diff --git a/classificationWorkflow.py b/classificationWorkflow.py
index f7983a61905e90a232f899fd02f409da1f31fd24..461558cdb87fe15ea6cfdda86836ff6ae45579d7 100644
--- a/classificationWorkflow.py
+++ b/classificationWorkflow.py
@@ -66,7 +66,6 @@ def baseTrainingCmd(shp,stat_file,code,flds,params,model_file,confmat_file):
sh = True
else:
sys.exit("Platform not supported!")
-
if type(shp) == str:
shp = [shp]
@@ -110,6 +109,7 @@ def baseClassifyCmd(shp,stat_file,model_file,code,flds,out_file,compute_confiden
cmd += ['-out', out_file]
if compute_confidence:
cmd += ['-confmap', 1]
+ subprocess.call(cmd,sh)
elif platform.system() == 'Windows':
import otbApplication
app = otbApplication.Registry.CreateApplication('VectorClassifier')
@@ -270,6 +270,40 @@ def classify(shp_list,code,stat_file,model_file,out_fld,out_ext,feat,feat_mode =
else:
sys.exit('Platform not supported!')
+def addField(filein, nameField, valueField, valueType=None,
+ driver_name="ESRI Shapefile", fWidth=None):
+
+
+ driver = ogr.GetDriverByName(driver_name)
+ source = driver.Open(filein, 1)
+ layer = source.GetLayer()
+ layer_name = layer.GetName()
+ layer_defn = layer.GetLayerDefn()
+ field_names = [layer_defn.GetFieldDefn(i).GetName() for i in range(layer_defn.GetFieldCount())]
+ if not valueType:
+ try :
+ int(valueField)
+ new_field1 = ogr.FieldDefn(nameField, ogr.OFTInteger)
+ except :
+ new_field1 = ogr.FieldDefn(nameField, ogr.OFTString)
+ elif valueType == str:
+ new_field1 = ogr.FieldDefn(nameField, ogr.OFTString)
+ sqlite_type = 'varchar'
+ elif valueType == int:
+ new_field1 = ogr.FieldDefn(nameField, ogr.OFTInteger)
+ sqlite_type = 'int'
+ elif valueType == float:
+ new_field1 = ogr.FieldDefn(nameField, ogr.OFTFLOAT)
+ sqlite_type = 'float'
+ if fWidth:
+ new_field1.SetWidth(fWidth)
+
+ layer.CreateField(new_field1)
+ for feat in layer:
+ layer.SetFeature(feat)
+ feat.SetField(nameField, valueField)
+ layer.SetFeature(feat)
+
def splitShapefileByClasses(shp,code):
ds = ogr.Open(shp)
ly = ds.GetLayer(0)
@@ -385,6 +419,7 @@ def Hclassify(shp_list,stat_file,h_model_fld,feat,out_fld,out_ext,feat_mode = 'l
if not os.path.exists(h_model_fld):
sys.exit('Folder ' + h_model_fld + ' not exists!')
+ out_file_list = []
for shp in shp_list:
with open(h_model_fld + '/h-model.csv', mode='rb') as h_model_file:
toProcess = []
@@ -394,14 +429,22 @@ def Hclassify(shp_list,stat_file,h_model_fld,feat,out_fld,out_ext,feat_mode = 'l
for row in rdr:
if row[0] == 'ROOT':
in_shp = shp
- elif len(toProcess) > 0 and toProcess[-1].endswith('_p' + row[0] + '_' + row[1] + '.shp'):
- in_shp = toProcess.pop()
+ elif len(toProcess) > 0:
+ for i,process in enumerate(toProcess):
+ if process.endswith('_p' + row[0] + '_' + row[1] + '.shp') :
+ in_shp = toProcess.pop(i)
else:
continue
if os.path.exists(in_shp):
out_shp = in_shp.replace('.shp','_ROOT.shp') if row[0] == 'ROOT' else None
split_shp = out_shp if row[0] == 'ROOT' else in_shp
- baseClassifyCmd(in_shp,stat_file,row[2],'p'+row[3],flds,out_shp)
+ with open(row[2]) as model:
+ lines = model.readlines()
+ to_classify = int(lines[1].split(' ')[0]) != 1
+ if to_classify :
+ baseClassifyCmd(in_shp,stat_file,row[2],'p'+row[3],flds,out_shp)
