Re: [GRASS-user] v.class.mlR Error in data.frame : arguments imply differing number of rows

[Moritz Lennert]

On 16/04/19 16:50, Jamille Haarloo wrote:

Replacing
ids <- rownames(features)
with
ids <- rownames(predicted)
is the only edit I did after the previous try, so this should have 
solved the error.


Watch out: I'm not sure that this is really equivalent. I think I used 
the rownames(features) to get the original ids of the features, while 
rownames(predicted) apparently gives 1,2,3,4,etc. So you will not be 
able to link the results back to your original features...




If I understood correctly na.action = na.exclude can help to work around 
the NA values without deleting rows but somehow I did not work. The user 
can always compare the original data rows to the results, right? As in 
my case, comparing the results file to the vector file shows that 17849 
of 17851 segments were classified as expected from the error. I did not 
loose any original data and also saved a copy. If you mention in the 
manual that intermediate na values might block the analyses and will 
therefore be omitted from the final results, it should be perfectly fine.


You are right that na.exclude is probably better here than na.omit. 
IIUC, this will give you NAs in the prediction output as well.


So I think that na.exclude is probably the safest bet and shouldn't 
create too much of a surprise for the user.


I noticed a minor issue; not all results were added to the vector file - 
about 686 segments (almost 4% of the data) were somehow missed. 


That's weird. Maybe this is due to the different ids ?

Moritz
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Re: [GRASS-user] v.class.mlR Error in data.frame : arguments imply differing number of rows

[Jamille Haarloo]

Replacing
ids <- rownames(features)
with
ids <- rownames(predicted)
is the only edit I did after the previous try, so this should have solved
the error.

If I understood correctly na.action = na.exclude can help to work around
the NA values without deleting rows but somehow I did not work. The user
can always compare the original data rows to the results, right? As in my
case, comparing the results file to the vector file shows that 17849 of
17851 segments were classified as expected from the error. I did not loose
any original data and also saved a copy. If you mention in the manual that
intermediate na values might block the analyses and will therefore be
omitted from the final results, it should be perfectly fine.
I noticed a minor issue; not all results were added to the vector file -
about 686 segments (almost 4% of the data) were somehow missed. Fortunately
I do have the results as separate output file.

Best,
Jamille

On Tue, Apr 16, 2019 at 10:04 AM Moritz Lennert <
mlenn...@club.worldonline.be> wrote:

> On 16/04/19 14:37, Jamille Haarloo wrote:
> > Hi Moritz,
> >
> > Thank you! it worked.
>
> What worked, exactly ? ;-)
>
>
> > I did not find the line nor similar lines of 'features <-
> > na.omit(features)' in the v.class.mlR script/ R_script4 file.
>
> Sorry, I am working with a heavily modified version here on my computer
> currently, and didn't realize that this was part of my local modifications.
>
> I also see that in the manual I actually wrote "The module makes no
> effort to check the input data for NA values or anything else that might
> perturb the analyses. It is up to the user to proceed to relevant checks
> before launching the module."
>
> I could add an na.omit to the code. What is your opinion on that as a
> user ? Isn't it too invasive to just force this on the user ? I do
> acknowledge that in my local case it is convenient.
>
> Moritz
>
> >
> > Best,
> > Jamille
> >
> > On Mon, Apr 15, 2019 at 11:09 AM Moritz Lennert
> > mailto:mlenn...@club.worldonline.be>>
> wrote:
> >
> > Hi Jamille,
> >
> > On 15/04/19 14:49, Jamille Haarloo wrote:> Dear Moritz and other
> Grass-
> > users and developers,
> >   >
> >   > I tried dealing with the error myself by changing predicted <-
> >   > data.frame(predict(models.cv 
> > , features)) into
> >   > predicted <- data.frame(predict(models.cv 
> > , features,
> >   > na.action = na.exclude)), based on discussions online implying
> some
> >   > predictions might be invalid NaN values. I checked the script
> > output to
> >   > see if this change was implemented and it was, but I get the
> > same error.
> >   > Any suggestions what to try next?>
> >   > --
> >   > v.class.mlR -i --overwrite segments_map=nvSegW24IDM4DV4@LUP1
> >   > training_map=TrainingApril2019@LUP1
> train_class_column=class_code
> >   > output_class_column=output_class output_prob_column=probability
> >   > classifiers=svmLinear,rf,xgbTree folds=5 partitions=10
> tunelength=10
> >   > weighting_modes=bwwv,qbwwv weighting_metric=accuracy
> >   >
> >
>  
> classification_results=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\results_all_classifiers
> >
> >
> >   >
> >
>  
> accuracy_file=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\accuracy_classifiers
> >
> >
> >   >
> >
>  
> model_details=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\details_classifier_module_runs
> >
> >
> >   >
> >
>  
> bw_plot_file=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\box-whicker_classifier_performance
> >
> >
> >   >
> >
>  
> r_script_file=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\R_script4
> >
> >
> >   > processes=3
> > Normally, there should be no NA in the features as there is a line:
> >
> > features <- na.omit(features)
> >
> > early in the R script. Can you see it in the R_script4 file ?
> >
> >
> >   > Running R now. Following output is R output.
> >   > During startup - Warning messages:
> >   > 1: Setting LC_CTYPE=en_US.cp1252 failed
> >   > 2: Setting LC_COLLATE=en_US.cp1252 failed
> >   > 3: Setting LC_TIME=en_US.cp1252 failed
> >   > 4: Setting LC_MONETARY=en_US.cp1252 failed
> >   > Loading required package: caret
> >   > Loading required package: lattice
> >   > Loading required package: ggplot2
> >   > Warning messages:
> >   > 1: package 'caret' was built under R version 3.5.3
> >   > 2: package 'ggplot2' was built under R version 3.5.3
> >   > Loading required package: foreach
> >   > Loading required package: iterators
> >   > Loading required package: parallel
> >   > Warning messages:
> >   > 1: package 'doParallel' was built under R version 3.5.3
> >   > 2: package 'foreach' was built under R version 

Re: [GRASS-user] cannot install extension on Windows 10

[Mira Kattwinkel]

Hello,

yes, I assumed that the metadata is available, but on my system they 
seem to be not found. And I do not know what settings  to change. What 
would be the correct proxy settings?


Indeed, the installation was successful, I can use the extension. 
However Settings / Addons extensions / Manage installed extensions gives 
me an empty list.


Any ideas?

Thanks a lot, Mira

On 15/04/2019 20.20, Martin Landa wrote:

Hi,

po 15. 4. 2019 v 18:18 odesílatel Mira Kattwinkel
  napsal:

For example g.extension extension=r.hydrodem operation=add
Updating addons metadata file...

ERROR: Unable to read addons metadata file from the remote server:
[Errno socket error] [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify
failed (_ssl.c:661)

WARNING: NO addons metadate available. Addons metadate file not updated.

hm, metadata seems to be available [0]. I tested installing r.hydrodem
on my Windows 10 machine. No error, addons installed, possible to
launch. Something related to proxy or?


Installation of  successfully finished (it is not!)

Are you sure about that? Binary package [1] is available. Have you
tried to launch the module?

Ma

[0]https://grass.osgeo.org/addons/grass7/modules.xml
[1]https://wingrass.fsv.cvut.cz/grass76/x86_64/addons/grass-7.6.0/r.hydrodem.zip


--
Dr. Mira Kattwinkel
Quantitative Landscape Ecology
Institute for Environmental Sciences
University of Koblenz-Landau
Fortstraße 7
76829 Landau
Germany
Phone: + 49 6341 280-31553
Office: Building I, Room 2.02

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Re: [GRASS-user] v.class.mlR Error in data.frame : arguments imply differing number of rows

[Jamille Haarloo]

Hi Moritz,

Thank you! it worked.

I did not find the line nor similar lines of 'features <-
na.omit(features)' in the v.class.mlR script/ R_script4 file.

Best,
Jamille

On Mon, Apr 15, 2019 at 11:09 AM Moritz Lennert <
mlenn...@club.worldonline.be> wrote:

