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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.

Getting help

Having trouble? We’d like to help!

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.

Distributed. BigARTM is able to distribute all CPU-intensive processing onto several machines, interconnected by network. We aim to scale up to hundreds of machines, but the real scalability have not been fully tested yet.

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

Tutorials

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 BigARTM command line utility 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.

    • Add C:\BigARTM\bin folder to your PATH system variable,
    • 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.

Running BigARTM from Python 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.

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 https://github.com/bigartm/bigartm.git
cd bigartm
mkdir build && cd build
cmake ..
make

Running BigARTM from command line

There is a simple utility cpp_client, 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 && gunzip docword.kos.txt.gz
wget https://s3-eu-west-1.amazonaws.com/artm/vocab.kos.txt
~/bigartm/build/src/cpp_client/cpp_client -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/src/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/src/python/examples.

Troubleshooting

ubuntu@192.168.0.1:~/bigartm/src/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/src/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/src/python/artm/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/src/python:$PYTHONPATH".

ubuntu@192.168.0.1:~/bigartm/src/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/src/python/artm/library.py", line 179, in __init__
    lib = Library().lib_
  File "/home/ubuntu/bigartm/src/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.

Typical python example

This section describe all steps, included in a typical BigARTM script (for example example02_parse_collection.py).

Parse collection

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))

Networking

Other tutorials focus on how to use BigARTM locally on your machine, within single process. Now we are going to deploy BigARTM on several machines.

Network modus operandi

First think you do to distribute CPU-intensive processing onto several machines is to launch there a python_interface.NodeController component.

One simple way to do so is to use node_controller application, included in BigARTM distributive.

>node_controller.exe

Usage:
    ./node_controller <endpoint>

Example:
    ./node_controller tcp://*:5555

To connect to node_controller from master replace '*' with fully qualified DNS name of the host.

After NodeControler is launched, you may add its endpoint to MasterComponentConfig.node_connect_endpoint list and then use your python_interface.MasterComponent as usual. No further actions is required on the remote nodes.

Warning

Some requirements and limitations apply in network mode.

Proxy to MasterComponent

BigARTM also supports a special proxy mode, which allows you to perform experiments on a remote cluster. As before, you start by running node_controller executable on your target machines. Then you deploy MasterComponent in one of you target machines.

library = ArtmLibrary('artm.dll')

master_proxy_config = messages_pb2.MasterProxyConfig()
master_proxy_config.node_connect_endpoint = 'tcp://192.168.0.2:5555'

with library.CreateMasterComponent(master_proxy_config) as master_proxy:
  # Use master_proxy in the same way you usually use master component

Or, if you launched several nodes, you can utilize all of them by configuring your remote MasterComponent to work in Network modus operandi.

library = ArtmLibrary('artm.dll')

master_proxy_config = messages_pb2.MasterProxyConfig()
master_proxy_config.node_connect_endpoint = 'tcp://192.168.0.2:5555'
master_proxy_config.config.modus_operandi = MasterComponentConfig_ModusOperandi_Network
master_proxy_config.disk_path = '/fileshare'
master_proxy_config.node_connect_addpoint.append('tcp://192.168.0.3:5555');
master_proxy_config.node_connect_addpoint.append('tcp://192.168.0.4:5555');

with library.CreateMasterComponent(master_proxy_config) as master_proxy:
  # Use master_proxy in the same way you usually use master component

Combining network modus operandi with proxy

This python script assumes that you have started local node_controller process as follows:

set GLOG_logtostderr=1 & node_controller.exe tcp://*:5000 tcp://*:5556 tcp://*:5557

This python script will use ports as follows:

  • 5000 - port of the MasterComponent to communicate between MasterComponent and Proxy (this endpoint must be created by node_controller)
  • 5550 - port of the MasterComponent to communicate between MasterComponent and Nodes (this endpoint will be automatically created by the master component)
  • 5556, 5557 - ports of the NodeControllerComponent to communicate between MasterComponent and Nodes (this endpoint must be created by node_controller)
import artm.messages_pb2, artm.library, sys

# Network path of a shared folder with batches to process.
# The folder must be reachable from all remote node controllers.
target_folder = 'D:\\datasets\\nips'

