辅导EECS 280、讲解Machine Learning、辅导C/C++设计、C/C++编程语言调试解析Java程序|辅导R语言编程

EECS 280 Project 5: Machine Learning
Due Friday, 13 April 2018, 8pm
In this project, you will write a program that uses natural language processing and machine
learning techniques to automatically identify the subject of posts from the EECS 280 Piazza. You
will gain experience with recursion, binary trees, templates, comparators, and the map data
structure. Another goal is to prepare you for future courses (like EECS 281) or your own
independent programming projects, so we have given you a lot of freedom to design the structure
of your overall application.
The correctness portion of the final submission is worth approximately 70%, with the remaining
approximately 30% based on the thoroughness of your BST test cases and style grading. Your
test cases and style will both by graded by the autograder.
Winter 2018: We will use the same automated style grading on this project that we did for project
4. On this project, the automated style checks will be part of the grade. To run the tests on your
own, check out the style checking tutorial.
You may work alone or with a partner. Please see the syllabus for partnership rules.
Table of Contents
Project Roadmap
Project Introduction
Project Essentials
The BinarySearchTree ADT
Testing BinarySearchTree
The Map ADT
Testing Map
The Piazza Datasets
Classifying Piazza Posts with NLP and ML
The Bag of Words Model
Training the Classifier4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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Predicting a Label for a New Post
Implementing Your Top?Level Classifier Application
Classifier Application Interface
Output
Results
Appendix A: Map Example
Appendix B: Splitting a Whitespace?Delimited String
Project Roadmap
1. Set up your IDE
Use the tutorial from project 1 to get your visual debugger set up. Use this wget link
https://eecs280staff.github.io/p5‐ml/starter‐files.tar.gz .
Before setting up your visual debugger, you’ll need to rename each .h.starter file to a .h
file.
$ mv BinarySearchTree.h.starter BinarySearchTree.h
$ mv Map.h.starter Map.h
You’ll also need to create these new files and add function stubs.
$ touch main.cpp
These are the executables you’ll use in this project:
BinarySearchTree_compile_check.exe
BinarySearchTree_public_test.exe
BinarySearchTree_tests.exe
Map_compile_check.exe
Map_public_test.exe
main.exe
If you’re working in a partnership, set up version control for a team.
2. Read the Project Introduction and Project Essentials4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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See the first sections below for an introduction to the project as well as essential instructions
for successfully completing the project.
3. Test and implement the BinarySearchTree data structure
We’ve provided header files with comments. Test and implement those functions. Be sure to
use recursion and tail recursion where the comments require it.
4. Test and implement the Map data structure
Implement and test a Map ADT that internally uses your BinarySearchTree to provide an
interface that works (almost) exactly like std::map from the STL! Appendix A has an example.
5. Test and implement the Piazza Classifier Application
This specification describes the interface for the overall application, but it’s up to you how to
separate it into functions and data structures.
Appendix B has tips and tricks for this part.
Submit to the Autograder
Submit the following files to the autograder.
BinarySearchTree.h
Map.h
main.cpp
BinarySearchTree_tests.cpp
Project Introduction
The goal for this project is to write an intelligent program that can classify Piazza posts according
to topic. This task is easy for humans ? we simply read and understand the content of the post,
and the topic is intuitively clear. But how do we compose an algorithm to do the same? We can’t
just tell the computer to “look at it” and understand. This is typical of problems in artificial
intelligence and natural language processing.4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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We know this is about Euchre, but how can we write an algorithm that “knows” that?
With a bit of introspection, we might realize each individual word is a bit of evidence for the topic
about which the post was written. Seeing a word like “card”, “spades”, or even “bob” leads us
toward the Euchre project. We judge a potential label for a post based on how likely it is given all
the evidence. Along these lines, information about how common each word is for each topic
essentially constitutes our classification algorithm.
But we don’t have that information (i.e. that algorithm). You could try to sit down and write out a
list of common words for each project, but there’s no way you’ll get them all. For example, the
word “lecture” appears much more frequently in posts about exam preparation. This makes
sense, but we probably wouldn’t come up with it on our own. And what if the projects change? We
don’t want to have to put in all that work again.
