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Introduction

Warning

Please note that this is a beta version of the BigARTM library which is still undergoing final testing before its official release. Should you encounter any bugs, lack of functionality or other problems with our library, please let us know immediately. Your help in this regard is greatly appreciated.

This is the documentation for the BigARTM library. BigARTM is a tool to infer topic models, based on a novel technique called Additive Regularization of Topic Models. This technique effectively builds multi-objective models by adding the weighted sums of regularizers to the optimization criterion. BigARTM is known to combine well very different objectives, including sparsing, smoothing, topics decorrelation and many others. Such combinations of regularizers significantly improves several quality measures at once almost without any loss of the perplexity.

Online. BigARTM never stores the entire text collection in the main memory. Instead the collection is split into small chunks called ‘batches’, and BigARTM always loads a limited number of batches into memory at any time.

Parallel. BigARTM can concurrently process several batches, and by doing so it substantially improves the throughput on multi-core machines. The library hosts all computation in several threads withing a single process, which enables efficient usage of shared memory across application threads.

Extensible API. BigARTM comes with an API in Python, but can be easily extended for all other languages that have an implementation of Google Protocol Buffers.

Cross-platform. BigARTM is known to be compatible with gcc, clang and the Microsoft compiler (VS 2012). We have tested our library on Windows, Ubuntu and Fedora.

Open source. BigARTM is released under the New BSD License. If you plan to use our library commercially, please beware that BigARTM depends on ZeroMQ. Please, make sure to review ZeroMQ license.

Acknowledgements. BigARTM project is supported by Russian Foundation for Basic Research (grants 14-07-00847, 14-07-00908, 14-07-31176), Skolkovo Institute of Science and Technology (project 081-R), Moscow Institute of Physics and Technology.

RFBR Skoltech MIPT

Downloads

Formats

This page describes input data formats compatible with BigARTM. Currently all formats correspond to Bag-of-words representation, meaning that all linguistic processing (lemmatization, tokenization, detection of n-grams, etc) needs to be done outside BigARTM.

  1. Vowpal Wabbit is a single-format file, based on the following principles:

    • each document is depresented in a single line
    • all tokens are represented as strings (no need to convert them into an integer identifier)
    • token frequency defaults to 1.0, and can be optionally specified after a colon (:)
    • namespaces (Batch.class_id) can be identified by a pipe (|)

    Example 1

    doc1 Alpha Bravo:10 Charlie:5 |author Ola_Nordmann
doc2 Bravo:5 Delta Echo:3 |author Ivan_Ivanov

    Example 2

    user123 |track-like track2 track5 track7 |track-play track1:10 track2:25 track3:2 track7:8 |track-skip track2:3 track8:1 |artist-like artist4:2 artist5:6 |artist-play artist4:100 artist5:20
user345 |track-like track2 track5 track7 |track-play track1:10 track2:25 track3:2 track7:8 |track-skip track2:3 track8:1 |artist-like artist4:2 artist5:6 |artist-play artist4:100 artist5:20

  2. UCI Bag-of-words format consists of two files - vocab.*.txt and docword.*.txt. The format of the docword.*.txt file is 3 header lines, followed by NNZ triples:

    D
W
NNZ
docID wordID count
docID wordID count
...
docID wordID count

    The file must be sorted on docID. Values of wordID must be unity-based (not zero-based). The format of the vocab.*.txt file is line containing wordID=n. Note that words must not have spaces or tabs. In vocab.*.txt file it is also possible to specify the namespace (Batch.class_id) for tokens, as it is shown in this example:

    token1 @default_class
token2 custom_class
token3 @default_class
token4

    Use space or tab to separate token from its class. Token that are not followed by class label automatically get ''@default_class‘’ as a label (see ‘’token4’’ in the example).

    Unicode support. For non-ASCII characters save vocab.*.txt file in UTF-8 format.

  3. Batches (binary BigARTM-specific format).

    This is compact and efficient format, based on several protobuf messages in public BigARTM interface (Batch, Item and Field).

    • A batch is a collection of several items
    • An item is a collection of several fields
    • A field is a collection of pairs (token_id, token_weight).

    The following example shows a Python code that generates a synthetic batch.

    import artm.messages, random, uuid

num_tokens = 60
num_items = 100
batch = artm.messages.Batch()
batch.id = str(uuid.uuid4())
for token_id in range(0, num_tokens):
    batch.token.append('token' + str(token_id))

for item_id in range(0, num_items):
    item = batch.item.add()
    item.id = item_id
    field = item.field.add()
    for token_id in range(0, num_tokens):
        field.token_id.append(token_id)
        background_count = random.randint(1, 5) if (token_id >= 40) else 0
        topical_count = 10 if (token_id < 40) and ((token_id % 10) == (item_id % 10)) else 0
        field.token_weight.append(background_count + topical_count)

    Note that the batch has its local dictionary, batch.token. This dictionary which maps token_id into the actual token. In order to create a batch from textual files involve one needs to find all distinct words, and map them into sequential indices.

    batch.id must be set to a unique GUID in a format of 00000000-0000-0000-0000-000000000000.

Installation

Installation for Windows users

Download

Download latest binary distribution of BigARTM from https://github.com/bigartm/bigartm/releases. Explicit download links can be found at Downloads section (for 32 bit and 64 bit configurations).

The distribution will contain pre-build binaries, command-line interface and BigARTM API for Python. The distribution also contains a simple dataset. More datasets in BigARTM-compatible format are available in the Downloads section.

Refer to Windows distribution for details about other files, included in the binary distribution package.

Configure BigARTM Python API

  1. Install Python, for example from the following links:

    Remember that the version of BigARTM package must match your version Python installed on your machine. If you have 32 bit operating system then you must select 32 bit for Python and BigARTM package. If you have 64 bit operating system then you are free to select either version. However, please note that memory usage of 32 bit processes is limited by 2 GB. For this reason we recommend to select 64 bit configurations.

    Please note that you must use Python 2.7, because Python 3 is not supported by BigARTM.

    Also you need to have several Python libraries to be installed on your machine:

    • numpy >= 1.9.2
    • pandas >= 0.16.2

  2. Add C:\BigARTM\bin folder to your PATH system variable, and add C:\BigARTM\python to your PYTHONPATH system variable:

    set PATH=%PATH%;C:\BigARTM\bin
set PATH=%PATH%;C:\Python27;C:\Python27\Scripts
set PYTHONPATH=%PYTHONPATH%;C:\BigARTM\Python

    Remember to change C:\BigARTM and C:\Python27 with your local folders.

  3. Setup Google Protocol Buffers library, included in the BigARTM release package.

    • Copy C:\BigARTM\bin\protoc.exe file into C:\BigARTM\protobuf\src folder
    • Run the following commands from command prompt
    cd C:\BigARTM\protobuf\Python
python setup.py build
python setup.py install

    Avoid python setup.py test step, as it produces several confusing errors. Those errors are harmless. For further details about protobuf installation refer to protobuf/python/README.

Installation for Linux and Mac OS-X users

Currently there is no distribution package of BigARTM for Linux. BigARTM had been tested on several Linux OS, and it is known to work well, but you have to get the source code and compile it locally on your machine.

System dependencies

Building BigARTM requires the following components:

  • git (any recent version) – for obtaining source code;
  • cmake (at least of version 2.8), make, g++ or clang compiler with c++11 support, boost (at least of version 1.40) – for building library and binary executable;
  • python (version 2.7) – for building Python API for BigARTM.

To simplify things, you may type:

  • On deb-based distributions: sudo apt-get install git make cmake build-essential libboost-all-dev
  • On rpm-based distributions: sudo yum install git make cmake gcc-c++ glibc-static libstdc++-static boost boost-static python (for Fedora 22 or higher use dnf instead of yum)

Download sources and build

Clone the latest BigARTM code from our github repository, and build it via CMake as in the following script.

cd ~
git clone --branch=stable https://github.com/bigartm/bigartm.git
cd bigartm
mkdir build && cd build
cmake ..
make

Note for Linux users: By default building binary executable bigartm requiers static versions of Boost, C and C++ libraries. To alter it, run cmake command with option -DBUILD_STATIC_BIGARTM=OFF.

System-wide installation

To install command-line utility, shared library module and Python interface for BigARTM, you can type:

sudo make install

Normally this will install:

  • bigartm utility into folder /usr/local/bin/;
  • shared library libartm.so (artm.dylib for Max OS-X) into folder /usr/local/lib/;
  • Python interface for BigARTM into Python-specific system directories, along with necessary dependencies.

If you want to alter target folders for binary and shared library objects, you may specify common prefix while running cmake command via option -DCMAKE_INSTALL_PREFIX=path_to_folder. By default CMAKE_INSTALL_PREFIX=/usr/local/.

Configure BigARTM Python API

If you want to use only Python interface for BigARTM, you may run following commands:

# Step 1 - install Google Protobuf as dependency
cd ~/bigartm/3rdparty/protobuf/python
sudo python setup.py install

# Step 2 - install Python interface for BigARTM
cd ~/bigartm/python
sudo python setup.py install

# Step 3 - point ARTM_SHARED_LIBRARY variable to libartm.so (libartm.dylib) location
export ARTM_SHARED_LIBRARY=~/bigartm/build/src/artm/libartm.so        # for linux
export ARTM_SHARED_LIBRARY=~/bigartm/build/src/artm/libartm.dylib     # for Mac OS X

We strongly recommend system-wide installation as there is no need to keep BigARTM code after it, so you may safely remove folder ~bigartm/.

Troubleshooting

While building BigARTM you can see lines similar to the following:

Building python package protobuf 2.5.1-pre
  File "/home/ubuntu/bigartm/3rdparty/protobuf/python/setup.py", line 52
    print "Generating %s..." % output
                             ^
SyntaxError: Missing parentheses in call to 'print'

This error may happen during google protobuf installation. It indicates that you are using Python 3, which is not supported by BigARTM. (see this question on StackOverflow for more details on the error around print). Please use Python 2.7 (e.g Python 2.7.11) to workaround this issue.

Using BigARTM Python API you can encounter this error:

Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "build/bdist.linux-x86_64/egg/artm/wrapper/api.py", line 19, in __init__
File "build/bdist.linux-x86_64/egg/artm/wrapper/api.py", line 53, in _load_cdll
OSError: libartm.so: cannot open shared object file: No such file or directory
Failed to load artm shared library. Try to add the location of `libartm.so` file into your LD_LIBRARY_PATH system variable, or to set ARTM_SHARED_LIBRARY - a specific system variable which may point to `libartm.so` file, including the full path.

This error indicates that BigARTM’s python interface can not locate libartm.so (libartm.dylib) files. In such case type export ARTM_SHARED_LIBRARY=path_to_artm_shared_library.

BigARTM on Travis-CI

To get a live usage example of BigARTM you may check BigARTM’s .travis.yml script and the latest continuous integration build.

Tutorials

BigARTM command line utility

This document provides an overview of bigartm command-line utility shipped with BigARTM.

For a detailed description of bigartm command line interface refer to bigartm.exe notebook (in Russian).

In brief, you need to download some input data (a textual collection represented in bag-of-words format). We recommend to download vocab and docword files by links provided in Downloads section of the tutorial. Then you can use bigartm as described by bigartm --help:

BigARTM - library for advanced topic modeling (http://bigartm.org):

Input data:
  -c [ --read-vw-corpus ] arg         Raw corpus in Vowpal Wabbit format
  -d [ --read-uci-docword ] arg       docword file in UCI format
  -v [ --read-uci-vocab ] arg         vocab file in UCI format
  --read-cooc arg                     read co-occurrences format
  --batch-size arg (=500)             number of items per batch
  --use-batches arg                   folder with batches to use

Dictionary:
  --dictionary-min-df arg             filter out tokens present in less than N
                                      documents / less than P% of documents
  --dictionary-max-df arg             filter out tokens present in less than N
                                      documents / less than P% of documents
  --use-dictionary arg                filename of binary dictionary file to use

Model:
  --load-model arg                    load model from file before processing
  -t [ --topics ] arg (=16)           number of topics
  --use-modality arg                  modalities (class_ids) and their weights
  --predict-class arg                 target modality to predict by theta
                                      matrix

Learning:
  -p [ --passes ] arg (=0)            number of outer iterations
  --inner-iterations-count arg (=10)  number of inner iterations
  --update-every arg (=0)             [online algorithm] requests an update of
                                      the model after update_every document
  --tau0 arg (=1024)                  [online algorithm] weight option from
                                      online update formula
  --kappa arg (=0.699999988)          [online algorithm] exponent option from
                                      online update formula
  --reuse-theta                       reuse theta between iterations
  --regularizer arg                   regularizers (SmoothPhi,SparsePhi,SmoothT
                                      heta,SparseTheta,Decorrelation)
  --threads arg (=0)                  number of concurrent processors (default:
                                      auto-detect)
  --async                             invoke asynchronous version of the online
                                      algorithm
  --model-v06                         use legacy model from BigARTM v0.6.4

Output:
  --save-model arg                    save the model to binary file after
                                      processing
  --save-batches arg                  batch folder
  --save-dictionary arg               filename of dictionary file
  --write-model-readable arg          output the model in a human-readable
                                      format
  --write-dictionary-readable arg     output the dictionary in a human-readable
                                      format
  --write-predictions arg             write prediction in a human-readable
                                      format
  --write-class-predictions arg       write class prediction in a
                                      human-readable format
  --write-scores arg                  write scores in a human-readable format
  --force                             force overwrite existing output files
  --csv-separator arg (=;)            columns separator for
                                      --write-model-readable and
                                      --write-predictions. Use \t or TAB to
                                      indicate tab.
  --score-level arg (=2)              score level (0, 1, 2, or 3
  --score arg                         scores (Perplexity, SparsityTheta,
                                      SparsityPhi, TopTokens, ThetaSnippet, or
                                      TopicKernel)
  --final-score arg                   final scores (same as scores)

Other options:
  -h [ --help ]                       display this help message
  --response-file arg                 response file
  --paused                            start paused and waits for a keystroke
                                      (allows to attach a debugger)
  --disk-cache-folder arg             disk cache folder
  --disable-avx-opt                   disable AVX optimization (gives similar
                                      behavior of the Processor component to
                                      BigARTM v0.5.4)
  --use-dense-bow                     use dense representation of bag-of-words
                                      data in processors
  --time-limit arg (=0)               limit execution time in milliseconds

Examples:

* Download input data:
  wget https://s3-eu-west-1.amazonaws.com/artm/docword.kos.txt
  wget https://s3-eu-west-1.amazonaws.com/artm/vocab.kos.txt
  wget https://s3-eu-west-1.amazonaws.com/artm/vw.mmro.txt

* Parse docword and vocab files from UCI bag-of-word format; then fit topic model with 20 topics:
  bigartm -d docword.kos.txt -v vocab.kos.txt -t 20 --passes 10

* Parse VW format; then save the resulting batches and dictionary:
  bigartm --read-vw-corpus vw.mmro.txt --save-batches mmro_batches --save-dictionary mmro.dict

* Parse VW format from standard input; note usage of single dash '-' after --read-vw-corpus:
  cat vw.mmro.txt | bigartm --read-vw-corpus - --save-batches mmro2_batches --save-dictionary mmro2.dict

* Load and filter the dictionary on document frequency; save the result into a new file:
  bigartm --use-dictionary mmro.dict --dictionary-min-df 5 dictionary-max-df 40% --save-dictionary mmro-filter.dict

* Load the dictionary and export it in a human-readable format:
  bigartm --use-dictionary mmro.dict --write-dictionary-readable mmro.dict.txt

* Use batches to fit a model with 20 topics; then save the model in a binary format:
  bigartm --use-batches mmro_batches --passes 10 -t 20 --save-model mmro.model

* Load the model and export it in a human-readable format:
  bigartm --load-model mmro.model --write-model-readable mmro.model.txt

* Load the model and use it to generate predictions:
  bigartm --read-vw-corpus vw.mmro.txt --load-model mmro.model --write-predictions mmro.predict.txt

* Fit model with two modalities (@default_class and @target), and use it to predict @target label:
  bigartm --use-batches <batches> --use-modality @default_class,@target --topics 50 --passes 10 --save-model model.bin
  bigartm --use-batches <batches> --use-modality @default_class,@target --topics 50 --load-model model.bin
          --write-predictions pred.txt --csv-separator=tab
          --predict-class @target --write-class-predictions pred_class.txt --score ClassPrecision

* Fit simple regularized model (increase sparsity up to 60-70%):
  bigartm -d docword.kos.txt -v vocab.kos.txt --dictionary-max-df 50% --dictionary-min-df 2
          --passes 10 --batch-size 50 --topics 20 --write-model-readable model.txt
          --regularizer "0.05 SparsePhi" "0.05 SparseTheta"

* Fit more advanced regularize model, with 10 sparse objective topics, and 2 smooth background topics:
  bigartm -d docword.kos.txt -v vocab.kos.txt --dictionary-max-df 50% --dictionary-min-df 2
          --passes 10 --batch-size 50 --topics obj:10;background:2 --write-model-readable model.txt
          --regularizer "0.05 SparsePhi #obj"
          --regularizer "0.05 SparseTheta #obj"
          --regularizer "0.25 SmoothPhi #background"
          --regularizer "0.25 SmoothTheta #background"

* Configure logger to output into stderr:
  tset GLOG_logtostderr=1 & bigartm -d docword.kos.txt -v vocab.kos.txt -t 20 --passes 10

Running BigARTM from Python API

Refer to ARTM notebook (in Russian or in English), which describes high-level Python API of BigARTM.

Python Interface

This document describes all classes and functions in python interface of BigARTM library.

ARTM model

This page describes ARTM class.

class artm.ARTM(num_processors=0, topic_names=None, num_topics=10, class_ids=None, cache_theta=True, scores=None, regularizers=None, theta_columns_naming='id')

ARTM represents a topic model (public class)

Parameters:
  • num_processors (int) – how many threads will be used for model training, if not specified then number of threads will be detected by the lib
  • topic_names (list of str) – names of topics in model, if not specified will be auto-generated by lib according to num_topics
  • num_topics (int) – number of topics in model (is used if topic_names not specified), default=10
  • class_ids (dict) – list of class_ids and their weights to be used in model, key — class_id, value — weight, if not specified then all class_ids will be used
  • cache_theta (bool) – save or not the Theta matrix in model. Necessary if ARTM.get_theta() usage expects, default=True
  • scores (list) – list of scores (objects of artm.***Score classes), default=None
  • regularizers (list) – list with regularizers (objects of artm.***Regularizer classes), default=None
  • theta_columns_naming (string) – either ‘id’ or ‘title’, determines how to name columns (documents) in theta dataframe, default=’id’
Important public fields:
  • regularizers — contains dict of regularizers, included into model
  • scores — contains dict of scores, included into model
  • score_tracker — contains dict of scoring results; key — score name, value — ScoreTracker object, which contains info about values of score on each synchronization in list

Note

  • Here and anywhere in BigARTM empty topic_names or class_ids means that model (or regularizer, or score) should use all topics or class_ids.
  • If some fields of regularizers or scores are not defined by user — internal lib defaults would be used.
  • If field ‘topic_names’ is None, it will be generated by BigARTM and will be available using ARTM.topic_names().
create_dictionary(dictionary_name=None, dictionary_data=None)

ARTM.save_dictionary() — save the BigARTM dictionary of the collection on the disk

Parameters:
  • dictionary_name (str) – the name of the dictionary in the lib, default=None
  • dictionary_data (DictionaryData instance) – configuration of dictionary, default=None
filter_dictionary(dictionary_name=None, dictionary_target_name=None, class_id=None, min_df=None, max_df=None, min_df_rate=None, max_df_rate=None, min_tf=None, max_tf=None)

ARTM.filter_dictionary() — filter the BigARTM dictionary of the collection, which was already loaded into the lib

Parameters:
  • dictionary_name (str) – name of the dictionary in the lib to filter
  • dictionary_target_name (str) – name for the new filtered dictionary in the lib
  • class_id (str) – class_id to filter
  • min_df (float) – min df value to pass the filter
  • max_df (float) – max df value to pass the filter
  • min_df_rate (float) – min df rate to pass the filter
  • max_df_rate (float) – max df rate to pass the filter
  • min_tf (float) – min tf value to pass the filter
  • max_tf (float) – max tf value to pass the filter
fit_offline(batch_vectorizer=None, num_collection_passes=20, num_document_passes=1, reuse_theta=True, dictionary_filename='dictionary.dict')

ARTM.fit_offline() — proceed the learning of topic model in off-line mode

Parameters:
  • batch_vectorizer – an instance of BatchVectorizer class
  • num_collection_passes (int) – number of iterations over whole given collection, default=20
  • num_document_passes (int) – number of inner iterations over each document for inferring theta, default=1
  • reuse_theta (bool) – using theta from previous pass of the collection, default=True
  • dictionary_filename (str) – the name of file with dictionary to use in inline initialization, default=’dictionary’

Note

ARTM.initialize() should be proceed before first call ARTM.fit_offline(), or it will be initialized by dictionary during first call.

fit_online(batch_vectorizer=None, tau0=1024.0, kappa=0.7, update_every=1, num_document_passes=10, reset_theta_scores=False, dictionary_filename='dictionary.dict')

ARTM.fit_online() — proceed the learning of topic model in on-line mode

Parameters:
  • batch_vectorizer – an instance of BatchVectorizer class
  • update_every (int) – the number of batches; model will be updated once per it, default=1
  • tau0 (float) – coefficient (see kappa), default=1024.0
  • kappa (float) – power for tau0, default=0.7
  • num_document_passes (int) – number of inner iterations over each document for inferring theta, default=10
  • reset_theta_scores (bool) – reset accumulated Theta scores before learning, default=False
  • dictionary_filename (str) – the name of file with dictionary to use in inline initialization, default=’dictionary’

Note

The formulas for decay_weight and apply_weight:

  • update_count = current_processed_docs / (batch_size * update_every)
  • rho = pow(tau0 + update_count, -kappa)
  • decay_weight = 1-rho
  • apply_weight = rho

Note

ARTM.initialize() should be proceed before first call ARTM.fit_online(), or it will be initialized by dictionary during first call.

fit_transform(topic_names=None, remove_theta=False)

ARTM.fit_transform() — obsolete way of theta retrieval. Use get_theta instead.

gather_dictionary(dictionary_target_name=None, data_path=None, cooc_file_path=None, vocab_file_path=None, symmetric_cooc_values=False)

ARTM.gather_dictionary() — create the BigARTM dictionary of the collection, represented as batches and load it in the lib

Parameters:
  • dictionary_target_name (str) – the name of the dictionary in the lib, default=None
  • data_path (str) – full path to batches folder
  • cooc_file_path (str) – full path to the file with cooc info
  • vocab_file_path (str) – full path to the file with vocabulary. If given, the dictionary token will have the same order, as in this file, otherwise the order will be random, default=None
  • symmetric_cooc_values (bool) – if the cooc matrix should considered to be symmetric or not, default=False
get_phi(topic_names=None, class_ids=None, model_name=None)

ARTM.get_phi() — get custom Phi matrix of model. The extraction of the whole Phi matrix expects ARTM.phi_ call.

