Overview

From the UW-CSE Alchemy Page.

“This data set consists of information about the University of Washington Department of Computer Science and Engineering. The data has been anonymized to comply with the University of Washington’s privacy guidelines.”

As usual, the version here is a .zip with the necessary background and train/test folders.

Target: advisedby

The facts contain information on fourteen labels: courselevel, hasposition, inphase, professor, projectmember, publication, samecourse, sameperson, sameproject, student, ta, taughtby, tempadvisedby, yearsinprogram.

Download

Download: UW-CSE.zip (257 KB)

  • md5sum:

    5e8217ebdb835ff8b6ff94eb3880d96b

  • sha256sum:

    f16be492805bdac95cded02a3a3e590c29a68145f5ea59eb4180c300fb23b7e2

Setup

  1. After downloading, unzip UW-CSE.zip

    unzip UW-CSE.zip

  2. If you’re using a jar file, move it into the UW-CSE directory:

    mv (BoostSRL jar file) UW-CSE/
    mv (auc jar file) UW-CSE/

  3. Learning:

    java -jar BoostSRL.jar -l -train train/ -target advisedby -trees 10

  4. Inference:

java -jar BoostSRL.jar -i -test test/ -model train/models/ -aucJarPath . -target advisedby -trees 10

Modes

setParam: loadAllLibraries = false.
setParam: treeDepth=3.
setParam: nodeSize=1.
setParam: numOfClauses=8.
setParam: numOfCycles=8.
importLibrary:  listsInLogic.
queryPred: advisedby/2.
mode: professor(+Person).
mode: student(+Person).
mode: publication(+Title, -Person).
mode: publication(-Title, +Person).
mode: taughtby(+Course, +Person, -Quarter).
mode: taughtby(+Course, -Person, +Quarter).
mode: taughtby(-Course, +Person, -Quarter).
mode: courselevel(+Course, +Level).
mode: courselevel(+Course, #Level).
mode: hasposition(+Person, +Position!1).
mode: hasposition(+Person, #Position).
mode: multiclass_hasposition(+Person).
okIfUnknown: multiclass_hasposition/1.
mode: projectmember(+Project, -Person).
mode: projectmember(-Project, +Person).
range: Position={faculty_affiliate,faculty,faculty_adjunct,faculty_emeritus}.
range: Phase={pre_quals,post_generals,post_quals}.
mode: position(+Position).
mode: phase(+Phase).
position(faculty_affiliate).
position(faculty).
position(faculty_adjunct).
position(faculty_emeritus).
phase(pre_quals).
phase(post_generals).
phase(post_quals).
mode: advisedby(+Person, +Person).
mode: inphase(+Person, +Phase!1).
mode: inphase(+Person, #Phase).
mode: multiclass_inphase(+Person).
okIfUnknown: multiclass_inphase/1.
mode: tempadvisedby(-Person, +Person).
mode: tempadvisedby(+Person, -Person).
mode: yearsinprogram(+Person, #Integer).
mode: ta(+Course, -Person, +Quarter).
mode: ta(+Course, +Person, -Quarter).
mode: ta(-Course, +Person, -Quarter).
mode: sameperson(+Person, +Person).
mode: samecourse(+Course, +Course).
mode: sameproject(+Project, +Project).
mode: have_more_than_n_pubs(+Person, #PThresh).
mode: have_more_than_n_common_pubs(+Person, -Person, #PThresh).
mode: have_more_than_n_common_pubs(-Person, +Person, #PThresh).
mode: count_taughtby(+Person, -PThresh).
mode: count_publications(+Person, -PThresh).
mode: count_common_pubs(+Person, -Person, -PThresh).
mode: count_common_pubs(-Person, +Person, -PThresh).
usePrologVariables: true.
precompute:
commonpub(Title, P1,P2) :- publication(Title, P1), publication(Title, P2),P1\==P2.
precompute:
commonta(C,Q,P1,P2) :- ta(C,P2,Q), taughtby(C,P1,Q).
precompute1:
count_taughtby(Person,N) :- taughtby(SomeC, Person, SomeQ), all([Course, Quarter], taughtby(Course, Person, Quarter), AllCourses), N is length(AllCourses).
precompute1:
count_publications(Person,N) :- publication(Somet, Person), all(Title, publication(Title, Person), AllTitles), N is length(AllTitles).
precompute1:
count_common_pubs(P1,P2,N) :- commonpub(Somet, P1,P2), all(Title, commonpub(Title, P1,P2), AllTitles),  N is length(AllTitles).
precompute2:
have_more_than_n_pubs(A,N) :-
	        count_publications(A,N2),
		member(N,[1, 3, 5, 7, 9,11,13,15]),
		        N2 > N.
precompute2:
have_more_than_n_common_pubs(A1,A2,N) :-
	        count_common_pubs(A1,A2,N2),
		member(N,[1, 3, 5, 7, 9,11,13,15]),
		        N2 > N.

Updated: