palma 2024.1.2

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Project for Automated Learning MAchine

This library aims at providing tools for an automatic machine learning approach. As many tools already exist to establish one or the other component of an AutoML approach, the idea of this library is to provide a structure rather than to implement a complete service. In this library, a broad definition of AutoML is used : it covers the optimization of hyperparameters, the historization of models, the analysis of performances etc. In short, any element that can be replicated and that must, in most cases, be included in the analysis results of the models. Also, thanks to the use of components, this library is designed to be modular and allows the user to add his own analyses.
It therefore contains the following elements

1. A vanilla approach described below (in basic usage section) and in the notebooks classification and regression

2. A collection of components that can be added to enrich analysis

Install it with

python -m pip install palma

Documentation

Access the full documentation here.

Basic usage

1. Start your project

To start using the library, use the project class

import pandas as pd
from sklearn import model_selection
from sklearn.datasets import make_classification
from palma import Project

X, y = make_classification(n_informative=2, n_features=100)
X, y = pd.DataFrame(X), pd.Series(y).astype(bool)
project = Project(problem="classification", project_name="default")
project.start(
    X, y,
    splitter=model_selection.ShuffleSplit(n_splits=10, random_state=42),
)

The instantiation defines the type of problem and the start method will set what is needed to carry out ML project :

• A testing strategy (argument splitter). That will define train and test instances. Note that we use cross validator from sklearn to do that. In the optimisation of hyper-parameters, a train test split will be operated, in this case, the first split will be used. This implies for instance that if you want 80/20 splitting method that shuffle the dataset, you should use

splitter = model_selection.ShuffleSplit(n_splits=5, random_state=42)

• Training data X and target y

2. Run hyper-optimisation

The hyper-optimisation process will look for the best model in pool of models that tend to perform well on various problem. For this specific task we make use of FLAML module. After hyper parametrisation, the metric to track can be computed

from palma import ModelSelector

ms = ModelSelector(engine="FlamlOptimizer",
                   engine_parameters=dict(time_budget=30))
ms.start(project)
print(ms.best_model_)

3. Tailoring and analysing your estimator

from palma import ModelEvaluation
from sklearn.ensemble import RandomForestClassifier

# Use your own
model = ModelEvaluation(estimator=RandomForestClassifier())
model.fit(project)

# Get the optimized estimator
model = ModelEvaluation(estimator=ms.best_model_)
model.fit(project)

Contributing

You are very welcome to contribute to the project, by requesting features, pointing out new tools that can be added as component, by identifying issues and creating new features. Development guidelines will be detailed in near future.

• Fork the repository
• Clone your forked repository git clone https://github.com/$USER/palma.git
• Test using pytest pip install pytest; pytest tests/
• Submit you work with a pull request.

Authors

Eurobios Mews Labs