62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
import os
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import sys
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import unittest
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import numpy as np
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import torch
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import scipy.stats as scst
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from torch.distributions import Exponential
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PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
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MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
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sys.path.append(MODULE_ROOT)
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from pytorch_wrapper.ptdm_cdf_tensor import PtdmCdfTensor
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class TestPtdfCdfTensor(unittest.TestCase):
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def setUp(self):
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"""Set up test instance."""
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self.test_cases = {
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"Exponential": [
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(1.0, 3.0, scst.expon.cdf(3.0, scale=1)),
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(1.0, [3.0, 4.0], scst.expon.cdf(np.array([3.0, 4.0], dtype=np.float32), scale=1))
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],
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}
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def test_distributions(self):
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"""Test CDF."""
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for dist_name, cases in self.test_cases.items(): # For each distribution
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for case in cases: # For each distribution's test cases
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with self.subTest(distribution=dist_name, params=case):
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if dist_name == "Exponential":
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distribution = Exponential(torch.tensor(case[0]))
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k_tensor = torch.tensor(case[1], dtype=torch.float32)
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else:
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raise ValueError("Only exponential is supported in this unit test.")
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cdf = PtdmCdfTensor().f(distribution, k_tensor)[0]
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# Ensure return values are tensors
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self.assertTrue(isinstance(cdf, torch.Tensor), "Probability output is not a tensor")
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# Convert tensors to floats for comparison
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if isinstance(case[1], float):
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cdf = cdf.item()
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# Check if computed values are approximately equal to expected values
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self.assertAlmostEqual(cdf, case[-1], places=4,
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msg=f"{dist_name}: Expected {case[-1]}, got {cdf}")
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elif isinstance(case[1], list):
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cdf = cdf.numpy()
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self.assertTrue(
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np.allclose(cdf, case[-1], atol=1e-5)
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)
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else:
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raise ValueError("Unsupported data type.")
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if __name__ == "__main__":
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unittest.main()
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