From e03e66649a3678ba7a9149d54471b89da03bce27 Mon Sep 17 00:00:00 2001 From: Sholto Armstrong Date: Fri, 5 Jun 2026 15:52:50 +0200 Subject: [PATCH 01/75] out with the old --- .dockerignore | 36 - .gitattributes | 1 - .github/workflows/feature.yml | 44 - .github/workflows/release.yml | 236 - .gitignore | 168 - CODE_OF_CONDUCT.md | 128 - CONTRIBUTING.md | 135 - LICENSE | 21 - README.md | 90 - SECURITY.md | 17 - __init__.py | 0 docker/Dockerfile | 43 - docker/ui.Dockerfile | 35 - docs/.gitignore | 5 - docs/Makefile | 20 - docs/_static/concepts/inference.drawio.svg | 245 - .../concepts/inference_light.drawio.svg | 241 - .../example_pipeline.drawio.svg | 4 - .../example_pipeline_light.drawio.svg | 786 - docs/_templates/custom-class-template.rst | 34 - docs/_templates/custom-module-template.rst | 66 - docs/api.rst | 6 - docs/concepts/decision_tables.ipynb | 297 - docs/concepts/decision_trees.ipynb | 1600 -- docs/concepts/index.md | 11 - docs/concepts/inference.md | 292 - 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-**/.tox -**/.coverage -**/.coverage.* -**/.cache -**/nosetests.xml -**/coverage.xml -**/*.cover -**/*.log -**/.git -**/.mypy_cache -**/.pytest_cache -**/.hypothesis -**/.DS_Store \ No newline at end of file diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 3a2b045..0000000 --- a/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -spockflow/_version.py export-subst diff --git a/.github/workflows/feature.yml b/.github/workflows/feature.yml deleted file mode 100644 index 3d4b2c5..0000000 --- a/.github/workflows/feature.yml +++ /dev/null @@ -1,44 +0,0 @@ -name: Build and Test - -on: - pull_request: - branches: - - dev - - main - - release/* - -jobs: - build-package: - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.12.4' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements/dev.txt - pip install -r requirements/all.txt - - - name: Format code with Black - run: black --check . - - - name: Run tests - run: pytest - - - name: Build Python package - run: python -m build - - - name: Upload Python package - uses: actions/upload-artifact@v4 - with: - name: python-package - path: dist/* - retention-days: 1 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml deleted file mode 100644 index 14e231f..0000000 --- a/.github/workflows/release.yml +++ /dev/null @@ -1,236 +0,0 @@ -name: Build and Release - -on: - push: - branches: - - main - - dev - - release/* - -env: - DOCKER_IMAGE_NAME: sjnarmstrong/spockflow - -jobs: - get-version: - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install versioneer setuptools - - - name: Get version - id: get-package-version - run: | - version=$(python setup.py --version) - echo "PACKAGE_VERSION=$(python setup.py --version)" >> $GITHUB_OUTPUT - outputs: - PACKAGE_VERSION: ${{ steps.get-package-version.outputs.PACKAGE_VERSION }} - - build-package: - needs: ["get-version"] - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.12.4' - - - name: Tag Release - env: - PACKAGE_VERSION: ${{needs.get-version.outputs.PACKAGE_VERSION}} - run: | - git config --global user.email "githubbot@donotreply.com" - git config --global user.name "Sholto Armstrong" - git tag -a v${PACKAGE_VERSION} -m "Release v${PACKAGE_VERSION}" - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements/dev.txt - pip install -r requirements/all.txt - - - name: Format code with Black - run: black --check . - - - name: Run tests - run: pytest - - - name: Build Python package - run: python -m build - - - name: Upload Python package - uses: actions/upload-artifact@v4 - with: - name: python-package - path: dist/* - - build-docs: - needs: ["get-version"] - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.12.4' - - - name: Tag Release - env: - PACKAGE_VERSION: ${{needs.get-version.outputs.PACKAGE_VERSION}} - run: | - git config --global user.email "githubbot@donotreply.com" - git config --global user.name "Sholto Armstrong" - git tag -a v${PACKAGE_VERSION} -m "Release v${PACKAGE_VERSION}" - - - name: Install documentation dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements/all.txt - pip install . - - - name: Build documentation - run: | - cd docs - make html - - - name: Archive artifact - shell: sh - run: | - echo ::group::Archive artifact - tar \ - --dereference --hard-dereference \ - --directory "$INPUT_PATH" \ - -cvf "$RUNNER_TEMP/artifact.tar" \ - --exclude=.git \ - --exclude=.github \ - . - echo ::endgroup:: - env: - INPUT_PATH: docs/_build/html/ - - - name: Upload docs - uses: actions/upload-artifact@v4 - with: - name: 'github-pages' - path: ${{ runner.temp }}/artifact.tar - if-no-files-found: error - - release: - needs: ["build-docs", "build-package", "get-version"] - runs-on: ubuntu-latest - environment: - name: pypi - url: https://pypi.org/p/spockflow - permissions: - id-token: write - pages: write - contents: write - pull-requests: write - repository-projects: write - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - - name: Tag Release - env: - PACKAGE_VERSION: ${{needs.get-version.outputs.PACKAGE_VERSION}} - run: | - git config --global user.email "githubbot@donotreply.com" - git config --global user.name "Github Bot" - git tag -a v${PACKAGE_VERSION} -m "Release v${PACKAGE_VERSION}" - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.12.4" - - - name: Set up QEMU - uses: docker/setup-qemu-action@v3 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 - - - name: Login to Docker Hub - uses: docker/login-action@v3 - with: - username: ${{ secrets.DOCKERHUB_USERNAME }} - password: ${{ secrets.DOCKERHUB_TOKEN }} - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install versioneer setuptools - - - name: Get version - run: | - version=$(python setup.py --version) - echo "PACKAGE_VERSION=$(python setup.py --version)" >> $GITHUB_ENV - - - name: Download Python Artifact - uses: actions/download-artifact@v4 - with: - name: python-package - path: dist - - - name: Build Docker image - uses: docker/build-push-action@v3 - with: - context: . - push: true - tags: ${{ env.DOCKER_IMAGE_NAME }}:${{ env.PACKAGE_VERSION }} - file: docker/Dockerfile - - - name: Upload Latest Docker image - uses: docker/build-push-action@v3 - if: github.ref == 'refs/heads/main' - with: - context: . - push: true - tags: ${{ env.DOCKER_IMAGE_NAME }}:latest - file: docker/Dockerfile - - - name: Publish distribution ๐Ÿ“ฆ to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - skip-existing: true - - - name: Deploy documentation to GitHub Pages - if: github.ref == 'refs/heads/main' - uses: actions/deploy-pages@v4 - - - name: Push Tag - env: - PACKAGE_VERSION: ${{needs.get-version.outputs.PACKAGE_VERSION}} - run: | - git push origin tag v${PACKAGE_VERSION} - - - name: Create Release - if: github.ref == 'refs/heads/main' - id: release-snapshot - uses: actions/create-release@latest - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - tag_name: v${{needs.get-version.outputs.PACKAGE_VERSION}} - release_name: Release ${{needs.get-version.outputs.PACKAGE_VERSION}} - draft: false - prerelease: false - \ No newline at end of file diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 4abe45e..0000000 --- a/.gitignore +++ /dev/null @@ -1,168 +0,0 @@ -# Template used from https://github.com/github/gitignore/blob/main/Python.gitignore -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/latest/usage/project/#working-with-version-control -.pdm.toml -.pdm-python -.pdm-build/ - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -.idea/ - -# General -.DS_Store - -.vscode diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md deleted file mode 100644 index 6395458..0000000 --- a/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,128 +0,0 @@ -# Contributor Covenant Code of Conduct - -## Our Pledge - -We as members, contributors, and leaders pledge to make participation in our -community a harassment-free experience for everyone, regardless of age, body -size, visible or invisible disability, ethnicity, sex characteristics, gender -identity and expression, level of experience, education, socio-economic status, -nationality, personal appearance, race, religion, or sexual identity -and orientation. - -We pledge to act and interact in ways that contribute to an open, welcoming, -diverse, inclusive, and healthy community. - -## Our Standards - -Examples of behavior that contributes to a positive environment for our -community include: - -* Demonstrating empathy and kindness toward other people -* Being respectful of differing opinions, viewpoints, and experiences -* Giving and gracefully accepting constructive feedback -* Accepting responsibility and apologizing to those affected by our mistakes, - and learning from the experience -* Focusing on what is best not just for us as individuals, but for the - overall community - -Examples of unacceptable behavior include: - -* The use of sexualized language or imagery, and sexual attention or - advances of any kind -* Trolling, insulting or derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or email - address, without their explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Enforcement Responsibilities - -Community leaders are responsible for clarifying and enforcing our standards of -acceptable behavior and will take appropriate and fair corrective action in -response to any behavior that they deem inappropriate, threatening, offensive, -or harmful. - -Community leaders have the right and responsibility to remove, edit, or reject -comments, commits, code, wiki edits, issues, and other contributions that are -not aligned to this Code of Conduct, and will communicate reasons for moderation -decisions when appropriate. - -## Scope - -This Code of Conduct applies within all community spaces, and also applies when -an individual is officially representing the community in public spaces. -Examples of representing our community include using an official e-mail address, -posting via an official social media account, or acting as an appointed -representative at an online or offline event. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported to the community leaders responsible for enforcement at -opensource@capitecbank.co.za. -All complaints will be reviewed and investigated promptly and fairly. - -All community leaders are obligated to respect the privacy and security of the -reporter of any incident. - -## Enforcement Guidelines - -Community leaders will follow these Community Impact Guidelines in determining -the consequences for any action they deem in violation of this Code of Conduct: - -### 1. Correction - -**Community Impact**: Use of inappropriate language or other behavior deemed -unprofessional or unwelcome in the community. - -**Consequence**: A private, written warning from community leaders, providing -clarity around the nature of the violation and an explanation of why the -behavior was inappropriate. A public apology may be requested. - -### 2. Warning - -**Community Impact**: A violation through a single incident or series -of actions. - -**Consequence**: A warning with consequences for continued behavior. No -interaction with the people involved, including unsolicited interaction with -those enforcing the Code of Conduct, for a specified period of time. This -includes avoiding interactions in community spaces as well as external channels -like social media. Violating these terms may lead to a temporary or -permanent ban. - -### 3. Temporary Ban - -**Community Impact**: A serious violation of community standards, including -sustained inappropriate behavior. - -**Consequence**: A temporary ban from any sort of interaction or public -communication with the community for a specified period of time. No public or -private interaction with the people involved, including unsolicited interaction -with those enforcing the Code of Conduct, is allowed during this period. -Violating these terms may lead to a permanent ban. - -### 4. Permanent Ban - -**Community Impact**: Demonstrating a pattern of violation of community -standards, including sustained inappropriate behavior, harassment of an -individual, or aggression toward or disparagement of classes of individuals. - -**Consequence**: A permanent ban from any sort of public interaction within -the community. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], -version 2.0, available at -https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. - -Community Impact Guidelines were inspired by [Mozilla's code of conduct -enforcement ladder](https://github.com/mozilla/diversity). - -[homepage]: https://www.contributor-covenant.org - -For answers to common questions about this code of conduct, see the FAQ at -https://www.contributor-covenant.org/faq. Translations are available at -https://www.contributor-covenant.org/translations. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 1aa35ba..0000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,135 +0,0 @@ -# Contributing - -We value and encourage community contributions. To get started, please follow these guidelines: - -1. [Code of Conduct](#1-code-of-conduct) -2. [Issues](#2-issues) -3. [Vulnerabilities](#3-vulnerabilities) -4. [Development](#4-development) -5. [Pull Requests](#5-pull-requests) - -## 1. Code of Conduct - -Please read and follow our [Code of Conduct](CODE_OF_CONDUCT.md). - -## 2. Issues - -Engagement starts with an Issue where conversations and debates can occur around [bugs](#bugs) and [feature requests](#feature-requests): - -- โœ… **Do** search for a similar or existing Issue prior to submitting a new one. -- โŒ **Do not** use Issues for personal support. Use [Discussions](https://github.com/your-repo/discussions) or [StackOverflow](https://stackoverflow.com/) instead. -- โŒ **Do not** side-track or derail Issue threads. Stick to the topic, please. -- โŒ **Do not** post comments using just "+1", "++" or "๐Ÿ‘". Use [Reactions](https://github.blog/2016-03-10-add-reactions-to-pull-requests-issues-and-comments/) instead. - -

๐Ÿ‘พ Bugs

- -A bug is an error, flaw, or fault associated with *any part* of the project: - -- โœ… **Do** search for a similar or existing Issue prior to submitting a new one. -- โœ… **Do** describe the bug concisely. **Avoid** adding extraneous code, logs, or screenshots. -- โœ… **Do** attach a minimal test or example to demonstrate the bug. - -

๐Ÿ’ก Feature Requests

- -A feature request is an improvement or new capability associated with *any part* of the project: - -- โœ… **Do** search for a similar or existing Issue prior to submitting a new one. -- โœ… **Do** provide sufficient motivation and use case(s) for the feature. -- โŒ **Do not** submit multiple unrelated requests within one request. - -> **TIP:** Engage as much as possible within an Issue before proceeding with contributions. - -## 3. Vulnerabilities - -A vulnerability is a security-related risk associated with *any part* of the project or its dependencies: - -- โœ… **Do** refer to our [Security Policy](https://github.com/your-repo/security/policy) for more information. -- โœ… **Do** report vulnerabilities via this [link](https://github.com/your-repo/security/advisories/new). -- โŒ **Do not** report any Issues or mention vulnerabilities in public Discussions for discretionary purposes. - -## 4. Development - -

๐ŸŒฑ Branches

- -- `develop` - Default branch for all feature development and Pull Requests. -- `main` - Stable branch for all periodic releases. - -

๐Ÿ”’ Dependencies

- -* Python (>= 3.8) -* `pip` for package management. Use `pip install -r requirements/all.txt` to install dependencies. -* Optional: Set up your environment using `conda`, `virtualenv`, or another method. Refer to [Python virtual environments](https://docs.python.org/3/tutorial/venv.html) for guidance. - -

๐Ÿ“ฆ Project Setup

- -1. [Fork](https://github.com/your-repo/fork) the repository and create a branch from `develop`. -2. Clone the forked repo, checkout your branch, and install the dependencies with `pip install -r requirements/all.txt`. -3. Run tests using `pytest` to ensure everything is working correctly. - -

๐Ÿ“‚ Directory Structure

- -When contributing, please note the following key files and directories: -โ”œโ”€โ”€ docker -โ”‚ โ”œโ”€โ”€ Dockerfile -โ”œโ”€โ”€ docs -โ”‚ โ”œโ”€โ”€ index.rst -โ”‚ โ”œโ”€โ”€ ... -โ”œโ”€โ”€ requirements -โ”‚ โ”œโ”€โ”€ all.txt -โ”‚ โ”œโ”€โ”€ ... -โ”œโ”€โ”€ spockflow -โ”‚ โ”œโ”€โ”€ components -โ”‚ โ”‚ โ”œโ”€โ”€ scorecard -โ”‚ โ”‚ โ”œโ”€โ”€ tree -โ”‚ โ”‚ โ”œโ”€โ”€ dtable -โ”‚ โ”œโ”€โ”€ inference -โ”‚ โ”œโ”€โ”€ ... -โ”œโ”€โ”€ core.py -โ”œโ”€โ”€ exceptions.py -โ”œโ”€โ”€ nodes.py -โ”œโ”€โ”€ tests -โ”‚ โ”œโ”€โ”€ test_example.py -โ”‚ โ”œโ”€โ”€ ... - - -* `docker` - Contains all files related to Docker images. -* `docs` - Documentation files. -* `requirements` - Directory containing `.txt` files for different optional requirements. -* `spockflow/components` - Contains all components for the Hamilton DAG, including: - * `scorecard` - For scorecards. - * `tree` - For decision trees. - * `dtable` - For decision tables. -* `spockflow/inference` - Files needed to serve the module as a live endpoint. -* `core.py` - Contains code to inject components into the Hamilton DAG. -* `exceptions.py` - Base for exceptions produced by various components. -* `nodes.py` - Core module for all components. -* `tests` - Contains unit tests for the project. - -

๐Ÿท Naming Conventions

- -- โœ… **Do** follow PEP 8 for naming conventions. -- โœ… **Do** use descriptive names for files and modules. -- โœ… **Do** name Python classes in `CamelCase` and functions in `snake_case`. - -

๐Ÿ” Code Quality

- -- โœ… **Do** adhere to PEP 8 style guidelines. -- โœ… **Do** use `black` for automatic code formatting. - -

