From c671d8aa2957f292c655030e4893651781993810 Mon Sep 17 00:00:00 2001 From: Fabrizio Riguzzi Date: Wed, 4 Sep 2013 21:12:09 +0200 Subject: [PATCH] slipcover manual --- packages/cplint/doc/bib.bib | 8 + packages/cplint/doc/manual.html | 457 ++++++++++++++++++++------------ packages/cplint/doc/manual.pdf | Bin 221427 -> 225798 bytes packages/cplint/doc/manual.tex | 87 ++++-- 4 files changed, 358 insertions(+), 194 deletions(-) diff --git a/packages/cplint/doc/bib.bib b/packages/cplint/doc/bib.bib index 329b071b3..50460c5c8 100644 --- a/packages/cplint/doc/bib.bib +++ b/packages/cplint/doc/bib.bib @@ -1,3 +1,11 @@ +@article{BelRig13-TPLP-IJ, + author = {Elena Bellodi and Fabrizio Riguzzi}, + title = {Structure Learning of Probabilistic Logic Programs by Searching the Clause Space}, + journal = {Theory and Practice of Logic Programming}, + publisher = {Cambridge University Press}, + copyright = {Cambridge University Press}, + year = {2013} +} @article{BelRig11-IDA, author = {Elena Bellodi and Fabrizio Riguzzi}, title = { Expectation {Maximization} over Binary Decision Diagrams for Probabilistic Logic Programs}, diff --git a/packages/cplint/doc/manual.html b/packages/cplint/doc/manual.html index 797aca095..566009765 100644 --- a/packages/cplint/doc/manual.html +++ b/packages/cplint/doc/manual.html @@ -7,7 +7,7 @@ - + @@ -21,18 +21,18 @@ class="cmr-12">Fabrizio Riguzzi
fabrizio.riguzzi@unife.it
July 16, 2013
+class="cmr-12">September 4, 2013

1 Introduction

cplint is a suite of programs for reasoning with ICL [13], LPADs [22, 23] and +href="#XDBLP:journals/ai/Poole97">14], LPADs [23, 24] and CP-logic programs [20, 21]. It contains programs both for inference and +href="#XVenDenBru-JELIA06">21, 22]. It contains programs both for inference and learning.

2

The coin example of [23] is represented as (see file 24] is represented as (see file coin.cpl)

@@ -169,13 +169,13 @@ class="cmtt-10">cplint contains various modules for answering queries.
  • lpadsld.pl: uses the top-down procedure described in in [14] and [15]. It +href="#XRig-AIIA07-IC">15] and [16]. It is based on SLDNF resolution and is an adaptation of the interpreter for ProbLog [9]. +href="#XDBLP:conf/ijcai/RaedtKT07">10].

    It was proved correct [15] with respect to the semantics of LPADs for +href="#XRig-RCRA07-IC">16] with respect to the semantics of LPADs for range restricted acyclic programs [1] without function symbols.

    It is also able to deal with extensions of LPADs and CP-logic: the clause @@ -188,8 +188,8 @@ class="cmtt-10">setof or bagof operator. These extended features have been introduced in order to represent CLP(BN) [19] programs and PRM models [12]: 20] programs and PRM models [13]: setof and bagof allow to express dependency of an attribute from an aggregate function of another @@ -199,11 +199,11 @@ class="cmtt-10">bagof allow to

  • picl.pl: performs inference on ICL programs [16] +href="#XRig09-LJIGPL-IJ">17]
  • lpad.pl: uses a top-down procedure based on SLG resolution [8]. As a +href="#XDBLP:journals/jacm/ChenW96">9]. As a consequence, it works for any sound LPADs, i.e., any LPAD such that each of its instances has a two valued well founded model.
    • @@ -217,28 +217,28 @@ class="cmtt-10">cpl.pl: uses a top-down procedure based on SLG resolution
    • deepit.pl performs iterative deepening [7] +href="#XBraRig10-ILP10-IC">8]
    • deepdyn.pl performs dynamic iterative deepening [7] +href="#XBraRig10-ILP10-IC">8]
    • bestk.pl performs k-Best [7] +href="#XBraRig10-ILP10-IC">8]
    • bestfirst.pl performs best first [7] +href="#XBraRig10-ILP10-IC">8]
    • montecarlo.pl performs Monte Carlo [7] +href="#XBraRig10-ILP10-IC">8]
    • mcintyre.pl: implements the algorithm MCINTYRE (Monte Carlo INference wiTh Yap REcord) [17]
    +href="#XRig11-CILC11-NC">18]
  • approx/exact.pl as P, i.e., sufficient causation, independent causation, no deus ex machina events and temporal precedence. It uses the definition of the semantics given in [21].
    • +href="#XDBLP:journals/tplp/VennekensDB09">22].

