     [file my-bib-macros.texi not included]
References

Copyright © 2016-2017 Franco Masotti franco.masotti@student.unife.it

Copying and distribution of this file, with or without modification, are permitted in any medium without royalty provided the copyright notice and this notice are preserved.

Cplint R
********

This manual is for Cplint R program version 1.0.0

About
*****

About
=====

The purpose of this library is to provide an interface between the Cplint (Cplint) suite for SWI Prolog (SWI Prolog) and R (R) to handle charts made with the ggplot2 (ggplot2) package.

This means that most C3js (C3js) charting functions of Cplint are also available for R.

Terminology
===========

In order to simplify the understanding of this document, a minimal set of terminology is necessary:

  * Any code between < > represents pseudocode.

  * The symbol # represents an unsigned integer or the root user, depending on the context.

  * Optional code or parameters are encapsuled between [ ].

Installation
************

Cplint R is provided as part of the Cplint on SWISH (Cplint on SWISH) package. This is the recomended and simplest way to obtain a working installation without too much trouble.

You can also install it manually with pack_install('cplint_r'). using the swipl command on UNIX-like systems. Unfortunately, doing this is not enough since you need to setup an R environment with the components described in the following section. For more information, have a look at the SWISH Installer Manual (SWISH Installer Manual)

Dependencies
============

Cplint R has both Prolog and R dependencies:

Prolog
------

  * Cplint

  * Rserve Client (Rserve Client)

  * R SWISH library (R SWISH)

R
-

  * ggplot2

  * Rserve (Rserve)

  * gridExtra (gridExtra)

Examples
********

Gaussian mean estimation
========================

The following is the content of gauss_mean_est_R.pl.

     [file example.pl not included]

Using ?- dens_lw(1000) as query the following chart is produced:

Other examples
==============

For other examples see all the files ending in "_R.pl" on https://github.com/friguzzi/swish/tree/master/examples/inference

Protocol
********

Interface
=========

The following is a list of exported predicates available to the library users'.

Each predicate corresponds to one of the following categories

  * Helper: specific of this library.

  * Pita: part of Cplint.

  * Mcintyre: part of Cplint.

  * Auc: part of Cplint.

Each argument of a predicate correponds to a data type. See the SWI Prolog data types manual (SWI Prolog data types) and the Learn Prolog Now manual (LPN). Have a look at the Cplint help manual (Cplint) to learn in details about the functionality of each predicate.

Helper build_xy_list (+X:list, +Y:list, -OUT:list) is det Given to lists X and Y build an output list OUT in the form [X1-Y1,X2-Y2,...,XN-YN].

Helper r_row (+X:atom, +Y:atom, -OUT:atom) is det Given two atoms X and Y, build the term r(X,Y) in OUT.

Helper get_set_from_xy_list (+L:list, -R:list) is det Given an input list L in the form [X1-Y1,X2-Y2,...,XN-YN], transform it in an output list R in the form [r(X1,Y1),r(X2,Y2),...,r(XN,YN)]. This means that R will contain an X-Y relationship which can be then passed to an R data frame.

Pita prob_bar_r (:QUERY:atom) is nondet The predicate computes and plots the probability of QUERY as a bar chart with a bar for the probability of QUERY true and a bar for the probability of QUERY false. If QUERY is not ground, it returns in backtracking all ground instantiations of QUERY together with their probabilities.

Pita prob_bar_r (:QUERY:atom, :EVIDENCE:atom) is nondet The predicate computes and plots the probability of QUERY given EVIDENCE as a bar chart with a bar for the probability of QUERY true and a bar for the probability of QUERY false given EVIDENCE. If QUERY / EVIDENCE are not ground, it returns in backtracking all ground instantiations of QUERY / EVIDENCE together with their probabilities.

Mcintyre mc_prob_bar_r (:QUERY:atom, -PROBABILITY:dict) is det See prob_bar_r/2.

Mcintyre mc_sample_bar_r (:QUERY:atom, +SAMPLES:int) is det The predicate samples QUERY a number of SAMPLES times and plots a bar chart with a bar for the number of successes and a bar for the number of failures. If QUERY is not ground, it considers it as an existential query.

