Module tomotopy

Python package tomotopy provides types and functions for various Topic Model including LDA, DMR, HDP, MG-LDA, PA and HPA. It is written in C++ for speed and provides Python extension.

What is tomotopy?

tomotopy is a Python extension of tomoto (Topic Modeling Tool) which is a Gibbs-sampling based topic model library written in C++. It utilizes a vectorization of modern CPUs for maximizing speed. The current version of tomoto supports several major topic models including

The most recent version of tomotopy is 0.4.1.

Star Issue

Getting Started

You can install tomotopy easily using pip. (https://pypi.org/project/tomotopy/) ::

$ pip install tomotopy

For Linux, it is neccesary to have gcc 5 or more for compiling C++14 codes. After installing, you can start tomotopy by just importing. ::

import tomotopy as tp
print(tp.isa) # prints 'avx2', 'avx', 'sse2' or 'none'

Currently, tomotopy can exploits AVX2, AVX or SSE2 SIMD instruction set for maximizing performance. When the package is imported, it will check available instruction sets and select the best option. If tp.isa tells none, iterations of training may take a long time. But, since most of modern Intel or AMD CPUs provide SIMD instruction set, the SIMD acceleration could show a big improvement.

Here is a sample code for simple LDA training of texts from 'sample.txt' file. ::

import tomotopy as tp
mdl = tp.LDAModel(k=20)
for line in open('sample.txt'):
    mdl.add_doc(line.strip().split())

for i in range(0, 100, 10):
    mdl.train(10)
    print('Iteration: {}\tLog-likelihood: {}'.format(i, mdl.ll_per_word))

for k in range(mdl.k):
    print('Top 10 words of topic #{}'.format(k))
    print(mdl.get_topic_words(k, top_n=10))

Performance of tomotopy

tomotopy uses Collapsed Gibbs-Sampling(CGS) to infer the distribution of topics and the distribution of words. Generally CGS converges more slowly than Variational Bayes(VB) that gensim's LdaModel uses, but its iteration can be computed much faster. In addition, tomotopy can take advantage of multicore CPUs with a SIMD instruction set, which can result in faster iterations.

Following chart shows the comparison of LDA model's running time between tomotopy and gensim. The input data consists of 1000 random documents from English Wikipedia with 1,506,966 words (about 10.1 MB). tomotopy trains 200 iterations and gensim trains 10 iterations.

↑ Performance in Intel i5-6600, x86-64 (4 cores)

↑ Performance in Intel Xeon E5-2620 v4, x86-64 (8 cores, 16 threads)

↑ Performance in AMD Ryzen7 3700X, x86-64 (8 cores, 16 threads)

Although tomotopy iterated 20 times more, the overall running time was 5~10 times faster than gensim. And it yields a stable result.

It is difficult to compare CGS and VB directly because they are totaly different techniques. But from a practical point of view, we can compare the speed and the result between them. The following chart shows the log-likelihood per word of two models' result.

Top words of topics generated by `tomotopy`
#1use, acid, cell, form, also, effect
#2use, number, one, set, comput, function
#3state, use, may, court, law, person
#4state, american, nation, parti, new, elect
#5film, music, play, song, anim, album
#6art, work, design, de, build, artist
#7american, player, english, politician, footbal, author
#8appl, use, comput, system, softwar, compani
#9day, unit, de, state, german, dutch
#10team, game, first, club, leagu, play
#11church, roman, god, greek, centuri, bc
#12atom, use, star, electron, metal, element
#13alexand, king, ii, emperor, son, iii
#14languag, arab, use, word, english, form
#15speci, island, plant, famili, order, use
#16work, univers, world, book, human, theori
#17citi, area, region, popul, south, world
#18forc, war, armi, militari, jew, countri
#19year, first, would, later, time, death
#20apollo, use, aircraft, flight, mission, first
Top words of topics generated by `gensim`
#1use, acid, may, also, azerbaijan, cell
#2use, system, comput, one, also, time
#3state, citi, day, nation, year, area
#4state, lincoln, american, war, union, bell
#5anim, game, anal, atari, area, sex
#6art, use, work, also, includ, first
#7american, player, english, politician, footbal, author
#8new, american, team, season, leagu, year
#9appl, ii, martin, aston, magnitud, star
#10bc, assyrian, use, speer, also, abort
#11use, arsen, also, audi, one, first
#12algebra, use, set, ture, number, tank
#13appl, state, use, also, includ, product
#14use, languag, word, arab, also, english
#15god, work, one, also, greek, name
#16first, one, also, time, work, film
#17church, alexand, arab, also, anglican, use
#18british, american, new, war, armi, alfr
#19airlin, vote, candid, approv, footbal, air
#20apollo, mission, lunar, first, crew, land

The SIMD instruction set has a great effect on performance. Following is a comparison between SIMD instruction sets.

Fortunately, most of recent x86-64 CPUs provide AVX2 instruction set, so we can enjoy the performance of AVX2.

