--- a
+++ b/src/testsuite.py
@@ -0,0 +1,82 @@
+import unittest
+import numpy as np
+import torch
+from utils.dataloader import Dataloader, tokenize_and_preserve_labels
+from utils.metric_tracking import MetricsTracking 
+from transformers import BertTokenizer
+import re
+
+class DataloaderTest(unittest.TestCase):
+
+    def test_tokenize_sentence(self):
+        label_to_ids = {
+           'B-MEDCOND': 0,
+           'I-MEDCOND': 1,
+           'O': 2
+        }
+        ids_to_label = {
+               0:'B-MEDCOND',
+               1:'I-MEDCOND',
+               2:'O'
+        }
+        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        max_tokens = 128
+
+        sentence = "Patient presents with glaucoma, characterized by a definitive diagnosis of pigmentary glaucoma. Intraocular pressure measures at 15 mmHg, while the visual field remains normal. Visual acuity is recorded as 20/50. The patient has not undergone prior cataract surgery, but has had LASIK surgery. Additionally, comorbid ocular diseases include macular degeneration."
+
+        tokens = "O O O B-MEDCOND O O O O O O O B-MEDCOND I-MEDCOND O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MEDCOND I-MEDCOND I-MEDCOND O B-MEDCOND I-MEDCOND O"
+
+        sentence = re.findall(r"\w+|\w+(?='s)|'s|['\".,!?;]", sentence.strip(), re.UNICODE)
+        tokens = tokens.split(" ")
+
+        t_sen, t_labl = tokenize_and_preserve_labels(sentence, tokens, tokenizer, label_to_ids, ids_to_label, max_tokens)
+
+        self.assertEqual(len(t_sen), len(t_labl))
+        self.assertEqual(t_labl.count("B-MEDCOND"), tokens.count("B-MEDCOND"))
+
+    def test_load_dataset(self):
+        label_to_ids = {
+           'B-MEDCOND': 0,
+           'I-MEDCOND': 1,
+           'O': 2
+        }
+        ids_to_label = {
+               0:'B-MEDCOND',
+               1:'I-MEDCOND',
+               2:'O'
+        }
+
+        dataloader = Dataloader(label_to_ids, ids_to_label)
+
+        dataset = dataloader.load_dataset(full = True)
+
+        self.assertEqual(len(dataset), 255)
+
+        sample = dataset.__getitem__(0)
+
+        self.assertEqual(len(sample), 4) #input_ids, attention_mask, token_type_ids, entity
+
+
+
+class MetricsTrackingTest(unittest.TestCase):
+
+    def test_avg_metrics(self):
+        predictions =  np.array([-100, 0, 0, 0, 1, 1, 1, 2, 2, 2])
+        ground_truth = np.array([-100, 0, 1, 0, 1, 2, 1, 2, 0, 2]) #arbitrary, should return 67% for each metric
+        
+        predictions = torch.from_numpy(predictions)
+        ground_truth = torch.from_numpy(ground_truth)
+
+        tracker = MetricsTracking()
+        tracker.update(predictions, ground_truth, 0.1)
+
+        metrics = tracker.return_avg_metrics(1) #tracker only updated once
+
+        self.assertEqual(metrics['acc'], 0.667)
+        self.assertEqual(metrics['f1'], 0.667)
+        self.assertEqual(metrics['precision'], 0.667)
+        self.assertEqual(metrics['recall'], 0.667)
+        self.assertEqual(metrics['loss'], 0.1)
+
+if __name__ == '__main__':
+    unittest.main()