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MLBox is a powerful Automated Machine Learning python library. It provides the following features:

• Fast reading and distributed data preprocessing/cleaning/formatting
• Highly robust feature selection and leak detection
• Accurate hyper-parameter optimization in high-dimensional space
• State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,...)
• Prediction with models interpretation

> For more details, please refer to the official documentation

> Experiments on Kaggle : Two Sigma Connect: Rental Listing Inquiries (rank : 85/2488)

> Articles from users : Tutorial on Automated Machine Learning using MLBox & MLBox : a short regression tutorial

> Webinar : Paris ML Hors Série #13: Automated Machine Learning

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Getting started: 30 seconds to MLBox

MLBox main package contains 3 sub-packages : preprocessing, optimisation and prediction. Each one of them are respectively aimed at reading and preprocessing data, testing or optimising a wide range of learners and predicting the target on a test dataset.

Here are a few lines to import the MLBox:

from mlbox.preprocessing import *
from mlbox.optimisation import *
from mlbox.prediction import *

Then, all you need to give is :

• the list of paths to your train datasets and test datasets
• the name of the target you try to predict (classification or regression)

paths = ["<file_1>.csv", "<file_2>.csv", ..., "<file_n>.csv"] #to modify
target_name = "<my_target>" #to modify

Now, let the MLBox do the job !

... to read and preprocess your files :

data = Reader(sep=",").train_test_split(paths, target_name)  #reading
data = Drift_thresholder().fit_transform(data)  #deleting non-stable variables

... to evaluate models (here default configuration):

Optimiser().evaluate(None, data)

... or to test and optimize the whole Pipeline [OPTIONAL]:

• missing data encoder, aka 'ne'
• categorical variables encoder, aka 'ce'
• feature selector, aka 'fs'
• meta-features stacker, aka 'stck'
• final estimator, aka 'est'

NB : please have a look at all the possibilities you have to configure the Pipeline (steps, parameters and values...)

space = {

        'ne__numerical_strategy' : {"space" : [0, 'mean']},

        'ce__strategy' : {"space" : ["label_encoding", "random_projection", "entity_embedding"]},

        'fs__strategy' : {"space" : ["variance", "rf_feature_importance"]},
        'fs__threshold': {"search" : "choice", "space" : [0.1, 0.2, 0.3]},

        'est__strategy' : {"space" : ["XGBoost"]},
        'est__max_depth' : {"search" : "choice", "space" : [5,6]},
        'est__subsample' : {"search" : "uniform", "space" : [0.6,0.9]}

        }

best = opt.optimise(space, data, max_evals = 5)

... finally to predict on the test set with the best parameters (or None for default configuration):

Predictor().fit_predict(best, data)

That's all ! You can have a look at the folder "save" where you can find :

• your predictions
• feature importances
• drift coefficients of your variables (0.5 = very stable, 1. = not stable at all)

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How to Contribute

MLBox has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

• Check out call for contributions to see what can be improved, or open an issue if you want something.
• Contribute to the tests to make it more reliable.
• Contribute to the documents to make it clearer for everyone.
• Contribute to the examples to share your experience with other users.
• Open issue if you met problems during development.

For more details, please refer to CONTRIBUTING.