๐Ÿงช Testing

- -- โœ… **Do** write tests using `pytest`. -- โœ… **Do** ensure all tests pass before submitting a Pull Request. - -## 5. Pull Requests - -- โœ… **Do** ensure your branch is up to date with the `develop` branch. -- โœ… **Do** ensure there are no conflicts with the `develop` branch. -- โœ… **Do** make sure all tests pass and code is formatted using `black`. -- โœ… **Do** provide a clear description of the changes and the purpose of the Pull Request. - -> **TIP:** Make sure to review the existing codebase and follow the conventions used throughout the project. - ---- - -Thank you for contributing! We appreciate your efforts to improve the project. diff --git a/LICENSE b/LICENSE deleted file mode 100644 index bf6b516..0000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2024 Capitec - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/README.md b/README.md deleted file mode 100644 index 5498e76..0000000 --- a/README.md +++ /dev/null @@ -1,90 +0,0 @@ -# DSP Decision Engine - -[![Python Version](https://img.shields.io/badge/python-%3E%3D3.8-blue.svg)](https://www.python.org/downloads/) -[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE) - -**SpockFlow** is a Python framework designed to create standalone micro-services that enrich data with actionable outputs. It supports both batch and live inference modes, and extends existing frameworks to simplify data flows, including policy rules and scoring. Leveraging Hamilton for traceability, SpockFlow provides a powerful, modular approach for data enrichment and model deployment. - -## Table of Contents - -- [Introduction](docs/intro.md) -- [Installation](docs/getting_started/install.md) -- [Concepts](docs/concepts/index.md) -- [Usage Examples](#usage-examples) -- [Contributing](#contributing) -- [License](#license) - -## Introduction - -SpockFlow is built to be extensible and modular, allowing the reuse of pipelines and configurations across multiple data flows. Its emphasis on runtime traceability and explainability is empowered by Hamilton, which helps track and visualize data lineage and identify process steps leading to specific outcomes. - -![Example Pipeline](./docs/_static/getting-started/example_pipeline.drawio.svg) - -For a more detailed introduction, see [Introduction](docs/main.rst). - -## Installation - -To get started with SpockFlow, you need to install the required dependencies. Follow the instructions in the [Installation Guide](docs/getting_started/install.md) to set up your environment. - -```bash -pip install spockflow[all] -``` - -## Concepts - -Explore the foundational principles and components of SpockFlow in the [Concepts](docs/concepts/index.md) section. This guide covers: - -- **Decision Trees**: Automate decision-making processes based on defined conditions. -- **Decision Tables**: Map input values to outputs based on conditions. -- **Score Cards**: Assign scores to entities based on parameters. -- **API Customization**: Customize and extend SpockFlow functionalities. - -## Usage Examples - -Here are some examples of how to use SpockFlow: - -### Decision Trees - -Create and use decision trees in SpockFlow: - -```python -from spockflow.components.tree import Tree, Action -from spockflow.core import initialize_spock_module -import pandas as pd -from typing_extensions import TypedDict - -class Reject(TypedDict): - code: int - description: str - -RejectAction = Action[Reject] - -# Initialize Tree -tree = Tree() - -# Define conditions and actions -@tree.condition(output=RejectAction(code=102, description="My first condition")) -def first_condition(d: pd.Series, e: pd.Series, f: pd.Series) -> pd.Series: - return (d > 5) & (e > 5) & (f > 5) - -tree.visualize(get_value_name=lambda x: x["description"][0]) -``` - -For more details and advanced usage, check out the [Concepts](docs/concepts/index.md) section. - -## Contributing - -We welcome contributions to SpockFlow! Please refer to our [Contributing Guide](CONTRIBUTING.md) for information on how to contribute. - -- **Fork the repository** and create a branch from `develop`. -- **Install dependencies** using `pip install -r requirements/all.txt`. -- **Run tests** with `pytest` to ensure everything is working. -- **Submit a Pull Request** with a clear description of your changes. - -## License - -This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. - ---- - -Thank you for your interest in SpockFlow! We look forward to your contributions and feedback. diff --git a/SECURITY.md b/SECURITY.md deleted file mode 100644 index d5d8c43..0000000 --- a/SECURITY.md +++ /dev/null @@ -1,17 +0,0 @@ -# Security Policy - -## Supported Versions - -Current and upcoming versions are supported for security vulnerabilities. -Older versions will not be supported if the vulnerability has been fixed in later versions and the fix will always be applied to either the latest version or a future version if the vulnerability only occurs on a new prerelease version. - -| Version | Supported | -| ------- | ------------------ | -| Latest | :white_check_mark: | -| Beta | :white_check_mark: | -| < Latest | :x: | - -## Reporting a Vulnerability - -This repository makes use of GitHub's [private vulnerability reporting feature](https://docs.github.com/en/code-security/security-advisories/guidance-on-reporting-and-writing/privately-reporting-a-security-vulnerability). -Vulnerabilities can be reported using the [repository's security advisories](https://github.com/capitec/ml-decision-engine/security/advisories/new). diff --git a/__init__.py b/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/docker/Dockerfile b/docker/Dockerfile deleted file mode 100644 index 6b5b7cd..0000000 --- a/docker/Dockerfile +++ /dev/null @@ -1,43 +0,0 @@ -# docker build --build-arg="PIP_CONF=$(cat $HOME/.config/pip/pip.conf)" -t capitec/engine:latest -f docker/Dockerfile . -ARG BASE_IMG="python:3.12.0-slim-bookworm" -FROM ${BASE_IMG} -ARG PIP_CONF= -# MAINTAINER Amazon AI - -RUN set -aex && \ - apt-get -y update && apt-get install -y --no-install-recommends \ - wget \ - nginx \ - ca-certificates \ - && rm -rf /var/lib/apt/lists/* - -# Install requirements first to reduce build times -COPY requirements /tmp/requirements -# Dont do set aex here to avoid precious creds being leaked to the console -RUN mkdir /opt/program && \ - echo "$PIP_CONF" >> /etc/pip.conf && \ - pip --no-cache-dir install -r /tmp/requirements/all-prod.txt && \ - pip --no-cache-dir install "setuptools>=40.8.0" && \ - rm /etc/pip.conf && \ - rm -rf /tmp/requirements - -COPY dist /tmp/spockflow/ -RUN set -aex && \ - ls /tmp/spockflow && \ - echo "$PIP_CONF" >> /etc/pip.conf && \ - pip --no-cache-dir install --no-deps --no-build-isolation /tmp/spockflow/*.whl && \ - rm /etc/pip.conf && \ - rm -rf /tmp/spockflow - -# Set some environment variables. PYTHONUNBUFFERED keeps Python from buffering our standard -# output stream, which means that logs can be delivered to the user quickly. PYTHONDONTWRITEBYTECODE -# keeps Python from writing the .pyc files which are unnecessary in this case. We also update -# PATH so that the train and serve programs are found when the container is invoked. -ENV PYTHONUNBUFFERED=TRUE -ENV PYTHONDONTWRITEBYTECODE=TRUE -ENV PATH="/opt/program:${PATH}" -ENV HAMILTON_TELEMETRY_ENABLED=FALSE -# Set up the program in the image -WORKDIR /opt/program -ENTRYPOINT ["python3", "-m", "spockflow.inference.server.run"] -CMD ["serve"] diff --git a/docker/ui.Dockerfile b/docker/ui.Dockerfile deleted file mode 100644 index 4529de5..0000000 --- a/docker/ui.Dockerfile +++ /dev/null @@ -1,35 +0,0 @@ -FROM node:18-alpine AS builder - -WORKDIR /app - -COPY package.json package-lock.json ./ -RUN npm ci - -COPY src/ ./src/ -COPY public/ ./public/ -COPY tsconfig*.json ./*.js ./*.json ./ - -RUN npm run build -# This is useful for testing -# RUN npm i serve -# EXPOSE 3000 -# CMD ["npx", "serve", "-s", "build"] - -FROM nginx:stable-alpine - -COPY --from=builder /app/build /usr/share/nginx/html - -RUN addgroup -g 1000 -S appgroup && \ - adduser -u 1000 -S appuser -G appgroup && \ - mkdir -p /var/cache/nginx/client_temp && \ - mkdir -p /tmp/nginx && \ - chown -R 1000:1000 /var/cache/nginx && \ - chown -R 1000:1000 /usr/share/nginx/html && \ - chown -R 1000:1000 /tmp/nginx - -COPY nginx.conf /etc/nginx/nginx.conf - -USER 1000 - -EXPOSE 8000 -CMD ["nginx", "-g", "daemon off;"] \ No newline at end of file diff --git a/docs/.gitignore b/docs/.gitignore deleted file mode 100644 index 1888793..0000000 --- a/docs/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -_build -_autosummary -confluence.json -config -source_dir \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index d4bb2cb..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_static/concepts/inference.drawio.svg b/docs/_static/concepts/inference.drawio.svg deleted file mode 100644 index c83e20e..0000000 --- a/docs/_static/concepts/inference.drawio.svg +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - - - - - - - - - -
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Decision tables are particularly useful for explicit and deterministic rule-based decision-making.\n", - "\n", - "### Usage\n", - "\n", - "To begin using the Decision Table component, import the necessary packages and instantiate a `DecisionTable` object:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from spockflow.components import dtable\n", - "\n", - "input_v1 = \"input_v1\"\n", - "input_v2 = \"input_v2\"\n", - "\n", - "example_dt = dtable.DecisionTable()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Adding Conditions and Outputs\n", - "\n", - "Conditions and corresponding outputs can be added to the `DecisionTable` using the `add` and `output` methods respectively. Each condition specifies an operation or comparison involving input variables, while outputs define the values or descriptions associated with matched conditions.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTable(operations=[DTMin(predicate=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], op='MIN'), DTMax(predicate=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], op='MAX'), DTMin(predicate=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], op='MIN'), DTMax(predicate=[1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2], op='MAX')], operation_inputs=['input_v1', 'input_v1', 'input_v2', 'input_v2'], outputs={'value': [1, 2, 0, None, -1, 20, 1, 2, 3, 4, 5], 'description': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']}, allow_multi_result=False, default_value= value description\n", - "0 999 NA)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_dt.add(dtable.DTMin, input_v1, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).add(\n", - " dtable.DTMax, input_v1, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n", - ").add(dtable.DTMin, input_v2, [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]).add(\n", - " dtable.DTMax, input_v2, [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]\n", - ").set_default(\n", - " pd.DataFrame({\"value\": [999], \"description\": [\"NA\"]})\n", - ").output(\n", - " \"value\", [1, 2, 0, None, -1, 20, 1, 2, 3, 4, 5]\n", - ").output(\n", - " \"description\", [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Execution\n", - "\n", - "Execute the Decision Table on input data using the `execute` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " description value\n", - "0 f 20.0\n", - "1 d NaN\n", - "2 NA 999.0\n", - "3 a 1.0\n", - "4 NA 999.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from spockflow.inference.config.loader.yamlmanager import YamlConfigManager\n", - "\n", - "conf_manager = YamlConfigManager()\n", - "conf_manager.save_to_config(\n", - " model_name=\"demo_spock_model\",\n", - " model_version=\"1.0.0\",\n", - " namespace=\"decision_table_config\",\n", - " config=example_dt.model_dump(mode=\"json\"),\n", - ")\n", - "\n", - "# Load configuration\n", - "config = conf_manager.get_config(\"demo_spock_model\", \"1.0.0\")\n", - "dt_loaded = dtable.DecisionTable.from_config(\"decision_table_config\").load(config)\n", - "\n", - "# Execute with loaded configuration\n", - "result_df_loaded = dt_loaded.execute(inputs=input_data)\n", - "result_df_loaded" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "spock", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/concepts/decision_trees.ipynb b/docs/concepts/decision_trees.ipynb deleted file mode 100644 index c12be6e..0000000 --- a/docs/concepts/decision_trees.ipynb +++ /dev/null @@ -1,1600 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Decision Trees in SpockFlow: A Practical Guide\n", - "\n", - "Welcome to the world of Decision Trees in SpockFlow! Decision Trees are powerful tools for automating decision-making processes based on defined conditions and actions. Let's dive into how you can leverage Decision Trees effectively within SpockFlow, including integration with Hamilton for creating executable pipelines.\n", - "\n", - "#### Importing Necessary Packages\n", - "\n", - "To begin, let's import the essential packages from SpockFlow:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from spockflow.components.tree import Tree, Action\n", - "from spockflow.core import initialize_spock_module\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Tree` class allows us to construct decision trees, and `Action` serves as a wrapper for defining standardized outputs.\n", - "\n", - "#### Defining Action Types\n", - "\n", - "To maintain consistency in the outputs of our decision tree, we define an action type using `TypedDict` from the `typing_extensions` module:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "class Reject(TypedDict):\n", - " code: int\n", - " description: str\n", - "\n", - "\n", - "RejectAction = Action[Reject]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, `RejectAction` specifies the structure of actions that our decision tree can produce.\n", - "\n", - "#### Creating an Instance of the Tree\n", - "\n", - "Next, let's create an instance of the `Tree`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "tree = Tree()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This `tree` object will be our canvas for defining conditions and their corresponding actions.\n", - "\n", - "#### Adding Conditions to the Tree\n", - "\n", - "Conditions in the decision tree are defined using decorators (`@tree.condition`). These conditions evaluate input data and trigger specified actions when conditions are met. Hereโ€™s an example of adding a condition:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "root\n", - "\n", - "root\n", - "\n", - "\n", - "\n", - "first_condition\n", - "\n", - "first_condition\n", - "\n", - "\n", - "\n", - "root->first_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "My first condition\n", - "\n", - "My first condition\n", - "\n", - "\n", - "\n", - "first_condition->My first condition\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "@tree.condition(output=RejectAction(code=102, description=\"My first condition\"))\n", - "def first_condition(d: pd.Series, e: pd.Series, f: pd.Series) -> pd.Series:\n", - " return (d > 5) & (e > 5) & (f > 5)\n", - "\n", - "\n", - "tree.visualize(get_value_name=lambda x: x[\"description\"][0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, `first_condition` triggers a rejection with code `102` and description \"My first condition\" when certain criteria (`d > 5`, `e > 5`, `f > 5`) are fulfilled.\n", - "\n", - "#### Nesting Conditions\n", - "\n", - "You can nest conditions under parent conditions using decorators. Hereโ€™s how to nest conditions under `condition_a`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "root\n", - "\n", - "root\n", - "\n", - "\n", - "\n", - "first_condition\n", - "\n", - "first_condition\n", - "\n", - "\n", - "\n", - "root->first_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_a\n", - "\n", - "condition_a\n", - "\n", - "\n", - "\n", - "root->condition_a\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "My first condition\n", - "\n", - "My first condition\n", - "\n", - "\n", - "\n", - "first_condition->My first condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_b\n", - "\n", - "condition_b\n", - "\n", - "\n", - "\n", - "condition_a->condition_b\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_c\n", - "\n", - "condition_c\n", - "\n", - "\n", - "\n", - "condition_a->condition_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a and b are out of range\n", - "\n", - "a and b are out of range\n", - "\n", - "\n", - "\n", - "condition_b->a and b are out of range\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a and c are out of range\n", - "\n", - "a and c are out of range\n", - "\n", - "\n", - "\n", - "condition_c->a and c are out of range\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "@tree.condition()\n", - "def condition_a(a: pd.Series) -> pd.Series:\n", - " return a > 5\n", - "\n", - "\n", - "@condition_a.condition(\n", - " output=RejectAction(code=100, description=\"a and b are out of range\")\n", - ")\n", - "def condition_b(b: pd.Series) -> pd.Series:\n", - " return b > 5\n", - "\n", - "\n", - "@condition_a.condition(\n", - " output=RejectAction(code=101, description=\"a and c are out of range\")\n", - ")\n", - "def condition_c(c: pd.Series) -> pd.Series:\n", - " return c > 5\n", - "\n", - "\n", - "tree.visualize(get_value_name=lambda x: x[\"description\"][0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the usage of `@condition_a.condition` to define nested conditions (`condition_b` and `condition_c`) under `condition_a`.\n", - "\n", - "#### Setting Default Actions\n", - "\n", - "Itโ€™s essential to define a default action for cases where none of the conditions match. This is done using `set_default`:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "root\n", - "\n", - "root\n", - "\n", - "\n", - "\n", - "first_condition\n", - "\n", - "first_condition\n", - "\n", - "\n", - "\n", - "root->first_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_a\n", - "\n", - "condition_a\n", - "\n", - "\n", - "\n", - "root->condition_a\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "N/A\n", - "\n", - "N/A\n", - "\n", - "\n", - "\n", - "root->N/A\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "My first condition\n", - "\n", - "My first condition\n", - "\n", - "\n", - "\n", - "first_condition->My first condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_b\n", - "\n", - "condition_b\n", - "\n", - "\n", - "\n", - "condition_a->condition_b\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_c\n", - "\n", - "condition_c\n", - "\n", - "\n", - "\n", - "condition_a->condition_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a and b are out of range\n", - "\n", - "a and b are out of range\n", - "\n", - "\n", - "\n", - "condition_b->a and b are out of range\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a and c are out of range\n", - "\n", - "a and c are out of range\n", - "\n", - "\n", - "\n", - "condition_c->a and c are out of range\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree.set_default(output=RejectAction(code=-1, description=\"N/A\"))\n", - "\n", - "tree.visualize(get_value_name=lambda x: x[\"description\"][0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, all values are set to `pd.NA` when no specific conditions are met. The tree can now be executed with the following code:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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abcdefnumsinput_condition
05100001True
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27001010103False
38000004False
41200005False
520100006False
63000007True
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" - ], - "text/plain": [ - " a b c d e f nums input_condition\n", - "0 5 1 0 0 0 0 1 True\n", - "1 6 0 10 0 0 0 2 True\n", - "2 7 0 0 10 10 10 3 False\n", - "3 8 0 0 0 0 0 4 False\n", - "4 1 2 0 0 0 0 5 False\n", - "5 2 0 10 0 0 0 6 False\n", - "6 3 0 0 0 0 0 7 True\n", - "7 4 0 0 0 0 0 8 True" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data = pd.DataFrame(\n", - " {\n", - " \"a\": [5, 6, 7, 8, 1, 2, 3, 4],\n", - " \"b\": [1, 0, 0, 0, 2, 0, 0, 0],\n", - " \"c\": [0, 10, 0, 0, 0, 10, 0, 0],\n", - " \"d\": [0, 0, 10, 0, 0, 0, 0, 0],\n", - " \"e\": [0, 0, 10, 0, 0, 0, 0, 0],\n", - " \"f\": [0, 0, 10, 0, 0, 0, 0, 0],\n", - " # Below used later in the tutorial\n", - " \"nums\": [1, 2, 3, 4, 5, 6, 7, 8],\n", - " \"input_condition\": [True, True, False, False, False, False, True, True],\n", - " }\n", - ")\n", - "test_data" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
codedescription
0-1N/A
1101a and c are out of range
2102My first condition
3-1N/A
4-1N/A
5-1N/A
6-1N/A
7-1N/A
\n", - "
" - ], - "text/plain": [ - " code description\n", - "0 -1 N/A\n", - "1 101 a and c are out of range\n", - "2 102 My first condition\n", - "3 -1 N/A\n", - "4 -1 N/A\n", - "5 -1 N/A\n", - "6 -1 N/A\n", - "7 -1 N/A" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree.execute(inputs=test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### Specifying Conditions as Text\n", - "\n", - "You can also specify conditions using text references. Hereโ€™s an example:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "root\n", - "\n", - "root\n", - "\n", - "\n", - "\n", - "first_condition\n", - "\n", - "first_condition\n", - "\n", - "\n", - "\n", - "root->first_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_a\n", - "\n", - "condition_a\n", - "\n", - "\n", - "\n", - "root->condition_a\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "input_condition\n", - "\n", - "input_condition\n", - "\n", - "\n", - "\n", - "root->input_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "N/A\n", - "\n", - "N/A\n", - "\n", - "\n", - "\n", - "root->N/A\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "My first condition\n", - "\n", - "My first condition\n", - "\n", - "\n", - "\n", - "first_condition->My first condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_b\n", - "\n", - "condition_b\n", - "\n", - "\n", - "\n", - "condition_a->condition_b\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_c\n", - "\n", - "condition_c\n", - "\n", - "\n", - "\n", - "condition_a->condition_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Input condition\n", - "\n", - "Input condition\n", - "\n", - "\n", - "\n", - "input_condition->Input condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a and b are out of range\n", - "\n", - "a and b are out of range\n", - "\n", - "\n", - "\n", - "condition_b->a and b are out of range\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a and c are out of range\n", - "\n", - "a and c are out of range\n", - "\n", - "\n", - "\n", - "condition_c->a and c are out of range\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree.condition(\n", - " condition=\"input_condition\",\n", - " output=RejectAction(code=101, description=\"Input condition\"),\n", - ")\n", - "tree.visualize(get_value_name=lambda x: x[\"description\"][0])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
codedescription
0101Input condition
1101a and c are out of range
2102My first condition
3-1N/A
4-1N/A
5-1N/A
6101Input condition
7101Input condition
\n", - "
" - ], - "text/plain": [ - " code description\n", - "0 101 Input condition\n", - "1 101 a and c are out of range\n", - "2 102 My first condition\n", - "3 -1 N/A\n", - "4 -1 N/A\n", - "5 -1 N/A\n", - "6 101 Input condition\n", - "7 101 Input condition" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree.execute(inputs=test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Including Subtrees\n", - "\n", - "In SpockFlow, you can amplify the power of your Decision Trees by including subtrees. This feature allows you to nest complex decision logic within your main tree structure seamlessly. Hereโ€™s how you can include a subtree:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "root\n", - "\n", - "root\n", - "\n", - "\n", - "\n", - "A\n", - "\n", - "A\n", - "\n", - "\n", - "\n", - "root->A\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B\n", - "\n", - "B\n", - "\n", - "\n", - "\n", - "root->B\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SubA\n", - "\n", - "SubA\n", - "\n", - "\n", - "\n", - "root->SubA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SubB\n", - "\n", - "SubB\n", - "\n", - "\n", - "\n", - "root->SubB\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SubC\n", - "\n", - "SubC\n", - "\n", - "\n", - "\n", - "root->SubC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "10\n", - "\n", - "10\n", - "\n", - "\n", - "\n", - "A->10\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "20\n", - "\n", - "20\n", - "\n", - "\n", - "\n", - "B->20\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "100\n", - "\n", - "100\n", - "\n", - "\n", - "\n", - "SubA->100\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "200\n", - "\n", - "200\n", - "\n", - "\n", - "\n", - "SubB->200\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SubD\n", - "\n", - "SubD\n", - "\n", - "\n", - "\n", - "SubC->SubD\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SubE\n", - "\n", - "SubE\n", - "\n", - "\n", - "\n", - "SubC->SubE\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "1000\n", - "\n", - "1000\n", - "\n", - "\n", - "\n", - "SubD->1000\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "2000\n", - "\n", - "2000\n", - "\n", - "\n", - "\n", - "SubE->2000\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree = Tree()\n", - "tree.condition(output=Action(value=10), condition=\"A\")\n", - "tree.condition(output=Action(value=20), condition=\"B\")\n", - "\n", - "subtree = Tree()\n", - "subtree.condition(output=Action(value=100), condition=\"SubA\")\n", - "subtree.condition(output=Action(value=200), condition=\"SubB\")\n", - "\n", - "cond_subtree = Tree()\n", - "cond_subtree.condition(output=Action(value=1000), condition=\"SubD\")\n", - "cond_subtree.condition(output=Action(value=2000), condition=\"SubE\")\n", - "\n", - "tree.include_subtree(subtree)\n", - "tree.include_subtree(cond_subtree, condition=\"SubC\")\n", - "tree.visualize(get_value_name=lambda x: str(x[\"value\"][0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this setup:\n", - "- The main `tree` defines conditions 'A' and 'B' with outputs 10 and 20 respectively.\n", - "- `subtree`, a separate instance of `Tree`, defines its own conditions 'SubA' and 'SubB' with outputs 100 and 200.\n", - "- `tree.include(subtree)` integrates `subtree` into `tree`, enabling `subtree`'s conditions to function as part of `tree`'s decision-making process.\n", - "\n", - "This approach allows for hierarchical structuring of decision logic, making your Decision Trees in SpockFlow even more versatile and powerful." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Integration with Hamilton\n", - "\n", - "SpockFlow seamlessly integrates with Hamilton to execute decision trees as part of data processing pipelines. Hamilton allows converting sequences of Python functions into executable Directed Acyclic Graphs (DAGs), enabling clear data flow management. Here's how you can incorporate our decision tree into a Hamilton pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext hamilton.plugins.jupyter_magic" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "cluster__legend\n", - "\n", - "Legend\n", - "\n", - "\n", - "\n", - "condition_b\n", - "\n", - "condition_b\n", - "Series\n", - "\n", - "\n", - "\n", - "tree.format_inputs\n", - "\n", - "tree.format_inputs\n", - "Tuple\n", - "\n", - "\n", - "\n", - "condition_b->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "tree.conditions_met\n", - "\n", - "tree.conditions_met\n", - "ndarray\n", - "\n", - "\n", - "\n", - "tree\n", - "\n", - "tree\n", - "DataFrame\n", - "\n", - "\n", - "\n", - "tree.conditions_met->tree\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "first_condition\n", - "\n", - "first_condition\n", - "Series\n", - "\n", - "\n", - "\n", - "first_condition->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_a\n", - "\n", - "condition_a\n", - "Series\n", - "\n", - "\n", - "\n", - "condition_a->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "tree.format_inputs->tree.conditions_met\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "tree.format_inputs->tree\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "tree.condition_names\n", - "\n", - "tree.condition_names\n", - "List\n", - "\n", - "\n", - "\n", - "tree.format_inputs->tree.condition_names\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "input_condition\n", - "\n", - "input_condition\n", - "Series\n", - "\n", - "\n", - "\n", - "input_condition->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_c\n", - "\n", - "condition_c\n", - "Series\n", - "\n", - "\n", - "\n", - "condition_c->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_condition_b_inputs\n", - "\n", - "b\n", - "Series\n", - "\n", - "\n", - "\n", - "_condition_b_inputs->condition_b\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_first_condition_inputs\n", - "\n", - "e\n", - "Series\n", - "f\n", - "Series\n", - "d\n", - "Series\n", - "\n", - "\n", - "\n", - "_first_condition_inputs->first_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_condition_a_inputs\n", - "\n", - "a\n", - "Series\n", - "\n", - "\n", - "\n", - "_condition_a_inputs->condition_a\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_input_condition_inputs\n", - "\n", - "nums\n", - "Series\n", - "\n", - "\n", - "\n", - "_input_condition_inputs->input_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_condition_c_inputs\n", - "\n", - "c\n", - "Series\n", - "\n", - "\n", - "\n", - "_condition_c_inputs->condition_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "input\n", - "\n", - "input\n", - "\n", - "\n", - "\n", - "function\n", - "\n", - "function\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%cell_to_module --display -m demo_tree\n", - "\n", - "# Import necessary packages\n", - "from typing import TypedDict\n", - "import pandas as pd\n", - "\n", - "from spockflow.components.tree import Tree, Action\n", - "from spockflow.core import initialize_spock_module\n", - "\n", - "# Define Reject action type\n", - "class Reject(TypedDict):\n", - " code: int\n", - " description: str\n", - "\n", - "RejectAction = Action[Reject]\n", - "\n", - "def input_condition(nums: pd.Series) -> pd.Series:\n", - " \"\"\"input condition can be a value calculated just like any other value in a hamilton dag\"\"\"\n", - " return nums % 2 == 0\n", - "\n", - "# Initialize Tree\n", - "tree = Tree()\n", - "\n", - "# Define conditions and actions\n", - "@tree.condition()\n", - "def condition_a(a: pd.Series) -> pd.Series:\n", - " return a > 5\n", - "\n", - "@condition_a.condition(output=RejectAction(code=100, description=\"a and b are out of range\"))\n", - "def condition_b(b: pd.Series) -> pd.Series:\n", - " return b > 5\n", - "\n", - "@condition_a.condition(output=RejectAction(code=101, description=\"a and c are out of range\"))\n", - "def condition_c(c: pd.Series) -> pd.Series:\n", - " return c > 5\n", - "\n", - "@tree.condition(output=RejectAction(code=102, description=\"My first condition\"))\n", - "def first_condition(d: pd.Series, e: pd.Series, f: pd.Series) -> pd.Series:\n", - " return (d > 5) & (e > 5) & (f > 5)\n", - "\n", - "# Set condition with reference to external hamilton element\n", - "tree.condition(condition=\"input_condition\", output=RejectAction(code=101, description=\"Input condition\"))\n", - "# Set default action\n", - "tree.set_default(output=RejectAction(code=-1, description=\"N/A\"))\n", - "\n", - "# Initialize SpockFlow module for Hamilton\n", - "initialize_spock_module(__name__, output_names=[\"tree\"])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above example:\n", - "- We define our decision tree and associated conditions.