freediscovery 1.1.2

Open source software for E-Discovery and Information Retrieval

Open Source e-Discovery and Information Retrieval Engine

FreeDiscovery is built on top of existing machine learning libraries (scikit-learn) and provides a REST API for information retrieval applications. It aims to benefit existing e-Discovery and information retrieval platforms with a focus on text categorization, semantic search, document clustering, duplicates detection and e-mail threading.

In addition, FreeDiscovery can be used as Python package and exposes several estimators with a scikit-learn compatible API.

Installation

FreeDiscovery requires Python 3.5+ and can be installed with conda: conda install -c conda-forge freediscovery

Alternatively, to install with pip,

1. Install scipy and numpy

2. Run pip install freediscovery[all]

Running the server

• freediscovery run

• to check that the server started successfully, curl -X GET http://localhost:5001/

Quick start

1. Install FreeDiscovery and start the server (see above)

2. Download the 20_newsgroup dataset: freediscovery download 20_newsgroups

1. Data ingestion

1. Create a new vectorized dataset with curl -X POST http://localhost:5001/api/v0/feature-extraction and save the returned hexadecimal id for later use with export FD_DATASET_ID=<returned-id>.

2. Ingest the dataset,

   curl -X POST -H 'Content-Type: application/json' -d '{
      "data_dir": "./20_newsgroups/"
   }'  http://localhost:5001/api/v0/feature-extraction/${FD_DATASET_ID}

3. Get the mapping between file_path of individial files and their document_id:

   curl -X POST http://localhost:5001/api/v0/feature-extraction/${FD_DATASET_ID}/id-mapping > ./fd_id_mapping.txt

   and save the results.

2. Latent Semantic Indexing (LSI)

The creation of an LSI index is necessary for clustering, nearest neighbor classification, semantic search and near-duplicates detection,

curl -X POST -H 'Content-Type: application/json' -d "{
   \"parent_id\": \"${FD_DATASET_ID}\"
}"  http://localhost:5001/api/v0/lsi/

Save the returned id for later use with export FD_LSI_ID=<returned-id>.

3. Semantic search

Search in the semantic space can be performed with,

curl -X POST -H 'Content-Type: application/json' -d "{
   \"parent_id\": \"${FD_LSI_ID}\",
   \"query\": \"Jupyter moon\", \"max_results\": 10
 }"  http://localhost:5001/api/v0/search/

4. Categorization

Create a categorization model,

curl -X POST -H 'Content-Type: application/json' -d "{
   \"parent_id\": \"${FD_DATASET_ID}\",
   \"method\": \"LogisticRegression\",
   \"data\": [{\"document_id\": 14000, \"category\": \"sci.space\"},
              {\"document_id\": 14003, \"category\": \"sci.space\"},
              {\"document_id\": 18780, \"category\": \"talk.politics.misc\"},
              {\"document_id\": 18784, \"category\": \"talk.politics.misc\"}
              ],
   \"training_scores\": true
 }"  http://localhost:5001/api/v0/categorization/

Save the returned id for later use with export FD_CAT_ID=<returned-id>.

Predictions for the other documents in the dataset can then be retrieved with,

curl -X GET -H 'Content-Type: application/json' -d "{
   \"max_results\": 10, \"max_result_categories\": 2, \"sort_by\": \"sci.space\"
 }"  http://localhost:5001/api/v0/categorization/${FD_CAT_ID}/predict

The correspondence of these results with ground truth categories can be checked in fd_id_mapping.txt.

5. Hierarchical clustering

Create a Birch hierarchical clustering model,

curl -X POST -H 'Content-Type: application/json' -d "{
   \"parent_id\": \"${FD_LSI_ID}\",
   \"min_similarity\": 0.7, \"max_tree_depth\": 2
 }"  http://localhost:5001/api/v0/clustering/birch/

Save the returned id for later use with export FD_BIRCH_ID=<returned-id>.

Finally retrieve the computed hierarchical clusters,

curl -X GET http://localhost:5001/api/v0/clustering/birch/${FD_BIRCH_ID}

See http://freediscovery.io/doc/stable/examples/ for more complete examples.

We would very much appreciate feedback on the existing functionality. Feel free to open new issues on Github or send any comments to the mailing list https://groups.google.com/forum/#!forum/freediscovery-ml.

Documentation

For more information see the documentation and API Reference,

• development version [master branch | documentation http://freediscovery.io/doc/dev/ ].

• stable version 1.1.2 [1.1.X branch | documentation http://freediscovery.io/doc/stable/ ].

Licence

FreeDiscovery is released under the 3-clause BSD licence.