Model Save and Load

tomotopy provides save and load method for each topic model class, so you can save the model into the file whenever you want, and re-load it from the file. ::

import tomotopy as tp

mdl = tp.HDPModel()
for line in open('sample.txt'):
    mdl.add_doc(line.strip().split())

for i in range(0, 100, 10):
    mdl.train(10)
    print('Iteration: {}\tLog-likelihood: {}'.format(i, mdl.ll_per_word))

# save into file
mdl.save('sample_hdp_model.bin')

# load from file
mdl = tp.HDPModel.load('sample_hdp_model.bin')
for k in range(mdl.k):
    if not mdl.is_live_topic(k): continue
    print('Top 10 words of topic #{}'.format(k))
    print(mdl.get_topic_words(k, top_n=10))

# the saved model is HDP model, 
# so when you load it by LDA model, it will raise an exception
mdl = tp.LDAModel.load('sample_hdp_model.bin')

When you load the model from a file, a model type in the file should match the class of methods.

See more at LDAModel.save() and LDAModel.load() methods.

Documents in the Model and out of the Model

We can use Topic Model for two major purposes. The basic one is to discover topics from a set of documents as a result of trained model, and the more advanced one is to infer topic distributions for unseen documents by using trained model.

We named the document in the former purpose (used for model training) as document in the model, and the document in the later purpose (unseen document during training) as document out of the model.

In tomotopy, these two different kinds of document are generated differently. A document in the model can be created by LDAModel.add_doc() method. add_doc can be called before LDAModel.train() starts. In other words, after train called, add_doc cannot add a document into the model because the set of document used for training has become fixed.

To acquire the instance of the created document, you should use LDAModel.docs like:

::

mdl = tp.LDAModel(k=20)
idx = mdl.add_doc(words)
if idx < 0: raise RuntimeError("Failed to add doc")
doc_inst = mdl.docs[idx]
# doc_inst is an instance of the added document

A document out of the model is generated by LDAModel.make_doc() method. make_doc can be called only after train starts. If you use make_doc before the set of document used for training has become fixed, you may get wrong results. Since make_doc returns the instance directly, you can use its return value for other manipulations.

::

mdl = tp.LDAModel(k=20)
# add_doc ...
mdl.train(100)
doc_inst = mdl.make_doc(unseen_words) # doc_inst is an instance of the unseen document

Inference for Unseen Documents

If a new document is created by LDAModel.make_doc(), its topic distribution can be inferred by the model. Inference for unseen document should be performed using LDAModel.infer() method.

::

mdl = tp.LDAModel(k=20)
# add_doc ...
mdl.train(100)
doc_inst = mdl.make_doc(unseen_words)
topic_dist, ll = mdl.infer(doc_inst)
print("Topic Distribution for Unseen Docs: ", topic_dist)
print("Log-likelihood of inference: ", ll)

The infer method can infer only one instance of Document or a list of instances of Document. See more at LDAModel.infer().

Examples

You can find an example python code of tomotopy at https://github.com/bab2min/tomotopy/blob/master/example.py .

You can also get the data file used in the example code at https://drive.google.com/file/d/18OpNijd4iwPyYZ2O7pQoPyeTAKEXa71J/view .

License

tomotopy is licensed under the terms of MIT License, meaning you can use it for any reasonable purpose and remain in complete ownership of all the documentation you produce.

History

  • 0.4.1 (2019-11-27)

    • A bug at init function of PLDAModel was fixed.

  • 0.4.0 (2019-11-18)

  • 0.3.1 (2019-11-05)

    • An issue where get_topic_dist() returns incorrect value when min_cf or rm_top is set was fixed.
    • The return value of get_topic_dist() of MGLDAModel document was fixed to include local topics.
    • The estimation speed with tw=ONE was improved.

  • 0.3.0 (2019-10-06)

    • A new model, LLDAModel was added into the package.
    • A crashing issue of HDPModel was fixed.
    • Since hyperparameter estimation for HDPModel was implemented, the result of HDPModel may differ from previous versions. If you want to turn off hyperparameter estimation of HDPModel, set optim_interval to zero.

  • 0.2.0 (2019-08-18)

    • New models including CTModel and SLDAModel were added into the package.
    • A new parameter option rm_top was added for all topic models.
    • The problems in save and load method for PAModel and HPAModel were fixed.
    • An occassional crash in loading HDPModel was fixed.
    • The problem that ll_per_word was calculated incorrectly when min_cf > 0 was fixed.

  • 0.1.6 (2019-08-09)

    • Compiling errors at clang with macOS environment were fixed.

  • 0.1.4 (2019-08-05)

    • The issue when add_doc receives an empty list as input was fixed.
    • The issue that PAModel.get_topic_words() doesn't extract the word distribution of subtopic was fixed.

  • 0.1.3 (2019-05-19)

    • The parameter min_cf and its stopword-removing function were added for all topic models.

