py-rete 0.0.7.dev39

A basic Python implementation of the RETE algorithm.

py_rete

Introduction

The py_rete project aims to implement a Rete engine in native python. This system is built using one the description of the Rete algorithms provided by Doorenbos (1995). It also makes heavy use of ideas from the Experta project (although no code is used from this project as it utilizes an LGPL license).

The purpose of this system is to support basic expert / production system AI capabilities in a way that is easy to integrate with other Python based AI/ML systems.

Installation

This package is installable via pip with the following command: pip install -U py_rete.

It can also be installed directly from GitHub with the following command: pip install -U git+https://github.com/cmaclell/py_rete@master

The Basics

The two high-level structures to support reasoning with py_rete are facts and productions.

Facts

Facts represent the basic units of knowledge that the productions match over. Here are a few examples of facts and how they work.

1. Facts are a subclass of dict, so you can treat them similar to dictionaries.

>>> f = Fact(a=1, b=2)
>>> f['a']
1

2. Similar to dictionaries, Facts do not maintain an internal order of items.

>>> Fact(a=1, b=2)
Fact(b=2, a=1)

3. Facts extend dictionaries, so they also support values without keys.

>>> f = Fact('a', 'b', 'c')
>>> f[0]
'a'

4. Facts can support mixed positional and named arguments, but positional must come before named and named arguments do not get positional references.

>>> f = Fact('a', 'b', c=3, d=4)
>>> f[0]
'a'
>>> f['c']
3

5. Facts support nesting with other facts.

>>> f = Fact(subfact=Fact())
Fact(subfact=Fact())

Note that there will be issues if facts contain other data structures that contain facts (they will not be properly added to the rete network or to productions).

Productions

Similar to Experta's rules, Productions are functions that are decorated with conditions that govern when they execute and bind the arguments necessary for their execution.

Productions have two components:

• Conditions, which are essentially facts that can pattern matching variables.
• A Function, which is executed for each rule match, with the arguments to the function being passed the bindings from pattern matching variables.

Here is an example of a simple Productions that binds with all Facts that have the color red and prints 'I found something red':

@Production(Fact(color='red'))
def alert_something_red():
    print("I found something red")

Productions also support logical operators to express more complex conditions.

@Production(AND(OR(Fact(color='red'),
                   Fact(color='blue')),
	        NOT(Fact(color='green'))))
def alert_something_complex():
    print("I found something red or blue without any green present")

Bitwise logical operators can be used as shorthand to make composing complex conditions easier.

@Production((Fact(color='red') | Fact(color='blue')) & ~Fact(color='green'))
def alert_something_complex2():
    print("I found something red or blue without any green present")

In addition to matching simple facts, pattern matching variables can be used to match wildcards, ensure variables are consistent across conditions, and to bind variables for functions.

@Production(Fact(firstname='Chris', lastname=V('lastname')) &
            Fact(first='John', lastname=V('lastname')))
def found_relatives(lastname):
    print("I found a pair of relatives with the lastname: {}".format(lastname))

It is also possible to employ functional tests (lambdas or other functions) in conditions. These tests operate over bound variables, so it is important for positive facts that bind with these variables to be listed in the production before the tests that use them.

@Production(Fact(value=V('a')) &
            Fact(value=V('b')) &
            Filter(lambda a, b: a > b) &
            Fact(value=V('c')) &
            Filter(lambda b, c: b > c))
def three_values(a, b, c):
    print("{} is greater than {} is greater than {}".format(a, b, c))

It is also possible to bind facts to variables as well. Note, variables bound in this way do not get matched against other occurances of the variable. This is primarily for binding variables for use in subsequent function calls.

@Production(V('name_fact') << Fact(name=V('name')))
def found_name(name_fact):
    print("I found a name fact {}".format(name_fact))

ReteNetwork

To engage in reasoning facts and productions are loaded into a ReteNetwork, which facilitates the matching and application of productions to facts.

Here is how you create a network:

net = ReteNetwork()

Once a network has been created, then facts can be added to it.

f1 = Fact(light_color="red")
net.add_fact(f1)

Note, facts added to the network cannot contain any variables or they will trigger an exception. Additionally, once a fact has been added to network it is assigned a unique internal identifier.

This makes it possible to update the fact.

f1['light_color'] = "green"
net.update_fact(f1)

It also make it possible to remove the fact.

net.remove_fact(f1)

When updating a fact, note that it is not updated in the network until the update_fact method is called on it. An update essentially equates to removing and re-adding the fact.

Productions can also be added to the network. When they are added, then the the rete_net variable is added to the scope of the function. This rete_net variable points to the network the production has been added to, and can be used to update the network.

>>> f1 = Fact(light_color="red")
>>> 
>>> @Production(V('fact') << Fact(light_color="red"))
>>> def make_green(fact):
>>>	print('making green')
>>>     fact['light_color'] = 'green'
>>>     rete_net.update_fact(fact)
>>> 
>>> @Production(V('fact') << Fact(light_color="green"))
>>> def make_red(fact):
>>>	print('making red')
>>>     fact['light_color'] = 'red'
>>>     rete_net.update_fact(fact)
>>> 
>>> light_net = ReteNetwork()
>>> light_net.add_fact(f1)
>>> light_net.add_production(make_green)
>>> light_net.add_production(make_red)

Once the above fact and productions have been added the network can be run.

>>> light_net.run(5)
making green
making red
making green
making red
making green

The number passed to run denotes how many rules the network should fire before terminating.

In addition to this high-level function for running the network, there are also some lower-level capabilities that can be used to more closely control the rule execution.

For example, you can get all the production matches from the matches property.

matches = list(light_net.matches)

You can also get just the new matches.

new = list(light_net.new_matches)

You can fire one of the matches.

matches[0].fire()