{
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        "author": "John Foley",
        "author_email": "jjfoley@smith.edu",
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            "License :: OSI Approved :: MIT License",
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        "description": "# FastRank [![Build Status](https://travis-ci.com/jjfiv/fastrank.svg?token=wqGZxUYsDSPaq1jz2zn6&branch=master)](https://travis-ci.com/jjfiv/fastrank) [![PyPI version](https://badge.fury.io/py/fastrank.svg)](https://badge.fury.io/py/fastrank)\n\n\nMy most frequently used learning-to-rank algorithms ported to rust for efficiency.\n\n## Python Usage \n\n```shell\npip install fastrank\n```\n\n### Configuring Models\n\n```python\nfrom fastrank import CModel, CDataset, CQRel, TrainRequest\n\nRANDOM_FOREST = False\n\nif RANDOM_FOREST:\n    train_request = TrainRequest.random_forest()\n    params = train_request.params\n    params.num_trees = 200\n    params.feature_sampling_rate = 0.5\n    params.instance_sampling_rate = 0.5\nelse:\n    train_request = TrainRequest.coordinate_ascent()\n    params = train_request.params\n    params.init_random = True\n    params.normalize = True\n    \n# No matter what, deterministic seed and limit print statements.\nparams.quiet = True\nparams.seed = 16710601535089033473\n```\n\n### Loading SVMrank/Ranklib files:\n\n```python\nimport os\n\nquery_dir = os.path.join(os.environ['HOME'], 'code', 'queries', 'trec_news')\nqrels = CQRel.load_file(os.path.join(query_dir, 'newsir18-entity.qrel'))\n\ndataset = CDataset.open_ranksvm(\n    os.path.join(data_dir, \"ent.ranklib.gz\"),\n    os.path.join(data_dir, \"feature_names.json\"),\n)\n```\n\n### Train & Evaluate Models\n\n```python\nfrom sklearn.model_selection import KFold\n\nEVAL_MEASURE = \"NDCG@5\"\n\nmodels = []\nevals = []\nfolds = KFold(n_splits=5, random_state=0, shuffle=False)\nfeatures = dataset.feature_names()\nfeatures.remove(\"0\") # ranksvm starts at 1 for many tools\nqueries = sorted(d2018.queries())\n\nfdataset = d2018.subsample_feature_names(features)\n\nfor train_idx, test_idx in folds.split(queries):\n    train_queries = [queries[i] for i in train_idx]\n    test_queries = [queries[i] for i in test_idx]\n    train = fdataset.subsample_queries(train_queries)\n    test = fdataset.subsample_queries(test_queries)\n    model = train.train_model(train_request)\n    eval_dict = test.evaluate(model, EVAL_MEASURE, qrels)\n    evals.append(eval_dict)\n    models.append(model)\n    print(\"  NDCG@5 = %1.3f\" % np.mean(list(eval_dict.values())))\n```\n\n## Code Structure\n\n### fastrank \n\nThe core algorithms and data structures are implemented in Rust.\n\n### cfastrank [![PyPI version](https://badge.fury.io/py/cfastrank.svg)](https://badge.fury.io/py/cfastrank)\n\nA very thin layer of rust code provides a C-compatible API. A manylinux version is published to pypi. Don't install this manually -- install the ``fastrank`` package and let it be pulled in as a dependency.\n\n### pyfastrank\n\nA pure-python libary accesses the core algorithms using cffi via cfastrank. A version is published to pypi.",
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