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{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"id": "daef9871-0fa5-4913-add2-bf82a6f3fa1a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: nbimporter in /opt/conda/lib/python3.11/site-packages (0.3.4)\n"
]
}
],
"source": [
"!pip install nbimporter"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e112e54c-6619-46b4-8681-c6315c05edf1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
".\n",
"----------------------------------------------------------------------\n",
"Ran 1 test in 0.351s\n",
"\n",
"OK\n"
]
}
],
"source": [
"import unittest\n",
"import numpy as np\n",
"import nbimporter\n",
"from main import preprocess_data\n",
"\n",
"class TestMainNotebook(unittest.TestCase):\n",
"\n",
" def setUp(self):\n",
" \"\"\"Set up mock data for testing.\"\"\"\n",
" # Sample data resembling MNIST\n",
" self.train_images = np.random.rand(100, 28, 28) # Shape: (100, 28, 28)\n",
" self.test_images = np.random.rand(20, 28, 28) # Shape: (20, 28, 28)\n",
"\n",
" def test_preprocess_data(self):\n",
" \"\"\"Test the data preprocessing function.\"\"\"\n",
" preprocessed_train, preprocessed_test = preprocess_data(self.train_images, self.test_images)\n",
"\n",
" # Verify shapes after preprocessing\n",
" self.assertEqual(preprocessed_train.shape, (100, 28, 28, 1))\n",
" self.assertEqual(preprocessed_test.shape, (20, 28, 28, 1))\n",
"\n",
" # Verify data normalization\n",
" self.assertTrue(np.all(preprocessed_train <= 1.0))\n",
" self.assertTrue(np.all(preprocessed_test <= 1.0))\n",
"\n",
"if __name__ == '__main__':\n",
" unittest.main(argv=[''], exit=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel) *",
"language": "python",
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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