enhance calculation of nb classes + tests (#45)

* update get_nb_classes method to handle 1-hot and scalar input

tests/test_model.py

@@ -2,7 +2,7 @@ import pytest
 import numpy as np
 
 from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, \
-    BlackboxClassifierPredictions, BlackboxClassifierPredictFunction
+    BlackboxClassifierPredictions, BlackboxClassifierPredictFunction, is_one_hot, get_nb_classes
 from apt.utils.datasets import ArrayDataset, Data
 from apt.utils import dataset_utils
 
@@ -13,6 +13,9 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Input
 
 
+from art.utils import to_categorical
+
+
 def test_sklearn_classifier():
     (x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
     underlying_model = RandomForestClassifier()
@@ -181,4 +184,33 @@ def test_blackbox_classifier_predict():
     score = model.score(train)
     assert (0.0 <= score <= 1.0)
 
+def test_is_one_hot():
+    (_, y_train), (_, _) = dataset_utils.get_iris_dataset_np()
+
+    assert (not is_one_hot(y_train))
+    assert (not is_one_hot(y_train.reshape(-1,1)))
+    assert (is_one_hot(to_categorical(y_train)))
+
+def test_get_nb_classes():
+    (_, y_train), (_, y_test) = dataset_utils.get_iris_dataset_np()
+
+    # shape: (x,) - not 1-hot
+    nb_classes_test = get_nb_classes(y_test)
+    nb_classes_train = get_nb_classes(y_train)
+    assert (nb_classes_test == nb_classes_train)
+    assert (nb_classes_test == 3)
+
+    # shape: (x,1) - not 1-hot
+    nb_classes_test = get_nb_classes(y_test.reshape(-1,1))
+    assert (nb_classes_test == 3)
+
+    # shape: (x,3) - 1-hot
+    y = to_categorical(y_test)
+    nb_classes = get_nb_classes(y)
+    assert (nb_classes == 3)
+
+    # gaps: 1,2,4 (0,3 missing)
+    y_test[y_test == 0] = 4
+    nb_classes = get_nb_classes(y_test)
+    assert (nb_classes == 5)