`ATL_Python` is a reconstruction of `ATL_Matlab` made by the same author, but using Python 3.6 and PyTorch (with autograd enabled and GPU support). The code is still not one-to-one and some differences in results can be found (specially on the data split methods in `DataManipulator`, however the network structure is correct and can be used by whoever is interested on this work in order to understand the structure or to build comparative results with your own research work.
2. Install [anaconda](https://www.anaconda.com/) or [miniconda](https://docs.conda.io/en/latest/miniconda.html).
3. Open Anaconda prompt and travel until ATL folder.
4. Run the following command `conda env create -f environment.yml`. This will create an environment called `atl` with every python packaged/library needed to run ATL.
5. Enable ATL environment by running the command `activate atl` or `conda activate atl`.
6. Provide a dataset by replacing the file `data.csv`
The current `data.csv` holds [SEA](https://www.researchgate.net/publication/221653408_A_Streaming_Ensemble_Algorithm_SEA_for_Large-Scale_Classification) dataset.
ATL will automatically normalize your data and split your data into 2 streams (Source and Target data streams) with a bias between them, as described in the paper.
- And a quick review of ATL structure (both discriminative and generative phases), where you can see how many automatically generated nodes were created.
As some datasets are too big, we can't upload them to GitHub. GitHub has a size limit of 35MB per file. Because of that, you can find all the datasets in a csv format on the anonymous link below. To test it, copy the desired dataset to the same folder as ATL and rename it to `data.csv`.