    4.1 Commands

    @@ -827,55 +827,55 @@ class="cmtt-10">examples:
  • alarm.cpl: representation of the Bayesian network in Figure 2 of [23]. +href="#XVenVer04-ICLP04-IC">24].
  • coin.cpl: coin example from [23]. +href="#XVenVer04-ICLP04-IC">24].
  • coin2.cpl: coin example with two coins.
  • dice.cpl: dice example from [23]. +href="#XVenVer04-ICLP04-IC">24].
  • twosideddice.cpl, threesideddice.cpl game with idealized dice with two or three sides. Used in the experiments in [15]. +href="#XRig-RCRA07-IC">16].
  • ex.cpl: first example in [15]. +href="#XRig-RCRA07-IC">16].
  • exapprox.cpl: example showing the problems of approximate inference (see [15]). +href="#XRig-RCRA07-IC">16]).
  • exrange.cpl: example showing the problems with non range restricted programs (see [15]). +href="#XRig-RCRA07-IC">16]).
  • female.cpl: example showing the dependence of probabilities in the head from variables in the body (from [23]). +href="#XVenVer04-ICLP04-IC">24]).
  • mendel.cpl, mendels.cpl: programs describing the Mendelian rules of inheritance, taken from [6]. +href="#XBlo04-ILP04WIP-IC">7].
  • paper_ref.cpl, paper_ref_simple.cpl: paper citations examples, showing reference uncertainty, inspired by [12]. +href="#XGetoor+al:JMLR02">13].
  • paper_ref_not.cpl: paper citations example showing that negation @@ -895,13 +895,13 @@ class="cmtt-10">school.cpl.
  • student.cpl: student example from Figure 1.3 of [11]. +href="#XGetFri01-BC">12].
  • win.cpl, light.cpl, trigger.cpl, throws.cpl, hiv.cpl,
    invalid.cpl: programs taken from [21]. 22]. invalid.cpl is an example of a program that is invalid but sound.

    • The files cplint contains the following learning algorithms: class="cmtt-10">cplint EM): an implementation of EM for learning parameters that is based on lpadsld.pl [18] +href="#XRigDiM11-ML-IJ">19]

  • RIB (Relational Information Bottleneck): an algorithm for learning parameters based on the Information Bottleneck [18] +href="#XRigDiM11-ML-IJ">19]
  • EMBLEM (EM over Bdds for probabilistic Logic programs Efficient Mining): an implementation of EM for learning parameters that computes @@ -935,14 +935,20 @@ href="#XBelRig11-IDA">5, 2, 3]
    • -
  • SLIPCASE (Structure LearnIng of ProbabilistiC logic progrAmS with Em - over bdds): an algorithm for learning the structure of program that is - based on EMBLEM [4]
    • -

    +

  • SLIPCASE (Structure LearnIng of ProbabilistiC logic progrAmS with + Em over bdds): an algorithm for learning the structure of programs by + searching directly the theory space [4] +
    • +
  • SLIPCOVER (Structure LearnIng of Probabilistic logic programs by + searChing OVER the clause space): an algorithm for learning the structure + of programs by searching the clause space and the theory space separatery + [6]
    • +

    5.1 Input

    -

    To execute the learning algorithms, prepare four files in the same folder: +

    To execute the learning algorithms, prepare four files in the same folder:

    • <stem>.kb: contains the example interpretations @@ -956,11 +962,13 @@ class="cmtt-10"><stem>.l: contains language bias informatio
    • <stem>.cpl: contains the LPAD for you which you want to learn the - parameters or the initial LPAD for SLIPCASE
    -

    where + +

    where <stem> is your dataset name. Examples of these files can be found in the dataset pages. -

    In

    In <stem>.kb the example interpretations have to be given as a list of Prolog facts initiated by begin(model(<name>)). and terminated by actor(2).  
    end(model(b1)).