Mcintyre mc_sample_arg_bar_r (:QUERY:atom, +SAMPLES:int, ?ARG:var) is det The predicate samples QUERY SAMPLES times. ARG should be a variable in QUERY. The predicate plots a bar chart with a bar for each possible value of L, the list of values of ARG for which QUERY succeeds in a world sampled at random. The size of the bar is the number of samples returning that list of values.

Mcintyre mc_sample_arg_first_bar_r (:QUERY:atom, +SAMPLES:int, ?ARG:var) is det The predicate samples QUERY SAMPLES times. ARG should be a variable in QUERY. The predicate plots a bar chart with a bar for each value of ARG returned as a first answer by QUERY in a world sampled at random. The size of the bar is the number of samples that returned that value. The value is failure if the query fails.

Mcintyre mc_rejection_sample_arg_bar_r (:QUERY:atom, :EVIDENCE:atom, +SAMPLES:int, ?ARG:var) is det The predicate calls mc_rejection_sample_arg/5 and builds an R graph of the results. It plots a bar chart with a bar for each possible value of L, the list of values of ARG for which QUERY succeeds given that EVIDENCE is true The size of the bar is the number of samples returning that list of values.

Mcintyre mc_mh_sample_arg_bar_r (:QUERY:atom, :EVIDENCE:atom, +SAMPLES:int, +LAG:int, ?ARG:var) is det The predicate calls mc_mh_sample_arg/6 and builds an R graph of the results. The predicate plots a bar chart with a bar for each possible value of L, the list of values of ARG for which QUERY succeeds in a world sampled at random. The size of the bar is the number of samples returning that list of values.

Mcintyre mc_mh_sample_arg_bar_r (:QUERY:atom, :EVIDENCE:atom, +SAMPLES:int, +MIX:int, +LAG:int, ?ARG:var) is det The predicate calls mc_mh_sample_arg/7 and builds an R graph of the results. The predicate plots a bar chart with a bar for each possible value of L, the list of values of ARG for which QUERY succeeds in a world sampled at random. The size of the bar is the number of samples returning that list of values.

Mcintyre histogram_r (+LIST:list, +NBINS:int) is det Draws a histogram of the samples in List dividing the domain in NBINS bins. LIST must be a list of couples of the form [V]-W or V-W where V is a sampled value and W is its weight.

Mcintyre density_r (+LIST:list) is det Display a smooth density estimate of a sets of samples. The samples are in LIST as couples V-W where V is a value and W its weigth.

Mcintyre densities_r (+PRIORLIST:list, +POSTLIST:list) is det Display a smooth density estimate of two sets of samples, usually prior and post observations. The samples from the prior are in PRIORLIST while the samples from the posterior are in POSTLIST as couples V-W where V is a value and W its weigth.

Auc compute_areas_diagrams_r (+LG:list, -AUCROC:float, -AUCPR:float) is det The predicate takes as input a list LG of pairs probability-literal in asceding order on probability where the literal can be an Atom (incading a positive example) or \+ Atom, indicating a negative example while the probability is the probability of Atom of being true. PR and ROC diagrams are plotted. The predicate returns:

  * AUCROC: the size of the area under the ROC curve

  * AUCPR: the size of the area under the PR curve

See http://cplint.lamping.unife.it/example/exauc.pl for an example.

Internals
=========

Important predicates in this library follow a common structure to avoid confusion and promote standardization.

Interface predicates are involved in the interaction between input data from a program and the plot of that same data. These predicates are usable from the programs.

As the name suggests, plotting predicates are only involved in plotting the data.

Finally there are other functions which handle the lists and other types of data.

Interface predicates
--------------------

All interface predicates have a similar structure. Their names end with _r (except the Helpers) in order to distinguish them from the original Cplint predicates.

First and last operations are always load_r_libraries and finalize_r_graph respectively.

Plotting is done right before the last operation with one of the geom_ predicates.

A skeleton of the structure follows.
<cplint_graphing_predicate>_r(<input>):-
    load_r_libraries,
    <operations on the input>,
    geom_<something>(<new input, possibly lists>),
    finalize_r_graph.

Plotting predicates
-------------------

Predicates directly involved in plotting all start with geom_ as prefix.

These predicates work with lists which are then transformed into R data frames, and, as a final instruction, a corresponding plot is generated.

