check_type_int for labels

pyxai/examples/DT/base.py

@@ -0,0 +1,23 @@
+from pyxai import Learning, Explainer
+
+learner = Learning.Scikitlearn("pyxai/tests/dermatology.csv", learner_type=Learning.CLASSIFICATION)
+model = learner.evaluate(method=Learning.HOLD_OUT, output=Learning.DT)
+instance, prediction = learner.get_instances(model, n=1, correct=True, predictions=[0])
+
+print("le:", learner.dict_labels)
+
+explainer = Explainer.initialize(model, instance)
+print("instance:", instance)
+print("binary representation:", explainer.binary_representation)
+
+sufficient_reason = explainer.sufficient_reason(n=1)
+print("sufficient_reason:", sufficient_reason)
+print("to_features:", explainer.to_features(sufficient_reason))
+
+instance, prediction = learner.get_instances(model, n=1, correct=False)
+explainer.set_instance(instance)
+contrastive_reason = explainer.contrastive_reason()
+print("contrastive reason", contrastive_reason)
+print("to_features:", explainer.to_features(contrastive_reason, contrastive=True))
+
+explainer.visualisation.screen(instance, contrastive_reason, contrastive=True)

pyxai/sources/learning/learner.py

@@ -166,11 +166,21 @@ def create_dict_labels(self, labels):
         index = 0
         self.dict_labels = OrderedDict()
         self.inverse_dict_labels = OrderedDict()
-        for p in labels:
-            if str(p) not in self.dict_labels:
-                self.dict_labels[str(p)] = index
-                self.inverse_dict_labels[index] = str(p)
-                index += 1
+        set_labels = set(labels)
+        check_type_int = all(numpy.issubdtype(x, numpy.integer) or isinstance(x, int) or (isinstance(x, str) and x.isnumeric()) for x in set_labels)
+        if check_type_int is True:
+            for p in labels:
+                if str(p) not in self.dict_labels:
+                    self.dict_labels[str(p)] = int(p)
+                    self.inverse_dict_labels[int(p)] = str(p)
+        else:
+            for p in labels:
+                if str(p) not in self.dict_labels:
+                    self.dict_labels[str(p)] = index
+                    self.inverse_dict_labels[index] = str(p)
+                    index += 1
+
+
 
 
     """