FreeDiscovery – Release History

Release history

Version 1.3.1

May 22, 2018

Enhancements

  • Added compatibility with scikit-learn 0.19.1
  • Possibility to specify custom model ids (#177)
  • Optionally prune identical cluster at different depth in BIRCH
Version 1.3.0

Oct 1, 2017

New features

  • Additional TF-IDF weighting schemes and pivoted normalization (#164)
  • Exposed the wrapper functions to visualize Birch hierarchical trees in the Python package (#175)
  • Better separation between the FD engine (REST API) and the FD Python package.
  • Support for both Python 2.7 and 3.5+ for the Python package. The FD engine remains Python 3.5+ only.

Enhancements

  • Improved documentation and examples
  • Added compatibility with scikit-learn 0.19.0 (#169) which fixed several issues found in 0.18.1.

API Changes

  • In POST/api/v0/feature-extraction parameters binary, use_idf and sublinear_tf are replaced by a single parameters weighting that defines term document weighting and normalization using the SMART notation (#164)
Version 1.2.0

Oct 1, 2017

New features

  • Additional TF-IDF weighting schemes and pivoted normalization (#164)
  • Exposed the wrapper functions to visualize Birch hierarchical trees in the Python package (#175)
  • Better separation between the FD engine (REST API) and the FD Python package.
  • Support for both Python 2.7 and 3.5+ for the Python package. The FD engine remains Python 3.5+ only.

Enhancements

  • Improved documentation and examples
  • Added compatibility with scikit-learn 0.19.0 (#169) which fixed several issues found in 0.18.1.

API Changes

  • In POST/api/v0/feature-extraction parameters binary, use_idf and sublinear_tf are replaced by a single parameters weighting that defines term document weighting and normalization using the SMART notation (#164)
Version 1.1.2

Jun 2, 2017

New features

  • Possibility to ingest documents via HTTP and to chunk document ingestion (#143)
  • Truncating the hierarchical BIRCH clustering tree
  • Dowloading the document collections from CLI (#149)

Enhancements / Bug fixes

  • Add subset_document_id parameter for categrization and semantic search (#145)
  • Larger number of supported dependencies (#148)
  • Documentation improvement (#151)
  • Capacity to infer the document_id from the file name (#150)

API Changes

  • Designed an unified interface for data ingestion (#143)
  • Added URL endpoint with server info (#147)
Version 1.0

May 2, 2017

New features

  • Ability to add / remove documents in an existing processed dataset using /api/v0/feature-extraction/{dsid}/append and /api/v0/feature-extraction/{dsid}/delete URL endpoints
  • Pagination in search and document categorization with the batch_id and batch_size parameters.

Enhancements

  • Better handling of data persistence, which leads to faster response time for all URL endpoints, and in particular semantic search and categorization. This breaks backward compatibility for the internal data format: datasets need to re-processed and models re-trained.
  • Additional tests for categorization and semantic search

API Changes

  • The nn_metric parameter was renamed to metric; a new metric cosine-positive was added
  • Breaking change: by default, the cosine similarity score is used.
  • The /email-parser/* endpoints are removed and merged into the /feature-extraction/ endpoint, thus unifying data ingestion.
Version 0.9

Jan 28, 2017

New features

  • Support for multi-class categorization and non integer class labels (PR #96)
  • In the case of binary categorization, recall at 20 % of documents is computed as part of the list of default scores (PR #106)

Enhancements

  • Categorization and semantic search support sorting and filtering of documents below a user provided threashold. (PR #96)
  • Categorization returns only max_result_categories categories with the highest score.
  • The similarity and ML scores can now be scaled to range using nn_metric and ml_output input parameters (PR #101).
  • The REST API documentation is generated automatically from the code (using an OpenAPI specification) which allows to enforce consistency between the code and the docs (PR #85)
  • Adapted clustering and duplicate detection API to return structured objects indexed by document_id( and optionally rendering_id)
  • Improved tests coverage and overall simplified the API

API Changes

  • The following endpoints accepting a request body are modified from GET to POST method (PR #94), in accordance with the HTTP/1.1 spec, section 4.3,
    • /api/v0/metrics/categorization
    • /api/v0/metrics/clustering
    • /api/v0/metrics/duplicate-detection
    • /api/v0/feature-extraction/{dsid}/id-mapping/flat
    • /api/v0/feature-extraction/{dsid}/id-mapping/nested
    • /api/v0/email-parser/{dsid}/index
  • Significant changes in the categorization REST API to accommodate for multi-class cases
  • The endpoint /api/v0/feature-extraction/{dsid}/id-mapping/flat is removed, while /api/v0/feature-extraction/{dsid}/id-mapping/nested is renamed to /api/v0/feature-extraction/{dsid}/id-mapping.
  • Removed the /categorization/<mid>/test which is superseded by /metrics/categorization.
  • The internal_id is no longer exposed in the public API
Version 0.8

Feb. 25, 2017

New features

  • NearestNeighbor search now can perform categorization both with positive/negative training documents (supervised) as well as with only a list of positive documents (unsupervised) (PR #50)
  • Document search and semantic search (PR #63)

Enhancements

  • In depth code reorganisation making each processing step a separate REST endpoint (PR #57)
  • More rubust default parameters (PR #65)

API Changes

  • Ability to associate external document_id, rendition_id fields when ingesting documents; document categorization can now be used with these external ids.
  • All the wrappers classes are made private in the Python API
  • The same categorization and clustering enpoints can operate ether in the document-term space or in the LSI space (PR #57)