CMT655

Cardiff School of Computer Science and Informatics
Module Code: CMT655
Module Title: Manipulating and Exploiting Data
Assessment Title: Course Portfolio
Assessment Number: 1
Date Set: Friday 26th March 2021
Submission Date and Time: May 31st 2021 9:30
Return Date: June 30th 2021
This assignment is worth 100% of the total marks available for this module. If coursework
is submitted late (and where there are no extenuating circumstances):
1 If the assessment is submitted no later than 24 hours after the deadline, the mark
for the assessment will be capped at the minimum pass mark;
2 If the assessment is submitted more than 24 hours after the deadline, a mark of 0
will be given for the assessment.
Your submission must include the official Coursework Submission Cover sheet, which can
be found here:
https://docs.cs.cf.ac.uk/down...
Submission Instructions
Your coursework should be submitted via Learning Central by the above deadline. It
consists of a portfolio divided in three assessments.
Assessment (1) consists of a set of exercises. The final deliverable consists of two
jupyter notebooks.
Assessment (2) is a machine-learning powered web service which is able to
train, evaluate and run predictions on unseen data, as well as storing model
configuration and results in a database. The deliverable is a zip file with the
application source code, a README.txt file and (optionally) a requirements.txt
file which lists dependencies and versions the app would require to run.
Assessment (3) is a reflective report (up to 2,000 words) describing solutions,
design choices and a reflection on the main challenges and ethical considerations
addressed during the development of solutions for assessments (1) and (2).
You have to upload the following files:
Any deviation from the submission instructions above (including the number and types of
files submitted) may result in a mark of zero for the assessment or question part.
Assignment
In this portfolio, students demonstrate their familiarity with the topics covered in the module
via three separate assessments.
Deliverable
Assessment 1
The deliverable for Assessment 1 consists of 2 jupyter notebook (.ipynb) files. They will be
submitted with all the output cells executed in a fresh run (i.e., Kernel -> Restart and run
all). 20 marks.
Assessment 2
The deliverable for Assessment 2 will be a zip file containing the webapp code, a
README.txt and an optional requirements.txt file, which will list the dependencies the app
requires. 25 marks.
Assessment 3
The deliverable for Assessment 3 will be a PDF file based on the .docx template provided
for this assessment in the starter package, available at Learning Central. 55 marks.
Description Type Name
Cover sheet Compulsory One PDF (.pdf) file [student number].pdf
Assessment
1
Compulsory One Jupyter notebook (.ipynb) assessment1_db_creation_[stude
nt number].ipynb
Assessment
1
Compulsory One Jupyter notebook (.ipynb) assessment1_queries_[student
number].ipynb
Assessment
2
Compulsory One zip file (.zip) assessment2_webapp_[student
number].zip
Assessment
3
Compulsory One PDF (.pdf) file assessment3_report_[student
number].pdf
Assessment 1
In Assessment 1, students solve two main types of challenges. These challenges are: (1)
data modeling and (2) database querying.
DATA MODELING AND QUERYING (20 Marks)
assessment1_db_creation_[student number].ipynb
assessment1_queries_[student number].ipynb

Data modeling (8 marks)
You are given an initial .csv dataset from Reddit (data_portfolio_21.csv,
available in the starter package in Learning Central). This data dump contains posts
extracted from Covid-related subreddits, as well as random subreddits. Your first
task is to process this dump and design, create and implement a relational
(MySQL) database, which you will then populate with all the posts and related data.
This dataset has information about three entities: posts, users and subreddits.
The column names are self-explanatory: columns starting with the prefix user_
describe users, those starting with the prefix subr_ describe subreddits, the column
subreddit is the subreddit name, and the rest of the columns are post attributes
(author, post date, post title and text, number of comments, score, favorited by,
etc.).
What to implement: Start from the notebook assessment1_db_creation_[student
number].ipynb, replacing [student number] with your student number. Implement
the following (not necessarily in this order):
-- Python logic for reading in the data. [2 marks]
-- SQL code for creating tables. [3 marks]
-- SQL code for populating tables. [3 marks]
Use comments or markdown along the way to explain how you dealt with
issues such as missing data, non-standard data types, multivalued columns, etc.
You are not required to explain the database design (normalization, integrity,
constraints, etc) process in this notebook, as there is a dedicated part of the report
in Assessment 3 for this. However, you can include pointers to design choices for
facilitating the understanding of your implementation.
All your code should be self-contained in Python code, and therefore you will have
to rely on a MySQL library for executing SQL statements and queries. Please use
pymysql, the one we have used in class.
You should submit your notebook with all the cells executed, from start to
finish, in a fresh run (i.e., first cell number should be [1], second [2], etc.). You can
achieve this by selecting Kernel -> Restart and run all. At the end of the run, your
notebook should have populated a database in the university server which you will
have created exclusively for this coursework.
Querying (12 marks)
You are given a set of questions in natural language, for which you must implement
queries to find the answer. While the queries will be answered in the provided
jupyter notebook, they will have to be written in SQL, i.e., you cannot use Python to
solve them.
