anirudh2290 closed pull request #13498: Fix flaky test
test_random:test_randint_generator
URL: https://github.com/apache/incubator-mxnet/pull/13498
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diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 26f7762ca9b..0a4d17dc266 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1849,12 +1849,12 @@ def chi_square_check(generator, buckets, probs,
nsamples=1000000):
If the generator is continuous, the buckets should contain tuples of
(range_min, range_max) \
and the probs should be the corresponding ideal probability within the
specific ranges. \
- Otherwise, the buckets should be the possible output of the discrete
distribution and the \
+ Otherwise, the buckets should contain all the possible values generated
over the discrete distribution and the \
probs should be groud-truth probability.
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::
@@ -1906,7 +1906,6 @@ def chi_square_check(generator, buckets, probs,
nsamples=1000000):
buckets_npy[i * 2 + 1] = buckets[i][1]
else:
continuous_dist = False
- buckets_npy = np.array(buckets)
expected_freq = (nsamples * np.array(probs,
dtype=np.float32)).astype(np.int32)
if continuous_dist:
sample_bucket_ids = np.searchsorted(buckets_npy, samples, side='right')
@@ -1923,7 +1922,7 @@ 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
@@ -1946,6 +1945,8 @@ 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
-------
@@ -1961,7 +1962,7 @@ 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"
diff --git a/tests/python/unittest/test_random.py
b/tests/python/unittest/test_random.py
index 3026d31c0f9..405602f073b 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -860,25 +860,26 @@ def test_randint_extremes():
assert a>=50000000 and a<=50000010
@with_seed()
[email protected]("Flaky test:
https://github.com/apache/incubator-mxnet/issues/13446";)
def test_randint_generator():
ctx = mx.context.current_context()
for dtype in ['int32', 'int64']:
- for low, high in [(50000000,
50001000),(-50000000,-9900),(-500,199),(-2147483647,2147483647)]:
+ for low, high in [(50000000,
50001000),(-50000100,-50000000),(-500,199)]:
scale = high - low
buckets, probs = gen_buckets_probs_with_ppf(lambda x:
ss.uniform.ppf(x, loc=low, scale=scale), 5)
# Quantize bucket boundaries to reflect the actual dtype and
adjust probs accordingly
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, nrepeat=100)
+ # Scipy uses alpha = 0.01 for testing discrete distribution
generator but we are using default alpha=0.05 (higher threshold ensures
robustness)
+ # Refer -
https://github.com/scipy/scipy/blob/9f12af697763fb5f9767d5cb1280ce62456a3974/scipy/stats/tests/test_discrete_basic.py#L45
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)
+ verify_generator(generator=generator_mx_same_seed,
buckets=buckets, probs=probs, nrepeat=100)
-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')
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