.. $Id: using_pyke.txt f3d7deb3181d 2009-10-19 mtnyogi $
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.. Copyright © 2007-2008 Bruce Frederiksen
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    crumb: Using Pyke
    page-description:
        How your Python program uses Pyke.  I.e., Pyke's API to Python.
    /description
    format: rest
    encoding: utf8
    output-encoding: utf8
    include: yes
    initialheaderlevel: 2
/restindex

uservalues
    filedate: $Id: using_pyke.txt f3d7deb3181d 2009-10-19 mtnyogi $
/uservalues

===================================
Using Pyke
===================================

This describes how to call Pyke from within your Python program.


Initializing Pyke
===============================

There are two steps to initializing a Pyke knowledge engine:

knowledge_engine.engine(\*paths, \*\*kws)
    The Pyke inference engine is offered as a class so that you can
    instantiate multiple copies of it with different rule bases to accomplish
    different tasks.

    Once you have a ``knowledge_engine.engine`` object; generally, all of
    the functions that you need are provided directly by this object:

    >>> from pyke import knowledge_engine
    >>> my_engine = knowledge_engine.engine('doc.examples')

    Pyke requires that all Pyke source files be placed (either directly or
    indirectly) under a Python package_ directory.  These packages must be
    on Python's search path for modules, and the directory for each dotted
    name must include an ``__init__.py`` file.

    Each ``paths`` argument represents a different Python package directory to
    search for Pyke source files.  These source packages may be passed either
    as package names (as a string in Python's standard dotted notation), as
    the packages themselves, or as any module within the package.  Thus, the
    example above could also be written:

    >>> from doc import examples
    >>> my_engine = knowledge_engine.engine(examples)

    or

    >>> from doc.examples import some_module
    >>> my_engine = knowledge_engine.engine(some_module)

    Pyke recursively walks each source package directory looking for Pyke
    source files (`.kfb files`_, `.krb files`_, and `.kqb files`_).  Each
    source file that it finds is compiled, if out of date, and then imported
    (depending on the keyword arguments, see below).  This causes all of the
    `rule bases`_ to be loaded and made ready to activate_.

    If you change some of your Pyke source files, you can create a new engine
    object to compile and reload the generated Python modules without
    restarting your program.  But note that you'll need to rerun the
    ``add_universal_fact`` calls that you made outside of your `.kfb files`_.

    .. note::
       It is OK for your source package directory to be part of a zipped
       `Python egg file`_.  But, in this case, Pyke can't rebuild and recompile
       the source files.  It will just import the compiled file that is there
       without complaining if the source file has a later modification time
       than the compiled file.

    All of the Python source files and pickle files generated from each source
    package are placed, by default, in a ``compiled_krb`` package directly
    subordinate to that source package.  You may specify a different
    destination package for any source package by passing that source package
    along with the destination package name as a 2-tuple.  The destination
    package name may be specified as an absolute Python module path; or as a
    relative path (with leading dots), relative to the source package.  Thus,
    specifying the default destination explicitly would look like:

    >>> my_engine = knowledge_engine.engine(('doc.examples', '.compiled_krb'))

    You may specify the same destination package for multiple source packages.

    The last component of the destination package will be created automatically
    if it does not already exist.

    You probably want to add ``compiled_krb`` (or whatever you've chosen to
    call it) to your source code repository's list of files to ignore.

    There are four optional keyword arguments that you may also pass to the
    ``engine`` constructor:

    - ``load_fc`` -- load forward-chaining rules
    - ``load_bc`` -- load backward-chaining rules
    - ``load_fb`` -- load fact bases and
    - ``load_qb`` -- load question bases

    These parameters must be passed as keyword parameters.  They all default
    to ``True``.
*some_engine*.add_universal_fact(kb_name, fact_name, arguments)
    The ``add_universal_fact`` function is called once per fact_.  These
    facts_ are never deleted and apply to all *cases*.
    
    Alternatively, you can place universal facts in a `.kfb file`_ so that
    they are loaded automatically.

