update v.0.3

f8cf2153 · Gilles Kratzer · ff33db7c · f8cf2153 · f8cf2153 · f8cf2153
Commit f8cf2153 authored Oct 13, 2019 by Gilles Kratzer
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,4 +8,4 @@

 ## mcmcabn 0.3:

-* update mcmcabn to make it compatible with constraints imported from the cache of scores
+* update mcmcabn to make it compatible with constraints imported from the cache of scores. Heating parameter to increase or decrease acceptance probability.
--- a/R/MBR.R
+++ b/R/MBR.R
-MBR <- function(n.var, dag.tmp, retain, ban, max.parents, sc,sc.scaled, score.cache, score, verbose) {
+MBR <- function(n.var, dag.tmp, retain, ban, max.parents, sc, score.cache, score, verbose, heating) {

    ## scores
    score.G <- 0
@@ -195,8 +195,8 @@ if(FALSE){
                #score.A <-  ( z.i.G.0 / z.star.i.G.prime.0  *  sum(is.finite(score.G)) / sum(is.finite(score.G.prime)))
                #score.A <- n.edges/n.edges.tilde * ((z.star.x.i.M.dot) / (z.star.x.j.M.dot) * (z.x.j.M.cross) / (z.x.i.M.tilde.cross))

-                s.proposed <- score.dag(dag.MBR,score.cache,sc.scaled)
-                s.current <- score.dag(dag.tmp,score.cache,sc.scaled)
+                s.proposed <- score.dag(dag.MBR,score.cache,sc)
+                s.current <- score.dag(dag.tmp,score.cache,sc)

                A <- min(exp(( s.proposed - s.current) * (n.edges/n.edges.tilde) ), 1)

@@ -205,7 +205,7 @@ if(FALSE){
                # A <- min(1, score.A)


-                if (runif(1)<A) {
+                if (runif(1)<(A**heating)) {
                  rejection <- 0
                  dag.tmp <- dag.MBR


--- a/R/REV.R
+++ b/R/REV.R
-REV <- function(n.var, dag.tmp, retain, ban, max.parents, sc,sc.scaled, score.cache, score, verbose) {
+REV <- function(n.var, dag.tmp, retain, ban, max.parents, sc, score.cache, score, verbose, heating) {

    rejection <- 1
    A <- 0
@@ -105,8 +105,8 @@ REV <- function(n.var, dag.tmp, retain, ban, max.parents, sc,sc.scaled, score.ca
                  ############################## Acceptance probability

                  #score.A <- min(exp((z.star.x.i.M.dot / (z.star.x.j.M.dot) * (z.x.j.M.cross) / (z.x.i.M.tilde.cross))*n.edges/n.edges.tilde),1)
-                  s.proposed <- score.dag(dag.M.tilde,score.cache,sc.scaled)
-                  s.current <- score.dag(dag.tmp,score.cache,sc.scaled)
+                  s.proposed <- score.dag(dag.M.tilde,score.cache,sc)
+                  s.current <- score.dag(dag.tmp,score.cache,sc)

                  A <- min(exp(( s.proposed - s.current) * (n.edges/n.edges.tilde) ), 1)

@@ -117,7 +117,7 @@ REV <- function(n.var, dag.tmp, retain, ban, max.parents, sc,sc.scaled, score.ca
                  #A <- min(1, score.A)

                  #if (rbinom(n = 1, size = 1, prob = A) == 1) {
-                  if (runif(1)<A){
+                  if (runif(1)<(A**heating)) {
                    rejection <- 0
                    dag.tmp <- dag.M.tilde
                    score <- score.dag(dag.M.tilde,score.cache,sc)

--- a/R/mc3.R
+++ b/R/mc3.R
-mc3 <- function(n.var, dag.tmp, retain, ban, max.parents, sc,sc.scaled, score.cache, score, prior.choice, prior.lambda, prior.dag, verbose, scaling.factor) {
+mc3 <- function(n.var, dag.tmp, retain, ban, max.parents, sc, score.cache, score, prior.choice, prior.lambda, prior.dag, verbose, heating) {

    ## construction of neighbours list
    neighbours.list <- NULL
@@ -141,8 +141,8 @@ mc3 <- function(n.var, dag.tmp, retain, ban, max.parents, sc,sc.scaled, score.ca
        }


-        score.Gprime.scaled <- score.dag(dag.gprime,score.cache,sc.scaled)
-        score.G.scaled <- score.dag(dag.tmp,score.cache,sc.scaled)
+        score.Gprime.scaled <- score.dag(dag.gprime,score.cache,sc)
+        score.G.scaled <- score.dag(dag.tmp,score.cache,sc)

        alpha <- min(exp(( score.Gprime.scaled - score.G.scaled) * (n.G/n.Gprime) * (prior.Gprime/prior.G)), 1)

@@ -150,7 +150,7 @@ mc3 <- function(n.var, dag.tmp, retain, ban, max.parents, sc,sc.scaled, score.ca

        score <- score.G

-        if (!is.null(dag.gprime) && runif(1)<alpha) {
+        if (!is.null(dag.gprime) && runif(1)<(alpha**heating)) {
            dag.tmp <- dag.gprime
            score <- score.Gprime
            rejection <- 0

