.. $Id: using_pyke.txt 72 2008-03-06 03:03:50Z mtnyogi $
.. 
.. Copyright © 2007 Bruce Frederiksen
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        How your python program uses pyke.
    /description
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===================================
Using Pyke
===================================

This describes how to use pyke from within your python program.

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

There are two steps to initializing a pyke knowledge engine:

knowledge_engine.engine(paths = ('.',), gen_dir = '.', gen_root_dir = 'compiled_krb', load_fc = True, load_bc = True)
    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('examples')

    This expects either a single directory or a sequence of directories as
    the ``paths`` argument.
    It recursively walks each directory looking for `.krb files`_.
    Each `.krb file`_ that it finds is compiled, if out of date, and then
    the resulting python modules imported (depending on ``load_fc`` and
    ``load_bc``).
    This causes all of the `rule bases`_ to be loaded and made ready to
    *activate* (see below).

    All generated python files are placed in a mirror directory structure
    under the *gen_root_dir* directory in *gen_dir*.  Thus, by default,
    this mirrored directory structure would be rooted under the
    "./compiled_krb" directory.  You probably want to add ``compiled_krb`` to
    your subversion ``global-ignores`` option.  *Gen_dir*, *gen_root_dir*
    and the mirrored directory structure will be created automatically if
    any of them do not already exist.

    If you change some of the .krb 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 your
    ``add_universal_fact`` calls.
*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*.
    
    >>> my_engine.add_universal_fact('family', 'son_of', ('bruce', 'thomas', 'norma'))

    Multiple facts with the same name are allowed.

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

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

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

    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 on 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', 'daughter_of', ('marilyn', 'arthur', 'kathleen'))
    >>> my_engine.assert_('family', 'daughter_of', ('sue', 'arthur', 'kathleen'))
    >>> my_engine.assert_('family', 'daughter_of', ('sue', 'arthur', 'kathleen'))

    Duplicates with universal facts are also ignored.

    >>> my_engine.assert_('family', 'son_of', ('bruce', 'thomas', 'norma'))
    >>> my_engine.get_kb('family').dump_specific_facts()
    daughter_of('marilyn', 'arthur', 'kathleen')
    daughter_of('sue', 'arthur', 'kathleen')
    >>> my_engine.get_kb('family').dump_universal_facts()
    son_of('bruce', 'thomas', 'norma')
    son_of('david', 'bruce', 'marilyn')

    There is no difference within the .krb files of how universal facts
    verses specific facts are used.  The only difference between the two
    types of facts is that the 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', 'norma')
    son_of('david', 'bruce', 'marilyn')

*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_ or an activated
    `rule base category`_.  The ``entity_name`` is the fact_name for fact_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
    group of arguments to the proof.  ``Num_returns`` specifies the number of
    additional `pattern variables`_ to be appended to the arguments for the
    proof.  The bindings of these pattern variables will be returned as 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 for ``$ans_0`` and ``$ans_1`` produced by
    the proof.

    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', 'child_parent', ('david', 'norma'), 3)
    ((('grand',), 'son', 'mother'), None)

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

    >>> my_engine.prove_1('bc_example', 'bogus', ('david', 'norma'), 3)
    Traceback (most recent call last):
        ...
    CanNotProve: Can not prove bc_example.bogus(david, norma, $ans_0, $ans_1, $ans_2)

*some_engine*.prove_n(kb_name, entity_name, fixed_args, num_returns)
    This is 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.

    >>> for ans in my_engine.prove_n('bc_example', 'child_parent', ('david',), 4):
    ...     print ans
    (('bruce', (), 'son', 'father'), None)
    (('marilyn', (), 'son', 'mother'), None)
    (('thomas', ('grand',), 'son', 'father'), None)
    (('norma', ('grand',), 'son', 'mother'), 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_.  Also, the program that
unpickles the plan_ does not have to import any pyke modules to be able
to run the plan_.  Just unpickle and call it.