What to implement: Start from the notebook assessment1_queries_[student
number].ipynb, replacing [student number] with your student number. All the logic
should be contained inside the provided (empty) functions. Then, a call to each
function should show the output of these queries. You are also required to submit
your notebook after a fresh run (Kernel -> Restart and run all).
The questions are:
- Users with highest scores over time [0.5 marks]
  ● Implement a query that returns the users with the highest aggregate scores (over all their
  posts) for the whole dataset. You should restrict your results to only those whose aggregated
  score is above 10,000 points, in descending order. Your query should return two columns:
  username and aggr_scores.
- Favorite subreddits with numbers but not 19 [0.5 marks]
  ● Implement a query that returns the set of subreddit names who have been favorited at least
  once and that contain any number in their name, but you should exclude those with the digit
  '19', as we want to filter out COVID-19 subreddit names. Your query should only return one
  column: subreddit.
- Most active users who add subreddits to their favorites. [0.5 marks]
  ● Implement a query that returns the top 20 users in terms of the number of subreddits they have
  favorited. Since several users have favorited the same number of subreddits, you need to
  order your results, first, by number of favourites per user, and secondly, alphabetically by user
  name. The alphabetical order should be, first any number, then A-Z (irrespective of case). Your
  query should return two columns: username and numb_favs.
- Awarded posts [0.5 marks]
  ● Implement a query that returns the number of posts who have received at least one award.
  Your query should return only one value.
- Find Covid subreddits in name and description. [1 mark]
  ● Implement a query that retrieves the name and description of all subreddits where the name
  starts with covid or corona and the description contains covid anywhere. The returned table
  should have two columns: name and description.
- Find users in haystack [1 mark]
  ● Implement a query that retrieves only the names of those users who have at least 3 posts with
  the same score as their number of comments, and their username contains the string meme
  anywhere. Your returned table should contain only one column: username.
- Subreddits with the highest average upvote ratio [1 mark]
  ● Implement a query that shows the 10 top subreddits in terms of the average upvote ratio of the
  users that posted in them. Your query should return two columns: subr_name and
  avg_upv_ratio.
- What are the chances [1 mark]
  ● Implement a query that finds those posts whose length (in number of characters) is exactly the
  same as the length of the description of the subreddit in which they were posted on. You
  should retrieve the following columns: subreddit_name, posting_user, user_registered_at,
  post_full_text, post_description and dif (which should show the difference in characters
  between the subreddit description and the post.
- Most active December 2020 days. [1 mark]
  ● Write a query that retrieves only a ranked list of the most prolific days in December 2020,
  prolific measured in number of posts per day. Your query should return those days in a
  single-column table (column name post_day) in the format YYYY-MM-DD.
- Top 'covid'-mentioning users. [1 mark]
  ● Retrieve the top 5 users in terms of how often they have mentioned the term 'covid' in their
  posts. Your query should return two columns: username and total_count. You will consider an
  occurrence of the word 'covid' only when it appears before and after a whitespace (i.e.,
  covid) and irrespective of case (both Covid and
  covid would be valid hits).
- Top 10 users whose posts reached the most users, but only in their favorite subreddits. [2
  marks]
  ● Write a query to retrieve a list of 10 users sorted in descending order by the number of users
  their posts reached, considering only the subset of users belonging to their favourite
  subreddits. Your query must return only one column: username.
- Users with high score for their posts. [2 marks]
  ● Retrieve the number of users with an average score for their posts which is higher than the
  average score for the posts in our dataset. Your query should return only one result, under the
  column result.
  Assessment 2
  In Assessment 2, you implement a Flask application which manages a machine-learning
  based text classifier and speaks to both MongDB and MySQL databases.
  WEBAPP (25 Marks)
  assessment2_webapp_[student number].zip
  In this assessment the goal is to set up a web service based on Flask which will sit on top
  of the database you built in Assessment 1, and will have a machine learning component.
  Specifically, the app will have several functionalities for training, evaluating and deploying
  a covid-or-not classifier, which will take as input a message posted in social media (e.g.,
  Reddit), and predicts whether it is about Covid-19 or not.
  What to implement: You will pull your data from the MySQL database that you
  implemented in Assessment 1. Then, your task is to develop a web application based on
  Flask which will have the following functionalities:
  a) Run a classification experiment and store results, models and configuration in a
  MongoDB database;
  b) Retrieve results for the experiments done so far, ranked based on a criterion of
  your choice; and
  c) Perform inference, i.e., given a piece of text provided by the user, predict whether
  it is about Covid-19 or not.
  You are provided with an ‘empty’ skeleton which contains starter HTML and Python code.
  Your task is to implement the backend logic following the detailed instructions below. The
  provided skeleton has an index.html page which has the layout shown in Figure 1:
  Figure 1: Landing page of the ML-powered Flask application.
  Then, your task will be to implement the backend logic that is triggered when each of the
  three buttons shown in Figure 1 are clicked on. The logic corresponding to each of these
  buttons is explained in detail below.
