Baunsgaard commented on a change in pull request #892: Extend Python API (rand,
lm, matrix multiplication)
URL: https://github.com/apache/systemml/pull/892#discussion_r410381855
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File path: src/main/python/tests/test_matrix_aggregations.py
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@@ -84,6 +86,63 @@ def test_var2(self):
def test_var3(self):
self.assertTrue(np.allclose(sds.matrix(m1).var(axis=1).compute(),
m1.var(axis=1, ddof=1).reshape(dim, 1)))
+ def test_rand_basic(self):
+ seed = 15
+ shape = (20, 20)
+ min_max = (0, 1)
+ sparsity = 0.2
+
+ m = sds.rand(rows=shape[0], cols=shape[1], pdf="uniform",
min=min_max[0], max=min_max[1],
+ seed=seed, sparsity=sparsity).compute()
+
+ self.assertTrue(m.shape == shape)
+ self.assertTrue((m.min() >= min_max[0]) and (m.max() <= min_max[1]))
+
+ # sparsity
+ m_flat = m.flatten('F')
+ count, bins, patches = plt.hist(m_flat)
+
+ non_zero_value_percent = sum(count[1:]) * 100 / count[0]
+ e = 0.05
+ self.assertTrue((non_zero_value_percent >= (sparsity - e) * 100)
+ and (non_zero_value_percent <= (sparsity + e) * 100))
+ self.assertTrue(sum(count) == (shape[0] * shape[1]))
Review comment:
Good practice is to only assert one thing in each test.
This is sometimes hard, to correlate with not duplicating code or repeating
steps, but possible if some thought is put into it.
(Low Priority)
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