+ else :
+ addField(in_shp,'p'+row[3],int(lines[1].split(' ')[1]))
if out_shp is not None:
toDelete.append(out_shp)
if row[4] == 'True':
@@ -410,11 +453,16 @@ def Hclassify(shp_list,stat_file,h_model_fld,feat,out_fld,out_ext,feat_mode = 'l
toDelete.extend(ds_dict.values())
elif row[4] == 'False':
toMerge.append(in_shp)
-
- out_file = out_fld + '/' + os.path.basename(shp).replace('.shp', out_ext + '.shp')
- mergeShapefiles(toMerge,out_file)
+ if len(toMerge) > 1 :
+ out_file = out_fld + '/' + os.path.basename(shp).replace('.shp', out_ext + '.shp')
+ mergeShapefiles(toMerge,out_file)
+ out_file_list.append(out_file)
+ elif len(toMerge) == 1 :
+ out_file = out_fld + '/' + os.path.basename(shp).replace('.shp', out_ext + '.shp')
+ os.rename(toMerge[0],out_file)
+ out_file_list.append(out_file)
drv = ogr.GetDriverByName('ESRI Shapefile')
for fn in toDelete:
- drv.DeleteDataSource(fn)
+ drv.DeleteDataSource(fn)
- return
\ No newline at end of file
+ return out_file_list
\ No newline at end of file
diff --git a/launchChain.py b/launchChain.py
index 3c1add5f1ddc9f6fa59228378924ff421321bf56..bf6da9cafa5a88a5b69d97f827c9bb616606ef80 100644
--- a/launchChain.py
+++ b/launchChain.py
@@ -670,7 +670,7 @@ def main(argv):
rfimpnodesize = int(config.get('TRAINING CONFIGURATION', 'rfimpnodesize'))
rfimpmaxfeat = int(config.get('TRAINING CONFIGURATION', 'rfimpmaxfeat'))
rfimpnruns = int(config.get('TRAINING CONFIGURATION', 'rfimpnruns'))
-
+ hierarchical_classif = config.get('TRAINING CONFIGURATION','hierarchicalclassif')
model_fld = output_fld + '/MODEL_' + setup_name
if not os.path.exists(model_fld):
@@ -684,16 +684,65 @@ def main(argv):
var_list.append(row[0])
if input_runlevel < 6:
+ if hierarchical_classif is False:
+ if ch_mode < 0:
+
+ nFolds = -ch_mode
+ dgt = int(floor(log10(nFolds)) + 1)
+
+ train_folds, test_folds = genKFolds(reference, cfieldlist[-1], nFolds)
+ kfold_train_samples, kfold_test_samples = kFoldRefToSamples(samples_fld + '/GT_samples.shp', val_fld + '/VAL_samples.shp', train_folds, test_folds)
+
+ for cfield in cfieldlist:
+
+ accuracies = []
+ kappas = []
+ fscores = {}
+
+ for i in range(nFolds):
- if ch_mode < 0:
+ mfld = model_fld + '/fold_' + str(i+1).zfill(dgt)
- nFolds = -ch_mode
- dgt = int(floor(log10(nFolds)) + 1)
+ if not os.path.exists(mfld):
+ os.mkdir(mfld)
- train_folds, test_folds = genKFolds(reference, cfieldlist[-1], nFolds)
- kfold_train_samples, kfold_test_samples = kFoldRefToSamples(samples_fld + '/GT_samples.shp', val_fld + '/VAL_samples.shp', train_folds, test_folds)
+ sfn, mfn = training(kfold_train_samples[i], cfield, mfld, params, var_list)
+ val_list = [kfold_test_samples[i]]
+ val_out = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp','_' + cfield + '.shp')
+ val_out_check = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp','_' + cfield + '_check.shp')
+ val_out_tmp_list = classify(val_list, 'p' + cfield, sfn, mfn, mfld,
+ '_' + cfield + '_tmp', var_list)
+ keepFields(val_out_tmp_list[0], val_out, ['Segment_ID', cfield, 'p' + cfield, 'confidence'])
+ shpd.DeleteDataSource(val_out_tmp_list[0])
+ txt_out = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp','_' + cfield + '_report.txt')
