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Update link to Hoffman paper (online VB LDA) #3133

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May 12, 2021
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Update link to Hoffman paper (online VB LDA) #3133

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c3bc1b8
Update link to Hoffman paper (online VB LDA)
jonaschn May 5, 2021
53109fa
Refactor links to Hoffman paper
jonaschn May 11, 2021
adb74a3
Fix flake8
jonaschn May 12, 2021
17a0e59
Update CHANGELOG.md
mpenkov May 12, 2021
a11c757
Merge branch 'develop' into patch-1
mpenkov May 12, 2021
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32 changes: 20 additions & 12 deletions gensim/models/atmodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,9 @@
<https://arxiv.org/abs/1207.4169>`_. The model correlates the authorship information with the topics to give a better
insight on the subject knowledge of an author.

.. _'Online Learning for LDA' by Hoffman et al.: online-lda_
.. _online-lda: https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf

Example
-------
.. sourcecode:: pycon
Expand Down Expand Up @@ -185,9 +188,12 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, author2doc=None, d
iterations : int, optional
Maximum number of times the model loops over each document.
decay : float, optional
Controls how old documents are forgotten.
A number between (0.5, 1] to weight what percentage of the previous lambda value is forgotten
when each new document is examined. Corresponds to :math:`\\kappa` from
`'Online Learning for LDA' by Hoffman et al.`_
offset : float, optional
Controls down-weighting of iterations.
Hyper-parameter that controls how much we will slow down the first steps the first few iterations.
Corresponds to :math:`\\tau_0` from `'Online Learning for LDA' by Hoffman et al.`_
alpha : {float, numpy.ndarray of float, list of float, str}, optional
A-priori belief on document-topic distribution, this can be:
* scalar for a symmetric prior over document-topic distribution,
Expand Down Expand Up @@ -618,15 +624,14 @@ def update(self, corpus=None, author2doc=None, doc2author=None, chunksize=None,

Notes
-----
This update also supports updating an already trained model (self)
with new documents from `corpus`: the two models are then merged in proportion to the number of old vs. new
documents. This feature is still experimental for non-stationary input streams.
This update also supports updating an already trained model (`self`) with new documents from `corpus`;
the two models are then merged in proportion to the number of old vs. new documents.
This feature is still experimental for non-stationary input streams.

For stationary input (no topic drift in new documents), on the other hand, this equals the online update of
`Hoffman et al. Stochastic Variational Inference
<http://www.jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf>`_ and is guaranteed to converge for any `decay`
in (0.5, 1.0>. Additionally, for smaller `corpus` sizes, an increasing `offset` may be beneficial (see
Table 1 in Hoffman et al.)
For stationary input (no topic drift in new documents), on the other hand, this equals the
online update of `'Online Learning for LDA' by Hoffman et al.`_
and is guaranteed to converge for any `decay` in (0.5, 1]. Additionally, for smaller corpus sizes, an
increasing `offset` may be beneficial (see Table 1 in the same paper).

If update is called with authors that already exist in the model, it will resume training on not only new
documents for that author, but also the previously seen documents. This is necessary for those authors' topic
Expand All @@ -653,9 +658,12 @@ def update(self, corpus=None, author2doc=None, doc2author=None, chunksize=None,
chunksize : int, optional
Controls the size of the mini-batches.
decay : float, optional
Controls how old documents are forgotten.
A number between (0.5, 1] to weight what percentage of the previous lambda value is forgotten
when each new document is examined. Corresponds to :math:`\\kappa` from
`'Online Learning for LDA' by Hoffman et al.`_
offset : float, optional
Controls down-weighting of iterations.
Hyper-parameter that controls how much we will slow down the first steps the first few iterations.
Corresponds to :math:`\\tau_0` from `'Online Learning for LDA' by Hoffman et al.`_
passes : int, optional
Number of times the model makes a pass over the entire training data.
update_every : int, optional
Expand Down
48 changes: 23 additions & 25 deletions gensim/models/ldamodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,12 @@

The core estimation code is based on the `onlineldavb.py script
<https://github.com/blei-lab/onlineldavb/blob/master/onlineldavb.py>`_, by
`Matthew D. Hoffman, David M. Blei, Francis Bach:
Online Learning for Latent Dirichlet Allocation, NIPS 2010
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_.
Matthew D. Hoffman, David M. Blei, Francis Bach:
`'Online Learning for Latent Dirichlet Allocation', NIPS 2010`_.

.. _'Online Learning for Latent Dirichlet Allocation', NIPS 2010: online-lda_
.. _'Online Learning for LDA' by Hoffman et al.: online-lda_
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.. _online-lda: https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf

The algorithm:

Expand Down Expand Up @@ -199,8 +202,7 @@ def blend(self, rhot, other, targetsize=None):

The number of documents is stretched in both state objects, so that they are of comparable magnitude.
This procedure corresponds to the stochastic gradient update from
`Hoffman et al. :"Online Learning for Latent Dirichlet Allocation"
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_, see equations (5) and (9).
`'Online Learning for LDA' by Hoffman et al.`_, see equations (5) and (9).

Parameters
----------
Expand Down Expand Up @@ -312,9 +314,7 @@ def load(cls, fname, *args, **kwargs):


class LdaModel(interfaces.TransformationABC, basemodel.BaseTopicModel):
"""Train and use Online Latent Dirichlet Allocation models as presented in
`Hoffman et al. :"Online Learning for Latent Dirichlet Allocation"
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_.
"""Train and use Online Latent Dirichlet Allocation models as presented in `'Online Learning for LDA' by Hoffman et al.`_

Examples
-------
Expand Down Expand Up @@ -396,13 +396,11 @@ def __init__(self, corpus=None, num_topics=100, id2word=None,
* 'auto': Learns an asymmetric prior from the corpus.
decay : float, optional
A number between (0.5, 1] to weight what percentage of the previous lambda value is forgotten
when each new document is examined. Corresponds to Kappa from
`Hoffman et al. :"Online Learning for Latent Dirichlet Allocation"
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_.
when each new document is examined.
Corresponds to :math:`\\kappa` from `'Online Learning for LDA' by Hoffman et al.`_
offset : float, optional
Hyper-parameter that controls how much we will slow down the first steps the first few iterations.
Corresponds to Tau_0 from `Hoffman et al. :"Online Learning for Latent Dirichlet Allocation"
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_.
Corresponds to :math:`\\tau_0` from `'Online Learning for LDA' by Hoffman et al.`_
eval_every : int, optional
Log perplexity is estimated every that many updates. Setting this to one slows down training by ~2x.
iterations : int, optional
Expand Down Expand Up @@ -862,13 +860,15 @@ def update(self, corpus, chunksize=None, decay=None, offset=None,

Notes
-----
This update also supports updating an already trained model with new documents; the two models are then merged
in proportion to the number of old vs. new documents. This feature is still experimental for non-stationary
input streams. For stationary input (no topic drift in new documents), on the other hand, this equals the
online update of `Hoffman et al. :"Online Learning for Latent Dirichlet Allocation"
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_
and is guaranteed to converge for any `decay` in (0.5, 1.0). Additionally, for smaller corpus sizes, an
increasing `offset` may be beneficial (see Table 1 in the same paper).
This update also supports updating an already trained model (`self`) with new documents from `corpus`;
the two models are then merged in proportion to the number of old vs. new documents.
This feature is still experimental for non-stationary input streams.

For stationary input (no topic drift in new documents), on the other hand,
this equals the online update of `'Online Learning for LDA' by Hoffman et al.`_
and is guaranteed to converge for any `decay` in (0.5, 1].
Additionally, for smaller corpus sizes,
an increasing `offset` may be beneficial (see Table 1 in the same paper).

