fix seed in random samples (rather than hardcoded) and fold definition

ml.py

@@ -223,7 +223,6 @@ class MLAlgorithm(SHPAlgorithm):
                 ## confusion matrix is a np array that does not fit in a json
                 best_metrics_dict.pop('conf_matrix', None)
                 best_metrics_dict.pop('class_report', None)
-                print(best_metrics_dict)
                 json.dump(best_metrics_dict, json_file, indent=4)
 
         self.infer_model(feedback)
@@ -296,7 +295,7 @@ class MLAlgorithm(SHPAlgorithm):
 
             feedback.pushInfo(f'before samples: {len(gdf)}')
             ## get random samples if geometry is not point based
-            gdf = get_random_samples_in_gdf(gdf, random_samples)
+            gdf = get_random_samples_in_gdf(gdf, random_samples, seed=self.seed)
 
             feedback.pushInfo(f'before extent: {len(gdf)}')
             bounds = box(
@@ -374,6 +373,7 @@ class MLAlgorithm(SHPAlgorithm):
             if fold_col.strip() != '' :
                 self.gdf[self.fold_col] = self.gdf[fold_col]
             else:
+                np.random.seed(self.seed)
                 self.gdf[self.fold_col] = np.random.randint(1, nfolds + 1, size=len(self.gdf))
         ## Else, self.gdf is the train set
         else:

tests/test_common.py

@@ -59,7 +59,7 @@ class TestSimAlgorithm(TestReductionAlgorithm):
 class TestMLAlgorithm(TestReductionAlgorithm):
     algorithm = MLAlgorithm()
     default_parameters = {'INPUT': INPUT,'OUTPUT': OUTPUT,'TEMPLATE':TEMPLATE_RF,'GT_COL': GT_COL}
-    possible_hashes = ['514fc247e4765ca34895a3d6cb9bffd6']
+    possible_hashes = ['bd22d66180347e043fca58d494876184']
     out_name = 'ml.tif'
 
 if __name__ == "__main__":