The PEAK Rules Core Framework

This document is for people who are extending the core framework in some way, e.g. adding custom action types to specialize method combination, or creating new kinds of engines or conditions. It isn't intended to be user documentation for the built-in rule facility.

Table of Contents

Roadmap and Design Overview
Method Combination
Rules Management
- Rule Objects
- RuleSet

Roadmap and Design Overview

Planned API

The peak.rules package will offer an API that looks something like this:

@rules.abstract(): Decorator to mark a function as abstract, meaning that it has no default rule defined. Various arguments and keyword options will be available to provide optimization hints or set the default method combination policy for the function.
@rules.when(f, condition=None): Decorator to extend an existing function (f), even if it is not already a generic function. Yes, that's right, you'll be able to extend any Python function. The condition will be optional, which means that if you don't specify one, your function will simply replace or wrap the original definition.
@rules.around(), @rules.before(), @rules.after(): Just like @rules.when(), except using different combination wrappers. As with when(), the condition will be optional and the function doesn't have to have been declared "abstract" ahead of time.

Once a function has been made extensible, the usual f.when() and other decorators will probably be available, but I'm not 100% decided on that as yet. Unlike RuleDispatch, PEAK-Rules will have an open-ended method combination system that doesn't rely on the generic function itself controlling the combination rules. So it might be cleaner just to always use @around(f, c) instead of e.g. @f.around(c), even though the latter looks a bit more pleasant to me.

In addition to these functions, there will probably be some exception classes, and maybe a few other specialty classes or functions, including perhaps some of the core framework's generic functions. None of those things are as yet well-defined enough to specify here.

Development Roadmap

The first versions will focus on developing a core framework for extensible functions that is itself implemented using extensible functions. This self-bootstrapping core will implement a type-tuple-caching engine using relatively primitive operations, and will then have a method combination system built on that. The core will thus be capable of implementing generic functions with multiple dispatch based on positional argument types, and the decorator APIs will be built around that.

The next phase of development will add alternative engines that are oriented towards predicate dispatch and more sophisticated ways of specifying regular class dispatch (e.g. being able to say things like isinstance(x,Foo) or isinstance(y,Foo)). To some extent this will be porting the expression machinery from RuleDispatch to work on the new core, but in a lot of ways it'll just be redone from scratch. Having type-based multiple dispatch available to implement the framework should enable a significant reduction in the complexity of the resulting library.

An additional phase will focus on adding new features not possible with the RuleDispatch engine, such as "predicate functions" (a kind of dynamic macro or rule expansion feature), "classifiers" (a way of priority-sequencing a set of alternative criteria) and others.

Finally, specialty features such as index customization, thread-safety, event-oriented rulesets, and such will be introduced.

There is no defined timeframe for these most of these phases, although I anticipate that at least the first one will be finished in June.

Design Concepts

Criterion

A criterion is a test that returns a boolean for a given value, for example by testing its type. The simplest criterion is just a class or type object, meaning that the value should be of that type.

Signature

A condition expressed purely in terms of simple tests "and"ed together, using no "or" operations of any kind. A signature specifies what argument expressions are tested, and which criteria should be applied to them. The simplest possible signature is a tuple of criteria, with each criterion applied to the corresponding argument in an argument tuple. An empty tuple matches any possible input.

Predicate

One or more signatures "or"ed together. (Note that this means that signatures are predicates, but predicates are not necessarily signatures.)

Rule

A combination of a predicate, an action type, and a body (usually a function.) The existence of a rule implies the existence of one or more actions of the given action type and body, one for each possible signature that could match the predicate.

Action Type

A factory that can produce an Action when supplied with a signature, body, and sequence. (Examples in peak.rules will include the MethodList, MethodChain, Around, Before, and After types.)

Action

An object representing the behavior of a single invocation of a generic function. Action objects may be combined (using a generic function of the form combine_actions(a1,a2)) to create combined methods ala RuleDispatch. Each action comprises at least a signature and a body, but actions of more complex types may include other information.

Rule Set

A collection of rules, combined with some policy information (such as the default action type) and optional optimization hints. A rule set does not directly implement dispatching. Instead, rule engines subscribe to rule sets, and the rule set informs them when actions are added and removed due to changes in the rule set's rules.

