yuvrajiro / rt_xgboost_binary_classifier

XGBoost Binary Classifier for ready-tensor

XGBoost Binary Classifier Implementation

Project Description

This repository is a dockerized implementation of the XGBoost classifier. It is implemented in flexible way that it can be used with any binary classification dataset with the use of JSON-formatted data schema file.

Here are the highlights of this implementation:

• Implementation contains a full-suite of functionality such as:
  • A standardized project structure
  • A generalized data schema structure to be used for binary classification datasets
  • A flexible preprocessing pipeline built using SciKit-Learn and feature-engine
  • A XGClassifier algorithm built using XGBoost
  • Hyperparameter-tuning using scikit-optimize
  • SHAP explainer using the shap package
  • FASTAPI inference service which provides endpoints for predictions and local explanations.
  • Pydantic data validation is used for the schema, training and test files, as well as the inference request data.
  • Error handling and logging using Python's logging module.
  • Comprehensive set of unit, integration, coverage and performance tests using pytest and pytest-cov.
  • Static code analysis using flake8, black, isort, safety, and radon.
  • Test automation using tox.
• It is flexible enough to be used with any binary classification dataset without requiring any code change albeit with the following restrictions:
  • The data must be in CSV format
  • The number of rows must not exceed 20,000. Number of columns must not exceed 200. The model may function with larger datasets, but it has not been performance tested on larger datasets.
  • Features must be one of the following two types: NUMERIC or CATEGORICAL. Other data types are not supported. Note that CATEGORICAL type includes boolean type
  • The train and test (or prediction) files must contain an ID field. The train data must also contain a target field.
• The data need not be preprocessed because the implementation already contains logic to handle missing values, categorical features, outliers, and scaling.

Project Structure

adaptive_binary_classifier/
├── examples/
├── model_inputs_outputs/
│   ├── inputs/
│   │   ├── data/
│   │   │   ├── testing/
│   │   │   └── training/
│   │   └── schema/
│   ├── model/
│   │   └── artifacts/
│   └── outputs/
│       ├── errors/
│       ├── hpt_outputs/
│       └── predictions/
├── requirements/
│   ├── requirements.txt
│   └── requirements_text.txt
├── src/
│   ├── config/
│   ├── data_models/
│   ├── hyperparameter_tuning/
│   ├── prediction/
│   ├── preprocessing/
│   ├── schema/
│   └── xai/
├── tests/
│   ├── integration_tests/
│   ├── performance_tests/
│   ├── test_resources/
│   ├── test_results/
│   │   ├── coverage_tests/
│   │   └── performance_tests/
│   └── unit_tests/
│       ├── (mirrors /src structure)
│       └── ...
├── tmp/
├── .dockerignore
├── .gitignore
├── docker-compose.yaml
├── Dockerfile
├── entrypoint.sh
├── LICENSE
├── pytest.ini
├── README.md
└── tox.ini

