MLBox, Machine Learning Box

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

To get it installed, please refer to README

For more details, please refer to docs

Experiments : https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries/leaderboard | Rank : 85/2488

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Getting started: 30 seconds to MLBox

MLBox main package is divided into 3 sub-packages : preprocessing, optimisation and prediction. Each one of them are respectively aimed at reading and preprocessing data, testing and 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' : {"search":"choice", "space":[0, 'mean']},
                              
        'ce__strategy' : {"search":"choice", "space":["label_encoding", "random_projection"]},
                          
        'fs__strategy' : {"search":"choice", "space":["variance", "l1"]},
        'fs__threshold': {"search":"choice", "space":[0.1,0.2,0.3]},             
        
        'est__strategy' : {"search":"choice", "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.