hpc...

Author: Ciaran1981

geospatial_learn/learning.py

@@ -465,7 +465,8 @@ class names in order of their numercial equivalents
 def create_model(X_train, outModel, clf='erf', group=None, random=False,
                  cv=5, params=None, pipe='default', cores=-1, strat=True, 
                  test_size=0.3, regress=False, return_test=True,
-                 scoring=None, class_names=None, save=True, cat_feat=None):
+                 scoring=None, class_names=None, save=True, cat_feat=None,
+                 plot=True):
 
     """
     Brute force or random model creating using scikit learn.
@@ -679,24 +680,25 @@ class names in order of their numercial equivalents
     testresult = grid.best_estimator_.predict(X_test)
 
     if regress == True:
-        regrslt = regression_results(y_test, testresult)
+        regrslt = regression_results(y_test, testresult, plot=plot)
         results = [grid]
 
     else:
         crDf = hp.plot_classif_report(y_test, testresult, target_names=class_names,
                                       save=outModel[:-3]+'._classif_report.png')
 
         confmat = metrics.confusion_matrix(testresult, y_test, labels=class_names)
-        disp = metrics.ConfusionMatrixDisplay(confusion_matrix=confmat,
-                                      display_labels=class_names)
-        disp.plot()
+        
+        if plot == True:
+            disp = metrics.ConfusionMatrixDisplay(confusion_matrix=confmat,
+                                          display_labels=class_names)
+            disp.plot()
 
         # confmat = hp.plt_confmat(X_test, y_test, grid.best_estimator_, 
         #                          class_names=class_names, 
         #                          cmap=plt.cm.Blues, 
         #                          fmt="%d", 
-        #                          save=outModel[:-3]+'_confmat.png')
-        
+        #                          save=outModel[:-3]+'_confmat.png')    
         results = [grid, crDf, confmat]
 
     if return_test == True:
@@ -861,7 +863,7 @@ class names in order of their numercial equivalents
     return comb, X_test, y_test 
 
 
-def regression_results(y_true, y_pred):
+def regression_results(y_true, y_pred, plot=True):
 
     # Regression metrics
     explained_variance = metrics.explained_variance_score(y_true, y_pred)
@@ -878,15 +880,16 @@ def regression_results(y_true, y_pred):
     print('MSE: ', round(mse,4))
     print('MedianAE', round(median_absolute_error, 4))
     print('RMSE: ', round(np.sqrt(mse),4))   
-    #TODO add when sklearn updated    
-    display = metrics.PredictionErrorDisplay.from_predictions(
-        y_true=y_true,
-        y_pred=y_pred,
-        kind="actual_vs_predicted",
-        #ax=ax,
-        scatter_kwargs={"alpha": 0.2, "color": "tab:blue"},
-        line_kwargs={"color": "tab:red"},
-    )
+    #TODO add when sklearn updated
+    if plot == True:    
+        display = metrics.PredictionErrorDisplay.from_predictions(
+            y_true=y_true,
+            y_pred=y_pred,
+            kind="actual_vs_predicted",
+            #ax=ax,
+            scatter_kwargs={"alpha": 0.2, "color": "tab:blue"},
+            line_kwargs={"color": "tab:red"},
+        )
 
 
 

geospatial_learn/shape.py

@@ -2426,7 +2426,7 @@ def meshgrid(inRaster, outShp, gridHeight=1, gridWidth=1):
     bwRas = inRaster[:-4]+'bw.tif'
     maskShp = inRaster[:-4]+'bwmask.shp'
     array2raster(bw, 1, inRaster, bwRas,  gdal.GDT_Byte)
-    polygonize(bwRas, maskShp, outField=None,  mask = True, band = 1)
+    polygonize(bwRas, maskShp, outField=None,  mask=True, band=1)
 
     inRas = None
     del bw, tempIm