Hi Roberto,
You do not need to scale here (you can remove the 3 first lines), the
point of the pipeline is actually to not have to do this:
After this I make the predictions
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
y_predictions = clf.predict(X_test)
You are calling the predict method of the (grid-searched) *Pipeline*
object which already includes the scaler: predict will call the
'transform' methods of every step in the pipeline (in your example
'scaler' and possibly 'binarizer') on X_test and then use the
transformed data to make the predictions using the final step (here
'linear_svm') predict method.
This is actually from the doc:
Definition: Pipeline.predict(self, X)
Docstring:
Applies transforms to the data, and the predict method of the
final estimator. Valid only if the final estimator implements
predict.
This means that when you do this:
X_test = scaler.transform(X_test)
y_predictions = clf.predict(X_test)
X_test if scaled twice, once by your scaler on the first line and once
by the Pipeline (not a big deal).
However if you also binarize the data before calling clf.predict I think
it would raise an issue as your /already binarized /data would be scaled
and binarized again (I would expect weird behaviour here!).
I don't think this answers all your questions but I'd suggest you clean
this part so that we can see more clearly about your other
interrogations ;).
Cheers,
Laurent
On 09/12/2014 06:31 PM, Pagliari, Roberto wrote:
Hi Andy,
I don’t think the accuracy is an issue. I explicitly provided a score
function and the problem persists.
With my own gridsearch I don’t use pipeline, just stratifiedKFold and
average for every combination of the parameters.
This is an example with scaling+svm using sklearn pipeline:
estimators = [('scaler', StandardScaler()),
('linear_svm', svm.LinearSVC(class_weight=’auto’,))]
clf_pipeline = Pipeline(estimators)
params = dict(linear_svm__C=<some array of values>)
clf = grid_search.GridSearchCV(clf_pipeline, param_grid=params)
clf.fit(X_train, y_train) # here I’m not scaling since I assume
gridsearch will do while searching
After this I make the predictions
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
y_predictions = clf.predict(X_test)
with binning, I would just add the Binarizer to the pipeline, and
right before computing y_predictions.
Is there anything wrong with what I’m doing?
Thank you
*From:*Andy [mailto:t3k...@gmail.com]
*Sent:* Friday, September 12, 2014 12:12 PM
*To:* scikit-learn-general@lists.sourceforge.net
*Subject:* Re: [Scikit-learn-general] getting different results with
sklearn gridsearchCV
Hi Roberto.
GridSearchCV uses accuracy for selection if not other method is
specified, so there should be no difference.
Could you provide code?
Do you also create a pipeline when using your own grid search? I would
imagine there is some difference in how you do the fitting in the
pipeline.
Cheers,
Andy
On 09/12/2014 05:09 PM, Pagliari, Roberto wrote:
Regarding my previous question, I suspect the difference lies in
the scoring function.
What is the default scoring function used by gridsearch?
In my own implementation I am using
number of correctly classified samples (no weighting) / total
number of samples
sklearn gridsearch function must be using something else, or maybe
the same, but with weighting?
Thanks,
*From:* Pagliari, Roberto
*Sent:* Friday, September 12, 2014 10:21 AM
*To:* 'scikit-learn-general@lists.sourceforge.net
<mailto:scikit-learn-general@lists.sourceforge.net>'
*Subject:* getting different results with sklearn gridsearchCV
I am comparing the results of sklearn cross-validation and my own
cross validation.
I tested linearSVC under the following conditions:
-Data scaling per grid search
-Data scaling + 2-level quantization, per grid search
Specifically, I have done the following:
Sklearn gridSearchCV
-Create a pipeline with [StandardScaler, LinearSVC] if no binning
is used, or [StandardScaler, Binarizer, LinearSVC], if binning is
used
-Invoke sklearn gridsearch (only C is provided as a parameter to
optimize over)
-When done with gridsearch,
oScale entire training set
oScale test set (with mean/std found on training set)
oQuantize, if quantization is used
o run LinearSVC, with best C value found
My own grid search
-Search over all possible values of C (same range as above)
-For each value of C, use stratifiedKFold with random_seed set to
a random number
oScale train cross-validation datased, and test cross validation
dataset with train cv mean and std
oIf binning is used, apply binary binning (my own function), on
top of StandardScaler
oFor each value of C compute average score over all partition,
where the score is defined as number of correctly classified
samples / total number of samples
-When done with gridsearch,
oScale entire training set
oScale test set (with mean/std found on training set)
oQuantize, if quantization is used
o run LinearSVC, with best C value found
For some reason, I’m getting different results. In particular,
sklearn gridsearch performs better than my own gridsearch when not
using quantization, and it gets worse with quantization. With my
own gridsearch I’m getting the opposite trend.
Is my understanding of sklearn gridsearch wrong, or are there any
issues with it?
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
Scikit-learn-general@lists.sourceforge.net
<mailto:Scikit-learn-general@lists.sourceforge.net>
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
Scikit-learn-general@lists.sourceforge.net
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
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