Parameters:
  • topic_names (list of str) – list with topics to extract, default=None (means all topics)
  • class_ids (list of str) – list with class ids to extract, default=None (means all class ids)
  • model_name (str) – self.model_pwt by default, self.model_nwt is also reasonable to extract unnormalized counters
Returns:

pandas.DataFrame (data, columns, rows), where:

  • columns — the names of topics in topic model
  • rows — the tokens of topic model
  • data — content of Phi matrix
get_theta(topic_names=None, remove_theta=False)

ARTM.get_theta() — get Theta matrix for training set of documents

Parameters:
  • topic_names (list of str) – list with topics to extract, default=None (means all topics)
  • remove_theta (bool) – flag indicates save or remove Theta from model after extraction, default=False
Returns:

pandas.DataFrame (data, columns, rows), where:

  • columns — the ids of documents, for which the Theta matrix was requested
  • rows — the names of topics in topic model, that was used to create Theta
  • data — content of Theta matrix
initialize(dictionary_name=None, seed=-1)

ARTM.initialize() — initialize topic model before learning

Parameters:
  • dictionary_name (str) – the name of loaded BigARTM collection dictionary, default=None
  • seed (unsigned int or -1) – seed for random initialization, default=-1 (no seed)
load(filename)

ARTM.load() — load the topic model, saved by ARTM.save(), from disk

Parameters:filename (str) – the name of file containing model, no default

Note

Loaded model will overwrite ARTM.topic_names and ARTM.num_topics fields. Also it will empty ARTM.score_tracker.

load_dictionary(dictionary_name=None, dictionary_path=None)

ARTM.load_dictionary() — load the BigARTM dictionary of the collection into the lib

Parameters:
  • dictionary_name (str) – the name of the dictionary in the lib, default=None
  • dictionary_path (str) – full file name of the dictionary, default=None
load_text_dictionary(dictionary_name=None, dictionary_path=None, encoding='utf-8')

ARTM.load_text_dictionary() — load the BigARTM dictionary of the collection from the disk in the human readable text format

Parameters:
  • dictionary_name (str) – the name for the dictionary in the lib, default=None
  • dictionary_path (str) – full file name of the text dictionary file, default=None
  • encoding (str) – an encoding of text in diciotnary
remove_dictionary(dictionary_name=None)

ARTM.remove_dictionary() — remove the loaded BigARTM dictionary from the lib

Parameters:dictionary_name (str) – the name of the dictionary in the lib, default=None
save(filename='artm_model')

ARTM.save() — save the topic model to disk

Parameters:filename (str) – the name of file to store model, default=’artm_model’
save_dictionary(dictionary_name=None, dictionary_path=None)

ARTM.save_dictionary() — save the BigARTM dictionary of the collection on the disk

Parameters:
  • dictionary_name (str) – the name of the dictionary in the lib, default=None
  • dictionary_path (str) – full file name for the dictionary, default=None
save_text_dictionary(dictionary_name=None, dictionary_path=None, encoding='utf-8')

ARTM.save_text_dictionary() — save the BigARTM dictionary of the collection on the disk in the human readable text format

Parameters:
  • dictionary_name (str) – the name of the dictionary in the lib, default=None
  • dictionary_path (str) – full file name for the text dictionary file, default=None
  • encoding (str) – an encoding of text in diciotnary
transform(batch_vectorizer=None, num_document_passes=1, predict_class_id=None)

ARTM.transform() — find Theta matrix for new documents

Parameters:
  • batch_vectorizer – an instance of BatchVectorizer class
  • num_document_passes (int) – number of inner iterations over each document for inferring theta, default = 1
  • predict_class_id (string) – class_id of a target modality to predict, default = None. When this option is enabled the resulting columns of theta matrix will correspond to unique labels of a target modality. The values will represent p(c|d), which give the probability of class label c for document d.
Returns:

pandas.DataFrame (data, columns, rows), where:

  • columns — the ids of documents, for which the Theta matrix was requested
  • rows — the names of topics in topic model, that was used to create Theta
  • data — content of Theta matrix.

BigARTM FAQ

Can I use BigARTM from other programming languages (not Python)?

Yes, as long as your language has an implementation of Google Protocol Buffers (the list can be found here). Note that Google officially supports C++, Python and Java.

The following figure shows how to call BigARTM methods directly on artm.dll (Windows) or artm.so (Linux).

Connecting to BigARTM from different languages

To write your API please refer to Plain C interface of BigARTM.

How to retrieve Theta matrix from BigARTM

Theta matrix is a matrix that contains the distribution of several items (columns of the matrix) into topics (rows of the matrix). There are three ways to retrieve such information from BigARTM, and the correct way depends on your scenario.

  1. You want to get Theta matrix for the same collection as you have used to infer the topic model.

    Set MasterComponentConfig.cache_theta to true prior to the last iteration, and after the iteration use MasterComponent::GetThetaMatrix() (in C++) or MasterComponent.GetThetaMatrix (in Python) to retrieve Theta matrix.

  2. You want to repeatedly monitor a small portion of the Theta matrix during ongoing iterations.

    In this case you should create Theta Snippet score, defined via ThetaSnippetScoreConfig, and then use MasterComponent::GetScoreAs<T>() to retrieve the resulting ThetaSnippetScore message.

    This configuration of Theta Snippet score require you to provide ThetaSnippetScoreConfig.item_id listing all IDs of the items that should have Theta’s collected. If you created the batches manually you should have specified such IDs in Item.id field. If you used other methods to parse the collection from disk then you shouldt try using sequential IDs, starting with 1.

    Remember that Theta snippet score is designed to handle only a small number of items. Attemp to retrieve 100+ items will have a negative effect on performance.

  3. You want to classify a new set of items with an existing model.

    In this case you need to create a Batch, containing your new items. Then copy this batch to GetThetaMatrixArgs.batch message, specify GetThetaMatrixArgs.model_name, and use MasterComponent::GetThetaMatrix() (in C++) or MasterComponent.GetThetaMatrix (in Python) to retrieve Theta matrix. In this case there is no need set MasterComponentConfig.cache_theta to true.

Check example11_get_theta_matrix.py for further examples.

BigARTM Developer’s Guide

This document describes the development process of BigARTM library.

You should not follow this guide if you are using pre-built BigARTM library via command-line interface or from Python environment. (refer to to Basic BigARTM tutorial for Windows users or Basic BigARTM tutorial for Linux and Mac OS-X users depending on your operating system).

Downloads (Windows)

Download and install the following tools:

All explicit links are given just for convenience if you are setting up new environment. You are free to choose other versions or tools, and most likely they will work just fine for BigARTM. Remember to match the following: * Visual Studio version must match Boost binaries version, unless you build Boost yourself * Use the same configuration (32 bit or 64 bit) for your Python and BigARTM binaries

Source code

BigARTM is hosted in public GitHub repository:

https://github.com/bigartm/bigartm

We maintain two branches: master and stable. master branch is the latest source code, potentially including some unfinished features. stable branch will be lagging behind master, and moved forward to master as soon as mainteiners decide that it is ready. Typically this should happen at the end of each month. At the same point we will introduce a new tag (something like v0.7.3 ) and produce a new release for Windows. In addition, stable branch also might receive small urgent fixes in between releases, typically to address critical issues reported by our users. Such fixes will be also included in master branch.

To contribute a fix you should fork the repository, code your fix and submit a pull request. against master branch. All pull requests are regularly monitored by BigARTM maintainers and will be soon merged. Please, keep monitoring the status of your pull request on travis, which is a continuous integration system used by BigARTM project.

Build C++ code on Windows

The following steps describe the procedure to build BigARTM’s C++ code on Windows.

  • Download and install GitHub for Windows.

  • Clone https://github.com/bigartm/bigartm/ repository to any location on your computer. This location is further refered to as $(BIGARTM_ROOT).

  • Download and install Visual Studio 2012 or any newer version. BigARTM will compile just fine with any edition, including any Visual Studio Express edition (available at www.visualstudio.com).

  • Install CMake (tested with cmake-3.0.1, Win32 Installer).

    Make sure that CMake executable is added to the PATH environmental variable. To achieve this either select the option “Add CMake to the system PATH for all users” during installation of CMake, or add it to the PATH manually.

  • Download and install Boost 1.55 or any newer version.

    We suggest to use the Prebuilt Windows Binaries. Make sure to select version that match your version of Visual Studio. You may choose to work with either x64 or Win32 configuration, both of them are supported.

  • Configure system variables BOOST_ROOT and Boost_LIBRARY_DIR.

    If you have installed boost from the link above, and used the default location, then the setting should look similar to this:

    setx BOOST_ROOT C:\local\boost_1_56_0
setx BOOST_LIBRARYDIR C:\local\boost_1_56_0\lib32-msvc-12.0

    For all future details please refer to the documentation of FindBoost module. We also encourage new CMake users to step through CMake tutorial.

  • Install Python 2.7 (tested with Python 2.7.6).

    You may choose to work with either x64 or Win32 version of the Python, but make sure this matches the configuration of BigARTM you have choosed earlier. The x64 installation of python will be incompatible with 32 bit BigARTM, and virse versus.

  • Use CMake to generate Visual Studio projects and solution files. To do so, open a command prompt, change working directory to $(BIGARTM_ROOT) and execute the following commands:

    mkdir build
cd build
cmake ..

    You might have to explicitly specify the cmake generator, especially if you are working with x64 configuration. To do so, use the following syntax:

    cmake .. -G"Visual Studio 12 Win64"

    CMake will generate Visual Studio under $(BIGARTM_ROOT)/build/.

  • Open generated solution in Visual Studio and build it as you would usually build any other Visual Studio solution. You may also use MSBuild from Visual Studio command prompt.

    The build will output result into the following folders:

    • $(BIGARTM_ROOT)/build/bin/[Debug|Release] — binaries (.dll and .exe)
    • $(BIGARTM_ROOT)/build/lib/[Debug|Release] — static libraries

At this point you should be able to run BigARTM tests, located here: $(BIGARTM_ROOT)/build/bin/*/artm_tests.exe.

Python code on Windows

  • Install Python 2.7 (this step is already done if you are following the instructions above),

  • Add Python to the PATH environmental variable

    http://stackoverflow.com/questions/6318156/adding-python-path-on-windows-7

  • Follow the instructions in README file in directory $(BIGARTM_ROOT)/3rdparty/protobuf/python/. In brief, this instructions ask you to run the following commands:

    python setup.py build
python setup.py test
python setup.py install

    On second step you fill see two failing tests:

    Ran 216 tests in 1.252s
FAILED (failures=2)

    This 2 failures are OK to ignore.

At this point you should be able to run BigARTM tests for Python, located under $(BIGARTM_ROOT)/python/tests/.

  • [Optional] Download and add to MSVS Python Tools 2.0. All necessary instructions can be found at https://pytools.codeplex.com/. This will allow you debug you Python scripts using Visual Studio. You may start with the following solution: $(BIGARTM_ROOT)/src/artm_vs2012.sln.

Build C++ code on Linux

Refer to Basic BigARTM tutorial for Linux and Mac OS-X users.

Working with iPython notebooks remotely

It turned out to be common scenario to run BigARTM on a Linux server (for example on Amazon EC2), while connecting to it from Windows through putty. Here is a convenient way to use ipython notebook in this scenario:

  1. Connect to the Linux machine via putty. Putty needs to be configured with dynamic tunnel for port 8888 as describe here on this page (port 8888 is a default port for ipython notebook). The same page describes how to configure internet properties:

    Clicking on Settings in Internet Explorer, or Proxy Settings in Google Chrome, should open this dialogue. Navigate through to the Advanced Proxy section and add localhost:9090 as a SOCKS Proxy.

  2. Start ipython notebook in your putty terminal.

  3. Open your favourite browser on Windows, and go to http://localhost:8888. Enjoy your notebook while the engine runs on remotely :)

Working with iPython notebooks remotely

Compiling .proto files on Windows

  1. Open a new command prompt

  2. Copy the following file into $(BIGARTM_ROOT)/src/

    • $(BIGARTM_ROOT)/build/bin/CONFIG/protoc.exe

    Here CONFIG can be either Debug or Release (both options will work equally well).

  3. Change working directory to $(BIGARTM_ROOT)/src/

  4. Run the following commands

    .\protoc.exe --cpp_out=. --python_out=. .\artm\messages.proto
.\protoc.exe --cpp_out=. .\artm\core\internals.proto

Code style

Configure Visual Studio

Open Tools / Text Editor / All languages / Tabs and configure as follows:

  • Indenting - smart,
  • Tab size - 2,
  • Indent size - 2,
  • Select “insert spaces”.

We also suggest to configure Visual Studio to show space and tab crlf characters (shortcut: Ctrl+R, Ctrl+W), and enable vertical line at 120 characters.

In the code we follow google code style with the following changes:

  • Exceptions are allowed
  • Indentation must be 2 spaces. Tabs are not allowed.
  • No lines should exceed 120 characters.

All .h and .cpp files under $(BIGARTM_ROOT)/src/artm/ must be verified for code style with cpplint.py script. Files, generated by protobuf compiler, are the only exceptions from this rule.

To run the script you need some version of Python installed on your machine. Then execute the script like this:

python cpplint.py --linelength=120 <filename>

On Windows you may run this master-script to check all required files:

$(BIGARTM_ROOT/utils/cpplint_all.bat.

Release Notes

BigARTM v0.7.0 Release notes

We are happy to introduce BigARTM v0.7.0, which brings you the following changes:

  • New-style models
  • Network modus operandi is removed
  • Coherence regularizer and scores (experimental)

New-style models

BigARTM v0.7.0 exposes new APIs to give you additional control over topic model inference:

  • ProcessBatches
  • MergeModel
  • RegularizeModel
  • NormalizeModel

Besides being more flexible, new APIs bring many additional benefits:

  • Fully deterministic inference, no dependency on threads scheduling or random numbers generation
  • Less bottlenecks for performance (DataLoader and Merger threads are removed)
  • Phi-matrix regularizers can be implemented externally
  • Capability to output Phi matrices directly into your NumPy matrices (scheduled for BigARTM v0.7.2)
  • Capability for store Phi matrices in sparse format (scheduled for BigARTM v0.7.3)
  • Capability for async ProcessBatches and non-blocking online algorithm (BigARTM v0.7.4)
  • Form solid foundation for high performance networking (BigARTM v0.8.X)

The picture below illustrates scalability of BigARTM v0.7.0 vs v0.6.4. Top chart (in green) corresponds to CPU usage at 28 cores on machine with 32 virtual cores (16 physical cores + hyper threading). As you see, new version is much more stable. In addition, new version consumes less memory.

BigARTM v0.7.0 vs v0.6.4

Refer to the following examples that demonstrate usage of new APIs for offline, online and regularized topic modelling:

Models, tuned with the new API are referred to as new-style models, as opposite to old-style models inferred with AddBatch, InvokeIteration, WaitIdle and SynchronizeModel APIs.

Warning

For BigARTM v0.7.X we will continue to support old-style models. However, you should consider upgrading to new-style models because old APIs (AddBatch, InvokeIteration, WaitIdle and SynchronizeModel) are likely to be removed in future releases.

The following flow chart gives a typical use-case on new APIs in online regularized algorithm:

Data flow in online regularized algorithm

Notes on upgrading existing code to new-style models

  1. New APIs can only read batches from disk. If your current script passes batches via memory (in AddBatchArgs.batch field) then you need to store batches on disk first, and then process them with ProcessBatches method.

  2. Initialize your model as follows:

    • For python_interface: using MasterComponent.InitializeModel method
    • For cpp_interface: using MasterComponent.InitializeModel method
    • For c_interface: using ArtmInitializeModel method

    Remember that you should not create ModelConfig in order to use this methods. Pass your topics_count (or topic_name list) as arguments to InitializeModel method.

  3. Learn the difference between Phi and Theta scores, as well as between Phi and Theta regularizes. The following table gives an overview:

    Object Theta Phi
    Scores
    • Perplexity
    • SparsityTheta
    • ThetaSnippet
    • ItemsProcessed
    • SparsityPhi
    • TopTokens
    • TopicKernel
    Regularizers
    • SmoothSparseTheta
    • DecorrelatorPhi
    • ImproveCoherencePhi
    • LabelRegularizationPhi
    • SmoothSparsePhi
    • SpecifiedSparsePhi

    Phi regularizers needs to be calculated explicitly in RegularizeModel, and then applied in NormalizeModel (via optional rwt argument). Theta regularizers needs to be enabled in ProcessBatchesArgs. Then they will be automatically calculated and applied during ProcessBatches.

    Phi scores can be calculated at any moment based on the new-style model (same as for old-style models). Theta scores can be retrieved in two equivalend ways:

    pwt_model = "pwt"
master.ProcessBatches(pwt_model, batches, "nwt")
perplexity_score.GetValue(pwt_model).value

    or

    pwt_model = "pwt"
process_batches_result = master.ProcessBatches(pwt_model, batches, "nwt")
perplexity_score.GetValue(scores = process_batches_result).value

    Second way is more explicit. However, the first way allows you to combine aggregate scores accross multiple ProcessBatches calls:

    pwt_model = "pwt"
master.ProcessBatches(pwt_model, batches1, "nwt")
master.ProcessBatches(pwt_model, batches2, "nwt", reset_scores=False)
perplexity_score.GetValue(pwt_model).value

    This works because BigARTM caches the result of ProcessBatches together (in association with pwt_model). The reset_scores switch disables the default behaviour, which is to reset the cache for pwt_model at the beginning of each ProcessBatch call.

  4. Continue using GetThetaMatrix and GetTopicModel to retrieve results from the library. For GetThetaMatrix to work you still need to enable cache_theta in master component. Remember to use the same model in GetThetaMatrix as you used as the input to ProcessBatches. You may also omit “target_nwt” argument in ProcessBatches if you are not interested in getting this output.

    master.ProcessBatches("pwt", batches)
theta_matrix = master.GetThetaMatrix("pwt")

  5. Stop using certain APIs:

    • For python_interface: stop using class Model and ModelConfig message
    • For cpp_interface: stop using class Model and ModelConfig message
    • For c_interface: stop using methods ArtmCreateModel, ArtmReconfigureModel, ArtmInvokeIteration, ArtmAddBatch, ArtmWaitIdle, ArtmSynchronizeModel

Notes on models handling (reusing, sharing input and output, etc)

Is allowed to output the result of ProcessBatches, NormalizeModel, RegularizeModel and MergeModel into an existing model. In this case the existing model will be fully overwritten by the result of the operation. For all operations except ProcessBatches it is also allowed to use the same model in inputs and as an output. For example, typical usage of MergeModel involves combining “nwt” and “nwt_hat” back into “nwt”. This scenario is fully supported. The output and input of ProcessBatches must refer to two different models. Finally, note that MergeModel will ignore all non-existing models in the input (and log a warning). However, if none of the input models exist then MergeModel will thrown an error.

Known differences

  1. Decorrelator regularizer will give slightly different result in the following scenario:

    master.ProcessBatches("pwt", batches, "nwt")
master.RegularizeModel("pwt", "nwt", "rwt", phi_regularizers)
master.NormalizeModel("nwt", "pwt", "rwt")

    To get the same result as from model.Synchronize() adjust your script as follows:

    master.ProcessBatches("pwt", batches, "nwt")
master.NormalizeModel("nwt", "pwt_temp")
master.RegularizeModel("pwt_temp", "nwt", "rwt", phi_regularizers)
master.NormalizeModel("nwt", "pwt", "rwt")

  2. You may use GetThetaMatrix(pwt) to retrieve Theta-matrix, previously calculated for new-style models inside ProcessBatches. However, you can not use GetThetaMatrix(pwt, batch) for new models. They do not have corresponding ModelConfig, and as a result you need to go through ProcessBatches to pass all parameters.

Network modus operandi is removed

Network modus operandi had been removed from BigARTM v0.7.0.

This decision had been taken because current implementation struggle from many issues, particularly from poor performance and stability. We expect to re-implement this functionality on top of new-style models.

