Skip to main content

Machine Learning Version Control made Simple

Project description

https://travis-ci.org/fridiculous/estimators.svg?branch=master Code Health

Estimators

Machine Learning Versioning made Simple

Intro

Estimators helps organize, track machine learning models and datasets. Estimators functions as an api for your machine learning models and datasets, to convieniently persist, retrieve and machine learning models and datasets.

This repo utilizes sqlalchemy as an ORM. If you’re using django, try django-estimators instead.

Installation

Estimators is not yet on PyPI, so just run:

pip install estimators

Environment Setup

First, we need to initialize our database and filesystem. This only needs to happen once per database/filesystem. In future releases, we anticipate this step will be simplified.

from estimators import Estimator, DataSet, DataBase
db = DataBase()
db.initialize_database()
Estimator.initialize_root_dir()
DataSet.initialize_root_dir()

Basic Usage

We can see the power of Estimators in 2 steps. Let’s say we are developing a classifier. We’ll load up the data, split it for validation, and then create and train a model.

from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier

digits = load_digits() # 1797 by 64
X = digits.data
y = digits.target

# simple splitting for validation testing
X_train, X_test = X[:1200], X[1200:]
y_train, y_test = y[:1200], y[1200:]

rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

1. First import an Evaluator object that instantiates an evaluation plan. Set the estimator, X_test and y_test to that evaluator object.

from estimators import Evaluator

plan = Evaluator()
plan.estimator = rfc
plan.X_test = X_test
plan.y_test = y_test

# persist all objects upon prediction
result = plan.evaluate()

# including our predictions
result.y_predicted

2. At a later date, we can retrieve the results, along with the original estimator, X_test dataset and y_test dataset using sqlalchemy orm.

from estimators import DataBase, EvaluationResult
db = DataBase()

result = db.Session.query(EvaluationResult).first()

# which has all our attributes
result.id
result.create_date
result.estimator
result.X_test
result.y_test
result.y_predicted

Advanced Usage

Continuing with the above example, we can pull specific estimators or datasets from our database.

from estimators import Estimator, DataSet

# to return an estimator proxy object
es = db.Session.query(Estimator)[-1]

# return our fitted RandomForestClassifier
es.estimator

# to returns all datasets as proxy objects

ds = db.Session.query(DataSet).all()
ds[0].data

But we can continue on to use all of sqlalchemy’s expressions

X_test_one = db.Session.query(DataSet).filter(DataSet.hash=='a381b220d0cd271d608a27eb52dfb654').first()
y_test_one = db.Session.query(DataSet).filter(DataSet.hash=='fe773b5c53aec02fd98ffc65feb4714d').first()

Furthermore, we can run more evaluations using our new proxy objects. The Evaluator object handles the proxy Estimator and DataSet objects just like regular data.

plan = Evaluator()
plan.estimator = es
plan.X_test = X_test_one
plan.y_test = y_test_one

result_two = plan.evaluate()

Additionally if we want to use a different database connection, we can pass the sqlalchemy session object to the evaluator.

from estimators import DataBase
db = DataBase(url='sqlite://')

plan = Evaluator()
plan.session = db.Session
# and continue as expected otherwise

Development Installation

To install the latest version of estimators, clone the repo, change directory to the repo, and pip install it into your current virtual environment.:

$ git clone git@github.com:fridiculous/estimators.git
$ cd estimators
$ <activate your project’s virtual environment>
(virtualenv) $ pip install -e .  # the dot specifies for this current repo

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
estimators-0.1.0.dev0-py2.py3-none-any.whl (17.6 kB) Copy SHA256 hash SHA256 Wheel py2.py3

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page