    -

    The interpretations may contain a fact of the form +

    The interpretations may contain a fact of the form

    prob(0.3).
    -

    assigning a probability (0.3 in this case) to the interpretations. If this is omitted, the +

    assigning a probability (0.3 in this case) to the interpretations. If this is omitted, the probability of each interpretation is considered equal to 1∕n where n is the total number of interpretations. prob/1 can be used to set different multiplicity for the different interpretations. -

    In order for RIB to work, the input interpretations must share the Herbrand +

    In order for RIB to work, the input interpretations must share the Herbrand universe. If this is not the case, you have to translate the interpretations in this was, see for example the sp1 files in RIB’s folder, that are the results of the conversion of the first fold of the IMDB dataset. -

    <stem>.bg can contain Prolog clauses that can be used to derive additional conclusions from the atoms in the interpretations. -

    <stem>.l contains the declarations of the input and output predicates, of the unseen predicates and the commands for setting the algorithms’ parameters. Output predicates are declared as @@ -1008,72 +1016,92 @@ predicates are declared as

    output(<predicate>/<arity>).
    -

    and define the predicates whose atoms in the input interpretations are used as the +

    and define the predicates whose atoms in the input interpretations are used as the goals for the prediction of which you want to optimize the parameters. Derivations for these goals are built by the systems. -

    Input predicates are those for the predictions of which you do not want to +

    Input predicates are those for the predictions of which you do not want to optimize the parameters. You can declare closed world input predicates with

    input_cw(<predicate>/<arity>).
    -

    For these predicates, the only true atoms are those in the interpretations, the +

    For these predicates, the only true atoms are those in the interpretations, the clauses in the input program are not used to derive atoms not present in the interpretations. -

    Open world input predicates are declared with +

    Open world input predicates are declared with

    input(<predicate>/<arity>).
    -

    In this case, if a subgoal for such a predicate is encountered when deriving the atoms +

    In this case, if a subgoal for such a predicate is encountered when deriving the atoms for the output predicates, both the facts in the interpretations and the clauses of the input program are used. -

    For RIB, if there are unseen predicates, i.e., predicates that are present in the +

    For RIB, if there are unseen predicates, i.e., predicates that are present in the input program but not in the interpretations, you have to declare them with

    unseen(<predicate>/<arity>).
    -

    -

    For SLIPCASE, you have to specify the language bias by means of mode -declarations in the style of

    +

    For SLIPCASE and SLIPCOVER, you have to specify the language bias by +means of mode declarations in the style of Progol .

    modeh(<recall>,<predicate>(<arg1>,...).
    -

    specifies the atoms that can appear in the head of clauses, while +

    specifies the atoms that can appear in the head of clauses, while

    modeb(<recall>,<predicate>(<arg1>,...).
    -

    specifies the atoms that can appear in the body of clauses.

    specifies the atoms that can appear in the body of clauses. <recall> can be an integer or * (currently unused). -

    The arguments are of the form +

    The arguments are of the form

    +<type>
    -

    for specifying an input variable of type

    for specifying an input variable of type <type>, or

    -<type>
    -

    for specifying an output variable of type

    for specifying an output variable of type <type>. or

    <constant>
    -

    for specifying a constant. -

    An example of language bias for the UWCSE domain is +

    for specifying a constant. +

    SLIPCOVER also allows the arguments

    +#<type> +
    +

    for specifying an argument which should be replaced by a constant of type <type> in +the bottom clause but should not be used for replacing input variables of the +following literals or + +

    +-#<type> +
    +

    for specifying an argument which should be replaced by a constant of type <type> in +the bottom clause and that should be used for replacing input variables of +the following literals. # and -# differ only in the creation of the bottom +clause. +

    An example of language bias for the UWCSE domain is + +

    output(advisedby/2).  
     
    input(student/1). @@ -1090,18 +1118,57 @@ output(advisedby/2).  
    modeb(*,samecourse(-course, +course)).  
    ....
    -

    -

    +

    SLIPCOVER also requires facts for the determination/2 predicate that indicate +which predicates can appear in the body of clauses. For example + +

    +determination(professor/1,student/1). + 
    determination(student/1,hasposition/2). +
    +

    state that student/1 can appear in the body of clauses for professor/1 and that +hasposition/2 can appear in the body of clauses for student/1. +

