metrics

ap_k

ndcg_k

<code>ap_k</code> calculates Average Precision at K (<code>ap@k</code>).
Please refer to <a href="https://en.wikipedia.org/wiki/Information_retrieval#Average_precision">Information retrieval wikipedia article</a>
<code>ndcg_k()</code> calculates Normalized Discounted Cumulative Gain at K (<code>ndcg@k</code>).
Please refer to <a href="https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG">Discounted cumulative gain</a>

Implements many algorithms for statistical learning on
sparse matrices - matrix factorizations, matrix completion,
elastic net regressions, factorization machines.
Also 'rsparse' enhances 'Matrix' package by providing methods for
multithreaded <sparse, dense> matrix products and native slicing of
the sparse matrices in Compressed Sparse Row (CSR) format.
List of the algorithms for regression problems:
1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL)
Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
List of algorithms for matrix factorization and matrix completion:
1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least
Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>)
2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro
(2005, <doi:10.1145/1102351.1102441>)
3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD,
Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder
et al. (2014, <doi:10.48550/arXiv.1410.2596>)
4) Linear-Flow matrix factorization, from 'Practical linear models for
large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al
(2016, ISBN:978-1-57735-770-4)
5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington,
Socher, Manning (2014, <https://aclanthology.org/D14-1162/>)
Package is reasonably fast and memory efficient - it allows to work with large
datasets - millions of rows and millions of columns. This is particularly useful
for practitioners working on recommender systems.

Dmitriy Selivanov

rsparse

Statistical Learning on Sparse Matrices

David Cortes

Drew Schmidt

Wei-Chen Chen

metrics function

<dl><dt>predictions</dt>
<dd>matrix of predictions. Predctions can be defined 2 ways:<ol>
<li><code>predictions</code> = <code>integer</code> matrix with item indices (correspond to column numbers in <code>actual</code>)</li>
<li><code>predictions</code> = <code>character</code> matrix with item identifiers (characters which correspond to <code>colnames(actual)</code>)
 which has attribute "indices" (<code>integer</code> matrix with item indices which correspond to column numbers in <code>actual</code>).</li>
</ol></dd>
<dt>actual</dt>
<dd>sparse Matrix of relevant items. Each non-zero entry considered as relevant item.
Value of the each non-zero entry considered as relevance for calculation of <code>ndcg@k</code>.
It should inherit from <code><a href="/link/Matrix%3A%3AsparseMatrix?package=rsparse&version=0.5.3" data-mini-rdoc="rsparse::Matrix::sparseMatrix">Matrix::sparseMatrix</a></code>. Internally <code>Matrix::RsparseMatrix</code> is used.</dd>
<dt>...</dt>
<dd>other arguments (not used at the moment)</dd></dl>

Arguments

<code>ap_k</code> calculates Average Precision at K (<code>ap@k</code>).
Please refer to <a href='https://en.wikipedia.org/wiki/Information_retrieval#Average_precision'>Information retrieval wikipedia article</a>
<code>ndcg_k()</code> calculates Normalized Discounted Cumulative Gain at K (<code>ndcg@k</code>).
Please refer to <a href='https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG'>Discounted cumulative gain</a>

Ranking Metrics for Top-K Items — metrics

<dl>

<dt>predictions</dt>
<dd>matrix of predictions. Predctions can be defined 2 ways:<ol>
<li><code>predictions</code> = <code>integer</code> matrix with item indices (correspond to column numbers in <code>actual</code>)</li>
<li><code>predictions</code> = <code>character</code> matrix with item identifiers (characters which correspond to <code>colnames(actual)</code>)
 which has attribute "indices" (<code>integer</code> matrix with item indices which correspond to column numbers in <code>actual</code>).</li>
</ol></dd>


<dt>actual</dt>
<dd>sparse Matrix of relevant items. Each non-zero entry considered as relevant item.
Value of the each non-zero entry considered as relevance for calculation of <code>ndcg@k</code>.
It should inherit from <code><a href='https://rdrr.io/pkg/Matrix/man/sparseMatrix.html'>Matrix::sparseMatrix</a></code>. Internally <code>Matrix::RsparseMatrix</code> is used.</dd>


<dt>...</dt>
<dd>other arguments (not used at the moment)</dd>

</dl>

metrics: Ranking Metrics for Top-K Items

Description

Usage

Arguments

Examples