PyTorch-RNN-Tutorial

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PyTorch-RNN-Tutorial

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PyTorch tutorial for using RNN and Encoder-Decoder RNN for time series forecasting

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# PyTorch tutorial on using RNNs and Encoder-Decoder RNNs for time series forcasting and hyperparameter tuning

## Some blabber

This package resulted from my effort to write a simple PyTorch based ML package that uses recurrent neural networks (RNN) to predict a given time series data.

You must be wondering why you should bother with this package since there is a lot of stuff on the internet on this topic. Well, let me tell you that I have traveled the internet lanes and I was really frustrated by how scattered the information is in this context. It was a lot of effort to collect all the relevant parts from the internet and construct this package.

I had only a basic background in ML and zero knowledge of PyTorch (using Keras doesn't prepare you for PyTorch :stuck_out_tongue:) when I started writing this package. But that actually ended up being a blessing in disguise. Since I was starting from scratch, I was able to write the code in a way that was intuitive and easy to understand for people who are new to the subject.

So if you're feeling lost and frustrated, give this package a try. It might just help you understand not only RNNs, but PyTorch as well. And who knows, you might even have a little fun along the way.

## Code Functionalities
1. Many-to-One prediction using PyTorch's vanilla versions of RNN, LSTM, and GRU.
2. Many-to-Many (or Seq2Seq) prediction using Encoder-Decoder architecture; base units could be RNN, LSTM, or GRU.
3. Hyperparameter Tuning! It uses the [Optuna](https://optuna.org/) library for that.
4. Save PyTorch models, as well as reload and train them further.
5. Works on any **univariate** and **equispaced** time series data.
6. Can use GPUs.

## Usage
Best way to figure out how to use this package is to check out the example notebooks available in the `Notebooks` folder.

I have also made a sample notebook available in Google Colab!
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1dsP3FhY-qghqfcmn6TLaUkuyCaWAl8WL?usp=sharing)

## Code Structure
I have structured the code so that different operations are abstracted away in Python Classes. Here is a brief summary:

* `Model`: Directory  -  contains classes which define the RNN models. _RNN_Vanilla.py_ defines the Many-to-One RNN; the traditional kind. _The EncDec.py_ file defines the Encode-Decoder class which uses the traditional RNN units as Encoder and Decoder modules, which are then combined together to provide a one-shot Many-to-Many prediction.

* `Notebooks`: Directory - example notebooks which demonstrate how to use the code on a sample time series data consisting of multi frequency sin waves. It also contains a notebook which demonstrates how to perform hyperparameter tuning using Optuna.

* `Saved_models`: Directory, empty - used to store the output from the _Create_and_Train.py_ file.

* `Utils` Directory - contains all the class files which do the data prep, training, testing, validation, and predicting. 
   * _Trainer.py_ contains the training loop, a test function to run the model on test data, as well as functions to make predictions. 
   * _SeqData.py_ file is used to create sequenced dataset, in torch tensors format, given a numpy 1D time series. 
   * _Create_and_Train.py_ is THE main file which creates a model (using the classes in the Model directory), runs the epoch loop, saves PyTorch models and train-test loss curves.

* `imports.py` file is used by the notebooks present in the `Notebooks` folder.

* `requirements.txt` file can be used in conjunction with pip to install the required packages.

## Limitations
I haven't generalized the code to use multivariate time series data for the sake of simplicty. But, it is relatively easy to do. If interested, report in the repo's _Issues_ section and we can collaborate!

### Note 
I also recommend checking out my colleague's [implementation](https://github.com/lkulowski/LSTM_encoder_decoder) of rnn in pytorch.

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