Rand Stats



Recommenders Workflows

In brief

This Raku (Perl 6) package has grammar classes and action classes for the parsing and interpretation of natural Domain Specific Language (DSL) commands that specify recommendations workflows.

The interpreters (actions) target different programming languages: R, Mathematica, Python, Raku. Also, different natural languages.

The generated pipelines are for the software monads "SMRMon-R" and "SMRMon-WL" implemented in R and WL respectively, [AAp2, AAp3], and the object oriented Python and Raku implementations [AAp4, AAp5].

Remark: "SMR" stands for "Sparse Matrix Recommender". "SBR" stands for "Streams Blending Recommender".

Remark: "WL" stands for "Wolfram Language". "Mathematica" and "WL" are used as synonyms.


Zef ecosystem:

zef install DSL::English::RecommenderWorkflows


zef install https://github.com/antononcube/Raku-DSL-English-RecommenderWorkflows.git


Programming languages

Here is a simple invocation:

use DSL::English::RecommenderWorkflows;

ToRecommenderWorkflowCode('recommend by profile action->10, drama->7', 'R::SMRMon');
# SMRMonRecommendByProfile( profile = c("action"=10, "drama"=7))

Here is a more complicated pipeline specification used to generate the code for recommender systems implemented in different languages:

my $command = q:to/END/;
create from dfTitanic; 
apply the LSI functions inverse document frequency, term frequency, and cosine;
recommend by profile female->3, 30->0.1; 
extend recommendations with dfTitanic; 
show pipeline value

say $_.key, "\n", $_.value, "\n"  for ($_ => ToRecommenderWorkflowCode($command, $_ ) for <R::SMRMon WL::SMRMon Python::SMRMon Raku>);
# R::SMRMon
# SMRMonCreate(data = dfTitanic) %>%
# SMRMonApplyTermWeightFunctions(globalWeightFunction = "IDF", localWeightFunction = "TermFrequency", normalizerFunction = "Cosine") %>%
# SMRMonRecommendByProfile( profile = c("female"=3, "30"=0.1)) %>%
# SMRMonJoinAcross( data = dfTitanic ) %>%
# SMRMonEchoValue()
# WL::SMRMon
# SMRMonUnit[] \[DoubleLongRightArrow] SMRMonCreate[dfTitanic] \[DoubleLongRightArrow]
# SMRMonApplyTermWeightFunctions["GlobalWeightFunction" -> "IDF", "LocalWeightFunction" -> "TermFrequency", "NormalizerFunction" -> "Cosine"] \[DoubleLongRightArrow]
# SMRMonRecommendByProfile[<|"female"->3, "30"->0.1|>] \[DoubleLongRightArrow]
# SMRMonJoinAcross[dfTitanic] \[DoubleLongRightArrow]
# SMRMonEchoValue[]
# Python::SMRMon
# obj = SparseMatrixRecommender().create_from_wide_form(data = dfTitanic).apply_term_weight_functions(global_weight_func = "IDF", local_weight_func = "TermFrequency", normalizer_func = "Cosine").recommend_by_profile( profile = {"female":3, "30":0.1}).join_across( data = dfTitanic ).echo_value()
# Raku
# my $sbrObj = ML::StreamsBlendingRecommender::CoreSBR.new;
# $sbrObj.makeTagInverseIndexesFromWideForm( dfTitanic);
# $sbrObj.applyTermWeightFunctions(globalWeightFunction => "IDF", localWeightFunction = "TermFrequency", normalizerFunction => "Cosine");
# $sbrObj.recommendByProfile( profile => %("female"=>3, "30"=>0.1));
# $sbrObj.joinAcross( dfTitanic );
# say $sbrObj.takeValue

The command above should print out R code for the R package SMRMon-R, [AAp1].

Natural languages

say $_.key, "\n", $_.value, "\n"  for ($_ => ToRecommenderWorkflowCode($command, $_ ) for <Bulgarian English Russian>);
# Bulgarian
# създай с таблицата: dfTitanic
# приложи тегловите функции: глобално-теглова функция: "IDF", локално-теглова функция: "TermFrequency", нормализираща функция: "Cosine"
# препоръчай с профила: {"female":3, "30":0.1}
# напречно съединение с таблицата: dfTitanic
# покажи лентовата стойност
# English
# create with data table: dfTitanic
# apply the term weight functions: global weight function: "IDF", local weight function: "TermFrequency", normalizing function: "Cosine"
# recommend with the profile: {"female":3, "30":0.1}
# join across with the data table: dfTitanic
# show the pipeline value
# Russian
# создать с таблицу: dfTitanic
# применять весовые функции: глобальная весовая функция: "IDF", локальная весовая функция: "TermFrequency", нормализующая функция: "Cosine"
# рекомендуй с профилю: {"female":3, "30":0.1}
# перекрестное соединение с таблицу: dfTitanic
# показать текущее значение конвейера


The original version of this Raku package was developed/hosted at [ AAp3 ].

A dedicated GitHub repository was made in order to make the installation with Raku's zef more direct. (As shown above.)


[AAp1] Anton Antonov, Recommender Workflows Raku Package, (2019), ConversationalAgents at GitHub.

[AAp2] Anton Antonov, Sparse Matrix Recommender Monad in R, (2019), R-packages at GitHub.

[AAp3] Anton Antonov, Monadic Sparse Matrix Recommender Mathematica package, (2018), MathematicaForPrediction at GitHub.

[AAp4] Anton Antonov, SparseMatrixRecommender Python package, (2021), Python-packages at GitHub.

[AAp5] Anton Antonov, ML::StreamsBlendingRecommender Raku package (2021), GitHub/antononcube.