Please, let us know if this caused issues for you, and we will consider to re-introduce networking in v0.8.0.

Coherence regularizer and scores (experimental)

Refer to example in example16_coherence_score.py.

BigARTM v0.7.1 Release notes

We are happy to introduce BigARTM v0.7.1, which brings you the following changes:

  • BigARTM noteboks — new source of information about BigARTM
  • ArtmModel — a brand new Python API
  • Much faster retrieval of Phi and Theta matrices from Python
  • Much faster dictionary imports from Python
  • Auto-detect and use all CPU cores by default
  • Fixed Import/Export of topic models (was broken in v0.7.0)
  • New capability to implement Phi-regularizers in Python code
  • Improvements in Coherence score

Before you upgrade to BigARTM v0.7.1 please review the changes that break backward compatibility.

BigARTM notebooks

BigARTM notebooks is your go-to links to read more ideas, examples and other information around BigARTM:

ArtmModel

Best thing about ArtmModel is that this API had been designed by BigARTM users. Not by BigARTM programmers. This means that BigARTM finally has a nice, clean and easy-to-use programming interface for Python. Don’t believe it? Just take a look and some examples:

That is cool, right? This new API allows you to load input data from several file formats, infer topic model, find topic distribution for new documents, visualize scores, apply regularizers, and perform many other actions. Each action typically takes one line to write, which allows you to work with BigARTM interactively from Python command line.

ArtmModel exposes most of BigARTM functionality, and it should be sufficiently powerful to cover 95% of all BigARTM use-cases. However, for the most advanced scenarios you might still need to go through the previous API (artm.library). When in doubt which API to use, ask bigartm-users@googlegroups.com — we are there to help!

Coding Phi-regularizers in Python code

This is of course one of those very advanced scenarios where you need to go down to the old API :) Take a look at this example:

First one tells how to use Phi regularizers, built into BigARTM. Second one provides a new capability to manipulate Phi matrix from Python. We call this Attach numpy matrix to the model, because this is similar to attaching debugger (like gdb or Visual Studio) to a running application.

To implement your own Phi regularizer in Python you need to to attach to rwt model from the first example, and update its values.

Other changes

Fast retrieval of Phi and Theta matrices. In BigARTM v0.7.1 dense Phi and Theta matrices will be retrieved to Python as numpy matrices. All copying work will be done in native C++ code. This is much faster comparing to current solution, where all data is transferred in a large Protobuf message which needs to be deserialized in Python. ArtmModel already takes advantage of this performance improvements.

Fast dictionary import. BigARTM core now supports importing dictionary files from disk, so you no longer have to load them to Python. ArtmModel already take advantage of this performance improvement.

Auto-detect number of CPU cores. You no longer need to specify num_processors parameter. By default BigARTM will detect the number of cores on your machine and load all of them. num_processors still can be used to limit CPU resources used by BigARTM.

Fixed Import/Export of topic models. Export and Import of topic models will now work. As simple as this:

master.ExportModel("pwt", "file_on_disk.model")
master.ImportModel("pwt", "file_on_disk.model")

This will also take care of very large models above 1 GB that does not fit into single protobuf message.

Coherence scores. Ask bigartm-users@googlegroups.com if you are interested :)

Breaking changes

  • Changes in Python methods MasterComponent.GetTopicModel and MasterComponent.GetThetaMatrix

    From BigARTM v0.7.1 and onwards method MasterComponent.GetTopicModel of the low-level Python API will return a tuple, where first argument is of type TopicModel (protobuf message), and second argument is a numpy matrix. TopicModel message will keep all fields as usual, except token_weights field which will became empty. Information from token_weights field had been moved to numpy matrix (rows = tokens, columns = topics).

    Similarly, MasterComponent.GetThetaMatrix will also return a tuple, where first argument is of type ThetaMatrix (protobuf message), and second argument is a numpy matrix. ThetaMatrix message will keep all fields as usual, except item_weights field which will became empty. Information from item_weights field had been moved to numpy matrix (rows = items, columns = topics).

    Updated examples:

    Warning

    Use the followign syntax to restore the old behaviour:

    • MasterComponent.GetTopicModel(use_matrix = False)
    • MasterComponent.GetThetaMatrix(use_matrix = False)

    This will return a complete protobuf message, without numpy matrix.

  • Python method ParseCollectionOrLoadDictionary is now obsolete

    • Use ParseCollection method to convert collection into a set of batches
    • Use MasterComponent.ImportDictionary to load dictionary into BigARTM
    • Updated example: example06_use_dictionaries.py

BigARTM v0.7.2 Release notes

We are happy to introduce BigARTM v0.7.2, which brings you the following changes:

  • Enhancements in high-level python API (ArtmModel -> ARTM)
  • Enhancements in low-level python API (library.py -> master_component.py)
  • Enhancements in CLI interface (cpp_client)
  • Status and information retrievals from BigARTM
  • Allow float token counts (token_count -> token_weight)
  • Allow custom weights for each batch (ProcessBatchesArgs.batch_weight)
  • Bug fixes and cleanup in the online documentation

Enhancements in Python APIs

Note that ArtmModel had been renamed to ARTM. The naming conventions follow the same pattern as in scikit learn (e.g. fit, transform and fit_transform methods).

Also note that all input data is now handled by BatchVectorizer class.

Refer to noteboods in English and in Russian for further details about ARTM interface.

Also note that previous low-level python API library.py is superseeded by a new API master_component.py. For now both APIs are available, but the old one will be removed in future releases. Refer to this folder for futher examples of the new low-level python API.

Remember that any use of low-level APIs is discouraged. Our recommendation is to always use the high-level python API ARTM, and e-mail us know if some functionality is not exposed there.

Enhancements in CLI interface

BigARTM command line interface cpp_client had been enhanced with the following options:

  • --load_model - to load model from file before processing
  • --save_model - to save the model to binary file after processing
  • --write_model_readable - to output the model in a human-readable format (CSV)
  • --write_predictions - to write prediction in a human-readable format (CSV)
  • --dictionary_min_df - to filter out tokens present in less than N documents / less than P% of documents
  • --dictionary_max_df - filter out tokens present in less than N documents / less than P% of documents
  • --tau0 - an option of the online algorith, describing the weight parameter in the online update formula. Optional, defaults to 1024.
  • --kappa - an option of the online algorithm, describing the exponent parameter in the online update formula. Optional, defaults to 0.7.

Note that for --dictionary_min_df and --dictionary_max_df can be treated as number, fraction, percent.

  • Use a percentage % sign to specify percentage value
  • Use a floating value in [0, 1) range to specify a fraction
  • Use an integer value (1 or greater) to indicate a number

BigARTM v0.7.3 Release notes

BigARTM v0.7.3 releases the following changes:

  • New command line tool for BigARTM
  • Support for classification in bigartm CLI
  • Support for asynchronous processing of batches
  • Improvements in coherence regularizer and coherence score
  • New TopicMass score for phi matrix
  • Support for documents markup
  • New API for importing batches through memory

New command line tool for BigARTM

New CLI is named bigartm (or bigrtm.exe on Windows), and it supersedes previous CLI named cpp_client. New CLI has the following features:

  • Parse collection in one of the Formats
  • Load dictionary
  • Initialize a new model, or import previously created model
  • Perform EM-iterations to fit the model
  • Export predicted probabilities for all documents into CSV file
  • Export model into a file

All command-line options are listed here, and you may see several exampels on BigARTM page at github. At the moment full documentation is only available in Russian.

Support for classification in BigARTM CLI

BigARTM CLI is now able to perform classification. The following example assumes that your batches have target_class modality in addition to the default modality (@default_class).

# Fit model
bigartm.exe --use-batches <your batches>
            --use-modality @default_class,target_class
            --topics 50
            --dictionary-min-df 10
            --dictionary-max-df 25%
            --save-model model.bin

# Apply model and output to text files
bigartm.exe --use-batches <your batches>
            --use-modality @default_class,target_class
            --topics 50
            --passes 0
            --load-model model.bin
            --predict-class target_class
            --write-predictions pred.txt
            --write-class-predictions pred_class.txt
            --csv-separator=tab
            --score ClassPrecision

Support for asynchronous processing of batches

Asynchronous processing of batches enables applications to overlap EM-iterations better utilize CPU resources. The following chart shows CPU utilization of bigartm.exe with (left-hand side) and without async flag (right-hand side).

BigARTM performance in asynchronous mode

TopicMass score for phi matrix

Topic mass score calculates cumulated topic mass for each topic. This is a useful metric to monitor balance between topics.

Support for documents markup

Document markup provides topic distribution for each word in a document. Since BigARTM v0.7.3 it is posible to extract this information to use it. A potential application includes color-highlighted maps of the document, where every work is colored according to the most probable topic of the document.

In the code this feature is refered to as ptdw matrix. It is possible to extract and regularizer ptdw matrices. In future versions it will be also possible to calculate scores based on ptdw matrix.

New API for importing batches through memory

New low-level APIs ArtmImportBatches and ArtmDisposeBatches allow to import batches from memory into BigARTM. Those batches are saved in BigARTM, and can be used for batches processing.

BigARTM v0.7.4 Release notes

BigARTM v0.7.4 is a big release that includes major rework of dictionaries and MasterModel.

bigartm/stable branch

Up until now BigARTM has only one master branch, containing the latest code. This branch potentially includes untested code and unfinished features. We are now introducing bigartm/stable branch, and encourage all users to stop using master and start fetching from stable. stable branch will be lagging behind master, and moved forward to master as soon as mainteiners decide that it is ready. At the same point we will introduce a new tag (something like v0.7.3 ) and produce a new release for Windows. In addition, stable branch also might receive small urgent fixes in between releases, typically to address critical issues reported by our users. Such fixes will be also included in master branch.

MasterModel

MasterModel is a new set of low-level APIs that allow users of C-interface to infer models and apply them to new data. The APIs are ArtmCreateMasterModel, ArtmReconfigureMasterModel, ArtmFitOfflineMasterModel, ArtmFitOnlineMasterModel and ArtmRequestTransformMasterModel, togehter with corresponding protobuf messages. For a usage example see src/bigartm/srcmain.cc.

This APIs should be easy to understand for the users who are familiar with Python interface. Basically, we take ARTM class in Python, and push it down to the core. Now users can create their model via MasterModelConfig (protobuf message), fit via ArtmFitOfflineMasterModel or ArtmFitOnlineMasterModel, and apply to the new data via ArtmRequestTransformMasterModel. This means that the user no longer has to orchestrate low-level building blocks such as ArtmProcessBatches, ArtmMergeModel, ArtmRegularizeModel and ArtmNormalizeModel.

ArtmCreateMasterModel is similar to ArtmCreateMasterComponent in a sence that it returns master_id, which can be later passed to all other APIs. This mean that most APIs will continue working as before. This applies to ArtmRequestThetaMatrix, ArtmRequestTopicModel, ArtmRequestScore, and many others.

Rework of dictionaries

Previous implementation of the dictionaries was really messy, and we are trying to clean this up. This effort is not finished yet, however we decided to release current version because it is a major improvement comparing to the previous version. At the low-level (c_interface), we now have the following methods to work with dictionaries:

  • ArtmGatherDictionary collects a dictionary based on a folder with batches,
  • ArtmFilterDictionary filter tokens from the dictinoary based on their term frequency or document frequency,
  • ArtmCreateDictionary creates a dictionary from a custom DictionaryData object (protobuf message),
  • ArtmRequestDictionary retrieves a dictionary as DictionaryData object (protobuf message),
  • ArtmDisposeDictionary deletes dictionary object from BigARTM,
  • ArtmImportDictionary import dictionary from binary file,
  • ArtmExportDictionary expor tdictionary into binary file.

All dictionaries are identified by a string ID (dictionary_name). Dictionaries can be used to initialize the model, in regularizers or in scores.

Note that ArtmImportDictionary and ArtmExportDictionary now uses a different format. For this reason we require that all imported or exported files end with .dict extension. This limitation is only introduced to make users aware of the change in binary format.

Warning

Please note that you have to re-generate all dictionaries, created in previous BigARTM versions. To force this limitation we decided that ArtmImportDictionary and ArtmExportDictionary will require all imported or exported files end with .dict extension. This limitation is only introduced to make users aware of the change in binary format.

Please note that in the next version (BigARTM v0.8.0) we are planing to break dictionary format once again. This is because we will introduce boost.serialize library for all import and export methods. From that point boost.serialize library will allow us to upgrade formats without breaking backwards compatibility.

The following example illustrate how to work with new dictionaries from Python.

# Parse collection in UCI format from D:\Datasets\docword.kos.txt and D:\Datasets\vocab.kos.txt
# and store the resulting batches into D:\Datasets\kos_batches
batch_vectorizer = artm.BatchVectorizer(data_format='bow_uci',
                                        data_path=r'D:\Datasets',
                                        collection_name='kos',
                                        target_folder=r'D:\Datasets\kos_batches')

# Initialize the model. For now dictionaries exist within the model,
# but we will address this in the future.
model = artm.ARTM(...)


# Gather dictionary named `dict` from batches.
# The resulting dictionary will contain all distinct tokens that occur
# in those batches, and their term frequencies
model.gather_dictionary("dict", "D:\Datasets\kos_batches")

# Filter dictionary by removing tokens with too high or too low term frequency
# Save the result as `filtered_dict`"
model.filter_dictionary(dictionary_name='dict',
                        dictionary_target_name='filtered_dict',
                        min_df=10, max_df_rate=0.4)

# Initialize model from `diltered_dict`
model.initialize("filtered_dict")

# Import/export functionality
model.save_dictionary("filtered_dict", "D:\Datasets\kos.dict")
model.load_dictionary("filtered_dict2",  "D:\Datasets\kos.dict")

Changes in the infrastructure

  • Static linkage for bigartm command-line executable on Linux. To disable static linkage use cmake -DBUILD_STATIC_BIGARTM=OFF ..
  • Install BigARTM python API via python setup.py install

Changes in core functionality

  • Custom transform function for KL-div regularizers
  • Ability to initialize the model with custom seed
  • TopicSelection regularizers
  • PeakMemory score (Windows only)
  • Different options to name batches when parsing collection (GUID as today, and CODE for sequential numbering)

Changes in Python API

  • ARTM.dispose() method for managing native memory
  • ARTM.get_info() method to retrieve internal state
  • Performance fixes
  • Expose class prediction functionality

Changes in C++ interface

  • Consume MasterModel APIs in C++ interface. Going forward this is the only C++ interface that we will support.

Changes in console interface

  • Better options to work with dictionaries
  • --write-dictionary-readable to export dictionary
  • --force switch to let user overwrite existing files
  • --help generates much better examples
  • --model-v06 to experiment with old APIs (ArtmInvokeIteration / ArtmWaitIdle / ArtmSynchronizeModel)
  • --write-scores switch to export scores into file
  • --time-limit option to time-box model inference(as an alternative to --passes switch)

Publications

  • Vorontsov, Konstantin and Frei, Oleksandr and Apishev, Murat and Romov, Peter and Suvorova, Marina and Yanina, Anastasia; Non-Bayesian Additive Regularization for Multimodal Topic Modeling of Large Collections // Proceedings of the 2015 Workshop on Topic Models: Post-Processing and Applications, PDF in English
  • Vorontsov K., Potapenko A., Plavin A. Additive Regularization of Topic Models for Topic Selection and Sparse Factorization. // Statistical Learning and Data Sciences. 2015 — pp. 193-202. PDF in English.
  • Vorontsov K., Frei O., Apishev M., Romov P., Dudarenko M. BigARTM: Open Source Library for Regularized Multimodal Topic Modeling of Large Collections Analysis of Images, Social Networks and Texts. 2015. Slides in English, Article in English
  • Vorontsov K. V. Additive Regularization for Topic Models of Text Collections // Doklady Mathematics. 2014, Pleiades Publishing, Ltd. — Vol. 89, No. 3, pp. 301–304. PDF in English, PDF in Russian.
  • Vorontsov K. V., Potapenko A. A. Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization // AIST‘2014, Analysis of Images, Social networks and Texts. Springer International Publishing Switzerland, 2014. Communications in Computer and Information Science (CCIS). Vol. 436. pp. 29–46. PDF in English.
  • Vorontsov K. V., Potapenko A. A. Additive Regularization of Topic Models // Machine Learning Journal, Special Issue “Data Analysis and Intelligent Optimization”, Springer, 2014. PDF in English, PDF in Russian.

Legacy documentation pages

Legacy pages are kept to preserve existing user’s links (favourites in browser, etc).

Typical python example

This page is obsolete, please use the high-level API described in ARTM notebook (in Russian or in English).

Examples of low-level API

Folder C:\BigARTM\python\examples contains several toy examples:

All examples does not have any parameters, and you may run them without arguments:

C:\BigARTM\python\examples>python example02_parse_collection.py

 No batches found, parsing them from textual collection...  OK.
 Iter#0 : Perplexity = 6885.223 , Phi sparsity = 0.050 , Theta sparsity = 0.012
 Iter#1 : Perplexity = 2409.510 , Phi sparsity = 0.113 , Theta sparsity = 0.063
 Iter#2 : Perplexity = 2075.445 , Phi sparsity = 0.203 , Theta sparsity = 0.174
 Iter#3 : Perplexity = 1855.196 , Phi sparsity = 0.293 , Theta sparsity = 0.261
 Iter#4 : Perplexity = 1728.749 , Phi sparsity = 0.370 , Theta sparsity = 0.302
 Iter#5 : Perplexity = 1661.044 , Phi sparsity = 0.429 , Theta sparsity = 0.317
 Iter#6 : Perplexity = 1621.851 , Phi sparsity = 0.475 , Theta sparsity = 0.327
 Iter#7 : Perplexity = 1596.965 , Phi sparsity = 0.511 , Theta sparsity = 0.331

 Top tokens per topic:
 Topic#1: poll(0.05) iraq(0.04) people(0.02) news(0.02) john(0.01) media(0.01)
 Topic#2: republican(0.02) party(0.02) state(0.02) general(0.01) democrats(0.01)
 Topic#3: dean(0.04) edwards(0.02) percent(0.02) primary(0.02) clark(0.02)
 Topic#4: forces(0.01) baghdad(0.01) iraqis(0.01) coburn(0.01) carson(0.01)
 Topic#5: military(0.01) officials(0.01) intelligence(0.01) american(0.01)
 Topic#6: electoral(0.04) labor(0.02) culture(0.02) exit(0.02) scoop(0.01)
 Topic#7: law(0.01) court(0.01) marriage(0.01) gay(0.01) amendment(0.01)
 Topic#8: president(0.03) administration(0.02) campaign(0.01) million(0.01)
 Topic#9: years(0.01) ballot(0.01) rights(0.01) nader(0.01) life(0.01)
 Topic#10: house(0.08) war(0.03) republicans(0.02) voting(0.02) vote(0.02)

 Snippet of theta matrix:
 Item#3000: 0.432 0.507 0.059 0.000 0.000 0.000 0.000 0.000 0.002 0.000
 Item#2991: 0.249 0.382 0.269 0.000 0.000 0.025 0.016 0.034 0.000 0.026
 Item#2992: 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.851 0.000 0.147
 Item#2993: 0.358 0.058 0.030 0.141 0.152 0.000 0.002 0.248 0.000 0.010
 Item#2994: 0.051 0.142 0.056 0.000 0.000 0.146 0.000 0.000 0.000 0.604
 Item#2995: 0.004 0.593 0.000 0.000 0.128 0.005 0.168 0.040 0.030 0.033
 Item#2996: 0.069 0.063 0.054 0.000 0.000 0.107 0.008 0.004 0.000 0.696
 Item#2997: 0.000 0.194 0.000 0.000 0.043 0.000 0.471 0.228 0.062 0.002
 Item#2998: 0.026 0.085 0.042 0.001 0.180 0.000 0.146 0.485 0.022 0.012
 Item#2999: 0.312 0.547 0.099 0.000 0.000 0.004 0.008 0.017 0.013 0.000

This simple example loads a text collection from disk and uses iterative scans over the collection to infer a topic model. Then it outputs top words in each topic and topic distributions of last processed documents. For further information about this example refer to Typical python example.

Parse collection step

The following python script parses docword.kos.txt and vocab.kos.txt files and converts them into a set of binary-serialized batches, stored on disk. In addition the script creates a dictionary with all unique tokens in the collection and stored it on disk. The script also detects if it had been already executed, and in this case it just loads the dictionary and save it in unique_tokens variable.

The same logic is implemented in a helper-method ParseCollectionOrLoadDictionary method.

data_folder = sys.argv[1] if (len(sys.argv) >= 2) else ''
target_folder = 'kos'
collection_name = 'kos'

batches_found = len(glob.glob(target_folder + "/*.batch"))
if batches_found == 0:
  print "No batches found, parsing them from textual collection...",
  parser_config = artm.messages_pb2.CollectionParserConfig();
  parser_config.format = artm.library.CollectionParserConfig_Format_BagOfWordsUci

  parser_config.docword_file_path = data_folder + 'docword.'+ collection_name + '.txt'
  parser_config.vocab_file_path = data_folder + 'vocab.'+ collection_name + '.txt'
  parser_config.target_folder = target_folder
  parser_config.dictionary_file_name = 'dictionary'
  unique_tokens = artm.library.Library().ParseCollection(parser_config);
  print " OK."
else:
  print "Found " + str(batches_found) + " batches, using them."
  unique_tokens  = artm.library.Library().LoadDictionary(target_folder + '/dictionary');

You may also download larger collections from the following links. You can get the original collection (docword file and vocab file) or an already precompiled batches and dictionary.