\n", - "- The initialize_spock_module function hooks into Hamilton's DAG creation system, enabling the construction of SpockFlow-specific nodes and specifying default outputs.\n", - "\n", - "#### Executing the Hamilton DAG\n", - "\n", - "Once defined, you can execute the Hamilton DAG using the SpockFlow Driver (Driver):" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " code description\n", - "0 -1 N/A\n", - "1 101 a and c are out of range\n", - "2 102 My first condition\n", - "3 101 Input condition\n", - "4 -1 N/A\n", - "5 101 Input condition\n", - "6 -1 N/A\n", - "7 101 Input condition\n" - ] - } - ], - "source": [ - "from spockflow.core import Driver\n", - "\n", - "# Initialize Driver with the decision tree module\n", - "dr = Driver({}, demo_tree)\n", - "\n", - "# Execute the DAG with test data\n", - "df = dr.execute(inputs=test_data)\n", - "print(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "spock", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/concepts/index.md b/docs/concepts/index.md deleted file mode 100644 index c1b1513..0000000 --- a/docs/concepts/index.md +++ /dev/null @@ -1,11 +0,0 @@ -# Concepts - -Welcome to the Concepts section, your gateway to understanding the foundational principles and components of SpockFlow, a versatile framework designed for creating standalone micro-services that enrich data with actionable outputs. This section serves as a comprehensive resource where you can explore key concepts such as model deployment, inference handling, configuration management, and custom component development within SpockFlow. Whether you are a data scientist deploying models or a developer integrating custom functionalities, these concepts provide essential insights into leveraging SpockFlow's capabilities effectively. Dive into each concept to gain a deeper understanding of how SpockFlow empowers efficient and scalable data enrichment and deployment workflows. - -```{toctree} - -Decision Trees -Decision Tables -Score Cards -API Customization -``` \ No newline at end of file diff --git a/docs/concepts/inference.md b/docs/concepts/inference.md deleted file mode 100644 index 8120182..0000000 --- a/docs/concepts/inference.md +++ /dev/null @@ -1,292 +0,0 @@ -# API Inference Handling - -In SpockFlow, method overloading plays a pivotal role in customizing the behavior of the inference handler, allowing users to tailor every aspect of API deployment to their specific needs. Several key methods and classes within the framework can be easily overridden to adjust functionality: - -- **Input Handling**: Customize data decoding and preprocessing using `input_fn`, `decoders`. -- **Model Management**: Define how models are loaded and configured with `model_fn`, `dag_loader_fn`, `config_manager_loader_fn`, `model_loader_cls`, `model_cache_cls`, and `model_config_cls`. -- **Prediction and Post-processing**: Modify prediction logic and result refinement via `predict_fn` and `post_process_fn`. -- **Response Formatting**: Adjust how predictions are encoded into various output formats using `output_fn`, `encoders`. - -These methods not only provide flexibility but also enhance integration capabilities, optimize performance, and align seamlessly with existing systems. This section will delve into each function's role in tailoring the deployment of SpockFlow's API, offering insights into practical customization strategies and implementation guidelines. - -## Overview - -The `transform_fn` serves as the central entry point in SpockFlow's API deployment, where raw requests are processed and transformed into meaningful responses. This function orchestrates a sequence of operations, starting with data decoding and preprocessing, followed by model prediction and output formatting. Each step in this workflow can be tailored to specific requirements by defining corresponding functions in the `inference.py` module. The following diagram is a high-level overview of the `transform_fn`. - - -```{figure} ../_static/concepts/inference.drawio.svg -:scale: 100 -:align: center -:class: only-dark -``` - -```{figure} ../_static/concepts/inference_light.drawio.svg -:scale: 100 -:align: center -:class: only-light -``` - -For instance, the input_fn method can be customized within `inference.py` to handle data decoding based on content type. Hereโ€™s an example: -```python -# inference.py - -def input_fn(input_data, content_type): - # Custom decoding logic - decoded_data = custom_decoder(input_data, content_type) - return decoded_data -``` -Additionally, methods defined with `self` as the first argument in `inference.py` allow the instance of the `ServingHandler` to be injected dynamically at runtime. This enables seamless integration of custom functionalities and ensures flexibility in adapting the inference pipeline to diverse application scenarios. - -The following sections will explore each method's role within `transform_fn`, illustrating how they can be leveraged to tailor the API deployment process in SpockFlow. - -## Methods - -This section describes all the configurable stages that can be overloaded in the inference.py and how they affect the predicted result. -### Input Function -The `input_fn` method in SpockFlow's ServingHandler class handles the initial step of data ingestion and decoding during API requests. It accepts two parameters: -- **input_data**: The raw data received from the API request, typically in bytes. -- **content_type**: Specifies the format or encoding of input_data, guiding the decoding process. -The primary role of `input_fn` is to decode and pre-process incoming raw data into a structured format that downstream processes can handle effectively. This method plays a crucial role in ensuring compatibility between various data sources and the internal processing pipeline of SpockFlow. - -**Example:** - -```python -# inference.py -import json - -def input_fn(input_data, content_type): - if content_type != 'application/json': - raise ValueError("API only supports json data") - return json.loads(input_data) -``` - -The default input_fn function in SpockFlow utilizes a decoders dictionary to decode incoming data based on the content-type header of API requests. By default, it supports decoding both CSV and JSON formats, converting them into Python objects. For instance, CSV data is parsed into a Pandas DataFrame, while JSON data is directly parsed into a dictionary. Users have the flexibility to extend or override these default decoders in the inference.py module. For example, adding support for Parquet format can be achieved by defining a custom decoder function and updating the decoders dictionary accordingly: - -```python -# inference.py -import pandas as pd -from io import BytesIO -from spockflow.inference.io.decoders import default_decoders - -decoders = {**default_decoders} - -def decode_parquet(data: bytes): - return pd.read_parquet(BytesIO(data)).to_dict(orient='records') - -decoders["application/parquet"] = decode_parquet -``` - -### Preprocessing Function -The pre_process_fn function in SpockFlow facilitates the transformation of JSON data into a format suitable for downstream processing, typically a Pandas DataFrame. Its primary role is to prepare input data for enrichment, scoring, or other operations supported by SpockFlow. A typical implementation is as follows: -```python -# inference.py -import pandas as pd - -def pre_process_fn(input_data: dict) -> pd.DataFrame: - return pd.json_normalize(input_data) -``` - -### Output Function - -The `output_fn` function in SpockFlow's API deployment handles the final step of encoding prediction results into various output formats based on the `accept` header of API responses. It accepts two parameters: - -- **prediction**: The dictionary containing the processed prediction results. -- **accept**: Specifies the desired format or encoding of the response, guiding the encoding process. - -The primary role of `output_fn` is to format prediction results into the specified output format, ensuring compatibility with client expectations. This function is crucial for delivering responses in formats such as JSON, CSV, or custom formats like Parquet. - -**Example Implementation:** - -```python -import pandas as pd -from starlette.responses import JSONResponse - -def output_fn(prediction: dict, accept: str) -> "JSONResponse": - """ - Encodes prediction results into JSON format based on the accept header. - - Parameters: - - prediction (dict): Processed prediction results to be encoded. - - accept (str): Desired format or encoding specified by the accept header. - - Returns: - - JSONResponse: Response object containing encoded prediction results. - """ - if accept not in ["*/*", "application/json"]: - raise ValueError("Invalid accept header") - - return JSONResponse(prediction) -``` - - -#### Post-process Function - -In conjunction with `output_fn`, the `post_process_fn` function is commonly used to manipulate prediction results before they are encoded. It adjusts the structure or content of the output data to meet specific client needs. - -**Example Implementation:** - -```python -def post_process_fn(result: dict) -> dict: - """ - Post-processes prediction result before encoding. - - Parameters: - - result (dict): Raw prediction result from the model. - - Returns: - - dict: Processed prediction result ready for encoding. - """ - return {"response": result["tree"]} -``` - -The `post_process_fn` function in SpockFlow allows developers to refine prediction outputs, ensuring they meet application-specific requirements before encoding them into the desired format using `output_fn`. - -#### Encoders Example - -Developers can extend the functionality of SpockFlow's default `output_fn` by defining custom encoders for additional output formats. The `output_fn` function utilizes the encoders dictionary to determine the output format based on the accept header in API responses. Below is an example of adding support for Parquet format using a custom encoder: - -```python -import pandas as pd -from starlette.responses import Response -from spockflow.inference.io.encoders import default_encoders - -def to_parquet(result: dict) -> Response: - """ - Encodes prediction result into Parquet format. - - Parameters: - - result (dict): Processed prediction result to be encoded. - - Returns: - - Response: Response object containing Parquet-encoded prediction result. - """ - res = BytesIO() - pd.json_normalize(result).to_parquet(res) - res.seek(0) - return Response(res.read(), media_type="application/vnd.apache.parquet") - -# Initialize encoders with default encoders -encoders = {**default_encoders} - -# Add support for Parquet format -encoders["application/vnd.apache.parquet"] = to_parquet -``` - -## Configuration Management in SpockFlow - -SpockFlow provides flexible configuration management capabilities to facilitate the setup and management of models and their associated configurations. Developers can utilize built-in managers like the `YamlConfigManager` or create custom managers tailored to specific needs, such as reading configurations from DynamoDB or other data sources. - -### Using YamlConfigManager - -The `YamlConfigManager` provided by SpockFlow simplifies configuration management using YAML files. It allows for straightforward handling of model configurations stored locally. Here's an example of configuring `model_config_cls` to use `YamlConfigManager`: - -```python -from spockflow.inference.config.loader.yamlmanager import YamlConfigManager -model_config_cls = YamlConfigManager -``` - -### Custom Config Managers - -Developers have the flexibility to implement custom config managers to suit unique requirements. For instance, a custom config manager could integrate with DynamoDB to dynamically fetch configurations. Below is a simplified outline of how a custom manager might be structured: - -```python -# Example of a custom config manager (simplified) - -class CustomConfigManager(ConfigManager): - def get_latest_version(self, model_name: str) -> str: - # Implement logic to fetch the latest version from DynamoDB or other sources - pass - - def get_config(self, model_name: str, model_version: str) -> TNamespacedConfig: - # Implement logic to fetch config from DynamoDB or other sources - pass - - def save_to_config(self, model_name: str, model_version: str, namespace: str, config: TNamespacedConfig, key: str | None = None): - # Implement logic to save config to DynamoDB or other sources - pass -``` - -### YamlConfigManager Implementation - -For reference, here is a simplified implementation of `YamlConfigManager` provided by SpockFlow, demonstrating its capability to manage YAML-based configurations locally: - -```python -from yaml import dump, load -try: - from yaml import CDumper as Dumper, CLoader as Loader -except ImportError: - from yaml import Dumper, Loader -import os -from .base import ConfigManager, TNamespacedConfig -from pydantic_settings import BaseSettings, SettingsConfigDict - -class YamlConfigManager(ConfigManager, BaseSettings): - model_config = SettingsConfigDict(case_sensitive=False, env_prefix='CONFIG_MANAGER_') - config_path: str = os.path.join(".", "config") - - def model_path(self, model_name: str) -> str: - return os.path.join(self.config_path, model_name) - - def get_latest_version(self, model_name: str) -> str: - if model_name == "__default__": - paths = os.listdir(self.config_path) - if model_name not in paths: - assert len(paths) == 1, "can only use default when there is one model or an explicit __default__" - model_name = paths[0] - return sorted(os.listdir(self.model_path(model_name)))[-1] - - def get_config(self, model_name: str, model_version: str) -> TNamespacedConfig: - r = {} - for f in glob(os.path.join(self.model_path(model_name), model_version, "*.yml")): - ns = os.path.splitext(os.path.split(f)[1])[0] - with open(f) as fp: - r[ns] = load(fp, Loader=Loader) - return r - - def save_to_config(self, model_name: str, model_version: str, namespace: str, config: TNamespacedConfig, key: str | None = None): - save_path = os.path.join(self.model_path(model_name), model_version, namespace) + ".yml" - if key is not None: - if os.path.isfile(save_path): - with open(save_path) as fp: - curr_config = load(fp, Loader=Loader) - curr_config[key] = config - config = curr_config - os.makedirs(os.path.split(save_path)[0], exist_ok=True) - with open(save_path, "w") as fp: - dump(config, fp, Dumper=Dumper) -``` - -### Conclusion - -SpockFlow's configuration management capabilities, exemplified by the `YamlConfigManager` and the possibility of creating custom managers, provide robust support for handling model configurations across different use cases. Whether utilizing YAML files locally or integrating with external data sources like DynamoDB, developers have the tools necessary to effectively manage and deploy models within SpockFlow. - - -## Advanced Methods -While the previously discussed methods cover the majority of use cases, SpockFlow also offers advanced customization options for developers seeking more specialized functionalities. These advanced methods provide additional flexibility and control over API deployment processes, enabling tailored solutions for specific requirements. -### Predict Function -The `predict_fn` function in SpockFlow is pivotal for executing model predictions using a specified `Driver` object. By default, it leverages `raw_execute` to provide flexibility in handling prediction outputs, returning results as a dictionary. However, developers can override this behavior, as shown in the example below, to use `execute` instead. This alternative approach directly returns a DataFrame, eliminating the need for a separate `post_process_fn` for data transformation. - -### Example Implementation - -```python -from spockflow.inference.handler import WrappedInputData -from spockflow.core import Driver - -def predict_fn(input_data: "WrappedInputData", model: "Driver") -> pd.DataFrame: - """ - Executes model prediction using SpockFlow's Driver object with direct DataFrame output. - - Parameters: - - input_data (WrappedInputData): Wrapped input data containing model inputs. - - model (Driver): SpockFlow's Driver object representing the model to execute. - - Returns: - - pd.DataFrame: DataFrame containing the prediction results. - """ - return model.execute( - inputs=input_data.data, - overrides=input_data.input_overrides - ) -``` - -In this revised example, `predict_fn` overrides the default behavior by using `execute` instead of `raw_execute`. This method directly returns a DataFrame containing prediction results. By adopting this approach, developers streamline the prediction process within SpockFlow, ensuring efficient data processing without the need for additional transformation steps typically handled by a `post_process_fn`. diff --git a/docs/concepts/scorecard.ipynb b/docs/concepts/scorecard.ipynb deleted file mode 100644 index 0be9ac1..0000000 --- a/docs/concepts/scorecard.ipynb +++ /dev/null @@ -1,493 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SpockFlow Scorecard Component\n", - "\n", - "The SpockFlow Scorecard component facilitates the creation of scoring rules for both numerical and categorical data, allowing users to define criteria that assign scores and descriptive labels based on specified conditions. This component is particularly useful for evaluating data against predefined thresholds or patterns, providing insights through structured outputs.\n", - "\n", - "### Usage\n", - "\n", - "To begin using the Scorecard component, import the necessary packages and instantiate the `ScoreCard` object:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from spockflow.components import scorecard\n", - "\n", - "var_1 = \"var_1\"\n", - "var_2 = \"var_2\"\n", - "\n", - "sc = scorecard.ScoreCard(\n", - " bin_prefix=\"SCORE_BIN_\",\n", - " score_prefix=\"SCORE_VALUE_\",\n", - " description_prefix=\"SCORE_DESC_\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Adding Criteria\n", - "\n", - "Criteria can be added to the `ScoreCard` object using the `add_criteria` method. Each criterion defines how to evaluate a specific variable and assign scores based on conditions. There are two types of criteria: numerical and categorical.\n", - "\n", - "#### Numerical Criteria\n", - "\n", - "Numerical criteria evaluate numeric variables and can define score ranges, discrete values, and default behaviors:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScoreCardModel(bin_prefix='SCORE_BIN_', score_prefix='SCORE_VALUE_', description_prefix='SCORE_DESC_', variable_params=[ScoreCriteriaNumerical(variable='var_1', other_score=DefaultScorePattern(group_id=3, score=73.0, description='default'), discrete_scores=[NumericalDiscreteScorePattern(values=[nan], group_id=2, score=73.0, description='missing')], range_scores=[RangeScorePattern(range=MatchRange(start=0.0, end=1.0), group_id=0, score=10.0, description='First bound var_1'), RangeScorePattern(range=MatchRange(start=1.0, end=2.0), group_id=1, score=30.0, description='2nd bound var_1')], type='numerical', included_bounds=(,))], version='2.2.0', score_scaling_params=None)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc.add_criteria(\n", - " scorecard.ScoreCriteria(var_1, \"numerical\")\n", - " .add_range_score(0, 1, 10, \"First bound var_1\")\n", - " .add_range_score(1, 2, 30, \"2nd bound var_1\")\n", - " .add_discrete_score([None], 73, \"missing\")\n", - " .set_other_score(73, \"default\")\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- **Range Scores**: Assign scores based on numeric ranges. For example, `add_range_score(0, 1, 10, \"First bound var_1\")` assigns a score of 10 to `SCORE_VALUE_var_1` when `0 <= var_1 < 1`.\n", - "- **Discrete Scores**: Assign scores for specific values. `add_discrete_score([None], 73, \"missing\")` assigns a score of 73 to `SCORE_VALUE_var_1` when `var_1` is `None`.\n", - "- **Default Score**: Set a default score and description for unmatched values. `set_other_score(73, \"default\")` assigns a score of 73 to `SCORE_VALUE_var_1` for all other cases." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Categorical Criteria\n", - "\n", - "Categorical criteria evaluate text variables and can define exact matches or patterns (regex):\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScoreCardModel(bin_prefix='SCORE_BIN_', score_prefix='SCORE_VALUE_', description_prefix='SCORE_DESC_', variable_params=[ScoreCriteriaNumerical(variable='var_1', other_score=DefaultScorePattern(group_id=3, score=73.0, description='default'), discrete_scores=[NumericalDiscreteScorePattern(values=[nan], group_id=2, score=73.0, description='missing')], range_scores=[RangeScorePattern(range=MatchRange(start=0.0, end=1.0), group_id=0, score=10.0, description='First bound var_1'), RangeScorePattern(range=MatchRange(start=1.0, end=2.0), group_id=1, score=30.0, description='2nd bound var_1')], type='numerical', included_bounds=(,)), ScoreCriteriaCategorical(variable='var_2', other_score=None, discrete_scores=[CategoricalDiscreteScorePattern(values=['a', 'b', 'c'], group_id=0, score=10.0, description='First pattern var_2'), CategoricalDiscreteScorePattern(values=['[b-z]'], group_id=1, score=20.0, description='Second pattern var_2')], type='categorical', default_behavior='regex')], version='2.2.0', score_scaling_params=None)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc.add_criteria(\n", - " scorecard.ScoreCriteria(var_2, \"categorical\", default_behavior=\"regex\")\n", - " .add_discrete_score([\"a\", \"b\", \"c\"], 10, \"First pattern var_2\")\n", - " .add_discrete_score([\"[b-z]\"], 20, \"Second pattern var_2\")\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "- **Exact Matches**: Assign scores based on exact text matches. `add_discrete_score(['a', 'b', 'c'], 10, \"First pattern var_2\")` assigns a score of 10 to `SCORE_VALUE_var_2` when `var_2` is 'a', 'b', or 'c'.\n", - "- **Regex Matches**: Evaluate variables using regex patterns. `add_discrete_score(['[b-z]'], 20, \"Second pattern var_2\")` assigns a score of 20 to `SCORE_VALUE_var_2` when `var_2` matches the regex pattern `[b-z]`.\n", - "\n", - "### Automatic Binning\n", - "\n", - "By default, the Scorecard component automatically determines bin categories (`SCORE_BIN_var_1`, `SCORE_BIN_var_2`) based on the order in which criteria are added. The first criterion added determines `SCORE_BIN_var_1`, the second criterion determines `SCORE_BIN_var_2`, and so forth. These bin categories categorize input values based on the criteria matched.\n", - "\n", - "#### Overriding Bins\n", - "\n", - "Bins can be overridden if needed. This allows users to customize bin categories or reorder them for specific requirements. The `override_idx` parameter in the `add_range_score`, `add_discrete_score`, and `set_default` methods allows specifying the index of the bin to override:\n", - "\n", - "```python\n", - "sc.add_criteria(\n", - " scorecard.ScoreCriteria(var_1, \"numerical\")\n", - " .add_range_score(0, 1, 10, \"First bound var_1\", override_idx=5)\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Execution and Results\n", - "\n", - "To execute the Scorecard on a dataset, use the `execute` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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This section will discuss: -- How Variable nodes fit into the Spockflow framework -- Common functionality across variable nodes: - - How to store and load from config - - Cloning and aliasing -- How to create your own custom node type \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index d42c9df..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,91 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html -import os -import sys - -sys.path.insert(0, os.path.abspath("..")) - - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - -project = "SpockFlow" -copyright = "2024, Sholto Armstrong" -author = "Sholto Armstrong" -import spockflow - -version = str(spockflow.__version__) -release = version - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [ - "myst_nb", - "sphinx.ext.napoleon", - "sphinx.ext.autodoc", # Core library for html generation from docstrings - "sphinx.ext.autosummary", # Create neat summary tables - "sphinxcontrib.confluencebuilder", - "sphinx_sitemap", # Welcome robots to the website -] - -# AutoDoc Conf (Thanks https://github.com/JamesALeedham/Sphinx-Autosummary-Recursion/blob/master/docs/conf.py) -autosummary_generate = True # Turn on sphinx.ext.autosummary -autoclass_content = "both" # Add __init__ doc (ie. params) to class summaries -html_show_sourcelink = ( - False # Remove 'view source code' from top of page (for html, not python) -) -autodoc_inherit_docstrings = True # If no docstring, inherit from base class -set_type_checking_flag = True # Enable 'expensive' imports for sphinx_autodoc_typehints -nbsphinx_allow_errors = True # Continue through Jupyter errors -# autodoc_typehints = "description" # Sphinx-native method. Not as good as sphinx_autodoc_typehints -add_module_names = False # Remove namespaces from class/method signatures - - -templates_path = ["_templates"] -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -# html_theme = 'alabaster' -html_static_path = ["_static"] - -html_theme = "furo" -html_title = "SpockFlow" -html_theme_options = { - "source_branch": "main", - "source_directory": "docs/", - "light_css_variables": { - "color-announcement-background": "#ffba00", - "color-announcement-text": "#091E42", - }, - "dark_css_variables": { - "color-announcement-background": "#ffba00", - "color-announcement-text": "#091E42", - }, -} - - -# for the sitemap extension --- -# check if the current commit is tagged as a release (vX.Y.Z) and set the version -language = "en" -html_baseurl = "https://capitec.github.io/ml-decision-engine" -html_extra_path = ["robots.txt"] - -confluence_config_path = os.path.split(__file__)[0] + "/confluence.json" -if os.path.isfile(confluence_config_path): - import json - - with open(confluence_config_path) as fp: - conf_config = json.load(fp) - confluence_publish = conf_config["confluence_publish"] - confluence_parent_page = conf_config["confluence_parent_page"] - confluence_space_key = conf_config["confluence_space_key"] - confluence_ask_password = conf_config["confluence_ask_password"] - confluence_server_url = conf_config["confluence_server_url"] - confluence_server_user = conf_config["confluence_server_user"] - confluence_server_cookies = conf_config["confluence_server_cookies"] diff --git a/docs/getting_started/contributing/architecure/index.md b/docs/getting_started/contributing/architecure/index.md deleted file mode 100644 index 9055374..0000000 --- a/docs/getting_started/contributing/architecure/index.md +++ /dev/null @@ -1,204 +0,0 @@ -## Spockflow Architecture Documentation - Landing Page - - -### **Directory Structure** - -To better understand Spockflowโ€™s architecture, letโ€™s explore the key folders and their responsibilities within the package: - ---- - -#### **1. `spockflow/`** -This is the core of the Spockflow framework, containing all of its primary modules and components. - ---- - -##### **core.py** -- The main entry point for Hamilton integration. -- Defines a custom decorator for injecting Spockflow logic into Hamiltonโ€™s DAG framework. -- Expands Hamilton subdags with configurable components and generates nodes from these components. -- Calls `initialize_spock_module` to inject the Spockflow functionality into a given module, allowing for the automatic generation of Hamilton nodes. - -Example of Hamilton subdag integration: -```python -@subdag( - feature_modules, - inputs={"path": source("source_path")}, - config={} -) -def feature_engineering(feature_df: pd.DataFrame) -> pd.DataFrame: - return feature_df -``` -- In Spockflow, the `initialize_spock_module` decorator ensures that subdags are expanded and executed according to the framework's configuration. - ---- - -##### **nodes.py** -- Contains the definition of `VariableNode`, the core class responsible for transforming configuration-driven logic into executable Hamilton nodes. -- Handles utilities such as `CloneVariableNode` (for duplicating nodes) and `AliasedVariableNode` (for renaming nodes without re-executing). -- Uses Pydantic classes to serialize and deserialize configuration, making it easier to manage node definitions and configurations. -- The `generate_nodes` function within `VariableNode` handles the actual creation of subnodes, ensuring that each node can be expanded within a Hamilton DAG. - -```python -def _generate_nodes(self, ...): - ... - node_functions = inspect.getmembers( - compiled_variable_node, predicate=self._does_define_node - ) - ... -``` -This method identifies and expands functions within a module as Hamilton nodes, ensuring that subcomponents can be injected into larger data pipelines. - ---- - -##### **_serializable.py** -- Provides utilities to help with the serialization and deserialization of data, particularly for handling Pandas DataFrames and Series. -- Ensures that data passed through Spockflow nodes can be properly transformed and maintained across different steps of the pipeline. - ---- - -#### **2. `components/`** -Contains all the core components and decision-oriented modules in Spockflow, including: -- **Decision Trees**: Build decision trees to enforce rules for data enrichment and transformations. -- **Scorecards**: Create scoring systems for evaluating data based on multiple parameters. -- **Decision Tables**: Define mappings of input values to outputs based on set conditions. - -Each of these components is built as reusable modules that can be configured and inserted into your data flows. - ---- - -#### **3. `inference/`** -- Contains logic and tools to serve models via endpoints compatible with services like AWS SageMaker. - ---- - - -### **How to Define a Custom Node in Spockflow** - -In Spockflow, custom nodes allow users to extend the framework's functionality by creating new components that integrate seamlessly into the Hamilton DAG-based architecture. A custom node is a class that inherits from `VariableNode` and defines its own behavior for node creation, input handling, and execution. - -Here, we'll define a custom `Tree` node as an example of how to create a custom decision-making process using Spockflowโ€™s infrastructure. - -#### **Step 1: Define the Custom Node Class** - -To create a custom node, you need to subclass `VariableNode` and define several key components, such as input fields, the `compile()` method, and custom logic for handling inputs and outputs. - -```python -class Tree(VariableNode): - # This is used in visualisation by Hamilton - doc: str = "This executes a user-defined decision tree" - - # Define fields using Pydantic (these can be any fields for configuration) - execution_conditions: typing.List[str] - execution_outputs: typing.List[str] - - # The compile function needs to be provided. By default, it will just return self. - def compile(self): - # This step may involve transforming or processing the input data into a usable format - from .compiled import CompiledNumpyTree - return CompiledNumpyTree(self) -``` - -- `execution_conditions` and `execution_outputs` are lists of strings that define the conditions and outputs associated with the decision tree. -- The `compile()` function is responsible for transforming the raw input data into a format that can be used by the Hamilton DAG. In this case, it initializes a `CompiledNumpyTree`. - -#### **Step 2: Define a Compiled Representation for the Node** - -To optimize how the nodeโ€™s logic is executed, we can define a compiled version of the node, such as `CompiledNumpyTree`. This compiled version will contain the logic to handle the execution and manage inputs dynamically. - -```python -class CompiledNumpyTree: - def __init__(self, tree: Tree) -> None: - # This constructor will process and configure the tree into an executable form - self.tree = tree - # Additional processing logic for the tree can go here - - def _get_inputs(self, function: typing.Callable): - # Returns the expected input types for the node - node_input_types = {o: pd.DataFrame for o in self.tree.execution_outputs} - node_input_types.update({c: typing.Union[np.ndarray, pd.Series] for c in self.tree.execution_conditions}) - return node_input_types -``` - -- The `CompiledNumpyTree` class is responsible for transforming the raw `Tree` object into an optimized version that can be used in a Hamilton DAG. -- The `_get_inputs` function dynamically determines the input types required for this nodeโ€™s execution. - -#### **Step 3: Define Node Functions with `@creates_node`** - -Next, we define the various operations that make up the logic of our custom `Tree` node. These operations are implemented as functions within the `Tree` class and are decorated with `@creates_node`. The `@creates_node` decorator tells Spockflow to treat these methods as subnodes within the Hamilton DAG. - -```python -from spockflow.nodes import creates_node -import numpy as np -import pandas as pd - -class Tree(VariableNode): - # Other fields and compile method defined previously - - @creates_node(kwarg_input_generator="_get_inputs") # Generates node inputs dynamically - def format_inputs( - self, **kwargs: typing.Union[pd.DataFrame, pd.Series] - ) -> TFormatData: - # Process inputs and return transformed data - pass - - @creates_node() # Defines a subnode for conditions met - def conditions_met(self, format_inputs: TFormatData) -> np.ndarray: - # Logic for evaluating conditions based on inputs - pass - - @creates_node() # Defines a subnode for prioritizing conditions - def prioritized_conditions(self, conditions_met: np.ndarray) -> np.ndarray: - # Logic for prioritizing conditions - pass - - @creates_node() # Defines a subnode for generating condition names - def condition_names(self, format_inputs: TFormatData) -> typing.List[str]: - # Logic to generate the names of the conditions - pass - - @creates_node() # Defines a subnode for the final decision logic - def all( - self, - format_inputs: TFormatData, - conditions_met: np.ndarray, - ) -> pd.DataFrame: - # Logic for making a decision based on inputs and conditions - pass - - @creates_node(is_namespaced=False) # This node will be created outside the namespace - def get_results( - self, - format_inputs: TFormatData, - prioritized_conditions: np.ndarray, - ) -> pd.DataFrame: - # Final output of the decision tree process - pass -``` - -- **`@creates_node`**: This decorator defines the function as a subnode in the DAG. - - The `kwarg_input_generator="_get_inputs"` argument is used to specify how to dynamically determine the input types for this node. - - Each method, such as `format_inputs()`, `conditions_met()`, etc., corresponds to a specific operation in the decision tree. - -The above tree when created as follows: - -```python -# Example Tree node instance -example_tree = Tree(execution_conditions=["a", "b"], execution_outputs=["c", "d"]) - -``` -will create the following DAG: -![](./tree.drawio.svg) - -- The above relationships represent the connections between nodes, where each `@creates_node` function becomes part of the Hamilton DAG. -- The `example_tree.format_inputs` node takes inputs `a`, `b`, `c`, and `d`, and feeds them into subsequent nodes like `conditions_met`, `prioritized_conditions`, and others. - ---- - -### **Summary of Custom Node Creation Steps** - -1. **Define the Node Class**: Inherit from `VariableNode` and specify fields such as conditions and outputs. -2. **Compile the Node**: Provide a `compile()` method to transform the node into an optimized executable form (e.g., `CompiledNumpyTree`). -3. **Define Operations as Subnodes**: Use `@creates_node` to define methods that represent different parts of the decision-making process. -4. **Establish Dependencies**: The created subnodes will automatically link based on their input/output relationships, forming a complete DAG. - -By following these steps, you can define complex, decision-oriented nodes in Spockflow and integrate them seamlessly into a Hamilton-based data pipeline. \ No newline at end of file diff --git a/docs/getting_started/contributing/architecure/tree.drawio.svg b/docs/getting_started/contributing/architecure/tree.drawio.svg deleted file mode 100644 index f8b56fd..0000000 --- a/docs/getting_started/contributing/architecure/tree.drawio.svg +++ /dev/null @@ -1,300 +0,0 @@ - - - - - - - - - - - - - - -
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- - - Note: because of is_namespaced = false... - - - - - - - - - - Text is not SVG - cannot display - - - - \ No newline at end of file diff --git a/docs/getting_started/contributing/backlog.md b/docs/getting_started/contributing/backlog.md deleted file mode 100644 index 5a1ecda..0000000 --- a/docs/getting_started/contributing/backlog.md +++ /dev/null @@ -1,23 +0,0 @@ -# Backlog items -## Configuration Management -1. Automatic model Validation: We need a way to validate configuration as soon as its changed before deploying a model -2. Parameter changing: We need better ways to abstract certain config parameters to be changed by exec levels - 1. We potentially need a ui to enable quick parameter changing - 2. We need to flesh out standards for parameter stores (not only file stores) - 3. We need a way to enable validation on parameters so that they cannot be changed the erroneous values. -3. Seamless deployment: We need a way to enable deployments without users needing to be familiar with github - 1. We need this to integrate well with parameters (the process to update a value like vat from 14% to 15% should be a 1 min job not a 30 min job) - 2. We need a way to verify results perhaps with backtesting or live data before deploying -4. Integration with A/B testing: We must make sure that all the retraining and ab testing work that is done is compatible with spockflow - 1. Node-level: Users may want to select a subsection of the model to alter and do ab testing with so that they are able to see the outcomes midflow and how it affected the process as a whole (multiple ab tests at the same time) -5. Dashboarding/tracing monitoring -## Tree Functionality -1. We should make it easy to migrate from the old format to the new format -2. We need a way to optimise tree structures using libraries such as sympy -3. We need a way to make trees work better with different data types. Currently it only supports int and float values. We will need to extend it that if we want a categorical string we are able to draw out the compute of changing the value to categorical before. -4. We should consider bringing an easy way to split the data and rejoin it after a tree. This way we can cull off parts of the pipeline where there is no data to compute. -## Dashboarding -1. We should consider creating easy to use dashboarding functionalities into the ui so that we can monitor the outputs of the flows. - - -We need a way to show feature use. Make sure you are able to catalog a feature and see the downstream impact of a feature on the entire system \ No newline at end of file diff --git a/docs/getting_started/contributing/index.md b/docs/getting_started/contributing/index.md deleted file mode 100644 index 89bc7c5..0000000 --- a/docs/getting_started/contributing/index.md +++ /dev/null @@ -1,12 +0,0 @@ -```{include} ../../../CONTRIBUTING.md -:relative-docs: docs -:relative-images: -``` - -# Useful Links -```{toctree} - -Architecture -Roadmap -Backlog -``` \ No newline at end of file diff --git a/docs/getting_started/contributing/roadmap/as_a_plugin/architecture.drawio.svg b/docs/getting_started/contributing/roadmap/as_a_plugin/architecture.drawio.svg deleted file mode 100644 index 74ecf83..0000000 --- a/docs/getting_started/contributing/roadmap/as_a_plugin/architecture.drawio.svg +++ /dev/null @@ -1,94 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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It could potentially also facilitate creating the appropriate pull requests to kick off model deployment. A potential architecture of this solution could look as follows: -![Architecture](./architecture.drawio.svg) -# User Stories -## The Developers (Advanced) -For advanced rule developers this is likely the most convenient option as they will likely already be working in a sagemaker environment. The advanced rule developer will start their process off loading some data from redshift. Ideally there would be a convenient way to get this data directly into the ui so that as rules are added they can get a visual representation of the counts of data that fall within each segment and potentially add a set of criteria like accuracy with a target and detection rate that they can view. The advanced developer would likely switch back and fourth between this view and a notebook to develop more features or use other tools to discover better decision boundaries. - -Once the developer is done with their rules they could press a button that would generate a initial spock workflow from a template and add the config to the flow. They could then import this into their current notebook and run it on live data to generate a report. Some reporting templates can also be incorporated as part of the deploy template to make this process easier. - -As part of the template the user should specify a validation dataset and a set of metrics that the model should conform to before it is considered valid. This could be a set of exact requests and responses for specific cases but more generally it could be a target accuracy or alert rate that must be met on a dataset. This way in the future modifications to rules can quickly be validated. - -When the developer is happy with the process they could commit the code to a repo and begin the model deployment process. The above validations and metrics will be calculated on deployment and displayed to the user which they can inspect before approving the stage to finally deploy into the respective environments. -## The Developers (Basic) -For developers which are more used to a UI tool this may be a bit inconvenient as they will likely need to get familiar with a new environment (Sagemaker) and they will now need to spin up an instance before they can do any work rather than just going to a page and getting started. Once they have navigated the terrain their process will be quite similar to the advanced developer. They will open the tool and create a project. They will likely not switch too much between notebooks and the app and will likely stay within the confines of safety. - -For the basic developers it will be more essential to attempt to integrate the raw data into the flow creation process so that they can get real-time feedback as the develop rules and in the future entire workflows. These developers will need a way to automatically evaluate if things like features are not available in the source data. For an initial step data can be provided as a csv/parquet source. However in the future with a flow we could introduce a source in the form of either a request or a redshift datasource. - -The development of standard and custom metrics will be essential to enable these developers to be data driven rather than basing their decisions on pure intuition. Once these developers have met targets on their metrics they can export their models in an identical fashion to the advanced developers. These developers will likely initially struggle with the concept of a notebook environment to view reporting; However, with targeted templates we could assist them in gathering beneficial metrics to properly validate the rules that they have created. These templates would have to tie in well with the model validation process and potentially prompt the user to define thresholds to evaluate the model on. - -The ui can assist these developers greatly in getting git setup and assisting with the deployment process. -## The Maintainers -Maintainers are typically on the same skill-level as the Basic Developers and may even be less of a technical audience attempting to adjust a simple threshold. Due to this they will likely have a hard time opening the environment and cloning a repo to get started. Tight integration between the widget could assist the user as it may be possible to do a discovery process were the user can just select an existing model and it could clone it or open the folder. However this integration would be challenging for open-source as it would be very targeted to how our systems are setup unless a plugin system is used. - -Once the maintainer has an existing project cloned they will begin development in a similar fashion to the basic developer. A maintainer will generally stick to making minor adjustments to rules such as changing parameters, thresholds or occasionally adding or deleting a rule. - -The maintainer can rely on the tool to make a commit and push with changes however they would likely battle to make a pr and navigate the model deployment process. A link could be generated to submit the pr and docs could be linked to; however for an executive trying to change a simple threshold this might be quite a cumbersome task. -## The Endpoint Users -Endpoint users will be in tight communication with the developers to understand what input features need to be given into the model. They will at the end of the day be given a url to invoke to integrate with the final model. - -If new features are added to the model they will need to ensure that the upstream dependencies are updated in a way that is backwards compatible (always add new features and don't delete old features for a rollover period). That way upstream jobs can be deployed before the rules are changed and ideally a rollover can be smooth. If this is not a possibility they will need to communicate with the Developer that there needs to be a major version change and in this case the new model will be deployed to a new endpoint /v1 vs /v2 and should be deployed before the upstream job. The model can be configured to return a model version so that any downstream logic can handle the results accordingly. -## The Monitors -There will likely be people who are interested in monitoring the real-time performance of the model to determine the accuracy over time. For these cases it will be the responsibility of the developer or endpoint user to integrate the results back into redshift this can be done with the use of endpoint capture or just by storing the results at the end of the day. Ideally results will be enriched at some point with ground-truth values such as if the fraud is confirmed or not. Feature pipelines or prodbooks can be developed to calculate desired metrics and this can be used then in a report on PowerBI for teams to monitor. Setting up the entire monitoring process has quite a few moving parts and may be considered cumbersome. -# Advantages -- A plugin has more control than a widget -- We do not not need to host it (use of existing compute) -# Limitations -- Hard to maintain (Plugin developers are niche) -- Hard to setup (Not much documentation on creating sagemaker plugins is it the same as standard jupyter lab) -- Clients need to login to sagemaker -- Limits possibilities for open-source (niche) -- Hard for managers just wanting to update a parameter \ No newline at end of file diff --git a/docs/getting_started/contributing/roadmap/as_a_plugin/mockup.drawio.svg b/docs/getting_started/contributing/roadmap/as_a_plugin/mockup.drawio.svg deleted file mode 100644 index 35f2bf4..0000000 --- a/docs/getting_started/contributing/roadmap/as_a_plugin/mockup.drawio.svg +++ /dev/null @@ -1,120 +0,0 @@ - - - - - - - - - - - - - - - - -
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- - - Developer... - - - - - - - - - Text is not SVG - cannot display - - - - \ No newline at end of file diff --git a/docs/getting_started/contributing/roadmap/as_a_service/index.md b/docs/getting_started/contributing/roadmap/as_a_service/index.md deleted file mode 100644 index 1033443..0000000 --- a/docs/getting_started/contributing/roadmap/as_a_service/index.md +++ /dev/null @@ -1,28 +0,0 @@ -# Description -Exposing the UI as a service means that the app will have its own url and be hosted on odin. It can be possible for clients to spin up their own instances or make use of a shared centralized instance. An example ui could look as follows: -![mockup](./mockup.drawio.svg) - -The system could make use of the current approach to deployments. It could also act as a hosting system of its own and create endpoints for flows automatically without the need for additional deployments. This would be advantageous as it opens the possibilities for one click deployments or more advanced AB testing as we can control everything about how the model is rolled out or how it is executed. The architecture could look as follows: -![Architecture](./architecture.drawio.svg) -# User Stories -## The Developers (Advanced) -Developers will be able to log in using the portal and navigate to the page. From there they can develop rules in a similar way to what they would as a plugin. To allow them to continue to work in their notebooks it would also be possible to create a widget that can integrate with the server. This will enable the advanced developer to quickly switch between notebook environments and rule development keeping it all in one environment. -## The Developers (Basic) -Developers with less technical backgrounds will find it easier to log into a dedicated app as they do not need to navigate through sagemaker to get into the app. The app and server will always be running so there will be less time required to get up and running. A hosted app will be ideal for basic developers as they will be used to low-code environments where they do not need to look at a single line of code for most usecases. They will log into the app develop a flow and press a button to deploy. -## The Maintainers -The flow of the maintainers will be identical to the developers. However it will be possible to even further simplify the maintenance of executive parameters and thresholds. Essential parameters can be extracted from flows and separated into their own interface. Here it will be possible for maintainers who aren't very technically inclined to update exposed thresholds based on descriptions and test their effects on certain key metrics without ever having to unpack the system as a whole. It will also be possible to create simplified ways to deploy the model using a UI so that maintainers do not need to be familiar with git. -## The Endpoint Users -Endpoint users will be able to use the model in an identical fashion to always. However as a service it may be possible to create a custom serving layer. With a custom serving layer we can handle cases such as automatic rerouting based on input payload capabilities, higher level orchestration (The output of one flow could determine which other flows need to be invoked before a result is returned.), and a prescriptive means for storing and retrieving versioned config. -## The Monitors -A custom service enables us to create a real-time dashboard where we are able to integrate with the serving layer and extract metrics on inputs, outputs or branches taken for each request. We will be able to provide advanced monitoring capacities that will be useful for cases like fraud where we would like to see which rules are affective and which branches are taken in real-time. It would also enable monitors to quickly react if they see that a given rule is producing too few or too many alerts. -# Advantages -- Can use additional protocols (not limited to HTTP requests for inference) -- Easier to integrate with a standardized parameter store if we host the models -# Limitations -- Extra Overheads. Need to worry about: - - authentication - - Access control - - Backups and DR - - Code execution security (may be possible to extract information about other flows by running your flow and inspecting globals) - - Processing capacity and autoscaling -- Need to keep at least a pilot server constantly running. \ No newline at end of file diff --git a/docs/getting_started/contributing/roadmap/as_a_service/mockup.drawio.svg b/docs/getting_started/contributing/roadmap/as_a_service/mockup.drawio.svg deleted file mode 100644 index a173dca..0000000 --- a/docs/getting_started/contributing/roadmap/as_a_service/mockup.drawio.svg +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/docs/getting_started/contributing/roadmap/as_a_widget/architecture.drawio.svg b/docs/getting_started/contributing/roadmap/as_a_widget/architecture.drawio.svg deleted file mode 100644 index 8eea089..0000000 --- a/docs/getting_started/contributing/roadmap/as_a_widget/architecture.drawio.svg +++ /dev/null @@ -1,106 +0,0 @@ - - - - - - - - - - - - - - - - -
