Errata in examples 2.1 and 2.4

[Joaoloula]

In section 2.1.3.1, the pipeline model is used to fit and predict X and y variables that weren't declared before in the tutorial: it seems these were taken from the example _plot_haxby_anovasvm.py, so something like this should probably be added:

mask_filename = haxby_dataset.mask nifti_masker = NiftiMasker(mask_img=mask_filename, standardize=True) func_filename = haxby_dataset.func[0] X = nifti_masker.fit_transform(func_filename) X = X[condition_mask]

with eventually a change in the name of the mask variables just to make it clear that they're different from those used at the beginning of the section. The y variable can just be replaced with the target variable declared before.

In the first paragraph of section 2.4.1, both links to [Grosenick et al. 2013] redirect to [Gramfort et al. 2013], they should probably be changed to http://www.ncbi.nlm.nih.gov/pubmed/23298747.

[KamalakerDadi]

the pipeline model is used to fit and predict X and y variables that weren't declared before in the tutorial:

The idea of this section being that more focussed on how to do feature selection procedure providing an idea. For me, I think user will know the meaning of X and y in fit and predict (as it explained above).

In the first paragraph of section 2.4.1, both links to [Grosenick et al. 2013] redirect to [Gramfort et al. 2013], they should probably be changed to http://www.ncbi.nlm.nih.gov/pubmed/23298747.

Thanks for spotting. PR welcome. :)

[Joaoloula]

Ok, I'll do a PR

[dohmatob]

On Tue, Jun 14, 2016 at 11:05 PM, Joao Loula notifications@github.com wrote:

In section 2.1.3.1, http://nilearn.github.io/decoding/decoding_tutorial.html#dimension-reduction-with-feature-selection the pipeline model is used to fit and predict X and y variables that weren't declared before in the tutorial: it seems these were taken from the example _plot_haxby_anovasvm.py, so something like this should probably be added:

mask_filename = haxby_dataset.mask nifti_masker = NiftiMasker(mask_img=mask_filename, standardize=True) func_filename = haxby_dataset.func[0] X = nifti_masker.fit_transform(func_filename) X = X[condition_mask]

with eventually a change in the name of the mask variables just to make it clear that they're different from those used at the beginning of the section. The y variable can just be replaced with the target variable declared before.

In the first paragraph of section 2.4.1 http://nilearn.github.io/decoding/space_net.html#the-spacenet-decoder, both links to [Grosenick et al. 2013] redirect to [Gramfort et al. 2013], they should probably be changed to http://www.ncbi.nlm.nih.gov/pubmed/23298747.

Good catch.

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[tsalo]

It looks like the link issues were resolved in #1184, so now the only problem is that Example 2.1 (An introduction to decoding) does not define the variables used in section 2.1.3.1 (Dimension reduction with feature selection)- specifically func_img and conditions.

The example uses the following lines from plot_haxby_anova_svm.py, which does not include the code used to define func_img and conditions: https://github.com/nilearn/nilearn/blob/0c6388a26950beaeec0925e67b9bfb55ccfa2fde/examples/02_decoding/plot_haxby_anova_svm.py#L56-L86