# Dictionary file (must be located on developer's box that runs python script)
dictionary_file = 'D:\\datasets\\nips\\dictionary'

unique_tokens = artm.library.Library().LoadDictionary(dictionary_file)

# Create master component and infer topic model
proxy_config = artm.messages_pb2.MasterProxyConfig()
proxy_config.node_connect_endpoint = 'tcp://localhost:5000'
proxy_config.communication_timeout = 10000  # timeout (in ms) for communication between proxy and master component
proxy_config.polling_frequency  = 50  # polling frequency (in ms) for long-lasting operations, for example WaitIdle()
proxy_config.config.modus_operandi = artm.library.MasterComponentConfig_ModusOperandi_Network
proxy_config.config.communication_timeout = 2000  # timeout (in ms) for communication between master component and nodes
proxy_config.config.disk_path = target_folder
proxy_config.config.create_endpoint = 'tcp://*:5550'
proxy_config.config.connect_endpoint = 'tcp://localhost:5550'
proxy_config.config.node_connect_endpoint.append('tcp://localhost:5556')
proxy_config.config.node_connect_endpoint.append('tcp://localhost:5557')
proxy_config.config.processors_count = 1  # number of processors to create at every node

with artm.library.MasterComponent(config = proxy_config) as master:
  dictionary = master.CreateDictionary(unique_tokens)
  perplexity_score = master.CreatePerplexityScore()
  model = master.CreateModel(topics_count = 10, inner_iterations_count = 10)
  model.EnableScore(perplexity_score)
  model.Initialize(dictionary)

  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

Whitepapers

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 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. Note that cach_theta flag does not work together with network modus operandi. Current workaround for the network modus operandi is to use the option no. 3 (described below).

  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

To contribute a fix you should fork the repository, code your fix and submit a pull request. All pull requests are regularly monitored by BigARTM maintainers and will be soon merged into BigARTM’s master branch. 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 here: $(BIGARTM_ROOT)/src/python_tests/python_tests.py.

  • [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

Simply run CMake on from the root of the project.

The following script had been tested in Ubuntu.

sudo apt-get install git make cmake build-essential libboost-all-dev -q -y
git clone https://github.com/bigartm/bigartm
cd ~/bigartm
mkdir build
cd build
cmake ..
make -j8

~/bigartm/build/src/artm_tests/artm_tests

It is also possible to use BigARTM from Python on Linux. Just make sure to setup protobuf library as described $(BIGARTM_ROOT)/3rdparty/protobuf/python/README, and then you can simply run python scripts under $(BIGARTM_ROOT)/python_tests/ or $(BIGARTM_ROOT)/python_client/.

Compiling .proto files on Windows

  1. Open a new command prompt

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

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

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

  3. Rename protoc-gen-cpp_rpcz.exe to protoc-gen-rpcz_plugin.exe.

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

  5. Run the following commands

    .\protoc.exe --cpp_out=. --python_out=. .\artm\messages.proto
.\protoc.exe --cpp_out=. --rpcz_plugin_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 100 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 100 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=100 <filename>

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

$(BIGARTM_ROOT/utils/cpplint_all.bat.

Intel Math Kernel Library

BigARTM can utilize Intel Math Kernel Library to achieve better performance. This only applies when ModelConfig.use_sparse_bow is false (in sparse version BigARTM has better built-in algorithm that does not use Intel MKL).

To enable MKL usage on Windows add the path to MKL library to your PATH system variable

set PATH=%PATH%;"C:\Program Files (x86)\Intel\Composer XE 2013 SP1\redist\intel64\mkl"

To enable MKL usage on Linux create a new system variable MKL_PATH and set it as follows

export MKL_PATH="/opt/intel/mkl/lib/intel64/"

BigARTM Reference

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 {
  enum ModusOperandi {
    Local = 0;
    Network = 1;
  }

  optional ModusOperandi modus_operandi = 1 [default = Local];
  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 string create_endpoint = 10;
  optional string connect_endpoint = 11;
  repeated string node_connect_endpoint = 12;
  optional bool online_batch_processing = 13 [default = false];  // obsolete in BigARTM v0.5.8
  optional int32 communication_timeout = 14 [default = 1000];
  optional string disk_cache_path = 15;
}
MasterComponentConfig.modus_operandi

A value that defines the modus operandi of the master component.