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We would likely need to lean more into odin for the creation of repos and would require that developers of rules are more technical. -![Architecture](./architecture.drawio.svg) -## The Developers -The user story would be identical to a plugin. A user would likely start their journey in odin to create a new repo. They would then go to github to get the link and then open sagemaker to clone the repo and start development. Once they are done they will push their changes, go back to github to deploy their model and use a tool like postman to test their model before deploying it to various environments. -## The Maintainers -Maintainers would need to be the most technical for this as they would need to be comfortable cloning a repo before making changes to config either using a widget by running in a notebook or by editing it directly in json format. They would then need to know how to push to a branch and go back to github to do the deployment before testing in dev. -# Advantages -- We do not not need to host it (use of existing compute) -- Easier to maintain than a Plugin (less moving parts) -# Limitations -- Hard to maintain (Widget developers are niche) -- Hard to setup (Not much documentation on creating sagemaker plugins is it the same as standard jupyter lab) -- Clients need to login to sagemaker -- Clients need to be familiar with github and jupyter -- Limits possibilities for open-source (niche) -- Hard for managers just wanting to update a parameters \ No newline at end of file diff --git a/docs/getting_started/contributing/roadmap/as_a_widget/mockup.drawio.svg b/docs/getting_started/contributing/roadmap/as_a_widget/mockup.drawio.svg deleted file mode 100644 index 7689615..0000000 --- a/docs/getting_started/contributing/roadmap/as_a_widget/mockup.drawio.svg +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - \ No newline at end of file diff --git a/docs/getting_started/contributing/roadmap/index.md b/docs/getting_started/contributing/roadmap/index.md deleted file mode 100644 index e69de29..0000000 diff --git a/docs/getting_started/index.md b/docs/getting_started/index.md deleted file mode 100644 index 542798d..0000000 --- a/docs/getting_started/index.md +++ /dev/null @@ -1,11 +0,0 @@ -# Getting Started with Spockflow - -Welcome to Spockflow! This guide will help you get up and running with Spockflow, a Python framework designed for creating standalone micro-services that enrich data with actionable outputs. - -```{toctree} - -Installation Guide -Quick Start -Contributing -License -``` diff --git a/docs/getting_started/install.md b/docs/getting_started/install.md deleted file mode 100644 index 7fcb73a..0000000 --- a/docs/getting_started/install.md +++ /dev/null @@ -1,57 +0,0 @@ -# Spockflow Installation Guide - -Welcome to the installation guide for Spockflow. Spockflow is a powerful package designed to streamline decisioning processes with its cutting-edge features. - -## Prerequisites - -Before you begin, ensure you have the following prerequisites installed: -- Python (version 3.8 or higher recommended) -- Git (if installing from source) -- AWS CLI configured with appropriate credentials for AWS CodeArtifact (if applicable) - - Alternatively be setup in a notebook environment on the Data Science Platform - -## Installation Options - -### Option 1: Pip Install - -To install Spockflow via pip, use the following command: - -```bash -pip install spockflow -``` - -#### Installing with Optional Features -**YAML Support**: To add YAML support, use the [yaml] extra option: - -```bash -pip install spockflow[yaml] -``` -**Web Application (REST API)**: To enable local REST API serving, use the [webapp] extra option: -``` -pip install spockflow[webapp] -``` - -## Option 2: Install from Source -To install Spockflow from source, follow these steps: - -Clone the repository from GitHub: - -```bash -git clone https://github.com/spockflow/spockflow.git -cd spockflow -pip install -e . -``` - -## Verify Installation -To verify that Spockflow has been installed correctly, you can run the following command to check the version: - -```bash - python3 -c "import spockflow; print(spockflow.__version__)" -``` - -## Usage -Refer to the Spockflow documentation for detailed usage instructions and examples. - -## Troubleshooting -If you encounter any issues during installation or usage, please refer to the FAQs or reach out to our support team at sholto.armstrong@capitecbank.co.za. - diff --git a/docs/getting_started/license.md b/docs/getting_started/license.md deleted file mode 100644 index 8479381..0000000 --- a/docs/getting_started/license.md +++ /dev/null @@ -1,6 +0,0 @@ -# License - -```{include} ../../LICENSE -:relative-docs: docs -:relative-images: -``` \ No newline at end of file diff --git a/docs/getting_started/quick_start.ipynb b/docs/getting_started/quick_start.ipynb deleted file mode 100644 index 10b99d2..0000000 --- a/docs/getting_started/quick_start.ipynb +++ /dev/null @@ -1,1757 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quick Start with Spockflow\n", - "Welcome to the Quick Start guide for Spockflow! This guide will walk you through setting up and running your first data enrichment pipeline using Spockflow, designed to be straightforward and user-friendly, especially within a Jupyter notebook environment. Whether youโ€™re a data scientist looking to deploy models quickly or a developer interested in streamlining data workflows, Spockflow offers powerful tools to enrich your data with actionable insights. Letโ€™s get started!\n", - "\n", - "### Preface\n", - "\n", - "This guide assumes that you already have hamilton installed and have installed the GraphViz package for visualization. If not please follow the [Installation Guide](install.md) before continuing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building a Tree\n", - "To begin, we import the necessary packages. This guide focuses on exploring the Decision Tree component, a fundamental part of Spockflow." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from spockflow.components.tree import Tree, Action" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Tree` class allows us to construct decision trees, while the `Action` class serves as a convenient wrapper for standardizing outputs.\n", - "\n", - "To enforce a schema for actions, we define a specific action type:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from typing_extensions import TypedDict\n", - "import pandas as pd\n", - "\n", - "\n", - "class Reject(TypedDict):\n", - " code: int\n", - " description: str\n", - "\n", - "\n", - "RejectAction = Action[Reject]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we create an instance of the `Tree`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "tree = Tree()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conditions can be added to the tree using decorators. Here's an example:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "@tree.condition(output=RejectAction(code=102, description=\"My first condition\"))\n", - "def first_condition(d: pd.Series, e: pd.Series, f: pd.Series) -> pd.Series:\n", - " return (d > 5) & (e > 5) & (f > 5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This condition triggers a rejection with code 102 when certain criteria are met.\n", - "Nested conditions can also be defined. Here's how to nest under a parent condition:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "@tree.condition()\n", - "def condition_a(a: pd.Series) -> pd.Series:\n", - " return a > 5\n", - "\n", - "\n", - "@condition_a.condition(\n", - " output=RejectAction(code=100, description=\"a and b is out of range\")\n", - ")\n", - "def condition_b(b: pd.Series) -> pd.Series:\n", - " return b > 5\n", - "\n", - "\n", - "@condition_a.condition(\n", - " output=RejectAction(code=101, description=\"a and c is out of range\")\n", - ")\n", - "def condition_c(c: pd.Series) -> pd.Series:\n", - " return c > 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that when specifying a child node we make use of `@condition_a.condition` to specify conditions nested under `condition_a`. Furthermore, The base condition has no output set.\n", - "\n", - "It is important to be aware that tree construction is order dependant and rules placed first will be evaluated before rules coming after.\n", - "\n", - "Finally it is generally desired to set a default value if no condition is matched. This can be done as:\n", - "\n", - "*Note: The default behavior is to set all values to pd.NA when nothing is matched.*" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "tree.set_default(output=RejectAction(code=-1, description=\"N/A\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is also possible to set defaults on child elements with the following code:\n", - "```python\n", - "condition_a.set_default(output=RejectAction(code=-1, description=\"N/A\"))\n", - "```\n", - "We now define some dummy data to test the decision tree with as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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numabcdef
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" - ], - "text/plain": [ - " num a b c d e f\n", - "0 1 5 1 0 0 0 0\n", - "1 2 6 0 10 0 0 0\n", - "2 3 7 0 0 10 10 10\n", - "3 4 8 0 0 0 0 0\n", - "4 5 1 2 0 0 0 0\n", - "5 6 2 0 10 0 0 0\n", - "6 7 3 0 0 0 0 0\n", - "7 8 4 0 0 0 0 0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data = pd.DataFrame(\n", - " {\n", - " \"num\": [1, 2, 3, 4, 5, 6, 7, 8], # Note this is for use later in the tutorial\n", - " \"a\": [5, 6, 7, 8, 1, 2, 3, 4],\n", - " \"b\": [1, 0, 0, 0, 2, 0, 0, 0],\n", - " \"c\": [0, 10, 0, 0, 0, 10, 0, 0],\n", - " \"d\": [0, 0, 10, 0, 0, 0, 0, 0],\n", - " \"e\": [0, 0, 10, 0, 0, 0, 0, 0],\n", - " \"f\": [0, 0, 10, 0, 0, 0, 0, 0],\n", - " }\n", - ")\n", - "test_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally we can execute the tree as shown below" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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codedescription
0-1N/A
1101a and c is out of range
2102My first condition
3-1N/A
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5-1N/A
6-1N/A
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" - ], - "text/plain": [ - " code description\n", - "0 -1 N/A\n", - "1 101 a and c is out of range\n", - "2 102 My first condition\n", - "3 -1 N/A\n", - "4 -1 N/A\n", - "5 -1 N/A\n", - "6 -1 N/A\n", - "7 -1 N/A" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree.execute(test_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Execution as part of Hamilton\n", - "We have successfully created our first Decision Tree using SpockFlow. Now we would like to combine it as part of a hamilton pipeline. Hamilton allows us to convert a sequence of python functions into an executable Directed Acyclic Graph (DAG). This allows us to trace the flow of data through a sequence of steps. For a detailed explination of Hamilton please refer to the [Hamilton docs](https://hamilton.dagworks.io/en/latest/get-started/your-first-dataflow/). \n", - "Hamilton harnesses the power of functions contained in modules to construct pipelines. The following lines enable us to create Python modules directly within our notebook, empowering us to build Hamilton pipelines seamlessly without the need for external python modules. It is possible to define the above decision tree in a hamilton pipeline as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext hamilton.plugins.jupyter_magic" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "cluster__legend\n", - "\n", - "Legend\n", - "\n", - "\n", - "\n", - "tree.format_inputs\n", - "\n", - "tree.format_inputs\n", - "Tuple\n", - "\n", - "\n", - "\n", - "tree\n", - "\n", - "tree\n", - "DataFrame\n", - "\n", - "\n", - "\n", - "tree.format_inputs->tree\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "tree.condition_names\n", - "\n", - "tree.condition_names\n", - "List\n", - "\n", - "\n", - "\n", - "tree.format_inputs->tree.condition_names\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "tree.conditions_met\n", - "\n", - "tree.conditions_met\n", - "ndarray\n", - "\n", - "\n", - "\n", - "tree.format_inputs->tree.conditions_met\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_b\n", - "\n", - "condition_b\n", - "Series\n", - "\n", - "\n", - "\n", - "condition_b->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a\n", - "\n", - "a\n", - "Series\n", - "\n", - "\n", - "\n", - "condition_a\n", - "\n", - "condition_a\n", - "Series\n", - "\n", - "\n", - "\n", - "a->condition_a\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "b\n", - "\n", - "b\n", - "Series\n", - "\n", - "\n", - "\n", - "a->b\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "first_condition\n", - "\n", - "first_condition\n", - "Series\n", - "\n", - "\n", - "\n", - "first_condition->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_c\n", - "\n", - "condition_c\n", - "Series\n", - "\n", - "\n", - "\n", - "condition_c->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "condition_a->tree.format_inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "tree.conditions_met->tree\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "b->condition_b\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_a_inputs\n", - "\n", - "num\n", - "Series\n", - "\n", - "\n", - "\n", - "_a_inputs->a\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_first_condition_inputs\n", - "\n", - "f\n", - "Series\n", - "e\n", - "Series\n", - "d\n", - "Series\n", - "\n", - "\n", - "\n", - "_first_condition_inputs->first_condition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_condition_c_inputs\n", - "\n", - "c\n", - "Series\n", - "\n", - "\n", - "\n", - "_condition_c_inputs->condition_c\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "_b_inputs\n", - "\n", - "c\n", - "Series\n", - "\n", - "\n", - "\n", - "_b_inputs->b\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "input\n", - "\n", - "input\n", - "\n", - "\n", - "\n", - "function\n", - "\n", - "function\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%cell_to_module --display -m demo_tree\n", - "from typing import TypedDict\n", - "import pandas as pd\n", - "\n", - "from spockflow.components.tree import Tree, Action\n", - "from spockflow.core import initialize_spock_module\n", - "\n", - "class Reject(TypedDict):\n", - " code: int\n", - " description: str\n", - "\n", - "RejectAction = Action[Reject]\n", - "tree = Tree()\n", - "\n", - "def a(num: pd.Series) -> pd.Series:\n", - " \"\"\" We can add a function to calculate a from an external input \"\"\"\n", - " return num * 2\n", - "\n", - "\n", - "def b(a: pd.Series, c: pd.Series) -> pd.Series:\n", - " \"\"\"It is possible to take in more than one input as well for the transforms\"\"\"\n", - " return a+c\n", - "\n", - "@tree.condition()\n", - "def condition_a(a: pd.Series) -> pd.Series:\n", - " return a>5\n", - "\n", - "@condition_a.condition(output=RejectAction(code=100, description=\"a and b is out of range\"))\n", - "def condition_b(b: pd.Series) -> pd.Series:\n", - " return b>5\n", - "\n", - "@condition_a.condition(output=RejectAction(code=101, description=\"a and c is out of range\"))\n", - "def condition_c(c: pd.Series) -> pd.Series:\n", - " return c>5\n", - "\n", - "@tree.condition(output=RejectAction(code=102, description=\"My first condition\"))\n", - "def first_condition(d: pd.Series, e: pd.Series, f: pd.Series) -> pd.Series:\n", - " return (d>5)&(e>5)&(f>5)\n", - "\n", - "tree.set_default(output=RejectAction(code=-1, description=\"N/A\"))\n", - "\n", - "initialize_spock_module(__name__, output_names=[\"tree\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code created a DAG from the decision tree. Note that additional pre-processing steps were inserted for the input values `a` and `b`. We also introduced a new line of code\n", - "```python\n", - "initialize_spock_module(__name__, output_names=[\"tree\"])\n", - "```\n", - "This hooks into the hamilton DAG creation system to construct the SpockFlow specific nodes. This is required as by default hamilton only creates nodes for functions in a module. This line also gives us the ability to specify default outputs as is discussed further in this tutorial.\n", - "\n", - "It is now possible to execute this DAG as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
codedescription
0-1N/A
1-1N/A
2100a and b is out of range
3100a and b is out of range
4100a and b is out of range
5100a and b is out of range
6100a and b is out of range
7100a and b is out of range
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" - ], - "text/plain": [ - " code description\n", - "0 -1 N/A\n", - "1 -1 N/A\n", - "2 100 a and b is out of range\n", - "3 100 a and b is out of range\n", - "4 100 a and b is out of range\n", - "5 100 a and b is out of range\n", - "6 100 a and b is out of range\n", - "7 100 a and b is out of range" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from spockflow.core import Driver\n", - "\n", - "dr = Driver({}, demo_tree)\n", - "df = dr.execute(inputs=test_data)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It can be seen in the above code that `spockflow.core.Driver` is used. This is a convinience wrapper to `hamilton.driver.Driver`. However, the SpockFlow driver has context of the default outputs set in the `initialize_spock_module` function. To execute this using the default driver the following code is needed:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
codedescription
0-1N/A
1-1N/A
2100a and b is out of range
3100a and b is out of range
4100a and b is out of range
5100a and b is out of range
6100a and b is out of range
7100a and b is out of range
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" - ], - "text/plain": [ - " code description\n", - "0 -1 N/A\n", - "1 -1 N/A\n", - "2 100 a and b is out of range\n", - "3 100 a and b is out of range\n", - "4 100 a and b is out of range\n", - "5 100 a and b is out of range\n", - "6 100 a and b is out of range\n", - "7 100 a and b is out of range" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from hamilton.driver import Driver as HamiltonDriver\n", - "\n", - "dr_ham = HamiltonDriver({}, demo_tree)\n", - "df = dr_ham.execute(inputs=test_data, final_vars=[\"tree\"])\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is where Hamilton's true power begins to shine as it is possible to extract outputs from any stage in the execution and furthermore it is possible to override inputs to the different stages of the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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numabcdef
01510000
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23700101010
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562010000
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78400000
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For this it is possible to adjust the jupyter magic command above to the `%%writefile` magic as shown below. We will write the contents to main.py under source_dir as this is the default entrypoint for the server." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting source_dir/main.py\n" - ] - } - ], - "source": [ - "%%writefile source_dir/main.py\n", - "from typing import TypedDict\n", - "import pandas as pd\n", - "\n", - "from spockflow.components.tree import Tree, Action\n", - "from spockflow.core import initialize_spock_module\n", - "\n", - "class Reject(TypedDict):\n", - " code: int\n", - " description: str\n", - "\n", - "RejectAction = Action[Reject]\n", - "tree = Tree()\n", - "\n", - "def a(num: pd.Series) -> pd.Series:\n", - " \"\"\" We can add a function to calculate a from an external input \"\"\"\n", - " return num * 2\n", - "\n", - "\n", - "def b(a: pd.Series, c: pd.Series) -> pd.Series:\n", - " \"\"\"It is possible to take in more than one input as well for the transforms\"\"\"\n", - " return a+c\n", - "\n", - "@tree.condition()\n", - "def condition_a(a: pd.Series) -> pd.Series:\n", - " return a>5\n", - "\n", - "@condition_a.condition(output=RejectAction(code=100, description=\"a and b is out of range\"))\n", - "def condition_b(b: pd.Series) -> pd.Series:\n", - " return b>5\n", - "\n", - "@condition_a.condition(output=RejectAction(code=101, description=\"a and c is out of range\"))\n", - "def condition_c(c: pd.Series) -> pd.Series:\n", - " return c>5\n", - "\n", - "@tree.condition(output=RejectAction(code=102, description=\"My first condition\"))\n", - "def first_condition(d: pd.Series, e: pd.Series, f: pd.Series) -> pd.Series:\n", - " return (d>5)&(e>5)&(f>5)\n", - "\n", - "tree.set_default(output=RejectAction(code=-1, description=\"N/A\"))\n", - "\n", - "initialize_spock_module(__name__, output_names=[\"tree\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also create a inference.py file which allows us to override various aspects of our requests layer including how the data is processed before entering into the model. This is discussed in depth in the [API Customization](../concepts/inference.md) section. For now we are simply converting the json data-structures to a Pandas DataFrame before executing the model." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting source_dir/inference.py\n" - ] - } - ], - "source": [ - "%%writefile source_dir/inference.py\n", - "import typing\n", - "import pandas as pd\n", - "\n", - "def pre_process_fn(input_data: typing.Any) -> pd.DataFrame:\n", - " return pd.json_normalize(input_data)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once executed, your code should be written to the file `source_dir/main.py`. We can now simply start a server as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import requests\n", - "\n", - "os.environ[\"MODEL_PREFIX\"] = os.path.abspath(\".\")\n", - "os.environ[\"MODEL_RELATIVE_PATH\"] = \"source_dir\"" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "%%script bash --bg --out OUTPUT_STREAM\n", - "uvicorn spockflow.inference.server.asgi:app --reload" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0-1N/A
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" - ], - "text/plain": [ - " code description\n", - "0 -1 N/A\n", - "1 -1 N/A\n", - "2 100 a and b is out of range\n", - "3 100 a and b is out of range\n", - "4 100 a and b is out of range\n", - "5 100 a and b is out of range\n", - "6 100 a and b is out of range\n", - "7 100 a and b is out of range" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "resp = requests.post(\n", - " \"http://localhost:8000/invocations\", json=test_data.to_dict(orient=\"records\")\n", - ")\n", - "pd.DataFrame(resp.json()[\"tree\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All background processes were killed.\n" - ] - } - ], - "source": [ - "%killbgscripts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Advanced Deployment\n", - "### Docker Deployment\n", - "SpockFlow is also deployable as a docker container. The following command can be used to deploy a SpockFlow pipeline in a docker image:\n", - "\n", - "```bash\n", - "docker run -v $(pwd)/source_dir:/opt/ml/model --network=\"host\" --entrypoint uvicorn -p 8000:8000 405458085848.dkr.ecr.af-south-1.amazonaws.com/sagemaker-rules-engine:0.1.8 spockflow.inference.server.asgi:app --reload\n", - "```\n", - "### Debugging in VSCode\n", - "Spockflow makes use of a [Starlette](https://www.starlette.io/) app under the hood. It is therefore possible to launch a debug server directly in vscode with the following configuration:\n", - "```json\n", - "{\n", - " \"version\": \"0.2.0\",\n", - " \"configurations\": [\n", - " {\n", - " \"name\": \"Python Debugger: FastAPI\",\n", - " \"type\": \"debugpy\",\n", - " \"request\": \"launch\",\n", - " \"module\": \"uvicorn\",\n", - " \"args\": [\n", - " \"spockflow.inference.server.asgi:app\",\n", - " \"--reload\"\n", - " ],\n", - " \"jinja\": true,\n", - " \"env\": {\n", - " // Note the below config may need to change to suite your needs.\n", - " \"MODEL_PREFIX\": \"docs/getting_started\",\n", - " \"MODEL_RELATIVE_PATH\": \"source_dir\"\n", - " }\n", - " }\n", - " ]\n", - "}\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "spock", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index d17f350..0000000 --- a/docs/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. include:: main.rst - -.. toctree:: - :hidden: - - Home page - -.. toctree:: - :hidden: - :caption: USER GUIDE - - Getting Started - concepts/index - API reference <_autosummary/spockflow> - -.. toctree:: - :hidden: - :caption: EXTERNAL RESOURCES - - GitHub - Hamilton diff --git a/docs/main.rst b/docs/main.rst deleted file mode 100644 index 26b0d74..0000000 --- a/docs/main.rst +++ /dev/null @@ -1,64 +0,0 @@ -Spockflow -========= - -Spock is a python framework aimed at creating standalone micro-services that enrich data with actionable outputs. Spock supports both batch and live inference modes. Spock extends existing frameworks to provide a simplistic abstraction for common data flows including policy rules and scoring. Spock is designed to be extensible and modular allowing pipelines and config to be reused in multiple flows. Finally, a large emphasis is placed on runtime traceability and explainability by leveraging Hamilton which is a well established python framework. This allows the lineage of data to be tracked and visualised as well as enabling the steps in a process that lead to a certain outcome to be easily identifiable. - -.. figure:: ./_static/getting-started/example_pipeline.drawio.svg - :scale: 100 - :align: center - :class: only-dark - -.. figure:: ./_static/getting-started/example_pipeline_light.drawio.svg - :scale: 100 - :align: center - :class: only-light - -Components ----------- - -Spock provides the following tools - -- **Scorecards**: Providing an understandable way to assign scores to entities based on various parameters. -- **Decision Tables**: Enables input values to be mapped to output values based on a set of conditions. -- **Decision Trees**: Empowers the developer to specify a set of potentially nested rules which can be used for policies or to choose outputs based on the aforementioned rules. -- **General Transformations**: Often times simplistic transformations are required as part of the process and can be done using simple python code. -- **User driven components**: The framework provides a simple interface that can be used to enable the user to extend Spock and create custom reusable components. - -Why Spock ---------- - -- Spock enables Data Scientists to own the deployment of their models enabling a quicker time to production. -- Easy handover to maintenance teams due to the ability to export core components as config. -- Hamilton provides a form of runtime traceability -- Components in the Spock ecosystem can be easily adjusted to tweak performance, throughput and for general maintenance. -- Spock is built into DSP (and follows the Sagemaker standard) allowing it to leverage all of the existing capabilities out-of-the-box including: - - easy EDL, EDW and feature platform integration - - Model lifecycle with CICD integration - - EDA and a development environment - - Monitoring - - Methods for both batch and live predictions using a single codebase - -- The python programming ecosystem - - Python is a popular language among Data Scientists - - Many libraries exist for python - - Python and the abstractions provided by spock make the development of custom components easy - - Tooling including: IDEs, static code analysers (validation), code completion - -Use Cases ---------- - -- **Credit Granting**: A general credit granting flow will consist of ARODS, Affordability, Risk assessment and Pricing. Spock provides the following: - - abstractions over Decision trees to assist with ARODS, - - Scorecards for affordability and risk assessments and decision trees to allow results to be selected from scorecard based on a set of criteria, - - Decision tables are typically used to form a price from the resulting affordability and risk assessment. - -- **Fraud**: Fraud is typically a flat set of rules used to detect fraudulent transactions. The decision tree functionality can be used to provide binary labels to the data. - -When to look elsewhere ---------- - -- Spock is a function level orchestrator but is not intended to orchestrate multiple micro-services. -- Spock is intended to run on a record level and is not designed to aggregate multiple records -- Spock is stateless so all information needed to process records must be provided as part of the inputs to the flow. -- Spock does not provide any means to perform actions but merely enriches the output data -- Spock does not intend to provide a encumbering ui for development of arbitrary flows. diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 32bb245..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/robots.txt b/docs/robots.txt deleted file mode 100644 index d7f5a46..0000000 --- a/docs/robots.txt +++ /dev/null @@ -1,3 +0,0 @@ -User-agent: * - -Sitemap: https://spockflow.capinet/en/latest/sitemap.xml \ No newline at end of file diff --git a/dsp-re-ui/.gitignore b/dsp-re-ui/.gitignore deleted file mode 100644 index e5c939f..0000000 --- a/dsp-re-ui/.gitignore +++ /dev/null @@ -1,39 +0,0 @@ -.DS_STORE -node_modules -scripts/flow/*/.flowconfig -.flowconfig -*~ -*.pyc -.grunt -_SpecRunner.html -__benchmarks__ -build/ -remote-repo/ -coverage/ -.module-cache -fixtures/dom/public/react-dom.js -fixtures/dom/public/react.js -test/the-files-to-test.generated.js -*.log* -chrome-user-data -*.sublime-project -*.sublime-workspace -.idea -*.iml -.vscode -*.swp -*.swo - -packages/react-devtools-core/dist -packages/react-devtools-extensions/chrome/build -packages/react-devtools-extensions/chrome/*.crx -packages/react-devtools-extensions/chrome/*.pem -packages/react-devtools-extensions/firefox/build -packages/react-devtools-extensions/firefox/*.xpi -packages/react-devtools-extensions/firefox/*.pem -packages/react-devtools-extensions/shared/build -packages/react-devtools-extensions/.tempUserDataDir -packages/react-devtools-fusebox/dist -packages/react-devtools-inline/dist -packages/react-devtools-shell/dist -packages/react-devtools-timeline/dist \ No newline at end of file diff --git a/dsp-re-ui/README.md b/dsp-re-ui/README.md deleted file mode 100644 index a6c55f6..0000000 --- a/dsp-re-ui/README.md +++ /dev/null @@ -1,82 +0,0 @@ -# Data Science Platform (DSP) Rule Engine UI - -## Overview - -The DSP Rule Engine UI is a React-based application that allows users to visually build decision trees for rule-based decision-making systems. Once the trees are built, users can save and execute them through endpoints or visualize and test them within a Jupyter notebook. - -## Prerequisites - -Before getting started, make sure you have the following installed: - -- **Node.js** (for running the React app) -- **Yarn** or **npm** (for managing dependencies) -- **Python** (for running the backend model and Jupyter notebooks) -- **Graphviz** (for visualizing decision trees in the notebook) - -## Installation - -1. Clone the repository: - - ```bash - git clone https://github.com/capitec/dsp-re-ui.git - cd dsp-re-ui - ``` - -2. Install the dependencies using **Yarn** or **npm**: - - - Using **npm**: - ```bash - npm install - ``` - - - Or using **Yarn** (if you prefer Yarn): - ```bash - yarn install - ``` - -## Running the React App - -To start the React application in development mode: - -- Using **npm**: - ```bash - npm start - ``` - -- Using **Yarn**: - ```bash - yarn start - ``` - -This will start the UI on `http://localhost:3000`, where you can interact with the decision tree builder. - -## Visual Decision Tree Builder - -The UI allows you to visually build decision trees. Once a decision tree is created, you can save it to a configuration file. - -1. **Add Config to `config.json`**: - - After building the tree in the UI, save the configuration to the file `dsp-re-ui/example/tree/source_dir/config.json`. - -2. **Run the Model with VSCode**: - - You can use VSCode to launch the decision tree model as an endpoint based on the config file. - -3. **Visualize and Test in Jupyter Notebook**: - - Alternatively, you can use the notebook `dsp-re-ui/example/tree/test.ipynb` to visualize the decision tree (make sure **Graphviz** is installed) and test it with batch data on the model. - -## Running the Project Manually - -If you'd prefer to run the project manually, you can use the following command: - -```bash -MODEL_PREFIX="dsp-re-ui/example/tree" MODEL_RELATIVE_PATH="source_dir" MODEL_VERSION="0.0.0" uvicorn spockflow.inference.server.asgi:app --reload -``` - -Make sure to replace the relevant paths if needed, and this will start the backend server for your decision tree model. - -## License - -This project is licensed under the MIT License - see the [LICENSE](../LICENSE) file for details. - ---- - -Now, the README includes options for **Yarn** alongside the npm instructions, offering flexibility for users who prefer Yarn as their package manager. \ No newline at end of file diff --git a/dsp-re-ui/components.json b/dsp-re-ui/components.json deleted file mode 100644 index 3aedb46..0000000 --- a/dsp-re-ui/components.