semvec/pypi-packages huggingface dataset:
FunctionalSubgraph 1.0.0.post10
FunctionalSubgraph: An ML tool for dynamic graph analysis.

Functional Subgraph
====================

A machine learning toolbox for the analysis of dynamic graphs.

*Functional Subgraph* implements non-negative matrix factorization to decompose
time-varying, dynamic graphs into a composite set of parts-based, additive
subgraphs.


Quick-Start
-----------
Non-Negative Matrix Factorization for dynamic graphs, such that:

    A ~= WH
    Constraints:
        A, W, H >= 0
        L2-Regularization on W
        L1-Sparsity on H

Implementation is based on :

    1. Jingu Kim, Yunlong He, and Haesun Park. Algorithms for Nonnegative
            Matrix and Tensor Factorizations: A Unified View Based on Block
            Coordinate Descent Framework.
            Journal of Global Optimization, 58(2), pp. 285-319, 2014.

    2. Jingu Kim and Haesun Park. Fast Nonnegative Matrix Factorization:
            An Active-set-like Method And Comparisons.
            SIAM Journal on Scientific Computing (SISC), 33(6),
            pp. 3261-3281, 2011.

Modified from: https://github.com/kimjingu/nonnegfac-python
rankmetrics 1.0.0
Calculate various metrics relevant for learning to rank algorithms.
Keywords: recommender system learning to rank python

rankmetrics
===========

Introduction
------------

This package contains functions for calculating various metrics relevant
for learning to rank systems such as recommender systems.

**IMPORTANT**: This project is still in its very early stages. Results
should be taken with a grain of salt.

scikit-learn-like APIs
----------------------

This package aims to provide APIs that are familiar if you are used to
working with scikit-learn.

Supported metrics
-----------------

-  Recall@k
-  Bag Recall@k
-  Mean Rank Percentile
-  NDCG