Entropy-controlled contexts in Python
Project description
Python module ordered
ordered
module is the opposite to random
- it maintains order in the program.
import random
x = 5
def increase():
global x
x += 7
def decrease():
global x
x -= 2
while x != 22:
random.choice([increase, decrease])()
# takes long time to exit ...
vs.
import random, ordered
x = 5
def increase():
global x
x += 7
def decrease():
global x
x -= 2
with ordered.orderedcontext(): # entropy-controlled context
while x != 22:
random.choice([increase, decrease])()
# exits immediately with correct result
Usage
ordered
is a Python 3.8+ module. Usecases include automated decisionmaking, manufacturing control, robotics, automated design, automated programming and others.
You describe the problem in Python language formalism in form of functions and methods that model some business process - these methods modify the state of the world. By defining the exit criteria of entropy-controlled context you ask the system to reach the goal state by applying available functions and methods as many times as needed. ordered
will automatically decide which objects available in memory to use with each call.
pip install ordered
Having pypy3 installed on the system is recommended for performance and will be automatically detected and used.
Entropy Context Objects
Ordered contexts are environments of controlled entropy. Contexts allow you to control which portions of the program will be guaranteed to exit with minimum state-changing steps. Raising any exceptions is also avoided by providing the correct "anti-random" choice()
results.
# ... normal python code
with ordered.orderedcontext():
# ... entropy-controlled context, guaranteed to exit without exceptions
# ... normal python code
-
ordered.orderedcontext()
Return a context manager and enter the context.
SchedulingError
will be raised if exit is not possible.Inside ordered context functions
random.choice
andordered.choice
are equivalent and no randomness is possible. Ifchoice()
is called without parameters thengc.get_objects()
(all objects in Python heap) is considered by default.Optional returned context object allows to set parameters and limits such as
timeout
andmax_states
.Warning: not all Python features are currently supported and thus
ordered
might fail with internal exception. In this case a rewrite of user code is needed to remove the usage of unsupported features (such as I/O, lists and for loops.)Warning:
ordered
requires all entropy-controlled code to be type-hinted.
# ...
def decrease():
global x
assert x > 25 # when run inside context this excludes cases when x <= 25
# thus increasing amount of overall steps needed to complete
x -= 2
# ...
with ordered.orderedcontext(): # entropy-controlled context
while x < 21: # exit if x >= 21
random.choice([increase, decrease])()
assert x < 23 # only x == 21 or 22 matches overall
ordered
.choice()
-
ordered.choice(objects=None)
Choose and return the object that maintains maximum order in the program (minimum entropy). Any exception increases entropy to infinity so choices leading to exceptions will be avoided. Inside the entropy controlled context,
random.choice
is equivalent toordered.choice
.objects
is a list of objects to choose from. Ifobjects
isNone
thengc.get_objects()
is assumed by default.Warning: current implementation of
while ... ordered
loop is hard-coded to the form shown in examples.while
loops with other statements than a single-linechoice()
are not supported. Add your code to other parts of context and/or functions and methods in your program
ordered
.side_effect(lambda: <code>)
-
ordered.side_effect(lamdba=[lambda function])
Execute the supplied lambda function as a side-effect avoiding the compilation and subsequent effect analysis by
ordered
. This is useful when I/O is easier schdeuled right within the entropy-controlled part of the program or when you know that the code to be executed has no useful effect on the state of the problem of interest.side_effect may only be used when importred into global namespace using
from ordered import side_effect
from ordered import side_effect def move(t: Truck, l: Location): "Move truck to any adjacent location" assert l in t.location.adjacent t.locaiton = l t.distance += 1 side_effect(lambda: print(f"This {__name__} code can have any Python construct and is not analysed. Current value is {t.distance}"))
Examples:
Object Oriented Code
Preferred way of implementing software models with ordered
is object-oriented:
import ordered
class MyVars:
x: int
steps: int
def __init__(self) -> None:
self.x = 0
self.steps = 0
def plus_x(self):
self.x += 3
self.count_steps()
def minus_x(self):
self.x -= 2
self.count_steps()
def count_steps(self):
self.steps += 1
m = MyVars()
m.x = 5
with ordered.orderedcontext():
while m.x != 12:
ordered.choice()()
print("Steps:", steps)
Pouring problem
A classic bottle pouring puzzle. You are in the possession of two bottles, one with a capacity of 3 litres and one with a capacity of 5 litres. Next to you is an infinitely large tub of water. You need to measure exactly 4 litres in one of the bottles. You are only allowed to entirely empty or fill the bottles. You can't fill them partially since there is no indication on the bottles saying how much liquid is in them. How do you measure exactly 4 litres?
