ensemblem 0.4
Dynamic Weighted Ensemble - Local Fusion
Requires Python: >=3.8

# Dynamic Weighted Ensemble - Local Fusion

This repository contains an implementation for the Dynamic Weighted Ensemble (DWE) - Local Fusion method. Find the paper in this ref on [IEEE](https://ieeexplore.ieee.org/document/8272838).

Local Fusion is an ensemble techinque that could be used to improve predictions by weighing appropriately the single models contribution.

![imgs](https://github.com/IvanVigor/IvanVigor.github.io/blob/main/imgs/dwe.png?raw=true)

## Installation

[Pypi](https://pypi.org/project/ensemblem)

```{r setup, include=FALSE}

	pip install ensemblem

```

[Docs - ReadTheDocs](https://local-fusion-dynamic-weighted-ensemble.readthedocs.io/en/latest/) (UnderReview)

## Usage

First of all, you need to define the KWEnsembler class. Next, it's required to provide the search-space (it could be the validation set / neighbours-set) in which the ensembler will find the nearest elements to the generic test sample.

```{r setup, include=FALSE}
	from ensemblem.model import KWEnsembler
	ensemble = KWEnsembler(5)
	ensemble.fit(X_validation, y_validation)
```

Finally, calling the prediction method the class will produce the forecasts.

```{r setup, include=FALSE}
	ensemble.predict(X_test,
                    features_space,
                    other_model_prediction_columns)
```

The class returns predictions in the same order in which they are provided. 
It supports one or multiple samples to forecasts. In this library, we refers to the neighbours-set as the space in which the ensembler will find the nearest elements to the generic test sample.



## Example of using the KWEnsembler class

You can find a detailed tutorial in the readthedocs webpage.

1. Load data
2. Split data into train, neighbours-set and test sets
3. Train multiple expert models on the train data
4. Generate predictions for the test data
5. Train the ensembler on neighbours-set
6. Generate predictions for the test dataset using ensembler
7. Compare the predictions from the ensembler with the predictions from the expert models


## Results & Benchmarks

|    | Model   |     MAPE |      MAE |      RMSE |    RMSLE |
|---:|:--------|---------:|---------:|----------:|---------:|
|  0 | ***Ensemble*** | ***0.304129*** | ***0.499381*** | ***0.0016118*** | ***0.211999*** |
|  1 | Tree 1  | 0.370919 | 0.593606 | 0.00755926 | 0.249373 |
|  2 | Tree 2  | 0.319638 | 0.511249 | 0.00224047 | 0.225012 |
|  3 | RidgeCV | 0.31537 | 0.531177 | 0.0131216 | 0.238018 |



## Credits

Algorithm Applications

- A dynamic weighting ensemble approach for wind energy production prediction [IEEE](https://ieeexplore.ieee.org/document/8272838)

- An ensemble approach to sensor fault detection and signal reconstruction for nuclear system control [Elsevier](https://www.sciencedirect.com/science/article/pii/S0306454910000927)


## Possible Improvements

- [Docs] General improvements over documentations

- [Code] Clean-code 

- [Engineering] When dealing with features coming with magnitude and different meaning, it's relevant to normalize values appropriately.

- [Engineering] Additional measures to the simple euclidean-space


## Licence
The code is provided with a MIT licence. 

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)