Local
Defines a master component that operates in the local mode.
In this mode master component is self-contained, and
does not require any external nodes to tune topic model.
Network
Defines a master component that operates in the network mode.
In this mode master component delegates all heavy processing
to externals nodes. The master component is then responsible
only for merging the results from external nodes into a single
topic model.
MasterComponentConfig.disk_path

A value that defines the disk location to store or load the collection. In network modus operandi this field is required, and it must point to a network file share, accessible by all nodes connected to the master component.

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(). This feature is currently not supported in network modus operandi.

MasterComponentConfig.processors_count

A value that defines the number of concurrent processor components. In network modus operandi this value defines the processors count at every remote node controller, connected to the master component. 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. In network modus operandi this value defines the maximal queue size at every remote node controller, connected to the master component. Recommendations regarding the maximal queue size are as follows:

  • the queue size should be at least as large as the number of concurrent processors;
  • the total size of the queues across all node controllers should not exceed the number of batches in the collection.
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.create_endpoint

A value that defines ZeroMQ endpoint to expose the master component service (example: tcp://*:5555). For further details about the format of endpoint string refer to ZeroMQ documentation (http://api.zeromq.org). The value is used only in the network modus operandi.

MasterComponentConfig.connect_endpoint

A value that defines ZeroMQ endpoint to expose the master component service (example: tcp://192.168.0.1:5555). // For further details about the format of endpoint string refer to ZeroMQ documentation (http://api.zeromq.org). The value is used only in the network modus operandi.

MasterComponentConfig.node_connect_endpoint

A set containing all ZeroMQ endpoints of the external node controllers (example: tcp://192.168.0.2:5556). For further details about the format of endpoint string refer to ZeroMQ documentation (http://api.zeromq.org). The value is used only in the network modus operandi. A node controller component at the remote machine must be created prior to configuring master component with its endpoint.

MasterComponentConfig.online_batch_processing

Obsolete in BigARTM v0.5.8.

MasterComponentConfig.communication_timeout

An communication timeout in milliseconds. Any remote network call that exceeds communication timeout will result in ARTM_NETWORK_ERROR error.

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.

NodeControllerConfig

class messages_pb2.NodeControllerConfig

Represents a configuration of a NodeController

message NodeControllerConfig {
  optional string create_endpoint = 1;
}
NodeControllerConfig.create_endpoint

A value that defines ZeroMQ endpoint to expose the node component service (example: tcp://*:5556). For further details about the format of endpoint string refer to ZeroMQ documentation (http://api.zeromq.org).

MasterProxyConfig

class messages_pb2.MasterProxyConfig

Represents a configuration of a proxy to MasterComponent. The purpose of the proxy is to operate MasterComponent from a remote machine. There is no requirement to run MasterComponent and its proxy in the same operating system (MasterComponent can run on linux while the proxy can be on Windows). Any type of MasterComponent (either in local or in network modus operandi) can be operated by a proxy.

message MasterProxyConfig {
  optional string node_connect_endpoint = 1;
  optional MasterComponentConfig config = 2;
  optional int32 communication_timeout = 3 [default = 1000];
  optional int32 polling_frequency = 4 [default = 50];
}
MasterProxyConfig.node_connect_endpoint

A value that defines ZeroMQ endpoint of an external node controller. (example: tcp://192.168.0.2:5556). For further details about the format of endpoint string refer to ZeroMQ documentation (http://api.zeromq.org). A node controller component at the remote machine must be created prior to configuring master component with its endpoint.

MasterProxyConfig.config

A message that defines MasterComponent configuration of a remote node.

MasterProxyConfig.communication_timeout

An communication timeout in milliseconds. Any remote network call that exceeds communication timeout will result in ARTM_NETWORK_ERROR error.

MasterProxyConfig.polling_frequency

Defines the frequency that the proxy object uses to repeatedly pool remote master component for a status.

When ArtmWaitIdle() on the remote master component reports ARTM_STILL_WORKING, the proxy object will retry the request within specified pooling frequency.

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];
}
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.

RegularizerConfig

class messages_pb2.RegularizerConfig

Represents a configuration of a general regularizer.

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

  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
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.

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 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.

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.

CreateNodeController(endpoint)

Creates a node controller at a specific endpoint.

