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        "author": "Sasho Savkov",
        "author_email": "me@sasho.io",
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        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": "# bioeval\n\n[![CircleCI](https://circleci.com/gh/savkov/bioeval/tree/master.svg?style=svg&circle-token=a7c321334dce133af9fca534f186d8e5816ee1fc)](https://circleci.com/gh/savkov/bioeval/tree/master)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nCoNLL-2000 style evaluation of data using BIO and BEISO representation for \nmutli-token entities (i.e. chunks).\n\n### Install\n\nIn the root folder execute:\n\n`pip install bioeval`\n\n### Change Log \n\n* [pypi release](https://pypi.org/project/bioeval/) and automated CI releases\n* `bioeval` now supports pandas `DataFame` objects through `bioeval.evaluate_df`.\n\n### Usage\n\nThe library supports two ways of evaluating span annotation. The first is the\nnative format way while the second uses a pandas DataFrame format.\n\n#### Native input format\n\nThe native input format is a set of tuples, where each tuple signifies the \ngroup of tokens in a span. Tokens are also denoted by tuples that are supposed\nto be unique. The user can achieve that uniqueness through adding a unique \nidentifier to each token as in the example bellow.\n\n```python\nfrom bioeval import evaluate\n\n\n# gold chunks\nchunk = {\n    ((1, 'Gold', 'N', 'B-NP'),),\n    ((2, 'is', 'V', 'B-MV'),),\n    ((3, 'green', 'J', 'B-AP'),),\n    ((4, '.', '.', 'B-NP'),),\n    (\n        (5, 'The', 'D', 'B-NP'),\n        (6, 'red', 'J', 'I-NP'),\n        (7, 'square', 'N', 'I-NP')\n    ),\n    ((8, 'is', 'V', 'B-MV'),),\n    (\n        (9, 'very', 'A', 'B-AP'),\n        (10, 'boring', 'J', 'I-AP')\n    ),\n    ((11, '.', '.', 'O'),)\n}\n\n# candidate chunks\nguess_chunk = {\n    ((1, 'Gold', 'N', 'B-NP'),),\n    ((2, 'is', 'V', 'I-NP'),),\n    ((3, 'green', 'J', 'B-AP'),),\n    ((4, '.', '.', 'B-NP'),),\n    (\n        (5, 'The', 'D', 'B-NP'),\n        (6, 'red', 'J', 'I-NP')\n    ),\n    ((7, 'square', 'N', 'O'),),\n    ((8, 'is', 'V', 'B-MV'),),\n    (\n        (9, 'very', 'A', 'B-AP'),\n        (10, 'boring', 'J', 'I-AP')\n    ),\n    ((8, '.', '.', 'O'),)\n}\n\n# evaluation\nf1, pr, re = evaluate(gold_sequence=chunk, guess_sequence=guess_chunk, chunk_col=3)\nprint(f1)\n# 71.43\n```\n\n#### Dataframe format\n\nThe library supports dataframes input through the use of the `evaluate_df`\nmethod, which needs the additional `chunkcol` and `guesscol` parameters to\nspecify the gold and candidate spans.\n\n```python\nimport pandas as pd\nfrom bioeval import evaluate_df\n\n# input data as a JSON parsed to a DataFrame object\ndf = pd.DataFrame(\n    [\n        {'chunktag': 'B-foo','guesstag': 'B-foo'},\n        {'chunktag': 'I-foo','guesstag': 'I-foo'},\n        {'chunktag': 'O','guesstag': 'O'},\n        {'chunktag': 'B-bar','guesstag': 'B-bar'},\n        {'chunktag': 'B-foo','guesstag': 'B-foo'},\n        {'chunktag': 'O','guesstag': 'O'},\n        {'chunktag': 'B-foo','guesstag': 'B-foo'},\n        {'chunktag': 'I-foo','guesstag': 'I-foo'},\n        {'chunktag': 'B-bar','guesstag': 'B-bar'},\n        {'chunktag': 'I-bar','guesstag': 'I-bar'},\n        {'chunktag': 'O','guesstag': 'O'},\n        {'chunktag': 'B-foo','guesstag': 'B-foo'},\n        {'chunktag': 'B-bar','guesstag': 'I-foo'},\n        {'chunktag': 'B-foo','guesstag': 'B-foo'},\n        {'chunktag': 'I-foo','guesstag': 'B-foo'}\n    ]\n)\n\nf1, pr, re = evaluate_df(df=df, chunkcol='chunktag', guesscol='guesstag')\n\nprint(f1)\n>>> 62.5\n```\n\n\n",
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