    >>> my_engine.add_universal_fact('family', 'son_of', ('bruce', 'thomas'))

    Multiple facts with the same name are allowed.

    >>> my_engine.add_universal_fact('family', 'son_of', ('david', 'bruce'))

    But duplicate facts (with the same arguments) are silently ignored.

    >>> my_engine.add_universal_fact('family', 'son_of', ('david', 'bruce'))
    >>> my_engine.get_kb('family').dump_universal_facts()
    son_of('bruce', 'thomas')
    son_of('david', 'bruce')

    These facts are accessed as ``kb_name.fact_name(arguments)`` within the
    `.krb files`_.


Setting up Each Case
===========================

Pyke is designed to be run multiple times for multiple *cases*.  In
general each case has its own set of starting facts_ and may use different
`rule bases`_, depending upon the situation.

Three functions initialize each case:

*some_engine*.reset()
    The ``reset`` function is called once to delete all of the `case specific
    facts`_ from the last run.  It also deactivates all `rule bases`_.
*some_engine*.assert_(kb_name, fact_name, arguments)
    Call ``assert_`` (or the equivalent, ``add_case_specific_fact``,
    see `Other Functions`_, below) for each starting fact_ for this case.
    Like universal facts, you may have multiple facts with the same name so
    long as they have different arguments.

    >>> my_engine.assert_('family', 'son_of', ('michael', 'bruce'))
    >>> my_engine.assert_('family', 'son_of', ('fred', 'thomas'))
    >>> my_engine.assert_('family', 'son_of', ('fred', 'thomas'))

    Duplicates with universal facts are also ignored.

    >>> my_engine.assert_('family', 'son_of', ('bruce', 'thomas'))
    >>> my_engine.get_kb('family').dump_specific_facts()
    son_of('michael', 'bruce')
    son_of('fred', 'thomas')
    >>> my_engine.get_kb('family').dump_universal_facts()
    son_of('bruce', 'thomas')
    son_of('david', 'bruce')

    There is no difference within the `.krb files`_ of how universal facts
    verses case specific facts are used.  The only difference between the two
    types of facts is that the case specific facts are deleted when a ``reset``
    is done.

    >>> my_engine.reset()
    >>> my_engine.get_kb('family').dump_specific_facts()
    >>> my_engine.get_kb('family').dump_universal_facts()
    son_of('bruce', 'thomas')
    son_of('david', 'bruce')

*some_engine*.activate(\*rb_names)
    Then call ``activate`` to activate the appropriate `rule bases`_.  This
    may be called more than once, if desired, or it can simply take multiple
    arguments.

    >>> my_engine.activate('bc_example')

Your Pyke engine is now ready to prove goals for this case!


Proving Goals
======================

Two functions are provided that cover the easy cases.  More general
functions are provided in `Other Functions`_, below.

*some_engine*.prove_1(kb_name, entity_name, fixed_args, num_returns)
    ``Kb_name`` may name either a `fact base`_, `question base`_ or
    `rule base category`_.
    
    The ``entity_name`` is the fact name for fact bases, question name for
    question bases or the name of the `backward chaining`_ goal for
    `rule bases`_.

    The ``fixed_args`` are a tuple of Python values.  These form the first
    set of arguments to the proof.  ``Num_returns`` specifies the number of
    additional `pattern variables`_ to be appended to these arguments for the
    proof.  The bindings of these pattern variables will be returned as a
    tuple in the answer for the proof.  For example:

        *some_engine*.prove_1(*some_rule_base_category*, *some_goal*, (1, 2, 3), 2)

    Proves the goal:

        *some_rule_base_category.some_goal* (1, 2, 3, $ans_0, $ans_1)

    And will return the bindings produced by the proof as (``$ans_0``,
    ``$ans_1``).

    Returns the first proof found as a 2-tuple: a tuple of the bindings for
    the ``num_returns`` pattern variables, and a plan_.  The plan is ``None``
    if no plan was generated; otherwise, it is a Python function as
    described below__.