Returns an instance of NodeController class.

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, proxy_endpoint = 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.

proxy_endpoint is an optional string value that provides connect endpoint of a remote node controller. When specified, the master component will operate in a proxy mode (that is, it will redirect all commands to the remote master component instantiated in the remote node controller).

Dispose()

Disposes the master component and releases all unmanaged resources (like memory, network connections, etc).

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.

This operation is not supported when MasterComponentConfig.modus_operandi is set to Network.

NodeController

class artm.library.NodeController(endpoint, lib = None)

Creates a node controller on a specific endpoint. See NodeControllerConfig for more details.

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.

Dispose()

Disposes the node controller and releases all unmanaged resources (like memory, network connections, etc).

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).

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.

exception artm.library.NetworkException

An exception class corresponding to ARTM_NETWORK_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
artm.library.MasterComponentConfig_ModusOperandi_Local
artm.library.MasterComponentConfig_ModusOperandi_Network

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.

ArtmCreateMasterProxy

int ArtmCreateMasterProxy(int length, const char* master_proxy_config)

Creates a proxy-object, connected to a remote master component.

Parameters:
  • master_proxy_config (const_char*) – Serialized MasterProxyConfig message, describing the configuration of the master proxy.
  • length (int) – The length in bytes of the master_proxy_config message.
Returns:

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

Prior to calling this method you must ensure that the remote host is running an instance of Node Controller component. The endpoint of the remote Node Controller should be specified in the master_proxy_config message.

The effect of this method is as follows:

  • a new Master Component is started on the remote Node Controller,
  • a proxy object to the remote Master Component is created withing current process.

The ID, returned by this operation, can be used in all methods that require master_id parameter. All API calls will be redirected to the remote Master Component.

ArtmReconfigureMasterComponent

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

Changes the configuration of the master component.

Parameters:
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:
  • master_id (int) – The ID of a master component or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() method. If master_id represents a master proxy, then this operation will dispose both remote master component and local proxy object.
Returns:

This operation always returns ARTM_SUCCESS.

This operation releases memory and other unmanaged resources, used by the master component. If the master component had been launched in the network modus operandi, then this operation also releases resources on the remote nodes and closes all network connections. If the master_id parameter represents a proxy-object to a remote master component, then both the remote master component and the local proxy are disposed.

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

ArtmCreateNodeController

int ArtmCreateNodeController(int length, const char* node_controller_config)

Creates a node controller component.

Parameters:
  • node_controller_config (const_char*) – Serialized NodeControllerConfig message, describing the configuration of the node controller.
  • length (int) – The length in bytes of the node_controller_config message.
Returns:

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

ArtmDisposeNodeController

int ArtmDisposeNodeController(int node_controller_id)

Disposes node controller and releases all unmanaged resources.

Parameters:
Returns:

This operation always returns ARTM_SUCCESS.

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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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:
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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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:
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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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:
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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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.

This operation is only allowed when MasterComponentConfig.online_batch_processing is set to True and MasterComponentConfig.modus_operandi is set to Local.

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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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:
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:
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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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 or a master proxy, returned by either ArtmCreateMasterComponent() or ArtmCreateMasterProxy() 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:
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:
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:
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:
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
#define ARTM_NETWORK_ERROR -9
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 neither MasterComponent nor MasterProxy exist with given ID.

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.

ARTM_NETWORK_ERROR

Network call did not reply withing the specified timeout.

C++ interface

This document explains C++ interface of BigARTM library.

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.

MasterComponent(const MasterProxyConfig& config)

Creates a proxy to remote master component with configuration defined by MasterProxyConfig 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).

NodeController

class NodeController
NodeController(const NodeControllerConfig& config)

Creates a node controller with configuration defined by NodeControllerConfig message.

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 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.

BigARTM command line utility

This document provides an overview of cpp_client, a simple command-line utility shipped with BigARTM.

