Gilles,

Thanks, I was trying to find the lowest # of trees that I could use while
getting around the same result - I'm expecting a cutoff score. I'll do the
second part though!

Thank you,
Ronnie


On Wed, Jan 16, 2013 at 2:37 PM, Gilles Louppe <[email protected]> wrote:

> 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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> >>
> >
> >
> >
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