Metadata-Version: 2.1
Name: rankerEval
Version: 0.2.0
Summary: A fast implementation of ranking metrics for information retrieval and recommendation.
Home-page: https://github.com/microsoft/rankerEval
Author: Tobias Schnabel
Author-email: tobias.schnabel@microsoft.com
License: MIT
Description: RankerEval
        ==========
        
        .. image:: https://img.shields.io/pypi/v/rankereval.svg
            :target: https://pypi.python.org/pypi/rankereval
            :alt: Latest PyPI version
        
        .. image:: https://github.com/microsoft/rankerEval/workflows/Python%20package/badge.svg
           :target: https://github.com/microsoft/rankerEval/actions
           :alt: Latest GitHub actions build status
        
        .. inclusion-marker-start
        
        A fast numpy/numba-based implementation of ranking metrics for information retrieval and recommendation.
        Coded with efficiency in mind and support for edge cases. 
        
        Find the full `documentation here <https://rankereval.readthedocs.io>`_.
        
        Features
        --------
        * Wide array of evaluation metrics for information retrieval and top-N recommender systems:
        
          * Binary labels: Recall, Precision, MAP, HitRate, MRR, MeanRanks, F1
          * Numeric and binary labels: DCG, nDCG
        * Minimal dependencies: Numpy and Numba (required), SciPy (optional)
        * Flexible input formats: Supports arrays, lists and sparse matrices 
        * Built-in support for confidence intervals via bootstrapping
          
        Usage
        -----
        .. code-block:: python
        
            from rankereval import BinaryLabels, Rankings, Recall
            
            y_true = BinaryLabels.from_positive_indices([[0,2], [0,1,2]])
            y_pred = Rankings.from_ranked_indices([[2,1], [1]])
        
            recall_at_3 = Recall(3).mean(y_true, y_pred)
            print(recall_at_3["score"])
        
        
        To get confidence intervals (95% by default), specify ``conf_interval=True``:
        
        .. code-block:: python
        
            recall_at_3 = Recall(3).mean(y_true, y_pred, conf_interval=True)
            print(recall_at_3["conf_interval"])
            
        Input formats
        +++++++++++++
        RankerEval allows for a variety of input formats, e.g., 
        
        .. code-block:: python
        
            # specify all labels as lists
            y_true = BinaryLabels.from_dense([[1,0,1], [1,1,1]])
            
            # specify labels as numpy array
            y_true = BinaryLabels.from_dense(np.asarray([[1,0,1], [1,1,1]]))
            
            # or use a sparse matrix
            import scipy.sparse as sp
            y_true = BinaryLabels.from_sparse(sp.coo_matrix([[1,0,1], [1,1,1]]))
        
        
            
        Installation
        ------------
        
        To install (requires Numpy 1.18 or newer):
        
        .. code-block:: bash
        
            pip install rankereval
        
        
        
        Licence
        -------
        This project is licensed under `MIT <https://choosealicense.com/licenses/mit/>`_.
        
        .. inclusion-marker-end
        
        Authors
        -------
        
        `RankerEval` was written by `Tobias Schnabel <tobias.schnabel@microsoft.com>`_.
        
        
        Contributing
        ------------
        
        This project welcomes contributions and suggestions.  Most contributions require you to agree to a
        Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
        the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
        
        When you submit a pull request, a CLA bot will automatically determine whether you need to provide
        a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
        provided by the bot. You will only need to do this once across all repos using our CLA.
        
        This project has adopted the `Microsoft Open Source Code of Conduct <https://opensource.microsoft.com/codeofconduct/>`_.
        For more information see the `Code of Conduct FAQ <https://opensource.microsoft.com/codeofconduct/faq/>`_ or
        contact `opencode@microsoft.com <mailto:opencode@microsoft.com>` with any additional questions or comments.
        
        
Keywords: ranking recommendation evaluation map mrr
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