MasterComponent

Master component is you main entry-point to all BigARTM functionality. The following script creates master component and configures it with several regularizers and score calculators.

with artm.library.MasterComponent(disk_path = target_folder) as master:
  perplexity_score     = master.CreatePerplexityScore()
  sparsity_theta_score = master.CreateSparsityThetaScore()
  sparsity_phi_score   = master.CreateSparsityPhiScore()
  top_tokens_score     = master.CreateTopTokensScore()
  theta_snippet_score  = master.CreateThetaSnippetScore()

  dirichlet_theta_reg  = master.CreateDirichletThetaRegularizer()
  dirichlet_phi_reg    = master.CreateDirichletPhiRegularizer()
  decorrelator_reg     = master.CreateDecorrelatorPhiRegularizer()

Master component must be configured with a disk path, which should contain a set of batches produced in the previous step of this tutorial.

Score calculators allows you to retrieve important quality measures for your topic model. Perplexity, sparsity of theta and phi matrices, lists of tokens with highest probability within each topic are all examples of such scores. By default BigARTM does not calculate any scores, so you have to create in master component. The same is true for regularizers, that allow you to customize your topic model.

For further details about master component refer to MasterComponentConfig.

Configure Topic Model

Topic model configuration defins the number of topics in the model, the list of scores to be calculated, and the list of regularizers to apply to the model. For further details about model configuration refer to ModelConfig.

model = master.CreateModel(topics_count = 10, inner_iterations_count = 10)
model.EnableScore(perplexity_score)
model.EnableScore(sparsity_phi_score)
model.EnableScore(sparsity_theta_score)
model.EnableScore(top_tokens_score)
model.EnableScore(theta_snippet_score)
model.EnableRegularizer(dirichlet_theta_reg, -0.1)
model.EnableRegularizer(dirichlet_phi_reg, -0.2)
model.EnableRegularizer(decorrelator_reg, 1000000)
model.Initialize(unique_tokens)    # Setup initial approximation for Phi matrix.

Note that on the last step we configured the initial approximation of Phi matrix. This step is optional — BigARTM is able to collect all tokens dynamically during first scan of the collection. However, a deterministic initial approximation helps to reproduce the same results from run to run.

Invoke Iterations

The following script performs several scans over the set of batches. Depending on the size of the collection this step might be quite time-consuming. It is good idea to output some information after every step.

for iter in range(0, 8):
  master.InvokeIteration(1)        # Invoke one scan of the entire collection...
  master.WaitIdle();               # and wait until it completes.
  model.Synchronize();             # Synchronize topic model.
  print "Iter#" + str(iter),
  print ": Perplexity = %.3f" % perplexity_score.GetValue(model).value,
  print ", Phi sparsity = %.3f" % sparsity_phi_score.GetValue(model).value,
  print ", Theta sparsity = %.3f" % sparsity_theta_score.GetValue(model).value

If your collection is very large you may want to utilize online algorithm that updates topic model several times during each iteration, as it is demonstrated by the following script:

master.InvokeIteration(1)        # Invoke one scan of the entire collection...
while True:
  done = master.WaitIdle(100)    # wait 100 ms
  model.Synchronize(0.9)         # decay weights in current topic model by 0.9,
  if (done):                     # append all increments and invoke all regularizers.
        break;

Retrieve and visualize scores

Finally, you are interested in retrieving and visualizing all collected scores.

artm.library.Visualizers.PrintTopTokensScore(top_tokens_score.GetValue(model))
artm.library.Visualizers.PrintThetaSnippetScore(theta_snippet_score.GetValue(model))

Basic BigARTM tutorial for Linux and Mac OS-X users

Currently there is no distribution package of BigARTM for Linux. BigARTM had been tested on several Linux OS, and it is known to work well, but you have to get the source code and compile it locally on your machine.

Download sources and build

Clone the latest BigARTM code from our github repository, and build it via CMake as in the following script.

sudo apt-get install git make cmake build-essential libboost-all-dev
cd ~
git clone --branch=stable https://github.com/bigartm/bigartm.git
cd bigartm
mkdir build && cd build
cmake ..
make

Running BigARTM from command line

There is a simple utility bigartm, which allows you to run BigARTM from command line. To experiment with this tool you need a small dataset, which you can get via the following script. More datasets are available through Downloads page.

cd ~/bigartm
mkdir datasets && cd datasets
wget https://s3-eu-west-1.amazonaws.com/artm/docword.kos.txt.gz
wget https://s3-eu-west-1.amazonaws.com/artm/vocab.kos.txt
gunzip docword.kos.txt.gz
../build/src/bigartm/bigartm -d docword.kos.txt -v vocab.kos.txt

Configure BigARTM Python API

For more advanced scenarios you need to configure Python interface for BigARTM. To use BigARTM from Python you need to use Google Protobuf. We recommend to use ‘protobuf 2.5.1-pre’, included in bigartm/3rdparty.

# Step 1 - add BigARTM python bindings to PYTHONPATH
export PYTHONPATH=~/bigartm/python:$PYTHONPATH

# Step 2 - install google protobuf
cd ~/bigartm
cp build/3rdparty/protobuf-cmake/protoc/protoc 3rdparty/protobuf/src/
cd 3rdparty/protobuf/python
python setup.py build
sudo python setup.py install

# Step 3 - point ARTM_SHARED_LIBRARY variable to libartm.so (libartm.dylib) location
export ARTM_SHARED_LIBRARY=~/bigartm/build/src/artm/libartm.so        # for linux
export ARTM_SHARED_LIBRARY=~/bigartm/build/src/artm/libartm.dylib     # for Mac OS X

At this point you may run examples under ~/bigartm/python/examples.

Troubleshooting

>python setup.py build
  File "setup.py", line 52
        print "Generating %s..." % output

SyntaxError: Missing parentheses in call to `print`

This error may happen during google protobuf installation. It indicates that you are using Python 3, which is not supported by BigARTM. (see this question on StackOverflow for more details on the error around print). Please use Python 2.7.9 to workaround this issue.

ubuntu@192.168.0.1:~/bigartm/python/examples$ python example01_synthetic_collection.py
Traceback (most recent call last):
  File "example01_synthetic_collection.py", line 6, in <module>
    import artm.messages_pb2, artm.library, random, uuid
ImportError: No module named artm.messages_pb2

This error indicate that python is unable to locate messages_pb2.py and ``library.py files. Please verify if you executed Step #1 in the instructions above.

ubuntu@192.168.0.1:~/bigartm/python/examples$ python example01_synthetic_collection.py
Traceback (most recent call last):
  File "example01_synthetic_collection.py", line 6, in <module>
    import artm.messages_pb2, artm.library, random, uuid
  File "/home/ubuntu/bigartm/python/messages_pb2.py", line 4, in <module>
    from google.protobuf import descriptor as _descriptor
ImportError: No module named google.protobuf

This error indicated that python is unable to locate protobuf library. Please verify if you executed Step #2 in the instructions above. If you do not have permissions to execute sudo python setup.py install step, you may also try to update PYTHONPATH manually: PYTHONPATH="/home/ubuntu/bigartm/3rdparty/protobuf/python:/home/ubuntu/bigartm/python:$PYTHONPATH".

ubuntu@192.168.0.1:~/bigartm/python/examples$ python example01_synthetic_collection.py
libartm.so: cannot open shared object file: No such file or directory,
fall back to ARTM_SHARED_LIBRARY environment variable
Traceback (most recent call last):
  File "example01_synthetic_collection.py", line 27, in <module>
    with artm.library.MasterComponent() as master:
  File "/home/ubuntu/bigartm/python/artm/library.py", line 179, in __init__
    lib = Library().lib_
  File "/home/ubuntu/bigartm/python/artm/library.py", line 107, in __init__
    self.lib_ = ctypes.CDLL(os.environ['ARTM_SHARED_LIBRARY'])
  File "/usr/lib/python2.7/UserDict.py", line 23, in __getitem__
    raise KeyError(key)
KeyError: 'ARTM_SHARED_LIBRARY'

This error indicate that BigARTM’s python interface can not locate libartm.so (libartm.dylib) files. Please verify if you executed Step #3 correctly.

BigARTM on Travis-CI

To get a live usage example of BigARTM you may check BigARTM’s .travis.yml script and the latest continuous integration build.

Basic BigARTM tutorial for Windows users

This tutorial gives guidelines for installing and running existing BigARTM examples via command-line interface and from Python environment.

Download

Download latest binary distribution of BigARTM from https://github.com/bigartm/bigartm/releases. Explicit download links can be found at Downloads section (for 32 bit and 64 bit configurations).

The distribution will contain pre-build binaries, command-line interface and BigARTM API for Python. The distribution also contains a simple dataset and few python examples that we will be running in this tutorial. More datasets in BigARTM-compatible format are available in the Downloads section.

Refer to Windows distribution for details about other files, included in the binary distribution package.

Running BigARTM from command line

No installation steps are required to run BigARTM from command line. After unpacking binary distribution simply open command prompt (cmd.exe), change current directory to bin folder inside BigARTM package, and run cpp_client.exe application as in the following example. As an optional step, we recommend to add bin folder of the BigARTM distribution to your PATH system variable.

>C:\BigARTM\bin>set PATH=%PATH%;C:\BigARTM\bin
>C:\BigARTM\bin>cpp_client.exe -v ../python/examples/vocab.kos.txt -d ../python/examples/docword.kos.txt -t 4
Parsing text collection... OK.
Iteration 1 took  197 milliseconds.
    Test perplexity = 7108.35,
    Train perplexity = 7106.18,
    Test spatsity theta = 0,
    Train sparsity theta = 0,
    Spatsity phi = 0.000144802,
    Test items processed = 343,
    Train items processed = 3087,
    Kernel size = 5663,
    Kernel purity = 0.958901,
    Kernel contrast = 0.292389
Iteration 2 took  195 milliseconds.
    Test perplexity = 2563.31,
    Train perplexity = 2517.07,
    Test spatsity theta = 0,
    Train sparsity theta = 0,
    Spatsity phi = 0.000144802,
    Test items processed = 343,
    Train items processed = 3087,
    Kernel size = 5559.5,
    Kernel purity = 0.956709,
    Kernel contrast = 0.298198
...
#1: november(0.054) poll(0.015) bush(0.013) kerry(0.012) polls(0.012) governor(0.011)
#2: bush(0.0083) president(0.0059) republicans(0.0047) house(0.0042) people(0.0039) administration(0.0036)
#3: bush(0.031) iraq(0.018) war(0.012) kerry(0.0096) president(0.0078) administration(0.0076)
#4: kerry(0.018) democratic(0.013) dean(0.012) campaign(0.0097) poll(0.0095) race(0.0082)
ThetaMatrix (last 7 processed documents, ids = 1995,1996,1997,1998,1992,2000,1994):
Topic0: 0.02104 0.02155 0.00604 0.00835 0.00965 0.00006 0.91716
Topic1: 0.15441 0.76643 0.06484 0.11643 0.20409 0.00006 0.00957
Topic2: 0.00399 0.16135 0.00093 0.03890 0.10498 0.00001 0.00037
Topic3: 0.82055 0.05066 0.92819 0.83632 0.68128 0.99987 0.07289

We recommend to download larger datasets, available in Downloads section. All docword and vocab files can be consumed by BigARTM exactly as in the previous example.

Internally BigARTM always parses such files into batches format (for example, enron_1k (7.1 MB)). If you have downloaded such pre-parsed collection, you may feed it into BigARTM as follows:

>C:\BigARTM\bin>cpp_client.exe --batch_folder C:\BigARTM\enron
Reuse 40 batches in folder 'enron'
Loading dictionary file... OK.
Iteration 1 took  2502 milliseconds.

For more information about cpp_client.exe refer to /ref/cpp_client section.

Configure BigARTM Python API

  1. Install Python, for example from the following links:

    Remember that the version of BigARTM package must match your version Python installed on your machine. If you have 32 bit operating system then you must select 32 bit for Python and BigARTM package. If you have 64 bit operating system then you are free to select either version. However, please note that memory usage of 32 bit processes is limited by 2 GB. For this reason we recommend to select 64 bit configurations.

    Also you need to have several Python libraries to be installed on your machine:

    • numpy >= 1.9.2
    • scipy >= 0.15.0
    • pandas >= 0.16.2
    • scikit-learn >= 0.16.1

  2. Add C:\BigARTM\bin folder to your PATH system variable, and add C:\BigARTM\python to your PYTHONPATH system variable:

    set PATH=%PATH%;C:\BigARTM\bin
set PATH=%PATH%;C:\Python27;C:\Python27\Scripts
set PYTHONPATH=%PYTHONPATH%;C:\BigARTM\Python

    Remember to change C:\BigARTM and C:\Python27 with your local folders.

  3. Setup Google Protocol Buffers library, included in the BigARTM release package.

    • Copy C:\BigARTM\bin\protoc.exe file into C:\BigARTM\protobuf\src folder
    • Run the following commands from command prompt
    cd C:\BigARTM\protobuf\Python
python setup.py build
python setup.py install

    Avoid python setup.py test step, as it produces several confusing errors. Those errors are harmless. For further details about protobuf installation refer to protobuf/python/README.

If you are getting errors when configuring or using Python API, please refer to Troubleshooting chapter in Basic BigARTM tutorial for Linux and Mac OS-X users. The list of issues is common between Windows and Linux.

Running BigARTM from Python API

Refer to ARTM notebook (in Russian or in English), which describes high-level Python API of BigARTM.

Enabling Basic BigARTM Regularizers

This paper describes the experiment with topic model regularization in BigARTM library using experiment02_artm.py. The script provides the possibility to learn topic model with three regularizers (sparsing Phi, sparsing Theta and pairwise topic decorrelation in Phi). It also allows the monitoring of learning process by using quality measures as hold-out perplexity, Phi and Theta sparsity and average topic kernel characteristics.

Warning

Note that perplexity estimation can influence the learning process in the online algorithm, so we evaluate perplexity only once per 20 synchronizations to avoid this influence. You can change the frequency using test_every variable.

We suggest you to have BigARTM installed in $YOUR_HOME_DIRECTORY. To proceed the experiment you need to execute the following steps:

  1. Download the collection, represented as BigARTM batches:

    This data represents a complete dump of the English Wikipedia (approximately 3.7 million documents). The size of one batch in first version is 1000 documents and 10000 in the second one. We used 10000. The decompressed folder with batches should be put into $YOUR_HOME_DIRECTORY. You also need to move there the dictionary file from the batches folder.

    The batch, you’d like to use for hold-out perplexity estimation, also must be placed into $YOUR_HOME_DIRECTORY. In our experiment we used the batch named 243af5b8-beab-4332-bb42-61892df5b044.batch.

  2. The next step is the script preparation. Open it’s code and find the declaration(-s) of variable(-s)

    • home_folder (line 8) and assign it the path $YOUR_HOME_DIRECTORY;
    • batch_size (line 28) and assign it the chosen size of batch;
    • batches_disk_path (line 36) and replace the string ‘wiki_10k’ with the name of your directory with batches;
    • test_batch_name (line 43) and replace the string with direct batch’s name with the name of your test batch;
    • tau_decor, tau_phi and tau_theta (lines 57-59) and substitute the values you’d like to use.

  3. If you want to estimate the final perplexity on another, larger test sample, put chosen batches into test folder (in $YOUR_HOME_DIRECTORY directory). Then find in the code of the script the declaration of variable save_and_test_model (line 30) and assign it True.

  4. After all launch the script. Current measures values will be printed into console. Note, that after synchronizations without perplexity estimation it’s value will be replaced with string ‘NO’. The results of synchronizations with perplexity estimation in addition will be put in corresponding files in results folder. The file format is general for all measures: the set of strings «(accumulated number of processed documents, measure value)»:

    (10000, 0.018)
(220000, 0.41)
(430000, 0.456)
(640000, 0.475)
...

    These files can be used for plot building.

If desired, you can easy change values of any variable in the code of script since it’s sense is clearly commented. If you used all parameters and data identical our experiment you should get the results, close to these ones

experiment02_artm

Here you can see the results of comparison between ARTM and LDA models. To make the experiment with LDA instead of ARTM you only need to change the values of variables tau_decor, tau_phi and tau_theta to 0, 1 / topics_count and 1 / topics_count respectively and run the script again.

Warning

Note, that we used machine with 8 cores and 15 Gb RAM for our experiment.

BigARTM as a Service

The following diagram shows a suggested topology for a query service that involve topic modelling on Big Data.

cloud_service

Here the main use for Hadoop / MapReduce is to process your Big Unstructured Data into a compact bag-of-words representation. Due to out-of-core design and extreme performance BigARTM will be able to handle this data on a single compute-optimized node. The resulting topic model should be replicated on all query instances that serve user requests.

To avoid query-time dependency on BigARTM component you may want to infer topic distributions theta_{td} for new documents in your code. This can be done as follows. Start from uniform topic assigment theta_{td} = 1 / |T| and update it in the following loop:

theta_update

where n_dw is the number of word w occurences in document d, phi_wt is an element of the Phi matrix. In BigARTM the loop is repeated ModelConfig.inner_iterations_count times (defaulst to 10). To precisely replicate BigARTM behavior one needs to account for class weights and include regularizers. Please contact us if you need more details.

BigARTM: The Algorithm Under The Hood

ToDo: link BigARTM to online batch PLSA algorithm.

ToDo: explain the notation in the algorithm.

ToDo: update the algortihm with regularization.

Algorithm of BigARTM

In this algorithm most CPU resources are consumed on steps 8-11 to infer topic distribution for each document. This operation can be executed concurrently across documents or batches. In BigARTM this parallelization is done across batches to avoid splitting the work into too small junks.

Processing each batch produces counters $tilde n_{wt}$ and $tilde n_{t}$, which should be then merged with the corresponding counters coming from other batches. Since this information is produced by multiple concurrent threads the merging process should be thread-safe and properly synchronised. Our solution is to store all counters $tilde n_{wt}$ and $tilde n_{t}$ into a single queue, from where they can be picked up by a single merger thread. This thread will then accumulate the counters without any locking.

Further in this text the term outer iteration loop stands for the loop at the step 2, and the term emph{inner iteration loop} stands for the loop at step 8. Instead of “repeat until it converges” criteria current implementation uses a fixed number of iterations, which is configured manually by the user.

Step 15 is incorporated into all steps that require $phi_{wt}$ (e.g. into steps 9, 10 and 11). These steps utilize counters from the previous iteration ($n^{i-1}_wt$ and $n^{i-1}_t$), which are no longer updated by the merger thread, hence they represent read-only data and can be accessed from multiple threads without any synchronization. At the same time the merger thread will accumulate counters for $n^i_{wt}$ and $n^i_t$ for the current iteration, again in a lock-free manner.

Messages

This document explains all protobuf messages that can be transfered between the user code and BigARTM library.

Warning

Remember that all fields is marked as optional to enhance backwards compatibility of the binary protobuf format. Some fields will result in run-time exception when not specified. Please refer to the documentation of each field for more details.

Note that we discourage any usage of fields marked as obsolete. Those fields will be removed in future releases.

DoubleArray

class messages_pb2.DoubleArray

Represents an array of double-precision floating point values.

message DoubleArray {
  repeated double value = 1 [packed = true];
}

FloatArray

class messages_pb2.FloatArray

Represents an array of single-precision floating point values.

message FloatArray {
  repeated float value = 1 [packed = true];
}

BoolArray

class messages_pb2.BoolArray

Represents an array of boolean values.

message BoolArray {
  repeated bool value = 1 [packed = true];
}

IntArray

class messages_pb2.IntArray

Represents an array of integer values.

message IntArray {
  repeated int32 value = 1 [packed = true];
}

Item

class messages_pb2.Item

Represents a unit of textual information. A typical example of an item is a document that belongs to some text collection.

message Item {
  optional int32 id = 1;
  repeated Field field = 2;
  optional string title = 3;
}
Item.id

An integer identifier of the item.

Item.field

A set of all fields withing the item.

Item.title

An optional title of the item.

Field

class messages_pb2.Field

Represents a field withing an item. The idea behind fields is that each item might have its title, author, body, abstract, actual text, links, year of publication, etc. Each of this entities should be represented as a Field. The topic model defines how those fields should be taken into account when BigARTM infers a topic model. Currently each field is represented as “bag-of-words” — each token is listed together with the number of its occurrences. Note that each Field is always part of an Item, Item is part of a Batch, and a batch always contains a list of tokens. Therefore, each Field just lists the indexes of tokens in the Batch.

message Field {
  optional string name = 1 [default = "@body"];
  repeated int32 token_id = 2;
  repeated int32 token_count = 3;
  repeated int32 token_offset = 4;

  optional string string_value = 5;
  optional int64 int_value = 6;
  optional double double_value = 7;
  optional string date_value = 8;

  repeated string string_array = 16;
  repeated int64 int_array = 17;
  repeated double double_array = 18;
  repeated string date_array = 19;
}

Batch

class messages_pb2.Batch

Represents a set of items. In BigARTM a batch is never split into smaller parts. When it comes to concurrency this means that each batch goes to a single processor. Two batches can be processed concurrently, but items in one batch are always processed sequentially.

message Batch {
  repeated string token = 1;
  repeated Item item = 2;
  repeated string class_id = 3;
  optional string description = 4;
  optional string id = 5;
}
Batch.token

A set value that defines all tokens than may appear in the batch.

Batch.item

A set of items of the batch.

Batch.class_id

A set of values that define for classes (modalities) of tokens. This repeated field must have the same length as token. This value is optional, use an empty list indicate that all tokens belong to the default class.

Batch.description

An optional text description of the batch. You may describe for example the source of the batch, preprocessing technique and the structure of its fields.