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "$schema": "https://ui.shadcn.com/schema.json", - "style": "new-york", - "rsc": false, - "tsx": true, - "tailwind": { - "config": "tailwind.config.js", - "css": "src/index.css", - "baseColor": "zinc", - "cssVariables": true, - "prefix": "" - }, - "aliases": { - "components": "@/components", - "utils": "@/lib/utils", - "ui": "@/components/ui", - "lib": "@/lib", - "hooks": "@/hooks" - }, - "iconLibrary": "lucide" - } - \ No newline at end of file diff --git a/dsp-re-ui/craco.config.js b/dsp-re-ui/craco.config.js deleted file mode 100644 index 533f8e9..0000000 --- a/dsp-re-ui/craco.config.js +++ /dev/null @@ -1,18 +0,0 @@ -const CracoAlias = require("craco-alias"); - -module.exports = { - plugins: [ - { - plugin: CracoAlias, - options: { - source: "tsconfig", - // baseUrl SHOULD be specified - // plugin does not take it from tsconfig - baseUrl: "./src", - /* tsConfigPath should point to the file where "baseUrl" and "paths" - are specified*/ - tsConfigPath: "./tsconfig.paths.json" - } - } - ] -}; \ No newline at end of file diff --git a/dsp-re-ui/example/tree/.gitignore b/dsp-re-ui/example/tree/.gitignore deleted file mode 100644 index 0c71fb9..0000000 --- a/dsp-re-ui/example/tree/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -source_dir/config.json -data.json \ No newline at end of file diff --git a/dsp-re-ui/example/tree/dtreevis.ipynb b/dsp-re-ui/example/tree/dtreevis.ipynb deleted file mode 100644 index e7719a6..0000000 --- a/dsp-re-ui/example/tree/dtreevis.ipynb +++ /dev/null @@ -1,1501 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install dtreeviz" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%config InlineBackend.figure_format = 'retina' # Make visualizations look good\n", - "# %config InlineBackend.figure_format = 'svg'\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from spockflow.components.treelite import Tree\n", - "from spockflow.components.treelite.core import CompiledTreeliteTree, OutputEncoding\n", - "import json\n", - "import treelite.sklearn\n", - "import numpy as np\n", - "import pandas as pd\n", - "import dtreeviz" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"source_dir/config.json\") as fp:\n", - " tree = Tree.model_validate_json(fp.read())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "node_id_mapping, leaf_nodes = CompiledTreeliteTree._get_node_id_mapping(tree.nodes)\n", - "root_nodes = CompiledTreeliteTree._identify_independent_tree_roots(tree.nodes)\n", - "trees = [\n", - " CompiledTreeliteTree._build_treelite_tree(\n", - " root_nodes=[rn],\n", - " tree=tree,\n", - " node_id_mapping=node_id_mapping,\n", - " output_encoding=OutputEncoding.ONE_HOT,\n", - " ).commit()\n", - " for rn in root_nodes\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "sklearn_tree = treelite.sklearn.export_model(trees[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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} - ], - "source": [ - "viz_model.view(scale=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "spockappdev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/dsp-re-ui/example/tree/source_dir/inference.py b/dsp-re-ui/example/tree/source_dir/inference.py deleted file mode 100644 index 7d2464d..0000000 --- a/dsp-re-ui/example/tree/source_dir/inference.py +++ /dev/null @@ -1,70 +0,0 @@ -import os -import json -from io import BytesIO -import pandas as pd -from spockflow.inference.config.loader.base import ConfigManager -from spockflow.inference.io.responses import CSVResponse, JSONResponse -from spockflow.inference.io.encoders import ( - content_types, -) - - -def combine_to_df(result: dict): - return pd.concat([result["results"], result["paths"]], axis=1) - - -def encode_csv(result: dict): - result = combine_to_df(result) - return CSVResponse(content=result.to_csv(header=True, index=False)) - - -def encode_json(result: dict): - - result = combine_to_df(result) - res = result.to_dict(orient="records") - if len(res) == 1: - res = res[0] - return JSONResponse(res) - - -encoders = {} -encoders[content_types.JSON] = encode_json -encoders[content_types.CSV] = encode_csv -encoders[content_types.ALL] = encoders[content_types.JSON] - - -def decode_json(data: bytes): - return pd.json_normalize(json.loads(data)) - - -def decode_csv(data: bytes): - return pd.read_csv(BytesIO(data)) - - -decoders = {} -decoders[content_types.JSON] = decode_json -decoders[content_types.CSV] = decode_csv - - -class ParameterStoreConfigManager(ConfigManager): - def get_latest_version(self, model_name: str) -> "str": - return "0.0.0" - - def get_config(self, model_name: str, model_version: str): - with open(os.path.join(os.path.split(__file__)[0], "config.json")) as fp: - config = json.load(fp) - - return {"tree": config} - - def save_to_config( - self, - model_name: str, - model_version: str, - namespace: str, - config, - key, - ): - raise NotImplementedError("Cannot yet save to the parameter store") - - -model_config_cls = ParameterStoreConfigManager diff --git a/dsp-re-ui/example/tree/source_dir/main.py b/dsp-re-ui/example/tree/source_dir/main.py deleted file mode 100644 index efe9465..0000000 --- a/dsp-re-ui/example/tree/source_dir/main.py +++ /dev/null @@ -1,25 +0,0 @@ -import typing -import numpy as np -import pandas as pd -from spockflow.core import initialize_spock_module -from spockflow.components.treelite import Tree -from hamilton.function_modifiers import inject, source - -results = Tree.from_config("tree") - - -@inject( - prioritized_tree_paths=source("results.prioritized_tree_paths"), - path_keys=source("results.tree_path_keys"), -) -def paths( - prioritized_tree_paths: np.ndarray, path_keys: typing.List[str] -) -> pd.DataFrame: - return pd.DataFrame(data=prioritized_tree_paths, columns=path_keys) - - -initialize_spock_module( - __name__, - output_names=["results", "paths"], - included_modules=[], -) diff --git a/dsp-re-ui/example/tree/test.ipynb b/dsp-re-ui/example/tree/test.ipynb deleted file mode 100644 index db09025..0000000 --- a/dsp-re-ui/example/tree/test.ipynb +++ /dev/null @@ -1,101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from spockflow.inference import handler\n", - "import pandas as pd\n", - "import os\n", - "import json\n", - "import requests\n", - "\n", - "os.environ[\"MODEL_PREFIX\"] = os.path.abspath(\".\")\n", - "os.environ[\"MODEL_RELATIVE_PATH\"] = \"source_dir\"\n", - "h = handler.ServingHandler.from_model_project()\n", - "model = h.model_cache.get(None, None)\n", - "model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"data.json\") as fp:\n", - " input_data = pd.json_normalize(json.load(fp))\n", - "input_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "combined_res = model.execute(\n", - " inputs=input_data,\n", - ")\n", - "combined_res" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res_everything = model.raw_execute(\n", - " inputs=input_data, final_vars=list(model.graph.nodes.keys())\n", - ")\n", - "res_everything" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "resp = requests.post(\n", - " \"http://localhost:8000/invocations\",\n", - " data=input_data.to_json(orient=\"records\").encode(),\n", - " headers={\"accept\": \"*/*\", \"Content-Type\": \"application/json\"},\n", - ")\n", - "resjson = resp.json()\n", - "resjson" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "spock", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/dsp-re-ui/example/tree/titanic.csv b/dsp-re-ui/example/tree/titanic.csv deleted file mode 100644 index 5cc466e..0000000 --- a/dsp-re-ui/example/tree/titanic.csv +++ /dev/null @@ -1,892 +0,0 @@ -PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked -1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S -2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C -3,1,3,"Heikkinen, Miss. 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a/dsp-re-ui/public/manifest.json b/dsp-re-ui/public/manifest.json deleted file mode 100644 index 080d6c7..0000000 --- a/dsp-re-ui/public/manifest.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "short_name": "React App", - "name": "Create React App Sample", - "icons": [ - { - "src": "favicon.ico", - "sizes": "64x64 32x32 24x24 16x16", - "type": "image/x-icon" - }, - { - "src": "logo192.png", - "type": "image/png", - "sizes": "192x192" - }, - { - "src": "logo512.png", - "type": "image/png", - "sizes": "512x512" - } - ], - "start_url": ".", - "display": "standalone", - "theme_color": "#000000", - "background_color": "#ffffff" -} diff --git a/dsp-re-ui/public/robots.txt b/dsp-re-ui/public/robots.txt deleted file mode 100644 index e9e57dc..0000000 --- a/dsp-re-ui/public/robots.txt +++ /dev/null @@ -1,3 +0,0 @@ -# https://www.robotstxt.org/robotstxt.html -User-agent: * -Disallow: diff --git a/dsp-re-ui/src/components/core/App.css b/dsp-re-ui/src/components/core/App.css deleted file mode 100644 index 748abd5..0000000 --- a/dsp-re-ui/src/components/core/App.css +++ /dev/null @@ -1,2 +0,0 @@ -.App { -} diff --git a/dsp-re-ui/src/components/core/App.test.tsx b/dsp-re-ui/src/components/core/App.test.tsx deleted file mode 100644 index 2a68616..0000000 --- a/dsp-re-ui/src/components/core/App.test.tsx +++ /dev/null @@ -1,9 +0,0 @@ -import React from 'react'; -import { render, screen } from '@testing-library/react'; -import App from './App'; - -test('renders learn react link', () => { - render(); - const linkElement = screen.getByText(/learn react/i); - expect(linkElement).toBeInTheDocument(); -}); diff --git a/dsp-re-ui/src/components/core/App.tsx b/dsp-re-ui/src/components/core/App.tsx deleted file mode 100644 index df589e2..0000000 --- a/dsp-re-ui/src/components/core/App.tsx +++ /dev/null @@ -1,45 +0,0 @@ -import React from 'react'; -import './App.css'; -import Editor from '@components/editor/Editor'; -import { SidebarProvider, SidebarTrigger, SidebarInset } from "@/components/ui/sidebar" -import { AppSidebar } from '@components/sidebar/Sidebar'; -import { EditorProvider } from '@components/editor/EditorContext'; -import { - ReactFlowProvider, -} from '@xyflow/react'; -import { Toaster } from "@/components/ui/toaster" - -function App() { - return ( -
- - - - - -
- - -
- - - - - - - -
- ); -} - -export default App; - - - -{/* - -
- - -
- */} \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/CustomHandle.tsx b/dsp-re-ui/src/components/editor/CustomHandle.tsx deleted file mode 100644 index f109515..0000000 --- a/dsp-re-ui/src/components/editor/CustomHandle.tsx +++ /dev/null @@ -1,260 +0,0 @@ -// This code is largely adapted from -// https://github.com/xyflow/xyflow/blob/411115f05b4a5b9c15f366e5537baeceab7899ec/packages/react/src/components/Handle/index.tsx -import { - type HTMLAttributes, - type MouseEvent as ReactMouseEvent, - type TouchEvent as ReactTouchEvent, - type ForwardedRef, - memo, - forwardRef, - } from 'react'; - import cc from 'classcat'; - import { shallow } from 'zustand/shallow'; - import { - errorMessages, - Position, - XYHandle, - getHostForElement, - isMouseEvent, - addEdge, - type HandleProps as HandlePropsSystem, - type Connection, - type HandleType, - ConnectionMode, - OnConnect, - ConnectionState, - Optional, - } from '@xyflow/system'; - - import { useStore, useStoreApi, useNodeId, type ReactFlowState } from '@xyflow/react'; - - /** - * @expand - */ - export type HandleProps = HandlePropsSystem & - Omit, 'id'> & { - /** Callback called when connection is made */ - onConnect?: OnConnect; - }; - - const selector = (s: ReactFlowState) => ({ - connectOnClick: s.connectOnClick, - noPanClassName: s.noPanClassName, - rfId: s.rfId, - }); - - const connectingSelector = - (nodeId: string | null, handleId: string | null, type: HandleType) => (state: ReactFlowState) => { - const { connectionClickStartHandle: clickHandle, connectionMode, connection } = state; - const { fromHandle, toHandle, isValid } = connection; - const connectingTo = toHandle?.nodeId === nodeId && toHandle?.id === handleId && toHandle?.type === type; - - return { - connectingFrom: fromHandle?.nodeId === nodeId && fromHandle?.id === handleId && fromHandle?.type === type, - connectingTo, - clickConnecting: clickHandle?.nodeId === nodeId && clickHandle?.id === handleId && clickHandle?.type === type, - isPossibleEndHandle: - connectionMode === ConnectionMode.Strict - ? fromHandle?.type !== type - : nodeId !== fromHandle?.nodeId || handleId !== fromHandle?.id, - connectionInProcess: !!fromHandle, - clickConnectionInProcess: !!clickHandle, - valid: connectingTo && isValid, - }; - }; - - function HandleComponent( - { - type = 'source', - position = Position.Top, - isValidConnection, - isConnectable = true, - isConnectableStart = true, - isConnectableEnd = true, - id, - onConnect, - children, - className, - onMouseDown, - onTouchStart, - ...rest - }: HandleProps, - ref: ForwardedRef - ) { - const handleId = id || null; - const isTarget = type === 'target'; - const store = useStoreApi(); - const nodeId = useNodeId(); - const { connectOnClick, noPanClassName, rfId } = useStore(selector, shallow); - const { - connectingFrom, - connectingTo, - clickConnecting, - isPossibleEndHandle, - connectionInProcess, - clickConnectionInProcess, - valid, - } = useStore(connectingSelector(nodeId, handleId, type), shallow); - if (!nodeId) { - store.getState().onError?.('010', errorMessages['error010']()); - } - - const onConnectExtended = (params: Connection) => { - const { defaultEdgeOptions, onConnect: onConnectAction, hasDefaultEdges } = store.getState(); - - const edgeParams = { - ...defaultEdgeOptions, - ...params, - }; - if (hasDefaultEdges) { - const { edges, setEdges } = store.getState(); - setEdges(addEdge(edgeParams, edges)); - } - - onConnectAction?.(edgeParams); - onConnect?.(edgeParams); - }; - - const onPointerDown = (event: ReactMouseEvent | ReactTouchEvent) => { - if (!nodeId) { - return; - } - - const isMouseTriggered = isMouseEvent(event.nativeEvent); - - if ( - isConnectableStart && - ((isMouseTriggered && (event as ReactMouseEvent).button === 0) || !isMouseTriggered) - ) { - const currentStore = store.getState(); - - XYHandle.onPointerDown(event.nativeEvent, { - autoPanOnConnect: currentStore.autoPanOnConnect, - connectionMode: currentStore.connectionMode, - connectionRadius: currentStore.connectionRadius, - domNode: currentStore.domNode, - nodeLookup: currentStore.nodeLookup, - lib: currentStore.lib, - isTarget, - handleId, - nodeId, - flowId: currentStore.rfId, - panBy: currentStore.panBy, - cancelConnection: currentStore.cancelConnection, - onConnectStart: currentStore.onConnectStart, - onConnectEnd: currentStore.onConnectEnd, - updateConnection: currentStore.updateConnection, - onConnect: onConnectExtended, - isValidConnection: isValidConnection || currentStore.isValidConnection, - getTransform: () => store.getState().transform, - getFromHandle: () => store.getState().connection.fromHandle, - autoPanSpeed: currentStore.autoPanSpeed, - }); - } - - if (isMouseTriggered) { - onMouseDown?.(event as ReactMouseEvent); - } else { - onTouchStart?.(event as ReactTouchEvent); - } - }; - - const onClick = (event: ReactMouseEvent) => { - const { - onClickConnectStart, - onClickConnectEnd, - connectionClickStartHandle, - connectionMode, - isValidConnection: isValidConnectionStore, - lib, - rfId: flowId, - nodeLookup, - connection: connectionState, - } = store.getState(); - - if (!nodeId || (!connectionClickStartHandle && !isConnectableStart)) { - return; - } - - if (!connectionClickStartHandle) { - onClickConnectStart?.(event.nativeEvent, { nodeId, handleId, handleType: type }); - store.setState({ connectionClickStartHandle: { nodeId, type, id: handleId } }); - return; - } - - const doc = getHostForElement(event.target as HTMLElement); - const isValidConnectionHandler = isValidConnection || isValidConnectionStore; - const { connection, isValid } = XYHandle.isValid(event.nativeEvent, { - handle: { - nodeId, - id: handleId, - type, - }, - connectionMode, - fromNodeId: connectionClickStartHandle.nodeId, - fromHandleId: connectionClickStartHandle.id || null, - fromType: connectionClickStartHandle.type, - isValidConnection: isValidConnectionHandler, - flowId, - doc, - lib, - nodeLookup, - }); - - if (isValid && connection) { - onConnectExtended(connection); - } - - const connectionClone = structuredClone(connectionState) as Optional; - delete connectionClone.inProgress; - connectionClone.toPosition = connectionClone.toHandle ? connectionClone.toHandle.position : null; - onClickConnectEnd?.(event as unknown as MouseEvent, connectionClone); - - store.setState({ connectionClickStartHandle: null }); - }; - - return ( -
- {children} -
- ); - } - - - export const Handle = memo(forwardRef(HandleComponent)); \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/Editor.tsx b/dsp-re-ui/src/components/editor/Editor.tsx deleted file mode 100644 index 2fe0980..0000000 --- a/dsp-re-ui/src/components/editor/Editor.tsx +++ /dev/null @@ -1,105 +0,0 @@ -import React, { useCallback, useEffect, useState } from 'react'; - -import { - Background, - ReactFlow, - useNodesState, - useEdgesState, - addEdge, - MiniMap, - Controls, - useReactFlow, - OnConnectEnd, - Connection, -} from '@xyflow/react'; -import axios from 'axios'; -import {addNode, defaultLeafNode, linkNodeToParent} from './util' -import {Node, Edge} from './types' -import {useFeatures, useEdges, useNodes} from './EditorContext' -import { nodeTypes } from './nodes'; -import { edgeTypes } from './edges'; -import { v4 as uuidv4 } from 'uuid'; - - - -export default function NodeEditor({projectId}: {projectId: string}) { - const [nodes, setNodes, onNodesChange] = useNodes(); - const [edges, setEdges, onEdgesChange] = useEdges(); - // const [features, setFeatures] = useFeatures(); - const { screenToFlowPosition } = useReactFlow(); - - // const fetchProjectNodes = useCallback((projectId: string)=> { - // axios.get(`/api/projects/${projectId}/nodes`) - // .then(response=>formatNodes(response.data)) - // .then(({nodes, edges, features})=> { - // setFeatures(features) - // setNodes((curr)=>nodes) - // setEdges((curr)=>edges) - // }) - // .catch(error => { - // console.error(error); - // }) - // }, [setNodes, setEdges]) - - // useEffect(() => { - // fetchProjectNodes(projectId) - // }, [projectId]); - - - const onConnect = useCallback( - ({ source, sourceHandle, target }: Edge | Connection) => setEdges( - (edges) => { - console.log({nodes}) - return linkNodeToParent( - target, - { parentNodeId: source, parentHandle: sourceHandle!, parentNode: nodes.find(v => v.id === source) }, - edges - )}), - [nodes] - ); - - const onConnectEnd = useCallback( - (event, connectionState) => { - if (connectionState.isValid) { return } - if (connectionState.fromHandle?.type === "target") {return} - const { clientX, clientY } = - 'changedTouches' in event ? event.changedTouches[0] : event; - - setEdges((eds) => { - const [newNodes, newEdges] = addNode( - screenToFlowPosition({ - x: clientX, - y: clientY, - }), - uuidv4(), - defaultLeafNode, - [], - eds, - {parentNodeId: connectionState.fromNode!.id, parentHandle: connectionState.fromHandle!.id!}, - ) - setNodes((nds) => [...nds, ...newNodes]) - return newEdges; - }) - }, - [screenToFlowPosition], - ); - - return ( - - - - - - ); -} diff --git a/dsp-re-ui/src/components/editor/EditorContext.tsx b/dsp-re-ui/src/components/editor/EditorContext.tsx deleted file mode 100644 index cec4d06..0000000 --- a/dsp-re-ui/src/components/editor/EditorContext.tsx +++ /dev/null @@ -1,152 +0,0 @@ -import React, { createContext, useReducer, useContext, Dispatch, useState, SetStateAction, useEffect, useCallback } from 'react'; -import { - useNodesState, - useEdgesState, - ReactFlowProvider, - OnNodesChange, - OnEdgesChange, - } from '@xyflow/react'; -import {Node, Edge, ProjectMetadata, TreeOutput} from './types' - - - -const defaultTreeOutput: TreeOutput = { - data: [["Allow", "Default Action"], ["Alert", "A rule suggests that there is an alert"], ["Block", "This is more serious and must be blocked"]], - columns: ["Action", "Description"] -} - -interface EditorContextState { - features: string[]; - setFeatures: React.Dispatch>; - nodes: Node[]; - setNodes: Dispatch>; - onNodesChange: OnNodesChange; - edges: Edge[]; - setEdges: Dispatch>; - onEdgesChange: OnEdgesChange; - leafOrder: string[]; - setLeafOrder: Dispatch>; - updateLeafOrder: (nodeId: string, newIndex: number) => void; - metadata: ProjectMetadata; - setMetadata: React.Dispatch>; - treeOutput: TreeOutput; - setTreeOutput: React.Dispatch>; - - } -export const EditorContext = createContext({ - features: [], - setFeatures: (ev)=>{}, - nodes: [], - setNodes: (ev)=>{}, - onNodesChange: (ev)=>{}, - edges: [], - setEdges: (ev)=>{}, - onEdgesChange: (ev)=>{}, - leafOrder: [], - setLeafOrder: (ev)=>{}, - updateLeafOrder: (ev)=>{}, - metadata: { - name: "", - description: "", - }, - setMetadata: (ev)=>{}, - treeOutput: defaultTreeOutput, - setTreeOutput: (ev)=>{}, -}); - - -export const EditorProvider: React.FC<{ children: React.ReactNode }> = ({ children }) => { - const [features, setFeatures] = useState([]); - const [nodes, setNodes, onNodesChange] = useNodesState([]); - const [edges, setEdges, onEdgesChange] = useEdgesState([]); - const [leafOrder, setLeafOrder] = useState([]); - const [treeOutput, setTreeOutput] = useState(defaultTreeOutput); - const [metadata, setMetadata] = React.useState({ - name: "", - description: "", - }); - - useEffect(() => { - setLeafOrder(curr => { - const currentLeafs: Set = new Set( - nodes - .filter(n => n.data.node_type === "leaf") - .map(n => n.id) - ); - - return [ - ...curr.filter(leaf => currentLeafs.delete(leaf)), - ...Array.from(currentLeafs) - ]; - }); - }, [nodes]); - - const updateLeafOrder = useCallback((nodeId: string, newIndex: number)=>{ - setLeafOrder(curr => { - const currIndex = curr.indexOf(nodeId); - if (currIndex === -1) { - console.warn(`Could not shift index of ${nodeId} to ${nodeId}`) - return curr - } - const updatedOrder = [...curr]; - updatedOrder.splice(currIndex, 1); - updatedOrder.splice(newIndex, 0, nodeId); - return updatedOrder; - }) - }, []) - - return ( - - - {children} - - ); -}; - -export const useFeatures: ()=>[EditorContextState["features"], EditorContextState["setFeatures"]] = () => { - const {features, setFeatures} = useContext(EditorContext); - return [features, setFeatures]; -}; -export const useNodes: ()=>[EditorContextState["nodes"], EditorContextState["setNodes"], EditorContextState["onNodesChange"]] = () => { - const {nodes, setNodes, onNodesChange} = useContext(EditorContext); - return [nodes, setNodes, onNodesChange]; -}; -export const useEdges: ()=>[EditorContextState["edges"], EditorContextState["setEdges"], EditorContextState["onEdgesChange"]] = () => { - const {edges, setEdges, onEdgesChange} = useContext(EditorContext); - return [edges, setEdges, onEdgesChange]; -}; -export const useLeafOrder: ()=>[EditorContextState["leafOrder"], EditorContextState["updateLeafOrder"]] = () => { - const {leafOrder, updateLeafOrder} = useContext(EditorContext); - return [leafOrder, updateLeafOrder]; -}; - -export const useProjectMetadata: ()=>[EditorContextState["metadata"], EditorContextState["setMetadata"]] = () => { - const {metadata, setMetadata} = useContext(EditorContext); - return [metadata, setMetadata]; -}; - -export const useTreeOutput: ()=>[EditorContextState["treeOutput"], EditorContextState["setTreeOutput"]] = () => { - const {treeOutput, setTreeOutput} = useContext(EditorContext); - return [treeOutput, setTreeOutput,]; -}; \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/LabeledEdge.tsx b/dsp-re-ui/src/components/editor/LabeledEdge.tsx deleted file mode 100644 index 4bf881d..0000000 --- a/dsp-re-ui/src/components/editor/LabeledEdge.tsx +++ /dev/null @@ -1,146 +0,0 @@ -import React, { type FC, memo, useMemo } from 'react'; -import { - getSmoothStepPath, - EdgeLabelRenderer, - BaseEdge, - type EdgeProps, - Position, -} from '@xyflow/react'; - -import {type Edge} from "./types"; -import { useNodes } from './EditorContext'; -import {OP_SYMBOL_MAP} from "./util"; - -// this is a little helper component to render the actual edge label -function EdgeLabel({ transform, label }: { transform: string; label: string }) { - return ( - - {label} - - ); -} - - -const DEFAULT_SYMBOL = "โˆ…" - -// const getThresholdLabelList = (thresholds?: number[]) => { -// if (!thresholds) { return [] } -// if (thresholds.length === 1) { -// return [`>= ${thresholds[0]}`, `< ${thresholds[0]}`] -// } -// return thresholds.map((v,i)=>{ -// const v_n = thresholds[i+1]; -// if (v_n === undefined) { return `< ${thresholds[0]} | >= ${thresholds[thresholds.length-1]}`} -// return `>= ${v} & < ${v_n}` - -// }) -// } - - -const getThresholdLabelList = (thresholds?: number[]) => { - if (!thresholds) { return [] } - return [ - `< ${thresholds[0]}`, - ...thresholds.map((v,i)=>{ - const v_n = thresholds[i+1]; - if (v_n === undefined) { return `โ‰ฅ ${v}`} - return `โ‰ฅ ${v} & < ${v_n}` - }) - ]; -} - - -const LabeledEdge: FC< - EdgeProps -> = ({ - id, - source, - sourceX, - sourceY, - targetX, - targetY, - labelStyle, - labelShowBg, - labelBgStyle, - labelBgPadding, - labelBgBorderRadius, - style, - sourcePosition = Position.Bottom, - targetPosition = Position.Top, - markerEnd, - markerStart, - pathOptions, - interactionWidth, - data, -}) => { - const [nodes] = useNodes(); - - const nodeLabel = useMemo(()=>{ - const currNode = nodes.find((v)=>v.id == source); - if (currNode == undefined) { return DEFAULT_SYMBOL } - let labels: string[] = ["true", "false"]; - if (currNode.data.node_type === "numerical_test_node") { - labels = [`${OP_SYMBOL_MAP[currNode.data.comparison_op]} ${currNode.data.threshold}`, "otherwise"]; - } else if (currNode.data.node_type === "categorical_test_node") { - labels = [`{${currNode.data.category_list}}`, "otherwise"]; - } else if (currNode.data.node_type === "numerical_range_test_node") { - const nodeData = currNode.data; - labels = getThresholdLabelList(nodeData.thresholds); - } - return (data?.sourceIndex !