{ "info": { "author": "Anton Dries", "author_email": "anton.dries@cs.kuleuven.be", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Prolog", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "ProbFOIL v2.1\n=============\n\nProbFOIL is a probabilistic extension of FOIL that is capable of learning probabilistic rules from\nprobabilistic data.\n\nProbFOIL 2.1 is a redesign of the Prob2FOIL algorithm that was introduced in https://lirias.kuleuven.be/handle/123456789/499989.\nIt works on top of ProbLog 2.1.\n\nIf you are looking for the version used in the paper, you should check out the tag ``paper_version``.\n\nInstallation\n------------\n\nProbFOIL 2.1 requires ProbLog 2.1.\nYou can install ProbLog by using the command:\n \n.. code-block:: python\n\n pip install problog\n\nProbFOIL does not require any further installation.\n\nUsage\n-----\n\nThe input of ProbFOIL consists of two parts: settings and data.\nThese are both specified in Prolog (or ProbLog) files, and they can be combined into one.\n\nThe data consists of (probabilistic) facts.\nThe settings define\n\n* target: the predicate we want to learn\n* modes: which predicates can be added to the rules\n* types: type information for the predicates\n* other settings related to the data\n\nTo use:\n\n.. code-block:: bash\n\n probfoil data.pl\n\nor, in the repository version\n\n.. code-block:: bash\n\n python probfoil/probfoil.py data.pl\n\nMultiple files can be specified and the information in them is concatenated.\n(For example, it is advisable to separate settings from data).\n\nSeveral command line arguments are available. Use ``--help`` to get more information.\n\nSettings format\n---------------\n\nTarget\n++++++\n\nThe target should be specified by adding a fact ``learn(predicate/arity)``.\n\nModes\n+++++\n\nThe modes should be specified by adding facts of the form ``mode(predicate(mode1, mode2, ...)``,\nwhere ``modeX`` is the mode specifier for argument X.\nPossible mode specifiers are:\n\n * ``+``: the variable at this position must already exist when the literal is added\n * ``-``: the variable at this position does not exist yet in the rule (note that this is stricter than usual)\n * ``c``: a constant should be introduced here; possible value are derived automatically from the data\n\nTypes\n+++++\n\nFor each relevant predicate (target and modes) there should be a type specifier.\nThis specifier is of the form ``base(predicate(type1, type2, ...)``, where ``typeX`` is a type identifier.\nType can be identified by arbitrary Prolog atoms (e.g. ``person``, ``a``, etc.)\n\nExample generation\n++++++++++++++++++\n\nBy default, examples are generated by quering the data for the target predicate.\nNegative examples can be specified by adding zero-probability facts, e.g.:\n\n.. code-block:: prolog\n\n 0.0::grandmother(john, mary).\n\nAlternatively, ProbFOIL can derive negative examples automatically by taking combinations of possible\nvalues for the target arguments. Note that this can lead to a combinatorial explosion.\nTo enable this behavior, you can specify the fact\n\n.. code-block:: prolog\n\n example_mode(auto).\n\n\nExample\n-------\n\n.. code-block:: prolog\n\n % Modes\n mode(male(+)).\n mode(parent(+,+)).\n mode(parent(+,-)).\n mode(parent(-,+)).\n\n % Type definitions\n base(parent(person,person)).\n base(male(person)).\n base(female(person)).\n base(mother(person,person)).\n base(grandmother(person,person)).\n base(father(person,person)).\n base(male_ancestor(person,person)).\n base(female_ancestor(person,person)).\n\n % Target\n learn(grandmother/2).\n\n % How to generate negative examples\n example_mode(auto).\n\nFurther examples can be found in the directory ``examples``.", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://dtai.cs.kuleuven.be/software/probfoil", "keywords": "probabilistic logic learning", "license": "Apache 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