Fix flaky test test_random:test_randint_generator

python/mxnet/test_utils.py

@@ -1854,7 +1854,7 @@ def chi_square_check(generator, buckets, probs, nsamples=1000000):
 
     Usually the user is required to specify the probs parameter.
 
-    After obtatining the p value, we could further use the standard p > 0.05 threshold to get \
+    After obtaining the p value, we could further use the standard p > 0.05 (alpha) threshold to get \
     the final result.
 
     Examples::
@@ -1920,7 +1920,7 @@ def chi_square_check(generator, buckets, probs, nsamples=1000000):
     _, p = ss.chisquare(f_obs=obs_freq, f_exp=expected_freq)
     return p, obs_freq, expected_freq
 
-def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, success_rate=0.15):
+def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, success_rate=0.25, alpha=0.05):
     """Verify whether the generator is correct using chi-square testing.
 
     The test is repeated for "nrepeat" times and we check if the success rate is
@@ -1943,6 +1943,8 @@ def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, suc
         The times to repeat the test
     success_rate: float
         The desired success rate
+    alpha: float
+        The desired threshold for type-I error i.e. when a true null hypothesis is rejected
 
     Returns
     -------
@@ -1958,7 +1960,7 @@ def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, suc
         cs_ret_l.append(cs_ret)
         obs_freq_l.append(obs_freq)
         expected_freq_l.append(expected_freq)
-    success_num = (np.array(cs_ret_l) > 0.05).sum()
+    success_num = (np.array(cs_ret_l) > alpha).sum()
     if success_num < nrepeat * success_rate:
         raise AssertionError("Generator test fails, Chi-square p=%s, obs_freq=%s, expected_freq=%s."
                              "\nbuckets=%s, probs=%s"

tests/python/unittest/test_random.py

@@ -859,8 +859,8 @@ def test_randint_extremes():
     a = mx.nd.random.randint(dtype='int64', low=50000000, high=50000010, ctx=mx.context.current_context())
     assert a>=50000000 and a<=50000010
 
-@with_seed()
-@unittest.skip("Flaky test: https://github.com/apache/incubator-mxnet/issues/13446")
+# Seed set because the test is not robust enough to operate on random data
+@with_seed(1234)
 def test_randint_generator():
     ctx = mx.context.current_context()
     for dtype in ['int32', 'int64']:
@@ -871,14 +871,15 @@ def test_randint_generator():
             buckets = np.array(buckets, dtype=dtype).tolist()
             probs = [(buckets[i][1] - buckets[i][0]) / float(scale) for i in range(5)]
             generator_mx = lambda x: mx.nd.random.randint(low, high, shape=x, ctx=ctx, dtype=dtype).asnumpy()
-            verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+            verify_generator(generator=generator_mx, buckets=buckets, probs=probs, alpha=0.01)
+            # Scipy uses alpha = 0.01 for testing discrete distribution generator
             generator_mx_same_seed = \
                 lambda x: np.concatenate(
                     [mx.nd.random.randint(low, high, shape=x // 10, ctx=ctx, dtype=dtype).asnumpy()
                         for _ in range(10)])
             verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs)
 
-with_seed()
+@with_seed()
 def test_randint_without_dtype():
     a = mx.nd.random.randint(low=50000000, high=50000010, ctx=mx.context.current_context())
     assert(a.dtype, 'int32')