This would almost be better named an "action set" than a "rule set", in that it's (virtually speaking) a collection of actions rather than rules. However, you do add and remove entries from it by specifying rules; the actions are merely implied by the rules.

Generic functions will have a __rules__ attribute that points to their rule set, so that the various decorators can add rules to them. You will probably be able to subclass the base RuleSet class or create alternate implementations, as might be useful for supporting persistent or database-stored rules. (Although you'd probably also need a custom rule engine for that.)

Rule Engine

An object that manages the dispatching of a given rule set to implement a specific generic function. Generic functions will have an __engine__ attribute that points to their current engine. Engines will be responsible for doing any indexing, caching, or code generation that may be required to implement the resulting generic function.

The default engine will implement simple type-based multiple dispatch with type-tuple caching. For simple generic functions this is likely to be faster than almost anything else, even C-assisted RuleDispatch. It also should have far less definition-time overhead than a RuleDispatch-style engine would.

Engines will be pluggable, and in fact there will be a mechanism to allow engines to be switched at runtime when certain conditions are met. For example, the default engine could switch automatically to a RuleDispatch-like engine if a rule is added whose conditions can't be translated to simple type dispatching. There will also be some type of hint system to allow users to suggest what kind of engine implementation or special indexing might be appropriate for a particular function.

Method Combination

Method combination is performed using the combine_actions() API function:

>>> from peak.rules import combine_actions

combine_actions() takes two arguments: a pair of actions. They are compared using implies() to see if one is more specific than the other. If so, the more specific action's override() method is called, passing in the less-specific action. If neither action can override the other, the first action's merge() method is called, passing in the other action.

In either case, the result of calling the merge() or override() method is returned.

So, to define a custom action type for method combination, it needs to support implies() operations on its signature(s), and it needs to implement merge() and override() methods.

Signature Implication

The implies() function is used to determine the logical implication relationship between two signatures or methods. A signature s1 implies signature s2 if s2 will always match an invocation matched by s1. (Action implication is based on signature implication; see the Action Types section below for more details.)

For the simplest signatures (tuples of types), this corresponds to a subclass relationship between the elements of the tuples:

>>> from peak.rules import implies

>>> implies(int, object)
True
>>> implies(object, int)
False

>>> implies(int, str)
False

>>> implies(int, int)
True

>>> implies( (int,str), (object,object) )
True

>>> implies( (object,int), (object,str) )
False

It's possible for a longer tuple to imply a shorter one:

>>> implies( (int,int), (object,) )
True

But not the other way around:

>>> implies( (int,), (object,object) )
False

And as a special case of type implication, any classic class implies both object and InstanceType -- and nothing else:

>>> class X: pass
>>> implies(X, object)
True
>>> implies(X, type(X()))
True

Action Types

Method

The default action type (for rules with no specified action type) is Method. A Method combines a body, signature, precedence, and an optional "chained" action that it can fall back to. All of these values are optional, except for the body:

>>> from peak.rules import Method

>>> def dummy(*args, **kw):
...     print "called with", args, kw

>>> meth = Method.make(dummy, (object,), 1)
>>> meth
Method(<...dummy...>, (<type 'object'>,), 1, None)

Calling a Method invokes the wrapped body:

>>> meth(1,2,x=3)
called with (1, 2) {'x': 3}

One Method implies another if and only if its signature implies the other's:

>>> implies(Method.make(dummy,(int,int)), Method.make(dummy,(object,object)))
True

>>> implies(Method.make(dummy,(object,object)), Method.make(dummy,(int,int)))
False

When a method overrides another, you get the overriding method:

>>> meth.override(Method.make(dummy))
Method(<...dummy...>, (<type 'object'>,), 1, None)

Unless the overriding method's body is a function whose first parameter is named next_method, in which case a chain of methods is created via the "tail" of a copy of the overriding method:

>>> def overriding_fn(next_method, etc):
...     print "calling", next_method
...     return next_method(etc)

>>> chain = Method.make(overriding_fn).override(Method.make(dummy))
>>> chain
Method(<...overriding_fn...>, (), 0, Method(<...dummy...>, (), 0, None))

The resulting chain is a callable Method, and the next_method is passed in to the first function of the chain:

>>> chain(42)
calling Method(<...dummy...>, (), 0, None)
called with (42,) {}

Around

Around methods are identical to normal Method objects, except that whenever an Around method and a regular Method are combined, the Around method overrides the regular one. This forces all the regular methods to be further down the chain than all of the "around" methods.

