v3.4.1

dzetsaka.py

@@ -41,6 +41,20 @@
 
 from .dzetsaka_provider import dzetsakaProvider
 
+### In order to use RF/SVM/KNN
+try:
+    import pip
+except:
+    execfile(os.path.join(self.plugin_dir, get_pip.py))
+    import pip
+    # just in case the included version is old
+    pip.main(['install','--upgrade','pip'])
+
+try:
+    from sklearn import datasets
+except:
+    pip.main(['install','-U' , 'scikit-learn'])
+
 
 class dzetsakaGUI ( QDialog ):
     """QGIS Plugin Implementation."""
@@ -103,7 +117,7 @@ def __init__(self, iface):
         #self.run()
 
     def rememberLastSaveDir(self,fileName):
-        """!@brief Remember last saved dir when saving or loading file"""
+        """!@brief Remember last sd dir when saving or loading file"""
         if fileName != '':
             self.lastSaveDir = fileName
             self.settings.setValue('/dzetsaka/lastSaveDir',self.lastSaveDir)

metadata.txt

@@ -10,7 +10,7 @@
 name=dzetsaka : Classification tool
 qgisMinimumVersion=3.0
 description=Fast and Easy Classification plugin for Qgis
-version=3.4
+version=3.4.1
 author=Nicolas Karasiak
 [email protected]
 
@@ -29,6 +29,9 @@ repository=http://www.github.com/lennepkade/dzetsaka
 
 # Uncomment the following line and add your changelog:
 changelog=
+        3.4.1
+	  * Automatically install sklearn (if pip is installed)
+	  * Precise in the confusion matrix that lines are references and columns prediction.
 	3.4
 	  * Add welcome message if first installation, with, I hope, good tips for new users
     	3.3.1

scripts/mainfunction.py

@@ -497,7 +497,7 @@ def __init__(self,inRaster,inVector,inField='Class',outModel=None,inSplit=100,in
                 if outMatrix is not None:
                     if not os.path.exists(os.path.dirname(outMatrix)):
                         os.makedirs(os.path.dirname(outMatrix))
-                    np.savetxt(outMatrix,CONF.confusion_matrix,delimiter=',',fmt='%1.4d')
+                    np.savetxt(outMatrix,CONF.confusion_matrix,delimiter=',',header='Columns=prediction,Lines=Reference.',fmt='%1.4d')
 
                 if inClassifier !='GMM':
                     for key in param_grid.keys():