Metadata-Version: 2.1
Name: paretoset
Version: 1.2.0
Summary: Compute the Pareto (non-dominated) set, i.e., skyline operator/query.
Home-page: https://github.com/tommyod/paretoset
Author: tommyod
Author-email: tommy.odland@gmail.com
License: MIT
Description: # paretoset ![Build Status](https://github.com/tommyod/paretoset/workflows/Python%20CI/badge.svg?branch=master) [![](https://badge.fury.io/py/paretoset.svg)](https://pypi.org/project/paretoset/) [![](https://pepy.tech/badge/paretoset)](https://pepy.tech/project/paretoset) [![](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
        
        Compute the Pareto (non-dominated) set, i.e., skyline operator/query.
        
        ## Installation
        
        The software is available through GitHub, and through [PyPI](https://pypi.org/project/paretoset/).
        You may install the software using `pip`.
        
        ```bash
        pip install paretoset
        ```
        
        ## Examples - Skyline queries for data analysis and insight
        
        ### Hotels that are cheap and close to the beach
        
        In the database context, the Pareto set is called the *skyline* and computing the Pareto set is called a *skyline query*.
        The folllowing example is from the paper "*The Skyline Operator*" by Börzsönyi et al.
        
        > Suppose you are going on holiday and you are looking for a hotel that is cheap and close to the beach. 
          These two goals are complementary as the hotels near the beach tend to be more expensive. 
          The database system is unable to decide which hotel is best for you, but it can at least present you all interesting hotels. 
          Interesting are all hotels that are not worse than any other hotel in both dimensions. 
          You can now make your final decision, weighing your personal preferences for price and distance to the beach.
        
        Here's an example showing hotels in the Pareto set.
        
        ```python
        from paretoset import paretoset
        import pandas as pd
        
        hotels = pd.DataFrame({"price": [50, 53, 62, 87, 83, 39, 60, 44], 
                               "distance_to_beach": [13, 21, 19, 13, 5, 22, 22, 25]})
        mask = paretoset(hotels, sense=["min", "min"])
        paretoset_hotels = hotels[mask]
        ```
        
        ![](https://raw.githubusercontent.com/tommyod/paretoset/master/scripts/example_hotels.png)
        
        ### Top performing salespeople
        
        Suppose you wish to query a database for salespeople that might be eligible for a raise.
        To find top performers (low salary, but high sales) for every department:
        
        ```python
        from paretoset import paretoset
        import pandas as pd
        
        salespeople = pd.DataFrame(
            {
                "salary": [94, 107, 67, 87, 153, 62, 43, 115, 78, 77, 119, 127],
                "sales": [123, 72, 80, 40, 64, 104, 106, 135, 61, 81, 162, 60],
                "department": ["c", "c", "c", "b", "b", "a", "a", "c", "b", "a", "b", "a"],
            }
        )
        mask = paretoset(salespeople, sense=["min", "max", "diff"])
        top_performers = salespeople[mask]
        ```
        
        ![](https://raw.githubusercontent.com/tommyod/paretoset/master/scripts/example_salespeople.png)
        
        ## Example - Pareto efficient solutions in multiobjective optimization
        
        Suppose you wish to query a database for salespeople that might be eligible for a raise.
        To find top performers (low salary, but high sales) for every department:
        
        ```python
        from paretoset import paretoset
        import numpy as np
        from collections import namedtuple
        
        # Create Solution objects holding the problem solution and objective values
        Solution = namedtuple("Solution", ["solution", "obj_value"])
        solutions = [Solution(solution=object, obj_value=np.random.randn(2)) for _ in range(999)]
        
        # Create an array of shape (solutions, objectives) and compute the non-dominated set
        objective_values_array = np.vstack([s.obj_value for s in solutions])
        mask = paretoset(objective_values_array, sense=["min", "max"])
        
        # Filter the list of solutions, keeping only the non-dominated solutions
        efficient_solutions = [solution for (solution, m) in zip(solutions, mask) if m]
        ```
        
        ![](https://raw.githubusercontent.com/tommyod/paretoset/master/scripts/example_optimization.png)
        
        ## Contributing
        
        You are very welcome to scrutinize the code and make pull requests if you have suggestions and improvements.
        Your submitted code must be PEP8 compliant, and all tests must pass.
        
        ## Performance
        
        The graph below shows how long it takes to compute the Pareto set.
        Gaussian data has only a few observations in the Pareto set, while uniformly distributed data on a simplex has every observations in the Pareto set.
        
        ![](https://raw.githubusercontent.com/tommyod/paretoset/master/scripts/times_pareto_set.png)
        
        
        ## References
        
        - "*The Skyline Operator*" by Börzsönyi et al. introduces the *Skyline Operator* in the database context.
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