+ classes, cm, acc, kappa, prf = surfaceValidation(test_folds[i], val_out, val_out_check, cfield)
+ formatValidationTxt(classes, cm, acc, kappa, prf, txt_out)
+
+ accuracies.append(acc)
+ kappas.append(kappa)
+ for c,fs in zip(classes,prf[2]):
+ if c not in fscores.keys():
+ fscores[c] = []
+ fscores[c].append(fs)
+
+ kFoldReport(fscores,accuracies,kappas,model_fld + '/kFold_report_' + cfield + '.txt')
for cfield in cfieldlist:
+ training(samples_fld + '/GT_samples.shp',cfield,model_fld,params,var_list)
+
+ if rfimp == True:
+ for cfield in cfieldlist:
+ getVariableImportance(samples_fld + '/GT_samples.shp',var_list,cfield,samples_fld + '/var_importance_' + cfield + '.csv', nbtrees=rfimpntrees, nodesize=rfimpnodesize, mtry=rfimpmaxfeat, nruns=rfimpnruns, field_names=samples_fld + '/field_names.csv')
+ else :
+ from classificationWorkflow import Htraining, Hclassify
+ if ch_mode < 0:
+
+ nFolds = -ch_mode
+ dgt = int(floor(log10(nFolds)) + 1)
+ train_folds, test_folds = genKFolds(reference, cfieldlist[-1], nFolds)
+ kfold_train_samples, kfold_test_samples = kFoldRefToSamples(samples_fld + '/GT_samples.shp',
+ val_fld + '/VAL_samples.shp', train_folds,
+ test_folds)
accuracies = []
kappas = []
@@ -701,38 +750,47 @@ def main(argv):
for i in range(nFolds):
- mfld = model_fld + '/fold_' + str(i+1).zfill(dgt)
+ mfld = model_fld + '/fold_' + str(i + 1).zfill(dgt)
if not os.path.exists(mfld):
os.mkdir(mfld)
-
- sfn, mfn = training(kfold_train_samples[i], cfield, mfld, params, var_list)
+ sfn, mfn = Htraining(kfold_train_samples[i], cfieldlist, mfld, params, var_list)
val_list = [kfold_test_samples[i]]
- val_out = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp','_' + cfield + '.shp')
- val_out_check = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp','_' + cfield + '_check.shp')
- val_out_tmp_list = classify(val_list, 'p' + cfield, sfn, mfn, mfld,
- '_' + cfield + '_tmp', var_list)
- keepFields(val_out_tmp_list[0], val_out, ['Segment_ID', cfield, 'p' + cfield, 'confidence'])
- shpd.DeleteDataSource(val_out_tmp_list[0])
- txt_out = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp','_' + cfield + '_report.txt')
- classes, cm, acc, kappa, prf = surfaceValidation(test_folds[i], val_out, val_out_check, cfield)
- formatValidationTxt(classes, cm, acc, kappa, prf, txt_out)
-
- accuracies.append(acc)
- kappas.append(kappa)
- for c,fs in zip(classes,prf[2]):
- if c not in fscores.keys():
- fscores[c] = []
- fscores[c].append(fs)
-
- kFoldReport(fscores,accuracies,kappas,model_fld + '/kFold_report_' + cfield + '.txt')
-
- for cfield in cfieldlist:
- training(samples_fld + '/GT_samples.shp',cfield,model_fld,params,var_list)
-
- if rfimp == True:
- for cfield in cfieldlist:
- getVariableImportance(samples_fld + '/GT_samples.shp',var_list,cfield,samples_fld + '/var_importance_' + cfield + '.csv', nbtrees=rfimpntrees, nodesize=rfimpnodesize, mtry=rfimpmaxfeat, nruns=rfimpnruns, field_names=samples_fld + '/field_names.csv')
+ val_out = mfld + '/' + os.path.basename('_'.join(cfieldlist))
+ val_out_check = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp',
+ '_' + '_'.join(cfieldlist) + '_check.shp')
