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--- |
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language: |
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- en |
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license: |
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- unknown |
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--- |
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# JSON Schema Dataset |
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This dataset consists of a collection of JSON Schema documents collected from GitHub by searching using the Sourcegraph API. |
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# Step 1: Find a list of JSON Schema paths |
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The [Sourcegraph](https://sourcegraph.com/) code search API is used to find files with a .json extension and containing `{\n "$schema": "https://json-schema.org/"`. |
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This is somewhat restrictive, but still manages to find a large number of schemas. |
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pipenv run python slurp.py --outfile repos.csv |
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# Step 2: Fetch the history information for each file |
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We fetch every revision of each JSON Schema file. |
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Before downloading the files, we use the GitHub API to get the list of commit hashes. |
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The resulting data is saved to `commits.json`. |
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pipenv run python fetch_history.py |
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# Step 3: Download the JSON Schema files |
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This script will download each schema which comes from GitHub and save it into subfolders in the `data` directory. |
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./fetch_files.sh |
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# Step 4: Validate each JSON Schema |
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The following script will read each schema in the `data` directory and confirm that it is a valid JSON Schema. |
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A copy of all valid schemas will be placed in the `valid_data` directory. |
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Note that schemas are parsed as [JSON5](https://json5.org/) to be more permissive on what syntax is allowed but the final schemas are written as standard JSON. |
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pipenv run python validate_schemas.py |
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# Step 5: Split into train, test, and validation |
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Finally data is split into training, test, and validation sets. |
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Schemas are always grouped together in the same set based on the GitHub organization they are from. |
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Schemas can also be checked for similarity so that very similar schemas are grouped together. |
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pipenv run python train_split.py |
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