• examples/: This directory contains example files for the titanic dataset. Three files are included: titanic_schema.json, titanic_train.csv and titanic_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. 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 hyperparameter tuning outputs, and prediction results.
• requirements/: This directory contains the requirements files. We have multiple requirements files for different purposes:
  • requirements.txt for the main code in the src directory
  • requirements_text.txt for dependencies required to run tests in the tests directory.
  • requirements_quality.txt for dependencies related to formatting and style checks.
• src/: This directory holds the source code for the project. It is further divided into various subdirectories :
  • config/: for configuration files such as hyperparameters, model configuration, hyperparameter tuning-configuration specs, paths, and preprocessing configuration.
  • data_models/: for data models for input validation including the schema, training and test files, and the inference request data.
  • hyperparameter_tuning/: for hyperparameter-tuning (HPT) functionality using scikit-optimize
  • prediction/: for prediction model (XGBoost) script
  • preprocessing/: for data preprocessing scripts including the preprocessing pipeline, target encoder, and custom transformers.
  • schema/: for schema handler script.
  • xai/: explainable AI scripts (SHAP explainer) Furthermore, we have the following scripts in src:
  • logger.py: This script contains the logger configuration.
  • 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/.
  • serve.py: This script is used to serve the model as a REST API. It loads the artifacts and creates a FastAPI server to serve the model. It provides 3 endpoints: /ping, /infer, and /explain. The /ping endpoint is used to check if the server is running. The /infer endpoint is used to make predictions. The /explain endpoint is used to get local explanations for the predictions.
  • serve_utils.py: This script contains utility functions used by the serve.py script.
  • 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/. It also saves a SHAP explainer object in the path ./model/artifacts/. When the train task is run with a flag to perform hyperparameter tuning, it also saves the hyperparameter tuning results in the path ./model_inputs_outputs/outputs/hpt_outputs/.
  • utils.py: This script contains utility functions used by the other scripts.
• tests/: This directory contains all the tests for the project and associated resources and results.
  • integration_tests/: This directory contains the integration tests for the project. We cover four main workflows: data preprocessing, training, prediction, and inference service.
  • performance_tests/: This directory contains performance tests for the training and batch prediction workflows in the script test_train_predict.py. It also contains performance tests for the inference service workflow in the script test_inference_apis.py. Helper functions are defined in the script performance_test_helpers.py. Fixtures and other setup are contained in the script conftest.py.
  • test_resources/: This folder contains various resources needed in the tests, such as trained model artifacts (including the preprocessing pipeline, target encoder, explainer, etc.). These resources are used in integration tests and performance tests.
  • test_results/: This folder contains the results for the performance tests. These are persisted to disk for later analysis.
  • unit_tests/: This folder contains all the unit tests for the project. It is further divided into subdirectories mirroring the structure of the src folder. Each subdirectory contains unit tests for the corresponding script in the src folder.
• tmp/: This directory is used for storing temporary files which are not necessary to commit to the repository.
• .dockerignore: This file specifies the files and folders that should be ignored by Docker.
• .gitignore: This file specifies the files and folders that should be ignored by Git.
• docker-compose.yaml: This file is used to define the services that make up the application. It is used by Docker Compose to run the application.
• 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.
• pytest.ini: This is the configuration file for pytest, the testing framework used in this project.
• README.md: This file contains the documentation for the project, explaining how to set it up and use it.
• tox.ini: This is the configuration file for tox, the primary test runner used in this project.

Usage

Preparing your data

• If you plan to run this model implementation on your own binary classification 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. You can also refer to the tutorial on Using Data Schemas for more information on how to create a schema file.

To run locally (without Docker)

• Create your virtual environment and install dependencies listed in requirements.txt which is inside the requirements directory.
• Move the three example files (titanic_schema.json, titanic_train.csv and titanic_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 XGBclassifier model. This will save the model artifacts, including the preprocessing pipeline and label encoder, in the path ./model_inputs_outputs/model/artifacts/. If you want to run with hyperparameter tuning then include the -t flag. This will also save the hyperparameter tuning results in the path ./model_inputs_outputs/outputs/hpt_outputs/.
• 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/.
• Run the script src/serve.py to start the inference service, which can be queried using the /ping, /infer and /explain endpoints. The service runs on port 8080.

To run with Docker

1. 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.

2. Build the image. You can use the following command:
   docker build -t classifier_img .
   Here classifier_img is the name given to the container (you can choose any name).

3. Note the following before running the container for train, batch prediction or inference service:

   • The train, batch predictions tasks and inference service 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 the inference service task is run, the container will keep running until you stop or kill it.
   • 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 or inference service tasks, 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.
   • The inference service runs on the container's port 8080. Use the -p flag to map a port on local host to the port 8080 in the container.
   • 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.

4. You can run training with or without hyperparameter tuning:

   • To run training without hyperparameter tuning (i.e. using default hyperparameters), run the container with the following command container:
     docker run -v <path_to_mount_on_host>/model_inputs_outputs:/opt/model_inputs_outputs classifier_img train
     where classifier_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 training with hyperparameter tuning, issue the command:
     docker run -v <path_to_mount_on_host>/model_inputs_outputs:/opt/model_inputs_outputs classifier_img train -t
     This will tune hyperparameters,and used the tuned hyperparameters to train the model and save the artifacts in the model_inputs_outputs/model/artifacts directory in the bind mount. It will also save the hyperparameter tuning results in the model_inputs_outputs/outputs/hpt_outputs directory in the bind mount.

5. 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 classifier_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.

6. To run the inference service, issue the following command on the running container:
   docker run -p 8080:8080 -v <path_to_mount_on_host>/model_inputs_outputs:/opt/model_inputs_outputs classifier_img serve
   This starts the service on port 8080. You can query the service using the /ping, /infer and /explain endpoints. More information on the requests/responses on the endpoints is provided below.

