymongo==3.5.1
pynisher==0.4.2
pyOpenSSL==16.2.0
pyparsing==2.1.10
pyrfr==0.6.1
pytest==2.9.2
python-dateutil==2.6.1
pytz==2017.3
PyYAML==3.12
pyzmq==15.4.0
QtAwesome==0.4.4
qtconsole==4.2.1
QtPy==1.3.1
redis==2.10.5
requests==2.12.4
rope-py3k==0.9.4.post1
rpy2==2.8.5
ruamel-yaml===-VERSION
scikit-image==0.12.3
scikit-learn==0.18.2
scikit-neuralnetwork==0.7
scikit-rf==0.14.5
scipy==0.19.1
simplegeneric==0.8.1
singledispatch==3.4.0.3
six==1.11.0
sklearn-deap==0.2.2
smac==0.6.0
snowballstemmer==1.2.1
sockjs-tornado==1.0.3
Sphinx==1.4.6
sphinx-rtd-theme==0.2.4
spyder==3.2.4
SQLAlchemy==1.0.13
statsmodels==0.6.1
sympy==1.0
tables==3.2.3.1
tensorflow==1.2.0
terminado==0.6
Theano==0.9.0
toolz==0.8.0
tornado==4.4.1
traitlets==4.3.0
typing==3.6.2
unicodecsv==0.14.1
wcwidth==0.1.7
Werkzeug==0.12.2
widgetsnbextension==1.2.6
wrapt==1.10.6
xlrd==1.0.0
XlsxWriter==0.9.3
xlwings==0.10.0
xlwt==1.1.2

from sklearn-deap.

I also have this suddenly

from sklearn-deap.

For some reason I had to roll back to scikit-learn==0.18.2, I tried upgrading to 0.19.0 now, and the error went away.

from sklearn-deap.

I have the same issue, but upgrading to sklearn 0.19.1 did not solve the issue.

from sklearn-deap.

I solved it by removing these fromt init

    #self.best_score_ = None
    #self.best_params_ = None

Looks like they are already declared in the base module, and it creates some trouble for some strange reasons... Any ideas?

Seems to be working just fine without declaring None

Edit: It didn't work. It fails in the end with the same error at:

    self._best_score = current_best_score_
    self._best_params = current_best_params_

Edit2: It did work in the end by just renaming to something else.

from sklearn-deap.

Maybe something changed with the new version of scikit-learn, I'll see if I have some time to fix this bug.

from sklearn-deap.

This shows how to solve this problem: https://stackoverflow.com/q/35950741/4013571

class Test(BaseSearchCV):
     best_score_ = None
     def __init__(self):
         self.best_score_ = None

 t = Test()

from sklearn-deap.

Hi,

Getting the attribute error, as shown. installed whatever is mentioned in this thread, still the same error.

AttributeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_10444\1573941315.py in
3 from imblearn.ensemble import BalancedRandomForestClassifier
4 rf = BalancedRandomForestClassifier(n_estimators = 100)
----> 5 rf = rf.fit(X_train, y_train)

~\anaconda3\envs\PythonData\envs\mlenv\lib\site-packages\imblearn\ensemble_forest.py in fit(self, X, y, sample_weight)
433
434 # Remap output
--> 435 , self.n_features = X.shape
436
437 y = np.atleast_1d(y)

AttributeError: can't set attribute

from sklearn-deap.