Batch.id

Unique identifier of the batch in a form of a GUID (example: 4fb38197-3f09-4871-9710-392b14f00d2e). This field is required.

Stream

class messages_pb2.Stream

Represents a configuration of a stream. Streams provide a mechanism to split the entire collection into virtual subsets (for example, the ‘train’ and ‘test’ streams).

message Stream {
  enum Type {
    Global = 0;
    ItemIdModulus = 1;
  }

  optional Type type = 1 [default = Global];
  optional string name = 2 [default = "@global"];
  optional int32 modulus = 3;
  repeated int32 residuals = 4;
}
Stream.type

A value that defines the type of the stream.

Global
Defines a stream containing all items in the collection.
ItemIdModulus
Defines a stream containing all items with ID that
matches modulus and residuals. An item belongs to the
stream iff the modulo reminder of item ID is contained
in the residuals field.
Stream.name

A value that defines the name of the stream. The name must be unique across all streams defined in the master component.

MasterComponentConfig

class messages_pb2.MasterComponentConfig

Represents a configuration of a master component.

message MasterComponentConfig {
  optional string disk_path = 2;
  repeated Stream stream = 3;
  optional bool compact_batches = 4 [default = true];
  optional bool cache_theta = 5 [default = false];
  optional int32 processors_count = 6 [default = 1];
  optional int32 processor_queue_max_size = 7 [default = 10];
  optional int32 merger_queue_max_size = 8 [default = 10];
  repeated ScoreConfig score_config = 9;
  optional bool online_batch_processing = 13 [default = false];  // obsolete in BigARTM v0.5.8
  optional string disk_cache_path = 15;
}
MasterComponentConfig.disk_path

A value that defines the disk location to store or load the collection.

MasterComponentConfig.stream

A set of all data streams to configure in master component. Streams can overlap if needed.

MasterComponentConfig.compact_batches

A flag indicating whether to compact batches in AddBatch() operation. Compaction is a process that shrinks the dictionary of each batch by removing all unused tokens.

MasterComponentConfig.cache_theta

A flag indicating whether to cache theta matrix. Theta matrix defines the discrete probability distribution of each document across the topics in topic model. By default BigARTM infers this distribution every time it processes the document. Option ‘cache_theta’ allows to cache this theta matrix and re-use theha values when the same document is processed on the next iteration. This option must be set to ‘true’ before calling method ArtmRequestThetaMatrix().

MasterComponentConfig.processors_count

A value that defines the number of concurrent processor components. The number of processors should normally not exceed the number of CPU cores.

MasterComponentConfig.processor_queue_max_size

A value that defines the maximal size of the processor queue. Processor queue contains batches, prefetch from disk into memory. Recommendations regarding the maximal queue size are as follows:

  • the queue size should be at least as large as the number of concurrent processors;
MasterComponentConfig.merger_queue_max_size

A value that defines the maximal size of the merger queue. Merger queue size contains an incremental updates of topic model, produced by processor components. Try reducing this parameter if BigARTM consumes too much memory.

MasterComponentConfig.score_config

A set of all scores, available for calculation.

MasterComponentConfig.online_batch_processing

Obsolete in BigARTM v0.5.8.

MasterComponentConfig.disk_cache_path

A value that defines a writtable disk location where this master component can store some temporary files. This can reduce memory usage, particularly when cache_theta option is enabled. Note that on clean shutdown master component will will be cleaned this folder automatically, but otherwise it is your responsibility to clean this folder to avoid running out of disk.

ModelConfig

class messages_pb2.ModelConfig

Represents a configuration of a topic model.

message ModelConfig {
  optional string name = 1 [default = "@model"];
  optional int32 topics_count = 2 [default = 32];
  repeated string topic_name = 3;
  optional bool enabled = 4 [default = true];
  optional int32 inner_iterations_count = 5 [default = 10];
  optional string field_name = 6 [default = "@body"];  // obsolete in BigARTM v0.5.8
  optional string stream_name = 7 [default = "@global"];
  repeated string score_name = 8;
  optional bool reuse_theta = 9 [default = false];
  repeated string regularizer_name = 10;
  repeated double regularizer_tau = 11;
  repeated string class_id = 12;
  repeated float class_weight = 13;
  optional bool use_sparse_bow = 14 [default = true];
  optional bool use_random_theta = 15 [default = false];
  optional bool use_new_tokens = 16 [default = true];
  optional bool opt_for_avx = 17 [default = true];
}
ModelConfig.name

A value that defines the name of the topic model. The name must be unique across all models defined in the master component.

ModelConfig.topics_count

A value that defines the number of topics in the topic model.

ModelConfig.topic_name

A repeated field that defines the names of the topics. All topic names must be unique within each topic model. This field is optional, but either topics_count or topic_name must be specified. If both specified, then topics_count will be ignored, and the number of topics in the model will be based on the length of topic_name field. When topic_name is not specified the names for all topics will be autogenerated.

ModelConfig.enabled

A flag indicating whether to update the model during iterations.

ModelConfig.inner_iterations_count

A value that defines the fixed number of iterations, performed to infer the theta distribution for each document.

ModelConfig.field_name

Obsolete in BigARTM v0.5.8

ModelConfig.stream_name

A value that defines which stream the model should use.

ModelConfig.score_name

A set of names that defines which scores should be calculated for the model.

ModelConfig.reuse_theta

A flag indicating whether the model should reuse theta values cached on the previous iterations. This option require cache_theta flag to be set to ‘true’ in MasterComponentConfig.

ModelConfig.regularizer_name

A set of names that define which regularizers should be enabled for the model. This repeated field must have the same length as regularizer_tau.

ModelConfig.regularizer_tau

A set of values that define the regularization coefficients of the corresponding regularizer. This repeated field must have the same length as regularizer_name.

ModelConfig.class_id

A set of values that define for which classes (modalities) to build topic model. This repeated field must have the same length as class_weight.

ModelConfig.class_weight

A set of values that define the weights of the corresponding classes (modalities). This repeated field must have the same length as class_id. This value is optional, use an empty list to set equal weights for all classes.

ModelConfig.use_sparse_bow

A flag indicating whether to use sparse representation of the Bag-of-words data. The default setting (use_sparse_bow = true) is best suited for processing textual collections where every token is represented in a small fraction of all documents. Dense representation (use_sparse_bow = false) better fits for non-textual collections (for example for matrix factorization).

Note that class_weight and class_id must not be used together with use_sparse_bow=false.

ModelConfig.use_random_theta

A flag indicating whether to initialize p(t|d) distribution with random uniform distribution. The default setting (use_random_theta = false) sets p(t|d) = 1/T, where T stands for topics_count. Note that reuse_theta flag takes priority over use_random_theta flag, so that if reuse_theta = true and there is a cache entry from previous iteration the cache entry will be used regardless of use_random_theta flag.

ModelConfig.use_new_tokens

A flag indicating whether to automatically include new tokens into the topic model. This setting is set to True by default. As a result, every new token observed in batches is automatically incorporated into topic model during the next model synchronization (ArtmSynchronizeModel()). The n_wt_ weights for new tokens randomly generated from [0..1] range.

ModelConfig.opt_for_avx

An experimental flag that allows to disable AVX optimization in processor. By default this option is enabled as on average it adds ca. 40% speedup on physical hardware. You may want to disable this option if you are running on Windows inside virtual machine, or in situation when BigARTM performance degrades from iteration to interation.

This option does not affect the results, and is only intended for advanced users experimenting with BigARTM performance.

RegularizerConfig

class messages_pb2.RegularizerConfig

Represents a configuration of a general regularizer.

message RegularizerConfig {
  enum Type {
    SmoothSparseTheta = 0;
    SmoothSparsePhi = 1;
    DecorrelatorPhi = 2;
    LabelRegularizationPhi = 4;
  }

  optional string name = 1;
  optional Type type = 2;
  optional bytes config = 3;
}
RegularizerConfig.name

A value that defines the name of the regularizer. The name must be unique across all names defined in the master component.

RegularizerConfig.type

A value that defines the type of the regularizer.

SmoothSparseTheta Smooth-sparse regularizer for theta matrix
SmoothSparsePhi Smooth-sparse regularizer for phi matrix
DecorrelatorPhi Decorrelator regularizer for phi matrix
LabelRegularizationPhi Label regularizer for phi matrix
RegularizerConfig.config

A serialized protobuf message that describes regularizer config for the specific regularizer type.

SmoothSparseThetaConfig

class messages_pb2.SmoothSparseThetaConfig

Represents a configuration of a SmoothSparse Theta regularizer.

message SmoothSparseThetaConfig {
  repeated string topic_name = 1;
  repeated float alpha_iter = 2;
}
SmoothSparseThetaConfig.topic_name

A set of topic names that defines which topics in the model should be regularized. This value is optional, use an empty list to regularize all topics.

SmoothSparseThetaConfig.alpha_iter

A field of the same length as ModelConfig.inner_iterations_count that defines relative regularization weight for every iteration inner iterations. The actual regularization value is calculated as product of alpha_iter[i] and ModelConfig.regularizer_tau.

To specify different regularization weight for different topics create multiple regularizers with different topic_name set, and use different values of ModelConfig.regularizer_tau.

SmoothSparsePhiConfig

class messages_pb2.SmoothSparsePhiConfig

Represents a configuration of a SmoothSparse Phi regularizer.

message SmoothSparsePhiConfig {
  repeated string topic_name = 1;
  repeated string class_id = 2;
  optional string dictionary_name = 3;
}
SmoothSparsePhiConfig.topic_name

A set of topic names that defines which topics in the model should be regularized. This value is optional, use an empty list to regularize all topics.

SmoothSparsePhiConfig.class_id

This set defines which classes in the model should be regularized. This value is optional, use an empty list to regularize all classes.

SmoothSparsePhiConfig.dictionary_name

An optional value defining the name of the dictionary to use. The entries of the dictionary are expected to have DictionaryEntry.key_token, DictionaryEntry.class_id and DictionaryEntry.value fields. The actual regularization value will be calculated as a product of DictionaryEntry.value and ModelConfig.regularizer_tau.

This value is optional, if no dictionary is specified than all tokens will be regularized with the same weight.

DecorrelatorPhiConfig

class messages_pb2.DecorrelatorPhiConfig

Represents a configuration of a Decorrelator Phi regularizer.

message DecorrelatorPhiConfig {
  repeated string topic_name = 1;
  repeated string class_id = 2;
}
DecorrelatorPhiConfig.topic_name

A set of topic names that defines which topics in the model should be regularized. This value is optional, use an empty list to regularize all topics.

DecorrelatorPhiConfig.class_id

This set defines which classes in the model should be regularized. This value is optional, use an empty list to regularize all classes.

LabelRegularizationPhiConfig

class messages_pb2.LabelRegularizationPhiConfig

Represents a configuration of a Label Regularizer Phi regularizer.

message LabelRegularizationPhiConfig {
  repeated string topic_name = 1;
  repeated string class_id = 2;
  optional string dictionary_name = 3;
}
LabelRegularizationPhiConfig.topic_name

A set of topic names that defines which topics in the model should be regularized.

LabelRegularizationPhiConfig.class_id

This set defines which classes in the model should be regularized. This value is optional, use an empty list to regularize all classes.

LabelRegularizationPhiConfig.dictionary_name

An optional value defining the name of the dictionary to use.

RegularizerInternalState

class messages_pb2.RegularizerInternalState

Represents an internal state of a general regularizer.

message RegularizerInternalState {
  enum Type {
    MultiLanguagePhi = 5;
  }

  optional string name = 1;
  optional Type type = 2;
  optional bytes data = 3;
}

DictionaryConfig

class messages_pb2.DictionaryConfig

Represents a static dictionary.

message DictionaryConfig {
  optional string name = 1;
  repeated DictionaryEntry entry = 2;
  optional int32 total_token_count = 3;
  optional int32 total_items_count = 4;
}
DictionaryConfig.name

A value that defines the name of the dictionary. The name must be unique across all dictionaries defined in the master component.

DictionaryConfig.entry

A list of all entries of the dictionary.

DictionaryConfig.total_token_count

A sum of DictionaryEntry.token_count across all entries in this dictionary. The value is optional and might be missing when all entries in the dictionary does not carry the DictionaryEntry.token_count attribute.

DictionaryConfig.total_items_count

A sum of DictionaryEntry.items_count across all entries in this dictionary. The value is optional and might be missing when all entries in the dictionary does not carry the DictionaryEntry.items_count attribute.

DictionaryEntry

class messages_pb2.DictionaryEntry

Represents one entry in a static dictionary.

message DictionaryEntry {
  optional string key_token = 1;
  optional string class_id = 2;
  optional float value = 3;
  repeated string value_tokens = 4;
  optional FloatArray values = 5;
  optional int32 token_count = 6;
  optional int32 items_count = 7;
}
DictionaryEntry.key_token

A token that defines the key of the entry.

DictionaryEntry.class_id

The class of the DictionaryEntry.key_token.

DictionaryEntry.value

An optional generic value, associated with the entry. The meaning of this value depends on the usage of the dictionary.

DictionaryEntry.token_count

An optional value, indicating the overall number of token occurrences in some collection.

DictionaryEntry.items_count

An optional value, indicating the overall number of documents containing the token.

ScoreConfig

class messages_pb2.ScoreConfig

Represents a configuration of a general score.

message ScoreConfig {
  enum Type {
    Perplexity = 0;
    SparsityTheta = 1;
    SparsityPhi = 2;
    ItemsProcessed = 3;
    TopTokens = 4;
    ThetaSnippet = 5;
    TopicKernel = 6;
  }

  optional string name = 1;
  optional Type type = 2;
  optional bytes config = 3;
}
ScoreConfig.name

A value that defines the name of the score. The name must be unique across all names defined in the master component.

ScoreConfig.type

A value that defines the type of the score.

Perplexity Defines a config of the Perplexity score
SparsityTheta Defines a config of the SparsityTheta score
SparsityPhi Defines a config of the SparsityPhi score
ItemsProcessed Defines a config of the ItemsProcessed score
TopTokens Defines a config of the TopTokens score
ThetaSnippet Defines a config of the ThetaSnippet score
TopicKernel Defines a config of the TopicKernel score
ScoreConfig.config

A serialized protobuf message that describes score config for the specific score type.

ScoreData

class messages_pb2.ScoreData

Represents a general result of score calculation.

message ScoreData {
  enum Type {
    Perplexity = 0;
    SparsityTheta = 1;
    SparsityPhi = 2;
    ItemsProcessed = 3;
    TopTokens = 4;
    ThetaSnippet = 5;
    TopicKernel = 6;
  }

  optional string name = 1;
  optional Type type = 2;
  optional bytes data = 3;
}
ScoreData.name

A value that describes the name of the score. This name will match the name of the corresponding score config.

ScoreData.type

A value that defines the type of the score.

Perplexity Defines a Perplexity score data
SparsityTheta Defines a SparsityTheta score data
SparsityPhi Defines a SparsityPhi score data
ItemsProcessed Defines a ItemsProcessed score data
TopTokens Defines a TopTokens score data
ThetaSnippet Defines a ThetaSnippet score data
TopicKernel Defines a TopicKernel score data
ScoreData.data

A serialized protobuf message that provides the specific score result.

PerplexityScoreConfig

class messages_pb2.PerplexityScoreConfig

Represents a configuration of a perplexity score.

message PerplexityScoreConfig {
  enum Type {
    UnigramDocumentModel = 0;
    UnigramCollectionModel = 1;
  }

  optional string field_name = 1 [default = "@body"];  // obsolete in BigARTM v0.5.8
  optional string stream_name = 2 [default = "@global"];
  optional Type model_type = 3 [default = UnigramDocumentModel];
  optional string dictionary_name = 4;
  optional float theta_sparsity_eps = 5 [default = 1e-37];
  repeated string theta_sparsity_topic_name = 6;
}
PerplexityScoreConfig.field_name

Obsolete in BigARTM v0.5.8

PerplexityScoreConfig.stream_name

A value that defines which stream should be used in perplexity calculation.

PerplexityScore

class messages_pb2.PerplexityScore

Represents a result of calculation of a perplexity score.

message PerplexityScore {
  optional double value = 1;
  optional double raw = 2;
  optional double normalizer = 3;
  optional int32 zero_words = 4;
  optional double theta_sparsity_value = 5;
  optional int32 theta_sparsity_zero_topics = 6;
  optional int32 theta_sparsity_total_topics = 7;
}
PerplexityScore.value

A perplexity value which is calculated as exp(-raw/normalizer).

PerplexityScore.raw

A numerator of perplexity calculation. This value is equal to the likelihood of the topic model.

PerplexityScore.normalizer

A denominator of perplexity calculation. This value is equal to the total number of tokens in all processed items.

PerplexityScore.zero_words

A number of tokens that have zero probability p(w|t,d) in a document. Such tokens are evaluated based on to unigram document model or unigram colection model.

PerplexityScore.theta_sparsity_value

A fraction of zero entries in the theta matrix.

SparsityThetaScoreConfig

class messages_pb2.SparsityThetaScoreConfig

Represents a configuration of a theta sparsity score.

message SparsityThetaScoreConfig {
  optional string field_name = 1 [default = "@body"];  // obsolete in BigARTM v0.5.8
  optional string stream_name = 2 [default = "@global"];
  optional float eps = 3 [default = 1e-37];
  repeated string topic_name = 4;
}
SparsityThetaScoreConfig.field_name

Obsolete in BigARTM v0.5.8

SparsityThetaScoreConfig.stream_name

A value that defines which stream should be used in theta sparsity calculation.

SparsityThetaScoreConfig.eps

A small value that defines zero threshold for theta probabilities. Theta values below the threshold will be counted as zeros when calculating theta sparsity score.

SparsityThetaScoreConfig.topic_name

A set of topic names that defines which topics should be used for score calculation. The names correspond to ModelConfig.topic_name. This value is optional, use an empty list to calculate the score for all topics.

SparsityThetaScore

class messages_pb2.SparsityThetaScoreConfig

Represents a result of calculation of a theta sparsity score.

message SparsityThetaScore {
  optional double value = 1;
  optional int32 zero_topics = 2;
  optional int32 total_topics = 3;
}
SparsityThetaScore.value

A value of theta sparsity that is calculated as zero_topics / total_topics.

SparsityThetaScore.zero_topics

A numerator of theta sparsity score. A number of topics that have zero probability in a topic-item distribution.

SparsityThetaScore.total_topics

A denominator of theta sparsity score. A total number of topics in a topic-item distributions that are used in theta sparsity calculation.

SparsityPhiScoreConfig

class messages_pb2.SparsityPhiScoreConfig

Represents a configuration of a sparsity phi score.

message SparsityPhiScoreConfig {
  optional float eps = 1 [default = 1e-37];
  optional string class_id = 2;
  repeated string topic_name = 3;
}
SparsityPhiScoreConfig.eps

A small value that defines zero threshold for phi probabilities. Phi values below the threshold will be counted as zeros when calculating phi sparsity score.

SparsityPhiScoreConfig.class_id

A value that defines the class of tokens to use for score calculation. This value corresponds to ModelConfig.class_id field. This value is optional. By default the score will be calculated for the default class ('@default_class‘).

SparsityPhiScoreConfig.topic_name

A set of topic names that defines which topics should be used for score calculation. This value is optional, use an empty list to calculate the score for all topics.

SparsityPhiScore

class messages_pb2.SparsityPhiScore

Represents a result of calculation of a phi sparsity score.

message SparsityPhiScore {
  optional double value = 1;
  optional int32 zero_tokens = 2;
  optional int32 total_tokens = 3;
}
SparsityPhiScore.value

A value of phi sparsity that is calculated as zero_tokens / total_tokens.

SparsityPhiScore.zero_tokens

A numerator of phi sparsity score. A number of tokens that have zero probability in a token-topic distribution.

SparsityPhiScore.total_tokens

A denominator of phi sparsity score. A total number of tokens in a token-topic distributions that are used in phi sparsity calculation.

ItemsProcessedScoreConfig

class messages_pb2.ItemsProcessedScoreConfig

Represents a configuration of an items processed score.

message ItemsProcessedScoreConfig {
  optional string field_name = 1 [default = "@body"];  // obsolete in BigARTM v0.5.8
  optional string stream_name = 2 [default = "@global"];
}
ItemsProcessedScoreConfig.field_name

Obsolete in BigARTM v0.5.8

ItemsProcessedScoreConfig.stream_name

A value that defines which stream should be used in calculation of processed items.

ItemsProcessedScore

class messages_pb2.ItemsProcessedScore

Represents a result of calculation of an items processed score.

message ItemsProcessedScore {
  optional int32 value = 1;
}
ItemsProcessedScore.value

A number of items that belong to the stream ItemsProcessedScoreConfig.stream_name and have been processed during iterations. Currently this number is aggregated throughout all iterations.

TopTokensScoreConfig

class messages_pb2.TopTokensScoreConfig

Represents a configuration of a top tokens score.

message TopTokensScoreConfig {
  optional int32 num_tokens = 1 [default = 10];
  optional string class_id = 2;
  repeated string topic_name = 3;
}
TopTokensScoreConfig.num_tokens

A value that defines how many top tokens should be retrieved for each topic.

TopTokensScoreConfig.class_id

A value that defines for which class of the model to collect top tokens. This value corresponds to ModelConfig.class_id field.

This parameter is optional. By default tokens will be retrieved for the default class ('@default_class‘).

TopTokensScoreConfig.topic_name

A set of values that represent the names of the topics to include in the result. The names correspond to ModelConfig.topic_name.

This parameter is optional. By default top tokens will be calculated for all topics in the model.

