Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix epsilon according to dtype in LdaModel #1770

Merged
merged 5 commits into from
Dec 7, 2017
Merged

Fix epsilon according to dtype in LdaModel #1770

Changes from 1 commit
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 6 additions & 2 deletions gensim/models/ldamodel.py
View file
Open in desktop
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 choice one of numpy.float16, numpy.float32 or numpy.float64")
Copy link
Owner

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

choice => choose


self.dtype = dtype

# store user-supplied parameters
Expand Down Expand Up @@ -498,7 +501,8 @@ 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?
Copy link
Owner

@piskvorky piskvorky Dec 7, 2017 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Should update comment too: 1e-100 => epsilon.

phinorm = np.dot(expElogthetad, expElogbetad) + 1e-100
eps = 1e-100 if self.dtype == np.float64 else (1e-35 if self.dtype == np.float32 else 1e-5)
Copy link
Owner

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Easier to read and maintain as a mapping?

dtype_to_eps = {
    np.float64: 1e-100,
    np.float32: 1e-35,
    np.float16: 1e-5,
}

And then also if dtype not in dtype_to_eps: ...

phinorm = np.dot(expElogthetad, expElogbetad) + eps

# Iterate between gamma and phi until convergence
for _ in xrange(self.iterations):
Expand All @@ -509,7 +513,7 @@ 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