benjamin-croker / simpyl

A python library and web interface to manage running, configuring and logging computational simulations

simpyl

Track and log computational simulations and machine learning runs with a handful of Python decorators - via a web or code interface.

A Simple Example

examle.py shows how Simpyl can be used with a basic Random Forest classifier and the Iris dataset.

Import the library, create a Simply object, and point it to an SQLite database.

from simpyl import Simpyl
import simpyl.database as db

sl = Simpyl()
db.use_database('example.db')

Register a function to be controlled by Simpyl. The function arguments are automatically picked up.

@sl.add_procedure('trainer')
def main_trainer(n_estimators, min_samples_split):
    X, y = load_data()
    trained_classifier = train_classifier(X, y, n_estimators, min_samples_split)
    ...

Inside this function we can log information, and cache the trained estimator.

    ...
    sl.log("Overall accuracy: {}%".format(100.0 * score))
    sl.write_cache(trained_classifier, "classifier.rf")
    ...

Plots can also be saved. sl.savefig() will save any active Matplotlib plot.

@sl.add_procedure('plots')
def feature_importance():
    trained_classifier = sl.read_cache("classifier.rf")
    ...
    # Plot a figure with matplotlib
    ...
    sl.savefig("Feature Importances")

Start the webserver with sl.start(). This gives us a web interface to build runs with our registered functions.

A page with arguments, results and saved plots is automatically created.

Runs can be built an initiated via normal Python code as well.

sl.run(
    [
        ('trainer', {'n_estimators': 3, 'min_samples_split': 10}),
        ('plots', {})
    ],
    description="Run from code"
)

These will still be visible in the web interface.