Fixes to script

benchmarks/train_on_subset_of_points.py

@@ -6,25 +6,24 @@
 from cropharvest.datasets import CropHarvest, Task, CropHarvestLabels
 from cropharvest.engineer import TestInstance
 from cropharvest.columns import RequiredColumns
+from cropharvest.bbox import BBox
 
 from sklearn.ensemble import RandomForestClassifier
 
 
-def select_points(evaluation_dataset: CropHarvest, all_labels: CropHarvestLabels) -> pd.DataFrame:
+def select_points(bounding_box: BBox, all_labels: CropHarvestLabels) -> pd.DataFrame:
     """
     This is what participants would implement.
-    Given an evaluation dataset, they would need
+    Given an evaluation dataset's bounding box, they would need
     to implement some method of selecting points against
-    which a model will be trained. In this example code, I
-    show two examples - one uses bounding boxes, the other directly
-    overwrites the CropHarvestLabels geojson
+    which a model will be trained
     """
     # let's manually select points in the labels to be within the bounding box.
     # 1. Make a new geojson. We do it according to the bounding boxes but this could be done
     # in any way
     filtered_geojson = all_labels.filter_geojson(
         all_labels.as_geojson(),
-        evaluation_dataset.task.bounding_box,
+        bounding_box,
         include_external_contributions=True,
     )
 
@@ -91,8 +90,8 @@ def main():
     results_folder = DATAFOLDER_PATH / "data_centric_test"
     results_folder.mkdir(exist_ok=True)
 
-    togo_eval = [x for x in evaluation_datasets if "Togo" in x.task.name]
-    training_points_df = select_points(togo_eval, all_labels)
+    togo_eval = [x for x in evaluation_datasets if "Togo" in x.task.id][0]
+    training_points_df = select_points(togo_eval.task.bounding_box, all_labels)
     train_and_eval(training_points_df, togo_eval, results_folder)