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Sorting Algorithms

Sorting Algorithms

Project Goals

This engineering effort invites you to implement and use a program called listsorting that performs a doubling experiment to evaluate the performance of several different sorting algorithms. First, you will follow the Sorting Algorithms in Python tutorial from Real Python and add certain sorting algorithms, like bubble sort and quick sort, to your project. After you have implemented all of the required sorting algorithms, you will use the provided benchmarking framework to conduct a doubling experiment. This doubling experiment will invoke a specific sorting algorithm while repeatedly double the size of the input to the sorting algorithm for a specific number of doubling rounds. Since the doubling experiment enables you to calculate a doubling ratio, it enables you to experimentally predict the likely worst-case time complexity of each sorting algorithms. In addition to implementing the sorting algorithms and extending the benchmarking framework, you will use a comprehensive command-line interface, implemented with Typer, that allows you to easily control the execution of a doubling experiment. Finally, you will use your empirical results from using the listsorting program to better understand the performance trade-offs associated with sorting algorithms implemented in Python.

Project Access

If you are a student enrolled in a Computer Science class at Allegheny College, you can access this assignment by clicking the link provided to you in Discord. Once you click this link it will create a GitHub repository that you can clone to your computer by following the general-purpose instructions in the description of the technical skills. Specifically, you will need to use the git clone command to download the project from GitHub to your computer. Now you are ready to add source code and documentation to the project!

Expected Output

This project invites you to implement a Python program, called listsorting that performs a doubling experiment to evaluate the performance of several different sorting algorithms. After you finish a correct implementation of all the program's features, running it with the command poetry run listsorting --starting-size 100 --number-doubles 5 --approach insertion, causes it to produce output like the following. With that said, please remember that when you run the listsorting program your computer it will likely produce different performance results! Importantly, this output shows that the listsorting program ran the insertion sort algorithm, denoted insertion, for a total of 5 rounds in a doubling experiment that created input sizes that ranged from 100 to 1600. When listsorting runs the experiment, it uses the timeit package to measure the min, max, and avg execution time of the algorithm.

✨ Conducting an experiment to measure the performance of list sorting!

   The chosen sorting algorithm: insertion
   Starting size of the data container: 100
   Number of doubles to execute: 5

✨ Here are the results from running the experiment!

  Input Size    Min time (s)    Max time (s)    Avg time (s)
------------  --------------  --------------  --------------
         100         0.00198         0.00228         0.0021
         200         0.00791         0.00831         0.00804
         400         0.03091         0.03179         0.03129
         800         0.1397          0.14232         0.141
        1600         0.56098         0.58918         0.57665

These experimental results suggest that insertion sort has a doubling ratio of \(\frac{0.57665}{0.141} \approx 4.0897\). If you look at the last row of the data table you will see that, for the input sizes of 1600 and 800, the average execution time for insertion sort was \(0.57665\) and \(0.141\) seconds, respectively. Dividing the execution time for the larger input size by the execution time of the smaller input size yields the doubling ratio of approximately \(4.0897\), suggestion that insertion sort is a \(O(n^2)\) algorithm because a doubling of the input size caused a quadrupling of the execution time. Moreover, don't forget that you can display listsorting's help menu and learn more about its features by typing poetry run listsorting --help to show the following output. Finally, remember that the listsorting program should also run experiments for the other sorting algorithms, such as bubble sort and quick sort! You can also run listsorting with larger input sizes or more rounds of input doubling --- but be aware of the fact that your experiments could take a long time to finish for certain algorithms!

Usage: listsorting [OPTIONS]

  Conduct a doubling experiment to measure the performance of list
  sorting for various algorithms.

Options:
  --starting-size INTEGER         [default: 1000000]
  --maximum-value INTEGER         [default: 10000]
  --number-doubles INTEGER        [default: 10]
  --approach [bubble|insertion|merge|quick|tim]
                                  [default: bubble]
  --install-completion            Install completion for the current
                                  shell.

  --show-completion               Show completion for the current shell,
                                  to copy it or customize the
                                  installation.

  --help                          Show this message and exit.

Please note that the provided source code does not contain all of the functionality to produce the output displayed in this section. As the next section explains, you should add the features needed to ensure that listsorting produces the expected output! Drawing from the source code provided in the aforementioned Sorting Algorithms in Python tutorial from Real Python, this project invites you to add all of the sorting algorithms, use the listsorting program to conduct a doubling experiment for each of the sorting algorithms and, finally, leverage the data tables of empirical results to calculate a doubling ratio and predict the likely worst-case time complexity of each sorting algorithm.

Note

Don't forget that if you want to run the listsorting program you must use your terminal window to first go into the GitHub repository containing this project and then go into the listsorting/ directory that contains the project's source code. Finally, remember that before running the program you must run poetry install to add its dependencies, such as Pytest for automated testing and Rich for colorful output!