  • 0.1.0 (2019-05-12)

    • First version of tomotopy
Expand source code 
"""
Python package `tomotopy` provides types and functions for various Topic Model 
including LDA, DMR, HDP, MG-LDA, PA and HPA. It is written in C++ for speed and provides Python extension.

.. include:: ./documentation.rst
"""
from enum import IntEnum

class TermWeight(IntEnum):
    """
    This enumeration is for Term Weighting Scheme and it is based on following paper:
    
    > * Wilson, A. T., & Chew, P. A. (2010, June). Term weighting schemes for latent dirichlet allocation. In human language technologies: The 2010 annual conference of the North American Chapter of the Association for Computational Linguistics (pp. 465-473). Association for Computational Linguistics.
    
    There are three options for term weighting and the basic one is ONE. The others also can be applied for all topic models in `tomotopy`. 
    """

    ONE = 0
    """ Consider every term equal (default)"""

    IDF = 1
    """ 
    Use Inverse Document Frequency term weighting.
    
    Thus, a term occurring at almost every document has very low weighting
    and a term occurring at a few document has high weighting. 
    """

    PMI = 2
    """
    Use Pointwise Mutual Information term weighting.
    """

isa = ''
"""
Indicate which SIMD instruction set is used for acceleration.
It can be one of `'avx2'`, `'avx'`, `'sse2'` and `'none'`.
"""

# This code is an autocomplete-hint for IDE.
# The object imported here will be overwritten by _load() function.
try: from _tomotopy import *
except: pass

def _load():
    import importlib, os
    from cpuinfo import get_cpu_info
    flags = get_cpu_info()['flags']
    env_setting = os.environ.get('TOMOTOPY_ISA', '').split(',')
    if not env_setting[0]: env_setting = []
    isas = ['avx2', 'avx', 'sse2', 'none']
    isas = [isa for isa in isas if (env_setting and isa in env_setting) or (not env_setting and (isa in flags or isa == 'none'))]
    if not isas: raise RuntimeError("No isa option for " + str(env_setting))
    for isa in isas:
        try:
            mod_name = '_tomotopy' + ('_' + isa if isa != 'none' else '')
            globals().update({k:v for k, v in vars(importlib.import_module(mod_name)).items() if not k.startswith('_')})
            return
        except:
            if isa == isas[-1]: raise
_load()
import os
if os.environ.get('TOMOTOPY_LANG') == 'kr':
    __doc__ = """`tomotopy` 패키지는 Python에서 사용가능한 다양한 토픽 모델링 타입과 함수를 제공합니다.
이 모듈은 c++로 작성되어 컴파일되기 때문에 빠른 속도를 자랑합니다.

.. include:: ./documentation.kr.rst
"""
    __pdoc__ = {}
    __pdoc__['isa'] = """현재 로드된 모듈이 어떤 SIMD 명령어 세트를 사용하는지 표시합니다. 
이 값은 `'avx2'`, `'avx'`, `'sse2'`, `'none'` 중 하나입니다."""
    __pdoc__['TermWeight'] = """용어 가중치 기법을 선택하는 데에 사용되는 열거형입니다. 여기에 제시된 용어 가중치 기법들은 다음 논문을 바탕으로 하였습니다:
    
> * Wilson, A. T., & Chew, P. A. (2010, June). Term weighting schemes for latent dirichlet allocation. In human language technologies: The 2010 annual conference of the North American Chapter of the Association for Computational Linguistics (pp. 465-473). Association for Computational Linguistics.

총 3가지 가중치 기법을 사용할 수 있으며 기본값은 ONE입니다. 기본값뿐만 아니라 다른 모든 기법들도 `tomotopy`의 모든 토픽 모델에 사용할 수 있습니다. """
    __pdoc__['TermWeight.ONE'] = """모든 용어를 동일하게 간주합니다. (기본값)"""
    __pdoc__['TermWeight.IDF'] = """역문헌빈도(IDF)를 가중치로 사용합니다.

따라서 모든 문헌에 거의 골고루 등장하는 용어의 경우 낮은 가중치를 가지게 되며, 
소수의 특정 문헌에만 집중적으로 등장하는 용어의 경우 높은 가중치를 가지게 됩니다."""
    __pdoc__['TermWeight.PMI'] = """점별 상호정보량(PMI)을 가중치로 사용합니다."""
del _load, IntEnum, os

Global variables

var isa

Indicate which SIMD instruction set is used for acceleration. It can be one of 'avx2', 'avx', 'sse2' and 'none'.

Classes

class CTModel (*args, **kwargs)

Added in version: 0.2.0

This type provides Correlated Topic Model (CTM) and its implementation is based on following papers:

  • Blei, D., & Lafferty, J. (2006). Correlated topic models. Advances in neural information processing systems, 18, 147.
  • Mimno, D., Wallach, H., & McCallum, A. (2008, December). Gibbs sampling for logistic normal topic models with graph-based priors. In NIPS Workshop on Analyzing Graphs (Vol. 61).

CTModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k=1, smoothing_alpha=0.1, eta=0.01, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int
the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.
k : int
the number of topics between 1 ~ 32767.
alpha : float
hyperparameter of Dirichlet distribution for document-topic
eta : float
hyperparameter of Dirichlet distribution for topic-word
seed : int
random seed. The default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var num_beta_sample

the number of times to sample beta parameters, default value is 10.

CTModel samples num_beta_sample beta parameters for each document. The more beta it samples, the more accurate the distribution will be, but the longer time it takes to learn. If you have a small number of documents in your model, keeping this value larger will help you get better result.

var num_tmn_sample

the number of iterations for sampling Truncated Multivariate Normal distribution, default value is 5.

If your model shows biased topic correlations, increasing this value may be helpful.

var prior_cov

the covariance matrix of prior logistic-normal distribution the for topic distribution (read-only)

var prior_mean

the mean of prior logistic-normal distribution for the topic distribution (read-only)

Methods

def get_correlations(self, topic_id)

Return correlations between the topic topic_id and other topics. The returned value is a list of floats of size LDAModel.k.

Parameters

topic_id : int
an integer in range [0, k), indicating the topic

Inherited members

class DMRModel (*args, **kwargs)

This type provides Dirichlet Multinomial Regression(DMR) topic model and its implementation is based on following papers:

  • Mimno, D., & McCallum, A. (2012). Topic models conditioned on arbitrary features with dirichlet-multinomial regression. arXiv preprint arXiv:1206.3278.

DMRModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k=1, alpha=0.1, eta=0.01, sigma=1.0, alpha_epsilon=1e-10, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int

Added in version: 0.2.0

the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.

k : int
the number of topics between 1 ~ 32767
alpha : float
exponential of mean of normal distribution for lambdas
eta : float
hyperparameter of Dirichlet distribution for topic - word
sigma : float
standard deviation of normal distribution for lambdas
alpha_epsilon : float
small smoothing value for preventing exp(lambdas) to be zero
seed : int
random seed. default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var alpha_epsilon

the smooting value alpha-epsilon (read-only)

var f

the number of metadata features (read-only)

var lambdas

a list of paramter lambdas (read-only)

var metadata_dict

a dictionary of metadata in type Dictionary (read-only)

var sigma

the hyperparamter sigma (read-only)

Methods

def add_doc(self, words, metadata='')

Add a new document into the model instance with metadata and return an index of the inserted document.

Parameters

words : iterable of str
an iterable of str
metadata : str
metadata of the document (e.g., author, title or year)
def make_doc(self, words, metadata='')

Return a new Document instance for an unseen document with words and metadata that can be used for LDAModel.infer() method.

Parameters

words : iterable of str
an iteratable of str
metadata : str
metadata of the document (e.g., author, title or year)

Inherited members

class Dictionary (*args, **kwargs)

list-like Dictionary interface for vocabularies

class Document (*args, **kwargs)

This type provides abstract model to access documents to be used Topic Model.

An instance of this type can be acquired from LDAModel.make_doc() method or LDAModel.docs member of each Topic Model instance.

Instance variables

var beta

a list of beta parameters for each topic (for only CTModel model, read-only)

Added in version: 0.2.0

var labels

a list of (label, probability) of the document (for only LLDAModel model, read-only)

Added in version: 0.3.0

var metadata

"metadata of document (for only DMRModel model, read-only)

var subtopics

a list of sub topics for each word (for only PAModel and HPAModel model, read-only)

var topics

a list of topics for each word (read-only)

This represents super topics in PAModel and HPAModel model.

var vars

a list of response variables (for only SLDAModel model, read-only)

Added in version: 0.2.0

var weight

a weight of the document (read-only)

var windows

a list of window IDs for each word (for only MGLDAModel model, read-only)

var words

a list of IDs for each word (read-only)

Methods

def get_topic_dist(self)

Return a distribution of the topics in the document.

def get_topics(self, top_n=10)

Return the top_n topics with its probability of the document.

class HDPModel (*args, **kwargs)

This type provides Hierarchical Dirichlet Process(HDP) topic model and its implementation is based on following papers:

  • Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2005). Sharing clusters among related groups: Hierarchical Dirichlet processes. In Advances in neural information processing systems (pp. 1385-1392).
  • Newman, D., Asuncion, A., Smyth, P., & Welling, M. (2009). Distributed algorithms for topic models. Journal of Machine Learning Research, 10(Aug), 1801-1828.

Since version 0.3.0, hyperparameter estimation for alpha and gamma has been added. You can turn off this estimation by setting optim_interval to zero.

HDPModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, initial_k=2, alpha=0.1, eta=0.01, gamma=0.1, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int

Added in version: 0.2.0

the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.

initial_k : int
the initial number of topics between 2 ~ 32767. The number of topics will be adjusted for data during training.
Since version 0.3.0, the default value has been changed to 2 from 1.
alpha : float
concentration coeficient of Dirichlet Process for document-table
eta : float
hyperparameter of Dirichlet distribution for topic-word
gamma : float
concentration coeficient of Dirichlet Process for table-topic
seed : int
random seed. default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var gamma

the hyperparameter gamma (read-only)

var live_k

the number of alive topics (read-only)

var num_tables

the number of total tables (read-only)

Methods

def is_live_topic(self, topic_id)

Return True if the topic topic_id is alive, otherwise return False.

