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ICS 46 Data Structure Implementation and Analysis
Project #4: Set the Controls for the Heart of the Sun
Introduction
Search structures
In recent lectures, we've discussed several different data structures that can be used for the purposes of searching. Each of these data structures is organized differently, and each has different performance characteristics (measured in terms of time and memory usage). But they all share one goal: Given a key that we're searching for, find that key and any information that's been associated with it.
Broadly, we say that a set is a kind of data structure that stores a collection of unique keys, allowing us to add new keys, remove existing keys, and determine whether a particular key is stored. We say that a map is a similar kind of data structure, except that we associate a value with each key, so that keys function as the tool that lets us identify other information uniquely and find it easily. In lecture, we discussed several approaches to solving the problems of implementing search structures like sets and maps.
- We talked about using single-dimension search structures like arrays, vectors, and linked lists, but all had significant limitations. Either they required linear time to search (by looking at every element in sequence), or they could be searched more quickly (e.g., using binary search) but required linear time to add or remove a key.
- We discovered that binary search trees offer the ability to do searches, insertions, and removals in logarithmic time, which is a significant improvement on the linear-time bound on at least some operations on single-dimension search structures. But there was a catch: We only got logarithmic-time operations if the tree stayed balanced, but it could fall out of balance in circumstances that weren't necessarily rare in practice (such as adding keys in ascending or descending order).
- We found that all was not lost, because there are well-known algorithms for detecting and correcting binary search trees that have fallen out of balance. In particular, we learned about a binary search tree variant called AVL trees, which guarantee logarithmic-time searches, adds, and removals, by rearranging the nodes in the tree whenever a significant imbalance is detected.
- We explored skip lists, a very different solution that allows a variant of binary search to be performed on a linked list. It employs randomness to achieve its goal of providing logarithmic-time searches, adds, and removals, by storing some keys in two places, a few others in three places, a handful of others in four places, and so on. While skip lists don't actually guarantee logarithmic-time operations, if a good random number generator is used, skip lists have an extremely high probability of performing their operations in logarithmic-time, especially for large collections of keys.
- Finally, we discussed how hash tables offer the possibility of not having to search. Every key has a place where it "belongs," so if we only ever store keys where they belong, we'll never have to look anywhere except there. Of course, in practice, it doesn't turn out be quite as magical as that in practice, but it can perform very well, provided that we spread keys around so not too many of them belong in any one place.
As we often see when there are multiple solutions to the same kind of problem, there are important tradeoffs here. At first blush, hash tables with "good" hash functions appear to be the clear winner, as their search times tend toward Θ(1). But it's not always that simple. For example, you might not want to choose a hash table if you don't know enough about your keys to choose a suitable hash function. There's another reason, too, why you might prefer a balanced binary search tree or a skip list over a hash table: Since they impose ordering on their keys, binary search trees and skip lists can be iterated in ascending order of their keys in linear time. Hash tables can't, since they impose no meaningful ordering on their keys.
Spell checkers, as a case study of search structures
In this project, you'll build portions of a spell checker, similar to one you might find in a word processor, a text editor, or an email client. A spell checker's job is relatively simple: Given a list of correctly-spelled words (which we'll call a word set) and the input text, a spell checker reports all of the misspellings (i.e., words that are not found in the word set) in the input. When a misspelling is found in the input, a good spell checker will also suggest words appearing in the word set that are somewhat like each of the misspelled words, since there's a reasonably good chance that one of them will be the word the writer intended.
As an example, suppose that the word set contains the following words:
bake cake main rain vase
If the input text contains the word vake, a good spell checker will state that vake is misspelled, since it does not appear in the word set, and will suggest that perhaps bake, cake, or vase was the word that was actually intended, since all three of these words are reasonably similar to the word vake, differing by only one letter each. (An even smarter spell checker might employ contextual clues to guess the word that is most appropriate in the sentence that surrounds it, but this is beyond the scope of our work here.)