Instead, let’s write a program to comb through Piazza posts from previous terms (which are
already tagged according to topic) and learn which words go with which topics. Essentially, the
result of our program is an algorithm! This approach is called (supervised) machine learning. Once
we’ve trained the classifier on some set of Piazza posts, we can apply it to new ones written in the
future.4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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Authors
This project was developed for EECS 280, Fall 2016 at the University of Michigan. Andrew DeOrio
and James Juett wrote the original project and specification. Amir Kamil contributed to code
structure, style, and implementation details.
Project Essentials
The project consists of three main phases:
1. Implement and test the static _impl member functions in BinarySearchTree .
2. Implement and test Map by using the has?a pattern on top of BinarySearchTree .
3. Design, implement, and test the top?level classifier application.
The focus of part 1 is on working with recursive data structures and algorithms. The framework
and some of the implementation for BinarySearchTree is provided for you, but you must
implement the core functionality in several static member functions. Be mindful of requirements
for which implementations must use certain kinds of recursion.
Part 2 should not require a lot of additional implementation code. Make sure to reuse the
functionality already present in BinarySearchTree wherever possible.
For your top?level application, you must use std::map in place of Map . This means a bug in
parts 1 or 2 will not jeopardize your ability to complete part 3. Additionally, the implementation of
BinarySearchTree (and consequently Map ) we have you write will not be fast enough for the
classifier.
Requirements and Restrictions
DO DO NOT
Put all top?level application code in main.cpp.
Create additional files other than
main.cpp.
Create any ADTs or functions you wish for your
top?level classifier application.
Modify the BinarySearchTree or Map
public interfaces
Use any part of the STL for your top level classifier
application, including map and set.
Use STL containers in your
implementation of BinarySearchTree or
Map.4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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DO DO NOT
Use any part of the STL except for containers in
your BinarySearchTree and Map implementations.
Use your Map implementation for the
top level application. It will be too slow.
Use recursion for the BST _impl functions.
Use iteration for the BST _impl
functions.
Follow course style guidelines. Use static or global variables.
Starter Files
The following table describes each file included in the starter code. As you begin development,
rename files to remove .starter .
Filename Description
BinarySearchTree.h.starter Defines an ADT for a binary search tree.
BinarySearchTree_tests.cpp Add your BST tests to this file.
BinarySearchTree_public_test.cpp A public test for BinarySearchTree
BinarySearchTree_compile_check.cpp A compilation test for BinarySearchTree.h
TreePrint.h
Auxiliary file to support printing trees. You do not
need to look at this file. Do not modify it.
Map.h.starter Map ADT
Map_public_test.cpp
A sample test for Map. You are encouraged to write
map tests, but do not submit them.
Map_public_test.out.correct Correct output for the Map public test.
Map_compile_check.cpp A compilation test for Map.h.
Piazza Datasets (Four .csv files)
Piazza post data from several past EECS 280 terms in
Comma Separated Value (CSV) format.
csvstream.h A library for reading data in CSV format.4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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Filename Description
train_small.csv
test_small.csv
test_small.out.correct
test_small_debug.out.correct
Sample input training and testing files for the
classifier application, as well as the corresponding
correct output when run with those files.
Makefile
Used by the make command to compile the
executable.
unit_test_framework.h
unit_test_framework.cpp
The unit test framework you must use to write your
test cases.
The BinarySearchTree ADT
A binary search tree supports efficiently storing and searching for elements.
Template Parameters
BinarySearchTree has two template parameters:
T ? The type of elements stored within the tree.
Compare ? The type of comparator object (a functor) that should be used to determine
whether one element is less than another. The default type is std::less , which compares
two T objects with the < operator. To compare elements in a different fashion, a custom
comparator type must be specified.
No Duplicates Invariant
In the context of this project, duplicate values are NOT allowed in a BST. This does not need to be
the case, but it avoids some distracting complications.
Sorting Invariant
A binary search tree is special in that the structure of the tree corresponds to a sorted ordering of
elements and allows efficient searches (i.e. in logarithmic time).
