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An automated machine learning framework

Project description

Build Status Python 3.6 PyPI version Documentation Status Binder


Automatic Tool Kit for Machine Learning and Datascience.

The objective is to provide tools to ease the repetitive part of the DataScientist job and so that he/she can focus on modelization. This package is still in alpha and more features will be added. Its mains features are:

  • improved and new "scikit-learn like" transformers ;
  • GraphPipeline : an extension of sklearn Pipeline that handles more generic chains of tranformations ;
  • an AutoML to automatically search throught several transformers and models.

Full documentation is available here:

You can run examples here, thanks to Binder.


The GraphPipeline object is an extension of sklearn.pipeline.Pipeline but the transformers/models can be chained with any directed graph.

The objects takes as input two arguments:

  • models: dictionary of models (each key is the name of a given node, and each corresponding value is the transformer corresponding to that node)
  • edges: list of tuples that links the nodes to each other


gpipeline = GraphPipeline(
    models = {
        "vect": CountVectorizerWrapper(analyzer="char",
                                       ngram_range=(1, 4),
                                       columns_to_use=["text1", "text2"]),
        "cat": NumericalEncoder(columns_to_use=["cat1", "cat2"]),
        "rf": RandomForestClassifier(n_estimators=100)
    edges = [("vect", "rf"), ("cat", "rf")]

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Aikit contains an AutoML part which will test several models and transformers for a given dataset.

For example, you can create the following python script

from aikit.datasets import load_dataset, DatasetEnum
from aikit.ml_machine import MlMachineLauncher

def loader():
    dfX, y, *_ = load_dataset(DatasetEnum.titanic)
    return dfX, y

def set_configs(launcher):
    """ modify that function to change launcher configuration """
    launcher.job_config.score_base_line = 0.75
    launcher.job_config.allow_approx_cv = True
    return launcher

if __name__ == "__main__":
    launcher = MlMachineLauncher(base_folder = "~/automl/titanic",
                                 name = "titanic",
                                 loader = loader,
                                 set_configs = set_configs)

And then run the command:

python run -n 4

To run the automl using 4 workers, the results will be stored in the specified folder You can aggregate those result using:

python result

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