You can visualize the structure with the following pseudocode:
geom_<something>(<Lists and/or other input>) :-
    <handle lists>,
    <create one or more R data frame with the lists data>,
    <rename data frame colnames to avoid using default ones>,
    <- ggplot <something>

List handling
-------------

List handling is useful to pass information between Prolog and R. This is done thanks to build_xy_list/3, r_row/3 and get_set_from_xy_list/2 predicates, described in the interface section.

In case there are multiple distributions we simply have to call get_set_from_xy_list/2 the appropriate number of times, like: get_set_from_xy_list(<something>,R#).

Main data frame creation
------------------------

As descibed before, a data frame is useful to pass structured information between Prolog and R.

In Cplint R in particular, we use r_data_frame_from_rows/2 provided by the Rserve Client library, in the following manner:
r_data_frame_from_rows(df[#], R[#])

For each distribution the optional number is incremented by one.

In case it is the last (or only) data frame then its name will simply be df.

Helpers
-------

What follows are extracts of some trivial predicates indicated as internal helpers.

bin_width(Min,Max,NBins,Width) :-
  D is Max-Min,
  Width is D/NBins.

load_r_libraries :-
    <- library("ggplot2").

finalize_r_graph :-
    r_download.

Development
***********

Changing the library
====================

You are welcomed to change this library. In order to test it you simply have to place you modified version in the correct path and restart SWISH. If you have used the packages provided with SWISH Installer this can be achieved with the following:
# mv <your_modified_cplint_r_library> /home/swish/lib/swipl/pack/cplint_r/prolog/cplint_r.pl
# systemctl restart swish-cplint

Compiling this documentation
============================

The source of this documentation is under the doc directory of the repository.

To be able to compile it, you have to install several tex packages (for example: texlive-most and texi2html if you are using Arch Linux) that contain the following binaries:

makeinfo
texi2dvi
docbook2html
docbook2pdf
docbook2txt
texi2html
perl

After running make, a directory named manual will be created and you can access all the files by opening index.html with a browser.

Thanks
******

Fabrizio Riguzzi for developement, testing and feedback.

Raivo Laanemets for a blog article (Prolog pack development experience) on how to write SWI Prolog packs.

References
**********

Some quotations reported here are taken directly from the respective web sites.

     Cplint "A suite of programs for reasoning with probabilistic logic programs". See https://github.com/friguzzi/cplint and https://github.com/friguzzi/cplint/blob/master/doc/help-cplint.pdf for the Cplint help manual.

     SWI Prolog "SWI-Prolog offers a comprehensive free Prolog environment. Since its start in 1987, SWI-Prolog development has been driven by the needs of real world applications. SWI-Prolog is widely used in research and education as well as commercial applications". See http://www.swi-prolog.org/

     R "R is an integrated suite of software facilities for data manipulation, calculation and graphical display". See https://cran.r-project.org/doc/manuals/r-release/R-intro.html

     ggplot2 "ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics". See http://ggplot2.org/

     gridExtra "Miscellaneous Functions for "Grid" Graphics. Provides a number of user-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables". See https://github.com/baptiste/gridextra

     C3js "C3 enables deeper integration of charts into your application". See http://c3js.org/

     SWI Prolog data types See http://www.swi-prolog.org/datatypes.html.

     LPN "Learn Prolog Now". See http://www.learnprolognow.org/lpnpage.php?pagetype=html&pageid=lpn-htmlch1.

     Cplint on SWISH "A set of packages that are able to build and install SWISH, Cplint on SWISH and an R environment". See https://frnmst.github.io/swish-installer/

     Rserve Client "SWI-Prolog/SWISH client for Rserve". "The container exposes Rserve using a Unix-domain socket in /home/rserve/socket". See https://github.com/JanWielemaker/rserve_client

     R SWISH A library that talks between SWISH and Rserve. See https://github.com/SWI-Prolog/swish/blob/master/lib/r_swish.pl

     Rserve "Rserve is a TCP/IP server which allows other programs to use facilities of R (see www.r-project.org) from various languages without the need to initialize R or link against R library." See https://www.rforge.net/Rserve/

     SWISH Installer Manual See https://frnmst.github.io/swish-installer/

     Prolog pack development experience See https://rlaanemets.com/post/show/prolog-pack-development-experience