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https://github.com/surfiniaburger/pool-volume-prediction
Pool Volume Forecasting
https://github.com/surfiniaburger/pool-volume-prediction
cairo giza wavenet
Last synced: about 2 months ago
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Pool Volume Forecasting
- Host: GitHub
- URL: https://github.com/surfiniaburger/pool-volume-prediction
- Owner: surfiniaburger
- Created: 2024-02-25T11:14:24.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-03-08T21:25:53.000Z (11 months ago)
- Last Synced: 2024-03-08T22:29:46.806Z (11 months ago)
- Topics: cairo, giza, wavenet
- Language: Jupyter Notebook
- Homepage: https://actions-server-surfiniaburger-dblzzhtf5q-ew.a.run.app/dashboard
- Size: 22.8 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Pool Volume Forecasting
## Project description
The project involves forecasting pool volumes using time-series data. It begins with loading data from Polar into a DataFrame, extracting relevant features such as day of the week, month, and year, and performing one-hot encoding for categorical variables. After standardizing numerical features and preparing the input sequences by sliding a window across the time-series data, a WaveNet model is built and trained using TensorFlow/Keras. The trained model is then utilized to predict pool volumes on unseen data. The predictions are further processed using ONNX, EZKL, with the verifiable=True setting converting them to Cairo format, optimizing them for deployment or downstream tasks.
## Tech Stack
- Giza Actions SDK
- Giza cli
- Giza Virtual Environment
- Giza Dataset
- WaveNet
- Jupyter Notebook
- Tensorflow
- Poetry
- Cairo
- EZKL
- ONNX
Welcome to the Giza Actions SDK template! The [Giza Actions SDK](https://actions.gizatech.xyz/welcome/giza-actions-sdk) is tailored to assist you in designing your ZKML workflows efficiently. This project provides pre-configured actions ready for deployment on the Giza platform. The purpose of this template is to demonstrate how to construct your ZKML workflows using the Giza Actions SDK.
*Note: This template is based on the [MNIST tutorial](https://actions.gizatech.xyz/tutorials/build-a-verifiable-neural-network-with-giza-actions). Please be aware that certain steps, such as transpiling the model and deploying the generated model on Giza Plateform, are required between action executions. For a more comprehensive understanding, refer to the tutorial.*
## Requirements
- Python 3.11
- Poetry
## Get Started
```bash
$ poetry shell
$ poetry install
```
## Structure
Within the `yearn` directory, you'll discover multiple generated files:
- `train_action.py`: Contains actions for training your model.
- `predict_onnx_action.py`: Includes actions for making predictions with an ONNX model.
- `predict_cairo_action.py`: Includes actions for making verifiable predictions with the Orion Cairo model.
## Usage
To use this project, follow these steps:
1. Install the required dependencies.
2. Execute any of the provided action scripts using the command `python yearn/{action_file}.py`, for example, `python yearn/{train_action}.py`.
## Learn More
Explore more about the Giza Actions SDK [here](https://actions.gizatech.xyz/welcome/giza-actions-sdk).
## Acknowledgement
This template was generated using cookiecutter.