Run a classification experiment [15 marks]
In this exercise, you have to implement the following workflow:
a) Reset, create and verify two VIEWS, which you will call training_data and
test_data. These VIEWS should contain non-overlapping posts which you will use
to train and evaluate your classifier. The logic will be implemented in the following
(empty) functions, located in the main.py script, provided in the starter package:
reset_views() - Drop (if they exist) and create the views. [1 mark]
create_training_view() - Create an SQL VIEW for training data. This
view will have two columns, the text and the label. It is up to you to decide the size
of the dataset and the proportion of Covid vs. non-Covid posts. And also which part
of the post you take (title, body or both). You will justify these choices in
Assessment 3. We will make the strong assumption that any post submitted to a
Covid-related subreddit will be about Covid-19, and that it will be non-related to
Covid if it is submitted somewhere else. [3 marks]
create_test_view() - Create view for test data. This view will have two
columns, the text and the label. The same principles as in the previous case apply.
[3 marks]
check_views() - Retrieve all records in training_data and test_data, and
print to the console their size. This is a small sanity check. [1 mark]
b) Retrieve data from the views you created in step 1, train and evaluate a
classifier, and return the results as a JSON object that is rendered in the browser.
Implement this functionality in the experiment() method, again in the main.py
script. It is up to you to decide on the classifier and its configuration. There is an
opportunity to reflect on these choices in Assessment 3. [5 marks]
c) Take the model binaries, model configuration and the classification results you
obtained in step (b), as well as the time in which the experiment was performed,
and store this information in a dedicated collection. This exercise is open, i.e., there
is no suggested format on how to store this data, what information to store for your
models or the evaluation metrics you use. There is an opportunity to reflect on
these choices in Assessment 3. [2 marks]
Retrieve information on the experiments conducted so far (5 marks)
a) In this exercise, you query the collection you implemented in step 1c, and show
the top 3 experiments based on a certain criterion (best scoring according to
metric X, the most recent experiments, the fastest experiments in training time,
etc.). Your results will be returned as JSON objects and rendered in the browser. [5
marks]
Implement a ‘covid-or-not’ predictor (5 marks)
a) In this exercise, you implement a functionality for predicting ‘on the fly’ whether a
piece of text is Covid-19-related or not. To this end, you will use the top-ranked
model according to the ranking you implemented in step 2a. This model will then be
applied to the input text and the results will be rendered in the browser as a JSON
object with the format:
{
"input_text": some_input_text,
"prediction": the_prediction_of_your_classifier
}. [5 marks]
Assessment 3
Report (55 Marks)
assessment3_report_[student number].pdf
In Assessment 3, you write a technical report on Assessments 1 and 2, and discuss
ethical, legal and social implications of the development of this Covid-19 application in the
context of the UK Data Ethics Framework. You are strongly encouraged to follow a
scholarly approach, e.g., with peer-reviewed references as support, and with additional
empirical evidence (your own tests) for justifying your decisions (e.g., performance or
physical storage for DBMS, training time or accuracy for the ML webapp solution).
Maximum 2,000 words (not counting references, index and table and figure captions).
This report should cover the following aspects, discussing challenges and problems
encountered and the solutions implemented to overcome them. The mark will be divided
between 3 expected sections:
o [3a] Database Creation (DB choice, design, etc.), i.e., the research and
findings stemming from the development of Assessment 1. Specifically, you
should discuss any business rules that can be inferred from the dataset
(reverse-engineering), normalization (identifying partial and transitive
dependencies, if any, unnormalized relations, etc.), data integrity and
constraints, bad data, etc. Moreover, the expectation is that any design decision
(or lack thereof) will be empirically (e.g., with performance tests) and/or
theoretically (pointing to peer-reviewed publications) supported. [20 Marks]
o [3b] ML Application, explaining the implementation of the training and test
VIEWS; the ML algorithm chosen (based on main features, hyperparameters
used in the application, training speed as opposed to other alternatives, etc);
evaluation metrics; the overall logic followed by the app for storing and retrieving
experimental results; and finally any further details that may be relevant for the
‘covid-or-not’ inference functionality. You should also discuss the rationale
behind the MongoDB interaction with pointers both to the database and the code
that interacts with it. [20 Marks]
o [3c] Ethics and Bias in Data-driven Solutions in the specific context of this
dataset and the broader area of application of this project (automatic
categorization of social media content to enable easier screening of public
opinion). You should map your discussion to one of the five actions outlined
in the UK’s Data Ethics Framework. You should prioritize the action that, in your
opinion, is the weakest. Then, justify your choice by critically analyzing the three
key principles outlined in the Framework, namely transparency, accountability
and fairness. Finally, you should propose one solution that explicitly addresses
one point related to one of these three principles, reflecting on how your solution
would improve the data cycle in this particular use case. [15 Marks]
Learning Outcomes Assessed
This coursework covers the 7 LOs listed in the module description.
Criteria for assessment
Credit will be awarded against the following criteria.
Assessment 1 (20 Marks) Criteria for assessment are detailed below:
Data Modeling (8 marks)

CMT655

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