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Publish version 1.0.0 of mlrose-ky to pypi
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‎mlrose_ky/mlrose_ky.egg-info/PKG-INFO

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Metadata-Version: 2.1
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Name: mlrose_ky
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Version: 1.0.0
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Summary: MLROSe-ky: Machine Learning, Randomized Optimization and Search
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Author: Genevieve Hayes, Andrew Rollings
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Author-email: Kyle Nakamura <knakamura13dev@gmail.com>
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Maintainer-email: Kyle Nakamura <knakamura13dev@gmail.com>
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License: BSD-3-Clause
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Project-URL: Homepage, https://github.com/knakamura13/mlrose-ky
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Project-URL: Documentation, https://nkapila6.github.io/mlrose-ky/
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Keywords: machine learning,randomized optimization,search algorithms,neural networks,genetic algorithm,simulated annealing,hill climbing,MIMIC,Python,OMSCS,CS,7641,mlrose-hiive
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Classifier: Intended Audience :: Education
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Classifier: Intended Audience :: Science/Research
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Classifier: Natural Language :: English
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Classifier: License :: OSI Approved :: BSD License
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
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Classifier: Topic :: Scientific/Engineering
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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Classifier: Topic :: Scientific/Engineering :: Mathematics
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Classifier: Topic :: Software Development :: Libraries
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Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Requires-Python: >=3.10
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Description-Content-Type: text/markdown
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License-File: LICENSE
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Requires-Dist: joblib
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Requires-Dist: networkx
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Requires-Dist: numpy
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Requires-Dist: pandas
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Requires-Dist: scikit-learn
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Requires-Dist: scipy
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# mlrose-ky: Machine Learning, Randomized Optimization, and SEarch
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mlrose-ky is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.
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## Project Background
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mlrose-ky is a fork of the `mlrose-hiive` repository, which itself was a fork of the original `mlrose` repository. The original mlrose package was developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
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This repository includes implementations of all randomized optimization algorithms taught in the course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.
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## Main Features
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#### *Randomized Optimization Algorithms*
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- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm, and (discrete) MIMIC;
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- Solve both maximization and minimization problems;
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- Define the algorithm's initial state or start from a random state;
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- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay, or exponential decay.
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#### *Problem Types*
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- Solve discrete-value (bit-string and integer-string), continuous-value, and tour optimization (travelling salesperson) problems;
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- Define your own fitness function for optimization or use a pre-defined function.
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- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens, and Max-K Color optimization problems.
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#### *Machine Learning Weight Optimization*
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- Optimize the weights of neural networks, linear regression models, and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm, or gradient descent;
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- Supports classification and regression neural networks.
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## Project Improvements and Updates
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The `mlrose-ky` project is undergoing significant improvements to enhance code quality, documentation, and testing. Below is a list of tasks that have been completed or are in progress:
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1. **Fix Python Warnings and Errors**: All Python warnings and errors have been addressed, except for a few unavoidable ones like "duplicate code." ✅
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2. **Add Python 3.10 Type Hints**: Type hints are being added to all function and method definitions, as well as method properties (e.g., `self.foo: str = 'bar'`), to improve code clarity and maintainability.
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3. **Enhance Documentation**: NumPy-style docstrings are being added to all functions and methods, with at least a one-line docstring at the top of every file summarizing its contents. This will make the codebase more understandable and easier to use for others.
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4. **Increase Test Coverage**: Tests are being added using Pytest, with a goal of achieving 100% code coverage to ensure the robustness of the codebase.
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5. **Resolve TODO/FIXME Comments**: A thorough search is being conducted for any TODO, FIXME, or similar comments, and their respective issues are being resolved.
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6. **Optimize Code**: Vanilla Python loops are being optimized where possible by vectorizing them with NumPy to enhance performance.
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7. **Improve Code Quality**: Any other sub-optimal code, bugs, or code quality issues are being addressed to ensure a high standard of coding practices.
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8. **Clean Up Codebase**: All commented-out code is being removed to keep the codebase clean and maintainable.
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## Installation
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mlrose-ky was written in Python 3 and requires NumPy, SciPy, and Scikit-Learn (sklearn).
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The latest version can be installed using `pip`:
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```bash
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pip install mlrose-ky
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```
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Once it is installed, simply import it like so:
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```python
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import mlrose_ky
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```
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## Documentation
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The official mlrose-ky documentation can be found [here](https://mlrose.readthedocs.io/).
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A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).
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## Licensing, Authors, Acknowledgements
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mlrose-ky was forked from the `mlrose-hiive` repository, which was a fork of the original `mlrose` repository.
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The original `mlrose` was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE).
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You can cite mlrose-ky in research publications and reports as follows:
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* Nakamura, K. (2024). ***mlrose-ky: Machine Learning, Randomized Optimization, and SEarch package for Python***. https://github.com/your-repo-url. Accessed: *day month year*.
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Please also keep the original authors' citations:
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* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*.
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* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.
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Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).
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BibTeX entry:
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```bibtex
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@misc{Nakamura24,
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author = {Nakamura, K.},
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title = {{mlrose-ky: Machine Learning, Randomized Optimization and SEarch package for Python}},
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year = 2024,
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howpublished = {\url{https://github.com/your-repo-url}},
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note = {Accessed: day month year}
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}
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@misc{Rollings20,
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author = {Rollings, A.},
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title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},
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year = 2020,
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howpublished = {\url{https://github.com/hiive/mlrose}},
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note = {Accessed: day month year}
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}
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@misc{Hayes19,
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author = {Hayes, G.},
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title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
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year = 2019,
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howpublished = {\url{https://github.com/gkhayes/mlrose}},
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note = {Accessed: day month year}
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}
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```

