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@haarlooj_Ben_Test training_map=Training_Ben5@haarlooj_Ben_Test raster_segments_map=best5_myregion1_at_haarlooj_Ben_Test_rank1@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.haar...@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 < > mlenn...@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@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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