Every node in a well?formed binary search tree must obey this sorting invariant:
It represents an empty tree (i.e. a null Node* ).
OR 4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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The left subtree obeys the sorting invariant, and every element in the left subtree is less than
the root element (i.e. this node).
The right subtree obeys the sorting invariant, and the root element (i.e. this node) is less than
every element in the right subtree.
Put briefly, go left and you’ll find smaller elements. Go right and you’ll find bigger ones. For
example, the following are all well?formed sorted binary trees:
Data Representation
The data representation for BinarySearchTree is a tree?like structure of nodes similar to that
described in lecture. Each Node contains an element and pointers to left and right subtrees. The
structure is self?similar. A null pointer indicates an empty tree. You must use this data
representation. Do not add member variables to BinarySearchTree or Node .
Public Member Functions and Iterator Interface
The public member functions and iterator interface for BinarySearchTree are already implemented
in the starter code. DO NOT modify the code for any of these functions. They delegate the work
to private, static implementation functions, which you will write.
Implementation Functions4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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The core of the implementation for BinarySearchTree is a collection of private, static member
functions that operate on tree?like structures of nodes. You are responsible for writing the
implementation of several of these functions.
To disambiguate these implementation functions from the public interface functions, we have
used names ending with _impl . (This is not strictly necessary, because the compiler can
differentiate them based on the Node* parameter.)
There are a few keys to thinking about the implementation of these functions:
The functions have no idea that such a thing as the BinarySearchTree class exists, and
they shouldn’t. A “tree” is not a class, but simply a tree?shaped structure of Node s. The
parameter node points to the root of these nodes.
A recursive implementation depends on the idea of similar subproblems, so a “subtree” is
just as much a tree as the “whole tree”. That means you shouldn’t need to think about “where
you came from” in your implementation.
Every function should have a base case! Start by writing this part.
You only need to think about one “level” of recursion at a time. Avoid thinking about the
contents of subtrees and take the recursive leap of faith.
We’ve structured the starter code so that the first bullet point above is actually enforced by the
language. Because they are static member functions, they do not have access to a receiver
object (i.e. there’s no this pointer). That means it’s actually impossible for these functions to try
to do something bad with the BinarySearchTree object (e.g. trying to access the root member
variable).
Instead, the implementation functions are called from the regular member functions to perform
specific operations on the underlying nodes and tree structure, and are passed only a pointer to
the root Node of the tree/subtree they should work with.
The empty_impl function must run in constant time. It must must be able to determine and return
its result immediately, without using either iteration or recursion. The rest of the implementation
functions must be recursive. There are additional requirements on the kind of recursion that must
be used for some functions. See comments in the starter code for details. Iteration (i.e. using
loops) is not allowed in any of the _impl functions.
Using the Comparator
The _impl functions that need to compare data take in a comparator parameter called less .
Make sure to use less rather than the < operator to compare elements!
The insert_impl Function4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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The key to properly maintaining the sorting invariant lies in the implementation of the insert_impl
function this is essentially where the tree is built, and this function will make or break the whole
ADT. Your insert_impl function should follow this procedure:
1. Handle an originally empty tree as a special case.
2. Insert the element into the appropriate place in the tree, keeping in mind the sorting invariant.
You’ll need to compare elements for this, and to do so make sure to use the less comparator
passed in as a parameter.
3. Use the recursive leap of faith and call insert_impl itself on the left or right subtree. Hint:
You do need to use the return value of the recursive call. (Why?)
Important: When recursively inserting an item into the left or right subtree, be sure to replace the
old left or right pointer of the current node with the result from the recursive call. This is essential,
because in some cases the old tree structure (i.e. the nodes pointed to by the old left or right
pointer) is not reused. Specifically, if the subtree is empty, the only way to get the current node to
“know” about the newly allocated node is to use the pointer returned from the recursive call.
Technicality: In some cases, the tree structure may become unbalanced (i.e. too many nodes on
one side of the tree, causing it to be much deeper than necessary) and prevent efficient operation
for large trees. You don’t have to worry about this.
