[GRASS-user] v.class.mlR Error

Fri Jun 8 12:14:45 PDT 2018

Hello Moritz,

This time I asked a vector to be created with the stats and used this to
extract training polygons in QGIS and imported the training map in GRASS.
I had to do some interventions regarding the column names to make sure they
are the same except for the class.
I still get an error, and the only thing I could trace is the fact that
values are missing in some rows  for both vectors. I am not sure if I
should correct this/ retry it all.

This is the command output:

(Fri Jun 08 15:48:28 2018)

v.class.mlR -i --overwrite
segments_map=Segments_vector_Stats_Ben_test at haarlooj_Ben_Test
training_map=Training_Ben5 at haarlooj_Ben_Test
raster_segments_map=best5_myregion1_at_haarlooj_Ben_Test_rank1 at haarlooj_Ben_Test
train_class_column=Ecosystem output_class_column=vote
output_prob_column=prob classifiers=svmRadial,rf,C5.0 folds=5 partitions=10
tunelength=10 weighting_modes=smv,qbwwv weighting_metric=accuracy
classification_results=C:\Users\haarlooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_Classifier-results
accuracy_file=C:\Users\haarlooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_Classifier-accuracy
model_details=C:\Users\haarlooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_Classifier-module-runs
bw_plot_file=C:\Users\haarlooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_Classifier-performance
r_script_file=C:\Users\haarlooj\Documents\CELOS\v.class.mIRR_optional_output\Ben_test_R_script
processes=3
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
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
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
Warning message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,  :
  There were missing values in resampled performance measures.
Error in `$<-.data.frame`(`*tmp*`, vote_qbwwv, value = numeric(0)) :
  replacement has 0 rows, data has 1965
Calls: $<- -> $<-.data.frame
Execution halted
ERROR: There was an error in the execution of the R script.
Please check the R output.
(Fri Jun 08 15:49:32 2018) Command finished (1 min 4 sec)

Best,
Jamille

On Thu, Jun 7, 2018 at 11:09 AM, Jamille Haarloo <j.r.haarloo at gmail.com>
wrote:

> Hello Moritz,
>
> No worries. Thankful these modules are made available for newbies in RS
> like me and also happy these interactions are possible for learning.
> Hope to get back soon after some adjustments.
>
> Best,
> Jamille
>
> On Thu, Jun 7, 2018 at 10:44 AM, Moritz Lennert <
> mlennert at club.worldonline.be> wrote:
>
>> Thanks
>>
>> On 07/06/18 15:17, Jamille Haarloo wrote:
>>
>>> The first 20+ lines of Stats_Training_Ben_test:
>>>
>>> cat,area,perimeter,compact_circle,compact_square,fd,WV_Benat
>>> imofo_1_min,WV_Benatimofo_1_max,WV_Benatimofo_1_range,WV_Ben
>>> atimofo_1_mean,WV_Benatimofo_1_stddev,WV_Benatimofo_1_varia
>>> nce,WV_Benatimofo_1_coeff_var,WV_Benatimofo_1_sum,WV_
>>> Benatimofo_1_first_quart,WV_Benatimofo_1_median,WV_Benatim
>>> ofo_1_third_quart,WV_Benatimofo_2_min,WV_Benatimofo_2_max,
>>> WV_Benatimofo_2_range,WV_Benatimofo_2_mean,WV_Benatimofo_2_
>>> stddev,WV_Benatimofo_2_variance,WV_Benatimofo_2_coeff_var,
>>> WV_Benatimofo_2_sum,WV_Benatimofo_2_first_quart,WV_
>>> Benatimofo_2_median,WV_Benatimofo_2_third_quart,WV_Benatimof
>>> o_3_min,WV_Benatimofo_3_max,WV_Benatimofo_3_range,WV_Benat
>>> imofo_3_mean,WV_Benatimofo_3_stddev,WV_Benatimofo_3_varianc
>>> e,WV_Benatimofo_3_coeff_var,WV_Benatimofo_3_sum,WV_
>>> Benatimofo_3_first_quart,WV_Benatimofo_3_median,WV_Benatim
>>> ofo_3_third_quart,WV_Benatimofo_4_min,WV_Benatimofo_4_max,
>>> WV_Benatimofo_4_range,WV_Benatimofo_4_mean,WV_Benatimofo_4_
>>> stddev,WV_Benatimofo_4_variance,WV_Benatimofo_4_coeff_var,
>>> WV_Benatimofo_4_sum,WV_Benatimofo_4_first_quart,WV_
>>> Benatimofo_4_median,WV_Benatimofo_4_third_quart
>>> 1144,3832.000000,1256.000000,5.723635,0.197144,1.729624,13,7
>>> 6,63,46.4097077244259,9.98454911351384,99.69122100017,21.513
>>> 9237092391,177842,40,47,53,40,138,98,90.2687891440501,15.250
>>> 0825418009,232.565017531741,16.8940812061464,345910,81,92,
>>> 100,15,61,46,40.8582985386221,7.82663897784868,61.2562776895
>>> 802,19.1555675536767,156569,36,42,47,28,124,96,68.42536534
>>> 44676,13.5774536655369,184.347248039801,19.8427200164517,262206,59,68,77
>>> 1145,12092.000000,2282.000000,5.854120,0.192750,1.645226,13,
>>> 94,81,51.386288455177,10.5294376761475,110.869057775874,20.4
>>> 907534532914,621363,45,52,59,21,220,199,114.230731061859,23.
>>> 3590328249442,545.644414516822,20.4489917973953,1381278,101,
>>> 114,128,7,76,69,46.4219318557724,8.42747122371732,71.
>>> 0222712265835,18.1540726264915,561334,42,48,52,17,198,181,
>>> 97.2732385047966,22.492313569247,505.904169697333,23.
>>> 1228176577445,1176228,84,97,110
>>>
>>> [...]
>>
>> ---------------------
>>> All the lines of the output of v.db.select Training_Ben2 at haarlooj_Ben_Tes
>>> t:
>>>
>>> cat|id|Type|code
>>> 1|4|B29|18
>>> 2|5|B31|19
>>> 3|3|B28|17
>>>
>>
>>
>> Again a lack of clear documentation on my side: both the training and the
>> segment info should contain the same attributes, with only additional one
>> column ('code' in your case) present in the training data.
>>
>> It should be possible to do this differently, i.e. provide the module
>> with the features of all segments, and only the id/cat of each training
>> segment with the relevant class and have the module merge the two, but this
>> is not implemented, yet.
>>
>> I also just notice that you have the word 'Training' in both names.
>>
>> The segment_file/segment_map contains the info (cat + all feature
>> variables) of all segments you wish to classify, either in the form of a
>> csv file or in the form of a vector map with the info in the attribute
>> table.
>>
>> The training_file/training_map contains the info (cat + all feature
>> variables + class) of the training data. Often this is an extract of the
>> former, but not necessarily.
>>
>> All columns in the training file have to be present in the segment file,
>> except for the class column (your 'code').
>>
>> Sorry for the lack of docs. This module has mostly been used internally
>> here and so we are not always aware of the unclear and missing parts.
>> Having your feedback has been very useful !
>>
>> Moritz
>>
>>
>
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