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# An Adventure In Packaging: An exercise in research software engineering.¶

In this exercise, you will convert the already provided solution to the programming challenge defined in this Jupyter notebook, into a proper Python package.

The code to actually solve the problem is already given, but as roughly sketched out code in a notebook.

Your job will be to convert the code into a formally structured package, with unit tests, a command line interface, and demonstrating your ability to use git version control.

The exercise will be semi-automatically marked, so it is very important that you adhere in your solution to the correct file and folder structure, as defined in the rubric below. An otherwise valid solution which doesn't work with our marking tool will not be given credit.

First, we set out the problem we are solving, and it's informal solution. Next, we specify in detail the target for your tidy solution. Finally, to assist you in creating a good solution, we state the marks scheme we will use.

# Treasure Hunting for Beginners: an AI testbed¶

We are going to look at a simple game, a modified version of one with a long history. Games of this kind have been used as test-beds for development of artificial intelligence.

A dungeon is a network of connected rooms. One or more rooms contain treasure. Your character, the adventurer, moves between rooms, looking for the treasure. A troll is also in the dungeon. The troll moves between rooms at random. If the troll catches the adventurer, you lose. If you find treasure before being eaten, you win. (In this simple version, we do not consider the need to leave the dungeon.)

The starting rooms for the adventurer and troll are given in the definition of the dungeon.

The way the adventurer moves is called a strategy. Different strategies are more or less likely to succeed.

We will consider only one strategy this time - the adventurer will also move at random.

We want to calculate the probability that this strategy will be successful for a given dungeon.

We will use a "monte carlo" approach - simply executing the random strategy many times, and counting the proportion of times the adventurer wins.

Our data structure for a dungeon will be somewhat familiar from the Maze example:

In [1]:
dungeon1 = {
'treasure' : [1], # Room 1 contains treasure
'troll': 2, # The troll starts in room 2
'network': [[1], #Room zero connects to room 1
[0,2], #Room one connects to rooms 0 and 2
[1] ] #Room 2 connects to room 1
}


So this example shows a 3-room linear corridor: with the adventurer at one end, the troll at the other, and the treasure in the middle.

With the adventurer following a random walk strategy, we can define a function to update a dungeon:

In [2]:
import random

def random_move(network, current_loc):
targets=network[current_loc]
return random.choice(targets)

In [3]:
def update_dungeon(dungeon):
dungeon['troll']=random_move(dungeon['network'], dungeon['troll'])

In [4]:
update_dungeon(dungeon1)

dungeon1

Out[4]:
{'treasure': [1], 'adventurer': 1, 'troll': 1, 'network': [[1], [0, 2], [1]]}

We can also define a function to test if the adventurer has won, died, or if the game continues:

In [5]:
def outcome(dungeon):
return -1
return 1
return 0

In [6]:
outcome(dungeon1)

Out[6]:
-1

So we can loop, to determine the outcome of an adventurer in a dungeon:

In [7]:
import copy

def run_to_result(dungeon):
dungeon=copy.deepcopy(dungeon)
max_steps=1000
for _ in range(max_steps):
result= outcome(dungeon)
if result != 0:
return result
update_dungeon(dungeon)
# don't run forever, return 0 (e.g. if there is no treasure and the troll can't reach the adventurer)
return result

In [8]:
dungeon2 = {
'treasure' : [1], # Room 1 contains treasure
'troll': 2, # The troll starts in room 2
'network': [[1], #Room zero connects to room 1
[0,2], #Room one connects to rooms 0 and 2
[1,3], #Room 2 connects to room 1 and 3
[2]] # Room 3 connects to room 2

}

In [9]:
run_to_result(dungeon2)

Out[9]:
-1

Note that we might get a different result sometimes, depending on how the adventurer moves, so we need to run multiple times to get our probability:

In [10]:
def success_chance(dungeon):
trials=10000
successes=0
for _ in range(trials):
outcome = run_to_result(dungeon)
if outcome == 1:
successes+=1
success_fraction = successes/trials
return success_fraction

In [11]:
success_chance(dungeon2)

Out[11]:
0.4913

Make sure you understand why this number should be a half, given a large value for trials.

In [12]:
dungeon3 = {
'treasure' : [2], # Room 2 contains treasure
'troll': 4, # The troll starts in room 4
'network': [[1], #Room zero connects to room 1
[0,2], #Room one connects to rooms 0 and 2
[1,3], #Room 2 connects to room 1 and 3
[2, 4], # Room 3 connects to room 2 and 4
[3]] # Room 4 connects to room 3

}

In [13]:
success_chance(dungeon3)

Out[13]:
0.3999

[Not for credit] Do you understand why this number should be 0.4? Hint: The first move is always the same. In the next state, a quarter of the time, you win. 3/8 of the time, you end up back where you were before. The rest of the time, you lose (eventually). You can sum the series: $\frac{1}{4}(1+\frac{3}{8}+(\frac{3}{8})^2+...)=\frac{2}{5}$.

