def test_remove_scan_op():
with pm.Model():
pm.Normal('n', 0, 1)
inference = ADVI()
buff = io.StringIO()
inference.run_profiling(n=10).summary(buff)
assert 'theano.scan_module.scan_op.Scan' not in buff.getvalue()
buff.close()
def test_remove_scan_op():
with pm.Model():
pm.Normal('n', 0, 1)
inference = ADVI()
buff = six.StringIO()
inference.run_profiling(n=10).summary(buff)
assert 'theano.scan_module.scan_op.Scan' not in buff.getvalue()
buff.close()
def test_remove_scan_op():
with pm.Model():
pm.Normal("n", 0, 1)
inference = ADVI()
buff = io.StringIO()
inference.run_profiling(n=10).summary(buff)
assert "aesara.scan.op.Scan" not in buff.getvalue()
buff.close()
def test_clear_cache():
pymc3.memoize.clear_cache()
with pm.Model():
pm.Normal('n', 0, 1)
inference = ADVI()
inference.fit(n=10)
assert len(pm.variational.opvi.Approximation.logp.fget.cache) == 1
del inference
assert len(pm.variational.opvi.Approximation.logp.fget.cache) == 0
for c in pymc3.memoize.CACHE_REGISTRY:
assert len(c) == 0
def test_clear_cache():
import pickle
with pm.Model():
pm.Normal("n", 0, 1)
inference = ADVI()
inference.fit(n=10)
assert any(len(c) != 0 for c in inference.approx._cache.values())
inference.approx._cache.clear()
# should not be cleared at this call
assert all(len(c) == 0 for c in inference.approx._cache.values())
new_a = pickle.loads(pickle.dumps(inference.approx))
assert not hasattr(new_a, "_cache")
inference_new = pm.KLqp(new_a)
inference_new.fit(n=10)
assert any(len(c) != 0 for c in inference_new.approx._cache.values())
inference_new.approx._cache.clear()
assert all(len(c) == 0 for c in inference_new.approx._cache.values())
def test_clear_cache():
import pickle
pymc3.memoize.clear_cache()
assert all(len(c) == 0 for c in pymc3.memoize.CACHE_REGISTRY)
with pm.Model():
pm.Normal('n', 0, 1)
inference = ADVI()
inference.fit(n=10)
assert any(len(c) != 0 for c in inference.approx._cache.values())
pymc3.memoize.clear_cache(inference.approx)
# should not be cleared at this call
assert all(len(c) == 0 for c in inference.approx._cache.values())
new_a = pickle.loads(pickle.dumps(inference.approx))
assert not hasattr(new_a, '_cache')
inference_new = pm.KLqp(new_a)
inference_new.fit(n=10)
assert any(len(c) != 0 for c in inference_new.approx._cache.values())
pymc3.memoize.clear_cache(inference_new.approx)
assert all(len(c) == 0 for c in inference_new.approx._cache.values())
def test_clear_cache():
import pickle
pymc3.memoize.clear_cache()
assert all(len(c) == 0 for c in pymc3.memoize.CACHE_REGISTRY)
with pm.Model():
pm.Normal('n', 0, 1)
inference = ADVI()
inference.fit(n=10)
assert any(len(c) != 0 for c in inference.approx._cache.values())
pymc3.memoize.clear_cache(inference.approx)
# should not be cleared at this call
assert all(len(c) == 0 for c in inference.approx._cache.values())
new_a = pickle.loads(pickle.dumps(inference.approx))
assert not hasattr(new_a, '_cache')
inference_new = pm.KLqp(new_a)
inference_new.fit(n=10)
assert any(len(c) != 0 for c in inference_new.approx._cache.values())
pymc3.memoize.clear_cache(inference_new.approx)
assert all(len(c) == 0 for c in inference_new.approx._cache.values())
def test_from_advi(self):
with models.multidimensional_model()[1]:
advi = ADVI()
full_rank = FullRankADVI.from_advi(advi)
full_rank.fit(20)
def test_from_mean_field(self):
with models.multidimensional_model()[1]:
advi = ADVI()
full_rank = FullRankADVI.from_mean_field(advi.approx)
full_rank.fit(20)
