Hi,

Just to let you know, it is basically useless to grid-search over the
n_estimators parameter in your forests. The higher, the better.
However, you might try to tune min_sample_split (from 1 to
n_features). It is one of the few parameters that will actually lead
to any improvement in terms of accuracy.

Best,

Gilles

On 16 January 2013 19:54, Ronnie Ghose <[email protected]> wrote:
> well items is actually neverused so that is irrelevant.
>
> gridEngines is:
>
> GRID_SEARCH={
>
> #'Poly SVM':{'eng':svm.SVR(),
> #            'params':{'kernel':('poly','rbf'),'degree':[2,4,6,8,10],
> #                      'C':[.1,1,5,10,50,100,500,1000]}},
>
> 'Poly SVM':{'eng':svm.SVR(cache_size=svm_cs,max_iter=svm_maxiter),
>
> 'params':{'kernel':('poly','rbf'),'degree':[2,5,8,10,15,20,30,40],
>                       'C':[.1,1,5,10,20,40,80,160,320]}},
>
>
> 'RandomForestRegressor':{'eng':ens.RandomForestRegressor(),
>
> 'params':{'n_estimators':[10,20,30,40,50,100,150,200,350,500,750,1000,2000,3000]}},
> 'ExtraForestRegressor':{'eng':ens.ExtraTreesRegressor(),
>
> 'params':{'n_estimators':[10,20,30,40,50,100,200,500,750,1000,2000,3000],}},
> 'LogisticRegression':{'eng':lm.LogisticRegression(),
>                     'params':{
>                               'C':[.01,.1,10,20,30,40,50,100,200],
>                              }},
>
> 'DecisionTrees':{'eng':tr.DecisionTreeRegressor(),
>                 'params':{
>                 'min_samples_split':[2,3,4],
>                 'min_samples_leaf':[1,2,3,4],
>                 'min_density':[.05,.1,.2,.3,.4,.5],
>                 }
>                 },
>  #   "Stoch. Grad. Regressor" :{'eng':lm.SGDRegressor(),
>  #                           'params':{
>  #                           'alpha':[.0001,.001],
>  #                          }},
> }
>
>
> it runs GridSearchCV with the engine and the params passed.
>
> that's all that is 'mine' really, so I'm not sure how it is having what
> seems to be race conditions with itself?
>
>
> On Wed, Jan 16, 2013 at 1:49 PM, Andrew Winterman <[email protected]>
> wrote:
>>
>> could you include definitions of some of those global variables that are
>> used in the function?
>>
>> On Wed, Jan 16, 2013 at 9:02 AM, Ronnie Ghose <[email protected]>
>> wrote:
>>>
>>> Anyway I can get around the following?
>>>
>>> I seem to get this Pool Closed error frequently:
>>>
>>>
>>> sghose@adapt-ghose:~$ python -m ADAPT.engine.engine
>>> num samples: 417
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> [Parallel] Pool seems closed
>>> Traceback (most recent call last):
>>>   File "/usr/lib/python2.7/runpy.py", line 162, in _run_module_as_main
>>>     "__main__", fname, loader, pkg_name)
>>>   File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
>>>     exec code in run_globals
>>>   File "/home/sghose/ADAPT/engine/engine.py", line 86, in <module>
>>>     main()
>>>   File "/home/sghose/ADAPT/engine/engine.py", line 81, in main
>>>     dns()
>>>   File "/home/sghose/ADAPT/engine/engine.py", line 78, in dns
>>>     print m.average_score(10)
>>>   File "ADAPT/genclf/model.py", line 275, in average_score
>>>     _ = self.asynch_cv(gridsearch=True)
>>>   File "ADAPT/genclf/model.py", line 246, in asynch_cv
>>>     _ = mapfunc(items)
>>>   File "ADAPT/genclf/model.py", line 228, in mapfunc
>>>     self._gridENGINES[k]=eng.fit(X_train,Y_train)
>>>   File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py",
>>> line 372, in fit
>>>     for clf_params in grid for train, test in cv)
>>>   File
>>> "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py",
>>> line 513, in __call__
>>>     for function, args, kwargs in iterable:
>>> ValueError: generator already executing
>>>
>>>
>>>
>>> that line is
>>>
>>>
>>>
>>>        try:
>>>             svm_maxiter
>>>         except NameError:
>>>             _defined_svm_maxiter=False
>>>         #ks - keys
>>>         @catchwarnings(_defined_svm_maxiter)
>>>         def mapfunc(items):
>>>             if not gridsearch:
>>>                 a = parmap(fit_and_score,items)
>>>                 sleep(1)
>>>             elif gridsearch:
>>>                 # gridsearch parallelizes itself ~ as in the
>>>                 # library already does that
>>>                 for k,eng in self._gridENGINES.items():
>>>                     vprint('Currently CVing' + " "+ str(k))
>>>                     self._gridENGINES[k]=eng.fit(X_train,Y_train)
>>>             else:
>>>                 raise RuntimeError,"I have no clue how you are here. It's
>>> a\
>>>                                     binary comparison"
>>>
>>>
>>>
>>>
>>>
>>> self._gridENGINES is as expected a dictionary of names and classifiers.
>>> I'm running this in serial (gridsearch == 1), so why is joblib having this
>>> issue?
>>>
>>> Thank you,
>>> Shomiron Ghose
>>>
>>>
>>>
>>>
>>>
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>>
>>
>>
>> --
>> Andrew Winterman
>> 714 362 6823
>>
>>
>> ------------------------------------------------------------------------------
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>> and much more. Keep your Java skills current with LearnJavaNow -
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>>
>
>
> ------------------------------------------------------------------------------
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