Welcome to fasttransform

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/AnswerDotAI/fasttransform.git

or from pypi:

$ pip install fasttransform

Quick start

Transform

Transform is a class that lets you create reusable data transformations. You initialize a Transform by passing in or decorating a raw function. The Transform then provides an enhanced version of that function via Transform.encodes, which can be used in your data pipeline.

It provides various conveniences:

• Reversibility. You can collect the raw function and its inverse into one transform object.
• Customized initialization You can customize the exact behavior of a transform function on initialization.
• Type-based mulitiple dispatch. Transforms can specialize their behavior based on the runtime types of their arguments.
• Type conversion/preservation. Transforms help you maintain desired return types.

The simplest way to create a Transform is by decorating a function:

from fasttransform import Transform, Pipeline

@Transform
def add_one(x): 
    return x + 1

# Usage
add_one(2)

3

Reversibility

To make a transform reversible, you provide the raw function and its inverse. This is useful in data pipelines where, for instance, you might want to normalize and then de-normalize numerical values, or encode to category indexes and then decode back to categories.

def enc(x): return x*2
def dec(x): return x//2

t = Transform(enc,dec)

t(2), t.decode(2), t.decode(t(2))

(4, 1, 2)

Customized initialization

You can customize an individual Transform instance at initialization time, so that it can depend on aggregate properties of the data set.

Here we define a z-score normalization Transform by defining encodes and decodes methods directly:

import statistics

class NormalizeMean(Transform):
    def setups(self, items): 
        self.mean = statistics.mean(items)
        self.std  = statistics.stdev(items)
    
    def encodes(self, x): 
        return (x - self.mean) / self.std
    
    def decodes(self, x): 
        return x * self.std + self.mean

normalize = NormalizeMean()
normalize.setup([1, 2, 3, 4, 5])
normalize.mean

3

Type-based multiple dispatch

Instead of providing one raw functions, you can provide multiple raw functions which differ in their parameter types. Tranform will use type-based dispatch to automatically execute the correct function.

This is handy when your inputs come in different types (eg., different image formats, different numerical types).

def inc1(x:int): return x+1
def inc2(x:str): return x+"a"

t = Transform(enc=(inc1,inc2))

t(5), t('b')

(6, 'ba')

If an input type does not match any of the type annotations then the original input is returned.

add_one(2.0)

3.0

normalize(3.0)

0.0

Type conversion/preservation

You initialize a Transform by passing in or decorating a raw function.

A Transform encodes or decodes will note the return type of its raw function, which may be defined explicitly or implicitly, and enhance type-handling behavior in three ways:

1. Guaranteed return type. It will always return the return type of the raw function, promoting values if necessary.

2. Type Preservation. It will return the runtime type of its argument, whenever that is a subtype of the return type.

3. Opt-out conversion. If you explicitly mark the raw function’s return type as None, then it will not perform any type conversion or preservation.

Examples help make this clear:

Guaranteed return type

Say you define FS, a subclass of float. The usual Python type promotion behavior means that an FS times a float is still a float:

class FS(float):
  def __repr__(self): return f'FS({float(self)})'
 
f1 = float(1)
FS2 = FS(2)

val = f1 * FS2
type(val) # => float

float

With Transform, you can define a new multiplication operation which will be guaranteed to return a FS, because Transform reads the required raw function’s annotated return type:

def double_FS(x)->FS: return FS(2)*x
t = Transform(double_FS)
val = t(1) 
assert isinstance(val,FS)
val

FS(2.0)

Type preservation

Let us say that we define a transform without any return type annotation, so that the raw function is defined only by the behavior of multiplying its argument by the float 2.0.

Multiplying the subtype FS with the float value 2 would normally return a float. However, Transform’s encodes will preserve the runtime type of its argument, so that it returns FS:

def double(x): return x*2.0  # no type annotation
t = Transform(double)
fs1 = FS(1)
val = t(fs1)
assert isinstance(val,FS)
val # => FS(2), an FS value of 2

FS(2.0)

Opt-out conversion

Sometimes you don’t want Transform to do any type-based logic. You can opt-out of this system by declaring that your raw function’s return type is None:

def double_none(x) -> None: return x*2.0  # "None" returnt type means "no conversion"
t = Transform(double_none)
fs1 = FS(1)
val = t(fs1)
assert isinstance(val,float)
val # => 2.0, a float of 2, because of fallback to standard Python type logic

2.0

Pipelines

Transforms can be combined into larger Pipelines:

def double(x): return x*2.0 
def halve(x): return x/2.0
dt = Transform(double,halve)

class NormalizeMean(Transform):
    def setups(self, items): 
        self.mean = statistics.mean(items)
        self.std  = statistics.stdev(items)
    
    def encodes(self, x):
        return (x - self.mean) / self.std
    
    def decodes(self, x):
        return x * self.std + self.mean


p = Pipeline((dt, normalize))

v = p(5)
v

4.427188724235731

p.decode(v)

5.0

Documentation

This was just a quickstart. Learn more by reading the documentation.