Of course, a spell checker's task is centered around searching for words in a potentially large word set. An efficient search structure is critical to the performance of the spell checker, since it will be necessary to search not only for the words in the input, but possibly hundreds of potential suggestions that it might like to make for each misspelling. As you will see, a poor technique can render a spell checker — or any system that requires a large number of insertions and searches to be performed on a large search structure — effectively useless.
Getting started
Before you begin work on this project, there are a couple of chores you'll need to complete on your ICS 46 VM to get it set up to proceed.
Refreshing your ICS 46 VM environment
Even if you previously downloaded your ICS 46 VM, you will probably need to refresh its environment before proceeding with this project. Log into your VM and issue the command ics46 version to see what version of the ICS 46 environment you currently have stored on your VM. Note, in particular, the timestamp; if you see a version with a timestamp older than the one listed below, you'll want to refresh your environment by running the command ics46 refresh to download the latest one before you proceed with this project.
2022-05-09 07:31:27 Project 4 template added
If you're unable to get outgoing network access to work on the ICS 46 VM — something that afflicts a handful of students each quarter — then the ics46 refresh command won't work, but an alternative approach is to download the latest environment from the link below, then to upload the file on to your ICS 46 VM using SCP. (See the Project #0 write-up for more details on using SCP.) Once the file is on your VM, you can run the command ics46 refresh_local NAME_OF_ENVIRONMENT_FILE, replacing NAME_OF_ENVIRONMENT_FILE with the name of the file you uploaded; note that you'd need to be in the same directory where the file is when you run the command.
Creating your project directory on your ICS 46 VM
A project template has been created specifically for this project, containing a similar structure to the basic template you saw in Project #0, but including a fair amount of code (both source code and compiled libraries) that is being provided as a starting point. So you'll absolutely need to use the project4 template for this project, as opposed to the basic one.
Decide on a name for your project directory, then issue the command ics46 start YOUR_CHOSEN_PROJECT_NAME project4 to create your new project directory using the project4 template. (For example, if you wanted to call your project directory p4, you would issue the command ics46 start p4 project4 to create it.) Now you're ready to proceed!
The project directory
Change into your project directory and take a look around. Having already completed earlier projects, what you will see will look somewhat familiar, though there are a couple of differences.
Once again, your project directory is capable of building (any or all of) three separate programs that you can run by issuing the commands ./run app, ./run exp, or ./run gtest. A provided directory contains a set of source code for a mostly-complete spell checker that you will not be allowed to modify in any way, but that you might find instructive to read through as you work. An include directory contains declarations of ancillary tools that we needed to build the program, but that you are not likely to need yourself. The usual app, core, exp, and gtest directories are present, as well, and serve the same purpose as previously; app contains a main.cpp (and may not need anything else), core is where you'll do your primary work, exp is a place for you to write any experiments you need along the way, and gtest is where you can write Google Tests to validate that your code works as intended.
The program
Your work on this project begins with a partially-working spell checker. It allows a user to specify some configuration via the standard input (i.e., std::cin) and writes its output to standard output (i.e., std::cout). In short, it is capable of doing two different things:
- Given a file containing a word set and another file containing input text, it can check spelling in the input text and report on misspellings, along with suggestions about potentially correct spellings.
- Given a file containing a word set and another file containing input text, it can do the work of a spell checker without displaying a result, instead displaying the CPU time required to perform the task. This allows us to test different search structures to see what effect they have on performance.
Much of the program is already written and its complete source code has been provided. I will only be requiring you to implement two relatively small parts of it:
- A class called WordChecker that checks the spelling of words and makes appropriate suggestions when they're misspelled.
-
Three class templates that derive from an existing class template Set
, which implements the concept of a set (i.e., a collection of search keys). Only a handful of operations is supported in each, and you are not required to implement anything not declared in Set , other than a handful of additional functions that is specific to each (which are there to allow us to test details about its internal structure). There are actually three different class templates that you can write, but you are only required to choose two of them — you can choose any two of the three you'd like. -
AVLSet
, which is an AVL tree (or can be configured to skip the balancing and act as a binary search tree). Substantial partial credit is available on this one if you correctly implement only the binary search tree with no balancing. -
SkipListSet
, which is a skip list. -
HashSet
, which is a hash table with separate chaining, implemented as a dynamically-allocated array of linked lists.
