project代写 | Python | AI代做 | database | 机器学习代写 | machine learning代写| 算法代写 – ECE241 PROJECT 2: Thinking in Graph

2020-05-25

ECE241 PROJECT 2: Thinking in Graph

project代写 | Python | AI代做 | database | 机器学习代写 | machine learning代写 | 算法代写 – 这是一个机器学习相关的practice, 考察database的理解, 涉及了Python/AI/database等代写方面, 这个项目是project代写的代写题目

python代写 代写python

Introduction:

We live in a world where a lot of things are connected, especially with the development of the Internet. Six degrees of separation is the idea that everything is six or fewer steps away from each other so that a chain of “a friend of a friend” statements can be made to connect any two people in a maximum of six steps. In order to understand the world better, we need to think in a graph way, everything is connected. In this project, we are going to model our Million Song Dataset in a new way, Graph.

In this assignment, you will still read in a subset of the Million Song Dataset from an Excel file. Beside the fields such as title, artist, song duration, track ID in the previous assignment, there is a new attribute of the collaborative artists, which have contributed to produce the songs. You can find interest connections when you analyze this information, such as a new collaborative artist to recommend.

Task Overview

In this project, you will perform the following specific tasks.

  1. Manage your data in Python classes and objects. You should store songs in the Song object, manage songs in SongLibrary and build an artist graph in ArtistConnections class. a. For the Song class and the SongLibrary class, you can use the same one from project 1. (You can just include the basic structure, or you can add your own functions such as sorting or searching at your own convenience.) b. For the ArtistConnections class, you should build a graph based on the connections of the artists. You should have a Vertex class that store the basic information of an artist, which generally contains artist name, the songs he/she writes (an array) and the collaborative artists (an array) he/she has written songs together. (The information on the last column of the csv file is the collaborative artist list, they are separated using ;. Take the first song for example: 0,Qing Yi Shi,Leon Lai,203.38893, ,Nick Ingman;Tree;DJ Spinn and DJ Rashad;Ray Pillow;Soul Embraced;Green Day;Erja Lyytinen
For artist Leon Lai, there are 7 artists who collaborated with him on the song
Qing Yi Shi. So, when you make a new vertex of Leon Lai, you need to add
those artists into his coArtist dictionary (key as the name, value as 1 since there is
one song that they work together). If there is another song that have Leon Lai
and Nick Ingman together, you should update the value in their coArtist dictionary
as 2. NOTE that, you only need to connect Leon L AI and the 7 artists. There is
no need to connect those 7 artists pairwise.
  1. Build the artist graph from the database of songs using the load_graph method in ArtistConnections class. The method takes the input data file name and reads the artist information.
  2. Implement a search_artist function to search for the basic information of an artist based on the artist name. Return a tuple (the number of songs he/she wrote, the collaborative artist list). For example, if Leon Lai wrote 10 songs and has collaborative artists [A, B, C], you can just use return 10 , [“A”, “B”, “C”]
  3. In the class ArtistConnections, i mplement a find_new_friends function that returns a list of two-hop neighbors of a given artist. The two-hop neighbor means the artist that does not write songs with you but have written songs with your collaborative artists.
  4. In the class ArtistConnections, i mplement a recommend_new_collaborator function to find the (one) most potential artist you will work with next. We quantify the potential here as the number of songs that the artist from two-hop neighbors (list from the previous task) have written with your collaborative artists, the more the better. You need to return the name of that artist and the total number of songs he has written with your collaborative artists. (If there are several artists with the same total number, just return one of them.)
  5. In the class ArtistConnections, implement a shortest_path function to compute the shortest path (number of hops away) from a given artist to all the other artists. You should return a dictionary for all the vertices, where the key is the artist name and value is the path length.

Hints and suggestions

Successfully completing the project and achieving a good grade requires completing the project as described above and clearly commenting the code. As always, it makes sense to start the project early. Unless you are an amazing programmer, you probably wont be able to finish in one day. Build your project code step by step. For example, verify that you have successfully read in the database before attempting a BFS or DFS search. Then, make sure the search works before writing and testing code for find_new_friends or shortest_path , etc.

What to submit :

For Task 1 – 6, you should submit your code to Gradescope for auto-grading. Remember to comment your code properly.

Reminder: The course honesty policy requires you to write all code yourself, except for the code that we give to you. Your submitted code will be compared with all other submitted code for the course to identify similarities. Note that our checking program is not confused by changed variable or method names.

Grading:

  • Code works on Gradescope ( 9 0%)
  • Program structure and comments (10%)