removed "b"

39deb049 · Romain Vuillemot · b70952fc · 39deb049
Commit 39deb049 authored 1 year ago by Romain Vuillemot
--- a/assignments/assignment-01.ipynb
+++ b/assignments/assignment-01.ipynb
@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "markdown",
-   "id": "d8c5b7a8",
+   "id": "a016336c",
   "metadata": {},
   "source": [
    "NAME:"
@@ -58,7 +58,7 @@
    "data_file = 'users.csv'\n",
    "\n",
    "with open(data_file, 'r') as file:\n",
-    "    lines = file.readlines()b\n",
+    "    lines = file.readlines()\n",
    "    data = [tuple(line.strip().split(',')) for line in lines[1:]]"
   ]
  },

-%% Cell type:markdown id:d8c5b7a8 tags:
+%% Cell type:markdown id:a016336c tags:
 NAME:
 %% Cell type:markdown id:db477b79 tags:
 <center>
    <h3>ASSIGNMENT#1 (Algorithms) - Nov. 27, 2023</h3>
 </center>
 Due date: **December 8th, 2023, 5pm** (no extension will be allowed).
 Submit your solution by email: romain.vuillemot@ec-lyon.fr
 You may work as a group (max 2) but all members are required to submit their solution and indicate their collaborator (if any).
 ## Goal of this assignment
 In this assignment we provide you with a dataset of characters from a movie. Your role will be to answer the questions below programmatically, using Pyhon. **Please note you need to answer with fully working python code embedded in this notebook as solution (no external modules or files can be included).** You may then replace the code below with your answer for each question:
 ```python
 # YOUR CODE HERE
 raise NotImplementedError()
 ```
 ## Grading
 - 20% for the results to the questions
 - 60% for the code quality
 - 20% for the notebook presentation
 -  +10% bonus question
 ## Getting started
 We provide you with a dataset containing movie characters. Each character is represented as a row, along with connections to other characters, based on the movie script. You will use those connections to create a graph-based data structure and answer the questions.
 To get you started with the dataset, we provide you with the code that loads it:
 %% Cell type:code id:cf409997-537f-4f89-a039-c2b3494972f2 tags:
 ``` python
 data_file = 'users.csv'
 with open(data_file, 'r') as file:
-    lines = file.readlines()b
+    lines = file.readlines()
    data = [tuple(line.strip().split(',')) for line in lines[1:]]
 ```
 %% Cell type:markdown id:c48b6391-d858-4a35-b428-b99434ee2d57 tags:
 The `data` variable contains the list of characters. You may look at the `users.csv` file to grasp the values of this variable. Here is a sample:
 ```
 [('Tony_Stark',
  '40',
  'Male',
  '1.85',
  'Steve_Rogers Natasha_Romanoff Bruce_Banner Thor_Odinson'),
 ('Steve_Rogers',
  '98',
  'Male',
  '1.88',
  'Tony_Stark Natasha_Romanoff Sam_Wilson Bucky_Barnes'),
 ...
 ```
 %% Cell type:markdown id:70e93e78-f4dd-435f-b0f7-d56d928d7051 tags:
 **Question 1 -** How many characters are there in the dataset?
 %% Cell type:code id:9022b450-6fa6-41d0-999e-e019917cda79 tags:
 ``` python
 # YOUR CODE HERE
 raise NotImplementedError()
 ```
 %% Cell type:markdown id:0a1a4c6c-f95c-4dc3-8bb3-863d1048ae79 tags:
 **Question 2 -** Write a function that turns the `data` variable into a dictionnary data structure like the one below. You may then be able to plot it with the `plot_character_connections` function provided in the next cell:
 ```
 {'Tony_Stark': ['Steve_Rogers', 'Natasha_Romanoff', 'Bruce_Banner', 'Thor_Odinson'], 'Steve_Rogers': ['Tony_Stark', 'Natasha_Romanoff', 'Sam_Wilson', 'Bucky_Barnes'],
 ..
 ```
 %% Cell type:code id:7139b3cf-1c00-43bb-bc1b-67b4a8e2a92d tags:
 ``` python
 adjacency_list = {}
 # YOUR CODE HERE
 raise NotImplementedError()
 ```
 %% Cell type:code id:fbbdd57e-6895-4d3c-b561-406d5e6668a9 tags:
 ``` python
 # please run the cells below the "Utils" section first
 plot_character_connections(adjacency_list)
 ```
 %% Cell type:markdown id:4a62b350-2bc8-476d-b9c6-13a08d649bdc tags:
 **Question 3 -**  Is there a character self-connected to her/himself?
 %% Cell type:code id:232f3d2a-8300-40c1-a908-ebfa5e380ae1 tags:
 ``` python
 # YOUR CODE HERE
 raise NotImplementedError()
 ```
 %% Cell type:markdown id:b79c97bc-e7d5-4ff4-bb1c-5bdf1e3abae2 tags:
 **Question 4 -** How many communities can you identify? We define a community as a sub-graph of connected people by a path (i.e. a connected component).
 %% Cell type:code id:98186a3a-bd4c-4bf8-9ebd-2f350a83c856 tags:
 ``` python
 # YOUR CODE HERE
 raise NotImplementedError()
 ```
 %% Cell type:markdown id:840a8875-2f71-4da9-bbe7-0f1aab5f612b tags:
 **Question 5 -** Are connections mutual in the graph?
 %% Cell type:code id:12d15b4d-cf98-45aa-994c-e41070b9ea3a tags:
 ``` python
 # YOUR CODE HERE
 raise NotImplementedError()
 ```
 %% Cell type:markdown id:5e1b0409-1dff-4d8b-a5d8-a1efa1c10125 tags:
 **Question 6 (BONUS) -**  Among the community of characters, is there a fully connected one? (i.e. where all characters are directly connected to the others).
 %% Cell type:code id:d8a1900d-c15f-4ef4-83d8-1ecfbc8bfcf8 tags:
 ``` python
 # YOUR CODE HERE
 raise NotImplementedError()
 ```
 %% Cell type:markdown id:00c753ed-0905-4132-b276-4b415ba31408 tags:
 ## Utils
 %% Cell type:code id:d9f71538-98b6-4730-adc1-7ca84aa3748e tags:
 ``` python
 import networkx as nx
 import matplotlib.pyplot as plt
 def plot_character_connections(adjacency_list):
    G = nx.DiGraph()
    for character, connections in adjacency_list.items():
        G.add_node(character)
        for connection in connections:
            G.add_edge(character, connection)
    plt.figure(figsize=(12, 8))
    pos = nx.spring_layout(G, seed=42)
    nx.draw(
        G,
        pos,
        with_labels=True,
        node_size=500,
        node_color='skyblue',
        font_weight='bold',
        font_size=10
    )
    plt.title('Movie characters')
    plt.show()
 ```