Topik Projects

Topik can be run by chaining the output of one step directly to the input of another:

>>> from topik import read_input, tokenize, vectorize, run_model, visualize
>>> raw_data = read_input("./reviews/")
>>> content_field = "text"
>>> raw_data = ((hash(item[content_field]), item[content_field]) for item in raw_data)
>>> tokens = tokenize(raw_data)
>>> vectors = vectorize(tokens)
>>> model_output = run_model(vectors, ntopics=4)
>>> plot = visualize(model_output)

However, this does not handle any kind of persistence to disk. Your changes will be lost when you exit your Python session. To unify saving all the output from all the steps together, Topik provides projects. Here’s a comparable project usage to the example above:

>>> from topik import TopikProject
>>> with TopikProject("my_project") as project:
>>>     project.read_input("./reviews/", content_field="text")
>>>     project.tokenize()
>>>     project.vectorize()
>>>     project.run_model(ntopics=4)
>>>     project.visualize()
>>> # when project goes out of scope, any files are flushed to disk, and the project can later be loaded.

Projects create files on disk when instantiated if none already exist. When project files already exist, the contents of those files are loaded:

>>> from topik import TopikProject
>>> with TopikProject("my_project") as project:
>>>     project.visualize()

Output formats

Output formats are how your data are represented to further processing and modeling. Projects use a particular output format to store your intermediate results. To ensure a uniform interface, output formats implement the interface described by OutputInterface. Presently, two such backends are implemented: InMemoryOutput and ElasticSearchOutput. Available outputs can be examined by checking the keys of the topik.fileio.registered_outputs dictionary:

>>> from topik.fileio import registered_outputs
>>> list(registered_outputs.keys())

The default output is the InMemoryOutput. No additional arguments are necessary. InMemoryOutput stores everything in a Python dictionary. As such, it is memory intensive. All operations done with a InMemoryOutput block until complete. InMemoryOutput is the simplest to use, but it will ultimately limit the size of analyses that you can perform.

The ElasticSearchOutput can be specified to TopikProject using the output_type argument. It must be accompanied by another keyword argument, output_args, which should be a dictionary containing connection details and any additional arguments.

>>> output_args = {"source": "localhost", "index": "destination_index", content_field="text"}
>>> project = TopikProject("my_project", output_type="ElasticSearchOutput",

ElasticSearchOutput stores everything in an Elasticsearch instance that you specify. Operations do not block, and have “eventual consistency”: the corpus will eventually have all of the documents you sent available, but not necessarily immediately after the read_input function returns. This lag time is due to Elasticsearch indexing the data on the server side.

Saving and loading projects

Projects are designed to help you go back to some earlier state. There are several dictionary-like objects accessible on the project object:

>>> project = TopikProject("my_project")
>>> project.output.tokenized_corpora
>>> project.output.vectorized_corpora
>>> project.output.modeled_corpora

These are more quickly accessible as selected properties of the project:

>>> project.selected_filtered_corpus
>>> project.selected_tokenized_corpus
>>> project.selected_vectorized_corpus
>>> project.selected_modeled_corpus

These selected properties keep track of the last-used technique, and give you the corresponding data.

You can change the selected state using the select_tokenized_corpus(), select_modeled_corpus(), and select_modeled_corpus() methods.

Project objects also persist their state to disk. This is done in two or more files, dependent on the output backend in use. There will always be two files:

  • a .topikproject file, describing the project metadata and how to load the project
  • a .topikdata file, containing or describing how to obtain the data contained in the project.

Each of the above files are JSON format. Additional files may store data in binary format. If you move your outputs on disk, make sure to move all of them, or Topik will not be able to load your results.

If using the project with a context manager, data is saved and connections are closed when the context ends. Otherwise, call the save() to write data to disk, or the close() method to write data to disk and close connections.

Loading projects is achieved by providing simply the project name that you provided when creating the project. Additional connection details will be loaded from disk automatically.

>>> project = TopikProject("my_project")