# Packaging the Treasure: your exercise¶

You must submit your exercise solution to Moodle as a single uploaded Zip format archive. (You must use only the zip tool, not any other archiver, such as .tgz or .rar. If we cannot unzip the archiver with zip, you will receive zero marks.)

The folder structure inside your zip archive must have a single top-level folder, whose folder name is your student number, so that on running unzip this folder appears. This top level folder must contain all the parts of your solution. You will lose marks if, on unzip, your archive creates other files or folders at the same level as this folder, as we will be unzipping all the assignments in the same place on our computers when we mark them!

Inside your top level folder, you should create a setup.py file to make the code installable. You should also create some other files, per the lectures, that should be present in all research software packages. (Hint, there are three of these.)

Your tidied-up version of the solution code should be in a sub-folder called adventure which will be the python package itself. It will contain an init.py file, and the code itself must be in a file called dungeon.py. This should define a class Dungeon: instead of a data structure and associated functions, you must refactor this into a class and methods.

Thus, if you run python in your top-level folder, you should be able to from adventure.dungeon import Dungeon. If you cannot do this, you will receive zero marks.

You must create a command-line entry point, called hunt. This should use the entry_points facility in setup.py, to point toward a module designed for use as the entry point, in adventure/command.py. This should use the Argparse library. When invoked with hunt mydungeon.yml --samples 500 the command must print on standard output the probability of finding the treasure in the specified dungeon, using the random walk strategy, after the specified number of test runs.

The dungeon.yml file should be a yml file containing a structure representing the dungeon state. Use the same structure as the sample code above, even though you'll be building a Dungeon object from this structure rather than using it directly.

You must create unit tests which cover a number of examples. These should be defined in adventure/tests/test_dungeon.py. Don't forget to add an init.py file to that folder too, so that at the top of the test file you can " from ..dungeon import Dungeon." If your unit tests use a fixture file to DRY up tests, this must be called adventure/tests/fixtures.yml. For example, this could contain a yaml array of many dungeon structures.

You should git init inside your student-number folder, as soon as you create it, and git commit your work regularly as the exercise progresses.

Due to our automated marking tool, only work that has a valid git repository, and follows the folder and file structure described above, will receive credit.

Due to the need to avoid plagiarism, do not use a public github repository for your work - instead, use git on your local disk (with git commit but not git push), and *ensure the secret .git folder is part of your zipped archive.

# Marks Scheme¶

Note that because of our automated marking tool, a solution which does not match the standard solution structure defined above, with file and folder names exactly as stated, may not receive marks, even if the solution is otherwise good. "Follow on marks" are not guaranteed in this case.

• Code in dungeon.py, implementing the random walk strategy (5 marks)
• Which works (1 mark)
• Cleanly laid out and formatted - PEP8 (1 mark)
• Defining the class Dungeon with a valid object oriented structure (1 mark)
• Breaking down the solution sensibly into subunits (1 mark)
• Structured so that it could be used as a base for other strategies (1 mark)
• Command line entry point (4 marks)
• Accepting a dungeon definition text file as input (1 mark)
• With an optional parameter to control sample size (1 mark)
• Which prints the result to standard out (1 mark)
• Which correctly uses the Argparse library (1 mark)
• Which is itself cleanly laid out and formatted (1 mark)
• setup.py file (5 marks)
• Which could be used to pip install the project (1 mark)
• With appropriate metadata, including version number and author (1 mark)
• Which packages code (but not tests), correctly. (1 mark)
• Which specifies library dependencies (1 mark)
• Which points to the entry point function (1 mark)
• Three other metadata files: (3 marks)
• Hint: Who did it, how to reference it, who can copy it.
• Unit tests: (5 marks)
• Which test some obvious cases (1 mark)
• Which correctly handle approximate results within an appropriate tolerance (1 mark)
• Which test how the code fails when invoked incorrectly (1 mark)
• Which use a fixture file or other approach to avoid overly repetitive test code (1 mark)
• Which are themselves cleanly laid out code (1 mark)
• Version control: (2 marks)
• Sensible commit sizes (1 mark)
• Appropriate commit comments (1 mark)

Total: 25 marks

In [ ]: