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📃 Our Paper on CBR

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Casebased - A Python Library for Case-Based Reasoning

Casebased is a Python library that provides a flexible and extensible framework for implementing case-based reasoning (CBR) systems. CBR is a problem-solving paradigm that focuses on solving new problems by adapting solutions that were used to solve similar past problems.

Features

Feature	Description
Case Representation	Represent cases using a variety of data structures, including dictionaries, JSON, and custom classes.
Case Retrieval	Implement different case retrieval algorithms, such as nearest neighbor, fuzzy matching, and knowledge-guided search.
Case Adaptation	Provide functions to adapt retrieved cases to fit the current problem.
Case Evaluation	Assess the quality of the adapted solution and provide feedback to the system.
Case Learning	Automatically learn from successful and unsuccessful problem-solving experiences to improve future performance.
Extensibility	Easily integrate the library with your own domain-specific data and algorithms.

Installation

You can install the casebased library using pip:

pip install casebased

Usage

Here's a simple example of how to use the casebased library:

from casebased.case_base import CaseBase from casebased.retrieval import NearestNeighborRetriever

Create a case base

case_base = CaseBase()

Add cases to the case base

case_base.add_case({
"problem": {
"symptoms": ["fever", "headache", "sore throat"],
"duration": 3
},
"solution": {
"medication": "paracetamol",
"dosage": "500mg every 6 hours"
}
})

case_base.add_case({
"problem": {
"symptoms": ["cough", "runny nose", "fatigue"],
"duration": 5
},
"solution": {
"medication": "ibuprofen",
"dosage": "400mg every 8 hours"
}
})

Retrieve the most similar case

new_problem = {
"symptoms": ["fever", "headache", "sore throat"],
"duration": 2
}

retriever = NearestNeighborRetriever()
similar_case = retriever.retrieve(case_base, new_problem)

print(f"Similar case: {similar_case}")
print(f"Suggested solution: {similar_case['solution']}")

📈 Releases

You can see the list of available releases on the GitHub Releases page.

We follow Semantic Versions specification.

We use Release Drafter. As pull requests are merged, a draft release is kept up-to-date listing the changes, ready to publish when you’re ready. With the categories option, you can categorize pull requests in release notes using labels.

List of labels and corresponding titles

Label	Title in Releases
enhancement, feature	🚀 Features
bug, refactoring, bugfix, fix	🔧 Fixes & Refactoring
build, ci, testing	📦 Build System & CI/CD
breaking	💥 Breaking Changes
documentation	📝 Documentation
dependencies	⬆️ Dependencies updates

You can update it in release-drafter.yml.

GitHub creates the bug, enhancement, and documentation labels for you. Dependabot creates the dependencies label. Create the remaining labels on the Issues tab of your GitHub repository, when you need them.

🛡 License

This project is licensed under the terms of the MIT license. See LICENSE for more details.

📃 Citation

@misc{casebased,
  author = {casebased},
  title = {CaseBased is a python library that implements the priciples of case-based reasoning.},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/casebased/casebased}}
}

Credits

This project was generated with python-package-template