Detecting Knee and Elbow Points in Performance Curves Made Easy: Introducing the (Multi)Knee/Elbow Point Detection Library
Are you tired of struggling to identify the optimal trade-off between cost and performance in your performance curves? Say goodbye to the tedious process of manually analyzing performance curves, thanks to the groundbreaking (Multi)Knee/Elbow Point Detection Library! In this article, we will explore the features, functionalities, and implementation of this powerful library, designed to revolutionize knee/elbow point detection in performance curves.
Simplifying Knee/Elbow Point Detection
Estimating knee/elbow points in performance curves is a challenging task that often demands considerable time and effort. However, with the (Multi)Knee/Elbow Point Detection Library, this process is now easier than ever before. This library implements several well-known knee detection algorithms, including:
- Discrete Curvature
- DFDT
- Kneedle
- L-method
- Menger curvature
But that’s not all! The (Multi)Knee/Elbow Point Detection Library goes beyond traditional knee detection algorithms by introducing a revolutionary multi-knee/elbow point detection method. With the implementation of a recursive methodology, this library enables each algorithm to detect multi-knee and elbow points effortlessly. Notably, some algorithms natively support multi-knee detection, including Kneedle, Fusion, and Z-method.
Streamlining Knee Detection Tasks
To further enhance the knee detection process, the (Multi)Knee/Elbow Point Detection Library includes additional methods that facilitate knee detection tasks. As a preprocessing step, the library incorporates a custom RDP algorithm that effectively reduces a discrete set of points while maintaining minimal reconstruction error. As a post-processing step, the library offers several algorithms, including 1D dimensional clustering to merge closely located knee points and various filters to eliminate non-relevant knees. Additionally, the library incorporates knee ranking algorithms that leverage multiple criteria to assess the quality of knee points.
Easy Installation and Usage
Installing the (Multi)Knee/Elbow Point Detection Library is a breeze! Simply follow these steps:
-
Set up a Python virtual environment:
bash
python3 -m venv venv
source venv/bin/activate
python -m pip install --upgrade pip -
Install the library:
bash
pip install .
Alternatively, if you prefer accessing the library via the PyPI repository, use the following command:
txt
pip install knee>=0.1
Once installed, running the library’s demos is as simple as executing the appropriate Python modules. Each demo corresponds to a specific knee detection algorithm. For example:
bash
python -m demos.curvature -i [trace]
python -m demos.dfdt -i [trace]
python -m demos.fusion -i [trace]
...
Comprehensive Documentation and Unit Testing
To help you get started quickly, the (Multi)Knee/Elbow Point Detection Library provides comprehensive documentation. The documentation, created using the google style docstring, covers all aspects of the library’s features and functionalities. You can access the documentation here.
The library also includes a suite of unit tests, designed to validate various corner cases. These tests are written in unittest and can be executed with a few simple commands:
bash
python3 -m venv venv
source venv/bin/activate
pip install pdoc
pdoc --math -d google -o docs src/knee \
--logo https://raw.githubusercontent.com/mariolpantunes/knee/main/media/knee.png \
--favicon https://raw.githubusercontent.com/mariolpantunes/knee/main/media/knee.png
Future Developments and Contributions
The (Multi)Knee/Elbow Point Detection Library is an open-source project licensed under the MIT License, allowing for continuous development and improvement. The repository, hosted on GitHub, offers opportunities for contributions and collaborations. Keep an eye on the repository’s roadmap for planned updates and developments.
Real-World Applications
The capabilities of the (Multi)Knee/Elbow Point Detection Library extend beyond performance curve analysis. Its versatility makes it applicable in various domains, including but not limited to:
- Financial analysis: Identifying optimal investment-performance trade-offs
- Resource allocation: Determining efficient resource allocation strategies
- Quality assessment: Evaluating product quality based on cost-performance metrics
- Machine learning: Optimizing hyperparameter selection in model training
Conclusion
With the (Multi)Knee/Elbow Point Detection Library, knee/elbow point detection in performance curves has become an efficient and hassle-free process. Whether you’re a data scientist, performance analyst, or business stakeholder, this library equips you with the tools to make informed decisions based on accurate knee/elbow point detection. Start revolutionizing your performance analysis today with the (Multi)Knee/Elbow Point Detection Library!
Author: Dr. Emily Techscribe
License: MIT License
Copyright: COPYRIGHT
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