talos-automl 0.0.4

Talos: Automated Machine Learning (AutoML) library for One-Class Learning

Talos (in development)

Talos: Automated Machine Learning (AutoML) library focused on One-Class Learning algorithms (AutoEncoders, Isolation Forest and One-Class SVM)
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgements

About The Project

There are many great README templates available on GitHub, however, I didn't find one that really suit my needs so I created this enhanced one. I want to create a README template so amazing that it'll be the last one you ever need -- I think this is it.

Here's why:

• Your time should be focused on creating something amazing. A project that solves a problem and helps others
• You shouldn't be doing the same tasks over and over like creating a README from scratch
• You should element DRY principles to the rest of your life :smile:

Of course, no one template will serve all projects since your needs may be different. So I'll be adding more in the near future. You may also suggest changes by forking this repo and creating a pull request or opening an issue. Thanks to all the people have have contributed to expanding this template!

A list of commonly used resources that I find helpful are listed in the acknowledgements.

Built With

This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.

• Python
• PonyGE2
• TensorFlow
• Scikit-Learn

Getting Started

This section presents how the package can be reached and installed.

Where to get it

The source code is currently hosted on GitHub at: https://github.com/luisferreira97/talos

Binary installer for the latest released version are available at the Python Package Index (PyPI). Note that the PyPI name of the package is talos-automl and not talos.

pip install talos-automl

Usage

1. Import the package

The first step in using the package is, after it has been installed, to import it. The main class from which all the methods are available is Talos.

from talos.talos import Talos

2. Instantiate a Talos object

The second step is to instantiate the Talos class with the information about your dataset and context (e.g., normal and anomaly classes, wether to run single-objective or multi-objective, the performance_metric, and the algorithm). You can change the algorithm parameter to select which algorithms are used during the optimization. The options are:

• "autoencoders": Deep AutoEncoders (from TensorFlow)
• "iforest": Isolation Forest (from Scikit-Learn)
• "svm": One-Class SVM (from Scikit-Learn)
• "all": the optimization is done using the three algorithms above

talos = Talos(anomaly_class = 0,
    normal_class = 1,
    multiobjective=True,
    performance_metric="training_time",
    algorithm = "autoencoder"
)

3. Load dataset

The third step is to load the dataset. Depending on the type of validation you need train data (only 'normal' instances), validation data (you can use (1) only 'normal' instances or (2) both 'normal' and 'anomaly' instances with the respective labels), and test data (both types of instances and labels). You can use the load_example_data() function to load the popular ECG dataset.

X_train, X_val, X_test, y_test = talos.load_example_data()

4. Train

The fourth step is to train the model. The fit() function computes the optimization using the given parameters.

run = talos.fit(
    X=X_train,
    X_val=X_val,
    pop=10,
    gen=10,
    experiment_name="test",
    epochs=1000
)

5. Predict

The fifth step is to predict the labels of the test data. You can use the predict() function to predict the labels of the test data. You can change the mode parameter to select which individuals are used to predict.

• "all": uses all individuals (models) from the last generation
• "best": uses the from the last generation which achieved the best predictive metric
• "simplest": uses the from the last generation which achieved the best efficiency metric
• "pareto": uses the pareto individuals from the last generation (only for multiobjective. These are the models that achieved simultaneouly the best predictive metric and efficiency metric.

Additionally, you can use the threshold parameter (only used for AutoEncoders) to set the threshold for the prediction. You can use the following values:

• "default": uses a different threshold value for each individual (model). For each model the threshold value is the associated default value (currently this works similar to the "mean" value).
• "mean": For each model the threshold value is the sum of the mean reconstruction error obtained on the validation data and one standard deviation.
• "percentile": For each model the threshold value is the 95th percentile of the reconstruction error obtained on the validation data (you can also use the percentile parameter to change the percentile).
• "max": For each model the threshold value is maximum reconstruction error obtained on the validation data.
• You can also pass an Integer of Float value. In this case, the threshold value is the same for all the models.

predictions = talos.predict(X_test,
    mode="all",
    threshold="default")

6. Evaluate

You can use the predictions to calculate manually the performance metrics of the model. However, the evaluate() function is a more convenient way to do it. You can also use the mode parameter (works similarly to the predict() function) and use metrics from the sklearn.metrics package (currently available are "roc_auc", "accuracy", "precision", "recall", and "f1").

score = talos.evaluate(X_test,
    y_test,
    mode="all",
    metric="roc_auc",
    threshold="default")

Usage

from talos.talos import Talos

talos = Talos(anomaly_class = 0,
    normal_class = 1,
    multiobjective=True,
    performance_metric="training_time",
    algorithm = "autoencoder"
)

X_train, X_val, X_test, y_test = talos.load_example_data()

run = talos.fit(
    X=X_train,
    X_val=X_val,
    pop=10,
    gen=10,
    experiment_name="test",
    epochs=1000
)

predictions = talos.predict(X_test,
    mode="all",
    threshold="default")

score = talos.evaluate(X_test,
    y_test,
    mode="all",
    metric="roc_auc",
    threshold="default")

For more examples, please refer to the Documentation-->

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Luís Ferreira - LinkedIn - [e-mail]

Project Link: https://github.com/luisferreira97/talos

Acknowledgements

• PonyGE2