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Fix epsilon according to dtype in LdaModel #1770

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merged 5 commits into from
Dec 7, 2017
Merged

Fix epsilon according to dtype in LdaModel #1770

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db1ce0f
add type check + adopt eps for different dtypes
menshikh-iv Dec 7, 2017
5f56a1c
fix typo
menshikh-iv Dec 7, 2017
488dfd1
improve readability
menshikh-iv Dec 7, 2017
e4e96e4
move mapping to module level, remove duplication
menshikh-iv Dec 7, 2017
3d46db8
fix typo + remove duplication
menshikh-iv Dec 7, 2017
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13 changes: 11 additions & 2 deletions gensim/models/ldamodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -286,6 +286,9 @@ def __init__(self, corpus=None, num_topics=100, id2word=None,
>>> lda = LdaModel(corpus, num_topics=50, alpha='auto', eval_every=5) # train asymmetric alpha from data

"""
if dtype not in {np.float16, np.float32, np.float64}:
raise ValueError("Incorrect 'dtype', please choose one of numpy.float16, numpy.float32 or numpy.float64")

self.dtype = dtype

# store user-supplied parameters
Expand Down Expand Up @@ -498,7 +501,13 @@ def inference(self, chunk, collect_sstats=False):
# The optimal phi_{dwk} is proportional to expElogthetad_k * expElogbetad_w.
# phinorm is the normalizer.
# TODO treat zeros explicitly, instead of adding 1e-100?
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@piskvorky piskvorky Dec 7, 2017 •
edited
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Should update comment too: 1e-100 => epsilon.

phinorm = np.dot(expElogthetad, expElogbetad) + 1e-100
dtype_to_eps = {
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Better defined at module level, so __init__ can make use of the allowed keys? Now the same information is in three places, not DRY.

np.float16: 1e-5,
np.float32: 1e-35,
np.float64: 1e-100,
}
eps = dtype_to_eps[self.dtype]
phinorm = np.dot(expElogthetad, expElogbetad) + eps

# Iterate between gamma and phi until convergence
for _ in xrange(self.iterations):
Expand All @@ -509,7 +518,7 @@ def inference(self, chunk, collect_sstats=False):
gammad = self.alpha + expElogthetad * np.dot(cts / phinorm, expElogbetad.T)
Elogthetad = dirichlet_expectation(gammad)
expElogthetad = np.exp(Elogthetad)
phinorm = np.dot(expElogthetad, expElogbetad) + 1e-100
phinorm = np.dot(expElogthetad, expElogbetad) + eps
# If gamma hasn't changed much, we're done.
meanchange = np.mean(abs(gammad - lastgamma))
if meanchange < self.gamma_threshold:
Expand Down