TopTokensScore

class messages_pb2.TopTokensScore

Represents a result of calculation of a top tokens score.

message TopTokensScore {
  optional int32 num_entries = 1;
  repeated string topic_name = 2;
  repeated int32 topic_index = 3;
  repeated string token = 4;
  repeated float weight = 5;
}

The data in this score is represented in a table-like format. sorted on topic_index. The following code block gives a typical usage example. The loop below is guarantied to process all top-N tokens for the first topic, then for the second topic, etc.

for (int i = 0; i < top_tokens_score.num_entries(); i++) {
  // Gives a index from 0 to (model_config.topics_size() - 1)
  int topic_index = top_tokens_score.topic_index(i);

  // Gives one of the topN tokens for topic 'topic_index'
  std::string token = top_tokens_score.token(i);

  // Gives the weight of the token
  float weight = top_tokens_score.weight(i);
}
TopTokensScore.num_entries

A value indicating the overall number of entries in the score. All the remaining repeated fiels in this score will have this length.

TopTokensScore.token

A repeated field of num_entries elements, containing tokens with high probability.

TopTokensScore.weight

A repeated field of num_entries elements, containing the p(t|w) probabilities.

TopTokensScore.topic_index

A repeated field of num_entries elements, containing integers between 0 and (ModelConfig.topics_count - 1).

TopTokensScore.topic_name

A repeated field of num_entries elements, corresponding to the values of ModelConfig.topic_name field.

ThetaSnippetScoreConfig

class messages_pb2.ThetaSnippetScoreConfig

Represents a configuration of a theta snippet score.

message ThetaSnippetScoreConfig {
  optional string field_name = 1 [default = "@body"];  // obsolete in BigARTM v0.5.8
  optional string stream_name = 2 [default = "@global"];
  repeated int32 item_id = 3 [packed = true];  // obsolete in BigARTM v0.5.8
  optional int32 item_count = 4 [default = 10];
}
ThetaSnippetScoreConfig.field_name

Obsolete in BigARTM v0.5.8

ThetaSnippetScoreConfig.stream_name

A value that defines which stream should be used in calculation of a theta snippet.

ThetaSnippetScoreConfig.item_id

Obsolete in BigARTM v0.5.8.

ThetaSnippetScoreConfig.item_count

The number of items to retrieve. ThetaSnippetScore will select last item_count processed items and return their theta vectors.

ThetaSnippetScore

class messages_pb2.ThetaSnippetScore

Represents a result of calculation of a theta snippet score.

message ThetaSnippetScore {
  repeated int32 item_id = 1;
  repeated FloatArray values = 2;
}
ThetaSnippetScore.item_id

A set of item ids for which theta snippet have been calculated. Items are identified by the item id.

ThetaSnippetScore.values

A set of values that define topic probabilities for each item. The length of these repeated values will match the number of item ids specified in ThetaSnippetScore.item_id. Each repeated field contains float array of topic probabilities in the natural order of topic ids.

TopicKernelScoreConfig

class messages_pb2.TopicKernelScoreConfig

Represents a configuration of a topic kernel score.

message TopicKernelScoreConfig {
  optional float eps = 1 [default = 1e-37];
  optional string class_id = 2;
  repeated string topic_name = 3;
  optional double probability_mass_threshold = 4 [default = 0.1];
}
  • Kernel of a topic model is defined as the list of all tokens such that the probability p(t | w) exceeds probability mass threshold.
  • Kernel size of a topic t is defined as the number of tokens in its kernel.
  • Topic purity of a topic t is defined as the sum of p(w | t) across all tokens w in the kernel.
  • Topic contrast of a topic t is defined as the sum of p(t | w) across all tokens w in the kernel defided by the size of the kernel.
TopicKernelScoreConfig.eps

Defines the minimum threshold on kernel size. In most cases this parameter should be kept at the default value.

TopicKernelScoreConfig.class_id

A value that defines the class of tokens to use for score calculation. This value corresponds to ModelConfig.class_id field. This value is optional. By default the score will be calculated for the default class ('@default_class‘).

TopicKernelScoreConfig.topic_name

A set of topic names that defines which topics should be used for score calculation. This value is optional, use an empty list to calculate the score for all topics.

TopicKernelScoreConfig.probability_mass_threshold

Defines the probability mass threshold (see the definition of kernel above).

TopicKernelScore

class messages_pb2.TopicKernelScore

Represents a result of calculation of a topic kernel score.

message TopicKernelScore {
  optional DoubleArray kernel_size = 1;
  optional DoubleArray kernel_purity = 2;
  optional DoubleArray kernel_contrast = 3;
  optional double average_kernel_size = 4;
  optional double average_kernel_purity = 5;
  optional double average_kernel_contrast = 6;
}
TopicKernelScore.kernel_size

Provides the kernel size for all requested topics. The length of this DoubleArray is always equal to the overall number of topics. The values of -1 correspond to non-calculated topics. The remaining values carry the kernel size of the requested topics.

TopicKernelScore.kernel_purity

Provides the kernel purity for all requested topics. The length of this DoubleArray is always equal to the overall number of topics. The values of -1 correspond to non-calculated topics. The remaining values carry the kernel size of the requested topics.

TopicKernelScore.kernel_contrast

Provides the kernel contrast for all requested topics. The length of this DoubleArray is always equal to the overall number of topics. The values of -1 correspond to non-calculated topics. The remaining values carry the kernel contrast of the requested topics.

TopicKernelScore.average_kernel_size

Provides the average kernel size across all the requested topics.

TopicKernelScore.average_kernel_purity

Provides the average kernel purity across all the requested topics.

TopicKernelScore.average_kernel_contrast

Provides the average kernel contrast across all the requested topics.

TopicModel

class messages_pb2.TopicModel

Represents a topic model. This message can contain data in either dense or sparse format. The key idea behind sparse format is to avoid storing zero p(w|t) elements of the Phi matrix. Please refer to the description of TopicModel.topic_index field for more details.

To distinguish between these two formats check whether repeated field TopicModel.topic_index is empty. An empty field indicate a dense format, otherwise the message contains data in a sparse format. To request topic model in a sparse format set GetTopicModelArgs.use_sparse_format field to True when calling ArtmRequestTopicModel().

message TopicModel {
  enum OperationType {
    Initialize = 0;
    Increment = 1;
    Overwrite = 2;
    Remove = 3;
    Ignore = 4;
  }

  optional string name = 1 [default = "@model"];
  optional int32 topics_count = 2;
  repeated string topic_name = 3;
  repeated string token = 4;
  repeated FloatArray token_weights = 5;
  repeated string class_id = 6;

  message TopicModelInternals {
    repeated FloatArray n_wt = 1;
    repeated FloatArray r_wt = 2;
  }

  optional bytes internals = 7;  // obsolete in BigARTM v0.6.3
  repeated IntArray topic_index = 8;
  repeated OperationType operation_type = 9;
}
TopicModel.name

A value that describes the name of the topic model (TopicModel.name).

TopicModel.topics_count

A value that describes the number of topics in this message.

TopicModel.topic_name

A value that describes the names of the topics included in given TopicModel message. This values will represent a subset of topics, defined by GetTopicModelArgs.topic_name message. In case of empty GetTopicModelArgs.topic_name this values will correspond to the entire set of topics, defined in ModelConfig.topic_name field.

TopicModel.token

The set of all tokens, included in the topic model.

TopicModel.token_weights

A set of token weights. The length of this repeated field will match the length of the repeated field TopicModel.token. The length of each FloatArray will match the TopicModel.topics_count field (in dense representation), or the length of the corresponding IntArray from TopicModel.topic_index field (in sparse representation).

TopicModel.class_id

A set values that specify the class (modality) of the tokens. The length of this repeated field will match the length of the repeated field TopicModel.token.

TopicModel.internals

Obsolete in BigARTM v0.6.3.

TopicModel.topic_index

A repeated field used for sparse topic model representation. This field has the same length as TopicModel.token, TopicModel.class_id and TopicModel.token_weights. Each element in topic_index is an instance of IntArray message, containing a list of values between 0 and the length of TopicModel.topic_name field. This values correspond to the indices in TopicModel.topic_name array, and tell which topics has non-zero p(w|t) probabilities for a given token. The actual p(w|t) values can be found in TopicModel.token_weights field. The length of each IntArray message in TopicModel.topic_index field equals to the length of the corresponding FloatArray message in TopicModel.token_weights field.

Warning

Be careful with TopicModel.topic_index when this message represents a subset of topics, defined by GetTopicModelArgs.topic_name. In this case indices correspond to the selected subset of topics, which might not correspond to topic indices in the original ModelConfig message.

TopicModel.operation_type

A set of values that define operation to perform on each token when topic model is used as an argument of ArtmOverwriteTopicModel().

Initialize Indicates that a new token should be added to the topic model. Initial n_wt counter will be initialized with random value from [0, 1] range. TopicModel.token_weights is ignored. This operation is ignored if token already exists.
Increment Indicates that n_wt counter of the token should be increased by values, specified in TopicModel.token_weights field. A new token will be created if it does not exist yet.
Overwrite Indicates that n_wt counter of the token should be set to the value, specified in TopicModel.token_weights field. A new token will be created if it does not exist yet.
Remove Indicates that the token should be removed from the topic model. TopicModel.token_weights is ignored.
Ignore Indicates no operation for the token. The effect is the same as if the token is not present in this message.

ThetaMatrix

class messages_pb2.ThetaMatrix

Represents a theta matrix. This message can contain data in either dense or sparse format. The key idea behind sparse format is to avoid storing zero p(t|d) elements of the Theta matrix. Sparse representation of Theta matrix is equivalent to sparse representation of Phi matrix. Please, refer to TopicModel for detailed description of the sparse format.

message ThetaMatrix {
  optional string model_name = 1 [default = "@model"];
  repeated int32 item_id = 2;
  repeated FloatArray item_weights = 3;
  repeated string topic_name = 4;
  optional int32 topics_count = 5;
  repeated string item_title = 6;
  repeated IntArray topic_index = 7;
}
ThetaMatrix.model_name

A value that describes the name of the topic model. This name will match the name of the corresponding model config.

ThetaMatrix.item_id

A set of item IDs corresponding to Item.id values.

ThetaMatrix.item_weights

A set of item ID weights. The length of this repeated field will match the length of the repeated field ThetaMatrix.item_id. The length of each FloatArray will match the ThetaMatrix.topics_count field (in dense representation), or the length of the corresponding IntArray from ThetaMatrix.topic_index field (in sparse representation).

ThetaMatrix.topic_name

A value that describes the names of the topics included in given ThetaMatrix message. This values will represent a subset of topics, defined by GetThetaMatrixArgs.topic_name message. In case of empty GetTopicModelArgs.topic_name this values will correspond to the entire set of topics, defined in ModelConfig.topic_name field.

ThetaMatrix.topics_count

A value that describes the number of topics in this message.

ThetaMatrix.item_title

A set of item titles, corresponding to Item.title values. Beware that this field might be empty (e.g. of zero length) if all items did not have title specified in Item.title.

ThetaMatrix.topic_index

A repeated field used for sparse theta matrix representation. This field has the same length as ThetaMatrix.item_id, ThetaMatrix.item_weights and ThetaMatrix.item_title. Each element in topic_index is an instance of IntArray message, containing a list of values between 0 and the length of TopicModel.topic_name field. This values correspond to the indices in ThetaMatrix.topic_name array, and tell which topics has non-zero p(t|d) probabilities for a given item. The actual p(t|d) values can be found in ThetaMatrix.item_weights field. The length of each IntArray message in ThetaMatrix.topic_index field equals to the length of the corresponding FloatArray message in ThetaMatrix.item_weights field.

Warning

Be careful with ThetaMatrix.topic_index when this message represents a subset of topics, defined by GetThetaMatrixArgs.topic_name. In this case indices correspond to the selected subset of topics, which might not correspond to topic indices in the original ModelConfig message.

CollectionParserConfig

class messages_pb2.CollectionParserConfig

Represents a configuration of a collection parser.

message CollectionParserConfig {
  enum Format {
    BagOfWordsUci = 0;
    MatrixMarket = 1;
  }

  optional Format format = 1 [default = BagOfWordsUci];
  optional string docword_file_path = 2;
  optional string vocab_file_path = 3;
  optional string target_folder = 4;
  optional string dictionary_file_name = 5;
  optional int32 num_items_per_batch = 6 [default = 1000];
  optional string cooccurrence_file_name = 7;
  repeated string cooccurrence_token = 8;
  optional bool use_unity_based_indices = 9 [default = true];
}
CollectionParserConfig.format

A value that defines the format of a collection to be parsed.

BagOfWordsUci
A bag-of-words collection, stored in UCI format.
UCI format must have two files - vocab.*.txt
and docword.*.txt, defined by
docword_file_path
and vocab_file_path.
The format of the docword.*.txt file is 3 header
lines, followed by NNZ triples:
D
W
NNZ
docID wordID count
docID wordID count
...
docID wordID count
The file must be sorted on docID.
Values of wordID must be unity-based (not zero-based).
The format of the vocab.*.txt file is line containing wordID=n.
Note that words must not have spaces or tabs.
In vocab.*.txt file it is also possible to specify
Batch.class_id for tokens, as it is shown in this example:
token1 @default_class
token2 custom_class
token3 @default_class
token4
Use space or tab to separate token from its class.
Token that are not followed by class label automatically
get ''@default_class‘’ as a lable (see ‘’token4’’ in the example).
MatrixMarket
See the description at http://math.nist.gov/MatrixMarket/formats.html
In this mode parameter docword_file_path must refer to a file
in Matrix Market format. Parameter vocab_file_path
is also required and must refer to a dictionary file exported in
gensim format (dictionary.save_as_text()).
CollectionParserConfig.docword_file_path

A value that defines the disk location of a docword.*.txt file (the bag of words file in sparse format).

CollectionParserConfig.vocab_file_path

A value that defines the disk location of a vocab.*.txt file (the file with the vocabulary of the collection).

CollectionParserConfig.target_folder

A value that defines the disk location where to stores all the results after parsing the colleciton. Usually the resulting location will contain a set of batches, and a DictionaryConfig that contains all unique tokens occured in the collection. Such location can be further passed MasterComponent via MasterComponentConfig.disk_path.

CollectionParserConfig.dictionary_file_name

A file name where to save the DictionaryConfig message that contains all unique tokens occured in the collection. The file will be created in target_folder.

This parameter is optional. The dictionary will be still collected even when this parameter is not provided, but the resulting dictionary will be only returned as the result of ArtmRequestParseCollection, but it will not be stored to disk.

In the resulting dictionary each entry will have the following fields:

Use ArtmRequestLoadDictionary method to load the resulting dictionary.

CollectionParserConfig.num_items_per_batch

A value indicating the desired number of items per batch.

CollectionParserConfig.cooccurrence_file_name

A file name where to save the DictionaryConfig message that contains information about co-occurrence of all pairs of tokens in the collection. The file will be created in target_folder.

This parameter is optional. No cooccurrence information will be collected if the filename is not provided.

In the resulting dictionary each entry will correspond to two tokens (‘<first>’ and ‘<second>’), and carry the information about co-occurrence of this tokens in the collection.

  • DictionaryEntry.key_token - a string of the form ‘<first>~<second>’, produced by concatenation of two tokens together via the tilde symbol (‘~’). <first> tokens is guarantied lexicographic less than the <second> token.
  • DictionaryEntry.class_id - the label of the default class (“@DefaultClass”).
  • DictionaryEntry.items_count - the number of documents in the collection, containing both tokens (‘<first>’ and ‘<second>’)

Use ArtmRequestLoadDictionary method to load the resulting dictionary.

CollectionParserConfig.cooccurrence_token

A list of tokens to collect cooccurrence information. A cooccurrence of the pair <first>~<second> will be collected only when both tokens are present in CollectionParserConfig.cooccurrence_token.

CollectionParserConfig.use_unity_based_indices

A flag indicating whether to interpret indices in docword file as unity-based or as zero-based. By default ‘use_unity_based_indices = True`, as required by UCI bag-of-words format.

SynchronizeModelArgs

class messages_pb2.SynchronizeModelArgs

Represents an argument of synchronize model operation.

message SynchronizeModelArgs {
  optional string model_name = 1;
  optional float decay_weight = 2 [default = 0.0];
  optional bool invoke_regularizers = 3 [default = true];
  optional float apply_weight = 4 [default = 1.0];
}
SynchronizeModelArgs.model_name

The name of the model to be synchronized. This value is optional. When not set, all models will be synchronized with the same decay weight.

SynchronizeModelArgs.decay_weight

The decay weight and apply_weight define how to combine existing topic model with all increments, calculated since the last ArtmSynchronizeModel(). This is best described by the following formula:

n_wt_new = n_wt_old * decay_weight + n_wt_inc * apply_weight,

where n_wt_old describe current topic model, n_wt_inc describe increment calculated since last ArtmSynchronizeModel(), n_wt_new define the resulting topic model.

Expected values of both parameters are between 0.0 and 1.0. Here are some examples:

  • Combination of decay_weight=0.0 and apply_weight=1.0 states that the previous Phi matrix of the topic model will be disregarded completely, and the new Phi matrix will be formed based on new increments gathered since last model synchronize.
  • Combination of decay_weight=1.0 and apply_weight=1.0 states that new increments will be appended to the current Phi matrix without any decay.
  • Combination of decay_weight=1.0 and apply_weight=0.0 states that new increments will be disregarded, and current Phi matrix will stay unchanged.
  • To reproduce Online variational Bayes for LDA algorighm by Matthew D. Hoffman set decay_weight = 1 - rho and apply_weight = rho, where parameter rho is defined as rho = exp(tau + t, -kappa). See Online Learning for Latent Dirichlet Allocation for further details.
SynchronizeModelArgs.apply_weight

See decay_weight for the description.

SynchronizeModelArgs.invoke_regularizers

A flag indicating whether to invoke all phi-regularizers.

InitializeModelArgs

class messages_pb2.InitializeModelArgs

Represents an argument of ArtmInitializeModel() operation. Please refer to example14_initialize_topic_model.py for further information.

message InitializeModelArgs {
  enum SourceType {
    Dictionary = 0;
    Batches = 1;
  }

  message Filter {
    optional string class_id = 1;
    optional float min_percentage = 2;
    optional float max_percentage = 3;
    optional int32 min_items = 4;
    optional int32 max_items = 5;
    optional int32 min_total_count = 6;
    optional int32 min_one_item_count = 7;
  }

  optional string model_name = 1;
  optional string dictionary_name = 2;
  optional SourceType source_type = 3 [default = Dictionary];

  optional string disk_path = 4;
  repeated Filter filter = 5;
}
InitializeModelArgs.model_name

The name of the model to be initialized.

InitializeModelArgs.dictionary_name

The name of the dictionary containing all tokens that should be initialized.

GetTopicModelArgs

Represents an argument of ArtmRequestTopicModel() operation.

message GetTopicModelArgs {
  enum RequestType {
    Pwt = 0;
    Nwt = 1;
  }

  optional string model_name = 1;
  repeated string topic_name = 2;
  repeated string token = 3;
  repeated string class_id = 4;
  optional bool use_sparse_format = 5;
  optional float eps = 6 [default = 1e-37];
  optional RequestType request_type = 7 [default = Pwt];
}
GetTopicModelArgs.model_name

The name of the model to be retrieved.

GetTopicModelArgs.topic_name

The list of topic names to be retrieved. This value is optional. When not provided, all topics will be retrieved.

GetTopicModelArgs.token

The list of tokens to be retrieved. The length of this field must match the length of class_id field. This field is optional. When not provided, all tokens will be retrieved.

GetTopicModelArgs.class_id

The list of classes corresponding to all tokens. The length of this field must match the length of token field. This field is only required together with token, otherwise it is ignored.

GetTopicModelArgs.use_sparse_format

An optional flag that defines whether to use sparse format for the resulting TopicModel message. See TopicModel message for additional information about the sparse format. Note that setting use_sparse_format = true results in empty TopicModel.internals field.

GetTopicModelArgs.eps

A small value that defines zero threshold for p(w|t) probabilities. This field is only used in sparse format. p(w|t) below the threshold will be excluded from the resulting Phi matrix.

GetTopicModelArgs.request_type

An optional value that defines what kind of data to retrieve in this operation.

Pwt Indicates that the resulting TopicModel message should contain p(w|t) probabilities. This values are normalized to form a probability distribution (sum_w p(w|t) = 1 for all topics t).
Nwt Indicates that the resulting TopicModel message should contain internal n_wt counters of the topic model. This values represent an internal state of the topic model.

Default setting is to retrieve p(w|t) probabilities. This probabilities are sufficient to infer p(t|d) distributions using this topic model.

n_wt counters allow you to restore the precise state of the topic model. By passing this values in ArtmOverwriteTopicModel() operation you are guarantied to get the model in the same state as you retrieved it. As the result you may continue topic model inference from the point you have stopped it last time.

p(w|t) values can be also restored via c:func:ArtmOverwriteTopicModel operation. The resulting model will give the same p(t|d) distributions, however you should consider this model as read-only, and do not call ArtmSynchronizeModel() on it.

GetThetaMatrixArgs

Represents an argument of ArtmRequestThetaMatrix() operation.

message GetThetaMatrixArgs {
  optional string model_name = 1;
  optional Batch batch = 2;
  repeated string topic_name = 3;
  repeated int32 topic_index = 4;
  optional bool clean_cache = 5 [default = false];
  optional bool use_sparse_format = 6 [default = false];
  optional float eps = 7 [default = 1e-37];
}
GetThetaMatrixArgs.model_name

The name of the model to retrieved theta matrix for.

GetThetaMatrixArgs.batch

The Batch to classify with the model.

GetThetaMatrixArgs.topic_name

The list of topic names, describing which topics to include in the Theta matrix. The values of this field should correspond to values in ModelConfig.topic_name. This field is optional, by default all topics will be included.

GetThetaMatrixArgs.topic_index

The list of topic indices, describing which topics to include in the Theta matrix. The values of this field should be an integers between 0 and (ModelConfig.topics_count - 1). This field is optional, by default all topics will be included.

Note that this field acts similar to GetThetaMatrixArgs.topic_name. It is not allowed to specify both topic_index and topic_name at the same time. The recommendation is to use topic_name.

GetThetaMatrixArgs.clean_cache

An optional flag that defines whether to clear the theta matrix cache after this operation. Setting this value to True will clear the cache for a topic model, defined by GetThetaMatrixArgs.model_name. This value is only applicable when MasterComponentConfig.cache_theta is set to True.

GetThetaMatrixArgs.use_sparse_format

An optional flag that defines whether to use sparse format for the resulting ThetaMatrix message. See ThetaMatrix message for additional information about the sparse format.

GetThetaMatrixArgs.eps

A small value that defines zero threshold for p(t|d) probabilities. This field is only used in sparse format. p(t|d) below the threshold will be excluded from the resulting Theta matrix.

GetScoreValueArgs

Represents an argument of get score operation.

message GetScoreValueArgs {
  optional string model_name = 1;
  optional string score_name = 2;
  optional Batch batch = 3;
}
GetScoreValueArgs.model_name

The name of the model to retrieved score for.

GetScoreValueArgs.score_name

The name of the score to retrieved.

GetScoreValueArgs.batch

The Batch to calculate the score. This option is only applicable to cumulative scores. When not provided the score will be reported for all batches processed since last ArtmInvokeIteration().

AddBatchArgs

Represents an argument of ArtmAddBatch() operation.

message AddBatchArgs {
  optional Batch batch = 1;
  optional int32 timeout_milliseconds = 2 [default = -1];
  optional bool reset_scores = 3 [default = false];
  optional string batch_file_name = 4;
}
AddBatchArgs.batch

The Batch to add.

AddBatchArgs.timeout_milliseconds

Timeout in milliseconds for this operation.

AddBatchArgs.reset_scores

An optional flag that defines whether to reset all scores before this operation.

AddBatchArgs.batch_file_name

An optional value that defines disk location of the batch to add. You must choose between parameters batch_file_name or batch (either of them has to be specified, but not both at the same time).

InvokeIterationArgs

Represents an argument of ArtmInvokeIteration() operation.

message InvokeIterationArgs {
  optional int32 iterations_count = 1 [default = 1];
  optional bool reset_scores = 2 [default = true];
  optional string disk_path = 3;
}
InvokeIterationArgs.iterations_count

An integer value describing how many iterations to invoke.

InvokeIterationArgs.reset_scores

An optional flag that defines whether to reset all scores before this operation.

InvokeIterationArgs.disk_path

A value that defines the disk location with batches to process on this iteration.

WaitIdleArgs

Represents an argument of ArtmWaitIdle() operation.

message WaitIdleArgs {
  optional int32 timeout_milliseconds = 1 [default = -1];
}
WaitIdleArgs.timeout_milliseconds

Timeout in milliseconds for this operation.

ExportModelArgs

Represents an argument of ArtmExportModel() operation.

message ExportModelArgs {
  optional string file_name = 1;
  optional string model_name = 2;
}
ExportModelArgs.file_name

A target file name where to store topic model.

ExportModelArgs.model_name

A value that describes the name of the topic model. This name will match the name of the corresponding model config.

ImportModelArgs

Represents an argument of ArtmImportModel() operation.

message ImportModelArgs {
  optional string file_name = 1;
  optional string model_name = 2;
}
ImportModelArgs.file_name

A target file name from where to load topic model.

ImportModelArgs.model_name

A value that describes the name of the topic model. This name will match the name of the corresponding model config.

Python Interface

This document explains all classes in python interface of BigARTM library.

Library

class artm.library.Library(artm_shared_library = "")

Creates an ArtmLibrary object, wrapping the BigARTM shared library.

The artm_shared_library is an optional argument, which provides full file name of artm shared library (a disk path plus artm.dll on Windows or artm.so on Linux). When artm_shared_library is not specified the shared library will be searched in folders listed in PATH system variable. You may also configure ARTM_SHARED_LIBRARY system variable to provide full file name of artm shared library.

CreateMasterComponent(config = None)

Creates and returns an instance of MasterComponent class. config defines an optional MasterComponentConfig parameter that may carry the configuration of the master component.

SaveBatch(batch, disk_path)

Saves a given Batch into a disk location specified by disk_path.

ParseCollection(collection_parser_config)

Parses a text collection as defined by collection_parser_config (CollectionParserConfig). Returns an instance of DictionaryConfig which carry all unique words in the collection and their frequencies.

For more information refer to ArtmRequestParseCollection() and ArtmRequestLoadDictionary().

LoadDictionary(full_filename)

Loads a DictionaryConfig from the file, defined by full_filename argument.

For more information refer to ArtmRequestLoadDictionary().

LoadBatch(full_filename)

Loads a Batch from the file, defined by full_filename argument.

For more information refer to ArtmRequestLoadBatch().

ParseCollectionOrLoadDictionary(docword_file_path, vocab_file_path, target_folder)

A simple helper method that runs ParseCollection() when target_folder is empty, otherwise tried to use LoadDictionary() to load the dictionary from target_folder.

The docword_file_path and vocab_file_path arguments should provide the disk location of docword and vocab files of the collection to be parsed.

MasterComponent

class artm.library.MasterComponent(config = None, lib = None, disk_path = None)

Creates a master component.

config is an optional instance of MasterComponentConfig, providing an initial configuration of the master component.

lib is an optional argument pointing to Library. When not specified, a default library will be used. Check the constructor of Library for more details.

disk_path is an optional value providing the disk folder with batches to process by this master component. Changing disk_path is not supported (you must recreate a new instance MasterComponent to do so). Use InvokeIteration() will process all batches, located under disk_path. Alternatively use AddBatch() to add a specific batch into processor queue.

Dispose()

Disposes the master component and releases all unmanaged resources.

config()

Returns current MasterComponentConfig of the master component.

CreateModel(config=None, topics_count=None, inner_iterations_count=None, class_ids=None, class_weights=None,
topic_names=None, use_sparse_format=None, request_type=None)

Creates and returns an instance of Model class based on a given ModelConfig. Note that the model has to be further tuned by several iterative scans over the text collection. Use InvokeIteration() to perform such scans.

All parameters will override values, specifed in config.

RemoveModel(model)

Removes an instance of Model from the master component. After this operation the model object became invalid and must not be used.

CreateRegularizer(name, type, config)

Creates and returns an instance of Regularizer component. name can be any unique identifier, that you can further use to identify regularizer (for example, in ModelConfig.regularizer_name). type can be any regularizer type (for example, the RegularizerConfig_Type_DirichletTheta). config can be any regularizer config (for example, a SmoothSparseThetaConfig).

CreateSmoothSparseThetaRegularizer(name = None, config = None)

Creates an instance of SmoothSparseThetaRegularizer. config is an optional argument of SmoothSparseThetaConfig type.

CreateSmoothSparsePhiRegularizer(name = None, config = None, topic_names=None, class_ids=None)

Creates an instance of SmoothSparsePhiRegularizer. config is an optional argument of SmoothSparsePhiConfig type.

CreateDecorrelatorPhiRegularizer(name = None, config = None, topic_names=None, class_ids=None)

Creates an instance of DecorrelatorPhiRegularizer. config is an optional argument of DecorrelatorPhiConfig type.

RemoveRegularizer(regularizer)

Removes an instance of Regularizer from the master component. After this operation the regularizer object became invalid and must not be used.

CreateScore(name, type, config)

Creates a score calculator inside the master component. name can be any unique identifier, that you can further use to identify the score (for example, in ModelConfig.score_name). type can be any score type (for example, the ScoreConfig_Type_Perplexity). config can be any score config (for example, a PerplexityScoreConfig).

CreatePerplexityScore(self, name = None, config = None, stream_name = None, class_ids=None)

Creates an instance of PerplexityScore. config is an optional argument of PerplexityScoreConfig type.

CreateSparsityThetaScore(self, name = None, config = None, topic_names=None)

Creates an instance of SparsityThetaScore. config is an optional argument of SparsityThetaScoreConfig type.

CreateSparsityPhiScore(self, name = None, config = None, topic_names=None, class_id=None)

Creates an instance of SparsityPhiScore. config is an optional argument of SparsityPhiScoreConfig type.

CreateItemsProcessedScore(self, name = None, config = None)

Creates an instance of ItemsProcessedScore. config is an optional argument of ItemsProcessedScoreConfig type.

CreateTopTokensScore(self, name = None, config = None, num_tokens = None, class_id = None, topic_names=None)

Creates an instance of TopTokensScore. config is an optional argument of TopTokensScoreConfig type.

CreateThetaSnippetScore(self, name = None, config = None)

Creates an instance of ThetaSnippetScore. config is an optional argument of ThetaSnippetScoreConfig type.

CreateTopicKernelScore(self, name = None, config = None, topic_names=None, class_id=None)

Creates an instance of TopicKernelScore. config is an optional argument of TopicKernelScoreConfig type.

RemoveScore(name)

Removes a score calculator with the specific name from the master component.

CreateDictionary(config)

Creates and returns an instance of Dictionary class component with a specific DictionaryConfig.

RemoveDictionary(dictionary)

Removes an instance of Dictionary from the master component. After this operation the dictionary object became invalid and must not be used.

Reconfigure(config = None)

Updates the configuration of the master component with new MasterComponentConfig value, provided by config parameter. Remember that some changes of the configuration are not allowed (for example, the MasterComponentConfig.disk_path must not change). Such configuration parameters must be provided in the constructor of MasterComponent.

AddBatch(self, batch = None, batch_filename = None, timeout = None, reset_scores = False, args=None)

Adds an instance of Batch class to the processor queue. Master component creates a copy of the batch, so any further changes of the batch object will not be picked up. batch_filename is an alternative to file with binary-serialized batch (you must use either batch or batch_filename option, but not both at the same time).

This operation awaits until there is enough space in processor queue. It returns True if await succeeded within the timeout, otherwise returns False. The provided timeout is in milliseconds. By default it allows an infinite time for AddBatch() operation.

args is an optional argument of AddBatchArgs type.

InvokeIteration(iterations_count = 1, disk_path = None, args=None)

Invokes several iterations over the collection. The recommended value for iterations_count is 1. disk_path defines the disk location with batches to process on this iteration. For more iterations use for loop around InvokeIteration() method. This operation is asynchronous. Use WaitIdle() to await until all iterations succeeded.

args is an optional argument of InvokeIterationArgs type.

WaitIdle(timeout = None, args=None)

Awaits for ongoing iterations. Returns True if iterations had been finished within the timeout, otherwise returns False. The provided timeout is in milliseconds. Use timeout = -1 to allow infinite time for WaitIdle() operation. Remember to call Model.Synchronize() operation to synchronize each model that you are currently processing.

args is an optional argument of WaitIdleArgs type.

CreateStream(stream)

Creates a data stream base on the stream (Stream).

RemoveStream(stream_name)

Removes a stream with the specific name from the master component.

GetTopicModel(model = None, args = None)

Retrieves and returns an instance of TopicModel class, carrying all the data of the topic model (including the Phi matrix). Parameter model should be an instance of Model class. For more settings use args parameter (see GetTopicModelArgs for all available options).

GetRegularizerState(regularizer_name)

Retrieves and returns the internal state of a regularizer with the specific name.

GetThetaMatrix(model = None, batch = None, clean_cache = None, args = None)

Retrieves an instance of ThetaMatrix class. The content depends on batch parameter. When batch is provided, the resulting ThetaMatrix will contain theta values estimated for all documents in the batch. When batch is not provided, the resulting ThetaMatrix will contain theta values gathered during the last iteration.

Parameter model should be an instance of Model class. For more settings use args parameter (see GetThetaMatrixArgs for all available options).

When used without batch, this operation require MasterComponentConfig.cache_theta to be set to True before starting the last iteration. In this case the entire ThetaMatrix must fit into CPU memory, and for this reason MasterComponentConfig.cache_theta is turned off by default.

Model

class artm.library.Model

This constructor must not be used explicitly. The only correct way of creating a Model is through MasterComponent.CreateModel() method.

name()

Returns the string name of the model.

Reconfigure(config = None)

Updates the configuration of the topic model with new ModelConfig value, provided by config parameter. When config is not specified the configuration is updated with config() value. Remember that some changes of the configuration are applied immediately after this call. For example, changes to ModelConfig.topics_count or ModelConfig.topic_name will be applied only during the next Synchronize call.

Note that changes ModelConfig.topics_count or ModelConfig.topic_name are only supported on an idle master component (e.g. in between iterations). Changing these values during an ongoing iteration may cause unexpected results.

topics_count()

Returns the number of topics in the model.

config()

Returns current ModelConfig of the topic model.

Synchronize(decay_weight = 0.0, apply_weight = 1.0, invoke_regularizers = True, args=None)

This operation updates the Phi matrix of the topic model with all model increments, collected since the last call to Synchronize() method. The Phi matrix is calculated according to decay_weight and apply_weight (refer to SynchronizeModelArgs.decay_weight for more details). Depending on invoke_regularizers parameter this operation may also invoke all regularizers.

Remember to call Synchronize() operation every time after call MasterComponent.WaitIdle().

For more settings use args parameter (see SynchronizeModelArgs for all available options).

Initialize(dictionary = None, args=None)

Generates a random initial approximation for the Phi matrix of the topic model.

dictionary must be an instance of Dictionary class.

For more settings use args parameter (see InitializeModelArgs for all available options).

Export(filename)

Exports topic model into a file.

Import(filename)

Imports topic model from a file.

Overwrite(topic_model, commit = True)

Updates the model with new Phi matrix, defined by topic_model (TopicModel). This operation can be used to provide an explicit initial approximation of the topic model, or to adjust the model in between iterations.

Depending on the commit flag the change can be applied immediately (commit = true) or queued (commit = false). The default setting is to use commit = true. You may want to use commit = false if your model is too big to be updated in a single protobuf message. In this case you should split your model into parts, each part containing subset of all tokens, and then submit each part in separate Overwrite operation with commit = false. After that remember to call MasterComponent.WaitIdle() and Model.Synchronize() to propagate your change.

Enable()

Sets ModelConfig.enabled to True for the current topic model. This means that the model will be updated on MasterComponent.InvokeIteration().

EnableScore(score)

By default model does calculate any scores even if they are created with MasterComponent.CreateScore(). Method EnableScore tells to the model that score should be applied to the model. Parameter tau defines the regularization coefficient of the regularizer. score must be an instance of Score class.

EnableRegularizer(regularizer, tau)

By default model does not use any regularizers even if they are created with MasterComponent.CreateRegularizer(). Method EnableRegularizer tells to the model that regularizer should be applied to the model. Parameter tau defines the regularization coefficient of the regularizer. regularizer must be an instance of Regularizer class.

Disable()

Sets ModelConfig.enabled to False` for the current topic model. This means that the model will not be updated on MasterComponent.InvokeIteration(), but the the scores for the model still will be collected.

Regularizer

class artm.library.Regularizer

This constructor must not be used explicitly. The only correct way of creating a Regularizer is through MasterComponent.CreateRegularizer() method (or similar methods in MasterComponent class, dedicated to a particular type of the regularizer).

name()

Returns the string name of the regularizer.

Reconfigure(type, config)

Updates the configuration of the regularizer with new regularizer configuration, provided by config parameter. The config object can be, for example, of SmoothSparseThetaConfig type (or similar). The type must match the current type of the regularizer.

Score

class artm.library.Score

This constructor must not be used explicitly. The only correct way of creating a Score is through MasterComponent.CreateScore() method (or similar methods in MasterComponent class, dedicated to a particular type of the score).

name()

Returns the string name of the score.

GetValue(model = None, batch = None)

Retrieves the score for a specific model. For cumulative scores such as Perplexity of ThetaSparsity score it is possible to use batch argument.

Dictionary

class artm.library.Dictionary(master_component, config)

This constructor must not be used explicitly. The only correct way of creating a Dictionary is through MasterComponent.CreateDictionary() method.

name()

Returns the string name of the dictionary.

Reconfigure(config)

Updates the configuration of the dictionary with new DictionaryConfig value, provided by config parameter.

Visualizers

class artm.library.Visualizers

This class provides a set of static method to visualize some scores.

PrintTopTokensScore(top_tokens_score)

Prints the TopTokensScore.

PrintThetaSnippetScore(theta_snippet_score)

Prints the ThetaSnippetScore.

Exceptions

exception artm.library.InternalError

An exception class corresponding to ARTM_INTERNAL_ERROR error code.

exception artm.library.ArgumentOutOfRangeException

An exception class corresponding to ARTM_ARGUMENT_OUT_OF_RANGE error code.

exception artm.library.InvalidMasterIdException

An exception class corresponding to ARTM_INVALID_MASTER_ID error code.

exception artm.library.CorruptedMessageException

An exception class corresponding to ARTM_CORRUPTED_MESSAGE error code.

exception artm.library.InvalidOperationException

An exception class corresponding to ARTM_INVALID_OPERATION error code.

exception artm.library.DiskReadException

An exception class corresponding to ARTM_DISK_READ_ERROR error code.

exception artm.library.DiskWriteException

An exception class corresponding to ARTM_DISK_WRITE_ERROR error code.

Constants

artm.library.Stream_Type_Global
artm.library.Stream_Type_ItemIdModulus
artm.library.RegularizerConfig_Type_DirichletTheta
artm.library.RegularizerConfig_Type_DirichletPhi
artm.library.RegularizerConfig_Type_SmoothSparseTheta
artm.library.RegularizerConfig_Type_SmoothSparsePhi
artm.library.RegularizerConfig_Type_DecorrelatorPhi
artm.library.ScoreConfig_Type_Perplexity
artm.library.ScoreData_Type_Perplexity
artm.library.ScoreConfig_Type_SparsityTheta
artm.library.ScoreData_Type_SparsityTheta
artm.library.ScoreConfig_Type_SparsityPhi
artm.library.ScoreData_Type_SparsityPhi
artm.library.ScoreConfig_Type_ItemsProcessed
artm.library.ScoreData_Type_ItemsProcessed
artm.library.ScoreConfig_Type_TopTokens
artm.library.ScoreData_Type_TopTokens
artm.library.ScoreConfig_Type_ThetaSnippet
artm.library.ScoreData_Type_ThetaSnippet
artm.library.ScoreConfig_Type_TopicKernel
artm.library.ScoreData_Type_TopicKernel
artm.library.PerplexityScoreConfig_Type_UnigramDocumentModel
artm.library.PerplexityScoreConfig_Type_UnigramCollectionModel
artm.library.CollectionParserConfig_Format_BagOfWordsUci

Plain C interface of BigARTM

This document explains all public methods of the low level BigARTM interface.

Introduction

The goal of low level BigARTM interface is to expose all functionality of the library in a set of simple functions written in good old plain C language. This makes it easier to consume BigARTM from various programming environments. For example, the Python Interface of BigARTM uses ctypes module to call the low level BigARTM interface. Most programming environments also have similar functionality: PInvoke in C#, loadlibrary in Matlab, etc.

Note that most methods in this API accept a serialized binary representation of some Google Protocol Buffer message. Please, refer to Messages for more details about each particular message.

All methods in this API return an integer value. Negative return values represent an error code. See error codes for the list of all error codes. To get corresponding error message as string use ArtmGetLastErrorMessage(). Non-negative return values represent a success, and for some API methods might also incorporate some useful information. For example, ArtmCreateMasterComponent() returns the ID of newly created master component, and ArtmRequestTopicModel() returns the length of the buffer that should be allocated before calling ArtmCopyRequestResult().

ArtmCreateMasterComponent

int ArtmCreateMasterComponent(int length, const char* master_component_config)

Creates a master component.

Parameters:
  • master_component_config (const_char*) – Serialized MasterComponentConfig message, describing the configuration of the master component.
  • length (int) – The length in bytes of the master_component_config message.
Returns:

In case of success, a non-negative ID of the master component, otherwise one of the error codes.

The ID, returned by this operation, is required by most methods in this API. Several master components may coexist in the same process. In such case any two master components with different IDs can not share any common data, and thus they are completely independent from each other.

ArtmReconfigureMasterComponent

int ArtmReconfigureMasterComponent(int master_id, int length, const char* master_component_config)

Changes the configuration of the master component.

Parameters:
  • master_id (int) – The ID of a master component returned by ArtmCreateMasterComponent() method.
  • master_component_config (const_char*) – Serialized MasterComponentConfig message, describing the new configuration of the master component.
  • length (int) – The length in bytes of the master_component_config message.
Returns:

A zero value if operation succeeded, otherwise one of the error codes.

ArtmDisposeMasterComponent

int ArtmDisposeMasterComponent(int master_id)

Disposes master component together with all its models, regularizers and dictionaries.

Parameters:
Returns:

This operation always returns ARTM_SUCCESS.

This operation releases memory and other unmanaged resources, used by the master component.

After this operation the master_id value becames invalid and must not be used in other operations.

