https://github.com/antononcube/raku-ml-streamsblendingrecommender
Raku implementation of a Streams Blending Recommender (SBR).
https://github.com/antononcube/raku-ml-streamsblendingrecommender
Last synced: 12 months ago
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Raku implementation of a Streams Blending Recommender (SBR).
- Host: GitHub
- URL: https://github.com/antononcube/raku-ml-streamsblendingrecommender
- Owner: antononcube
- License: gpl-3.0
- Created: 2021-03-02T15:13:45.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-09-23T01:53:14.000Z (almost 2 years ago)
- Last Synced: 2025-02-08T11:13:21.723Z (over 1 year ago)
- Language: Raku
- Homepage:
- Size: 829 KB
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README-work.md
- License: LICENSE
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README
# Raku Streams Blending Recommender
[](http://sparrowhub.io:2222)
[](https://www.gnu.org/licenses/gpl-3.0)
Raku implementation of a Streams Blending Recommender (SBR) framework.
Generally speaking, SBR is a "computer scientist" implementation of a recommendation system
based on sparse linear algebra. See the article
["Mapping Sparse Matrix Recommender to Streams Blending Recommender"](https://github.com/antononcube/MathematicaForPrediction/tree/master/Documentation/MappingSMRtoSBR),
[AA1], for detailed theoretical description of the data structures and operations with them.
This implementation is loosely based on the:
- Software monad
["MonadicSparseMatrixRecommender"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicSparseMatrixRecommender.m), [AAp1],
in Mathematica
- Software monad ["SMRMon-R"](https://github.com/antononcube/R-packages/tree/master/SMRMon-R), [AAp2], in R
- Object-Oriented Programming (OOP) implementation
["SparseMatrixRecommender"](https://pypi.org/project/SparseMatrixRecommender/), [AAp3], in Python
Instead of "monads" the implementations in this package and [AAp3] use OOP classes.
Instead of "monadic pipelines" method chaining is used.
See the org-mode file
["Work-plan.org"](./org/Work-plan.org)
for detailed status (including a TODO list.)
--------
## Installation
From GitHub:
```shell
zef install https://github.com/antononcube/Raku-ML-StreamsBlendingRecommender.git
```
From zef-ecosystem:
```shell
zef install ML::StreamsBlendingRecommender
```
--------
## Usage
In this section we show how to use the package by making a (nearest neighbors) classifier with it.
Here are the steps:
1. Take an example dataset
- Titanic data from "Data::Reshapers", [AAp4].
2. Make a recommender for that dataset
3. Use the `classify` method of the recommender over a random selection of rows.
- Classify for the labels "died" or "survived" of Titanic's dataset column "passengerAge".
4. Evaluate the classification results
- Using Receiver Operating Characteristic (ROC) statistics via "ML::ROCFunctions", [AAp5].
```perl6
use Data::Reshapers;
use Data::Summarizers;
my @dsTitanic = get-titanic-dataset();
records-summary(@dsTitanic)
```
Here is a sample of the data:
```perl6
to-pretty-table(@dsTitanic.roll(4));
```
Here we make the recommender object:
```perl6
use ML::StreamsBlendingRecommender;
my ML::StreamsBlendingRecommender::CoreSBR $sbrObj .= new;
$sbrObj.makeTagInverseIndexesFromWideForm(@dsTitanic, tagTypes => @dsTitanic[0].keys.grep({ $_ ne 'id' }).Array, itemColumnName => , :!addTagTypesToColumnNames).transposeTagInverseIndexes;
```
Here we classify by profile
```perl6
$sbrObj.classifyByProfile('passengerSurvival', ['1st', 'female']):!object
```
**Remark:** Since we want to see the result and "dot-chain" with further method call we use
the adverb `:!object`.
Here are the classification results of 5 randomly selected rows from the dataset:
```perl6
my @clRes = @dsTitanic.pick(5).map({ $sbrObj.classifyByProfile('passengerSurvival', $_):!object }).Array;
```
*TBF...*
-------
## Implementation
### UML diagram
Here is a UML diagram that shows package's structure:
The
[PlantUML spec](./resources/class-diagram.puml)
and
[diagram](./resources/class-diagram.png)
were obtained with the CLI script `to-uml-spec` of the package "UML::Translators", [AAp6].
Here we get the [PlantUML spec](./resources/class-diagram.puml):
```shell
to-uml-spec ML::StreamsBlendingRecommender > ./resources/class-diagram.puml
```
Here get the [diagram](./resources/class-diagram.png):
```shell
to-uml-spec ML::StreamsBlendingRecommender | java -jar ~/PlantUML/plantuml-1.2022.5.jar -pipe > ./resources/class-diagram.png
```
## Object-oriented design
--------
## References
### Articles
[AA1] Anton Antonov,
["Mapping Sparse Matrix Recommender to Streams Blending Recommender"](https://github.com/antononcube/MathematicaForPrediction/tree/master/Documentation/MappingSMRtoSBR),
(2019),
[GitHub/antononcube](https://github.com/antononcube).
### Packages, repositories
[AAp1] Anton Antonov,
[Monadic Sparse Matrix Recommender Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicSparseMatrixRecommender.m),
(2018),
[GitHub/antononcube](https://github.com/antononcube/).
[AAp2] Anton Antonov,
[Sparse Matrix Recommender Monad R package](https://github.com/antononcube/R-packages/tree/master/SMRMon-R),
(2018),
[R-packages at GitHub/antononcube](https://github.com/antononcube/R-packages).
[AAp3] Anton Antonov,
[SparseMatrixRecommender Python package](https://github.com/antononcube/Python-packages/tree/main/SparseMatrixRecommender),
(2021),
[Python-packages at GitHub/antononcube](https://github.com/antononcube/Python-packages).
[AAp4] Anton Antonov,
[Data::Reshapers Raku package](https://github.com/antononcube/Raku-Data-Reshapers),
(2021),
[GitHub/antononcube](https://github.com/antononcube).
[AAp5] Anton Antonov,
[ML::ROCFunctions Raku package](https://github.com/antononcube/Raku-ML-ROCFunctions),
(2022),
[GitHub/antononcube](https://github.com/antononcube).
[AAp6] Anton Antonov,
[UML::Translators Raku package](https://github.com/antononcube/Raku-UML-Translators),
(2022),
[GitHub/antononcube](https://github.com/antononcube).