Parameters

topic_id : int
an integer in range [0, k) indicating the topic

Inherited members

class HLDAModel (*args, **kwargs)

This type provides Hierarchical LDA topic model and its implementation is based on following papers:

  • Griffiths, T. L., Jordan, M. I., Tenenbaum, J. B., & Blei, D. M. (2004). Hierarchical topic models and the nested Chinese restaurant process. In Advances in neural information processing systems (pp. 17-24).

Added in version: 0.4.0

HLDAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, depth=2, alpha=0.1, eta=0.01, gamma=0.1, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int
the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.
depth : int
the maximum depth level of hierarchy between 2 ~ 32767.
alpha : float
hyperparameter of Dirichlet distribution for document-topic
eta : float
hyperparameter of Dirichlet distribution for topic-word
gamma : float
concentration coeficient of Dirichlet Process
seed : int
random seed. default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var depth

the number of depth (read-only)

var gamma

the hyperparameter gamma (read-only)

var live_k

the number of alive topics (read-only)

Methods

def children_topics(self, topic_id)

Return a list of topic IDs with children of a topic topic_id.

Parameters

topic_id : int
an integer in range [0, k) indicating the topic
def is_live_topic(self, topic_id)

Return True if the topic topic_id is alive, otherwise return False.

Parameters

topic_id : int
an integer in range [0, k) indicating the topic
def level(self, topic_id)

Return the level of a topic topic_id.

Parameters

topic_id : int
an integer in range [0, k) indicating the topic
def num_docs_of_topic(self, topic_id)

Return the number of documents belonging to a topic topic_id.

Parameters

topic_id : int
an integer in range [0, k) indicating the topic
def parent_topic(self, topic_id)

Return the topic ID of parent of a topic topic_id.

Parameters

topic_id : int
an integer in range [0, k) indicating the topic

Inherited members

class HPAModel (*args, **kwargs)

This type provides Hierarchical Pachinko Allocation(HPA) topic model and its implementation is based on following papers:

  • Mimno, D., Li, W., & McCallum, A. (2007, June). Mixtures of hierarchical topics with pachinko allocation. In Proceedings of the 24th international conference on Machine learning (pp. 633-640). ACM.

HPAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k1=1, k2=1, alpha=0.1, eta=0.01, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int

Added in version: 0.2.0

the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.

k1 : int
the number of super topics between 1 ~ 32767
k2 : int
the number of sub topics between 1 ~ 32767
alpha : float
initial hyperparameter of Dirichlet distribution for document-topic
eta : float
hyperparameter of Dirichlet distribution for topic-word
seed : int
random seed. default value is a random number from std::random_device{} in C++

Ancestors

Methods

def get_topic_word_dist(self, topic_id)

Return the word distribution of the topic topic_id. The returned value is a list that has len(vocabs) fraction numbers indicating probabilities for each word in current topic.

Parameters

topic_id : int
0 indicates the top topic, a number in range [1, 1 + k1) indicates a super topic and a number in range [1 + k1, 1 + k1 + k2) indicates a sub topic.
def get_topic_words(self, topic_id, top_n=10)

Return the top_n words and its probability in the topic topic_id. The return type is a list of (word:str, probability:float).

Parameters

topic_id : int
0 indicates the top topic, a number in range [1, 1 + k1) indicates a super topic and a number in range [1 + k1, 1 + k1 + k2) indicates a sub topic.

Inherited members

class LDAModel (*args, **kwargs)

This type provides Latent Dirichlet Allocation(LDA) topic model and its implementation is based on following papers:

  • Blei, D.M., Ng, A.Y., &Jordan, M.I. (2003).Latent dirichlet allocation.Journal of machine Learning research, 3(Jan), 993 - 1022.
  • Newman, D., Asuncion, A., Smyth, P., &Welling, M. (2009).Distributed algorithms for topic models.Journal of Machine Learning Research, 10(Aug), 1801 - 1828.

LDAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k=1, alpha=0.1, eta=0.01, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int

Added in version: 0.2.0

the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.

k : int
the number of topics between 1 ~ 32767.
alpha : float
hyperparameter of Dirichlet distribution for document-topic
eta : float
hyperparameter of Dirichlet distribution for topic-word
seed : int
random seed. The default value is a random number from std::random_device{} in C++

Subclasses

Static methods

def load(filename)

Return the model instance loaded from file filename.

Instance variables

var alpha

the hyperparameter alpha (read-only)

var burn_in

get or set the burn-in iterations for optimizing parameters

Its default value is 0.

var docs

a list-like interface of Document in the model instance (read-only)

var eta

the hyperparameter eta (read-only)

var k

K, the number of topics (read-only)

var ll_per_word

a log likelihood per-word of the model (read-only)

var num_vocabs

the number of vocabuluaries after words with a smaller frequency were removed (read-only)

This value is 0 before train called.

var num_words

the number of total words (read-only)

This value is 0 before train called.

var optim_interval

get or set the interval for optimizing parameters

Its default value is 10. If it is set to 0, the parameter optimization is turned off.

var perplexity

a perplexity of the model (read-only)

var removed_top_words

a list of str which is a word removed from the model if you set rm_top greater than 0 at initializing the model (read-only)

var tw

the term weighting scheme (read-only)

var vocab_freq

a list of vocabulary frequencies included in the model (read-only)

var vocabs

a dictionary of vocabuluary as type Dictionary (read-only)

Methods

def add_doc(self, words)

Add a new document into the model instance and return an index of the inserted document.

Parameters

words : iterable of str
an iterable of str
def get_count_by_topics(self)

Return the number of words allocated to each topic.

def get_topic_word_dist(self, topic_id)

Return the word distribution of the topic topic_id. The returned value is a list that has len(vocabs) fraction numbers indicating probabilities for each word in the current topic.

Parameters

topic_id : int
an integer in range [0, k) indicating the topic
def get_topic_words(self, topic_id, top_n=10)

Return the top_n words and its probability in the topic topic_id. The return type is a list of (word:str, probability:float).

Parameters

topic_id : int
an integer in range [0, k), indicating the topic
def infer(self, doc, iter=100, tolerance=-1, workers=0, together=False)

Return the inferred topic distribution from unseen docs. The return type is (a topic distribution of doc, log likelihood) or (a list of topic distribution of doc, log likelihood)

Parameters

doc : Document or list of Document
an instance of Document or a list of instances of Document to be inferred by the model. It can be acquired from LDAModel.make_doc() method.
iter : int
an integer indicating the number of iteration to estimate the distribution of topics of doc. The higher value will generate a more accuracy result.
tolerance : float
isn't currently used.
workers : int
an integer indicating the number of workers to perform samplings. If workers is 0, the number of cores in the system will be used.
together : bool
all docs are infered together in one process if True, otherwise each doc is infered independently. Its default value is False.
def make_doc(self, words)

Return a new Document instance for an unseen document with words that can be used for LDAModel.infer() method.

Parameters

words : iterable of str
an iterable of str
def save(self, filename, full=True)

Save the model instance to file filename. Return None.

If full is True, the model with its all documents and state will be saved. If you want to train more after, use full model. If False, only topic paramters of the model will be saved. This model can be only used for inference of an unseen document.

def train(self, iter=10, workers=0)

Train the model using Gibbs-sampling with iter iterations. Return None. After calling this method, you cannot LDAModel.add_doc() more.

Parameters

iter : int
the number of iterations of Gibbs-sampling
workers : int
an integer indicating the number of workers to perform samplings. If workers is 0, the number of cores in the system will be used.
class LLDAModel (*args, **kwargs)

This type provides Labeled LDA(L-LDA) topic model and its implementation is based on following papers:

  • Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009, August). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1 (pp. 248-256). Association for Computational Linguistics.

Added in version: 0.3.0

LLDAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k=1, alpha=0.1, eta=0.01, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int
the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.
k : int
the number of topics between 1 ~ 32767.
alpha : float
hyperparameter of Dirichlet distribution for document-topic
eta : float
hyperparameter of Dirichlet distribution for topic-word
seed : int
random seed. The default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var topic_label_dict

a dictionary of topic labels in type Dictionary (read-only)

Methods

def add_doc(self, words, labels=[])

Add a new document into the model instance with labels and return an index of the inserted document.

Parameters

words : iterable of str
an iterable of str
labels : iterable of str
labels of the document
def make_doc(self, words, labels=[])

Return a new Document instance for an unseen document with words and labels that can be used for LDAModel.infer() method.

Parameters

words : iterable of str
an iteratable of str
labels : iterable of str
labels of the document

Inherited members

class MGLDAModel (*args, **kwargs)

This type provides Multi Grain Latent Dirichlet Allocation(MG-LDA) topic model and its implementation is based on following papers:

  • Titov, I., & McDonald, R. (2008, April). Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web (pp. 111-120). ACM.

MGLDAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k_g=1, k_l=1, t=3, alpha_g=0.1, alpha_l=0.1, alpha_mg=0.1, alpha_ml=0.1, eta_g=0.01, eta_l=0.01, gamma=0.1, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int

Added in version: 0.2.0

the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.

k_g : int
the number of global topics between 1 ~ 32767
k_l : int
the number of local topics between 1 ~ 32767
t : int
the size of sentence window
alpha_g : float
hyperparameter of Dirichlet distribution for document-global topic
alpha_l : float
hyperparameter of Dirichlet distribution for document-local topic
alpha_mg : float
hyperparameter of Dirichlet distribution for global-local selection (global coef)
alpha_ml : float
hyperparameter of Dirichlet distribution for global-local selection (local coef)
eta_g : float
hyperparameter of Dirichlet distribution for global topic-word
eta_l : float
hyperparameter of Dirichlet distribution for local topic-word
gamma : float
hyperparameter of Dirichlet distribution for sentence-window
seed : int
random seed. default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var alpha_g

the hyperparamter alpha_g (read-only)

var alpha_l

the hyperparamter alpha_l (read-only)

var alpha_mg

the hyperparamter alpha_mg (read-only)

var alpha_ml

the hyperparamter alpha_ml (read-only)

var eta_g

the hyperparamter eta_g (read-only)

var eta_l

the hyperparamter eta_l (read-only)

var gamma

the hyperparamter gamma (read-only)

var k_g

the hyperparamter k_g (read-only)

var k_l

the hyperparamter k_l (read-only)

var t

the hyperparamter t (read-only)

Methods

def add_doc(self, words, delimiter='.')

Add a new document into the model instance and return an index of the inserted document.

Parameters

words : iterable of str
an iterable of str
delimiter : str
a sentence separator. words will be separated by this value into sentences.
def get_topic_word_dist(self, topic_id)

Return the word distribution of the topic topic_id. The returned value is a list that has len(vocabs) fraction numbers indicating probabilities for each word in the current topic.

Parameters

topic_id : int
A number in range [0, k_g) indicates a global topic and a number in range [k_g, k_g + k_l) indicates a local topic.
def get_topic_words(self, topic_id, top_n=10)

Return the top_n words and its probability in the topic topic_id. The return type is a list of (word:str, probability:float).

Parameters

topic_id : int
A number in range [0, k_g) indicates a global topic and a number in range [k_g, k_g + k_l) indicates a local topic.
def make_doc(self, words, delimiter='.')

Return a new Document instance for an unseen document with words that can be used for LDAModel.infer() method.

Parameters

words : iterable of str
an iteratable of str
delimiter : str
a sentence separator. words will be separated by this value into sentences.

Inherited members

class PAModel (*args, **kwargs)

This type provides Pachinko Allocation(PA) topic model and its implementation is based on following papers:

  • Li, W., & McCallum, A. (2006, June). Pachinko allocation: DAG-structured mixture models of topic correlations. In Proceedings of the 23rd international conference on Machine learning (pp. 577-584). ACM.

PAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k1=1, k2=1, alpha=0.1, eta=0.01, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int

Added in version: 0.2.0

the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.

k1 : int
the number of super topics between 1 ~ 32767
k2 : int
the number of sub topics between 1 ~ 32767
alpha : float
initial hyperparameter of Dirichlet distribution for document-super topic
eta : float
hyperparameter of Dirichlet distribution for sub topic-word
seed : int
random seed. default value is a random number from std::random_device{} in C++

Ancestors

Subclasses

Instance variables

var k1

k1, the number of super topics (read-only)

var k2

k2, the number of sub topics (read-only)

Methods

def get_sub_topic_dist(self, super_topic_id)

Return a distribution of the sub topics in a super topic super_topic_id. The returned value is a list that has k2 fraction numbers indicating probabilities for each sub topic in the current super topic.

Parameters

super_topic_id : int
indicating the super topic, in range [0, k1)
def get_sub_topics(self, super_topic_id, top_n=10)

Added in version: 0.1.4

Return the top_n sub topics and its probability in a super topic super_topic_id. The return type is a list of (subtopic:int, probability:float).

Parameters

super_topic_id : int
indicating the super topic, in range [0, k1)
def get_topic_word_dist(self, sub_topic_id)

Return the word distribution of the sub topic sub_topic_id. The returned value is a list that has len(vocabs) fraction numbers indicating probabilities for each word in the current sub topic.

Parameters

sub_topic_id : int
indicating the sub topic, in range [0, k2)
def get_topic_words(self, sub_topic_id, top_n=10)

Return the top_n words and its probability in the sub topic sub_topic_id. The return type is a list of (word:str, probability:float).

Parameters

sub_topic_id : int
indicating the sub topic, in range [0, k2)

Inherited members

class PLDAModel (*args, **kwargs)

This type provides Labeled LDA(L-LDA) topic model and its implementation is based on following papers:

  • Ramage, D., Manning, C. D., & Dumais, S. (2011, August). Partially labeled topic models for interpretable text mining. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 457-465). ACM.

Added in version: 0.4.0

PLDAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, latent_topics=0, topics_per_label=1, alpha=0.1, eta=0.01, seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int
the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.
latent_topics : int
the number of latent topics, which are shared to all documents, between 1 ~ 32767.
topics_per_label : int
the number of topics per label between 1 ~ 32767.
alpha : float
hyperparameter of Dirichlet distribution for document-topic
eta : float
hyperparameter of Dirichlet distribution for topic-word
seed : int
random seed. The default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var latent_topics

the number of latent topics (read-only)

var topic_label_dict

a dictionary of topic labels in type Dictionary (read-only)

var topics_per_label

the number of topics per label (read-only)

Methods

def add_doc(self, words, labels=[])

Add a new document into the model instance with labels and return an index of the inserted document.

