test fidelity: adapt to harmonization

tests/metrics/test_fidelity.py

@@ -4,6 +4,7 @@
 from ..utils import generate_model, generate_timeseries_model, generate_data, almost_equal
 from xplique.metrics import Insertion, Deletion, MuFidelity, InsertionTS, DeletionTS
 
+
 def test_mu_fidelity():
     # ensure we can compute the metric with consistents arguments
     input_shape, nb_labels, nb_samples = ((32, 32, 3), 10, 20)
@@ -50,20 +51,23 @@ def test_perturbation_metrics():
     model = generate_timeseries_model(input_shape, nb_labels)
     explanations = np.random.uniform(0, 1, x.shape)
 
-    for step in [-1, 2, 10]:
-        for max_percentage_perturbed in [0.2, 1.0]:
-            for baseline_mode in [0.0, "zero", "inverse", "negative"]:
+    for step in [-1, 10]:
+        for baseline_mode in [0.0, "inverse"]:
+            for metric in ["loss", "accuracy"]:
                 score_insertion = InsertionTS(
-                    model, x, y, metric="loss", baseline_mode=baseline_mode,
-                    steps=step, max_percentage_perturbed=max_percentage_perturbed,
+                    model, x, y, metric=metric, baseline_mode=baseline_mode,
+                    steps=step, max_percentage_perturbed=0.2,
                 )(explanations)
                 score_deletion = DeletionTS(
-                    model, x, y, metric="loss", baseline_mode=baseline_mode,
-                    steps=step, max_percentage_perturbed=max_percentage_perturbed,
+                    model, x, y, metric=metric, baseline_mode=baseline_mode,
+                    steps=step, max_percentage_perturbed=0.2,
                 )(explanations)
 
                 for score in [score_insertion, score_deletion]:
-                    assert 0.0 < score < 1
+                    if metric == "loss":
+                        assert 0.0 < score
+                    elif score == "accuracy":
+                        assert 0.0 <= score <= 1.0
 
 
 def test_perfect_correlation():

tests/utils.py

@@ -30,7 +30,8 @@ def generate_timeseries_model(input_shape=(20, 10), output_shape=10):
     model.add(GlobalAveragePooling1D())
     model.add(Dense(output_shape))
     model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy', optimizer='sgd')
+    model.compile(loss='categorical_crossentropy', optimizer='sgd',
+                  metrics=['accuracy'])
 
     return model