DSCI-553代写、代写Python语言编程

日期：2023-03-29 08:22

DSCI-553 Foundations and Applications of Data Mining

Assignment 4

1. Overview of the Assignment

In this assignment, you will explore the spark GraphFrames library as well as implement your own

Girvan-Newman algorithm using the Spark Framework to detect communities in graphs. You will use the

ub_sample_data.csv dataset to find users who have similar business tastes. The goal of this assignment is

to help you understand how to use the Girvan-Newman algorithm to detect communities in an efficient

way within a distributed environment.

2. Requirements

2.1 Programming Requirements

a. For Task 1, you can use the Spark DataFrame and GraphFrames library. For task 2 you can ONLY use

Spark RDD and standard Python or Scala libraries. There will be a 10% bonus for each task if you also

submit a Scala implementation and both your Python and Scala implementations are correct.

2.2 Programming Environment

Python 3.6, JDK 1.8, Scala 2.12, and Spark 3.1.2

We will use these library versions to compile and test your code. There will be no point if we cannot run

your code on Vocareum.

2.3 Write your own code

Do not share code with other students!!

For this assignment to be an effective learning experience, you must write your own code! We

emphasize this point because you will be able to find Python implementations of some of the required

functions on the web. Please do not look for or at any such code!

TAs will combine all the code we can find from the web (e.g., Github) as well as other students’ code

from this and other (previous) sections for plagiarism detection. We will report all detected plagiarism.

2.4 What you need to turn in

You need to submit the following files on Vocareum:

a. [REQUIRED] two Python scripts, named: task1.py, task2.py

b1. [OPTIONAL, REQUIRED FOR SCALA] two Scala scripts, named: task1.scala, task2.scala

b2. [OPTIONAL, REQUIRED FOR SCALA] one jar package, named: hw4.jar

c. [OPTIONAL] You can include other scripts called by your main program.

d. You don’t need to include your results. We will grade your code with our testing data (data will be in

the same format).

3. Datasets

We have generated a sub-dataset, ub_sample_data.csv, from the Yelp review dataset containing user_id

and business_id. You can find the data on Vocareum under resource/asnlib/publicdata/.

4. Tasks

4.1 Graph Construction

To construct the social network graph, assume that each node is uniquely labeled and that links are

undirected and unweighted.

Each node represents a user. There should be an edge between two nodes if the number of common

businesses reviewed by two users is greater than or equivalent to the filter threshold. For example,

suppose user1 reviewed set{business1, business2, business3} and user2 reviewed set{business2,

business3, business4, business5}. If the threshold is 2, there will be an edge between user1 and user2.

If the user node has no edge, we will not include that node in the graph.

The filter threshold will be given as an input parameter when running your code.

4.2 Task1: Community Detection Based on GraphFrames (2 pts)

In task1, you will explore the Spark GraphFrames library to detect communities in the network graph you

constructed in 4.1. In the library, it provides the implementation of the Label Propagation Algorithm

(LPA) which was proposed by Raghavan, Albert, and Kumara in 2007. It is an iterative community

detection solution whereby information “flows” through the graph based on underlying edge structure.

In this task, you do not need to implement the algorithm from scratch, you can call the method provided

by the library. The following websites may help you get started with the Spark GraphFrames:

https://docs.databricks.com/spark/latest/graph-analysis/graphframes/user-guide-python.html

https://docs.databricks.com/spark/latest/graph-analysis/graphframes/user-guide-scala.html

4.2.1 Execution Detail

The version of the GraphFrames should be 0.6.0.

(For your convenience, graphframes0.6.0 is already installed for python on Vocareum. The corresponding

jar package can also be found under the $ASNLIB/public folder. )

For Python (in local machine):

● [Approach 1] Run “python3.6 -m pip install graphframes” in the terminal to install the package.

● [Approach 2] In PyCharm, you add the sentence below into your code to use the jar package

os.environ["PYSPARK_SUBMIT_ARGS"] = "--packages

graphframes:graphframes:0.8.2-spark3.1-s_2.12 pyspark-shell"

● In the terminal, you need to assign the parameter “packages” of the spark-submit:

--packages graphframes:graphframes:0.8.2-spark3.1-s_2.12

For Scala (in local machine):

● In Intellij IDEA, you need to add library dependencies to your project

“graphframes” % “graphframes” % “0.8.2-spark3.1-s_2.12”

“org.apache.spark” %% “spark-graphx” % sparkVersion

● In the terminal, you need to assign the parameter “packages” of the spark-submit:

--packages graphframes:graphframes:0.8.2-spark3.1-s_2.12

For the parameter “maxIter” of the LPA method, you should set it to 5.

4.2.2 Output Result

In this task, you need to save your result of communities in a txt file. Each line represents one

community and the format is:

‘user_id1’, ‘user_id2’, ‘user_id3’, ‘user_id4’,…

Your result should be firstly sorted by the size of communities in ascending order, and then the first

user_id in the community in lexicographical order (the user_id is of type string). The user_ids in each

community should also be in the lexicographical order.

If there is only one node in the community, we still regard it as a valid community.