from ordered import orderedcontext, choice
class Bottle:
volume: int
fill: int
def __init__(self, volume: int):
self.volume = volume
self.fill = 0
def fill_in(self):
self.fill += self.volume
assert self.fill == self.volume
def pour_out(self, bottle: "Bottle"):
assert self != bottle
can_fit = bottle.volume - bottle.fill
sf = self.fill
bf = bottle.fill
if self.fill <= can_fit:
bottle.fill += self.fill
self.fill = 0
assert self.fill == 0
assert bottle.fill == bf + sf
else:
bottle.fill += can_fit
self.fill -= can_fit
assert bottle.fill == bottle.volume
assert self.fill == sf - can_fit
def empty(self):
self.fill = 0
b1 = Bottle(3)
b2 = Bottle(5)
with orderedcontext():
while b2.fill != 4:
choice([Bottle])()
pass
NOTE: Be careful with importing from a module into global namespace and using choice()()
without parameters in global scope. Current implementation load all global objects including the orderedcontext
and choice
and cause an error
Learning a function
ordered
can be used
from ordered import choice, orderedcontext
from dataclasses import dataclass
@dataclass
class Point:
x: int
y: int
data = [Point(1,1), Point(2,4), Point(3,9)]
# TODO: create_function creates a nonrandom function out of Node objects with `ordered.choice`
# TODO: run_function runs a node tree with a value and returns result
with orderedcontext():
f = create_function()
for point in data:
assert run_function(f, point.x) == point.y
# context exit guarantees that create_function() constructs a correct function to describe input
# TODO: approximate function learning example
Work-in-progress functions
ordered
.relaxedcontext()
Guaranteed to find an exit. Modifies the program if required.
Method ordered
.def(heap_in_out: List)
Defines a function from a list of input and output heaps. The more examples of heaps are supplied, the better is the function.
Status
Although the system is in use by several industry organizations, ordered
is under heavy development. Expect rapid changes in language support, performance and bugs.
Limitations
Python Language
Overall we have a relatively complete support of 'basic' use of object-oriented programming style. However, there are some hard limitaions and work-in-progress items that are yet to be documented.
Try to avoid multiline code as we have several places where line continuation may break during compilation.
Built-ins support is minimal. No I/O can be executed except for in explicit side_effect()
calls.
None of the "ordered data structures" are supported: this includes list
, dict
and tuple
. Use set
or create your own data structures based on objects and classes.
Loops are not supported, including while
and for
besides the main while..choice()
loop as described above - define your problem by creating functions that can be iteratively called by while.. choice()
to overcome this.
Support of missing features is a current work in progress.
Integer Math
Math implementation is simple and works up to count 20-50 depedning on available resources. Future development includes switching to register-based math and monotonic-increase heuristics to support any numbers.
Symbolic Execution Performance
Current implementaion of Python code compilation is naive and doesn't scale well. The simpler your code, the faster it will compile. Future development includes implementing smarter symboic execution heuristics, pre-calculated database and statistical methods.
Model Universality
Current model can efficiently handle a limited set of problem classes and might require significantly more resources than would be needed with a more complete model. HyperC team provides more complete models for specific industry per request. Future development includes adding a universal pruning based on statistical methods as amount of data available to HyperC team grows.
Science behind ordered
ordered
is based on translating a Python program to AI planning problem and uses a customized fast-downward as a backend. Additionally, we're implementing machine learning and pre-computed matrices on various levels to vastly improve performance on larger problems.
Credits
Module ordered
is developed and maintained by HyperC team, https://hyperc.com (CriticalHop Inc.)
For any questions and inquries please contact Andrew Gree, andrewg@hyperc.com.
Investor Relations
HyperC is fundraising! Please contact at andrewg@hyperc.com.
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