--- a/R/mcmc.R
+++ b/R/mcmc.R
@@ -5,13 +5,13 @@
 ##-------------------------------------------------------------------------

 mcmcabn <- function(score.cache = NULL, score = "mlik", data.dists = NULL, max.parents = 1, mcmc.scheme = c(100, 1000,
-    1000), seed = 42, verbose = FALSE, start.dag = NULL, prior.dag = NULL, prior.lambda = NULL, prob.rev = 0.05, prob.mbr = 0.05, scaling = 0,
+    1000), seed = 42, verbose = FALSE, start.dag = NULL, prior.dag = NULL, prior.lambda = NULL, prob.rev = 0.05, prob.mbr = 0.05, heating = 1,
    prior.choice = 2) {

    #################################################### Tests

    .tests.mcmcabn(score.cache, data.dists, max.parents, mcmc.scheme, seed, verbose, start.dag, prior.dag, prior.lambda,
-        prob.rev, prob.mbr, prior.choice,scaling)
+        prob.rev, prob.mbr, prior.choice,heating)

    ## end of tests

@@ -123,11 +123,11 @@ mcmcabn <- function(score.cache = NULL, score = "mlik", data.dists = NULL, max.p
        sc <- cbind(tmp, score.cache[[score]])
    }

-    sc.scaled <- sc
-    tmp.fact <- abs(max(sc[,ncol(sc)]))
-    if(scaling>0){
-      sc.scaled[,ncol(sc)] <- sc[,ncol(sc)]/(scaling * tmp.fact)
-    }
+    # sc.scaled <- sc
+    # tmp.fact <- abs(max(sc[,ncol(sc)]))
+    # if(scaling>0){
+    #   sc.scaled[,ncol(sc)] <- sc[,ncol(sc)]/(scaling * tmp.fact)
+    # }


    ## scoring init
@@ -166,22 +166,22 @@ mcmcabn <- function(score.cache = NULL, score = "mlik", data.dists = NULL, max.p
        #if(heating=="auto"){factor <- 1/(j+1)}

        switch(method.choice, MC3 = {
-            out <- mc3(n.var, (dag.tmp), retain, ban, max.parents, sc,sc.scaled, score.cache, score, prior.choice, prior.lambda, prior.dag,
-                verbose)
+            out <- mc3(n.var, (dag.tmp), retain, ban, max.parents, sc, score.cache, score, prior.choice, prior.lambda, prior.dag,
+                verbose, heating)
            dag.tmp <- out$dag.tmp
            score <- out$score
        }, REV = {
            if (verbose) {
                print("REV move")
            }
-            out <- REV(n.var, (dag.tmp),retain, ban, max.parents, sc,sc.scaled, score.cache, score, verbose)
+            out <- REV(n.var, (dag.tmp),retain, ban, max.parents, sc, score.cache, score, verbose, heating)
            dag.tmp <- out$dag.tmp
            score <- out$score
        }, MBR = {
            if (verbose) {
                print("MBR move")
            }
-            out <- MBR(n.var, dag.tmp,retain, ban, max.parents, sc,sc.scaled, score.cache, score, verbose)
+            out <- MBR(n.var, dag.tmp,retain, ban, max.parents, sc, score.cache, score, verbose, heating)
            dag.tmp <- out$dag.tmp
            score <- out$score
        })
@@ -207,8 +207,8 @@ mcmcabn <- function(score.cache = NULL, score = "mlik", data.dists = NULL, max.p
            method.choice <- sample(x = c("MC3", "REV", "MBR"), size = 1, prob = c(prob.mc3, prob.rev, prob.mbr))

            switch(method.choice, MC3 = {
-                out <- mc3(n.var, (dag.tmp), retain, ban, max.parents, sc,sc.scaled, score.cache, score, prior.choice, prior.lambda, prior.dag,
-                  verbose)
+                out <- mc3(n.var, (dag.tmp), retain, ban, max.parents, sc, score.cache, score, prior.choice, prior.lambda, prior.dag,
+                  verbose, heating)
                dag.tmp <- out$dag.tmp
                score <- out$score
                alpha <- out$alpha
@@ -217,7 +217,7 @@ mcmcabn <- function(score.cache = NULL, score = "mlik", data.dists = NULL, max.p
                if (verbose) {
                  print("REV move")
                }
-                out <- REV(n.var, (dag.tmp),retain, ban, max.parents, sc,sc.scaled, score.cache, score, verbose)
+                out <- REV(n.var, (dag.tmp),retain, ban, max.parents, sc, score.cache, score, verbose, heating)
                dag.tmp <- out$dag.tmp
                score <- out$score
                alpha <- out$alpha
@@ -226,7 +226,7 @@ mcmcabn <- function(score.cache = NULL, score = "mlik", data.dists = NULL, max.p
                if (verbose) {
                  print("MBR move")
                }
-                out <- MBR(n.var, (dag.tmp), retain, ban, max.parents, sc,sc.scaled, score.cache, score, verbose)
+                out <- MBR(n.var, (dag.tmp), retain, ban, max.parents, sc, score.cache, score, verbose, heating)
                dag.tmp <- out$dag.tmp
                score <- out$score
                alpha <- out$alpha