Testing BinarySearchTree
You must write and submit tests for the BinarySearchTree class. Your test cases MUST use the
unit test framework, otherwise the autograder will not be able to evaluate them. Since unit tests
should be small and run quickly, you are limited to 50 TEST() items per file, and your whole test
suite must finish running in less than 5 seconds. Please bear in mind that you DO NOT need 50
unit tests to catch all the bugs. Writing targeted test cases and avoiding redundant tests can help
catch more bugs in fewer tests.
How We Grade Your Tests
We will autograde your BinarySearchTree unit tests by running them against a number of
implementations of the module. If a test of yours fails for one of those implementations, that is
considered a report of a bug in that implementation.
We grade your tests by the following procedure:
1. We compile and run your test cases with a correct solution. Test cases that pass are
considered valid. Tests that fail (i.e. falsely report a bug in the solution) are invalid. The
autograder gives you feedback about which test cases are valid/invalid. Since unit tests4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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should be small and run quickly, your whole test suite must finish running in less than 5
seconds.
2. We have a set of intentionally incorrect implementations that contain bugs. You get points
for each of these “buggy” implementations that your valid tests can catch.
3. How do you catch the bugs? We compile and run all of your valid test cases against each
buggy implementation. If any of these test cases fail (i.e. report a bug), we consider that you
have caught the bug and you earn the points for that bug.
The Map ADT
The Map ADT works just like std::map . Map has three template parameters for the types of keys
and values, as well as a customizable comparator type for comparing keys. The most important
functions are find, insert, and the [] operator. The RMEs and comments in Map.h provide the
details, and appendix A includes an example.
Note: Although you must implement Map , use std::map instead in your top?level application. Our
implementation of Map is not fast enough for the classifier.
Building on the BST
The operation of a map is quite similar to that of a BST. The additional consideration for a map is
that we want to store key?value pairs instead of single elements, but also have any comparisons
(e.g. for searching) only depend on the key and be able to freely change the stored values without
messing up the BST sorting invariant. We can employ the has?a pattern using a BinarySearchTree
as the data representation for Map:
BST template parameter: T
Instantiate with: Pair_type
We’ve provided a using declaration in the starter code for Pair_type :
using Pair_type = std::pair;
std::pair is basically like a struct that stores two objects together. Key_type and
Value_type are whatever template parameters were used to instantiate Map .
BST template parameter: Compare
Instantiate with: PairComp4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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You’ll need to define your own comparator by declaring a functor type called PairComp (or
whatever you want to call it) in your Map class. The overloaded () operator should accept
two objects of Pair_type and return whether the key of the LHS is less than the key of the
RHS (according to Key_compare ).
Finally, we can even reuse the iterators from the BST class, since the interface we want (based on
std::map ) calls for iterators to yield a key?value pair when dereferenced. Since the element type
T of the BST is our Pair_type , BST iterators will yield pairs and will work just fine. We’ve
provided this using declaration with the starter code to make Map::Iterator simply an alias for
iterators from the corresponding BST:
using Iterator = typename BinarySearchTree::Iterator;
Testing Map
You are encouraged to write tests for the Map ADT, but they are not required for the project
submission. Do not submit them to the autograder.
The Piazza Datasets
For this project, we retrieved archived Piazza posts from EECS 280 in past terms. We will focus on
two different ways to divide Piazza posts into labels (i.e. categories).
By topic. Labels: “exam”, “calculator”, “euchre”, “image”, “recursion”, “statistics”
Example: Posts extracted from w16_projects_exam.csv
label content
exam will final grades be posted within 72 hours
calculator can we use the friend class list in stack
euchre weird problem when i try to compile euchrecpp
image is it normal for the horses tests to take 10 minutes
recursion is an empty tree a sorted binary tree
statistics are we supposed to have a function for summary
… …4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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By author. Labels: “instructor”, “student”
Example: Posts extracted from w14?f15_instructor_student.csv
label content
instructor disclaimer not actually a party just extra OH
student how can you use valgrind with calccpp
student could someone explain to me what the this keyword means
… …
The Piazza datasets are Comma Separated Value (CSV) files. The label for each post is found in
the “tag” column, and the content in the “content” column. There may be other columns in the
CSV file; your code should ignore all but the “tag” and “content” columns. You may assume all
Piazza files are formatted correctly, and that post content and labels only contain
lowercase characters, numbers, and no punctuation. We recommend using the csvstream.h
library (see https://github.com/awdeorio/csvstream for documentation) to read CSV files in your
application. The csvstream.h file itself is included with the starter code.
