Re: [Scikit-learn-general] sklearn.utils.cs_graph_components in a broken state?
On 06/06/2013 07:02 AM, Robert Layton wrote: OK, it's up as PR https://github.com/scikit-learn/scikit-learn/pull/2037 and ready to go. Does that still assume a symmetric matrix? The easy solution to you problem would have been to feed mst + mst.T to the components algorithm ;) I fell into that trap yesterday :-/ Cheers, Andy -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
[Scikit-learn-general] On interpreting lasso stability path...
On using the lasso_stability_path, some paths in my feature set have a bell-curve shape. How should I interpret this? The real problem is that I don't have a quantitative understanding of this (yet). Many of my feature sets have the shape shown in the sklearn manual, but one particular - not so great set - gives me some paths that have a bell-curve shape. What's the explanation for this? I'm referring to charts similar to: http://scikit-learn.org/stable/_images/plot_sparse_recovery_1.png In some cases, all the paths are bunched up and have the same shape - nothing like the 'relevant features' in the link above. Is there something I can tweek to tease out the most important set of variables? Best regards. -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
[Scikit-learn-general] test failed after installaing scikit
all, IN my ubuntu(uname -a): Linux ubuntu 3.2.0-29-generic-pae #46-Ubuntu SMP Fri Jul 27 17:25:43 UTC 2012 i686 i686 i386 GNU/Linux, after installing the scikit-learn from source package followd by https://pypi.python.org/pypi/scikit-learn/ , run 'nosetests --exe sklearn' ,following error happens: root@ubuntu:~/scikit-learn# nosetests --exe sklearn E == ERROR: Failure: ImportError (No module named _check_build ___ Contents of /root/scikit-learn/sklearn/__check_build: _check_build.pyx setup.pyc __init__.py _check_build.c__init__.pyc setup.py ___ It seems that scikit-learn has not been built correctly. If you have installed scikit-learn from source, please do not forget to build the package before using it: run `python setup.py install` or `make` in the source directory. If you have used an installer, please check that it is suited for your Python version, your operating system and your platform.) -- Traceback (most recent call last): File /usr/lib/python2.7/dist-packages/nose/loader.py, line 390, in loadTestsFromName addr.filename, addr.module) File /usr/lib/python2.7/dist-packages/nose/importer.py, line 39, in importFromPath return self.importFromDir(dir_path, fqname) File /usr/lib/python2.7/dist-packages/nose/importer.py, line 86, in importFromDir mod = load_module(part_fqname, fh, filename, desc) File /root/scikit-learn/sklearn/__init__.py, line 31, in module from . import __check_build File /root/scikit-learn/sklearn/__check_build/__init__.py, line 46, in module raise_build_error(e) File /root/scikit-learn/sklearn/__check_build/__init__.py, line 41, in raise_build_error %s % (e, local_dir, ''.join(dir_content).strip(), msg)) ImportError: No module named _check_build ___ Contents of /root/scikit-learn/sklearn/__check_build: _check_build.pyx setup.pyc __init__.py _check_build.c__init__.pyc setup.py ___ It seems that scikit-learn has not been built correctly. If you have installed scikit-learn from source, please do not forget to build the package before using it: run `python setup.py install` or `make` in the source directory. If you have used an installer, please check that it is suited for your Python version, your operating system and your platform. -- Ran 1 test in 0.001s FAILED (errors=1) There is no any errors during building and installing, could you help me? Thanks Aaron -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] test failed after installaing scikit
could it be that the folder you're in (~/scikit-learn) contains the scikit-learn sources? 2013/6/6 linxpwww linxp...@163.com all, IN my ubuntu(uname -a): Linux ubuntu 3.2.0-29-generic-pae #46-Ubuntu SMP Fri Jul 27 17:25:43 UTC 2012 i686 i686 i386 GNU/Linux, after installing the scikit-learn from source package followd by https://pypi.python.org/pypi/scikit-learn/ , run 'nosetests --exe sklearn' ,following error happens: root@ubuntu:~/scikit-learn# nosetests --exe sklearn E == ERROR: Failure: ImportError (No module named _check_build ___ Contents of /root/scikit-learn/sklearn/__check_build: _check_build.pyx setup.pyc __init__.py _check_build.c__init__.pyc setup.py ___ It seems that scikit-learn has not been built correctly. If you have installed scikit-learn from source, please do not forget to build the package before using it: run `python setup.py install` or `make` in the source directory. If you have used an installer, please check that it is suited for your Python version, your operating system and your platform.) -- Traceback (most recent call last): File /usr/lib/python2.7/dist-packages/nose/loader.py, line 390, in loadTestsFromName addr.filename, addr.module) File /usr/lib/python2.7/dist-packages/nose/importer.py, line 39, in importFromPath return self.importFromDir(dir_path, fqname) File /usr/lib/python2.7/dist-packages/nose/importer.py, line 86, in importFromDir mod = load_module(part_fqname, fh, filename, desc) File /root/scikit-learn/sklearn/__init__.py, line 31, in module from . import __check_build File /root/scikit-learn/sklearn/__check_build/__init__.py, line 46, in module raise_build_error(e) File /root/scikit-learn/sklearn/__check_build/__init__.py, line 41, in raise_build_error %s % (e, local_dir, ''.join(dir_content).strip(), msg)) ImportError: No module named _check_build ___ Contents of /root/scikit-learn/sklearn/__check_build: _check_build.pyx setup.pyc __init__.py _check_build.c__init__.pyc setup.py ___ It seems that scikit-learn has not been built correctly. If you have installed scikit-learn from source, please do not forget to build the package before using it: run `python setup.py install` or `make` in the source directory. If you have used an installer, please check that it is suited for your Python version, your operating system and your platform. -- Ran 1 test in 0.001s FAILED (errors=1) There is no any errors during building and installing, could you help me? Thanks Aaron -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Peter Prettenhofer -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
[Scikit-learn-general] Re-cycling pipeline stages in GridSearchCV?
Hi, I noticed that GridSearchCV fits a new estimator from scratch for each grid point. But when working with pipelines where multiple steps have tuning parameters, some time could be saved by fitting an early step once and then fitting the later steps along a sequence of grid points while using the precomputed early step. This seems to make sense particularly with compute-intensive feature selection algorithms. However, it appears to me that this optimization would add a lot of complexity. Any thoughts? Regards, Michal -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Re-cycling pipeline stages in GridSearchCV?
Using in a clever way a joblib.Memory would be the way I would like to address this. I have no precise idea on how I would do this, though. G -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Re-cycling pipeline stages in GridSearchCV?
I proposed something that did this among a more general solution for warm starts without memoizing a couple of weeks ago, but I think memoizing is neater and handles most cases. To handle it generally, you could add a memoize parameter to Pipeline. Then I guess you'd have to do some subset of: * memoize the step estimator for each fit, given its parameters and the parameters of all preceding estimators, and the input to Pipeline.fit. (A enhanced version could take advantage of an estimator specifying that changing certain parameters will affect the result of transform without refitting.) * possibly memoize the transformed output for each step estimator given its parameters and the parameters of all preceding estimators, and the input to Pipeline.fit. Pipeline methods could then precede by looking for the latest memoized transform output and start new calculations from there. Or is there a cleverer way? - Joel On Fri, Jun 7, 2013 at 7:22 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: Using in a clever way a joblib.Memory would be the way I would like to address this. I have no precise idea on how I would do this, though. G -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general On Fri, Jun 7, 2013 at 7:22 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: Using in a clever way a joblib.Memory would be the way I would like to address this. I have no precise idea on how I would do this, though. G -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Random Forest with a mix of categorical and lexical features
On Tue, Jun 4, 2013 at 8:16 PM, Peter Prettenhofer peter.prettenho...@gmail.com wrote: I believe more in my results than in my expertise - and so should you :-) ** +1! There's very very few examples of theory trumping data in history... And a bajillion of the converse. I also think Joel put it quite nicely with all these trees can represent the same hypothesis space, it just might require a deeper tree to represent the same thing. Christian's results seem in no way contradictory to me, just pleasantly surprising. -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Random Forest with a mix of categorical and lexical features
I believe more in my results than in my expertise - and so should you :-) +1! There's very very few examples of theory trumping data in history... And a bajillion of the converse. I guess I didn't express myself clearly: I didn't mean to say that I mistrust my results per se.. I'm not that much into skepticism! What I meant rather is that when I'm experimenting with something new (to me), and observe something weird or not in line with what I expect, my a priori belief is that I most likely made a mistake, rather than discovered some previously unnoticed flaw. -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
