remove positive_terms parameters and clarify classes values

descriptor.json

@@ -1,5 +1,5 @@
 {
-  "command-line": "python run.py CYTOMINE_HOST CYTOMINE_PUBLIC_KEY CYTOMINE_PRIVATE_KEY CYTOMINE_ID_PROJECT CYTOMINE_ID_SOFTWARE CYTOMINE_ID_USERS CYTOMINE_ID_IMAGES CYTOMINE_ID_TERMS CYTOMINE_POSITIVE_TERMS CYTOMINE_REVIEWED CYTOMINE_ZOOM_LEVEL CYTOMINE_BINARY PYXIT_TARGET_WIDTH PYXIT_TARGET_HEIGHT N_JOBS PYXIT_TRANSPOSE PYXIT_N_SUBWINDOWS PYXIT_INTERPOLATION PYXIT_FIXED_SIZE PYXIT_COLORSPACE PYXIT_MIN_SIZE PYXIT_MAX_SIZE FOREST_N_ESTIMATORS FOREST_MIN_SAMPLES_SPLIT FOREST_MAX_FEATURES SVM_C SEED SVM",
+  "command-line": "python run.py CYTOMINE_HOST CYTOMINE_PUBLIC_KEY CYTOMINE_PRIVATE_KEY CYTOMINE_ID_PROJECT CYTOMINE_ID_SOFTWARE CYTOMINE_ID_USERS CYTOMINE_ID_IMAGES CYTOMINE_ID_TERMS CYTOMINE_REVIEWED CYTOMINE_ZOOM_LEVEL CYTOMINE_BINARY PYXIT_TARGET_WIDTH PYXIT_TARGET_HEIGHT N_JOBS PYXIT_TRANSPOSE PYXIT_N_SUBWINDOWS PYXIT_INTERPOLATION PYXIT_FIXED_SIZE PYXIT_COLORSPACE PYXIT_MIN_SIZE PYXIT_MAX_SIZE FOREST_N_ESTIMATORS FOREST_MIN_SAMPLES_SPLIT FOREST_MAX_FEATURES SVM_C SEED SVM",
   "inputs": [
     {
       "name": "Cytomine host",
@@ -93,27 +93,14 @@
     {
       "default-value": false,
       "name": "Binary classification",
-      "description": "Whether (true) or not (false) to treat the problem as a binary classification or not. If true cytomine_positive_terms should contain the list of terms considered as positive. Otherwise, there will be as many classes as terms.",
+      "description": "Whether or not to treat the problem as a binary classification or not. Annotations with terms given in cytomine_id_terms are considered foreground, all others are considered background.",
       "set-by-server": false,
       "value-key": "@ID",
       "optional": true,
       "id": "cytomine_binary",
       "type": "Boolean",
       "command-line-flag": "--@id"
     },
-    {
-      "default-value": "",
-      "name": "Cytomine positive terms",
-      "description": "Contains the ids of the terms that represent the positive class.",
-      "set-by-server": false,
-      "value-key": "@ID",
-      "optional": true,
-      "id": "cytomine_positive_terms",
-      "type": "ListDomain",
-      "uri": "/api/project/$currentProject$/term.json",
-      "uri-print-attribute": "name",
-      "command-line-flag": "--@id"
-    },
     {
       "default-value": 0,
       "name": "Cytomine reviewed",

run.py

@@ -31,27 +31,28 @@ def main(argv):
 
         # transform classes
         cj.job.update(progress=50, statusComment="Transform classes...")
-        classes = parse_domain_list(cj.parameters.cytomine_id_terms)
-        positive_classes = parse_domain_list(cj.parameters.cytomine_positive_terms)
-        classes = np.array(classes) if len(classes) > 0 else np.unique(y)
-        n_classes = classes.shape[0]
+        foreground_classes = parse_domain_list(cj.parameters.cytomine_id_terms)
+        foreground_classes = np.array(foreground_classes) if len(foreground_classes) > 0 else np.unique(y)
+        n_classes = foreground_classes.shape[0] + 1
 
         # filter unwanted terms
         cj.logger.info("Size before filtering:")
         cj.logger.info(" - x: {}".format(x.shape))
         cj.logger.info(" - y: {}".format(y.shape))
-        keep = np.in1d(y, classes)
+        keep = np.in1d(y, foreground_classes)
         x, y = x[keep], y[keep]
         cj.logger.info("Size after filtering:")
         cj.logger.info(" - x: {}".format(x.shape))
         cj.logger.info(" - y: {}".format(y.shape))
 
         if cj.parameters.cytomine_binary:
+            # 0 (background) vs 1 (classes from cytomine_id_terms)
             cj.logger.info("Will be training on 2 classes ({} classes before binarization).".format(n_classes))
-            y = np.in1d(y, positive_classes).astype(np.int)
+            y = np.ones(y.shape)
         else:
+            # 0 (background vs 1 vs 2 vs ... n (n classes from cytomine_id_terms)
             cj.logger.info("Will be training on {} classes.".format(n_classes))
-            y = np.searchsorted(classes, y)
+            y = np.searchsorted(foreground_classes, y) + 1  # +1 to prevent 0 to be used as a foreground class
 
         # build model
         cj.job.update(progress=55, statusComment="Build model...")
@@ -91,9 +92,8 @@ def main(argv):
             domainClassName="be.cytomine.processing.Job"
         ).upload()
 
-        Property(cj.job, key="classes", value=stringify(classes)).save()
+        Property(cj.job, key="foreground_classes", value=stringify(foreground_classes)).save()
         Property(cj.job, key="binary", value=cj.parameters.cytomine_binary).save()
-        Property(cj.job, key="positive_classes", value=stringify(positive_classes)).save()
 
         cj.job.update(status=Job.TERMINATED, status_comment="Finish", progress=100)