Your classifier should not hardcode any labels. Instead, it should use the exact set of
labels that appear in the training data.
Appendix B contains code for splitting a string of content into a set of individual words.
We have included several Piazza datasets with the project:
train_small.csv ? Made up training data intended for small?scale testing.
test_small.csv ? Made up test data intended for small?scale testing.
w16_projects_exam.csv ? (Train) Real posts from W16 labeled by topic.
sp16_projects_exam.csv ? (Test) Real posts from Sp16 labeled by topic.
w14‐f15_instructor_student.csv ? (Train) Real posts from four terms labeled by author.
w16_instructor_student.csv ? (Test) Real posts from W16 Piazza labeled by author.
For the real datasets, we have indicated which are intended for training vs. testing.
Classifying Piazza Posts with NLP and ML
At a high level, the classifier we’ll implement works by assuming a probabilistic model of how
Piazza posts are composed, and then finding which label (e.g. our categories of “euchre”, “exam”,4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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etc.) is the most probable source of a particular post.
All the details of natural language processing (NLP) and machine learning (ML) techniques you
need to implement the project are described here. You are welcome to consult other resources,
but there are many kinds of classifiers that have subtle differences. The classifier we describe
here is a simplified version of a “Multi?Variate Bernoulli Naive Bayes Classifier”. If you find other
resources, but you’re not sure they apply, make sure to check them against this specification.
This document provides a more complete description of the way the classifier works, in case
you’re interested in the math behind the formulas here.
The Bag of Words Model
We will treat a Piazza post as a “bag of words” ? each post is simply characterized by which
words it includes. The ordering of words is ignored, as are multiple occurrences of the same word.
These two posts would be considered equivalent:
“the left bower took the trick”
“took took trick the left bower bower”
Thus, we could imagine the post generation process as a person sitting down and going through
every possible word and deciding which to toss into a bag.
Background: Conditional Probabilities and Notation
We write to denote the probability (a number between 0 and 1) that some event will
occur. denotes the probability that event will occur given that we already know event
has occurred. For example, . This means that if a Piazza post is
about the euchre project, there is a 0.7% chance it will contain the word bower (we should say “at
least once”, technically, because of the bag of words model).
Training the Classifier
Before the classifier can make predictions, it needs to be trained on a set of previously labeled
Piazza posts (e.g. train_small.csv or w16_projects_exam.csv ). Your application should process
each post in the training set, and record the following information:
The total number of posts in the entire training set.
The number of unique words in the entire training set. (The vocabulary size.)
For each word , the number of posts in the entire training set that contain .
For each label , the number of posts with that label.4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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For each label and word , the number of posts with label that contain .
Predicting a Label for a New Post
Given a new Piazza post , we must determine the most probable label , based on what the
classifier has learned from the training set. A measure of the likelihood of C is the log probability
score given the post:
Important: Because we’re using the bag?of?words model, the words w , w , …, w in this formula
are only the unique words in the post, not including duplicates! To ensure consistent results, make
sure to add the contributions from each word in alphabetic order.
The classifier should predict whichever label has the highest log?probability score for the post. If
multiple labels are tied, predict whichever comes first alphabetically.
is the log prior probability of label and is a reflection of how common it is:
is the log likelihood of a word given a label , which is a measure of how likely it is
to see word in posts with label . The regular formula for is:
However, if was never seen in a post with label in the training data, we get a log?likelihood of∞, which is no good. Instead, use one of these two alternate formulas:
(Use when does not occur in posts labeled but does occur in the training data overall.)
(Use when does not occur anywhere at all in the training set.)
