adressed #845

enmapboxprocessing/algorithm/classificationperformancesimplealgorithm.py

@@ -28,7 +28,8 @@ def shortDescription(self) -> str:
                'Stehman (2014): https://doi.org/10.1080/01431161.2014.930207. ' \
                'Note that (simple) random sampling is a special case of stratified random sampling, ' \
                'with exactly one stratum. \n' \
-               'Observed and predicted categories are matched by name.'
+               'Observed and predicted categories are matched by name, if possible. ' \
+               'Otherwise, categories are matched by order (in this case, a warning message is logged).'
 
     def helpParameters(self) -> List[Tuple[str, str]]:
         return [

enmapboxprocessing/algorithm/classificationperformancestratifiedalgorithm.py

@@ -9,14 +9,14 @@
 
 import numpy as np
 
+from enmapbox.typeguard import typechecked
 from enmapboxprocessing.algorithm.rasterizecategorizedvectoralgorithm import RasterizeCategorizedVectorAlgorithm
 from enmapboxprocessing.algorithm.translatecategorizedrasteralgorithm import TranslateCategorizedRasterAlgorithm
 from enmapboxprocessing.enmapalgorithm import EnMAPProcessingAlgorithm, Group
 from enmapboxprocessing.rasterreader import RasterReader
 from enmapboxprocessing.reportwriter import HtmlReportWriter, CsvReportWriter, MultiReportWriter
 from enmapboxprocessing.utils import Utils
 from qgis.core import (QgsProcessingContext, QgsProcessingFeedback, QgsVectorLayer, QgsRasterLayer, QgsUnitTypes)
-from enmapbox.typeguard import typechecked
 
 
 @typechecked
@@ -34,7 +34,8 @@ def displayName(cls) -> str:
     def shortDescription(self) -> str:
         return 'Estimates map accuracy and area proportions for stratified random sampling as described in ' \
                'Stehman (2014): https://doi.org/10.1080/01431161.2014.930207. \n' \
-               'Observed and predicted categories are matched by name.'
+               'Observed and predicted categories are matched by name, if possible. ' \
+               'Otherwise, categories are matched by order (in this case, a warning message is logged).'
 
     def helpParameters(self) -> List[Tuple[str, str]]:
         return [
@@ -168,17 +169,22 @@ def processAlgorithm(
             yMap = arrayPrediction[valid].astype(np.float32)
             # - remap class ids by name
             yMapRemapped = yMap.copy()  # this initial state is correct for matching by order (see #845)
+            classNamesMatching = list()
             for i, cP in enumerate(categoriesPrediction):
                 found = False
                 for cR in categoriesReference:
                     if cR.name == cP.name:
                         yMapRemapped[yMap == cP.value] = cR.value
                         found = True
+                        classNamesMatching.append([cP.name, cR.name])
                 if not found:
                     feedback.pushWarning(
-                        f'predicted class "{categoriesPrediction[i].name}" not found in reference classes, '
-                        f'and will be matched by order to class "".'
+                        f'predicted class "{categoriesPrediction[i].name}" not found in reference classes. '
+                        f'class will be matched by order: '
+                        f'"{cP.name}" -> "{categoriesReference[i].name}".'
                     )
+                    classNamesMatching.append([cP.name, categoriesReference[i].name])
+
             yMap = yMapRemapped
             # - prepare strata
             stratum = arrayStratification[valid]
@@ -197,7 +203,10 @@ def processAlgorithm(
             stats = stratifiedAccuracyAssessment(stratum, yReference, yMap, h, N_h, classValues, classNames)
             pixelUnits = QgsUnitTypes.toString(classification.crs().mapUnits())
             pixelArea = classification.rasterUnitsPerPixelX() * classification.rasterUnitsPerPixelY()
-            self.writeReport(filename, stats, pixelUnits=pixelUnits, pixelArea=pixelArea)
+
+            self.writeReport(
+                filename, stats, pixelUnits=pixelUnits, pixelArea=pixelArea, classNamesMatching=classNamesMatching
+            )
             # dump json
             with open(filename + '.json', 'w') as file:
                 file.write(json.dumps(stats.__dict__, indent=4))
@@ -211,7 +220,10 @@ def processAlgorithm(
         return result
 
     @classmethod
-    def writeReport(cls, filename: str, stats: 'StratifiedAccuracyAssessmentResult', pixelUnits='pixel', pixelArea=1.):
+    def writeReport(
+            cls, filename: str, stats: 'StratifiedAccuracyAssessmentResult', pixelUnits='pixel', pixelArea=1.,
+            classNamesMatching: list = None
+    ):
 
         def smartRound(obj, ndigits):
             if isinstance(obj, list):
@@ -242,6 +254,9 @@ def confidenceIntervall(mean, se):
             report.writeParagraph(f'Sample size: {stats.n} px')
             report.writeParagraph(f'Area size: {smartRound(stats.N, 2)} {pixelUnits}')
 
+            if classNamesMatching is not None:
+                report.writeTable(classNamesMatching, 'Class matching', ['predicted', 'observed'])
+
             values = smartRound(stats.confusion_matrix_counts, 2)
             report.writeTable(
                 values, 'Adjusted confusion matrix counts: predicted (rows) vs. observed (columns)',