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Other Functions

Running and Pickling Plans

Once you've obtained a plan from prove_goal or prove_1_goal, 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, that need to be registered with copy_reg if you are running Python 2.x:

>>> 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).

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_related0', 'grand_father_son')
>>> my_engine.prove_1_goal('bc_related0.father_son(thomas, david, $depth)')
bc_related0.grand_father_son('thomas', 'david', '$depth')
bc_related0.grand_father_son succeeded with ('thomas', 'david', ('grand',))
({'depth': ('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_related0', 'grand_father_son')
>>> my_engine.prove_1_goal('bc_related0.father_son(thomas, david, $depth)')
({'depth': ('grand',)}, None)


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(__file__)
>>> my_engine.activate('error_test')
>>> try:                                            # doctest: +ELLIPSIS
...     my_engine.prove_1_goal('error_test.goal()')
... except:
...     krb_traceback.print_exc(None, sys.stdout)   # sys.stdout needed for doctest
Traceback (most recent call last):
  File "<doctest other_functions.txt[19]>", line 2, in <module>
  File "", line 366, in prove_1_goal
    return goal.compile(goal_str).prove_1(self, **args)
  File "", line 47, in prove_1
    return iter(it).next()
  File "", line 50, in next
  File "", line 41, in from_iterable
    for x in iterable: yield x
  File "", line 41, in from_iterable
    for x in iterable: yield x
  File "...error_test.krb", line 26, in rule1
  File "...error_test.krb", line 31, in rule2
  File "...error_test.krb", line 36, in rule3
  File "...error_test.krb", line 41, in rule4
    check $bar > 0
  File "", line 231, in lookup_data
    raise KeyError("$%s not bound" % var_name)
KeyError: '$bar not bound'


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

some_engine.add_case_specific_fact(kb_name, fact_name, args)
This is an alternate to the assert_ function.
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.
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 an Inference Engine Object

How to create a Pyke inference engine object.

Asserting New Facts

How to dynamically assert new facts from your Python program.

Proving Goals

Using Pyke's API to prove goals from your Python program.

Other Functions

Other miscellaneous functions available that you might be interested in, such as tracing rules and smart error tracebacks that show lines from your .krb files.

Page last modified Fri, Mar 12 2010.