程序代写代做代考 data mining python Inf553 – Foundations and Applications of Data
Inf553 – Foundations and Applications of Data
Mining
Fall 2018
The 4nd USC Informatics Data Mining Competition
Starting Date: Oct 12 Friday 2018
End Date: Nov 29 Thursday 2018 11:59 PM PST
1 Competition Overview
This competition is based on the assignment 3, recommendation system. You
need to keep improving the performance of your recommendation system on Yelp
challenge dataset. You can use any method (including SparkMLlib) to improve
your rating prediction. You can use Scala or Python for this competition.
Environment Requirements
Python: 2.7 Scala: 2.11 Spark: 2.3.1
Student must use python to complete both Task1 and Task2.
There will be 10% bonus if you use Scala for both Task1 and Task2 (i.e. 10
– 11; 9 – 10).
IMPORTANT: We will use these versions to compile and test your code. If
you use other versions, there will be a 20% penalty since we will not be able to
grade it automatically.
Write your own code!
For this assignment to be an effective learning experience, you must write your
own code! I emphasize this point because you will be able to find Python
implementations of most or perhaps even all of the required functions on the
web. Please do not look for or at any such code!
TA will combine some python code on Github which can be searched by key-
word ”INF553” and every students’ code, using some software tool for detecting
Plagiarism.
Do not share code with other students in the class!!
1
Submission Details
For the competition you will need to turn in a Python or Scala program de-
pending on your language of preference.
Your submission must be a .zip file with name: Firstname Lastname competition.zip.
The structure of your submission should be identical as shown below.
The Firstname Lastname Description.pdf file contains helpful instructions
on how to run your code along with other necessary information as described
in the following sections. This file also need to contain the description of the
method you use to improve the performance, and other detail of the implemen-
tation, as detailed as possible
The OutputFiles directory contains the deliverable output files for each prob-
lem and the Solution directory contains your source code.
Figure 1: Submission Structure
Data
In this assignment, we will use the yelp challenge dataset, please download the
”yelp challenge data” from this link: Yelp Challenge. In order to download the
dataset, you need to use your email to sign up individually in the Yelp challenge
website. Detailed introduction of the data can also be found through the link,
in the document tab. After download and unzip the data, the dataset contain
6 .json file and two .pdf file.
In this assignment, need the reviews.json file and three columns of the review
will be used: user id, business id, stars.
About Competition
In the competition of the recommendation system, you can use all other file in
this dataset to improve the performance. You can use the property of the user,
business, or even tips and use any method you know to make the improvement.
For instance, you can use different hybrid recommendation system mentioned
in the lecture, or some machine learning methods like regression to make the
improvement.
However, you can only use the information related to the user and business
in the training file. You cannot use other users and businesses that don’t in the
training file to make the improvement.
2
https://www.yelp.com/dataset/challenge
Here is the useful link contain some example of the dataset Yelp Dataset
Examples. And also you can find some papers using the dataset Paper about
Dataset.
More details of the Yelp dataset and challenge can be find from the official
website. You still have a chance to win rewards, Fight on!
Dataset Description
yelp academic dataset business.json : 188,593 records
Attributes: Business ID, address, name, city, Business hours, Categories,
rating and reviews count
yelp academic dataset review.json : 5,996,996 records
Attributes: review ID, user ID, business ID, rating, comments
yelp academic dataset user.json : 1,518,169 records
Attributes: user ID, name, review count, Yelp join date
yelp academic dataset checkin.json : 157,075 records
Attributes: Business ID, time
yelp academic dataset tip.json : 1,185,348 records
Attributes: user ID, Business ID, text, likes, date
yelp academic dataset photo.json : 280,992 records
Attributes: photo ID, Business ID, text
1.1 Task of Recomendation System
The task of this the recommendation system is to use the records in the train.csv
to predict the stars for businesses in the test.csv. Then, you need to use
the stars in testing data as the ground truth to evaluate the accuracy of your
recommendation system.
Example: Assuming train.csv contains 1 million records and the test.csv con-
tains two records: (12345, 2, 3) and (12345, 13, 4). You will use the records
in the train.csv to train a recommendation system (1 million). Finally, given
the user id 12345 and business id 2 and 13, your system should produce rating
predictions as close as 3 and 4, respectively.
You are going to predict the testing datasets mentioned above. In your
code, you can set the parameters yourself to reach a better performance. You
can make any improvement to your recommendation system: speed, accuracy.
After achieving the prediction for ratings (stars), you need to compare your
result to the correspond ground truth and compute the absolute differences.
You need to divide the absolute differences into 5 levels and count the number
of your prediction for each level as following:
>=0 and <1: 12345 (there are 12345 predictions with a < 1 difference from the ground truth) 3 https://github.com/Yelp/dataset-examples https://github.com/Yelp/dataset-examples https://scholar.google.com/scholar?q=citation%3A+Yelp+Dataset&btnG=&hl=en&as_sdt=0%2C5 https://scholar.google.com/scholar?q=citation%3A+Yelp+Dataset&btnG=&hl=en&as_sdt=0%2C5 >=1 and <2: 123 >=2 and <3: 1234 >=3 and <4: 1234 >=4: 12
Additionally, you need to compute the RMSE (Root Mean Squared Error)
by using following formula:
EMSE =
√
1
n
∑
(Predi −Ratei)2
Where Predi is the prediction for movie i, Ratei is the true rating for movie
i, n is the total number of the movies. Read the Microsoft paper mentioned in
class to know more about how to use RMSE for evaluating your recommendation
system.
Result Format
1. Save the predication results in a text file. The result is ordered by user id
and business Id in ascending order.
Example Format:
user1, business2, prediction12
user1, business3, prediction13
. . .
usern, businessk, predictionnk
2. Print the accuracy information in terminal, and copy this value in
your description file.
>=0 and <1: 12345
>=1 and <2: 123
>=2 and <3: 1234
>=3 and <4: 1234
>=4: 12
RMSE: 1.23456789
Time: 123 sec
Description File
Please include the following content in your description file:
1. Mention the Spark version and Python version
2. Describe how to run your program for both tasks
3. The precision and recall.
4. Same baseline table as mentioned in task 2 to record your accuracy and
run time of programs in task 2
4
5. If you make any improvement in your recommendation system, please
also describe it in your description file.
Submission Details
Your submission must be a .zip file with name: Firstname Lastname hw3.zip
Please include all the files in the right directory as following:
1. A description file: Firstname Lastname desription.pdf
2. All Scala scripts:
Firstname Lastname competition.scala
3. A jar package for all Scala file: Firstname Lastname competition.jar
If you use Scala, please make all *.scala file into ONLY ONE
Firstname Lastname competition.jar file and strictly follow the class name
mentioned above. And DO NOT include any data or unrelated libraries into
your jar.
4. If you use Python, then all python scripts:
Firstname Lastname competition.py
5. Required result files for competition:
Firstname Lastname competition.txt
Ranking Criteria
Every week, Yuanbin will check the submission of the competition and post the
result on the discussion board, the format will like a Ladder.
We will rank the competition based on your RMSE accuracy. After the last
day of the competition, the submission having the highest accuracy will receive
a 4% bonus on their final grade. The 2nd highest accuracy will receive a 3%
bonus on their final grade. The 3rd highest accuracy will receive a 2% bonus
on their final grade. Others will receive a 1% bonus on their final grade. In
addition, you need to beat Yuanbin’s system to be able to receive the bonus.
Yuanbin will continuously improve her system and will announce her accuracy
every Friday along with the rankings for the previous week.
5
Competition Overview
Task of Recomendation System
