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autoparser

Test Status Documentation Status codecov Code style: black

autoparser helps in the generation of ADTL parsers as TOML files, which can then be processed by adtl to transform files from the source schema to a specified schema.

Documentation: ReadTheDocs

Contains functionality to:

  1. Create a basic data dictionary from a raw data file (create-dict)
  2. Use an LLM (currently via either OpenAI or Google's Gemini) to add descriptions to the data dictionary, to enable better parser auto-generation (add-descriptions)
  3. Create a mapping csv file linking source to target data fields and value mappings using the LLM, which can be edited by a user (create-mapping)
  4. Create a TOML parser file ready for use with ADTL, based on a JSON schema (rules-based from the mapping file; create-parser).

All 4 functions have both a command-line interface, and a python function associated.

Parser construction process (CLI)

  1. Data: Get the data as CSV or Excel and the data dictionary if available.

  2. Creating autoparser config: Optional step if the data is not in REDCap (English) format. The autoparser config (example) specifies most of the variable configuration settings for autoparser.

  3. Preparing the data dictionary: If the data dictionary is not in CSV, or split across multiple Excel sheets, then it needs to be combined to a single CSV. If a data dictionary does not already exist, one can be created using

     autoparser create-dict <path to data> -o <parser-name>

    Here, -o sets the output name, and will create <parser-name>.csv. For optional arguments (such as using a custom configuration which was created in step 2), see autoparser create-dict --help.

  4. Generate intermediate mappings (CSV): Run with config and data dictionary to generate mappings:

    autoparser create-mapping <path to data dictionary> <path to schema> <language> <api key> -o <parser-name>

    Here language refers to the language of the original data, e.g. "fr" for french language data. autoparser defaults to using OpenAI as the LLM API, so the api key provided should be for the OpenAi platform. In future, alternative API's and/or a self-hosted llm are planned to be provided as options.

  5. Curate mappings: The intermediate mappings must be manually curated, as the LLM may have generated false matches, or missed certain fields or value mappings.

  6. Generate TOML: This step is automated and should produce a TOML file that conforms to the parser schema.

    For example:

    autoparser create-toml parser.csv <path to schema> -n parser

    will create parser.toml (specified using the -n flag) from the intermediate mappings parser.csv file.

  7. Review TOML: The TOML file may contain errors, so it is recommended to check it and alter as necessary.

  8. Run adtl: Run adtl on the TOML file and the data source. This process will report validation errors, which can be fixed by reviewing the TOML file and looking at the source data that is invalid.

Parser construction process (Python)

An example notebook has been provided using the test data to demonstrate the process of constructing a parser using the Python functions of autoparser.

Troubleshooting autogenerated parsers

  1. "I get validation errors like "'x' must be date": ADTL expects dates to be provided in ISO format (i.e. YYY-MM-DD). If your dates are formatted differently, e.g. "dd/mm/yyyy", you can add a line in the header of the TOML file (e.g. underneath the line "returnUnmatched=True") like this:
defaultDateFormat = "%d/%m/%Y"

which should automatically convert the dates for you.

  1. ADTL can't find my schema (error: No such file or directory ..../x.schema.json) AutoParser puts the path to the schema at the top of the TOML file, relative to the current location of the parser (i.e, where you ran the autoparser command from). If you have since moved the parser file, you will need to update the schema path at the top of the TOML parser.