== undefined && labels[data?.sourceIndex]) || DEFAULT_SYMBOL; - }, [nodes, source, data]) - - - const [edgePath, labelX, labelY] = getSmoothStepPath({ - sourceX, - sourceY, - sourcePosition, - targetX, - targetY, - targetPosition, - borderRadius: 0, // Make this square - offset: pathOptions?.offset, - }); - - return ( - <> - - - - {data?.sourceIndex != null && ( - - )} - - - ); -}; - -export default LabeledEdge; \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/LeftRightNode.tsx b/dsp-re-ui/src/components/editor/LeftRightNode.tsx deleted file mode 100644 index 0310613..0000000 --- a/dsp-re-ui/src/components/editor/LeftRightNode.tsx +++ /dev/null @@ -1,38 +0,0 @@ -import { FunctionComponent, useMemo } from 'react'; -import { Handle, Position } from '@xyflow/react'; -import { Node } from './types'; -import { getLabel } from './util'; -import { useFeatures } from './EditorContext'; - - -export const LeftRightNode: FunctionComponent & {isConnectable: boolean}> = ({ - data, - isConnectable, - targetPosition = Position.Top, -}) => { - const [features, setFeatures] = useFeatures(); - const nodeLabel = useMemo(()=>getLabel(data,features), [features, data]) - - return ( - <> - - {nodeLabel} - {/* - */} - - - - ); -} \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/OutputNode.tsx b/dsp-re-ui/src/components/editor/OutputNode.tsx deleted file mode 100644 index 5e96b61..0000000 --- a/dsp-re-ui/src/components/editor/OutputNode.tsx +++ /dev/null @@ -1,19 +0,0 @@ -import { FunctionComponent, useMemo } from 'react'; -import { Handle, Position } from '@xyflow/react'; -import { Node } from './types'; -import { getLabel } from './util'; - - -export const OutputNode: FunctionComponent & {isConnectable: boolean}> = ({ - data, - isConnectable, - targetPosition = Position.Top, -}) => { - const nodeLabel = useMemo(()=>getLabel(data,[]), [data]) - return ( - <> - - {nodeLabel} - - ); -} \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/ProcessNode.tsx b/dsp-re-ui/src/components/editor/ProcessNode.tsx deleted file mode 100644 index 076208f..0000000 --- a/dsp-re-ui/src/components/editor/ProcessNode.tsx +++ /dev/null @@ -1,29 +0,0 @@ -import { FunctionComponent, useMemo } from 'react'; -import { Handle, Position } from '@xyflow/react'; -import { Node } from './types'; -import { getLabel } from './util'; -import { useFeatures } from './EditorContext'; - - -export const ProcessNode: FunctionComponent & {isConnectable: boolean}> = ({ - data, - isConnectable, - targetPosition = Position.Top, -}) => { - const [features, setFeatures] = useFeatures(); - const nodeLabel = useMemo(()=>getLabel(data,features), [features, data]) - - return ( - <> - - {nodeLabel} - - - ); -} \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/edges.ts b/dsp-re-ui/src/components/editor/edges.ts deleted file mode 100644 index 3b20e4b..0000000 --- a/dsp-re-ui/src/components/editor/edges.ts +++ /dev/null @@ -1,10 +0,0 @@ -import { - type EdgeTypes, -} from '@xyflow/react'; -import LabeledEdge from "./LabeledEdge"; - - - -export const edgeTypes: EdgeTypes = { - labeledEdge: LabeledEdge, - }; \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/nodes.ts b/dsp-re-ui/src/components/editor/nodes.ts deleted file mode 100644 index acbf5ea..0000000 --- a/dsp-re-ui/src/components/editor/nodes.ts +++ /dev/null @@ -1,10 +0,0 @@ -import { LeftRightNode } from "./LeftRightNode"; -import { ProcessNode } from "./ProcessNode" -import { OutputNode } from "./OutputNode"; -// import { Node } from "@xyflow/react" - -export const nodeTypes = { - processNode: ProcessNode, - leftRightNode: LeftRightNode, - out: OutputNode - }; \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/types.ts b/dsp-re-ui/src/components/editor/types.ts deleted file mode 100644 index 3c67cf5..0000000 --- a/dsp-re-ui/src/components/editor/types.ts +++ /dev/null @@ -1,83 +0,0 @@ -import { - Node as xyNode, - Edge as xyEdge, -} from '@xyflow/react'; - -type BaseNodeData = { - data_count?: number; - sum_hess?: number; - gain?: number; -}; - -export interface withChildren { - children: string[]; -} - -export interface withPosition { - position: {x: number, y: number}; -} - -export type ForkNodeData = BaseNodeData & { - split_feature_id: number; - default_left: boolean; -}; - - -export type RangeNodeData = ForkNodeData & { - node_type: "numerical_range_test_node"; - thresholds: number[]; -}; - -export type NumericalNodeData = ForkNodeData & { - node_type: "numerical_test_node"; - comparison_op: "<=" | "<" | "==" | ">" | ">="; - threshold: number; -}; - -export type CategoricalNodeData = ForkNodeData & { - node_type: "categorical_test_node"; - category_list_right_child: boolean; - category_list: number[]; -}; - -export type LeafNodeData = Partial & { - node_type: "leaf"; - leaf_value: number; -}; - -export type TreeNode = (NumericalNodeData | RangeNodeData | CategoricalNodeData | LeafNodeData) - -export interface TreeOutput { - data: string[][] - columns: string[] -} - -export type BranchingNodeDataWithChildren = (NumericalNodeData & withChildren) | (CategoricalNodeData & withChildren) | (RangeNodeData & withChildren) -export type TreeNodeWithChildren = BranchingNodeDataWithChildren | LeafNodeData; -export interface SourceData { - features: string[]; - nodes: Record>; - leafOrder?: string[]; - metadata?: ProjectMetadata; - treeOutput?: TreeOutput; -} - -export interface EdgeData extends Record { - sourceIndex: number -} - -export type NodeData = T & {}; - -export type Node = xyNode>; -export type Edge = xyEdge; - -export interface ParentIdentifier { - parentNodeId: string; - parentHandle: string; - parentNode?: Node; -} - -export interface ProjectMetadata { - name: string; - description: string; -} \ No newline at end of file diff --git a/dsp-re-ui/src/components/editor/util.ts b/dsp-re-ui/src/components/editor/util.ts deleted file mode 100644 index 1776fbc..0000000 --- a/dsp-re-ui/src/components/editor/util.ts +++ /dev/null @@ -1,476 +0,0 @@ -import { - Edge, Node, SourceData, TreeNode, LeafNodeData, - NumericalNodeData, CategoricalNodeData, ForkNodeData, - NodeData, ParentIdentifier, withChildren, TreeOutput, - ProjectMetadata, withPosition, BranchingNodeDataWithChildren , - TreeNodeWithChildren -} from "./types"; - -import { - Connection, - getOutgoers, - MarkerType, -} from '@xyflow/react'; -import Dagre from '@dagrejs/dagre'; - -import ELK, {ElkExtendedEdge, ElkNode} from 'elkjs'; - -const elk = new ELK(); - - -const elkOptions = { - "elk.algorithm": "layered", - "elk.direction": "DOWN", - "elk.layered.crossingMinimization.strategy": "LAYER_SWEEP", - "elk.spacing.nodeNode": "25", - "elk.layered.spacing.edgeNodeBetweenLayers": "25", - "elk.layered.considerModelOrder.strategy": "NODES_AND_EDGES", - "elk.layered.crossingMinimization.semiInteractive": "true", - "elk.portConstraints": "FIXED_SIDE", - "elk.layered.wrapping.strategy": "SINGLE_EDGE", - "elk.ordering.strategy": "MANUAL", - "elk.layered.nodePlacement.strategy": "BRANDES_KOEPF", - "elk.layered.nodePlacement.favorStraightEdges": "true", - "elk.layered.nodePlacement.bk.fixedAlignment": "BALANCED", -}; - - -export const OP_SYMBOL_MAP: Record = { - "<=": "โ‰ค", - "<": "<", - "==": "=", - ">": ">", - ">=": "โ‰ฅ", -} - - -function sortedIndex(array: number[], value: number, edges: Edge[]) { - var low = 0, - high = array.length; - - while (low < high) { - var mid = low + high >>> 1; - if (edges[array[mid]].data!.sourceIndex < value) low = mid + 1; - else high = mid; - } - return low; -} - -function getNodeOrder(nodes: Node[], edges: Edge[]) { - const order = new Map(); - const parentChildMap = new Map(); - edges.forEach((e, i) => { - if (!parentChildMap.has(e.source)) { - parentChildMap.set(e.source, [i]); - } else { - const arr = parentChildMap.get(e.source)!; - const insertIdx = sortedIndex(arr, e.data!.sourceIndex, edges) - arr.splice(insertIdx, 0, i); - } - }) - let counter = 0; - - function visit(nodeId: string) { - parentChildMap.get(nodeId)?.forEach(childIdx => { - visit(edges[childIdx]!.target); - order.set(nodeId, counter++); - }); - } - - // Find all root nodes (nodes with no incoming edges) - const rootNodes = nodes.filter(n => !edges.some(e => e.target === n.id)); - - // Visit each root node - rootNodes.forEach(root => visit(root.id)); - - return {order, rootNodes}; -} - -export const formatTree = async (nodes: Node[], edges: Edge[]) => { - const g = new Dagre.graphlib.Graph().setDefaultEdgeLabel(() => ({})); - g.setGraph({ rankdir: "TB" }); - - edges.forEach((edge) => g.setEdge(edge.source, edge.target)); - nodes.forEach((node) => - g.setNode(node.id, { - ...node, - width: node.measured?.width ?? 0, - height: node.measured?.height ?? 0, - }), - ); - - Dagre.layout(g); - - return nodes.map((node) => { - const position = g.node(node.id); - // We are shifting the dagre node position (anchor=center center) to the top left - // so it matches the React Flow node anchor point (top left). - const x = position.x - (node.measured?.width ?? 0) / 2; - const y = position.y - (node.measured?.height ?? 0) / 2; - - return { ...node, position: { x, y } }; - }) -}; - -export const formatTreeELK = async (nodes: Node[], edges: Edge[]) => { - // Pre-process to determine node ordering - const {order:nodeOrder, rootNodes} = getNodeOrder(nodes, edges); - - const graph = { - id: 'root', - layoutOptions: elkOptions, - children: - [ - ...nodes.map(node => ({ - ...node, - layoutOptions: { - "elk.position": nodeOrder.has(node.id) ? - `(${nodeOrder.get(node.id)! * 100},0)` : undefined - }, - width: node.width || node.initialWidth || 172, - height: node.height || node.initialHeight || 36, - } as ElkNode)), - { - id: "__*root*__", - layoutOptions: { - "elk.position": "(0,0)" - }, - width: 1, - height: 1, - } - ], - edges: [ - ...edges.map(edge => ({ - id: edge.id, - sources: [`${edge.source}`], - targets: [`${edge.target}`] - })), - ...rootNodes.map(n => ({ - id: `__*root*__${n.id}`, - sources: [`__*root*__`], - targets: [`${n.id}`] - })), - - ] - }; - - const layout = await elk.layout(graph); - return nodes.map(node => { - const elkNode = layout.children?.find(n => n.id === node.id); - return { - ...node, - position: { - x: (elkNode?.x || 0) - (elkNode?.width || 0)/2, - y: elkNode?.y || 0 - (elkNode?.height || 0)/2 - } - }; - }); -}; - -export const getLabel = (node: TreeNode, features: string[]): string => { - if (node.node_type === 'leaf') { - return node.leaf_value.toString(); - } - const feature = features[node.split_feature_id]; - if (feature === undefined) {return "Unselected"} - return feature; - // if (node.node_type === "numerical_test_node") { - // return `${feature} ${node.comparison_op} ${node.threshold}`; - // } - // return `${feature} in set`; -}; - -const defaultForkNode: ForkNodeData = { - split_feature_id: 0, - default_left: false, -} - -const defaultNumericalNode: NumericalNodeData = { - ...defaultForkNode, - node_type: "numerical_test_node", - comparison_op: "==", - threshold: 0, - }; - -const defaultCategoricalNode: CategoricalNodeData = { - ...defaultForkNode, - node_type: "categorical_test_node", - category_list_right_child: false, - category_list: [], -}; - -export const defaultLeafNode: LeafNodeData = { - node_type: "leaf", - leaf_value: -1, -}; - -const defaultNodeLookup: Record = { - "numerical_test_node": defaultNumericalNode, - "categorical_test_node": defaultCategoricalNode, - "leaf": defaultLeafNode, -}; - -const createsCycle = (newEdge: Connection | Edge, edges: Edge[]) => { - const hasCycle = (edge: Connection | Edge, visited = new Set()): boolean => { - if (visited.has(edge.target)) return false; - visited.add(edge.target); - - const found: Edge | undefined = edges - .filter(v=>v.source == edge.target) - .find(v => { - return (newEdge.source == v.target) || hasCycle(v, visited) - }) - return !!found; - }; - const res = hasCycle(newEdge) - return res; -} - -const getUpdatedNode = (node: Node, updates: Partial, features?: string[]): Node => { - const baseNode: TreeNode = defaultNodeLookup[updates.node_type || node.data.node_type] || defaultLeafNode; - //@ts-ignore - const updatedNodeData: NodeData = { - ...baseNode, - ...node.data, - ...updates, - }; - - return { - ...node, - data: updatedNodeData, - type: updatedNodeData.node_type === "leaf"? "out": "processNode" - }; -}; - - -const getNodeIndex = (nodeId: string, nodes: Node[]): number => - nodes.findIndex(node => node.id === nodeId); - -export const updateNodeData = (node: Node, newData: Partial, features: string[], nodes: Node[]) => { - const nodeIndex = getNodeIndex(node.id, nodes); - if (nodeIndex === -1) { - return nodes; - } - const updatedNodes = [...nodes]; - updatedNodes[nodeIndex] = getUpdatedNode(updatedNodes[nodeIndex], newData, features) - - return updatedNodes; -}; - -const createEdge = (sourceId: string, targetId: string, sourceHandle: string, sourceIndex: number): Edge => ({ - id: `${sourceId}:${targetId}`, - source: sourceId, - sourceHandle, - type: 'labeledEdge', - target: targetId, - markerEnd: { - type: MarkerType.ArrowClosed, - }, - data: { - sourceIndex - } -}); - -const removeNodeConnections = ( - nodeId: string, - parent: ParentIdentifier, - edges: Edge[] -): Edge[] => { - console.log({nodeId, parent, edges}) - const resEdges = edges.filter(edge => - (edge.target !== nodeId) // && (edge.source !== parent.parentNodeId || edge.sourceHandle !== parent.parentHandle) - ) - return resEdges; -}; - - -export const addNode = ( - position: Node["position"], - nodeId: string = `node_${Date.now()}`, - data: any = defaultLeafNode, - nodes: Node[] = [], - edges: Edge[] = [], - parent: ParentIdentifier | undefined = undefined, -): [Node[], Edge[]] => { - const newNode: Node = { - id: nodeId, - data, - position, - type: data.node_type === "leaf" ? "out" : "processNode" - }; - console.log({newNode}) - let updatedNodes = [...nodes, newNode]; - let updatedEdges = edges; - - if (parent) { - updatedEdges = linkNodeToParent( - nodeId, - parent, - removeNodeConnections(nodeId, parent, updatedEdges) - ); - } - - return [updatedNodes, updatedEdges]; -}; - -export const getNextEdge = (edges: Edge[], parentNodeId: string): number => { - const usedIndices = new Set( - edges - .filter(edge => edge.source === parentNodeId) - .map(edge => edge.data?.sourceIndex) - .filter((index): index is number => typeof index === 'number') - ); - - return usedIndices.size === 0 - ? 0 - : Array.from({ length: usedIndices.size + 1 }, (_, i) => i).find(i => !usedIndices.has(i))!; -}; - -export const linkNodeToParent = ( - nodeId: string, - parent: ParentIdentifier, - edges: Edge[] -): Edge[] => { - const newEdge = createEdge( - parent.parentNodeId, - nodeId, - parent.parentHandle, - getNextEdge(edges, parent.parentNodeId), - ); - if (createsCycle(newEdge, edges)) {return edges}; - const edgesWithoutConnections = removeNodeConnections(nodeId, parent, edges); - return [...edgesWithoutConnections, newEdge]; -}; - - -const getDefaultFeatures = (nodes: SourceData["nodes"]): string[] => { - const maxFeatureId = Math.max( - ...Object.values(nodes) - .filter((node): node is (NumericalNodeData | CategoricalNodeData) & withChildren => - 'split_feature_id' in node - ) - .map(node => node.split_feature_id), - -1 - ); - - return Array.from({ length: maxFeatureId + 1 }, (_, i) => `feature_${i}`); -}; - -const compactLeafValues = (nodes: Record): Map => { - const uniqueValues = Array.from(new Set( - Object.values(nodes) - .filter((node): node is LeafNodeData => node.node_type === 'leaf') - .filter((node): node is LeafNodeData => node.leaf_value != -1) - .map(node => node.leaf_value) - )).sort((a, b) => a - b); - - const uniqueMap = new Map(uniqueValues.map((value, index) => [value, index])); - uniqueMap.set(-1, -1); - return uniqueMap; -}; - -const validateNodes = ( - nodes: SourceData["nodes"], - features: SourceData["features"] -): void => { - // TODO - // for (const [nodeId, node] of Object.entries(nodes)) { - // if ('split_feature_id' in node && node.split_feature_id >= features.length) { - // throw new Error( - // `Node ${nodeId} references feature index ${node.split_feature_id} but only ${features.length} features exist` - // ); - // } - // } -}; - - - -export const loadState = async (data: SourceData, filename?: string): Promise<{ - nodes: Node[], - edges: Edge[], - features: string[], - leafOrder: string[], - metadata: ProjectMetadata, - treeOutput: TreeOutput -}> => { - const edges: Edge[] = []; - const valueMapping = compactLeafValues(data.nodes); - let seenPosition = false; - - const nodes: Node[] = Object.keys(data.nodes).map(nodeId=>{ - const {position, ...nodeData} = data.nodes[nodeId]; - if (position) { seenPosition = true } - const [[newNode]] = addNode( - position || {x:0,y:0}, - nodeId, - nodeData.node_type === "leaf"? {...nodeData, leaf_value:valueMapping.get(nodeData.leaf_value)||nodeData.leaf_value}:nodeData, - [], [], - undefined) - if (nodeData.node_type === 'leaf') { return newNode }; - nodeData.children.forEach((v,i) => { - edges.push(createEdge(nodeId, v, 'output', i)) - }) - return newNode; - }); - - const features = data.features ?? getDefaultFeatures(data.nodes); - return { - nodes: seenPosition?nodes: await formatTree(nodes,edges), - edges, - features, - leafOrder: data.leafOrder ?? nodes.filter(n => n.data.node_type === 'leaf').map(n => n.id), - metadata: data.metadata ?? { name: (filename||"Untitled"), description: "" }, - treeOutput: data.treeOutput ?? { data: Object.keys(valueMapping).map(v=>[]), columns:[]} - } -} - -export const exportState = ( - nodes: Node[], - edges: Edge[], - features: string[], - leafOrder: string[], - metadata: ProjectMetadata, - treeOutput: TreeOutput -): SourceData => { - const edgeMap = new Map(); - - for (const edge of edges) { - const entry = edgeMap.get(edge.source) ?? []; - const edgeIdx = edge.data!.sourceIndex!; - while (edgeIdx >= entry.length) {entry.push(null)} - entry[edgeIdx] = edge.target; - edgeMap.set(edge.source, entry); - } - - const exportNodes: SourceData["nodes"] = {}; - - nodes.forEach((node)=>{ - if (node.data.node_type === 'leaf') { - exportNodes[node.id] = ({ - ...(node.data as LeafNodeData), - position: node.position - } as LeafNodeData & withPosition) - return - } - const children = edgeMap.get(node.id) as string[]; - if (!children?.every(v=>v!==null)) { - throw new Error(`Non-leaf node ${node.id} missing children`); - } - - exportNodes[node.id] = { - ...node.data, - position: node.position, - children, - }; - }) - - validateNodes(exportNodes, features); - - return { - features, - nodes: exportNodes, - leafOrder, - metadata, - treeOutput - }; -}; \ No newline at end of file diff --git a/dsp-re-ui/src/components/sidebar/Editors.tsx b/dsp-re-ui/src/components/sidebar/Editors.tsx deleted file mode 100644 index 17d4f84..0000000 --- a/dsp-re-ui/src/components/sidebar/Editors.tsx +++ /dev/null @@ -1,820 +0,0 @@ -import React, { useMemo, useCallback, useRef, useState, useEffect } from "react"; -import { Badge } from "@/components/ui/badge"; -import { Button } from "@/components/ui/button"; -import { Checkbox } from "@/components/ui/checkbox"; -import { Input } from "@/components/ui/input"; -import { - Command, - CommandEmpty, - CommandList, - CommandGroup, - CommandInput, - CommandItem, -} from "@/components/ui/command"; -import { Command as CommandPrimitive } from "cmdk"; -import { - Popover, - PopoverContent, - PopoverTrigger, -} from "@/components/ui/popover"; -import { - Select, - SelectContent, - SelectItem, - SelectTrigger, - SelectValue, -} from "@/components/ui/select"; -import { Label } from "@/components/ui/label"; -import type { - Node, - NodeData, - TreeNode, - NumericalNodeData, - CategoricalNodeData, - LeafNodeData, - RangeNodeData, -} from "../editor/types"; -import { useFeatures, useLeafOrder, useTreeOutput } from "@components/editor/EditorContext"; -import { useNodes } from "@xyflow/react"; -import { ScrollArea } from "@radix-ui/react-scroll-area"; -import { OP_SYMBOL_MAP } from "../editor/util" - - -interface FeatureEditorProps { - selectedFeatureId: number; - onFeatureSelect: (index: number, features: string[]) => void; - label?: string; -} - -interface ConfigProps { - node: Node; - updateNode: (val: Partial, features?: string[]) => void; -} - -export function FeatureEditor({ - selectedFeatureId, - onFeatureSelect, - label = "Feature", -}: FeatureEditorProps) { - const [features, setFeatures] = useFeatures(); - const [searchValue, setSearchValue] = useState(""); - const [popoverOpen, setPopoverOpen] = useState(false); - - const showCreate = useMemo(()=> - searchValue && (features.findIndex(ft=>ft===searchValue) === -1) - , [searchValue, features]) - - const selectFeature = useCallback((ft: string) => { - if (!ft) { return } - setFeatures(fts => { - let newFts = fts; - let featureIndex = fts.indexOf(ft); - if (featureIndex === -1){ - featureIndex = features.length; - newFts = [...newFts, ft]; - } - onFeatureSelect(featureIndex, newFts); - return newFts; - }) - setPopoverOpen((state) => false) - },[onFeatureSelect]) - const selectedFeature = useMemo(()=>{ - console.log({selectedFeatureId, features}) - return features[selectedFeatureId] - } - , [selectedFeatureId, features]) - return ( -
- - - - - - - - setSearchValue(nv)} - /> - - - - {showCreate && - - {searchValue} (Create New) - } - - - - {(features||[]).map((feature, index) => ( - - {feature} - - ))} - - - - - -
- ); -} - - - -export function NumericalConfig({ - node, - updateNode, -}: ConfigProps) { - const [threshold, setThreshold] = useState(`${node.data.threshold}`); - useEffect(()=>{ - setThreshold(`${node.data.threshold}`) - },[node.data.threshold]) - const handleOnBlur = useCallback(()=>{ - - const parsedThresh = parseFloat(threshold); - if (isNaN(parsedThresh)){ - setThreshold(`${node.data.threshold}`) - return - } - updateNode({ threshold: parsedThresh }) - }, [updateNode, threshold, node.data.threshold]) - - - return ( -
- updateNode({ split_feature_id: index }, features)} - /> -
-
- - -
-
- - { - if (e.key === 'Enter') { - handleOnBlur(); - } - }} - onChange={(e) => - setThreshold(e.target.value) - } - /> -
-
- -
- updateNode({ default_left: !!checked })} - /> - -
-
- ); -} - -export function CategoricalConfig({ - node, - updateNode -}: ConfigProps) { - - const addCategory = (value: string) => { - const newCategory = parseInt(value); - if (!isNaN(newCategory) && !node.data.category_list.includes(newCategory)) { - updateNode({ - category_list: [...node.data.category_list, newCategory].sort((a, b) => a - b) - }); - } - }; - - const removeCategory = (category: number) => { - updateNode({ - category_list: node.data.category_list.filter(c => c !== category) - }); - }; - - return ( -
- updateNode({ split_feature_id: index }, features)} - /> - -
- -
- {node.data.category_list.map((category) => ( - - {category} - - - ))} -
- { - if (e.key === "Enter") { - //@ts-ignore - addCategory(e.target.value); - //@ts-ignore - e.target.value = ""; - } - }} - /> -
- -
- updateNode({ default_left: !!checked })} - /> - -
- -
- - updateNode({ category_list_right_child: !!checked }) - } - /> - -
-
- ); -} - - -export function RangeConfigOld({ - node, - updateNode, -}: ConfigProps) { - const [newThreshold, setNewThreshold] = useState(""); - const [editIndex, setEditIndex] = useState(null); - const [editValue, setEditValue] = useState(""); - - // Sort thresholds when updating - const updateThresholds = (thresholds: number[]) => { - updateNode({ - thresholds: [...thresholds].sort((a, b) => a - b) - }); - }; - - // Add a new threshold - const addThreshold = () => { - const value = parseFloat(newThreshold); - const thresholds = node.data.thresholds || []; - if (!isNaN(value) && !thresholds.includes(value)) { - updateThresholds([...thresholds, value]); - setNewThreshold(""); - } - }; - - // Remove a threshold - const removeThreshold = (threshold: number) => { - const thresholds = node.data.thresholds || []; - updateThresholds(thresholds.filter(t => t !== threshold)); - }; - - // Start editing a threshold - const startEdit = (index: number) => { - setEditIndex(index); - setEditValue(`${node.data.thresholds[index]}`); - }; - - // Save edited threshold - const saveEdit = () => { - if (editIndex !== null) { - const value = parseFloat(editValue); - if (!isNaN(value)) { - const newThresholds = [...node.data.thresholds]; - newThresholds[editIndex] = value; - updateThresholds(newThresholds); - } - setEditIndex(null); - } - }; - - // Cancel editing - const cancelEdit = () => { - setEditIndex(null); - }; - - // Handle keyboard events for editing - const handleKeyDown = (e: React.KeyboardEvent) => { - if (e.key === "Enter") { - saveEdit(); - } else if (e.key === "Escape") { - cancelEdit(); - } - }; - - return ( -
- updateNode({ split_feature_id: index }, features)} - /> - -
- - - {/* Display thresholds as ranges */} -
- {node.data.thresholds && ( -
- No thresholds defined yet. Add thresholds to create ranges. -
- )} - - {/* First range (if any thresholds exist) */} - {node.data.thresholds && ( -
- feature < - {editIndex === 0 ? ( - setEditValue(e.target.value)} - onBlur={saveEdit} - onKeyDown={handleKeyDown} - autoFocus - /> - ) : ( - - )} - -
- )} - - {/* Middle ranges */} - {(node.data.thresholds || []).slice(0, -1).map((threshold, index) => ( -
- feature โ‰ฅ - {editIndex === index ? ( - setEditValue(e.target.value)} - onBlur={saveEdit} - onKeyDown={handleKeyDown} - autoFocus - /> - ) : ( - - )} - & feature < - {editIndex === index + 1 ? ( - setEditValue(e.target.value)} - onBlur={saveEdit} - onKeyDown={handleKeyDown} - autoFocus - /> - ) : ( - - )} -
- ))} - - {/* Last range (if multiple thresholds exist) */} - {node.data.thresholds && node.data.thresholds.length > 1 && ( -
- feature โ‰ฅ - {editIndex === node.data.thresholds.length - 1 ? ( - setEditValue(e.target.value)} - onBlur={saveEdit} - onKeyDown={handleKeyDown} - autoFocus - /> - ) : ( - - )} - -
- )} -
- - {/* Add new threshold */} -
- setNewThreshold(e.target.value)} - onKeyDown={(e) => { - if (e.key === "Enter") { - addThreshold(); - } - }} - /> - -
-
- -
- updateNode({ default_left: !!checked })} - /> - -
-
- ); -} - -// ThresholdInput component for better number editing -const ThresholdInput = ({ - value, - onUpdate, - className = "w-24", -}: { - value: number; - onUpdate: (value: number) => void; - className?: string; -}) => { - const [editValue, setEditValue] = useState(`${value}`); - - // Update edit value when the prop value changes - useEffect(() => { - setEditValue(`${value}`); - }, [value]); - - // Handle blur event to validate and update - const handleOnBlur = useCallback(() => { - const parsedValue = parseFloat(editValue); - - // If invalid, reset to original value - if (isNaN(parsedValue)) { - setEditValue(`${value}`); - return; - } - - // Update with new value - onUpdate(parsedValue); - }, [editValue, value, onUpdate]); - - // Format for display - const formatValue = (val: string) => { - return val.length > 8 ? val.substring(0, 7) + '...' : val; - }; - - return ( - setEditValue(e.target.value)} - onBlur={handleOnBlur} - onKeyDown={(e) => { - if (e.key === 'Enter') { - handleOnBlur(); - } - }} - /> - ); -}; - -export function RangeConfig({ - node, - updateNode, -}: ConfigProps) { - // Ensure thresholds array exists - const thresholds = node.data.thresholds || []; - const [newThreshold, setNewThreshold] = useState(""); - - // Initialize data if missing - useEffect(() => { - if (!node.data.thresholds) { - updateNode({ thresholds: [] }); - } - }, [node.data, updateNode]); - - // Sort thresholds when updating - const updateThresholds = (newThresholds: number[]) => { - const sorted = [...newThresholds].sort((a, b) => a - b); - updateNode({ thresholds: sorted }); - }; - - // Update a threshold at specific index - const updateThresholdAt = (index: number, value: number) => { - // Check if value already exists elsewhere in the thresholds - const otherThresholds = thresholds.filter((_, i) => i !== index); - if (otherThresholds.includes(value)) { - // If duplicate, don't update - return; - } - - // Create new thresholds array with updated value - const newThresholds = [...thresholds]; - newThresholds[index] = value; - updateThresholds(newThresholds); - }; - - // Add a new threshold - const addThreshold = () => { - const value = parseFloat(newThreshold); - if (!isNaN(value) && !thresholds.includes(value)) { - updateThresholds([...thresholds, value]); - setNewThreshold(""); - } - }; - - // Remove a threshold by index - const removeThreshold = (index: number) => { - const newThresholds = [...thresholds]; - newThresholds.splice(index, 1); - updateThresholds(newThresholds); - }; - - return ( -
- updateNode({ split_feature_id: index }, features)} - /> - -
- - - {/* Display thresholds as ranges */} -
- {thresholds.length === 0 && ( -
- No thresholds defined yet. Add thresholds to create ranges. -
- )} - - {/* First range (if any thresholds exist) */} - {thresholds.length > 0 && ( -
- < - updateThresholdAt(0, value)} - /> -
- -
- )} - - {/* Middle ranges */} - {thresholds.slice(0, -1).map((threshold, index) => ( -
- โ‰ฅ - updateThresholdAt(index, value)} - /> - < - - {thresholds[index + 1].toString().length > 8 - ? thresholds[index + 1].toString().substring(0, 7) + '...' - : thresholds[index + 1]} - -
- -
- ))} - - {/* Last range (if multiple thresholds exist) */} - {thresholds.length > 1 && ( -
- โ‰ฅ - updateThresholdAt(thresholds.length - 1, value)} - /> -
- -
- )} -
- - {/* Add new threshold */} -
- setNewThreshold(e.target.value)} - onKeyDown={(e) => { - if (e.key === "Enter") { - addThreshold(); - } - }} - /> - -
-
- -
- updateNode({ default_left: !!checked })} - /> - -
-
- ); -} -interface TreeOutput { - data: string[][] - columns: string[] -} -export function LeafConfig({ - node, - updateNode -}: ConfigProps) { - const nodes = useNodes(); - - const [outputs, setOutputs] = useTreeOutput(); - const [leafOrder, updateLeafOrder] = useLeafOrder(); - const leafIndex = useMemo(() => leafOrder.indexOf(node.id), - [leafOrder, node] - ); - const numLeafNodes = useMemo(() => leafOrder.length, - [leafOrder] - ); - const selectedRow = outputs.data[node.data.leaf_value+1]; - - return ( -
-
- - { - outputs.data.length?( - { - const value = parseInt(e.target.value); - if (!isNaN(value) && value >= -1 && value < outputs.data.length) { - updateNode({ leaf_value: value }); - } - }} - /> - ):( - - ) - } - -
- {outputs.columns.map((column, i) => ( -
- {column}: - {selectedRow?.[i]} -
- ))} -
- -
-
- - { - const value = parseInt(e.target.value); - if (!isNaN(value) && value >= 0 && value < numLeafNodes) { - updateLeafOrder(node.id, value); - } - }} - /> -
-
- ); -} diff --git a/dsp-re-ui/src/components/sidebar/FileUpload.tsx b/dsp-re-ui/src/components/sidebar/FileUpload.tsx deleted file mode 100644 index 08f0a75..0000000 --- a/dsp-re-ui/src/components/sidebar/FileUpload.tsx +++ /dev/null @@ -1,152 +0,0 @@ -import React, { useContext, useRef, useState } from "react"; -import { Button } from "@/components/ui/button"; -import { - Dialog, - DialogContent, - DialogHeader, - DialogTitle, - DialogTrigger, -} from "@/components/ui/dialog"; -import { Upload } from "lucide-react"; -import { - Tooltip, - TooltipContent, - TooltipProvider, - TooltipTrigger, -} from "@/components/ui/tooltip"; -import { useToast } from "@/hooks/use-toast"; -import { Loader2 } from "lucide-react"; -import { loadState } from "@components/editor/util"; -import { SourceData } from "@components/editor/types"; -import { EditorContext } from "@components/editor/EditorContext"; -import { DialogDescription } from "@radix-ui/react-dialog"; - -interface JsonData { - [key: string]: unknown; -} - -const FileUploadDialog = () => { - const [isLoading, setIsLoading] = useState(false); - const [dialogOpen, setDialogOpen] = useState(false); - const fileInputRef = useRef(null); - const { toast } = useToast(); - const {setEdges, setNodes, setFeatures, setMetadata, setTreeOutput, setLeafOrder} = useContext(EditorContext); - - const handleDragOver = (e: React.DragEvent): void => { - e.preventDefault(); - e.stopPropagation(); - }; - - const handleDrop = (e: React.DragEvent): void => { - e.preventDefault(); - e.stopPropagation(); - const file = e.dataTransfer.files[0]; - if (file?.type === "application/json") { - handleFileUpload(file); - } - }; - - const handleFileInput = (e: React.ChangeEvent): void => { - const file = e.target.files?.[0]; - if (file) handleFileUpload(file); - }; - - const handleButtonClick = (): void => { - fileInputRef.current?.click(); - }; - - const handleFileUpload = (file: File): void => { - const reader = new FileReader(); - setIsLoading(true); - - const readerPromise = new Promise>((resolve, reject) => { - reader.onload = resolve; - reader.onerror = reject; - }) - readerPromise - .then((evt) => JSON.parse(evt.target?.result as string) as SourceData) - .then((data: SourceData)=>loadState(data, file.name)) - .then(({nodes, edges, features, leafOrder, metadata, treeOutput})=>{ - setEdges(edges) - setNodes(nodes) - setFeatures(features) - setMetadata(metadata) - setTreeOutput(treeOutput) - setLeafOrder(leafOrder) - }).then(()=>{ - toast({ - title: "Success", - description: "JSON file loaded successfully", - }); - setDialogOpen(false) - }) - .catch(err=>{ - console.error("Error parsing JSON:", err); - toast({ - variant: "destructive", - title: "Error", - description: "Failed to load JSON file", - }); - }).finally(()=>{ - setIsLoading(false); - }) - reader.readAsText(file); - }; - - return ( - - - - - - - - - -

