--- a
+++ b/tests/test_data.py
@@ -0,0 +1,81 @@
+"""
+Test suite.
+"""
+
+import unittest
+import itertools
+import torch
+
+from continual.src.utils import data_processing
+
+BATCH_SIZES = (1, 10, 100)
+SEQ_LENS = (4, 12, 48)
+N_VARS = (2, 10, 100)
+N_CLASSES = (2, 10)
+N_LAYERS = (1, 2, 3, 4)
+HIDDEN_SIZES = (32, 64, 128)
+
+
+DEMOGRAPHICS = [
+    "age",
+    "gender",
+    "ethnicity",
+    "region",
+    "time_year",
+    "time_season",
+    "time_month",
+]
+OUTCOMES = ["ARF", "shock", "mortality"]
+DATASETS = ["MIMIC", "eICU"]
+
+
+class TestDataLoadingMethods(unittest.TestCase):
+    """
+    Data loading tests.
+    """
+
+    def test_modalfeatvalfromseq(self):
+        """
+        Test that mode of correct dim is returned.
+        """
+        for n_samples in BATCH_SIZES:
+            for seq_len in SEQ_LENS:
+                for n_feats in N_VARS:
+                    for i in range(n_feats):
+                        sim_data = (
+                            torch.randint(0, 1, (n_samples, seq_len, n_feats))
+                            .clone()
+                            .detach()
+                            .numpy()
+                        )
+                        modes = data_processing.get_modes(sim_data, feat=i)
+                        self.assertEqual(modes.shape, torch.Size([n_samples]))
+
+
+# CL task split tests
+class TestCLConstructionMethods(unittest.TestCase):
+    """
+    Test construction of Continual Learning task splits.
+    """
+
+    def ttest_taskidsnonoverlap(self):
+        for dataset in DATASETS:
+            for experiment in OUTCOMES:
+                for demographic in DEMOGRAPHICS:
+                    # JA: implement
+                    tasks = data_processing.load_data(dataset, demographic, experiment)
+                    for pair in itertools.combinations(tasks, repeat=2):
+                        self.assertTrue(pair[0][:, 0].intersection(pair[0][:, 0]) == {})
+
+    def ttest_tasktargets(self):
+        for dataset in DATASETS:
+            for experiment in OUTCOMES:
+                for demographic in DEMOGRAPHICS:
+                    # JA: implement
+                    tasks = data_processing.load_data(dataset, demographic, experiment)
+                    for task in tasks:
+                        self.assertTrue(len(task[:, -1].unique()) == 2)
+
+
+if __name__ == "__main__":
+    unittest.main()