‎mlrose_ky/mlrose_ky.egg-info/SOURCES.txt

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LICENSE
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README.md
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pyproject.toml
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mlrose_ky/algorithms/__init__.py
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mlrose_ky/algorithms/ga.py
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mlrose_ky/algorithms/gd.py
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mlrose_ky/algorithms/hc.py
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mlrose_ky/algorithms/mimic.py
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mlrose_ky/algorithms/rhc.py
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mlrose_ky/algorithms/sa.py
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mlrose_ky/algorithms/crossovers/__init__.py
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mlrose_ky/algorithms/crossovers/_crossover_base.py
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mlrose_ky/algorithms/crossovers/one_point_crossover.py
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mlrose_ky/algorithms/crossovers/tsp_crossover.py
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mlrose_ky/algorithms/crossovers/uniform_crossover.py
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mlrose_ky/algorithms/decay/__init__.py
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mlrose_ky/algorithms/decay/arithmetic_decay.py
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mlrose_ky/algorithms/decay/custom_decay.py
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mlrose_ky/algorithms/decay/exponential_decay.py
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mlrose_ky/algorithms/decay/geometric_decay.py
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mlrose_ky/algorithms/mutators/__init__.py
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mlrose_ky/algorithms/mutators/_mutator_base.py
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mlrose_ky/algorithms/mutators/discrete_mutator.py
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mlrose_ky/algorithms/mutators/gene_swap_mutator.py
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mlrose_ky/algorithms/mutators/single_gene_mutator.py
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mlrose_ky/algorithms/mutators/single_shift_mutator.py
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mlrose_ky/decorators/__init__.py
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mlrose_ky/decorators/short_name_decorator.py
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mlrose_ky/fitness/__init__.py
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mlrose_ky/fitness/_discrete_peaks_base.py
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mlrose_ky/fitness/continuous_peaks.py
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mlrose_ky/fitness/custom_fitness.py
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mlrose_ky/fitness/flip_flop.py
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mlrose_ky/fitness/four_peaks.py
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mlrose_ky/fitness/knapsack.py
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mlrose_ky/fitness/max_k_color.py
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mlrose_ky/fitness/one_max.py
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mlrose_ky/fitness/queens.py
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mlrose_ky/fitness/six_peaks.py
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mlrose_ky/fitness/travelling_salesperson.py
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mlrose_ky/generators/__init__.py
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mlrose_ky/generators/continuous_peaks_generator.py
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mlrose_ky/generators/flip_flop_generator.py
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mlrose_ky/generators/four_peaks_generator.py
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mlrose_ky/generators/knapsack_generator.py
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mlrose_ky/generators/max_k_color_generator.py
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mlrose_ky/generators/one_max_generator.py
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mlrose_ky/generators/queens_generator.py
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mlrose_ky/generators/six_peaks_generator.py
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mlrose_ky/generators/tsp_generator.py
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mlrose_ky/gridsearch/__init__.py
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mlrose_ky/gridsearch/grid_search_mixin.py
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mlrose_ky/mlrose_ky.egg-info/PKG-INFO
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mlrose_ky/mlrose_ky.egg-info/SOURCES.txt
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mlrose_ky/mlrose_ky.egg-info/dependency_links.txt
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mlrose_ky/mlrose_ky.egg-info/requires.txt
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mlrose_ky/mlrose_ky.egg-info/top_level.txt
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mlrose_ky/neural/__init__.py
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mlrose_ky/neural/_nn_base.py
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mlrose_ky/neural/linear_regression.py
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mlrose_ky/neural/logistic_regression.py
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mlrose_ky/neural/neural_network.py
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mlrose_ky/neural/nn_classifier.py
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mlrose_ky/neural/nn_core.py
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mlrose_ky/neural/activation/__init__.py
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mlrose_ky/neural/activation/identity.py
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mlrose_ky/neural/activation/leaky_relu.py
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mlrose_ky/neural/activation/relu.py
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mlrose_ky/neural/activation/sigmoid.py
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mlrose_ky/neural/activation/softmax.py
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mlrose_ky/neural/activation/tanh.py
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mlrose_ky/neural/fitness/__init__.py
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mlrose_ky/neural/fitness/network_weights.py
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mlrose_ky/neural/utils/__init__.py
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mlrose_ky/neural/utils/weights.py
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mlrose_ky/opt_probs/__init__.py
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mlrose_ky/opt_probs/continuous_opt.py
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mlrose_ky/opt_probs/discrete_opt.py
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mlrose_ky/opt_probs/flip_flop_opt.py
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mlrose_ky/opt_probs/knapsack_opt.py
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mlrose_ky/opt_probs/max_k_color_opt.py
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mlrose_ky/opt_probs/opt_prob.py
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mlrose_ky/opt_probs/queens_opt.py
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mlrose_ky/opt_probs/tsp_opt.py
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mlrose_ky/runners/__init__.py
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mlrose_ky/runners/_nn_runner_base.py
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mlrose_ky/runners/_runner_base.py
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mlrose_ky/runners/ga_runner.py
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mlrose_ky/runners/mimic_runner.py
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mlrose_ky/runners/nngs_runner.py
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mlrose_ky/runners/rhc_runner.py
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mlrose_ky/runners/sa_runner.py
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mlrose_ky/runners/skmlp_runner.py
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mlrose_ky/runners/utils.py
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mlrose_ky/samples/__init__.py
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mlrose_ky/samples/synthetic_data.py
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tests/test_activation.py
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tests/test_algorithms.py
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tests/test_decay.py
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tests/test_decorators.py
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tests/test_fitness.py
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tests/test_generators.py
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tests/test_gridsearch.py
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tests/test_neural.py
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tests/test_opt_probs.py
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tests/test_runners.py
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tests/test_samples.py
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tests/tests.py

