danielkorzekwa
commented
9 years ago error on cell 11:
In [6]:
:local-repo /tmp/snb/repo
res10: String = Repo changed to /tmp/snb/repo!
Out[6]: Repo changed to /tmp/snb/repo! In [7]:
:remote-repo sonasnap % default % https://oss.sonatype.org/content/repositories/snapshots/
res11: String = Remote repo added: sonasnap % default % https://oss.sonatype.org/content/repositories/snapshots/!
Out[7]: Remote repo added: sonasnap % default % https://oss.sonatype.org/content/repositories/snapshots/! In [10]:
:dp com.github.danielkorzekwa % bayes-scala_2.11 % 0.5-SNAPSHOT
warning: there were 2 feature warning(s); re-run with -feature for details jars: Array[String] = [Ljava.lang.String;@4cdddd res20: List[String] = List(/tmp/snb/repo/com/googlecode/efficient-java-matrix-library/ejml/0.20/ejml-0.20.jar, /tmp/snb/repo/com/github/fommil/netlib/netlib-native_ref-linux-armhf/1.1/netlib-native_ref-linux-armhf-1.1-natives.jar, /tmp/snb/repo/net/sf/opencsv/opencsv/2.3/opencsv-2.3.jar, /tmp/snb/repo/com/github/fommil/netlib/netlib-native_system-linux-x86_64/1.1/netlib-native_system-linux-x86_64-1.1-natives.jar, /tmp/snb/repo/com/github/fommil/netlib/netlib-native_ref-win-i686/1.1/netlib-native_ref-win-i686-1.1-natives.jar, /tmp/snb/repo/org/spire-math/spire_2.11/0.7.4/spire_2.11-0.7.4.jar, /tmp/snb/repo/org/scalanlp/breeze_2.11/0.11.2/breeze_2.11-0.11.2.jar, /tmp/snb/repo/org/scalanlp/breeze-macros_2.11/0.11.2/breeze-macros_2.11-0.11.2....
Out[10]:
In [11]:
import notebook.front.third.d3._
import notebook., front., widgets._
import notebook.JsonCodec._
import play.api.libs.json._
import dk.bayes.dsl.variable.Categorical
import dk.bayes.dsl.infer
type CategoricalWithInfo = (String, Categorical, Seq[String])
val loadedCode = {
val source = scala.io.Source.fromURL(" https://gist.githubusercontent.com/nightscape/c2fcccac859b3ae34c99/raw/d3_bayesian_network.js ")
val res = source.mkString
source.close()
res
}
import play.api.libs.json._
import play.api.libs.functional.syntax._
import dk.bayes.dsl.variable.Categorical
import dk.bayes.dsl.infer
case class ConditionalProbabilityTable(node: BnNode, parents: Seq[BnNode], probabilities: Seq[Double])
case class BnNode(name: String, states: Seq[String], currentState: Option[String] = None)
case class BnEdge(source: Int, target: Int)
def categoricalsToNetwork(marginalizer: Categorical => Seq[Double], cptExtractor: Categorical => Seq[Double] = _.cpd)(categoricalsWithNames: Seq[CategoricalWithInfo]): (Seq[(BnNode, ConditionalProbabilityTable, ConditionalProbabilityTable)], Seq[BnEdge]) = {
import breeze.linalg._
import breeze.numerics._
val nodes = categoricalsWithNames.map { case(name, categorical, states) =>
val currentState = categorical.getValue().map(states.apply)
BnNode(name, states, currentState)}
val categoricals = categoricalsWithNames.map(_._2)
val nodeMap = categoricals.zip(nodes).toMap
val cpts = categoricals.zip(nodes).map { case(cat, node) =>
val parents = cat.parents.map(nodeMap)
val numCols = node.states.size
val cpd = cptExtractor(cat)
val inferredCpd = infer(cat).cpd
val numRows = cpd.size / numCols
val cptArray = cpd.toArray
val cpt = new DenseMatrix(numCols, numRows, cptArray).t
(ConditionalProbabilityTable(node, parents, cpt.toArray),ConditionalProbabilityTable(node, Seq(), inferredCpd.toArray))
}
val edges = nodeMap.flatMap { case(cat, node) =>
val parents = cat.parents.map(nodeMap)
parents.map(p => BnEdge(nodes.indexOf(p), nodes.indexOf(node)))}.toSeq
(nodes.zip(cpts).map { case(n, (c, m)) => (n, c, m)}, edges)
}
object ConditionalProbabilityTable {
implicit val conditionalProbabilityTableWrites: Writes[ConditionalProbabilityTable] = Json.writes[ConditionalProbabilityTable]
}
object BnNode {
implicit val nodeWrites: Writes[BnNode] = Json.writes[BnNode]
}
object BnEdge {
implicit val edgeWrites: Writes[BnEdge] = Json.writes[BnEdge]
}
def networkToJson(nodesWithCpts: Seq[(BnNode, ConditionalProbabilityTable, ConditionalProbabilityTable)], edges: Seq[BnEdge]): JsObject = {
val nodeJs = nodesWithCpts.map { case(node, cpt, marginalized) =>
Json.toJson(node).asInstanceOf[JsObject] + ("cpt", Json.toJson(cpt)) +("marginalized", Json.toJson(marginalized))
}
Json.obj("nodes" -> Json.toJson(nodeJs), "edges" -> Json.toJson(edges))
}
val convertCategoricals: Seq[CategoricalWithInfo] => JsObject = (categoricalsToNetwork({cat: Categorical => infer(cat).cpd}) ).andThen((networkToJson ).tupled)
implicit val categoricalsCodec = new Codec[JsValue, Seq[CategoricalWithInfo]] {
def encode(x:JsValue):Seq[CategoricalWithInfo] = Seq()
def decode(x:Seq[CategoricalWithInfo]):JsValue = convertCategoricals(x)
}
val playgroundCode = s"""
function(dataPipe, e) {
$loadedCode
var bnGraph = BayesianNetworkGraph(e)
bnGraph(this.dataInit[0])
dataPipe.subscribe(function(d) {
bnGraph(d[0])})
}
"""
()
nightscape
andypetrella
Hi Daniel,
I've finally managed to create a visualization for Bayesian Networks constructed from Categoricals. Check out the README of the Gist here: https://gist.github.com/nightscape/c2fcccac859b3ae34c99#file-readme-md
Could you check if it runs on your machine? If so we can think about how to maybe integrate this into bayes-scala :)
Best and thanks again for your help! Martin