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Implementing Your Top?Level Classifier Application
For submission to the autograder, your top?level application code must be entirely
contained in a single file, main.cpp . However, the structure of your classifier application,
including which procedural abstractions and/or ADTs to use for the classifier, is entirely up to you.
Make sure your decisions are informed by carefully considering the classifier and top?level
application described in this specification.
We strongly suggest you make a class to represent the classifier ? the private data members for
the class should keep track of the classifier parameters learned from the training data, and the
public member functions should provide an interface that allows you to train the classifier and
make predictions for new piazza posts.
Here is some high?level guidance:
1. First, your application should read posts from a file (e.g. train_small.csv ) and use them to
train the classifier. After training, your classifier abstraction should store the information
mentioned in the “Training the Classifier” section above.
2. Your classifier should be able to compute the log?probability score of a post (i.e. a collection
of words) given a particular label. To predict a label for a new post, it should choose the label
that gives the highest log?probability score.
3. Read posts from a file (e.g. test_small.csv ) to use as testing data. For each post, predict a
label using your classifier.
Some of these steps have output associated with them. See the “output” section below for the
details.
You must also write RMEs and appropriate comments to describe the interfaces for the
abstractions you choose (ADTs, classes, functions, etc.). You should also write unit tests to verify
each component works on its own.
You are welcome to use any part of the STL in your top?level classifier application. In particular,
std::map and std::set will be useful.
Classifier Application Interface
Here is the usage message for the top?level application:
$ ./main.exe
Usage: main.exe TRAIN_FILE TEST_FILE [‐‐debug]4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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The main application always requires files for both training and testing, although the test file may
be empty. You may assume all files are in the correct format.
Use the provided small?scale files for initial testing and to check your output formatting:
$ ./main.exe train_small.csv test_small.csv
$ ./main.exe train_small.csv test_small.csv ‐‐debug
Correct output is in test_small.out.correct and test_small_debug.out.correct . The output
format is discussed in detail below.
Error Checking
The program checks that the command line arguments obey the following rules:
There are 3 or 4 arguments, including the executable name itself (i.e. argv[0] ).
The fourth argument (i.e. argv[3] ), if provided, must be ‐‐debug .
If any of these are violated, print out the usage message and then quit by returning a non?zero
value from main . Do not use the exit library function, as this fails to clean up local objects.
cout << "Usage: main.exe TRAIN_FILE TEST_FILE [‐‐debug]" << endl;
If any file cannot be opened, print out the following message, where filename is the name of the
file that could not be opened, and quit by returning a non?zero value from main .
cout << "Error opening file: " << filename << endl;
You do not need to do any error checking for command?line arguments or file I/O other than what
is described on this page. However, you must use precisely the error messages given here in
order to receive credit. (Just literally use the code given here to print them.)
As mentioned earlier, you may assume all Piazza data files are in the correct format.
Output
This section details the output your program should write to cout, using the small files mentioned
above as an example. Some lines are indented by two spaces. Output only printed when the ‐‐
debug flag is provided is indicated here with “(DEBUG)”.4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml
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Add this line at the beginning of your main function to set floating point precision:
cout.precision(3);
First, print information about the training data:
(DEBUG) Line?by?line, the label and content for each training document.
training data:
label = euchre, content = can the upcard ever be the left bower
label = euchre, content = when would the dealer ever prefer a card to the upcard
label = euchre, content = bob played the same card twice is he cheating
...
label = calculator, content = does stack need its own big three
label = calculator, content = valgrind memory error not sure what it means
The number of training posts.
trained on 8 examples
(DEBUG) The vocabulary size (the number of unique words in all training content).
vocabulary size = 49
An extra blank line
If the debug option is provided, also print information about the classifier trained on the training
posts. Whenever classes or words are listed, they are in alph

辅导EECS 280、讲解Machine Learning、辅导C/C++设计、C/C++编程语言调试 解析Java程序|辅导R语言编程

辅导EECS 280、讲解Machine Learning、辅导C/C++设计、C/C++编程语言调试解析Java程序|辅导R语言编程