ArtmCreateModel

int ArtmCreateModel(int master_id, int length, const char* model_config)

Defines a new topic model.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • model_config (const_char*) – Serialized ModelConfig message, describing the configuration of the topic model.
  • length (int) – The length in bytes of the model_config message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

Note that this method only defines the configuration a topic model, but does not tune it. Use ArtmInvokeIteration() method to process the collection of textual documents, and then ArtmRequestTopicModel() to retrieve the resulting topic model.

It is important to notice that model_config must have a unique value in the ModelConfig.name field, that can be further used to identify the model (for example in ArtmRequestTopicModel() call).

ArtmReconfigureModel

int ArtmReconfigureModel(int master_id, int length, const char* model_config)

Updates the configuration of topic model.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • model_config (const_char*) – Serialized ModelConfig message, describing the new configuration of the topic model.
  • length (int) – The length in bytes of the model_config message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmDisposeModel

int ArtmDisposeModel(int master_id, const char* model_name)

Explicitly delete a specific topic model. All regularizers within specific master component are also deleted automatically by ArtmDisposeMasterComponent().

After ArtmDisposeModel() the model_name became invalid and shell not be used in ArtmRequestScore(), ArtmRequestTopicModel(), ArtmRequestThetaMatrix() or any other method (or protobuf message) that require model_name.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • model_name (const_char*) – A string identified of the model that should be deleted.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmCreateRegularizer

int ArtmCreateRegularizer(int master_id, int length, const char* regularizer_config)

Creates a new regularizer.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • regularizer_config (const_char*) – Serialized RegularizerConfig message, describing the configuration of a new regularizer.
  • length (int) – The length in bytes of the regularizer_config message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

This operation only creates the regularizer so that it can be used by topic models. To actually apply the regularizer you should include its name in ModelConfig.regularizer_name list of a model config.

ArtmReconfigureRegularizer

int ArtmReconfigureRegularizer(int master_id, int length, const char* regularizer_config)

Updates the configuration of the regularizer.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • regularizer_config (const_char*) – Serialized RegularizerConfig message, describing the configuration of a new regularizer.
  • length (int) – The length in bytes of the regularizer_config message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmDisposeRegularizer

int ArtmDisposeRegularizer(int master_id, const char* regularizer_name)

Explicitly delete a specific regularizer. All regularizers within specific master component are also deleted automatically by ArtmDisposeMasterComponent().

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • regularizer_name (const_char*) – A string identified of the regularizer that should be deleted.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmCreateDictionary

int ArtmCreateDictionary(int master_id, int length, const char* dictionary_config)

Creates a new dictionary.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • dictionary_config (const_char*) – Serialized DictionaryConfig message, describing the configuration of a new dictionary.
  • length (int) – The length in bytes of the dictionary_config message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmReconfigureDictionary

int ArtmReconfigureDictionary(int master_id, int length, const char* dictionary_config)

Updates the dictionary.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • dictionary_config (const_char*) – Serialized DictionaryConfig message, describing the new configuration of the dictionary.
  • length (int) – The length in bytes of the dictionary_config message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmDisposeDictionary

int ArtmDisposeDictionary(int master_id, const char* dictionary_name)

Explicitly delete a specific dictionary. All dictionaries within specific master component are also deleted automatically by ArtmDisposeMasterComponent().

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • dictionary_name (const_char*) – A string identified of the dictionary that should be deleted.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmAddBatch

int ArtmAddBatch(int master_id, int length, const char* add_batch_args)

Adds batch for processing.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • add_batch_args (const_char*) – Serialized AddBatchArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the add_batch_args message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmInvokeIteration

int ArtmInvokeIteration(int master_id, int length, const char* invoke_iteration_args)

Invokes several iterations over the collection.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • char* invoke_iteration_args (const) – Serialized InvokeIterationArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the invoke_iteration_args message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmSynchronizeModel

int ArtmSynchronizeModel(int master_id, int length, const char* sync_model_args)

Synchronizes topic model.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • sync_model_args (const_char*) – Serialized SynchronizeModelArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the sync_model_args message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

This operation updates the Phi matrix of the topic model with all model increments, collected since last call to ArtmSynchronizeModel. In addition, this operation invokes all Phi-regularizers for the requested topic model.

ArtmInitializeModel

int ArtmInitializeModel(int master_id, int length, const char* init_model_args)

Initializes the phi matrix of a topic model with some random initial approximation.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • init_model_args (const_char*) – Serialized InitializeModelArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the init_model_args message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmExportModel

int ArtmExportModel(int master_id, int length, const char* export_model_args)

Exports phi matrix into a file.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • export_model_args (const_char*) – Serialized ExportModelArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the export_model_args message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmImportModel

int ArtmImportModel(int master_id, int length, const char* import_model_args)

Import phi matrix from a file.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • import_model_args (const_char*) – Serialized ImportModelArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the import_model_args message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmWaitIdle

int ArtmWaitIdle(int master_id, int length, const char* wait_idle_args)

Awaits for ongoing iterations.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • wait_idle_args (const_char*) – Serialized WaitIdleArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the wait_idle_args message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmOverwriteTopicModel

int ArtmOverwriteTopicModel(int master_id, int length, const char* topic_model)

This operation schedules an update of an entire topic model or of it subpart.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • topic_model (const_char*) – Serialized TopicModel message, describing the new topic model.
  • length (int) – The length in bytes of the topic_model message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

Note that this operation only schedules the update of a topic model. To make sure the update is completed you must call ArtmWaitIdle() and ArtmSynchronizeModel(). Remember that by default ArtmSynchronizeModel() will calculate all regularizers enabled in the configuration of the topic model. The may result in a different topic model than the one you passed as topic_model parameter. To avoid this behavior set SynchronizeModelArgs.invoke_regularizers to false.

ArtmRequestThetaMatrix

int ArtmRequestThetaMatrix(int master_id, int length, const char* get_theta_args)

Requests theta matrix. Use ArtmCopyRequestResult() to copy the resulting message.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • get_theta_args (const_char*) – Serialized GetThetaMatrixArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the get_theta_args message.
Returns:

In case of success, returns the length in bytes of a buffer that should be allocated on callers site and then passed to ArtmCopyRequestResult() method. This will populate the buffer with ThetaMatrix message, carrying the requested information. In case of a failure, returns one of the error codes.

ArtmRequestTopicModel

int ArtmRequestTopicModel(int master_id, int length, const char* get_model_args)

Requests topic model.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • get_model_args (const_char*) – Serialized GetTopicModelArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the get_model_args message.
Returns:

In case of success, returns the length in bytes of a buffer that should be allocated on callers site and then passed to ArtmCopyRequestResult() method. This will populate the buffer with TopicModel message, carrying the requested information. In case of a failure, returns one of the error codes.

ArtmRequestRegularizerState

int ArtmRequestRegularizerState(int master_id, const char* regularizer_name)

Requests state of a specific regularizer.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • regularizer_name (const_char*) – A string identified of the regularizer.
Returns:

In case of success, returns the length in bytes of a buffer that should be allocated on callers site and then passed to ArtmCopyRequestResult() method. This will populate the buffer with RegularizerInternalState message, carrying the requested information. In case of a failure, returns one of the error codes.

ArtmRequestScore

int ArtmRequestScore(int master_id, int length, const char* get_score_args)

Request the result of score calculation.

Parameters:
  • master_id (int) – The ID of a master component, returned by ArtmCreateMasterComponent() method.
  • const_char* – get_score_args: Serialized GetScoreValueArgs message, describing the arguments of this operation.
  • length (int) – The length in bytes of the get_score_args message.
Returns:

In case of success, returns the length in bytes of a buffer that should be allocated on callers site and then passed to ArtmCopyRequestResult() method. This will populate the buffer with ScoreData message, carrying the requested information. In case of a failure, returns one of the error codes.

ArtmRequestParseCollection

int ArtmRequestParseCollection(int length, const char* collection_parser_config)

Parses a text collection into a set of batches and stores them on disk. Returns a DictionaryConfig message that lists all tokens, occured in the collection.

Check the description of CollectionParserConfig message for more details about this operation.

Parameters:
  • const_char* – collection_parser_config: Serialized CollectionParserConfig message, describing the configuration the collection parser.
  • length (int) – The length in bytes of the collection_parser_config message.
Returns:

In case of success, returns the length in bytes of a buffer that should be allocated on callers site and then passed to ArtmCopyRequestResult() method. The buffer will contain DictionaryConfig message, that lists all unique tokens from the collection being parsed. In case of a failure, returns one of the error codes.

Warning

The following error most likelly indicate that you are trying to parse a very large file in 32 bit version of BigARTM.

InternalError :  failed mapping view: The parameter is incorrect

Try to use 64 bit BigARTM to workaround this issue.

ArtmRequestLoadDictionary

int ArtmRequestLoadDictionary(const char* filename)

Loads a DictionaryConfig message from disk.

Parameters:
  • const_char* – filename: A full file name of a file that contains a serialized DictionaryConfig message.
Returns:

In case of success, returns the length in bytes of a buffer that should be allocated on callers site and then passed to ArtmCopyRequestResult() method. The buffer will contain the resulting DictionaryConfig message. In case of a failure, returns one of the error codes.

This method can be used to load CollectionParserConfig.dictionary_file_name or CollectionParserConfig.cooccurrence_file_name dictionaries, saved by ArtmRequestParseCollection method.

ArtmRequestLoadBatch

int ArtmRequestLoadBatch(const char* filename)

Loads a Batch message from disk.

Parameters:
  • const_char* – filename: A full file name of a file that contains a serialized Batch message.
Returns:

In case of success, returns the length in bytes of a buffer that should be allocated on callers site and then passed to ArtmCopyRequestResult() method. The buffer will contain the resulting Batch message. In case of a failure, returns one of the error codes.

This method can be used to load batches saved by ArtmRequestParseCollection method or ArtmSaveBatch method.

ArtmCopyRequestResult

int ArtmCopyRequestResult(int length, char* address)

Copies the result of the last request.

Parameters:
  • const_char* – address: Target memory location to copy the data.
  • length (int) – The length in bytes of the address buffer.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmSaveBatch

int ArtmSaveBatch(const char* disk_path, int length, const char* batch)

Saves a Batch message to disk.

Parameters:
  • const_char* – disk_path: A floder where to save the batch.
  • batch (const_char*) – Serialized Batch message to save.
  • length (int) – The length in bytes of the batch message.
Returns:

Returns ARTM_SUCCESS value if operation succeeded, otherwise returns one of the error codes.

ArtmGetLastErrorMessage

const char* ArtmGetLastErrorMessage()

Retrieves the textual error message, occured during the last failing request.

Error codes

#define ARTM_SUCCESS 0
#define ARTM_STILL_WORKING -1
#define ARTM_INTERNAL_ERROR -2
#define ARTM_ARGUMENT_OUT_OF_RANGE -3
#define ARTM_INVALID_MASTER_ID -4
#define ARTM_CORRUPTED_MESSAGE -5
#define ARTM_INVALID_OPERATION -6
#define ARTM_DISK_READ_ERROR -7
#define ARTM_DISK_WRITE_ERROR -8
ARTM_SUCCESS

The API call succeeded.

ARTM_STILL_WORKING

This error code is applicable only to ArtmWaitIdle(). It indicates that library is still processing the collection. Try to retrieve results later.

ARTM_INTERNAL_ERROR

The API call failed due to internal error in BigARTM library. Please, collect steps to reproduce this issue and report it with BigARTM issue tracker.

ARTM_ARGUMENT_OUT_OF_RANGE

The API call failed because one or more values of an argument are outside the allowable range of values as defined by the invoked method.

ARTM_INVALID_MASTER_ID

An API call that require master_id parameter failed because MasterComponent with given ID does not exist.

ARTM_CORRUPTED_MESSAGE

Unable to deserialize protocol buffer message.

ARTM_INVALID_OPERATION

The API call is invalid in current state or due to provided parameters.

ARTM_DISK_READ_ERROR

The required files coult not be read from disk.

ARTM_DISK_WRITE_ERROR

The required files could not be writtent to disk.

C++ interface

BigARTM C++ interface is currently not documented. The main entry point is MasterModel class from src/artm/cpp_interface.cc. Please referto src/bigartm//srcmain.cc for usage examples, and ask questions at bigartm-users or open a new issue.

class MasterModel {
 public:
  explicit MasterModel(const MasterModelConfig& config);
  ~MasterModel();

  int id() const { return id_; }
  MasterComponentInfo info() const;  // misc. diagnostics information

  const MasterModelConfig& config() const { return config_; }
  MasterModelConfig* mutable_config() { return &config_; }
  void Reconfigure();  // apply MasterModel::config()

  // Operations to work with dictionary through disk
  void GatherDictionary(const GatherDictionaryArgs& args);
  void FilterDictionary(const FilterDictionaryArgs& args);
  void ImportDictionary(const ImportDictionaryArgs& args);
  void ExportDictionary(const ExportDictionaryArgs& args);
  void DisposeDictionary(const std::string& dictionary_name);

  // Operations to work with dictinoary through memory
  void CreateDictionary(const DictionaryData& args);
  DictionaryData GetDictionary(const GetDictionaryArgs& args);

  // Operatinos to work with batches through memory
  void ImportBatches(const ImportBatchesArgs& args);
  void DisposeBatch(const std::string& batch_name);

  // Operations to work with model
  void InitializeModel(const InitializeModelArgs& args);
  void ImportModel(const ImportModelArgs& args);
  void ExportModel(const ExportModelArgs& args);
  void FitOnlineModel(const FitOnlineMasterModelArgs& args);
  void FitOfflineModel(const FitOfflineMasterModelArgs& args);

  // Apply model to batches
  ThetaMatrix Transform(const TransformMasterModelArgs& args);
  ThetaMatrix Transform(const TransformMasterModelArgs& args, Matrix* matrix);

  // Retrieve operations
  TopicModel GetTopicModel(const GetTopicModelArgs& args);
  TopicModel GetTopicModel(const GetTopicModelArgs& args, Matrix* matrix);
  ThetaMatrix GetThetaMatrix(const GetThetaMatrixArgs& args);
  ThetaMatrix GetThetaMatrix(const GetThetaMatrixArgs& args, Matrix* matrix);

  // Retrieve scores
  ScoreData GetScore(const GetScoreValueArgs& args);
  template <typename T>
  T GetScoreAs(const GetScoreValueArgs& args);

Warning

What follows below in this page is really outdated.

In addition to this page consider to look at Plain C interface of BigARTM, Python Interface or Messages. These documentation files are also to certain degree relevant for C++ interface, because C++ interface is quite similar to Python interface and share the same Protobuf messages.

MasterComponent

class MasterComponent
MasterComponent(const MasterComponentConfig &config)

Creates a master component with configuration defined by MasterComponentConfig message.

void Reconfigure(const MasterComponentConfig &config)

Updates the configuration of the master component.

const MasterComponentConfig &config() const

Returns current configuration of the master component.

MasterComponentConfig *mutable_config()

Returns mutable configuration of the master component. Remember to call Reconfigure() to propagate your changes to master component.

void InvokeIteration(int iterations_count = 1)

Invokes certain number of iterations.

bool AddBatch(const Batch &batch, bool reset_scores)

Adds batch to the processing queue.

bool WaitIdle(int timeout = -1)

Waits for iterations to be completed. Returns true if BigARTM completed before the specific timeout, otherwise false.

std::shared_ptr<TopicModel> GetTopicModel(const std::string &model_name)

Retrieves Phi matrix of a specific topic model. The resulting message TopicModel will contain information about token weights distribution across topics.

std::shared_ptr<TopicModel> GetTopicModel(const GetTopicModelArgs &args)

Retrieves Phi matrix based on extended parameters, specified in GetTopicModelArgs message. The resulting message TopicModel will contain information about token weights distribution across topics.

std::shared_ptr<ThetaMatrix> GetThetaMatrix(const std::string &model_name)

Retrieves Theta matrix of a specific topic model. The resulting message ThetaMatrix will contain information about items distribution across topics. Remember to set MasterComponentConfig.cache_theta prior to the last iteration in order to gather Theta matrix.

std::shared_ptr<ThetaMatrix> GetThetaMatrix(const GetThetaMatrixArgs &args)

Retrieves Theta matrix based on extended parameters, specified in GetThetaMatrixArgs message. The resulting message ThetaMatrix will contain information about items distribution across topics.

std::shared_ptr<T> GetScoreAs<T>(const Model &model, const std::string &score_name)

Retrieves given score for a specific model. Template argument must match the specific ScoreData type of the score (for example, PerplexityScore).

Model

class Model
Model(const MasterComponent &master_component, const ModelConfig &config)

Creates a topic model defined by ModelConfig inside given MasterComponent.

void Reconfigure(const ModelConfig &config)

Updates the configuration of the model.

const std::string &name() const

Returns the name of the model.

const ModelConfig &config() const

Returns current configuration of the model.

ModelConfig *mutable_config()

Returns mutable configuration of the model. Remember to call Reconfigure() to propagate your changes to the model.

void Overwrite(const TopicModel &topic_model, bool commit = true)

Updates the model with new Phi matrix, defined by topic_model. This operation can be used to provide an explicit initial approximation of the topic model, or to adjust the model in between iterations.

Depending on the commit flag the change can be applied immediately (commit = true) or queued (commit = false). The default setting is to use commit = true. You may want to use commit = false if your model is too big to be updated in a single protobuf message. In this case you should split your model into parts, each part containing subset of all tokens, and then submit each part in separate Overwrite operation with commit = false. After that remember to call MasterComponent::WaitIdle() and Synchronize() to propagate your change.

void Initialize(const Dictionary &dictionary)

Initialize topic model based on the Dictionary. Each token from the dictionary will be included in the model with randomly generated weight.

void Export(const string &file_name)

Exports topic model into a file.

void Import(const string &file_name)

Imports topic model from a file.

void Synchronize(double decay_weight, double apply_weight, bool invoke_regularizers)

Synchronize the model.

This operation updates the Phi matrix of the topic model with all model increments, collected since the last call to Synchronize() method. The weights in the Phi matrix are set according to decay_weight and apply_weight values (refer to SynchronizeModelArgs.decay_weight for more details). Depending on invoke_regularizers parameter this operation may also invoke all regularizers.

Remember to call Model::Synchronize() operation every time after calling MasterComponent::WaitIdle().

void Synchronize(const SynchronizeModelArgs &args)

Synchronize the model based on extended arguments SynchronizeModelArgs.

Regularizer

class Regularizer
Regularizer(const MasterComponent &master_component, const RegularizerConfig &config)

Creates a regularizer defined by RegularizerConfig inside given MasterComponent.

void Reconfigure(const RegularizerConfig &config)

Updates the configuration of the regularizer.

const RegularizerConfig &config() const

Returns current configuration of the regularizer.

RegularizerConfig *mutable_config()

Returns mutable configuration of the regularizer. Remember to call Reconfigure() to propagate your changes to the regularizer.

Dictionary

class Dictionary
Dictionary(const MasterComponent &master_component, const DictionaryConfig &config)

Creates a dictionary defined by DictionaryConfig inside given MasterComponent.

void Reconfigure(const DictionaryConfig &config)

Updates the configuration of the dictionary.

const std::string name() const

Returns the name of the dictionary.

const DictionaryConfig &config() const

Returns current configuration of the dictionary.

Utility methods

void SaveBatch(const Batch &batch, const std::string &disk_path)

Saves Batch into a specific folder. The name of the resulting file will be autogenerated, and the extention set to .batch

std::shared_ptr<DictionaryConfig> LoadDictionary(const std::string &filename)

Loads the DictionaryConfig message from a specific file on disk. filename must represent full disk path to the dictionary file.

std::shared_ptr<Batch> LoadBatch(const std::string &filename)

Loads the Batch message from a specific file on disk. filename must represent full disk path to the batch file, including .batch extention.

std::shared_ptr<DictionaryConfig> ParseCollection(const CollectionParserConfig &config)

Parses a text collection as defined by CollectionParserConfig message. Returns an instance of DictionaryConfig which carry all unique words in the collection and their frequencies.

Windows distribution

This chapter describes content of BigARTM distribution package for Windows, available at https://github.com/bigartm/bigartm/releases.

bin/
Precompiled binaries of BigARTM for Windows.
This folder must be added to PATH system variable.
bin/artm.dll
Core functionality of the BigARTM library.
bin/cpp_client.exe
Command line utility allows to perform simple experiments
with BigARTM. Remember that not all BigARTM features are
available through cpp_client, but it can serve as a good
starting point to learn basic functionality. For further
details refer to /ref/cpp_client.
protobuf/
A minimalistic version of Google Protocol Buffers
(https://code.google.com/p/protobuf/)
library, required to run BigARTM from Python.
To setup this package follow the instructions in
protobuf/python/README file.
python/artm/
Python programming interface to BigARTM library.
This folder must be added to PYTHONPATH
system variable.
library.py
Implements all classes of BigARTM python interface.
messages_pb2.py
Contains all protobuf messages that can be transfered in
and out BigARTM core library. Most common features are
exposed with their own API methods, so normally you
do not use python protobuf messages to operate BigARTM.
python/examples/
Python examples of how to use BigARTM:
Files docword.kos.txt and vocab.kos.txt represent
a simple collection of text files in Bag-Of-Words format.
The files are taken from UCI Machine Learning Repository
(https://archive.ics.uci.edu/ml/datasets/Bag+of+Words).
src/
Several programming interfaces to BigARTM library.
src/c_interface.h
Low-level BigARTM interface in C.
cpp_interface.h,cc
C++ interface of BigARTM
messages.pb.h,cc
Protobuf messages for C++ interface
messages.proto
Protobuf description for all messages that appear in the
API of BigARTM. Documented here.
LICENSE License file of BigARTM.

Indices and tables