-
AVLSet
If you're interested in seeing an example implementation of the Set
Sanity-checking unit tests
As you saw in Project #0, a set of sanity-checking unit tests has been provided for each of the five classes you may implement in this project. Their role is not to test that the functionality works correctly, but instead to verify that your code remains compatible with the unit tests we'll be using as part of how we grade your work — for example, you haven't changed the signatures of the member functions, removed any of them, and so on. Initially, these sanity-checking unit tests will compile, since an empty implementation of every member function has been provided. You'll want to be sure that your code continues to compile with these sanity-checking unit tests.
Do not modify the sanity-checking unit tests, because this will invalidate the testing that they provide: Ensuring that your work compiles against our unit tests.
Insurance and partial credit
You are also welcome to implement all three of the Set
Additionally, you can rest assured that the parts of this project — WordChecker and the three Set
The input and output, in detail
The program takes input in a particular format and writes its output in particular formats, as well, though the code for this has already been written. Still, you'll need to understand these formats in order to complete your work, so they are described below.
The input
The program reads four lines of input from the standard input (i.e., std::cin), without printing any kind of prompts; it simply waits until each line of input arrives before proceeding.
-
The first line selects a search structure. It can be any of the following:
- EMPTY, which specifies an "empty set", a search structure that stores no keys and for which all searches return false. (This is used primarily in performance testing.)
- VECTOR, which specifies an unsorted std::vector implementation (which has been provided already).
- HASH ZERO, which specifies a hash table implementation using a hash function that always returns zero.
- HASH SUM, which specifies a hash table implementation using a hash function that sums the character codes for every character of a word.
- HASH PRODUCT, which specifies a hash table implementation using a hash function that includes multiplication by prime numbers in its calculation.
- AVL, which specifies an AVL tree.
- SKIPLIST, which specifies a skip list.
-
The second line specifies the path to a file that contains the word set (i.e., the set of correctly-specified words). The word set file will be assumed to have one correctly-spelled word on each line.
- You'll find a file called wordset.txt in your project directory, which is a large set of English words obtained from an open source wordlist project called wordlist.sourceforge.net.
- You might also want to create one or more smaller word sets to test with, since the provided word set has over 60,000 words.
-
The third line specifies the path to a file that contains the input text for which you want to check spelling. The file will be tokenized (i.e., broken into words, with punctuation and spaces ignored) and the words will be checked by your WordChecker class.
- You'll find a file called biginput.txt in your project directory, which is one very large test case that contains a combination of words that are spelled correctly and incorrectly. This, too, was obtained from the open source wordlist project at wordlist.sourceforge.net.
-
You'll almost certainly want to create one or more smaller test inputs, so you can determine whether your implementations of the various Set
class templates and your WordChecker class are correct.
-
The fourth line specifies the format of the output you'd like to see, which allows you to choose between seeing a display of the misspellings and suggestions, or whether you're just interested in seeing how much time the spell-checking process takes. This line needs to say either:
- DISPLAY, if you want a display of misspellings and suggestions.
- TIME, if you want to see only timing results.
Display output
When you ask for a display of misspellings and suggestions, for each misspelling, you'll see the line of text, what word was misspelled, and suggested correct spellings. For example, if the input text is this:
This is a lne of text that has a missspelling in it.
you would see the following output:
This is a lne of text that has a missspelling in it.
word not found: LNE
perhaps you meant:
LANE
LEE
LIE
LINE
LONE
ONE
This is a lne of text that has a missspelling in it.
word not found: MISSSPELLING
perhaps you meant:
MISS SPELLING
MISSPELLING
Timing output
When you ask for timing output, the following experiment is run:
- The word set is loaded from the file and added into your chosen search structure.
