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💡 About The Project

💡 About The Project

Longitudinal datasets contain information about the same cohort of individuals (instances) over time, with the same set of features (variables) repeatedly measured across different time points (also called waves) [1,2].

Scikit-Longitudinal, also called Sklongis a machine learning library designed to analyse longitudinal data, also called Panel data in certain fields. Today, Sklong is focussed on the Longitudinal Machine Learning Classification task. It offers tools and models for processing, analysing, and classify longitudinal data, with a user-friendly interface that integrates with the Scikit-learn ecosystem.

Auto-Scikit-Longitudinal (Auto-Sklong) is an Automated Machine Learning (AutoML) library, developed upon the General Machine Learning Assistant (GAMA) framework, introduces a brand-new search space leveraging both Sklong and Scikit-learn models to tackle the Longitudinal machine learning classification tasks.

Auto-Sklong comes with various search method to explore the search space introduced. Bayesian Optimisation via SMAC3, Random Search, Successive Halving, and Evolutionary Algorithms, via GAMA.

🛠️ Installation

  1. ✅ Install the latest version of Auto-Sklong:
pip install Auto-Sklong

Different Versions?

You can also install different versions of the library by specifying the version number, e.g., pip install Auto-Sklong==0.0.1. Refer to the Release Notes.

  1. 📦 [MANDATORY] Update the required dependencies (Why? See here)

Auto-Sklong incorporates via Sklong a modified version of Scikit-Learn called Scikit-Lexicographical-Trees, which can be found at this Pypi link.

This revised version guarantees compatibility with the unique features of Scikit-longitudinal. Nevertheless, conflicts may occur with other dependencies in Auto-Sklong that also require Scikit-Learn. Follow these steps to prevent any issues when running your project.

🫵 Simple Setup: Command Line Installation Say you want to try `Auto-Sklong` in a very simple environment. Such as without a proper `project.toml` file (`Poetry`, `PDM`, etc). Run the following command: 
pip uninstall scikit-learn scikit-lexicographical-trees && pip install scikit-lexicographical-trees
🫵 Project Setup: Using `PDM` (or any other such as `Poetry`, etc.) Imagine you have a project being managed by `PDM`, or any other package manager. The example below demonstrates `PDM`. Nevertheless, the process is similar for `Poetry` and others. Consult their documentation for instructions on excluding a package. Therefore, to prevent dependency conflicts, you can exclude `Scikit-Learn` by adding the provided configuration to your `pyproject.toml` file. 
[tool.pdm.resolution]
excludes = ["scikit-learn"]
*This exclusion ensures Scikit-Lexicographical-Trees (used as `Scikit-learn`) is used seamlessly within your project.*

💻 Developer Notes

For developers looking to contribute, please refer to the Contributing section of GAMA here and Scikit-Longitudinal here.

🛠️ Supported Operating Systems

Auto-Sklong is compatible with the following operating systems:

  • MacOS 
  • Linux 🐧
  • On Windows 🪟, you are recommended to run the library within a Docker container under a Linux distribution.

Warning

We haven't tested it on Windows without Docker.

🚀 Getting Started

To perform an AutoML search for your longitudinal machine learning classification task using Auto-Sklong, start by employing the LongitudinalDataset class to prepare your dataset (i.e, data itself, temporal vector, etc.). Next, instantiate a GamaLongitudinalClassifier object, which will set up the necessary configuration to run a search on your data, with the parameters you would have entered in the GamaLongitudinalClassifier constructor.

from sklearn.metrics import classification_report
from scikit_longitudinal.data_preparation import LongitudinalDataset
from gama.GamaLongitudinalClassifier import GamaLongitudinalClassifier

# Load your longitudinal dataset
dataset = LongitudinalDataset('./stroke.csv')
dataset.load_data_target_train_test_split(
  target_column="class_stroke_wave_4",
)

# Pre-set or manually set your temporal dependencies 
dataset.setup_features_group(input_data="elsa") # (1)

# Instantiate the AutoML system
automl = GamaLongitudinalClassifier(
    features_group=dataset.features_group(),
    non_longitudinal_features=dataset.non_longitudinal_features(), # (2)
    feature_list_names=dataset.data.columns,
    # (3)
)

# Run the AutoML system to find the best model and hyperparameters
model.fit(dataset.X_train, dataset.y_train)

# Predictions and prediction probabilities
label_predictions = automl.predict(X_test)
probability_predictions = automl.predict_proba(X_test)

print(classification_report(y_test, label_predictions))
automl.export_script()  # (4)
  1. Define the features_group manually or use a pre-set from the LongitudinalDataset class. If the data was from the ELSA database, you could have used the pre-sets such as .setup_features_group('elsa'). Read further in here
  2. Define the non-longitudinal features manually or use a pre-set from the LongitudinalDataset class. If the data was from the ELSA database, you could have used the pre-sets such as .setup_features_group('elsa'), then the non-longitudinal features would have been automatically set. Read further in here.
  3. The GamaLongtudinalClassifier comes with a variety of hyperparameters that can be set. Refer to the API for more information.
  4. The export_script method allows you to export the best model found by the AutoML system as a Python script. This script can be used to reproduce the model without the need for the AutoML system. Refer to the API for more information.

Wants to understand what's the feature_groups? How your temporal dependencies are set via pre-set or manually?

To understand how to set your temporal dependencies, please refer to the Temporal Dependency tab of the documentation.

Wants more control on hyper-parameters?

To see the full API reference, please refer to the API tab.

Wants more information on the Search Space Auto-Sklong comes with?

To see the full Search Space, please refer to the Search Space tab.

Wants more to grasp the idea?

To see more examples, please refer to the Examples tab of the documentation.

📚 References

[1] Kelloway, E.K. and Francis, L., 2012. Longitudinal research and data analysis. In Research methods in occupational health psychology (pp. 374-394). Routledge.

[2] Ribeiro, C. and Freitas, A.A., 2019. A mini-survey of supervised machine learning approaches for coping with ageing-related longitudinal datasets. In 3rd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL), held as part of IJCAI-2019 (num. of pages: 5).