Using the Inference Service

Getting Predictions

To get predictions for a single sample, use the following command:

curl -X POST -H "Content-Type: application/json" -d '{
  {
    "instances": [
        {
            "PassengerId": "879",
            "Pclass": 3,
            "Name": "Laleff, Mr. Kristo",
            "Sex": "male",
            "Age": None,
            "SibSp": 0,
            "Parch": 0,
            "Ticket": "349217",
            "Fare": 7.8958,
            "Cabin": None,
            "Embarked": "S"
        }
    ]
}' http://localhost:8080/infer

The key instances contains a list of objects, each of which is a sample for which the prediction is requested. The server will respond with a JSON object containing the predicted probabilities for each input record:

{
  "status": "success",
  "message": "",
  "timestamp": "<timestamp>",
  "requestId": "<uniquely generated id>",
  "targetClasses": ["0", "1"],
  "targetDescription": "A binary variable indicating whether or not the passenger survived (0 = No, 1 = Yes).",
  "predictions": [
    {
      "sampleId": "879",
      "predictedClass": "0",
      "predictedProbabilities": [0.97548, 0.02452]
    }
  ]
}

Getting predictions and local explanations

To get predictions and explanations for a single sample, use the following request to send to /explain endpoint (same structure as data for the /infer endpoint):

curl -X POST -H "Content-Type: application/json" -d '{
  {
    "instances": [
        {
            "PassengerId": "879",
            "Pclass": 3,
            "Name": "Laleff, Mr. Kristo",
            "Sex": "male",
            "Age": None,
            "SibSp": 0,
            "Parch": 0,
            "Ticket": "349217",
            "Fare": 7.8958,
            "Cabin": None,
            "Embarked": "S"
        }
    ]
}' http://localhost:8080/explain

The server will respond with a JSON object containing the predicted probabilities and locations for each input record:

{
  "status": "success",
  "message": "",
  "timestamp": "2023-05-22T10:51:45.860800",
  "requestId": "0ed3d0b76d",
  "targetClasses": ["0", "1"],
  "targetDescription": "A binary variable indicating whether or not the passenger survived (0 = No, 1 = Yes).",
  "predictions": [
    {
      "sampleId": "879",
      "predictedClass": "0",
      "predictedProbabilities": [0.92107, 0.07893],
      "explanation": {
        "baseline": [0.57775, 0.42225],
        "featureScores": {
          "Age_na": [0.05389, -0.05389],
          "Age": [0.02582, -0.02582],
          "SibSp": [-0.00469, 0.00469],
          "Parch": [0.00706, -0.00706],
          "Fare": [0.05561, -0.05561],
          "Embarked_S": [0.01582, -0.01582],
          "Embarked_C": [0.00393, -0.00393],
          "Embarked_Q": [0.00657, -0.00657],
          "Pclass_3": [0.0179, -0.0179],
          "Pclass_1": [0.02394, -0.02394],
          "Sex_male": [0.13747, -0.13747]
        }
      }
    }
  ],
  "explanationMethod": "Shap"
}

OpenAPI

Since the service is implemented using FastAPI, we get automatic documentation of the APIs offered by the service. Visit the docs at http://localhost:8080/docs.

Testing

Running through Tox

To run the tests: Tox is used for running the tests. To run the tests, simply run the command:

tox

This will run the tests as well as formatters black and isort and linter flake8. You can run tests corresponding to specific environment, or specific markers. Please check tox.ini file for configuration details.

Running through Pytest

• Run the command pytest from the root directory of the repository.
• To run specific scripts, use the command pytest <path_to_script>.
• To run slow-running tests (which take longer to run): use the command pytest -m slow.

Requirements

The requirements files are placed in the folder requirements.

Dependencies for the main model implementation in src are listed in the file requirements.txt.

For testing, dependencies are listed in the file requirements-test.txt.

Dependencies for quality-tests are listed in the file requirements-quality.txt. You can install these packages by running the following command:

pip install -r requirements.txt
pip install -r requirements-test.txt
pip install -r requirements-quality.txt

Alternatively, you can let tox handle the installation of test dependencies for you. To do this, simply run the command tox from the root directory of the repository. This will create the environments, install dependencies, and run the tests as well as quality checks on the code.

Contact Information

Repository created by Ready Tensor, Inc.