Parameters

words : iterable of str
an iterable of str
labels : iterable of str
labels of the document
def make_doc(self, words, labels=[])

Return a new Document instance for an unseen document with words and labels that can be used for LDAModel.infer() method.

Parameters

words : iterable of str
an iteratable of str
labels : iterable of str
labels of the document

Inherited members

class SLDAModel (*args, **kwargs)

This type provides supervised Latent Dirichlet Allocation(sLDA) topic model and its implementation is based on following papers:

  • Mcauliffe, J. D., & Blei, D. M. (2008). Supervised topic models. In Advances in neural information processing systems (pp. 121-128).
  • Python version implementation using Gibbs sampling : https://github.com/Savvysherpa/slda

Added in version: 0.2.0

SLDAModel(tw=TermWeight.ONE, min_cf=0, rm_top=0, k=1, vars='', alpha=0.1, eta=0.01, mu=[], nu_sq=[], glm_param=[], seed=None)

Parameters

tw : int or TermWeight
term weighting scheme in TermWeight. The default value is TermWeight.ONE
min_cf : int
minimum frequency of words. Words with a smaller collection frequency than min_cf are excluded from the model. The default value is 0, which means no words are excluded.
rm_top : int
the number of top words to be removed. If you want to remove too common words from model, you can set this value to 1 or more. The default value is 0, which means no top words are removed.
k : int
the number of topics between 1 ~ 32767.
vars : iterable of str

indicating types of response variables. The length of vars determines the number of response variables, and each element of vars determines a type of the variable. The list of available types is like below:

  • 'l': linear variable (any real value)
  • 'b': binary variable (0 or 1)
alpha : float
hyperparameter of Dirichlet distribution for document-topic
eta : float
hyperparameter of Dirichlet distribution for topic-word
mu : float or list of float
mean of regression coefficients
nu_sq : float or list of float
variance of regression coefficients
glm_param : float or list of float
the parameter for Generalized Linear Model
seed : int
random seed. The default value is a random number from std::random_device{} in C++

Ancestors

Instance variables

var f

the number of response variables (read-only)

Methods

def add_doc(self, words, y=[])

Add a new document into the model instance with response variables y and return an index of the inserted document.

Parameters

words : iterable of str
an iterable of str
y : list of float
response variables of this document. The length of y must be equal to the number of response variables of the model (SLDAModel.f).
def estimate(self, doc)

Return the estimated response variable for doc. If doc is an unseen document instance which is generated by SLDAModel.make_doc() method, it should be inferred by LDAModel.infer() method first.

Parameters

doc : Document
an instance of document to be used for estimating response variables
def get_regression_coef(self, var_id)

Return the regression coefficient of the response variable var_id.

Parameters

var_id : int
indicating the reponse variable, in range [0, f)
def get_var_type(self, var_id)

Return the type of the response variable var_id. 'l' means linear variable, 'b' means binary variable.

def make_doc(self, words, y=[])

Return a new Document instance for an unseen document with words and response variables y that can be used for LDAModel.infer() method.

Parameters

words : iterable of str
an iterable of str
y : list of float
response variables of this document. The length of y doesn't have to be equal to the number of response variables of the model (SLDAModel.f).

Inherited members

class TermWeight (*args, **kwargs)

This enumeration is for Term Weighting Scheme and it is based on following paper:

  • Wilson, A. T., & Chew, P. A. (2010, June). Term weighting schemes for latent dirichlet allocation. In human language technologies: The 2010 annual conference of the North American Chapter of the Association for Computational Linguistics (pp. 465-473). Association for Computational Linguistics.

There are three options for term weighting and the basic one is ONE. The others also can be applied for all topic models in tomotopy.

Expand source code 
class TermWeight(IntEnum):
    """
    This enumeration is for Term Weighting Scheme and it is based on following paper:
    
    > * Wilson, A. T., & Chew, P. A. (2010, June). Term weighting schemes for latent dirichlet allocation. In human language technologies: The 2010 annual conference of the North American Chapter of the Association for Computational Linguistics (pp. 465-473). Association for Computational Linguistics.
    
    There are three options for term weighting and the basic one is ONE. The others also can be applied for all topic models in `tomotopy`. 
    """

    ONE = 0
    """ Consider every term equal (default)"""

    IDF = 1
    """ 
    Use Inverse Document Frequency term weighting.
    
    Thus, a term occurring at almost every document has very low weighting
    and a term occurring at a few document has high weighting. 
    """

    PMI = 2
    """
    Use Pointwise Mutual Information term weighting.
    """

Ancestors

  • enum.IntEnum
  • builtins.int
  • enum.Enum

Class variables

var IDF

Use Inverse Document Frequency term weighting.

Thus, a term occurring at almost every document has very low weighting and a term occurring at a few document has high weighting.

var ONE

Consider every term equal (default)

var PMI

Use Pointwise Mutual Information term weighting.