.. __: #running-and-pickling-plans

    >>> my_engine.prove_1('bc_example', 'father_son', ('thomas', 'david'), 1)
    ((('grand',),), None)

    Raises ``pyke.knowledge_engine.CanNotProve`` if no proof is found.

    >>> my_engine.prove_1('bc_example', 'father_son', ('thomas', 'bogus'), 1)
    Traceback (most recent call last):
        ...
    CanNotProve: Can not prove bc_example.father_son(thomas, bogus, $ans_0)

*some_engine*.prove_n(kb_name, entity_name, fixed_args, num_returns)
    This returns a context manager for a generator yielding 2-tuples, a tuple
    whose length == ``num_returns`` and a plan_, for each possible proof.
    Like ``prove_1``, the plan is None if no plan was generated.  Unlike
    ``prove_1`` it does not raise an exception if no proof is found. 

    >>> from __future__ import with_statement
    >>> with my_engine.prove_n('bc_example', 'father_son', ('thomas',), 2) as gen:
    ...     for ans in gen:
    ...         print ans
    (('bruce', ()), None)
    (('david', ('grand',)), None)


Running and Pickling Plans
----------------------------

Once you've obtained a plan_ from ``prove_1`` or ``prove_n``, you just call
it like a normal Python function.  The arguments required are simply those
specified, if any, in the `taking clause`_ of the rule__ proving the top-level
goal.

You may call the plan function any number of times.  You may even pickle
the plan for later use.  But the plans are constructed out of
`functools.partial`_ functions, so you need to register this with copy_reg_
before pickling the plan:

    >>> import copy_reg
    >>> import functools
    >>> copy_reg.pickle(functools.partial,
    ...                 lambda p: (functools.partial, (p.func,) + p.args))

No special code is required to unpickle a plan.  Just unpickle and call it.
(Unpickling the plan only imports one small Pyke module to be able to run
the plan).

.. __: pyke_syntax/krb_syntax/bc_rule.html


Tracing Rules
-------------

Individual rules may be traced to aid in debugging.  The ``trace`` function
takes two arguments: the rule base name, and the name of the rule to trace:

    >>> my_engine.trace('bc_example', 'grand_father_son')
    >>> my_engine.prove_1('bc_example', 'father_son', ('thomas', 'david'), 1)
    bc_example.grand_father_son('thomas', 'david', '$ans_0')
    bc_example.grand_father_son succeeded with ('thomas', 'david', ('grand',))
    ((('grand',),), None)

This can be done either before or after rule base activation and will remain
in effect until you call ``untrace``:

    >>> my_engine.untrace('bc_example', 'grand_father_son')
    >>> my_engine.prove_1('bc_example', 'father_son', ('thomas', 'david'), 1)
    ((('grand',),), None)


Krb_traceback
---------------

A handy traceback module is provided to convert Python functions, lines and
line numbers to the `.krb file`_ rule names, lines and line numbers in a
Python traceback.  This makes it much easier to read the tracebacks that occur
during proofs.

The ``krb_traceback`` module has exactly the same functions as the standard
Python traceback_ module, but they convert the generated Python function
information into .krb file information.  They also delete the intervening
Python functions between subgoal proofs.

    >>> import sys
    >>> from pyke import knowledge_engine
    >>> from pyke import krb_traceback
    >>>
    >>> my_engine = knowledge_engine.engine('doc.examples')
    >>> my_engine.activate('error_test')
    >>> try:                                            # doctest: +ELLIPSIS
    ...     my_engine.prove_1('error_test', 'goal', (), 0)
    ... except:
    ...     krb_traceback.print_exc(None, sys.stdout)   # sys.stdout needed for doctest
    Traceback (most recent call last):
      File "<doctest using_pyke.txt[37]>", line 2, in <module>
        my_engine.prove_1('error_test', 'goal', (), 0)
      File "...knowledge_engine.py", line 288, in prove_1
        return iter(it).next()
      File "...knowledge_engine.py", line 272, in gen
        for plan in it:
      File "...rule_base.py", line 46, in next
        return self.iterator.next()
      File "...knowledge_engine.py", line 40, in from_iterable
        for x in iterable: yield x
      File "...knowledge_engine.py", line 40, in from_iterable
        for x in iterable: yield x
      File "...error_test.krb", line 26, in rule1
        goal2()
      File "...error_test.krb", line 31, in rule2
        goal3()
      File "...error_test.krb", line 36, in rule3
        goal4()
      File "...error_test.krb", line 41, in rule4
        check $bar > 0
      File "...contexts.py", line 227, in lookup_data
        raise KeyError("$%s not bound" % var_name)
    KeyError: '$bar not bound'