Figure 1: community output file format

4.3 Task 2: Community Detection Based on Girvan-Newman algorithm (5 pts)

In task 2, you will implement your own Girvan-Newman algorithm to detect the communities in the

network graph. You can refer to Chapter 10 from the Mining of Massive Datasets book for the algorithm

details.

Because your task1 and task2 code will be executed separately, you need to construct the graph again in

this task following the rules in section 4.1.

For task 2, you can ONLY use Spark RDD and standard Python or Scala libraries. Remember to delete

your code that imports graphframes. Usage of Spark DataFrame is NOT allowed in this task.

4.3.1 Betweenness Calculation (2 pts)

In this part, you will calculate the betweenness of each edge in the original graph you constructed in 4.1.

Then you need to save your result in a txt file. The format of each line is

(‘user_id1’, ‘user_id2’), betweenness value

Your result should be firstly sorted by the betweenness values in descending order and then the first

user_id in the tuple in lexicographical order (the user_id is type of string). The two user_ids in each tuple

should also be in lexicographical order.

For output, you should use the python built-in round() function to round the betweenness value to five

digits after the decimal point. (Rounding is for output only, please do not use the rounded numbers for

further calculation)

IMPORTANT: Please strictly follow the output format since your code will be graded automatically. We

will not regrade because of formatting issues.

Figure 2: betweenness output file format

4.3.2 Community Detection (3 pts)

You are required to divide the graph into suitable communities, which reaches the global highest

modularity. The formula of modularity is shown below:

According to the Girvan-Newman algorithm, after removing one edge, you should re-compute the

betweenness. The “m” in the formula represents the edge number of the original graph. (Hint: In each

remove step, “m”, “k_i” and “k_j” should not be changed, while ‘A’ is calculated based on the updated

graph.). In the step of removing the edges with the highest betweenness, if two or more edges have the

same (highest) betweenness, you should remove all those edges.

If the community only has one user node, we still regard it as a valid community.

You need to save your result in a txt file. The format is the same as the output file from task 1.

(Hint: For the second part of task 2, you should take into account the precision. For eg: stop the

modularity calculation only if there is a significant reduction in the new modularity)

4.4 Execution Format

Execution example:

Python:

spark-submit --packages graphframes:graphframes:0.8.2-spark3.1-s_2.12 task1.py

spark-submit task2.py

Scala:

spark-submit --packages graphframes:graphframes:0.8.2-spark3.1-s_2.12 –-class task1 hw4.jar threshold>

spark-submit –-class task2 hw4.jar

Input parameters:

1. : the filter threshold to generate edges between user nodes.

2.

: the path to the input file including path, file name and extension.

3. : the path to the betweenness output file including path, file name

and extension.

4. : the path to the community output file including path, file name and

extension.

Execution time:

The overall runtime limit of your task1 (from reading the input file to finishing writing the community

output file) is 400 seconds.

The overall runtime limit of your task 2 (from reading the input file to finishing writing the community

output file) is 400 seconds.

If your runtime exceeds the above limit, there will be no point for this task.

5. About Vocareum

a. Dataset is under the directory $ASNLIB/publicdata/, jar package is under $ASNLIB/public/

b. You should upload the required files under your workspace: work/, and click submit

c. You should test your scripts on both the local machine and the Vocareum terminal before

submission.

d. During the submission period, the Vocareum will automatically test task1 and task2.

e. During the grading period, the Vocareum will use another dataset that has the same format for

testing.

f. We do not test the Scala implementation during the submission period.

g. Vocareum will automatically run both Python and Scala implementations during the grading period.

h. Please start your assignment early! You can resubmit any script on Vocareum. We will only grade on

your last submission.

7. Common problems causing fail submission on Vocareum/FAQ

(If your program runs seems successfully on your local machine but fail on Vocareum, please check

these)

1. Try your program on Vocareum terminal. Remember to set python version as python3.6,

Use the latest Spark

/opt/spark/spark-3.1.2-bin-hadoop3.2/bin/spark-submit

Select JDK 8 by running the command

"export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64"

2. Check the input command line formats.

3. Check the output formats, for example, the headers, tags, typos.

4. Check the requirements of sorting the results.

5. Your program scripts should be named as task1.py task2.py etc.

6. Check whether your local environment fits the assignment description, i.e. version, configuration.

7. If you implement the core part in python instead of spark, or implement it with a high time

complexity (e.g. search an element in a list instead of a set), your program may be killed on the

Vocareum because it runs too slow.

8. You are required to only use Spark RDD in order to understand Spark operations more deeply. You

will not get any points if you use Spark DataFrame or DataSet. Don’t import sparksql.

9. Do not use Vocareum for debugging purposes, please debug on your local machine. Vocareum can

be very slow if you use it for debugging.

10. Vocareum is reliable in helping you to check the input and output formats, but its function on

checking the code correctness is limited. It can not guarantee the correctness of the code even with

a full score in the submission report.

11. Some students encounter an error like: the output rate…. has exceeded the allowed

value….bytes/s; attempting to kill the process.

To resolve this, please remove all print statements and set the Spark logging level such that it

limits the logs generated - that can be done using sc.setLogLevel . Preferably, set the log level to

either WARN or ERROR when submitting your code.