联系方式

  • QQ:99515681
  • 邮箱:99515681@qq.com
  • 工作时间:8:00-21:00
  • 微信:codinghelp

您当前位置:首页 >> CS作业CS作业

日期:2021-05-12 11:14

School of Computing and Information Systems

The University of Melbourne

COMP30027, Machine Learning, 2021

Project 2: How long will it take to cook this?

Task: Build a classifier to predict the cooking time of recipes

Due: Stage I: Wednesday 19 May, 5pm UTC+10 (Australian Eastern Standard Time)

Stage II: Wednesday 26 May, 5pm UTC+10 (Australian Eastern Standard Time)

Stage I: Friday 21 May, 5pm UTC+10 (Australian Eastern Standard Time)

Stage II: Friday 28 May, 5pm UTC+10 (Australian Eastern Standard Time)

Submission: Stage I: Report (PDF) and code to Canvas; test outputs to Kaggle in-class competition

Stage II: Peer reviews and reflection to Canvas

Marks: The Project will be marked out of 20, and will contribute 20% of your total mark.

Groups: Groups of 1 or 2, with commensurate expectations for each (see Sections 2 and 6).

1 Overview

The goal of this Project is to build and critically analyse supervised Machine Learning methods, to predict the

cooking time for recipes based on their steps, ingredients and other features. The cooking time of a recipe has

been categorised into three classes, corresponding to quick, medium and slow.

This assignment aims to reinforce the largely theoretical lecture concepts surrounding data representation, classifier

construction, and evaluation, by applying them to an open-ended problem. You will also have an opportunity

to practice your general problem-solving skills, written communication skills, and creativity.

This project has two stages. The main focus of these stages will be the written report, where you will demonstrate

the knowledge that you have gained and the critical analysis you have conducted in a manner that is

accessible to a reasonably informed reader.

2 Deliverables

More details about deliverables are given in the Submission (Section 6).

Stage I:

1. Report: an anonymous written report, of 1000-1500 words (for a group of one person) or 2000-2500

words (for a group of two people)

2. Output: the output of your classifiers, comprising predictions of labels for the test instances, submitted

to the Kaggle1

in-class competition described below.

3. Code: one or more programs, written in Python, which implement machine learning models, make

predictions, and evaluate the results.

Stage II:

1. Peer review: reviews of two reports written by other students, of 200-300 words each (for a group of one

person) or 300-400 words each (for a group of two people).

2. Reflection: a written reflection piece of 400-600 words. This deliverable is individual work.

1https://www.kaggle.com/

1

3 Terms of Use

The data has been collected from Food.com (formerly GeniusKitchen), under the provision that any resulting

work should cite this resource:

Generating Personalized Recipes from Historical User Preferences. Bodhisattwa Prasad Majumder,

Methods in Natural Language Processing and the 9th International Joint Conference on Natural

Language Processing (EMNLP-IJCNLP), 2019.

This reference must be cited in the bibliography. We reserve the right to mark any submission lacking this

reference with a 0, due to violation of the Terms of Use.

Please note that the dataset is a sample of actual data posted to the World Wide Web. As such, it may contain

information that is in poor taste, or that could be considered offensive. We would ask you, as much as possible,

to look beyond this to the task at hand. For example, it is generally not necessary to read individual records.

The opinions expressed within the data are those of the anonymised authors, and in no way express the official

views of the University of Melbourne or any of its employees; using the data in an educative capacity does not

constitute endorsement of the content contained therein.

If you object to these terms, please contact us (kris.ehinger@unimelb.edu.au or

ling.luo@unimelb.edu.au) as soon as possible.

4 Data

The data files are available via Canvas, and are described in a corresponding README.

The recipes are collected from Food.com2

, which is a platform that allows the user to publish recipes and

comments on others’ recipes. In our dataset, each recipe contains:

? recipe features: name, ingredients, steps, number of steps, and number of

ingredients

? text features: produced by various text encoding methods for name, ingredients, and steps. Each

feature is provided as a single file with rows corresponding to the file of recipe features.

? class label: the preparation time of a recipe duration (3 possible levels, 1, 2 or 3)

You will be provided with training set and a test set. The training set contains the recipe features, text features,

and the duration, which is the “class label” of our task. The test set only contains the recipe and text features

without the label.

The files provided are:

? recipe train.csv: recipe features and class label of training instances.

? recipe test.csv: recipe features of test instances.

? recipe text features *.zip: preprocessed text features for training and test sets, 1 zipped file for each text

encoding method. Details about using these text features are provided in README.

5 Task

You are expected to develop Machine Learning models to predict the preparation of a recipe based on its features

(e.g. name, ingredients, steps etc.). You will implement and compare different machine learning models and

explore the effective features for this task.

2https://www.food.com/

2

? The training-evaluation phase: The holdout or cross-validation approaches can be applied on the training

data provided.

? The test phase: the trained classifiers will be evaluated on the unlabelled test data. The predicted labels

of test cases should be submitted as part of the Stage I deliverable.

Various machine learning techniques have been (or will be) discussed in this subject (0R, Naive Bayes, Decision

Trees, kNN, SVM, neural network, etc.); many more exist. You may use any machine learning method you

consider suitable for this problem. You are strongly encouraged to make use of machine learning software

and/or existing libraries (such as sklearn) in your attempts at this project.

In addition to different learning algorithms, there are many different ways to encode text for these algorithms.

The files in recipe text features *.zip are some possible representations of the name, ingredients and steps of

recipes we have provided. For example, one of the encoding method is CountVectorizer in sklearn,

which converts text documents into “Bag of Words” – the documents are described by word occurrences while

ignoring the relative position information of the words. You can use these representations to develop your

classifiers, but you should also feel free to extract your own features from the raw recipe features, according to

your needs. Just keep in mind that any data representation you use for the text in the training set will need to be

able to generalise to the test set.

6 Submission

The report, code, peer reviews and reflections should be submitted via Canvas; the predictions on test data

should be submitted to Kaggle.

Stage I submissions will be open one week before the due date. Stage II submissions will be open as soon as

the reports are available (24 hours following the Stage I submission deadline).

6.1 Individual vs. Team Participation

You have the option of participating as a “group” of one individual, or in a group of two. In the case that you opt

to participate individually, you will be required to implement at least 1 and up to 4 distinct Machine Learning

models. Groups of two will be required to implement at least 3 and up to 5 distinct Machine Learning models,

of which one is to be an ensemble model – stacking based on the other models. The report length requirement

also differs, as detailed below:

Group size Distinct models required Report length

1 1–4 1,000–1,500 words

2 3–5 2,000–2,500 words

If you wish to form a group of 2, only one of the members needs to register on Canvas by Wednesday 5 May,

via the survey “Assignment 2 Group Registration”. For a group of 2, only one of the members needs to

submit deliverables.

Note that once you have signed up for a given group, you will not be allowed to change groups. If you do not

register before the deadline above, we will assume that you will be completing the assignment as an individual,

even if you were in a two-person group for Assignment 1.

6.2 Stage I: Report

The report should be 1,000-1,500 words (groups of one person) or 2,000-2,500 words (groups of two people)

in length and provide a basic description of:

1. the task, and a short summary of some related work

3

2. what you have done, including any learners that you have used, or features that you have engineered.

This should be at a conceptual level; a detailed description of the code is not appropriate for the report.

The description should be similar to what you would see in a machine learning conference paper.

3. evaluation of your classifiers.

You should also aim to have a more detailed discussion, which:

4. Contextualises the behaviour of the method(s), in terms of the theoretical properties we have identified in

the lectures

5. Attempts some error analysis of the method(s)

And don’t forget:

6. A bibliography, which includes the paper listed in Terms of Use, and other related work

Note that we are more interested in seeing evidence that you have thought about the task and investigated the

reasons for the relative performance of different methods, rather than in the raw scores of the different methods.

This is not to say that you should ignore the relative performance of different runs over the data, but rather that

you should think beyond simple numbers to the reasons that underlie them, and connect these to the theory that

we have discussed in this class.

Reports must be submitted in the form of a single PDF file. If a report is submitted in any format other than

PDF, we reserve the right to return the report with a mark of 0.

To facilitate anonymous peer-review, your name and student ID should not appear anywhere in the

report, including the metadata (filename, etc.).

6.3 Stage I: Predictions of test data

To give you the possibility of evaluating your models on the test set, we will be setting up this project as a Kaggle

in-class competition. You can submit results on the test set there, and get immediate feedback on your model’s

performance. There is a Leaderboard, that will allow you to see how well you are doing as compared to other

classmates participating on-line. The Kaggle in-class competition URL and instructions will be announced on

Canvas shortly.

You will receive marks for submitting (at least) one set of predictions for the unlabelled test dataset into the

competition; and get basically reasonable accuracy, e.g. better than Zero-R. The focus of this assignment is

on the quality of your critical analysis and your report, rather than the performance of your Machine Learning

models.

6.4 Stage II: Reviews

During the reviewing process, you will read two submissions by other students. This is to help you contemplate

some other ways of approaching the project, and to ensure that students get some extra feedback. For each

report, you should aim to write 200-400 words total (200-300 words if you work alone, or 300-400 words if

you work in a group of two people), responding to three “questions”:

? Briefly summarise what the author has done

? Indicate what you think that the author has done well, and why

? Indicate what you think could have been improved, and why

Please be courteous and professional in the reviewing process. A brief guideline for reviewers published

by IEEE can be found https://www.ieee.org/content/dam/ieee-org/ieee/web/org/members/

students/reviewer_guidelines_final.pdf.

4

6.5 Stage II: Reflections

A comprehensive written reflection piece summarising your critical reflection on the following topics within

400-600 words.

1. the process of completing this project

2. things that you are satisfied with and those can be improved in your Stage I deliverables, e.g. modelling,

evaluation, analysis and discussion.

3. if you have worked in a group of two people, what are the individual contributions?

The reflection report is individual and not anonymous. Everyone must submit their own reflection on Canvas.

7 Assessment Criteria

The Project will be marked out of 20, and is worth 20% of your overall mark for the subject. The mark

breakdown will be:

Report 14 marks

Performance of classifier 2 mark

Reviews 2 marks

Reflection 2 marks

TOTAL 20 marks

The report will be marked according to the rubric, which will be announced via the Canvas.

The performance of classifier (2 marks) is for submitting (at least) one set of model predictions to the Kaggle

competition; and get basically reasonable accuracy, e.g. better than Zero-R.

Since all of the documents exist on the World Wide Web, it is inconvenient but possible to “cheat” and identify

some of the class labels from the test data using non-Machine Learning methods. If there is any evidence of

this, the performance of classifier will be ignored, and you will instead receive a mark of 0 for this component.

The code will not be directly assessed, but if you do not submit it, it will be assumed that you are attempting

to circumvent the Machine Learning requirement, and you will receive a mark of 0 for the performance of

classifier.

8 Using Kaggle

The Kaggle in-class competition URL will be announced on Canvas shortly. To participate the competition:

? Each student should create a Kaggle account (unless they have one already) using your Student-ID

? You may make up to 8 submissions per day. An example submission file can be found on the Kaggle site.

? Submissions will be evaluated by Kaggle for accuracy, against just 30% of the test data, forming the

public leaderboard.

? Prior to competition close, you may select a final submission out of the ones submitted previously – by

default the submission with highest public leaderboard score is selected by Kaggle.

? After competition close, public 30% test scores will be replaced with the private leaderboard 100% test

scores.

5

9 Changes/Updates to the Assignment Specifications

We will use Canvas to advertise any (hopefully small-scale) changes or clarifications in the assignment speci-

fications. Any addendums made to the assignment specifications via Canvas will supersede information contained

in this version of the specifications.

10 Late Submissions

Late submissions will bring disruption to the reviewing process. You are strongly encouraged to submit by the

date and time specified above. If circumstances do not permit this, then the marks will be adjusted as follows:

? Each day (or part of the day) that the report is submitted after the specified due date and time for Stage I,

10% will be deducted from the marks available, up until 7 days (1 week) has passed, after which regular

submissions will no longer be accepted. A late report submission will mean that your report might not

participate in the reviewing process, and so you will probably receive less feedback.

? Any late submission of the reviews will incur a 50% penalty (i.e. 1 of the 2 marks), and will not be

accepted more than 7 days (1 week) after the Stage II deadline.

? Any late submission of the reflection will incur a 50% penalty (i.e. 1 of the 2 marks), and will not be

accepted more than 7 days (1 week) after the Stage II deadline.

11 Academic Honesty

While it is acceptable to discuss the assignment with others in general terms, excessive collaboration with

students outside of your group is considered cheating. Your submissions will be examined for originality and

will invoke the University’s Academic Misconduct policy here either inappropriate levels of collaboration or plagiarism are deemed to have taken place.

6


版权所有:编程辅导网 2020 All Rights Reserved 联系方式:QQ:99515681 电子信箱:99515681@qq.com
免责声明:本站部分内容从网络整理而来,只供参考!如有版权问题可联系本站删除。