Adding Functionality

If you study the file listsorting/listsorting/sorting.py you will see that it has many TODO markers that designate the sorting algorithms that you must implement so as to ensure that listsorting will produce correct output. For instance, you will need to provide an implementation of each sorting algorithm, like bubble sort, that has a signature like def bubble_sort(array: List[int]) -> List[int]. You will also need to resolve all of the TODO markers in listsorting/listsorting/main.py that involve calling the functions in listsorting/listsorting/experiment.py to run each of the steps in a doubling experiment. Specifically, you must ensure that the listsorting function in the main module calls the following run_sorting_algorithm_experiment_campaign function. Once you complete a task associated with a TODO marker, make sure that you delete it and revise the prompt associated with the marker into a meaningful comment.

def run_sorting_algorithm_experiment_campaign(
    algorithm: str,
    starting_size: int,
    maximum_value: int,
    number_doubles: int,
) -> List[List[Union[int, Tuple[float, float, float]]]]:
    data_table = []
    while number_doubles > 0:
        random_list = generate_random_container(starting_size, maximum_value)
        performance_data = run_sorting_algorithm(algorithm, random_list)
        data_table_row = [
            starting_size,
            performance_data[0],
            performance_data[1],
            performance_data[2],
        ]
        data_table.append(data_table_row)
        number_doubles = number_doubles - 1
        starting_size = starting_size * 2
    return data_table

Notably, the run_sorting_algorithm_experiment_campaign function completes all of the steps associated with running a specified sorting algorithm in a doubling experiment. Upon completion, this function returns a data_table that contains performance results for each round of the doubling experiment, as shown in the previous section. After finishing your implementation of listsorting, including the call to run_sorting_algorithm_experiment_campaign in main, you should repeatedly run the program in different configurations to conduct an experiment to evaluate the performance of each sorting algorithm that you implemented. This process will result in a data table that summarizes the results from a doubling experiment for each sorting algorithm. You can use the data in the table to calculate the doubling ratio and then use it to predict the likely worst-case time complexity of each sorting algorithm.

Running Checks

If you study the source code in the pyproject.toml file you will see that it includes the following section that specifies different executable tasks like lint. If you are in the listsorting directory that contains the pyproject.toml file and the poetry.lock file, the tasks in this section make it easy to run commands like poetry run task lint to automatically run all of the linters designed to check the Python source code in your program and its test suite. You can also use the command poetry run task black to confirm that your source code adheres to the industry-standard format defined by the black tool. If it does not adhere to the standard then you can run the command poetry run task fixformat and it will automatically reformat the source code.

Along with running tasks like poetry run task lint, you can leverage the relevant instructions in the technical skills to run the command gatorgrade --config config/gatorgrade.yml to check your work. If your work meets the baseline requirements and adheres to the best practices that proactive programmers adopt you will see that all the checks pass when you run gatorgrade. You can study the config/gatorgrade.yml file in your repository to learn how the GatorGrade program runs GatorGrader to automatically check your program and technical writing. If your program has all of the anticipated functionality, you can run the command poetry run task test and see that the test suite produces output like the following. Can you add comments to the test suite to explain how the test cases work? It is worth noting that the name of the test suite is test_sorting because the functions mentioned in the previous section exist in the listsorting module. Can you add comments to explain how these tests work? What are the key components of every test case created with Pytest?

tests/test_sorting.py ...

This project comes with other tasks that you can run once you have used Poetry to install all of the dependencies. For instance, if you find that your Python source code is not in adherence with the required formatting rules, you can run poetry run task black to automatically return it to the correct format! You can also run commands like poetry run task mypy to check the program's use of data types and poetry run task pylint to ensure that your source code adheres to other established programming conventions. You can use these built-in tasks to understand and improve your code's quality!

Note

Don't forget that when you commit source code or technical writing to your GitHub repository for this project, it will trigger the run of a GitHub Actions workflow. If you are a student at Allegheny College, then running this workflow consumes build minutes for the course's organization! As such, you should only commit to your repository once you have made substantive changes to your project and you are ready to confirm its correctness. Before you commit to your GitHub repository, you can still run checks on your own computer by using Poetry and GatorGrader.

Project Reflection

Once you have finished both of the previous technical tasks, you can use a text editor to answer all of the questions in the writing/reflection.md file. For instance, you should provide the output of the Python program in a fenced code block, explain the meaning of the Python source code segments that you implemented, and answer all of the other questions about your experiences in completing this project. A specific goal of the reflection for this project is to ensure that you can clearly write research questions and an experiment methodology for assessing the performance of a sorting algorithm. Once you have finished addressing the prompts in the writing/reflection.md file that have TODO makers given as reminders, make sure that you either delete the prompt or carefully integrate a revised version of it into your writing.

Project Assessment

Since this project is an engineering effort, it is aligned with the evaluating and creating levels of Bloom's taxonomy. You can learn more about how a proactive programming expert will assess your work by examining the assessment strategy. From the start to the end of this project you may make an unlimited number of reattempts at submitting source code and technical writing that meet all aspects of the project's specification.

Note

Before you finish all of the required deliverables required by this project is worth pausing to remember that the instructor will give advance feedback to any learner who requests it through GitHub and Discord at least 24 hours before the project's due date! Seriously, did you catch that? This policy means that you can have a thorough understanding of ways to improve your project before its final assessment! To learn more about this opportunity, please read the assessment strategy for this site.

Seeking Assistance

Emerging proactive programmers who have questions about this project are invited to ask them in either the GitHub discussions forum or the Proactive Programmers Discord server. Before you ask your question, please read the advice concerning how to best participate in the Proactive Programmers community. If you find a mistake in this project, please describe it and propose a solution by creating an issue in the GitHub Issue Tracker.


Updated: 2022-11-11   Created: 2021-08-12
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