    SLIPCOVER also allows mode declarations of the form + +

    +modeh(<r>,[<s1>,...,<sn>],[<a1>,...,<an>],[<P1/Ar1>,...,<Pk/Ark>]). +
    +

    These mode declarations are used to generate clauses with more than two head +atoms. In them, <s1>,...,<sn> are schemas, <a1>,...,<an> are atoms such that +<ai> is obtained from <si> by replacing placemarkers with variables, <Pi/Ari> are +the predicates admitted in the body. <a1>,...,<an> are used to indicate which +variables should be shared by the atoms in the head. An example of such a mode +declaration is + +

    +modeh(*, + 
      [advisedby(+person,+person),tempadvisedby(+person,+person)], + 
      [advisedby(A,B),tempadvisedby(A,B)], + 
      [professor/1,student/1,hasposition/2,inphase/2, + 
      publication/2,taughtby/3,ta/3,courselevel/2,yearsinprogram/2]). +
    +

    +

    5.2 Parameters

    -

    In order to set the algorithms’ parameters, you have to insert in

    In order to set the algorithms’ parameters, you have to insert in <stem>.l commands of the form -

    +
    :- set(<parameter>,<value>).
    -

    The available parameters are: +

    The available parameters are:

    -

    +class="cmtt-10">beamsize (values: integer, default value: 20, valid for SLIPCASE and + SLIPCOVER): size of the beam + +

  • megaex_bottom (values: integer, default value: 1, valid for SLIPCOVER): + number of mega-examples on which to build the bottom clauses +
    • +
  • initial_clauses_per_megaex (values: integer, default value: 1, valid for + SLIPCOVER): number of bottom clauses to build for each mega-example +
    • +
  • d (values: integer, default value: 10000, valid for SLIPCOVER): number + of saturation steps when building the bottom clause +
    • +
  • max_iter_structure (values: integer, default value: 1, valid for + SLIPCOVER): maximum number of theory search iterations +
    • +
  • background_clauses (values: integer, default value: 50, valid for + SLIPCOVER): maximum numbers of background clauses.
    • +

    5.3 Commands

    -

    To execute CEM, load

    To execute CEM, load em.pl with -

    +
    ?:- use_module(library(’cplint/em’)).
    -

    and call: - -

    -?:- em(stem). -
    -

    To execute RIB, load rib.pl with - -

    -?:- use_module(library(’cplint/rib’)). -
    -

    and call: - -

    -?:- ib_par(stem). -
    -

    To execute EMBLEM, load slipcase.pl with - -

    -?:- use_module(library(’cplint/slipcase’)). -
    -

    and call - -

    -?:- em(stem). -
    -

    To execute SLIPCASE, load slipcase.pl with +

    and call:

    -?:- use_module(library(’cplint/slipcase’)). +?:- em(stem).
    -

    and call +

    To execute RIB, load rib.pl with

    +?:- use_module(library(’cplint/rib’)). +
    +

    and call: + +

    +?:- ib_par(stem). +
    +

    To execute EMBLEM, load slipcase.pl with + +

    +?:- use_module(library(’cplint/slipcase’)). +
    +

    and call + +

    +?:- em(stem). +
    +

    To execute SLIPCASE, load slipcase.pl with + +

    +?:- use_module(library(’cplint/slipcase’)). +
    +

    and call + +

    ?:- sl(stem).
    -

    +

    To execute SLIPCOVER, load slipcover.pl with + +

    +?:- use_module(library(’cplint/slipcover’)). +
    +

    and call + +

    +?:- sl(stem). +
    +

    5.4 Learning Examples

    -

    The subfolders

    The subfolders em, rib and slipcase of the packages/cplint folder in Yap -git distribution contain examples of input and output files for the learning +class="cmtt-10">rib, slipcase and slipcover of the packages/cplint folder in +Yap git distribution contain examples of input and output files for the learning algorithms. -

    +

    6 License

    -

    cplint, as Yap, follows the Artistic License 2.0 that you can find in Yap CVS root dir. The copyright is by Fabrizio Riguzzi. -

    The modules in the approx subdirectory use SimplecuddLPADs, a modification of +

    The modules in the approx subdirectory use SimplecuddLPADs, a modification of the Simplecudd library whose copyright is by Katholieke Universiteit Leuven and that follows the Artistic License 2.0. -

    Some modules use the library

    Some modules use the library CUDD for manipulating BDDs that is included in glu. For the use of CUDD, the following license must be accepted: -

    Copyright (c) 1995-2004, Regents of the University of Colorado -

    All rights reserved. -

    Redistribution and use in source and binary forms, with or without modification, +

    Copyright (c) 1995-2004, Regents of the University of Colorado +

    All rights reserved. +

    Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

    • Redistributions of source code must retain the above copyright notice, this @@ -1334,7 +1433,7 @@ are permitted provided that the following conditions are met: contributors may be used to endorse or promote products derived from this software without specific prior written permission.
    -

    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS

    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS
    AND CONTRIBUTORS ”AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR @@ -1348,7 +1447,7 @@ class="newline" />AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -

    lpad.pl, semlpad.pl and cpl.pl are based on the SLG system by Weidong @@ -1402,6 +1501,14 @@ class="cmti-10">Intel. Data Anal.,

    [6]   Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic + logic programs by searching the clause space. Theory and Practice of Logic + Programming, 2013. +

    +

    + [7]   H. Blockeel. Probabilistic logical models for mendel’s experiments: An exercise. In Inductive Logic Programming (ILP 2004), Work in Progress @@ -1409,7 +1516,7 @@ class="cmti-10">Inductive Logic Programming (ILP 2004), Work in Progress class="cmti-10">Track, 2004.

    - [7]      Stefano Bragaglia and Fabrizio Riguzzi. Approximate inference for logic programs with annotated disjunctions. In Paolo Frasconi and Francesca Lisi, editors, ILP 2010, Florence, Italy, June 27-30, 2010. Revised PapersLNCS, pages 30–37. Springer, 2011.

    - [8]      Weidong Chen and David Scott Warren. Tabled evaluation with delaying for general logic programs. Journal of the ACM, 43(1):20–74, 1996.

    - [9]      L. De Raedt, A. Kimmig, and H. Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. In International Joint @@ -1434,22 +1541,22 @@ class="cmti-10">International Joint class="cmti-10">Conference on Artificial Intelligence, pages 2462–2467, 2007.

    - [10]      G. Elidan and N. Friedman. Learning hidden variable networks: The information bottleneck approach. Journal of Machine Learning Research, 6:81–127, 2005. -

    -

    - [11]   L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning - probabilistic relational models. In Saso Dzeroski and Nada Lavrac, editors, - Relational Data Mining. Springer-Verlag, Berlin, 2001.

    [12]   L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning + probabilistic relational models. In Saso Dzeroski and Nada Lavrac, editors, + Relational Data Mining. Springer-Verlag, Berlin, 2001. +

    +

    + [13]   L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning probabilistic models of relational structure. Journal of Machine Learning @@ -1457,13 +1564,13 @@ class="cmti-10">Journal of Machine Learning class="cmti-10">Research, 3:679–707, December 2002.

    - [13]      David Poole. The independent choice logic for modelling multiple agents under uncertainty. Artificial Intelligence, 94(1-2):7–56, 1997.

    - [14]      Fabrizio Riguzzi. A top down interpreter for LPAD and CP-logic. In Congress of the Italian Association for Artificial Intelligence, volume 4733 @@ -1471,7 +1578,7 @@ class="cmti-10">Congress of the Italian Association for Artificial Intelligence< class="cmti-10">LNAI, pages 109–120. Springer, 2007.

    - [15]      Fabrizio Riguzzi. A top down interpreter for LPAD and CP-logic. In Proceedings of the 14th RCRA workshop Experimental Evaluation of @@ -1479,13 +1586,13 @@ class="cmti-10">Proceedings of the 14th RCRA workshop Experimental Evaluation of class="cmti-10">Algorithms for Solving Problems with Combinatorial Explosion, 2007.

    - [16]      Fabrizio Riguzzi. Extended semantics and inference for the Independent Choice Logic. Logic Journal of the IGPL, 17(6):589–629, 2009.

    - [17]      Fabrizio Riguzzi. MCINTYRE: A Monte Carlo algorithm for probabilistic logic programming. In Proceedings of the 26th Italian @@ -1495,14 +1602,15 @@ class="cmti-10">Conference on Computational Logic (CILC2011), Pescara, Ita class="cmti-10">August-2 September, 2011, 2011.

    - [18]      Fabrizio Riguzzi and Nicola Di Mauro. Applying the information bottleneck to statistical relational learning. Machine Learning, 2011. To appear. +

    - [19]      V. Santos Costa, D. Page, M. Qazi, and J. Cussens. CLP(BN): class="cmti-10">Uncertainty in Artificial Intelligence. Morgan Kaufmann, 2003. -

    - [20]      J. Vennekens, M. Denecker, and M. Bruynooghe. Representing causal information about a probabilistic process. In Proceedings of the 10th @@ -1523,19 +1630,19 @@ class="cmti-10">European Conference on Logics in Artificial Intelligence, September 2006.

    - [21]      J. Vennekens, Marc Denecker, and Maurice Bruynooghe. CP-logic: A language of causal probabilistic events and its relation to logic programming. Theory Pract. Log. Program., 9(3):245–308, 2009.

    - [22]      J. Vennekens and S. Verbaeten. Logic programs with annotated disjunctions. Technical Report CW386, K. U. Leuven, 2003.

    - [23]      J. Vennekens, S. Verbaeten, and M. Bruynooghe. Logic programs with annotated disjunctions. 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zKp_)e%{MCue!P!?i($O^N#J0IcM}PL$#@Hr7zhc*=Q{~(g=G&in6q0fK8Zi95I1u~4V1}1%eBbu>4 ziB6&6H6&4~B$l9|Q;B#Q8K&T|DUl5F7_T&mPGi9fWSGpr*J@A$Xe?<*CWA77-yIpe zw1S03CgCjuYA8#QBE#sP&HWGm0jLM|j1vf&6aG8^@F|(bq6`Y^9&RofR5*OTlL7pj zXKAMW$)J1U7beqzI(UH~?O18_nLGw!{>2d)U{My!peT_=LD2FUc!3n4AfAx|@CRNX zg-B$Heo$Ch+cC&&+#0~e$lp}_hyOt91$)Nr7_&riv{KJ1V3VjLPpEWrwB;AesPn**qh2AbGR{ri_X zFz^x{p~B$kj?ZmCqu?764A2_>3Iq&370RQdL(sJGj{>iSqEUD>t%HNZ!8c$nf;Erk+901`@B|Wg=E;cQqC+!4 zjA617nWhg@X(nU?V*?`-07^kq(l;=s!ukffg#VAnK-Z0+tIHD>?im`sBh=f62dD.kb}: contains the example interpretations \item \texttt{.bg}: contains the background knowledge, i.e., knowledge valid for all interpretations \item \texttt{.l}: contains language bias information -\item \texttt{.cpl}: contains the LPAD for you which you want to learn the parameters or the initial LPAD for SLIPCASE +\item \texttt{.cpl}: contains the LPAD for you which you want to learn the parameters or the initial LPAD for SLIPCASE. For SLIPCOVER, this file should be absent \end{itemize} where \texttt{} is your dataset name. Examples of these files can be found in the dataset pages. @@ -503,7 +504,7 @@ For RIB, if there are unseen predicates, i.e., predicates that are present in th unseen(/). \end{verbatim} -For SLIPCASE, you have to specify the language bias by means of mode declarations in the style of +For SLIPCASE and SLIPCOVER, you have to specify the language bias by means of mode declarations in the style of \href{http://www.doc.ic.ac.uk/\string ~shm/progol.html}{Progol}. \begin{verbatim} modeh(,(,...). @@ -529,6 +530,16 @@ for specifying an output variable of type \texttt{}. or \end{verbatim} for specifying a constant. +SLIPCOVER also allows the arguments +\begin{verbatim} +# +\end{verbatim} +for specifying an argument which should be replaced by a constant of type \texttt{} in the bottom clause but should not be used for replacing input variables of the following literals or +\begin{verbatim} +-# +\end{verbatim} +for specifying an argument which should be replaced by a constant of type \texttt{} in the bottom clause and that should be used for replacing input variables of the following literals. \verb|#| and \verb|-#| differ only in the creation of the bottom clause. + An example of language bias for the UWCSE domain is \begin{verbatim} output(advisedby/2). @@ -547,6 +558,31 @@ modeb(*,samecourse(+course, -course)). modeb(*,samecourse(-course, +course)). .... \end{verbatim} +SLIPCOVER also requires facts for the \verb|determination/2| predicate that indicate which predicates can appear in the body of clauses. +For example +\begin{verbatim} +determination(professor/1,student/1). +determination(student/1,hasposition/2). +\end{verbatim} +state that \verb|student/1| can appear in the body of clauses for \verb|professor/1| and that \verb|hasposition/2| can appear in +the body of clauses for \verb|student/1|. + +SLIPCOVER also allows mode declarations of the form +\begin{verbatim} +modeh(,[,...,],[,...,],[,...,]). +\end{verbatim} +These mode declarations are used to