Other Functions
========================

There are a few more functions that may be useful in special situations.

The first two of these provide more general access to the fact_ lookup and
goal proof mechanisms.  The catch is that you must first convert **all**
arguments into patterns__ and create a *context* for these patterns.  This is
discussed below_.

.. __: #creating-your-own-patterns

*some_engine*.lookup(kb_name, entity_name, pattern_context, patterns)
    This returns a context manager for a generator that binds patterns__ to
    successive facts_.  Yields ``None`` for each successful match.
*some_engine*.prove(kb_name, entity_name, pattern_context, patterns)
    Returns a context manager for a generator that binds patterns__ to
    successive proofs.  Yields a *prototype_plan* or ``None`` for each
    successful match.  To turn the prototype_plan into a Python function,
    use *prototype_plan*.create_plan().  This returns the plan_ function.

.. __: #creating-your-own-patterns
.. __: #creating-your-own-patterns

The remaining functions are:

*some_engine*.add_case_specific_fact(kb_name, fact_name, args)
    This is an alternate to the ``assert_`` function.
*some_engine*.get_kb(kb_name)
    Finds and returns the `knowledge base`_ by the name ``kb_name``.  Raises
    ``KeyError`` if not found.  Note that for `rule bases`_, this returns the
    active `rule base`_ where ``kb_name`` is the `rule base category`_ name.
    Thus, not all `rule bases`_ are accessible through this call.
*some_engine*.get_rb(rb_name)
    Finds and returns the `rule base`_ by the name ``rb_name``.  Raises
    ``KeyError`` if not found.  This works for any `rule base`_, whether it
    is active_ or not.
*some_engine*.print_stats([f = sys.stdout])
    Prints a brief set of statistics for each knowledge base to file ``f``.
    These are reset by the ``reset`` function.  This will show how many facts
    were asserted, and counts of how many forward-chaining rules were fired
    and rerun, as well as counts of how many backward-chaining goals were
    tried, and how many backward-chaining rules matched, succeeded and failed.
    Note that one backward-chaining rule may succeed many times through
    backtracking.


Creating Your Own Patterns
----------------------------------

You'll need two more Pyke modules to create your own patterns__ and contexts:

.. __: logic_programming/pattern_matching/index.html

    >>> from pyke import pattern, contexts

There are four kinds of patterns:

pattern.pattern_literal(data)
    This matches the ``data`` provided.
pattern.pattern_tuple((elements), rest_var = None)
    This matches a tuple.  ``Elements`` must each be a pattern and must
    match the first *n* elements of the tuple.  ``Rest_var`` must be a
    variable (or anonymous).  It will match the rest of the tuple and is
    always bound to a (possibly empty) tuple.
contexts.variable(name)
    This will match anything the first time it is encountered and becomes
    bound to that value.  After that, it only matches this bound value each
    additional time it is encountered.  Calling the constructor twice with
    the same name produces the same variable and must match the same value
    in all of the places that it is used.
contexts.anonymous(name)
    This will match anything each time it is encountered.  Calling the
    constructor many times with the same name is not a problem.  The name
    must start with an underscore.

Finally, to create a *pattern context*, you need:

    contexts.simple_context()

You'll need to save this context to lookup your variable values after each
proof is yielded. This is done by either:

    | *some_context*.lookup_data(*variable_name*)
    | *some_variable*.as_data(*some_context*)

.. _below: `Creating Your Own Patterns`_


