I'm getting errors when using these parameters linear_svm__penalty linear_svm__loss linear_svm__dual
don't they have the same names? I tried linear_svc but it doesn't work either Thank you -----Original Message----- From: Laurent Direr [mailto:[email protected]] Sent: Thursday, September 11, 2014 12:57 PM To: [email protected] Subject: Re: [Scikit-learn-general] modify gridsearch to scale cross-validation training/test dataset Hi, If you test this code you will see it raises an error ;). The naming of the parameters in the param_grid should be consistent with the names in the Pipeline object. GridSearchCV performs grid search on the Pipeline object so it cannot understand what the 'LinearSVC__C' parameter means. If you replace it with 'linear_svm__C' it works just fine. On 09/11/2014 06:44 PM, Pagliari, Roberto wrote: > Hi, > Yes, I think you are right. > > Is the code below how it should be done (scaling+linearsvc)? > > [('scaler', Scaler()), ('linear_svm', LinearSVC())] clf = > Pipeline(estimators) params = dict(LinearSVC__C=[0.1, 10, 100]) gs = > GridSearchCV(clf, param_grid=params) > > Thank you, > > > -----Original Message----- > From: Laurent Direr [mailto:[email protected]] > Sent: Thursday, September 11, 2014 11:15 AM > To: [email protected] > Subject: Re: [Scikit-learn-general] modify gridsearch to scale > cross-validation training/test dataset > > Hello, > > I think a pipeline does precisely what you are asking for: > http://scikit-learn.org/stable/modules/pipeline.html > > If you include the scaler as a step in the pipeline it should behave the way > you described in your first email. > > Laurent > > On 09/11/2014 04:59 PM, Pagliari, Roberto wrote: >> I'm not trying to scale the dataset at the very beginning. I would like to >> scale while doing gridsearchCV. >> >> Thanks, >> >> >> -----Original Message----- >> From: Pagliari, Roberto [mailto:[email protected]] >> Sent: Thursday, September 11, 2014 10:52 AM >> To: [email protected] >> Subject: Re: [Scikit-learn-general] modify gridsearch to scale >> cross-validation training/test dataset >> >> I'm not sure how to do it when using gridsearch. Can you provide an example? >> >> Thank you, >> >> >> -----Original Message----- >> From: Gael Varoquaux [mailto:[email protected]] >> Sent: Thursday, September 11, 2014 10:50 AM >> To: [email protected] >> Subject: Re: [Scikit-learn-general] modify gridsearch to scale >> cross-validation training/test dataset >> >> Use a pipeline. >> >> G >> >> On Thu, Sep 11, 2014 at 02:47:48PM +0000, Pagliari, Roberto wrote: >>> Hello, >>> Gridsearch with CV is something like this at a high level: >> >>> for every combination of parameters: >>> for every partition of training data >>> split training into train_cv and test_cv >>> train_classifier(train_cv).predict(test_cv) >>> compute score >>> average score >>> if max so far, then update best params >> >>> I woud like to do something like this: >> >>> for every combination of parameters: >>> for every partition of training data >>> split training into train_cv and test_cv >>> scaler = StandardScaler() >>> scaler.fit(train_cv) >>> train_cv = scaler.transform(train_cv) >>> test_cv = scaler.transform(test_cv) >>> train_classifier(train_cv).predict(test_cv) >>> compute score >>> average score >>> if max so far, then update best params >> >>> basically, I would like to scale training data and test data (using >>> training data params) every time a CV train/test is generated. >>> Can someone suggest the best way to modify grid_search.py to do this? >> >>> Thank you, >> >> >>> -------------------------------------------------------------------- >>> - >>> - >>> -------- >>> Want excitement? >>> Manually upgrade your production database. >>> When you want reliability, choose Perforce Perforce version control. >>> Predictably reliable. >>> http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg. >>> clktrk >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> [email protected] >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ---------------------------------------------------------------------- > -------- > Want excitement? > Manually upgrade your production database. > When you want reliability, choose Perforce Perforce version control. > Predictably reliable. > http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg. > clktrk _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ---------------------------------------------------------------------- > -------- > Want excitement? > Manually upgrade your production database. > When you want reliability, choose Perforce Perforce version control. > Predictably reliable. > http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg. > clktrk _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Want excitement? Manually upgrade your production database. When you want reliability, choose Perforce Perforce version control. Predictably reliable. http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Want excitement? Manually upgrade your production database. When you want reliability, choose Perforce Perforce version control. Predictably reliable. http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