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_parent_and_child')
    >>> my_engine.prove_1('bc_example', 'child_parent', ('david', 'norma'), 3)
    bc_example.grand_parent_and_child('david', 'norma', '$ans_0', '$ans_1', '$ans_2')
    bc_example.grand_parent_and_child succeeded with ('david', 'norma', ('grand',), 'son', 'mother')
    ((('grand',), 'son', 'mother'), 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_parent_and_child')
    >>> my_engine.prove_1('bc_example', 'child_parent', ('david', 'norma'), 3)
    ((('grand',), 'son', 'mother'), 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 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('examples')
    >>> my_engine.activate('error_test')
    >>> try:
    ...     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[31]>", line 2, in <module>
        my_engine.prove_1('error_test', 'goal', (), 0)
      File "/home/bruce/python/workareas/sf.trunk/pyke/knowledge_engine.py", line 142, in prove_1
        return self.prove_n(kb_name, entity_name, fixed_args, num_returns) \
      File "/home/bruce/python/workareas/sf.trunk/pyke/knowledge_engine.py", line 129, in prove_n
        for arg in fixed_args) + vars):
      File "/home/bruce/python/workareas/sf.trunk/pyke/rule_base.py", line 37, in next
        return self.iterator.next()
      File "/home/bruce/python/workareas/sf.trunk/pyke/tmp_itertools.py", line 32, in chain
        for x in iterable: yield x
      File "/home/bruce/python/workareas/sf.trunk/pyke/tmp_itertools.py", line 32, in chain
        for x in iterable: yield x
      File "/home/bruce/python/workareas/sf.trunk/doc/examples/error_test.krb", line 26, in rule1
        goal2()
      File "/home/bruce/python/workareas/sf.trunk/doc/examples/error_test.krb", line 31, in rule2
        goal3()
      File "/home/bruce/python/workareas/sf.trunk/doc/examples/error_test.krb", line 36, in rule3
        goal4()
      File "/home/bruce/python/workareas/sf.trunk/doc/examples/error_test.krb", line 41, in rule4
        check $bar > 0
      File "/home/bruce/python/workareas/sf.trunk/pyke/contexts.py", line 224, in lookup_data
        raise KeyError("$%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_.

*some_engine*.lookup(kb_name, entity_name, pattern_context, patterns)
    This is a generator that binds patterns_ to successive facts_.  Yields None
    for each successful match.
*some_engine*.prove(kb_name, entity_name, pattern_context, patterns)
    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.

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 triggered
    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:

    >>> 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.  But then
    must match the first 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()
    This will match anything each time it is encountered.
    Calling the constructor many times is not a problem.

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*)

.. _backward chaining: overview/rules/backward_chaining.html
.. _below: `Creating Your Own Patterns`_
.. _case specific facts:
        overview/knowledge_bases/fact_bases.html#case-specific-facts
.. _copy_reg: http://docs.python.org/lib/module-copyreg.html
.. _fact: overview/knowledge_bases/fact_bases.html#facts
.. _fact_base: overview/knowledge_bases/fact_bases.html
.. _facts: fact_
.. _functools.partial: http://docs.python.org/lib/module-functools.html
.. _knowledge base: overview/knowledge_bases/index.html
.. _.krb file: krb_syntax/index.html
.. _.krb files: `.krb file`_
.. _pattern: krb_syntax/pattern.html
.. _pattern variables: krb_syntax/pattern.html#pattern-variable
.. _patterns: pattern_
.. _plan: overview/plans.html
.. _plans: plan_
.. _rule: overview/rules/index.html
.. _rules: rule_
.. _rule base: overview/knowledge_bases/rule_bases.html
.. _rule base category:
       overview/knowledge_bases/rule_bases.html#rule-base-categories
.. _rule bases: `rule base`_
.. _taking clause: krb_syntax/bc_rule.html#taking-clause
.. _traceback: http://docs.python.org/lib/module-traceback.html