> Hi Jamille,
>
> On 15/04/19 14:49, Jamille Haarloo wrote:> Dear Moritz and other Grass-
> users and developers,
>  >
>  > I tried dealing with the error myself by changing predicted <-
>  > data.frame(predict(models.cv , features)) into
>  > predicted <- data.frame(predict(models.cv , features,
>  > na.action = na.exclude)), based on discussions online implying some
>  > predictions might be invalid NaN values. I checked the script output to
>  > see if this change was implemented and it was, but I get the same error.
>  > Any suggestions what to try next?>
>  > --
>  > v.class.mlR -i --overwrite segments_map=nvSegW24IDM4DV4@LUP1
>  > training_map=TrainingApril2019@LUP1 train_class_column=class_code
>  > output_class_column=output_class output_prob_column=probability
>  > classifiers=svmLinear,rf,xgbTree folds=5 partitions=10 tunelength=10
>  > weighting_modes=bwwv,qbwwv weighting_metric=accuracy
>  >
> classification_results=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\results_all_classifiers
>
>
>  >
> accuracy_file=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\accuracy_classifiers
>
>
>  >
> model_details=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\details_classifier_module_runs
>
>
>  >
> bw_plot_file=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\box-whicker_classifier_performance
>
>
>  >
> r_script_file=C:\Users\haarlooj\Documents\CELOS\v.class.mlr_outputapril2019\R_script4
>
>
>  > processes=3
> Normally, there should be no NA in the features as there is a line:
>
> features <- na.omit(features)
>
> early in the R script. Can you see it in the R_script4 file ?
>
>
>  > Running R now. Following output is R output.
>  > During startup - Warning messages:
>  > 1: Setting LC_CTYPE=en_US.cp1252 failed
>  > 2: Setting LC_COLLATE=en_US.cp1252 failed
>  > 3: Setting LC_TIME=en_US.cp1252 failed
>  > 4: Setting LC_MONETARY=en_US.cp1252 failed
>  > Loading required package: caret
>  > Loading required package: lattice
>  > Loading required package: ggplot2
>  > Warning messages:
>  > 1: package 'caret' was built under R version 3.5.3
>  > 2: package 'ggplot2' was built under R version 3.5.3
>  > Loading required package: foreach
>  > Loading required package: iterators
>  > Loading required package: parallel
>  > Warning messages:
>  > 1: package 'doParallel' was built under R version 3.5.3
>  > 2: package 'foreach' was built under R version 3.5.3
>  > 3: package 'iterators' was built under R version 3.5.3
>  > During startup - Warning messages:
>  > 1: Setting LC_CTYPE=en_US.cp1252 failed
>  > 2: Setting LC_COLLATE=en_US.cp1252 failed
>  > 3: Setting LC_TIME=en_US.cp1252 failed
>  > 4: Setting LC_MONETARY=en_US.cp1252 failed
>  > During startup - Warning messages:
>  > 1: Setting LC_CTYPE=en_US.cp1252 failed
>  > 2: Setting LC_COLLATE=en_US.cp1252 failed
>  > 3: Setting LC_TIME=en_US.cp1252 failed
>  > 4: Setting LC_MONETARY=en_US.cp1252 failed
>  > During startup - Warning messages:
>  > 1: Setting LC_CTYPE=en_US.cp1252 failed
>  > 2: Setting LC_COLLATE=en_US.cp1252 failed
>  > 3: Setting LC_TIME=en_US.cp1252 failed
>  > 4: Setting LC_MONETARY=en_US.cp1252 failed
>  > Error in data.frame(id = rownames(features), predicted) :
>  >arguments imply differing number of rows: 17851, 17849
>  > Execution halted
> IDs are taken from the features and for some reasons there are two
> features which do not have a prediction. It might help if you could find
> out why.
>
> I cannot test right now, but you might want to check if you can replace
>
> ids <- rownames(features)
>
> with something like
>
> ids <- rownames(predicted)
>
> ?
>
> Moritz
>
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[GRASS-user] GRASS Community Sprint in Berlin May 2019

[Martin Landa]

Dear all,

it's pleasure for me to announce a next GRASS Community Sprint which
will be held in Berlin (thanks to DIW for free provision of meeting
rooms and internet access), Germany in May 17th - 20th.

Please register yourself at wiki [1] ASAP(!) to understand whether the
booked meeting room at DIW is big enough for our team :-)

More information at
https://grasswiki.osgeo.org/wiki/GRASS_GIS_Community_Sprint_Berlin_2019

Thanks, on behalf of organization team (here big thanks to Peter Loewe
from DIW for hard work with organizing the event at DIW). Hopefully
see you seen in Berlin, Martin

[1] 
https://grasswiki.osgeo.org/wiki/GRASS_GIS_Community_Sprint_Berlin_2019#In_person

-- 
Martin Landa
http://geo.fsv.cvut.cz/gwiki/Landa
http://gismentors.cz/mentors/landa
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[GRASS-user] Nice opportunity to show off GRASS functionality and topological vector format ?

[Moritz Lennert]

Hi,

If anyone has some time and wants to show off some GRASS GIS power:

https://www.ecmwf.int/en/learning/workshops/ecmwf-summer-weather-code-2019

"The Summer of Weather Code(ESoWC) programme by the European Centre for 
Medium-Range Weather Forecasts (ECMWF) is a collabrative online 
programme to promote the development of weather-related open-source 
software."


More specificially:

https://github.com/esowc/challenges_2019

And even more, I think this challenge is a fairly low hanging fruit 
using GRASS GIS tools:


https://github.com/esowc/challenges_2019/issues/3


APPLICATION DEADLINE: SUNDAY, 21 APRIL 23:59 GMT


Moritz
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