generate clauses with more than two head atoms. In them, \verb|,...,| are schemas, \verb|,...,| are atoms such that \verb|| is obtained from $\verb||$ by replacing placemarkers with variables, +\verb|| are the predicates admitted in the body. \verb|,...,| are used to indicate which variables should be shared by the atoms in the head. +An example of such a mode declaration is +\begin{verbatim} +modeh(*, + [advisedby(+person,+person),tempadvisedby(+person,+person)], + [advisedby(A,B),tempadvisedby(A,B)], + [professor/1,student/1,hasposition/2,inphase/2, + publication/2,taughtby/3,ta/3,courselevel/2,yearsinprogram/2]). +\end{verbatim} + + \subsection{Parameters} In order to set the algorithms' parameters, you have to insert in \texttt{.l} commands of the form @@ -556,20 +592,17 @@ In order to set the algorithms' parameters, you have to insert in \texttt{ The available parameters are: \begin{itemize} \item \verb|depth| (values: integer or \verb|inf|, default value: 3): depth of derivations if \verb|depth_bound| is set to \verb|true| -\item \verb|single_var| (values: \verb|{true,false}|, default value: \verb|false|, valid for CEM, EMBLEM and SLIPCASE): if set to \verb|true|, there is a random variable for each clauses, instead of a separate random variable for each grounding of a clause +\item \verb|single_var| (values: \verb|{true,false}|, default value: \verb|false|, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): if set to \verb|true|, there is a random variable for each clauses, instead of a separate random variable for each grounding of a clause \item \verb|sample_size| (values: integer, default value: 1000): total number of examples in case in which the models in the \verb|.kb| file contain a \verb|prob(P).| fact. In that case, one model corresponds to \verb|sample_size*P| examples -\item \verb|epsilon_em| (values: real, default value: 0.1, valid for CEM, EMBLEM and SLIPCASE): if the difference in the log likelihood in two successive EM iteration is smaller +\item \verb|epsilon_em| (values: real, default value: 0.1, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): if the difference in the log likelihood in two successive EM iteration is smaller than \verb|epsilon_em|, then EM stops -\item \verb|epsilon_em_fraction| (values: real, default value: 0.01, valid for CEM, EMBLEM and SLIPCASE): if the difference in the log likelihood in two successive EM iteration is smaller +\item \verb|epsilon_em_fraction| (values: real, default value: 0.01, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): if the difference in the log likelihood in two successive EM iteration is smaller than \verb|epsilon_em_fraction|*(-current log likelihood), then EM stops -\item \verb|iter| (values: integer, defualt value: 1, valid for EMBLEM and SLIPCASE): maximum number of iteration of EM parameter learning. If set to -1, no maximum number of iterations is imposed -\item \verb|iterREF| (values: integer, defualt value: 1, valid for SLIPCASE): +\item \verb|iter| (values: integer, defualt value: 1, valid for EMBLEM, SLIPCASE and SLIPCOVER): maximum number of iteration of EM parameter learning. If set to -1, no maximum number of iterations is imposed +\item \verb|iterREF| (values: integer, defualt value: 1, valid for SLIPCASE and SLIPCOVER): maximum number of iteration of EM parameter learning for refinements. If set to -1, no maximum number of iterations is imposed. - -\item \verb|random_restarts_number| (values: integer, default value: 1, valid for CEM, EMBLEM and SLIPCASE): number of random restarts of EM learning -\item \verb|random_restarts_REFnumber| (values: integer, default value: 1, valid for SLIPCASE): number of random restarts of EM learning for refinements - - +\item \verb|random_restarts_number| (values: integer, default value: 1, valid for CEM, EMBLEM, SLIPCASE and SLIPCOVER): number of random restarts of EM learning +\item \verb|random_restarts_REFnumber| (values: integer, default value: 1, valid for SLIPCASE and SLIPCOVER): number of random restarts of EM learning for refinements \item \verb|setrand| (values: rand(integer,integer,integer)): seed for the