Parameters
----------
Expand All @@ -879,13 +879,11 @@ def update(self, corpus, chunksize=None, decay=None, offset=None,
Number of documents to be used in each training chunk.
decay : float, optional
A number between (0.5, 1] to weight what percentage of the previous lambda value is forgotten
when each new document is examined. Corresponds to Kappa from
`Hoffman et al. :"Online Learning for Latent Dirichlet Allocation"
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_.
when each new document is examined. Corresponds to :math:`\\kappa` from
`'Online Learning for LDA' by Hoffman et al.`_
offset : float, optional
Hyper-parameter that controls how much we will slow down the first steps the first few iterations.
Corresponds to Tau_0 from `Hoffman et al. :"Online Learning for Latent Dirichlet Allocation"
<https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf>`_.
Corresponds to :math:`\\tau_0` from `'Online Learning for LDA' by Hoffman et al.`_
passes : int, optional
Number of passes through the corpus during training.
update_every : int, optional
Expand Down
28 changes: 15 additions & 13 deletions gensim/models/ldamulticore.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,13 @@
unseen documents. The model can also be updated with new documents for online training.

The core estimation code is based on the `onlineldavb.py script
<https://github.com/blei-lab/onlineldavb/blob/master/onlineldavb.py>`_, by `Hoffman, Blei, Bach:
Online Learning for Latent Dirichlet Allocation, NIPS 2010 <http://www.cs.princeton.edu/~mdhoffma>`_.
<https://github.com/blei-lab/onlineldavb/blob/master/onlineldavb.py>`_, by
Matthew D. Hoffman, David M. Blei, Francis Bach:
`'Online Learning for Latent Dirichlet Allocation', NIPS 2010`_.

.. _'Online Learning for Latent Dirichlet Allocation', NIPS 2010: online-lda_
.. _'Online Learning for LDA' by Hoffman et al.: online-lda_
.. _online-lda: https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf

Usage examples
--------------
Expand Down Expand Up @@ -147,13 +152,11 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, workers=None,
* 'auto': Learns an asymmetric prior from the corpus.
decay : float, optional
A number between (0.5, 1] to weight what percentage of the previous lambda value is forgotten
when each new document is examined. Corresponds to Kappa from
`Matthew D. Hoffman, David M. Blei, Francis Bach:
"Online Learning for Latent Dirichlet Allocation NIPS'10" <https://www.di.ens.fr/~fbach/mdhnips2010.pdf>`_.
when each new document is examined. Corresponds to :math:`\\kappa` from
`'Online Learning for LDA' by Hoffman et al.`_
offset : float, optional
Hyper-parameter that controls how much we will slow down the first steps the first few iterations.
Corresponds to Tau_0 from `Matthew D. Hoffman, David M. Blei, Francis Bach:
"Online Learning for Latent Dirichlet Allocation NIPS'10" <https://www.di.ens.fr/~fbach/mdhnips2010.pdf>`_.
Corresponds to :math:`\\tau_0` from `'Online Learning for LDA' by Hoffman et al.`_
eval_every : int, optional
Log perplexity is estimated every that many updates. Setting this to one slows down training by ~2x.
iterations : int, optional
Expand Down Expand Up @@ -198,14 +201,13 @@ def update(self, corpus, chunks_as_numpy=False):

Notes
-----
This update also supports updating an already trained model (`self`)
with new documents from `corpus`; the two models are then merged in
proportion to the number of old vs. new documents. This feature is still
experimental for non-stationary input streams.
This update also supports updating an already trained model (`self`) with new documents from `corpus`;
the two models are then merged in proportion to the number of old vs. new documents.
This feature is still experimental for non-stationary input streams.

For stationary input (no topic drift in new documents), on the other hand,
this equals the online update of Hoffman et al. and is guaranteed to
converge for any `decay` in (0.5, 1.0>.
this equals the online update of `'Online Learning for LDA' by Hoffman et al.`_
and is guaranteed to converge for any `decay` in (0.5, 1].

Parameters
----------
Expand Down