+ val_out_tmp_list = Hclassify(val_list, sfn, mfn, var_list,
+ mfld,'_h_classif')
+ for cfield in cfieldlist:
+ val_out = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp',
+ '_' + cfield +'.shp')
+ val_out_check = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp',
+ '_' + cfield +'_check.shp')
+
+ keepFields(val_out_tmp_list[0], val_out, ['Segment_ID', cfield, 'p' + cfield, 'confidence'])
+ txt_out = mfld + '/' + os.path.basename(kfold_test_samples[i]).replace('.shp',
+ '_' + cfield + '_report.txt')
+
+ classes, cm, acc, kappa, prf = surfaceValidation(test_folds[i], val_out, val_out_check, cfield)
+ formatValidationTxt(classes, cm, acc, kappa, prf, txt_out)
+
+ accuracies.append(acc)
+ kappas.append(kappa)
+ for c, fs in zip(classes, prf[2]):
+ if c not in fscores.keys():
+ fscores[c] = []
+ fscores[c].append(fs)
+
+ kFoldReport(fscores, accuracies, kappas, model_fld + '/kFold_report_' + cfield + '.txt')
+
+ Htraining(samples_fld + '/GT_samples.shp', cfieldlist, model_fld, params, var_list)
+
+ if rfimp == True:
+ for cfield in cfieldlist:
+ getVariableImportance(samples_fld + '/GT_samples.shp', var_list, cfield,
+ samples_fld + '/var_importance_' + cfield + '.csv', nbtrees=rfimpntrees,
+ nodesize=rfimpnodesize, mtry=rfimpmaxfeat, nruns=rfimpnruns,
+ field_names=samples_fld + '/field_names.csv')
if single_step:
sys.exit("Single step mode. Exiting.")
@@ -759,63 +817,120 @@ def main(argv):
shpd = ogr.GetDriverByName('ESRI Shapefile')
if input_runlevel < 7:
-
- for cfield in cfieldlist:
- model_file = model_fld + '/' + classifier + '_' + cfield + '.model'
- if not os.path.exists(model_file):
- warnings.warn('Error: Model file ' + model_file + ' not found. Skipping.')
- continue
+ if hierarchical_classif is False :
+ for cfield in cfieldlist:
+ model_file = model_fld + '/' + classifier + '_' + cfield + '.model'
+ if not os.path.exists(model_file):
+ warnings.warn('Error: Model file ' + model_file + ' not found. Skipping.')
+ continue
+
+ if ch_mode == 0 or ch_mode == 2:
+ ref_shp = config.get('GENERAL CONFIGURATION', 'validation')
+ val_mode = int(config.get('GENERAL CONFIGURATION', 'validmode'))
+ val_list = [val_fld + '/VAL_samples.shp']
+ val_out = final_fld + '/VAL_samples_' + cfield + '.shp'
+ val_out_check = final_fld + '/VAL_samples_' + cfield + '_check.shp'
+ val_out_tmp_list = classify(val_list, 'p'+cfield, stat_file, model_file, final_fld, '_' + cfield + '_tmp', var_list, compute_confidence=comp_conf)
+ keepFields(val_out_tmp_list[0],val_out,['Segment_ID',cfield,'p'+cfield,'confidence'])
+ shpd.DeleteDataSource(val_out_tmp_list[0])
+ txt_out = final_fld + '/VAL_samples.' + cfield + '.report.txt'
+ if val_mode == 0:
+ pixelValidation(ref_shp, val_out, seg_output, txt_out, cfield)
+ elif val_mode == 1:
+ classes,cm,acc,kappa,prf = surfaceValidation(ref_shp, val_out, val_out_check, cfield)
+ formatValidationTxt(classes, cm, acc, kappa, prf, txt_out)
+
+ shp_list = glob.glob(test_fld + '/segmentation_*.shp')
+ if ch_mode > 0 or (ch_mode < 0 and len(shp_list) > 0):
+ if not os.path.exists(final_fld + '/MAPS'):
+ os.mkdir(final_fld + '/MAPS')
+ if not os.path.exists(final_fld + '/MAPS/VECTOR_' + cfield):
+ os.mkdir(final_fld + '/MAPS/VECTOR_' + cfield)
+ map_list = []
+ ref_list = []
+
+ for cshp in shp_list:
+ ref_list.append(cshp.replace('.shp', '.tif'))
+
+ map_tmp_list = classify(shp_list,'p'+cfield,stat_file,model_file,final_fld + '/MAPS/VECTOR_' + cfield + '/','_' + cfield + '_tmp',var_list,Nproc=N_proc,compute_confidence=comp_conf)
+
+ for cshp in map_tmp_list:
+ map_out = cshp.replace('_tmp.shp','.shp')
+ keepFields(cshp,map_out,['Segment_ID','p'+cfield,'confidence'])
+ shpd.DeleteDataSource(cshp)
+ map_list.append(map_out)
+
+ if rasout == 'VRT':
+ if not os.path.exists(final_fld + '/MAPS/RASTER_' + cfield):
+ os.mkdir(final_fld + '/MAPS/RASTER_' + cfield)
+ ras_list = []
+ cmd_list = []
+ for map,ref in zip(map_list,ref_list):
+ ras_list.append(final_fld + '/MAPS/RASTER_' + cfield + '/' + os.path.basename(map).replace('.shp', '.tif'))
+ cmd = ['otbcli_Rasterization', '-in', map, '-im', ref, '-mode', 'attribute', '-mode.attribute.field', 'p'+cfield, '-out', ras_list[-1]]
+ cmd_list.append(cmd)
+ queuedProcess(cmd_list,N_processes=N_proc,shell=sh)
+
+ cmd = ['gdalbuildvrt', '-srcnodata', '0', '-vrtnodata', '0', final_fld + '/MAPS/RASTER_' + cfield + '/Classif_' + cfield + '.vrt'] + ras_list
+ subprocess.call(cmd, shell=sh)
+
+ else :
+ from classificationWorkflow import Hclassify
+ h_model_fld = os.path.join(model_fld,'H-MODEL_' + '_'.join(cfieldlist))
+ if len(glob.glob(os.path.join(h_model_fld,'*.model'))) == 0 :
+ sys.exit('Error: There is no model file in ' + h_model_fld + ' folder. Skipping.')
if ch_mode == 0 or ch_mode == 2:
ref_shp = config.get('GENERAL CONFIGURATION', 'validation')
val_mode = int(config.get('GENERAL CONFIGURATION', 'validmode'))
val_list = [val_fld + '/VAL_samples.shp']
- val_out = final_fld + '/VAL_samples_' + cfield + '.shp'
- val_out_check = final_fld + '/VAL_samples_' + cfield + '_check.shp'
- val_out_tmp_list = classify(val_list, 'p'+cfield, stat_file, model_file, final_fld, '_' + cfield + '_tmp', var_list, compute_confidence=comp_conf)
- keepFields(val_out_tmp_list[0],val_out,['Segment_ID',cfield,'p'+cfield,'confidence'])
+ val_out = final_fld + '/VAL_samples_' + '_'.join(cfieldlist) + '.shp'
+ val_out_check = final_fld + '/VAL_samples_' + '_'.join(cfieldlist) + '_check.shp'
+ val_out_tmp_list = Hclassify(val_list,stat_file,h_model_fld,var_list,final_fld,'_h_classif')
+ keepFields(val_out_tmp_list[0],val_out,['Segment_ID']+cfieldlist+['p'+c for c in cfieldlist]+['confidence'])
shpd.DeleteDataSource(val_out_tmp_list[0])
- txt_out = final_fld + '/VAL_samples.' + cfield + '.report.txt'
- if val_mode == 0:
- pixelValidation(ref_shp, val_out, seg_output, txt_out, cfield)
- elif val_mode == 1:
- classes,cm,acc,kappa,prf = surfaceValidation(ref_shp, val_out, val_out_check, cfield)
- formatValidationTxt(classes, cm, acc, kappa, prf, txt_out)
+ for cfield in cfieldlist:
+ txt_out = final_fld + '/VAL_samples.' + cfield + '.report.txt'
+ if val_mode == 0:
+ pixelValidation(ref_shp, val_out, seg_output, txt_out, cfield)
+ elif val_mode == 1:
+ classes,cm,acc,kappa,prf = surfaceValidation(ref_shp, val_out, val_out_check, cfield)
+ formatValidationTxt(classes, cm, acc, kappa, prf, txt_out)
shp_list = glob.glob(test_fld + '/segmentation_*.shp')
