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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 <https://github.com/fridiculous/django-estimators.git>_ 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