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Data Driven Programming

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Description: Shatter
=============

Data driven programming; input data and output nice functional code ;)


Introduction
=============

This is a [python 3.6+ project](https://pypi.python.org/pypi/shatter) that uses algorithms to transform a set of
conditions into functional python code. See some [examples](https://github.com/jisazaTappsi/shatter/tree/master/examples).


Package Setup
-------------

Install:

$ pip install shatter


Dependencies
------------

pyeda==0.28.0
pandas==0.19.1
sympy==1.1
Keras==2.0.6
numpy==1.13.1
pip==9.0.1
scikit_learn==0.19.0


Examples
=============


Get Started
-------------
Copy paste this snippet:

from shatter.solver import Rules


def my_func(a, b):
pass

r = Rules(a=True, b=True, output=True)
r.solve(my_func)

Run it and see how `my_func` code changes from `pass` to `return a and b`. We just
specified that when `a` and `b` are true then the output should be `True`, that is equivalent to the
logical `and` operator.

We can add further conditions and `shatter` will compute the optimal function to get there.


Adding more conditions
-------------

Now we add 2 additional conditions with `r.add()`:

from shatter.solver import Rules


def my_func(a, b):
pass


r = Rules(a=True, b=True, output=True)
r.add(a=False, b=True, output=True)
r.add(a=True, b=False, output=True)
r.solve(my_func)

In this case the solution is `a or b`.


If conditionals
-------------

What if the output for a given logical condition is not a boolean? In that case a programmer would use an if.
In the next example this package solves this case:

Change output to `1`:

from shatter.solver import Rules


def my_func(a, b):
pass

r = Rules(a=True, b=True, output=1)
r.solve(my_func)


The solution will be:

def my_func(a, b):

if a and b:
return 1

return False

Returns `1` or `False` otherwise.


Adding pieces of code
-------------

Say you want to add a arbitrary piece of code that evaluates to boolean, then:

from shatter.solver import Rules, Code


def any_code(a):
pass

r = Rules(condition=Code(code_str='isinstance(a, str)'), output=2)
r.solve(any_code)

The result should be:

def internal_code(a):

if isinstance(a, str):
return 2

return False

Here the piece of code `isinstance(a, str)` was added as the if condition to output `2`


Iteration
-------------

Run this code:

from shatter.solver import Rules, Code, Output


def recursive(a):
pass

a = Code()
args = {'a': a + 1}
out = Output(function=recursive, arguments=args)

r = Rules(stopping_condition= a > 2, output=a, default=out)
solution = r.solve(recursive)

The result this time will be a recursive counting function :)

def recursive(a):

if a > 2:
return a

return recursive(a + 1)

With `a = Code()` variable `a` is initialized as a code piece. Then with

args = {'a': a + 1}

A dictionary for the inputs of the `recursive` function is declared. Those inputs are fed into a `Output` object:

out = Output(function=recursive, arguments=args)

After `out` is passed via `default` keyword when initializing the `Rules` object. This `default` keyword
is used to override the last return statement of the `recursive` function.


Solve Small ML problem
----------------------

Copy paste this snippet:

import pandas as pd
from sklearn import datasets
from shatter.solver import Rules, solve


@solve()
def solve_iris(x1, x2, x3, x4):
pass


iris = datasets.load_iris()

x = iris.data
y = iris.target

data_frame = pd.DataFrame(x, columns=['x1', 'x2', 'x3', 'x4'])

# Make binary and add to df
data_frame['output'] = [int(bool(e)) for e in y]


print(data_frame)

r = Rules(data_frame)

solution = r.solve(solve_iris)


Outputs:

def solve_iris():
return x3 >= 2.45


Going deeper
=============

Setup
-------------

Clone repository:

`git clone git@github.com:jisazaTappsi/shatter.git`

More examples
-------------

See [examples](https://github.com/jisazaTappsi/shatter/tree/master/examples).


How does shatter work?
-------------

Takes a function and a truth table which is processed using the
[Quine-McCluskey Algorithm](https://en.wikipedia.org/wiki/Quine%E2%80%93McCluskey_algorithm).
Then finds an optimal boolean expression. This expression is inserted in the method definition.


Rules Class
=============

Is initialized with one rule. Other rules can be added with `Rules.add()` method. To generate
the solution call `Rules.solve()` method.

Each rule
-------------

The arguments of each rule are specified as optional arguments inside a `Rules` constructor or inside a
`Rules.add()` call. There are reserved keywords:

`output`: Determines the value to be returned when the given condition is True.

`output_args`: Dictionary with the values for the arguments when output is a function.

`default`: Value returned when non of the rules are True.


Arguments of `Rules.solve()`
-------------

- `function`: passed as a callable. This function is going to be filled with the solution to the present task.

- `unittest=None`: Test Case to be able to run and test the code generated each time the test runs.
See [example](https://github.com/jisazaTappsi/shatter/tree/master/examples/with_tests) for a deeper understanding.


Output Class
-------------

`solver.Output`: Class that helps define a function with arguments as an output. Has fields:

- `function`: A callable object.
- `arguments` Dictionary with the function inputs.

Code class
-------------

`solver.Code`: Class that helps represent pieces of code. The code is fed as a string (with optional argument `str_code`)
or it can be declared as variables. eg:

from shatter.solver import Code

a = Code()
b = Code()
print(a > b)

This will literally print the code `a > b` rather than the objects or any result.


Solution class
-------------

`solver.Solution`: Class that contains the solution of the problem it includes:

- `rules`: The information given by the user.
- `implementation`: Plain code.
- `ast`: Abstract syntax tree

Keywords: Quine McCluskey,Machine Learning,code,automatic code generation,expression
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools

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