AttributeError: can't set attribute

[vlatorre847]

This is quite puzzling. macos 10.9.5, python 3.5.2 with anaconda and spyder

My code:

import sklearn
import numpy
from sklearn.model_selection import KFold
import sklearn.neural_network
import sklearn.svm
import sklearn.ensemble
import sklearn.datasets
import time
from evolutionary_search import EvolutionaryAlgorithmSearchCV

dataset=sklearn.datasets.load_iris()
X=dataset.data
Y=dataset.target
seed=1
test_size = int( 0.2 * len( Y ) )
numpy.random.seed( seed )
indices = numpy.random.permutation(len(X))
X_train = X[ indices[:-test_size]]
Y_train = Y[ indices[:-test_size]]
X_test = X[ indices[-test_size:]]
Y_test = Y[ indices[-test_size:]]

network=sklearn.svm.SVC()
lb=[-15,-5]
ub=[3,15]
space_num=[]
space_points=[0]*len(lb)

for i in range(len(lb)):
    space_num.append(int(ub[i]-lb[i]+1))    
    space_points[i]=numpy.logspace(lb[i],ub[i],space_num[i],base=2)
param_grid={'gamma':space_points[0],'C':space_points[1]}

score=sklearn.metrics.make_scorer(sklearn.metrics.accuracy_score)
clf=EvolutionaryAlgorithmSearchCV(network,params=param_grid,cv=3,scoring=score,verbose=2,n_jobs=1)
time_begin=time.time()
clf.fit(X_train,Y_train)
time_end=time.time()-time_begin

print(time_end,sklearn.metrics.accuracy_score(clf.predict(X_test),Y_test))

I get the following error: File "~/anaconda/lib/python3.5/site-packages/evolutionary_search/cv.py", line 301, in init self.bestscore = None

AttributeError: can't set attribute

Any idea?

[rsteca]

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

[vlatorre847]

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

[kootenpv]

I also have this suddenly

[kootenpv]

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.

[mortendaehli]

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

[mortendaehli]

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.

[rsteca]

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

[flipdazed]

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()

[AbhilashMathews]

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

[rskronek]

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, sampleweight) 433 434 # Remap output --> 435 , self.nfeatures = X.shape 436 437 y = np.atleast_1d(y)

AttributeError: can't set attribute