test causal fidelity tabular

tests/metrics/test_fidelity.py

@@ -1,8 +1,8 @@
 import tensorflow as tf
 import numpy as np
 
-from ..utils import generate_model, generate_timeseries_model, generate_data, almost_equal
-from xplique.metrics import Insertion, Deletion, MuFidelity, InsertionTS, DeletionTS
+from ..utils import generate_model, generate_timeseries_model, generate_regression_model, generate_data, almost_equal
+from xplique.metrics import Insertion, Deletion, MuFidelity, InsertionTS, DeletionTS, InsertionTab, DeletionTab
 
 
 def test_mu_fidelity():
@@ -70,6 +70,32 @@ def test_perturbation_metrics():
                         assert 0.0 <= score <= 1.0
 
 
+def test_regression_metrics():
+    # ensure we can compute insertion/deletion metric with consistent arguments
+    input_shape, nb_labels, nb_samples = ((20, 10), 5, 50)
+    x, y = generate_data(input_shape, nb_labels, nb_samples)
+    model = generate_regression_model(input_shape, nb_labels)
+    explanations = np.random.uniform(0, 1, x.shape)
+
+    for step in [5, 10]:
+        for baseline_mode in [0.0, lambda x: x-0.5]:
+            for metric in ["loss", "accuracy"]:
+                score_insertion = InsertionTab(
+                    model, x, y, metric=metric, baseline_mode=baseline_mode,
+                    steps=step, max_percentage_perturbed=0.2,
+                )(explanations)
+                score_deletion = DeletionTab(
+                    model, x, y, metric=metric, baseline_mode=baseline_mode,
+                    steps=step, max_percentage_perturbed=0.2,
+                )(explanations)
+
+                for score in [score_insertion, score_deletion]:
+                    if metric == "loss":
+                        assert 0.0 < score
+                    elif score == "accuracy":
+                        assert 0.0 <= score <= 1.0
+
+
 def test_perfect_correlation():
     """Ensure we get perfect score if the correlation is perfect"""
     # we ensure perfect correlation if the model return the sum of the input,

tests/utils.py

@@ -42,8 +42,8 @@ def generate_regression_model(features_shape, output_shape=1):
     model.add(Dense(4, activation='relu'))
     model.add(Dense(4, activation='relu'))
     model.add(Dense(output_shape))
-    model.compile(loss='mean_absolute_error',
-                  optimizer='sgd')
+    model.compile(loss='mean_absolute_error', optimizer='sgd',
+                  metrics=['accuracy'])
 
     return model