NPTS Time-series Forecaster Model - AutoGluon

NPTS Forecaster for the Forecasting problem category as per Ready Tensor specifications.

Project Description

This repository is a dockerized implementation of the re-usable forecaster model. It is implemented in flexible way so that it can be used with any forecasting dataset with the use of CSV-formatted data, and a JSON-formatted data schema file. The main purpose of this repository is to provide a complete example of a machine learning model implementation that is ready for deployment. The following are the requirements for using your data with this model:

The data must be in CSV format.
The schema file must contain an idField and timeField columns.
The timeField data type must be either "DATE", "DATETIME" or "INT". Click here for more information on dataset specifications
The data type of the target field, past covariates and future covariates must be NUMERIC.
The train and test (or prediction) files must contain an ID field. The train data must also contain a target series.
The data need to be preprocessed because the implementation assumes the data is cleaned and has no missing values.

Covariates Support:

Past ❌

Future ❌

Static ❌

Here are the highlights of this implementation:

A NPTS Forecaster algorithm built using AutoGluon Additionally, the implementation contains the following features:
Data Validation: Pydantic data validation is used for the schema, training and test files, as well as the inference request data.
Error handling and logging: Python's logging module is used for logging and key functions include exception handling.

Project Structure

The following is the directory structure of the project:

examples/: This directory contains example files for the titanic dataset. Three files are included: smoke_test_forecasting_schema.json, smoke_test_forecasting_train.csv and smoke_test_forecasting_test.csv. You can place these files in the inputs/schema, inputs/data/training and inputs/data/testing folders, respectively.
model_inputs_outputs/: This directory contains files that are either inputs to, or outputs from, the model. When running the model locally (i.e. without using docker), this directory is used for model inputs and outputs. This directory is further divided into:
- /inputs/: This directory contains all the input files for this project, including the data and schema files. The data is further divided into testing and training subsets.
- /model/artifacts/: This directory is used to store the model artifacts, such as trained models and their parameters.
- /outputs/: The outputs directory contains sub-directories for error logs and prediction results.
src/: This directory holds the source code for the project. It is further divided into various subdirectories:
- config/: for configuration files for data preprocessing, model hyperparameters, paths, etc.
- data_models/: for data models for input validation including the schema, training and test files. It also contains the data model for the batch prediction results.
- schema/: for schema handler script. This script contains the class that provides helper getters/methods for the data schema.
- prediction/: Scripts for the forecaster model implemented using AutoGluon library.
- logger.py: This script contains the logger configuration using logging module.
- train.py: This script is used to train the model. It loads the data, preprocesses it, trains the model, and saves the artifacts in the path ./model_inputs_outputs/model/artifacts/.
- predict.py: This script is used to run batch predictions using the trained model. It loads the artifacts and creates and saves the predictions in a file called predictions.csv in the path ./model_inputs_outputs/outputs/predictions/.
- utils.py: This script contains utility functions used by the other scripts.
.gitignore: This file specifies the files and folders that should be ignored by Git.
Dockerfile: This file is used to build the Docker image for the application.
entry_point.sh: This file is used as the entry point for the Docker container. It is used to run the application. When the container is run using one of the commands train, predict or serve, this script runs the corresponding script in the src folder to execute the task.
LICENSE: This file contains the license for the project.
requirements.txt for the main code in the src directory
README.md: This file (this particular document) contains the documentation for the project, explaining how to set it up and use it.

Usage

In this section we cover the following:

How to prepare your data for training
How to run the model implementation locally (without Docker)
How to run the model implementation with Docker

Preparing your data

If you plan to run this model implementation on your own forecasting dataset, you will need your training and testing data in a CSV format. Also, you will need to create a schema file as per the Ready Tensor specifications. The schema is in JSON format, and it's easy to create. You can use the example schema file provided in the examples directory as a template.

To run locally (without Docker)

Create your virtual environment and install dependencies listed in requirements.txt which is inside the root directory.
Move the three example files (smoke_test_forecasting_schema.json, smoke_test_forecasting_train.csv and smoke_test_forecasting_test.csv) in the examples directory into the ./model_inputs_outputs/inputs/schema, ./model_inputs_outputs/inputs/data/training and ./model_inputs_outputs/inputs/data/testing folders, respectively (or alternatively, place your custom dataset files in the same locations).
Run the script src/train.py to train the forecaster model. This will save the model artifacts, including the preprocessing pipeline and label encoder, in the path ./model_inputs_outputs/model/artifacts/.
Run the script src/predict.py to run batch predictions using the trained model. This script will load the artifacts and create and save the predictions in a file called predictions.csv in the path ./model_inputs_outputs/outputs/predictions/.

To run with Docker

Set up a bind mount on host machine: It needs to mirror the structure of the model_inputs_outputs directory. Place the train data file in the model_inputs_outputs/inputs/data/training directory, the test data file in the model_inputs_outputs/inputs/data/testing directory, and the schema file in the model_inputs_outputs/inputs/schema directory.
Build the image. You can use the following command:
docker build -t forecaster_img .
Here forecaster_img is the name given to the container (you can choose any name).
Note the following before running the container for train, batch prediction:
- The train, batch predictions tasks require a bind mount to be mounted to the path /opt/model_inputs_outputs/ inside the container. You can use the -v flag to specify the bind mount.
- When you run the train or batch prediction tasks, the container will exit by itself after the task is complete.
- When you run training task on the container, the container will save the trained model artifacts in the specified path in the bind mount. This persists the artifacts even after the container is stopped or killed.
- When you run the batch prediction task, the container will load the trained model artifacts from the same location in the bind mount. If the artifacts are not present, the container will exit with an error.
- Container runs as user 1000. Provide appropriate read-write permissions to user 1000 for the bind mount. Please follow the principle of least privilege when setting permissions. The following permissions are required:
  - Read access to the inputs directory in the bind mount. Write or execute access is not required.
  - Read-write access to the outputs directory and model directories. Execute access is not required.
Run training:
- To run training, run the container with the following command container:
  docker run -v <path_to_mount_on_host>/model_inputs_outputs:/opt/model_inputs_outputs forecaster_img train
  where forecaster_img is the name of the container. This will train the model and save the artifacts in the model_inputs_outputs/model/artifacts directory in the bind mount.
To run batch predictions, place the prediction data file in the model_inputs_outputs/inputs/data/testing directory in the bind mount. Then issue the command:
docker run -v <path_to_mount_on_host>/model_inputs_outputs:/opt/model_inputs_outputs forecaster_img predict
This will load the artifacts and create and save the predictions in a file called predictions.csv in the path model_inputs_outputs/outputs/predictions/ in the bind mount.

Requirements

Dependencies for the main model implementation in src are listed in the file requirements.txt. You can install these packages by running the following command from the root of your project directory:

pip install -r requirements.txt

LICENSE

This project is provided under the Apache-2.0 License.

Contact Information

Repository created by Ready Tensor, Inc. (https://www.readytensor.ai/)

readytensor / rt_forecasting_autogluon_npts