https://github.com/augboost-anon/augboost

Gradient Boosting Enhanced with Step-Wise Feature Augmentation
https://github.com/augboost-anon/augboost

Last synced: 3 months ago
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Gradient Boosting Enhanced with Step-Wise Feature Augmentation

• Host: GitHub
• URL: https://github.com/augboost-anon/augboost
• Owner: augboost-anon
• License: other
• Created: 2019-02-24T20:24:35.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-01-13T14:43:55.000Z (almost 4 years ago)
• Last Synced: 2024-06-14T01:27:07.670Z (5 months ago)
• Language: Python
• Size: 51.8 KB
• Stars: 16
• Watchers: 1
• Forks: 17
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# AugBoost

Gradient Boosting Enhanced with Step-Wise Feature Augmentation. 

## About

The code in this repository is based heavily on scikit-learn's 'gradient_boosting.py'. 

We started this as a fork of sklearn, but split away when we saw it would be more convenient. Thanks! =]

## Prerequisites

* Python 3.6.5 (Ubuntu)

* sklearn.__version__ = '0.19.1'

* keras.__version__ = '2.2.0'

* tensorflow.__version__ = '1.8.0'

And a number of small packages which are included in Anaconda.

The most important prerequisite is probably the version of sklearn, although we haven't checked if any of them are necessary.

## Getting Started

After cloning the repository, the 2 modules in can be imported using these lines of code:

```python

from AugBoost import AugBoostClassifier as ABC

from AugBoost import AugBoostRegressor as ABR    #regression module has an issue and doesn't work yet

```

Meanwhile, only the code for classification tasks works =[

Create your model using code that looks like this:

```python

model = ABC(n_estimators=10, max_epochs=1000, learning_rate=0.1, \

    n_features_per_subset=round(len(X_train.columns)/3), trees_between_feature_update=10,\

    augmentation_method='nn', save_mid_experiment_accuracy_results=False)

```

And then train and predict like this:

```python

model.fit(X_train, y_train)

model.predict(X_val)

```

In the file 'notebook for experiments.ipynb' there is example of code for running experiments with AugBoost.