tests: tests online statistics

tests/attributions/test_online_statistics.py

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+"""
+Test that online statistics give similar results to normal ones
+"""
+
+import numpy as np
+import tensorflow as tf
+
+from xplique.attributions import SmoothGrad, VarGrad, SquareGrad
+
+from ..utils import almost_equal
+
+
+def test_online_mean():
+    method = SmoothGrad(lambda x: None)
+    for shape in [(1, 7, 4), (5, 2, 4, 4), (5, 7, 4, 4, 3)]:
+        samples = tf.reshape(tf.range(int(np.prod(shape)), dtype=tf.float32), shape)
+
+        normal_stat = tf.reduce_mean(samples, axis=1)
+        method._initialize_online_statistic()
+
+        for i in range(int(np.ceil(shape[1] / 2))):
+            sample = samples[:, 2 * i:2 * (i + 1)]
+            method._update_online_statistic(sample)
+
+        assert almost_equal(normal_stat, method._get_online_statistic_final_value())
+
+
+def test_online_square_mean():
+    method = SquareGrad(lambda x: None)
+    for shape in [(1, 7, 4), (5, 2, 4, 4), (5, 7, 4, 4, 3)]:
+        samples = tf.reshape(tf.range(int(np.prod(shape)), dtype=tf.float32), shape)
+
+        normal_stat = tf.reduce_mean(samples**2, axis=1)
+        method._initialize_online_statistic()
+
+        for i in range(int(np.ceil(shape[1] / 2))):
+            sample = samples[:, 2 * i:2 * (i + 1)]
+            method._update_online_statistic(sample)
+
+        assert almost_equal(normal_stat, method._get_online_statistic_final_value())
+
+
+def test_online_variance():
+    method = VarGrad(lambda x: None)
+    for shape in [(1, 7, 4), (5, 2, 4, 4), (5, 7, 4, 4, 3)]:
+        samples = tf.reshape(tf.range(int(np.prod(shape)), dtype=tf.float32), shape)
+
+        normal_stat = tf.math.reduce_variance(samples, axis=1) * (shape[1] / (shape[1] - 1))
+        method._initialize_online_statistic()
+
+        for i in range(int(np.ceil(shape[1] / 2))):
+            sample = samples[:, 2 * i:2 * (i + 1)]
+            method._update_online_statistic(sample)
+
+        assert almost_equal(normal_stat, method._get_online_statistic_final_value())