--- a/R/mcmcabn-utiles.R
+++ b/R/mcmcabn-utiles.R
@@ -5,7 +5,7 @@
 ##-------------------------------------------------------------------------

 .tests.mcmcabn <- function(score.cache, data.dists, max.parents, mcmc.scheme, seed, verbose, start.dag, prior.dag, prior.lambda,
-    prob.rev, prob.mbr, prior.choice,scaling) {
+    prob.rev, prob.mbr, prior.choice,heating) {
    # start tests
    if (is.null(score.cache))
        stop("A cache of score should be provided. You can produce it using the R package abn.")
@@ -55,12 +55,11 @@
    if (is.matrix(prior.dag) && dim(prior.dag)[1] != length(data.dists))
        stop("prior.dag should be a squared matrix with dimension equal to the number of variables.")

-  if(scaling<0)
-    stop("heating parameter shoud be between zero and one. Zero corresponds to a lower probability of accepting a lower score proposal. One corresponds to a larger probability of accepting a lowering of the score (values higher than one are possible but very unlikely to produce meaningful (or helpful) results.).")
+  if(heating<0 | !is.numeric(heating))
+    stop("heating parameter shoud be between a positive real number. One is neutral. Smaller than one is heating up and larger than one is cooling down the convergence rate.")

 }

-
 ##-------------------------------------------------------------------------
 ## Internal function that call multiple times strsplit() and remove space
 ##-------------------------------------------------------------------------

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@@ -8,21 +8,25 @@

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      <h1>Authors</h1>
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-        <p><strong><a href='https://gilleskratzer.netlify.com/'>Gilles Kratzer</a></strong>. Author, maintainer. <a href='https://orcid.org/0000-0002-5929-8935' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
+        <p><strong><a href='https://gilleskratzer.netlify.com/'>Gilles Kratzer</a></strong>. Author, maintainer. <a href='https://orcid.org/0000-0002-5929-8935' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID'></a>
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-        <p><strong><a href='https://user.math.uzh.ch/furrer/'>Reinhard Furrer</a></strong>. Contributor. <a href='https://orcid.org/0000-0002-6319-2332' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
+        <p><strong><a href='https://user.math.uzh.ch/furrer/'>Reinhard Furrer</a></strong>. Contributor. <a href='https://orcid.org/0000-0002-6319-2332' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID'></a>
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@@ -163,19 +171,23 @@ R package version 0.1, <a href="https://CRAN.R-project.org/package=mcmcabn">http
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 <title>Flexible Implementation of a Structural MCMC Sampler for DAGs • mcmcabn</title>
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 <meta property="og:description" content="Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) &lt;doi:10.1007/s10994-008-5057-7&gt; and the Markov blanket resampling from Su and Borsuk (2016) &lt;http://jmlr.org/papers/v17/su16a.html&gt;. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) &lt;http://dl.acm.org/citation.cfm?id=1005332.1005352&gt;, an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures. It allows to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it's magnitude is big enough to consider as a worthwhile intervention.">
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-<hr>
-<div id="mcmcabn-a-structural-mcmc-sampler-for-dags-learned-from-observed-systemic-datasets" class="section level1">
+      </header><div class="row">
+  <div class="contents col-md-9">
+<div id="mcmcabn-an-r-package-for-sampling-dags-using-structural-mcmc" class="section level1">
 <div class="page-header"><h1 class="hasAnchor">
-<a href="#mcmcabn-a-structural-mcmc-sampler-for-dags-learned-from-observed-systemic-datasets" class="anchor"></a>mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets</h1></div>
-<div id="quick-start" class="section level2">
+<a href="#mcmcabn-an-r-package-for-sampling-dags-using-structural-mcmc" class="anchor"></a>mcmcabn: An R Package for sampling DAGs using structural MCMC</h1></div>
+<p>(PUBLIC) !!! UNSTABLE VERSION !!!</p>
+<p>mcmcabn is a one-man-show (me!) and made of more than 10’000 lines of code which are not bug free! So use it with caution and awareness.</p>
+<div id="installation" class="section level2">
 <h2 class="hasAnchor">
-<a href="#quick-start" class="anchor"></a>Quick start</h2>
-<p>To install <code>mcmabn</code> you need two R packages: <a href="https://CRAN.R-project.org/package=abn">abn</a> and <a href="https://CRAN.R-project.org/package=gRbase">gRbase</a> which requires libraries not stored on <a href="https://cran.r-project.org/">CRAN</a> but on <a href="http://www.bioconductor.org/">bioconductor</a>. Hence you <strong>must</strong> install these packages <strong>before</strong> installing <code>mcmcabn</code>:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"