To run cpp_client you need to download 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 cpp_client as follows:

cpp_client -d docword.kos.txt -v vocab.kos.txt

You may append the following options to customize the resulting topic model:

  • -t or --num_topic sets the number of topics in the resulting topic model.
  • -i or --num_iters sets the number of iterative scans over the collection.
  • --num_inner_iters sets the number of updates of theta matrix performed on each iteration.
  • --reuse_theta enables caching of Theta matrix and re-uses last Theta matrix from the previous iteration as initial approximation on the next iteration. The default alternative without --reuse_theta switch is to generate random approximation of Theta matrix on each iteration.
  • --tau_phi, --tau_theta and --tau_decor allows you to specify weights of different regularizers. Currently cpp_client does not allow you to customize regularizer weights for different topics and for different iterations. This limitation is only related to cpp_client, and you can simply achieve this by using BigARTM interface (either in Python or in C++).
  • --update_every is a parameter of the online algorithm. When specified, the model will be updated every update_every documents.

You may also apply the following optimizations that should not change the resulting model

  • -p allows you to specify number of concurrent processors. The recommended value is to use the number of logical cores on your machine.
  • --no_scores disables calculation and visualization of all scores. This is a clean way of measuring pure performance of BigARTM, because at the moment some scores takes unnecessary long time to calculate.
  • --disk_cache_folder applies only together with --reuse_theta. This parameter allows you to specify a writable disk location where BigARTM can cache Theta matrix between iterations to avoid storing it in main memory.
  • --merger_queue_size limits the size of the merger queue. Decrease the size of the queue might reduce memory usage, but decrease CPU utilization of the processors.
>cpp_client --help
BigARTM - library for advanced topic modeling (http://bigartm.org):

Basic options:
  -h [ --help ]                        display this help message
  -d [ --docword ] arg                 docword file in UCI format
  -v [ --vocab ] arg                   vocab file in UCI format
  -b [ --batch_folder ] arg            If docword or vocab arguments are not
                                       provided, cpp_client will try to read
                                       pre-parsed batches from batch_folder
                                       location. Otherwise, if both docword and
                                       vocab arguments are provided, cpp_client
                                       will parse data and store batches in
                                       batch_folder location.
  -t [ --num_topic ] arg (=16)         number of topics
  -p [ --num_processors ] arg (=2)     number of concurrent processors
  -i [ --num_iters ] arg (=10)         number of outer iterations
  --num_inner_iters arg (=10)          number of inner iterations
  --reuse_theta                        reuse theta between iterations
  --dictionary_file arg (=dictionary)  filename of dictionary file
  --items_per_batch arg (=500)         number of items per batch
  --tau_phi arg (=0)                   regularization coefficient for PHI
                                       matrix
  --tau_theta arg (=0)                 regularization coefficient for THETA
                                       matrix
  --tau_decor arg (=0)                 regularization coefficient for topics
                                       decorrelation (use with care, since this
                                       value heavily depends on the size of the
                                       dataset)
  --paused                             wait for keystroke (allows to attach a
                                       debugger)
  --no_scores                          disable calculation of all scores
  --update_every arg (=0)              [online algorithm] requests an update of
                                       the model after update_every document
  --parsing_format arg (=0)            parsing format (0 - UCI, 1 - matrix
                                       market)
  --disk_cache_folder arg              disk cache folder
  --merger_queue_size arg              size of the merger queue

Networking options:
  --nodes arg                          endpoints of the remote nodes (enables
                                       network modus operandi)
  --localhost arg (=localhost)         DNS name or the IP address of the
                                       localhost
  --port arg (=5550)                   port to use for master node
  --proxy arg                          proxy endpoint
  --timeout arg (=1000)                network communication timeout in
                                       milliseconds

Examples:
        cpp_client -d docword.kos.txt -v vocab.kos.txt
        set GLOG_logtostderr=1 & cpp_client -d docword.kos.txt -v vocab.kos.txt

For further details please refer to the source code of cpp_client.

Windows distribution

This chapter describes content of BigARTM distribution package for Windows, available at https://github.com/bigartm/bigartm/releases.

Publications

  1. 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.
  2. 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.
  3. Vorontsov K. V., Potapenko A. A. Additive Regularization of Topic Models // Machine Learning Journal, Special Issue “Data Analysis and Intelligent Optimization”, Springer, 2014. (to appear). PDF in English, PDF in Russian.

Legacy documentation pages

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

Tutorial

Please refer to Basic BigARTM tutorial for Windows users or Basic BigARTM tutorial for Linux and Mac OS-X users depending on your operating system.

Indices and tables