random functions, see Yap manual for allowed values \item \verb|minimal_step| (values: [0,1], default value: 0.005, valid for RIB): minimal increment of $\gamma$ \item \verb|maximal_step| (values: [0,1], default value: 0.1, valid for RIB): maximal increment of $\gamma$ @@ -577,15 +610,23 @@ than \verb|epsilon_em_fraction|*(-current log likelihood), then EM stops \item \verb|delta| (values: negative integer, default value -10, valid for RIB): value assigned to $\log 0$ \item \verb|epsilon_fraction| (values: integer, default value 100, valid for RIB): in the computation of the step, the value of $\epsilon$ of \cite{DBLP:journals/jmlr/ElidanF05} is obtained as $\log |CH,T|\times$\verb|epsilon_fraction| \item \verb|max_rules| (values: integer, default value: 6000, valid for RIB and SLIPCASE): maximum number of ground rules. Used to set the size of arrays for storing internal statistics. Can be increased as much as memory allows. -\item \verb|logzero| (values: negative real, default value $\log(0.000001)$, valid for SLIPCASE): value assigned to $\log 0$ +\item \verb|logzero| (values: negative real, default value $\log(0.000001)$, valid for SLIPCASE and SLIPCOVER): value assigned to $\log 0$ \item \verb|examples| (values: \verb|atoms|,\verb|interpretations|, default value \verb|atoms|, valid for SLIPCASE): determines how BDDs are built: if set to \verb|interpretations|, a BDD for the conjunction of all the atoms for the target predicates in each interpretations is built. If set to \verb|atoms|, a BDD is built for the conjunction of a group of atoms for the target predicates in each interpretations. The number of atoms in each group is determined by the parameter \verb|group| \item \verb|group| (values: integer, default value: 1, valid for SLIPCASE): number of target atoms in the groups that are used to build BDDs -\item \verb|nax_iter| (values: integer, default value: 10, valid for SLIPCASE): number of interations of beam search -\item \verb|max_var| (values: integer, default value: 1, valid for SLIPCASE): maximum number of distinct variables in a clause +\item \verb|nax_iter| (values: integer, default value: 10, valid for SLIPCASE and SLIPCOVER): number of interations of beam search +\item \verb|max_var| (values: integer, default value: 1, valid for SLIPCASE and SLIPCOVER): maximum number of distinct variables in a clause \item \verb|verbosity| (values: integer in [1,3], default value: 1): level of verbosity of the algorithms -\item \verb|beamsize| (values: integer, default value: 20, valid for SLIPCASE): size of the beam in SLIPCASE - +\item \verb|beamsize| (values: integer, default value: 20, valid for SLIPCASE and SLIPCOVER): size of the beam +\item \verb|megaex_bottom| (values: integer, default value: 1, valid for SLIPCOVER): number of mega-examples on which to build the bottom clauses +\item \verb|initial_clauses_per_megaex| (values: integer, default value: 1, valid for SLIPCOVER): + number of bottom clauses to build for each mega-example +\item \verb|d| (values: integer, default value: 10000, valid for SLIPCOVER): + number of saturation steps when building the bottom clause +\item \verb|max_iter_structure| (values: integer, default value: 1, valid for SLIPCOVER): +maximum number of theory search iterations +\item \verb|background_clauses| (values: integer, default value: 50, valid for SLIPCOVER): + maximum numbers of background clauses. \end{itemize} \subsection{Commands} To execute CEM, load \texttt{em.pl} with @@ -620,8 +661,16 @@ and call \begin{verbatim} ?:- sl(stem). \end{verbatim} +To execute SLIPCOVER, load \texttt{slipcover.pl} with +\begin{verbatim} +?:- use_module(library('cplint/slipcover')). +\end{verbatim} +and call +\begin{verbatim} +?:- sl(stem). +\end{verbatim} \subsection{Learning Examples} -The subfolders \verb|em|, \verb|rib| and \verb|slipcase| of the \verb|packages/cplint| folder in Yap git distribution +The subfolders \verb|em|, \verb|rib|, \verb|slipcase| and \verb|slipcover| of the \verb|packages/cplint| folder in Yap git distribution contain examples of input and output files for the learning algorithms. \section{License}