if ch_mode > 0 or (ch_mode < 0 and len(shp_list) > 0):
if not os.path.exists(final_fld + '/MAPS'):
os.mkdir(final_fld + '/MAPS')
- if not os.path.exists(final_fld + '/MAPS/VECTOR_' + cfield):
- os.mkdir(final_fld + '/MAPS/VECTOR_' + cfield)
+ if not os.path.exists(final_fld + '/MAPS/VECTOR'):
+ os.mkdir(final_fld + '/MAPS/VECTOR')
map_list = []
ref_list = []
-
for cshp in shp_list:
ref_list.append(cshp.replace('.shp', '.tif'))
- map_tmp_list = classify(shp_list,'p'+cfield,stat_file,model_file,final_fld + '/MAPS/VECTOR_' + cfield + '/','_' + cfield + '_tmp',var_list,Nproc=N_proc,compute_confidence=comp_conf)
-
+ map_tmp_list = Hclassify(shp_list,stat_file,h_model_fld,var_list,os.path.join(final_fld, 'MAPS', 'VECTOR'),'_hclassif_tmp')
+ class_field = []
+ for cfield in cfieldlist:
+ class_field.append('p'+cfield)
for cshp in map_tmp_list:
map_out = cshp.replace('_tmp.shp','.shp')
- keepFields(cshp,map_out,['Segment_ID','p'+cfield,'confidence'])
+ keepFields(cshp,map_out,['Segment_ID']+class_field+['confidence'])
shpd.DeleteDataSource(cshp)
map_list.append(map_out)
if rasout == 'VRT':
- if not os.path.exists(final_fld + '/MAPS/RASTER_' + cfield):
- os.mkdir(final_fld + '/MAPS/RASTER_' + cfield)
- ras_list = []
- cmd_list = []
- for map,ref in zip(map_list,ref_list):
- ras_list.append(final_fld + '/MAPS/RASTER_' + cfield + '/' + os.path.basename(map).replace('.shp', '.tif'))
- cmd = ['otbcli_Rasterization', '-in', map, '-im', ref, '-mode', 'attribute', '-mode.attribute.field', 'p'+cfield, '-out', ras_list[-1]]
- cmd_list.append(cmd)
- queuedProcess(cmd_list,N_processes=N_proc,shell=sh)
-
- cmd = ['gdalbuildvrt', '-srcnodata', '0', '-vrtnodata', '0', final_fld + '/MAPS/RASTER_' + cfield + '/Classif_' + cfield + '.vrt'] + ras_list
- subprocess.call(cmd, shell=sh)
-
+ for cfield in cfieldlist :
+ if not os.path.exists(final_fld + '/MAPS/RASTER_' + cfield):
+ os.mkdir(final_fld + '/MAPS/RASTER_' + cfield)
+ ras_list = []
+ cmd_list = []
+ for map,ref in zip(map_list,ref_list):
+ ras_list.append(final_fld + '/MAPS/RASTER_' + cfield + '/' + os.path.basename(map).replace('.shp', '.tif'))
+ cmd = ['otbcli_Rasterization', '-in', map, '-im', ref, '-mode', 'attribute', '-mode.attribute.field', 'p'+cfield, '-out', ras_list[-1]]
+ cmd_list.append(cmd)
+ queuedProcess(cmd_list,N_processes=N_proc,shell=sh)
+ cmd = ['gdalbuildvrt', '-srcnodata', '0', '-vrtnodata', '0', final_fld + '/MAPS/RASTER_' + cfield + '/Classif_' + cfield + '.vrt'] + ras_list
+ subprocess.call(cmd, shell=sh)
if single_step:
sys.exit("Single step mode. Exiting.")
diff --git a/validationFramework.py b/validationFramework.py
index 55fbf56956af810f39ba600db3993f94960057ed..890de2a5c54a3c53b63dadb93e15a815f421e1f5 100644
--- a/validationFramework.py
+++ b/validationFramework.py
@@ -100,7 +100,7 @@ def surfaceValidation(ref_shp,val_shp,out,cfield,pfield=None):
acc = accuracy_score(y_true, y_pred, sample_weight=y_wght)
kappa = cohen_kappa_score(y_true, y_pred, sample_weight=y_wght)
prf = precision_recall_fscore_support(y_true, y_pred, sample_weight=y_wght)
- classes = sorted(np.unique(y_true))
+ classes = sorted(np.unique(y_true+y_pred))
return classes,cm,acc,kappa,prf