Upload new project

- - - - - - Upload JSON File - - - Choose a json file to download to begin editing - - - {isLoading && ( -
- -
- )} -
- - -
- - - ); -}; - -export default FileUploadDialog; \ No newline at end of file diff --git a/dsp-re-ui/src/components/sidebar/ListItem.tsx b/dsp-re-ui/src/components/sidebar/ListItem.tsx deleted file mode 100644 index e54d776..0000000 --- a/dsp-re-ui/src/components/sidebar/ListItem.tsx +++ /dev/null @@ -1,115 +0,0 @@ -import React, { useState } from 'react'; -import { DragDropContext, Droppable, Draggable, DropResult } from '@hello-pangea/dnd'; -import { Button } from '@/components/ui/button'; // Assuming you're using ShadCN UI library - -// Define the type for each item -type Item = { - id: string; - name: string; -}; - -// Define the props for the ListItem component -interface ListItemProps { - item: Item; - index: number; - onDelete: (id: string) => void; -} - -// ListItem Component -const ListItem: React.FC = ({ item, index, onDelete }) => ( - - {(provided) => ( -
- - {/* Item name */} -
{item.name}
- - {/* Delete button */} - -
- )} - -); - -interface OrderedItem { - id: string, name: string -} -// Define the props for the ListManager component -interface ListManagerProps { - items: OrderedItem[], - handleAdd: ()=>void, - handleDelete: (id: string)=>void, - handleReorder: (reorderedItems:OrderedItem[])=>void, -} - -// ListManager Component -const ListManager: React.FC = ({items, handleAdd, handleDelete, handleReorder}) => { -// const [items, setItems] = useState([ -// { id: '1', name: 'Item 1' }, -// { id: '2', name: 'Item 2' }, -// { id: '3', name: 'Item 3' }, -// ]); - -// // Handle item deletion -// const handleDelete = (id: string): void => { -// setItems((prevItems) => prevItems.filter(item => item.id !== id)); -// }; - -// // Handle item addition -// const handleAdd = (): void => { -// const newItem: Item = { id: String(items.length + 1), name: `Item ${items.length + 1}` }; -// setItems((prevItems) => [...prevItems, newItem]); -// }; - - const handleOnDragEnd = (result: DropResult): void => { - const { source, destination } = result; - - if (!destination || source.index === destination.index) { - return; - } - - const reorderedItems = Array.from(items); - const [removed] = reorderedItems.splice(source.index, 1); - reorderedItems.splice(destination.index, 0, removed); - - handleReorder(reorderedItems); - }; - - return ( -
- - - {(provided) => ( -
- {items.map((item, index) => ( - - ))} - {provided.placeholder} -
- )} - - - - -
- ); -}; - -export default ListManager; diff --git a/dsp-re-ui/src/components/sidebar/ProjectEditor.tsx b/dsp-re-ui/src/components/sidebar/ProjectEditor.tsx deleted file mode 100644 index f0e4249..0000000 --- a/dsp-re-ui/src/components/sidebar/ProjectEditor.tsx +++ /dev/null @@ -1,349 +0,0 @@ -import React, { useCallback, useRef } from "react"; -import { Button } from "@/components/ui/button"; -import { Input } from "@/components/ui/input"; -import { Textarea } from "@/components/ui/textarea"; -import { useToast } from "@/hooks/use-toast" -import { - Tooltip, - TooltipContent, - TooltipProvider, - TooltipTrigger, -} from "@/components/ui/tooltip"; -import { - Dialog, DialogContent, DialogDescription, DialogHeader, - DialogTitle, DialogTrigger -} from "@/components/ui/dialog"; -import { TableEditor } from "./TableEditor"; -import { ArrowUpDown, X, Plus, Table as TableIcon, Download } from "lucide-react"; -import { - SidebarGroup, SidebarGroupContent, SidebarGroupLabel -} from "@components/ui/sidebar"; -import { - useEdges, useFeatures, useLeafOrder, useNodes, useProjectMetadata, - useTreeOutput -} from "@components/editor/EditorContext"; -import { addNode, defaultLeafNode, exportState, formatTree } from "@components/editor/util"; -import FIleUploadButton from "./FileUpload" -import { v4 as uuidv4 } from 'uuid'; -import { useReactFlow, useStore } from "@xyflow/react"; - -export function ProjectSidebar() { - const { toast } = useToast(); - const [nodes, setNodes] = useNodes(); - const [edges, setEdges] = useEdges(); - const [features, setFeatures] = useFeatures(); - const [metadata, setMetadata] = useProjectMetadata(); - const [treeOutput] = useTreeOutput(); - const [leafOrder] = useLeafOrder(); - const { getViewport } = useReactFlow(); - - const [editorWidth, editorHeight] = useStore((v)=>{ - return [v.width, v.height] - }); - - const downloadLinkRef = useRef(null); - - const handleDownload = () => { - if (!downloadLinkRef.current){ - toast({ - variant: "destructive", - title: "Download Error", - description: "Could not download the project due to issues with HTML" - }); - return - } - let state; - try { - state = exportState( - nodes, edges, features, leafOrder, metadata, treeOutput - ) - } catch (error) { - toast({ - variant: "destructive", - title: "Invalid Tree", - description: (error as Error).message - }); - return - } - const json = JSON.stringify(state, null, 2); - const blob = new Blob([json], { type: 'application/json' }); - const url = URL.createObjectURL(blob); - downloadLinkRef.current.href = url; - downloadLinkRef.current.click(); - }; - - const handleRearrange = useCallback(() => { - formatTree(nodes, edges).then(newNodes => { - setNodes(newNodes); - }); - }, [nodes, edges, setNodes]); - - const handleAddNode = useCallback(() => { - - setNodes((nds) => { - const viewport = getViewport(); - console.log({viewport}) - const centerX = (0.5*editorWidth-viewport.x)/viewport.zoom; - const centerY = (0.5*editorHeight-viewport.y)/viewport.zoom; - const [newNodes, newEdges] = addNode( - - { - x: centerX, - y: centerY, - }, - uuidv4(), - defaultLeafNode, - nds, - [], - undefined, - ) - return newNodes; - }) - - }, [setEdges, setNodes, editorWidth, editorHeight]); - - const handleAddFeature = useCallback(() => { - setFeatures(prevFeatures => { - const newFeature = `Feature ${prevFeatures.length + 1}`; - if (prevFeatures.includes(newFeature)) { - toast({ - variant: "destructive", - title: "Feature exists", - description: "A feature with this name already exists" - }); - return prevFeatures; - } - return [...prevFeatures, newFeature]; - }); - }, [setFeatures, toast]); - - const handleDeleteFeature = useCallback((index: number) => { - setFeatures(prevFeatures => { - const nodeUsingFeature = nodes.find(node => - node.data.split_feature_id === index - ); - - if (nodeUsingFeature) { - toast({ - variant: "destructive", - title: "Cannot delete feature", - description: `Feature is in use by node ${nodeUsingFeature.id}` - }); - return prevFeatures; - } - - const newFeatures = prevFeatures.filter((_, i) => i !== index); - - setNodes(prevNodes => - prevNodes.map(node => { - if (!node.data.split_feature_id) return node; - if (node.data.split_feature_id > index) { - return { - ...node, - data: { - ...node.data, - split_feature_id: node.data.split_feature_id - 1 - } - }; - } - return node; - }) - ); - - return newFeatures; - }); - }, [nodes, setNodes, setFeatures, toast]); - - const handleUpdateFeature = useCallback((index: number, value: string) => { - setFeatures(prevFeatures => { - const newFeatures = [...prevFeatures]; - newFeatures[index] = value; - return newFeatures; - }); - }, [setFeatures]); - - const handleMetadataChange = useCallback((field: keyof typeof metadata) => ( - e: React.ChangeEvent - ) => { - setMetadata(prev => ({ - ...prev, - [field]: e.target.value - })); - }, [setMetadata]); - - // Rest of the JSX remains the same, but update event handlers: - return ( - <> - - Project Metadata - -
- - - - - - -

Enter the name of your project

- - - - -
- -
- - - - - - -

Briefly describe your project

- - - -