>>> from peak.rules import Around

>>> combine_actions(Method.make(dummy), Around(overriding_fn))
Around(<...overriding_fn...>, (), 0, Method(<...dummy...>, (), 0, None))

You will normally only want to use Around methods with functions that have a next_method parameter, since their purpose is to wrap "around" the calling of lower-precedence methods. If you don't do this, then the method chain will always end at that Around instance:

>>> combine_actions(Method.make(overriding_fn), Around(dummy))
Around(<...dummy...>, (), 0, None)

NoApplicableMethods

The simplest possible action type is NoApplicableMethods, meaning that there is no applicable action. When it's overridden by another method, it will of course get chained to the other method's tail (if appropriate).

>>> from peak.rules import NoApplicableMethods
>>> naf = NoApplicableMethods()
>>> meth = Method.make(overriding_fn)

>>> combine_actions(naf, meth)
Method(<...overriding_fn...>, (), 0, <...NoApplicableMethods...>)

>>> combine_actions(meth, naf)
Method(<...overriding_fn...>, (), 0, <...NoApplicableMethods...>)

Calling a NoApplicableMethods raises it, displaying the arguments it was called with:

>>> naf(1,2,x="y")
Traceback (most recent call last):
  ...
NoApplicableMethods: ((1, 2), {'x': 'y'})

Before, After, and MethodList

MethodList actions differ from normal method chain actions in a number of ways:

In case of ambiguity, they are ordered according to the sequence they were given in the underlying rule set.
They do not need to inspect or call a next_method(); the next method is always called automatically.

The Before and After action types are both MethodList subclasses. Before actions are invoked before their tail action, and After actions are invoked afterward:

>>> from peak.rules import Before, After

>>> def primary(*args,**kw):
...     print "primary method called"

>>> b = Before.make(dummy).override(Method.make(primary))
>>> a = After.make(dummy).override(Method.make(primary))

>>> b(23)
called with (23,) {}
primary method called

>>> a(42)
primary method called
called with (42,) {}

Notice that to create a MethodList with only one method, you must use the make() classmethod. Method also has this classmethod, but it has the same signature as the main constructor. The main constructor for MethodList has a different signature for its internal use.

The combination of before, after, primary, and around methods is as shown:

>>> b = Before.make(dummy)
>>> a = After.make(dummy)
>>> p = Method.make(primary)
>>> o = Around.make(overriding_fn)
>>> combine_actions(b, combine_actions(a, combine_actions(p, o)))(17)
calling Before(...dummy..., After(...dummy..., Method(...primary...)))
called with (17,) {}
primary method called
called with (17,) {}

Around methods take precedence over all other method types, so the around method's tail is a Before that wraps the After that wraps the primary method.

Within a MethodList, methods are ordered by signature implication first, and then by definition order within groups of ambiguous signatures:

>>> b1 = Before.make("b1", (), 1)
>>> b2 = Before.make("b2", (), 2)
>>> b3 = Before.make("b3", (int,), 3)

>>> combine_actions(b2, b3).sorted()
[((<type 'int'>,), 'b3'), ((), 'b2')]

>>> combine_actions(b2, b1).sorted()
[((), 'b1'), ((), 'b2')]

>>> combine_actions(b3, combine_actions(b1,b2)).sorted()
[((<type 'int'>,), 'b3'), ((), 'b1'), ((), 'b2')]

After methods sort the opposite way:

>>> a1 = After.make("a1", (), 1)
>>> a2 = After.make("a2", (), 2)
>>> a3 = After.make("a3", (int,), 3)

>>> combine_actions(a2, a3).sorted()
[((), 'a2'), ((<type 'int'>,), 'a3')]

>>> combine_actions(a2, a1).sorted()
[((), 'a2'), ((), 'a1')]

>>> combine_actions(a3, combine_actions(a1,a2)).sorted()
[((), 'a2'), ((), 'a1'), ((<type 'int'>,), 'a3')]

And lower-precedence duplicate bodies are automatically eliminated from the results:

>>> combine_actions(a1,a1).sorted()
[((), 'a1')]

>>> combine_actions(b1,b1).sorted()
[((), 'b1')]

>>> combine_actions(b1, Before.make("b1", (int,), 1)).sorted()
[((<type 'int'>,), 'b1')]

AmbiguousMethods

When you combine actions whose signatures are ambiguous (i.e. identical, overlapping, or mutually exclusive), you end up with an AmbiguousMethods object containing the ambiguous methods:

>>> am = combine_actions(meth, meth)
>>> am
AmbiguousMethods([Method(...), Method(...)])

