CS 152: Project 6

Title image Project 6
Spring 2020

Project 6: Optimizing the Simulation

This is the second project on elephant population simulation. In the first project, you developed the overall simulation and used it to figure out a single parameter: the percentage of female elephants to dart each year. This week we're going to explore how to optimize one or more parameters of a simulation automatically. This will involve a little bit of restructuring of your elephant simulation code and then the development of an optimizer that will run the simulation many times in order to figure out parameters that best achieve a specific outcome.


  1. Write a function to optimize the percent darted

    Create a new file called optimize.py. Have the file import the sys, elephant, and random packages. Then create a function called optimize with the following definition.

    # Executes a search to bring the result of the function optfunc to zero.
    # min: minimum parameter value to search
    # max: maximum parameter value to search
    # optfunc: function to optimize
    # parameters: optional parameter list to pass to optfunc
    # tolerance: how close to zero to get before terminating the search
    # maIterations: how many iterations to run before terminating the search
    # verbose: whether to print lots of information or not
    def optimize( min, max, optfunc, parameters = None, tolerance = 0.001, maxIterations = 20, verbose=False ):

    The optimize function is very similar to the binary search function you wrote in lab.

    1. Start by assigning to a variable done the value False.
    2. Start a loop that continues while done is equal to False.
    3. Inside the loop, assign to testValue the average of max and min. This is not (should not be) an integer calculation. If verbose is True, print out testValue.
    4. Assign to result the return value of calling optfunc with testValue and parameters as the arguments. If verbose is True, print out the result value.
    5. If the result is positive, assign to max the value of testValue. Else if the result is negative, assign to min the value of testValue. Else, assign to done the value True.
    6. If max - min is less than the tolerance value, then assign to done the value True.
    7. Decrement maxIterations. If maxIterations is less than or equal to zero, then set done to True.
    8. Outside the loop, return testValue.

    To test your optimize function, copy the following code and run optimize.py. As noted in the comments, try making tolerance smaller and see if it matches more digits in the target value.

    # a function that returns x - target
    def target(x, pars):
        return x - 0.73542618
    # Tests the binary search using a simple target function.
    # Try changing the tolerance to see how that affects the search.
    def testTarget():
        res = optimize( 0.0, 1.0, target, tolerance = 0.001, verbose=True)
        print( res )
    if __name__ == "__main__":
  2. Test the optimize function with your elephantSim

    The next step is to test the optimize function with your elephantSim function. Create a testEsim function (similar to testTarget above) that calls optimize with a min value of 0.0, a max value of 0.5, and passes it elephant.elephantSim as the target function. You probably want to set verbose=True as well. As with the testTarget function, assign the return value to a variable and then print the variable. At the bottom of your code, change testTarget() to testEsim() then run optimize.py. Does your optimize function find a value close to 0.43 for the percent darted?

  3. Automate varying a simulation parameter

    The next step is to automate the process of evaluating the effects of changing a simulation parameter across a range of values. This function will let us discover, for example, the effect on the dart percentage of changing the calfSurvival rate from 80% to 90% in steps of 1%. The function definition is given below.

    # Evaluates the effects of the selected parameter on the dart percentage
    # whichParameter: the index of the parameter to test
    # testmin: the minimum value to test
    # testmax: the maximum value to test
    # teststep: the step between parameter values to test
    # defaults: default parameters to use (default value of None)
    def evalParameterEffect( whichParameter, testmin, testmax, teststep, defaults=None, verbose=False ):
        # if defaults is None, assign to simParameters the result of calling elephant.defaultParameters.
        # else, assign to simParameters a copy of defaults  (e.g. simParameters = defaults[:]
        # create an empty list (e.g. results) to hold the results
        if verbose:
            print( "Evaluating parameter %d from %.3f to %.3f with step %.3f" % (whichParameter, testmin, testmax, teststep) )
        # assign to t the value testmin
        # while t is less than testmax
            # assign to the whichParameter element of simParameters (e.g. simParameters[whicParameter]) the value t
            # assign to percDart the result of calling optimize with the appropriate arguments, including simParameters
            # append to results the tuple (t, percDart)
            if verbose:
                print( "%8.3f \t%8.3f" % (t, percDart) )
            # increment t by the value teststep
        if verbose:
            print( "Terminating")
        # return the list of results

    Test your evalParameterEffects function by modifying your top level code at the bottom of your file to be the following.

    if __name__ == "__main__":
        evalParameterEffect( elephant.IDXProbAdultSurvival, 0.98, 1.0, 0.001, verbose=True )

    What does this do? What should you expect the output to be?

  4. Evaluate the effects of varying many parameters

    Your final task is to make the following evaluations, showing the effect on the dart percentage of the following parameter sweeps. Make a table or graph (or both) for each case. These five items should go in your report.

    1. Vary the adult survival probability from 0.98 to 1.0 in steps of 0.001.
    2. Vary the calf survival probability from 0.80 to 0.90 in steps of 0.01.
    3. Vary the senior survival probability from 0.1 to 0.5 in steps of 0.05.
    4. Vary the calving interval from 3.0 to 3.4 in steps of 0.05.
    5. Vary the max age from 56 to 66 in steps of 2.

    While not required, your goal should be to automate this process as much as possible, these include writing the output data to a CSV file or plotting the results using matplotlib. While debugging the automation process, you might want to reduce the carrying capacity even further to 200 or 500 elephants. Only run with 1000 elephants once everything works properly.

  1. What does an import statement do?
  2. What is binary search?
  3. Why is binary search faster than a linear search (e.g. going page by page to find a word in a dictionary)?
  4. What national park (of any country) would you most like to visit?


Extensions are your opportunity to customize your project, learn something else of interest to you, and improve your grade. The following are some suggested extensions, but you are free to choose your own. Be sure to describe any extensions you complete in your report.

Submit your code

Turn in your code (all files ending with .py) by putting it in a directory in the Courses server. On the Courses server, you should have access to a directory called CS152, and within that, a directory with your user name. Within this directory is a directory named Private. Files that you put into that private directory you can edit, read, and write, and the professor can edit, read, and write, but no one else. To hand in your code and other materials, create a new directory, such as project6, and then copy your code into the project directory for that week. Please submit only code that you want to be graded.

When submitting your code, double check the following.

  1. Is your name at the top of each code file?
  2. Does every function have a comment or docstring specifying what it does?
  3. Is your handin project directory inside your Private folder on Courses?

Write your project report

For CS 152 please use Google Docs to write your report. Create a new doc for each project. Start the doc with a title and your name. Attach the doc to your project on Google classroom. Make sure you click submit when you are done. The graders cannot provide feedback unless you click submit.

Your intended audience for your report is your peers not in the class. From week to week you can assume your audience has read your prior reports. Your goal should be to be able to use it to explain to friends what you accomplished in this project and to give them a sense of how you did it.

Your project report should contain the following elements. Please include a header for each section.

Thanks to Cathy Collins for the project idea and documentation. The original project concept and idea came from Therese Donovan, University of Vermont.