‎mlrose_ky/mlrose_ky.egg-info/dependency_links.txt

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‎mlrose_ky/mlrose_ky.egg-info/requires.txt

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joblib
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networkx
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numpy
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pandas
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scikit-learn
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scipy

‎mlrose_ky/mlrose_ky.egg-info/top_level.txt

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algorithms
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decorators
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fitness
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generators
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gridsearch
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neural
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opt_probs
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runners
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samples

‎pyproject.toml

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[build-system]
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requires = ["setuptools>=42", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "mlrose_ky"
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version = "1.0.0"
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description = "MLROSe-ky: Machine Learning, Randomized Optimization and Search"
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readme = "README.md"
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requires-python = ">=3.10"
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license = {text = "BSD-3-Clause"}
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authors = [
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{name = "Kyle Nakamura", email = "knakamura13dev@gmail.com"},
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{name = "Genevieve Hayes"},
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{name = "Andrew Rollings"}
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]
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maintainers = [
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{name = "Kyle Nakamura", email = "knakamura13dev@gmail.com"}
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]
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classifiers = [
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"Intended Audience :: Education",
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"Intended Audience :: Science/Research",
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"Natural Language :: English",
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"License :: OSI Approved :: BSD License",
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"Programming Language :: Python",
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"Programming Language :: Python :: 3",
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"Topic :: Scientific/Engineering",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Scientific/Engineering :: Mathematics",
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"Topic :: Software Development :: Libraries",
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"Topic :: Software Development :: Libraries :: Python Modules",
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]
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dependencies = [
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"joblib",
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"networkx",
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"numpy",
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"pandas",
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"scikit-learn",
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"scipy"
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]
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keywords = [
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"machine learning",
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"randomized optimization",
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"search algorithms",
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"neural networks",
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"genetic algorithm",
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"simulated annealing",
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"hill climbing",
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"MIMIC",
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"Python",
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"OMSCS",
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"CS",
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"7641",
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"mlrose-hiive"
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]
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[project.urls]
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"Homepage" = "https://github.com/knakamura13/mlrose-ky"
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"Documentation" = "https://nkapila6.github.io/mlrose-ky/"
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[tool.black]
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line-length = 140
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target-version = ['py310', 'py311', 'py312']
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skip_magic_trailing_comma = true
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skip_magic_trailing_comma = true
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[tool.setuptools.packages.find]
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where = ["mlrose_ky"]

‎setup.cfg

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