Ambiguous methods can be overridden by an action that would override all of the ambiguous actions:

>>> m1 = Method.make(dummy, (int,))
>>> combine_actions(am, m1) is m1
True
>>> combine_actions(m1, am) is m1
True

And if appropriate, the AmbiguousMethods will end up chained to the overriding method:

>>> m2 = Method.make(overriding_fn, (str,))
>>> combine_actions(am, m2)
Method(<...overriding_fn...>, (<type 'str'>,), 0, AmbiguousMethods(...))

>>> combine_actions(m2, am)
Method(<...overriding_fn...>, (<type 'str'>,), 0, AmbiguousMethods(...))

Ambiguous methods override and ignore anything that would be overridden by any of their members:

>>> am = combine_actions(m1, m1)
>>> combine_actions(am, meth) is am
True
>>> combine_actions(meth, am) is am
True

But anything that overlaps just results in a bigger AmbiguousMethods:

>>> combine_actions(m2,am)
AmbiguousMethods([Method(...), Method(...), Method(...)])

>>> combine_actions(am,m2)
AmbiguousMethods([Method(...), Method(...), Method(...)])

And invoking an AmbiguousMethods instance just outputs diagnostic info:

>>> am(1,2,x="y")
Traceback (most recent call last):
  ...
AmbiguousMethods: ([Method(...), Method(...)], (1, 2), {'x': 'y'})

Decorators

XXX decorators and how to create them: when, around, before, after

Creating Custom Combinations

XXX custom combination demo from RuleDispatch (compute upcharges+tax)

Rules Management

Rule Objects

Rule objects are essentially pure data objects, pairing a predicate, a body, and an action type that will be used as a factory to create the actions for the rule. At minimum, all a rule needs is a body. The predicate and action type both default to None if not specified:

>>> from peak.rules import Rule
>>> def dummy(): pass
>>> r = Rule(dummy)
>>> r
Rule(<function dummy ...>, (), None)

An action type of None (or any false value) means that the ruleset should decide what action type to use. Actually, it can decide anyway, since the rule set is always responsible for creating action objects; the rule's action type is really just advisory to begin with.

RuleSet

RuleSet objects hold the rules and policy information for a generic function, including the default action type and optional optimziation hints.

Iterating over a ruleset yields its actions:

>>> from peak.rules import RuleSet
>>> rs = RuleSet()
>>> list(rs)
[]

And rules can be added and removed with the add() and remove() methods:

>>> r = Rule(dummy)
>>> rs.add(r)
>>> list(rs)
[(<...Method...>, <function dummy ...>, (), 0)]

>>> rs.remove(r)
>>> list(rs)
[]

Observers can be added with the subscribe() and unsubscribe() methods. Observers have their actions_changed method called with an "added" set and a "removed" set of action definitions. (An action definition is a tuple of the form (actiontype, body, signature, precedence), and can thus be used to create action objects.)

>>> class DummyObserver:
...     def actions_changed(self, added, removed):
...         for a in added: print "Add:", a
...         for a in removed: print "Remove:", a
>>> do = DummyObserver()

>>> rs.subscribe(do)

>>> rs.add(r)
Add: (<...Method...>, <function dummy ...>, (), 1)

>>> rs.remove(r)
Remove: (<...Method...>, <function dummy ...>, (), 1)

>>> rs.unsubscribe(do)

When an observer is first added, it's notified of the current contents of the RuleSet, if any. As a result, observers don't need to do any special case handling for their initial setup. Everything can be handled via the normal operation of the actions_changed() method:

>>> rs.add(r)
>>> rs.subscribe(do)
Add: (<...Method...>, <function dummy ...>, (), 2)

Unsubscribing, however, does not send any removal messages:

>>> rs.unsubscribe(do)