Skip to content
/ fito Public
forked from elsonidoq/fito

Fito is a python library that helps to organize your data so you can access it in a more understandable and easy way

License

Notifications You must be signed in to change notification settings

azavalla/fito

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

About

Fito is to Data Science what SQLAlchemy is for data bases. Fito is the Data Science ORM.

It helps you organize you code and integrate different technologies while handling a consistent and clear object model.

Things like:

  • Mapping between config files and behaviour,
  • Caching results of execution (in memory, or in any key value store)
  • Or attaching metadata to executions (like metrics, scores, plots, etc)

become trivial

Functionality

Fito is a package that works around four concepts: First there are Specs. A Spec specifies an object. It provides the capability of specifiyng things, like models or data sources. Also an Spec can be combined with another Spec which allows them to specify things like experiments that combine both models and data sources. Specs are both json-serializable and hasheable.

An Operation is an Spec that computes something out of it. Can be though as a currified function

That leads us to the DataStore, whose capability is to index an Spec (or any subclass, of course). There are three implementations, one that uses python dictionaries, another uses the file system and a third one backed on MongoDB.

One nice combination of having this abstraction, is that we can do automatic caching. That can be performed just by linking operations and data stores together

Besides that, fito provides very helpful decorator, as_operation that turns any function into a subclass of Operation.

How does it look like?

It looks like this

from fito.data_store import DictDataStore
from fito import as_operation

ds = DictDataStore() # Can be any implementation of data store

@as_operation(cache_on=ds)
def f(x, y=1):
    return x + y

f(1).execute() # executed
f(1).execute() # retrieved from cache

That code is enough to cache the executions of f into memory

You can see more examples here:

Contributing

This is my first open source piece of software where I'm commiting myself to mantain for the next year.

Let me know if you happen to use it!

And please, do not hesitate on sending pull requests :D

Installation

pip install fito

About

Fito is a python library that helps to organize your data so you can access it in a more understandable and easy way

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 100.0%