- The input text is parsed, all words are compared against the word set, and all suggestions are generated. However, none of the misspellings or suggestions are displayed to he user.
- The word set is loaded from the file again, and instead added into the empty set search structure (i.e., one that doesn't store any keys and for which all searches return false).
- The input text is parsed again, all words again compared against the empty set, and suggestions generated.
Finally, three sets of times are shown: for everything (i.e., all of the work done using both the chosen search structure and the empty set), for just the empty set, and the difference. (The latter of these three timings is a reasonably good estimate of how much time was spent just in your chosen search structure, not including the time spent parsing the input or generating suggestions.) Each set of times indicates how much time was spent loading the word set and adding the words into the search structure, how much time was spent performing the spell checking, and the total of these times.
The results, which are indicated as CPU time measured in microseconds (i.e., millionths of a second), look like this:
RESULTS
LoadTime SpellCheckTime TotalTime
Everything 4040888usec 109778usec 4150666usec
Empty Set 36817usec 130usec 36947usec
Set Only 4004071usec 109648usec 4113719usec
This will give you some insight about how the various search structures perform relative to one another. Try your program using different search structures, word sets of different sizes and different organizations (e.g., sorted randomly, listed in ascending order), and input text of different kinds, and see what kind of insight you can gain about how the search structures compare to each other.
Generating suggestions for misspelled words
There are two popular text-mode spell checkers on Unix/Linux systems. One is called ispell; the other is a GNU "free software" program called aspell. They both use similar techniques for generating suggestions for misspelled words. While checking the spelling of an input file, if a word is not found in the word set, five techniques are used to generate possible suggestions. Each suggestion is looked up in the word set; any suggestion found in the word set is added to the list of suggestions, and no word should appear in the list of suggestions twice. The five techniques used are:
- Swapping each adjacent pair of characters in the word.
- In between each adjacent pair of characters in the word (also before the first character and after the last character), each letter from 'A' through 'Z' is inserted.
- Deleting each character from the word.
- Replacing each character in the word with each letter from 'A' through 'Z'.
- Splitting the word into a pair of words by adding a space in between each adjacent pair of characters in the word. It should be noted that this will only generate a suggestion if both words in the pair are found in the word set.
Your WordChecker class will need to generate suggestions using all five of these techniques, as well. (It should be noted that there are other ways to generate suggestions, including using algorithms that pay attention not only to the letters, but what the letters actually sound like. One such well-known algorithm is called the Soundex algorithm. You are not required to implement such algorithms for this project.)
Limitations
You are not permitted to modify any of the code in the provided directory, and note that the gather script (which gathers up your C++ source and header files for submission) will not include any of this code in your submission. What you submit must be compatible with what was already provided.
Note, too, that some of the search structures have limitations on how you're permitted to implement them: portions of the C++ Standard Library that are off-limits, certain techniques that must be used, or performance guarantees (say, certain operations that must run in O(log n) time). See the comments in each of the files (e.g., core/AVLSet.hpp) for more details.
Deliverables
After using the gather script in your project directory to gather up your C++ source and header files into a single project4.tar.gz file, then submit that file (and only that file!) to Canvas.
Understand that we will only accept projects submitted to the appropriate dropbox (for the appropriate assignment) on Canvas; we do not accept printed copies of your projects, nor do we accept them via email or other means under any circumstances.
You are responsible for submitting the version of your project that you want graded. We will grade the most recent submission made before the deadline. Accidentally submitting the wrong version, nor is there any remedy (outside of the late policy described above) for forgetting to submit your work at all.
Can I submit after the deadline?
Yes, it is possible, subject to the late work policy for this course, which is described in the section titled Late work at this link.
What do I do if Canvas slightly adjusts my filename?
Canvas will sometimes modify your filenames when you submit them (e.g., when you submit the same file twice, it will change the name of your second submission to end in -1.tar.gz instead of just .tar.gz). In general, this is fine; as long as the file you submitted has the correct name, we'll be able to obtain it with that same name, even if Canvas adjusts it.