WebValley 2022 Python Programming

Support Slides for the Python Programming Course for the FBK WebValley Summer School 2022 (https://webvalley.fbk.eu)

Transcript

1. Python Programming Class
   Valerio Maggio (he/him/his)
   @leriomaggio
   valerio.maggio@bristol.ac.uk
   

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2. Materials
   Shout out to Giuseppe Mastrandrea
   

   (GH @giuseppemastrandrea)
   • Lecture materials for this class has been adapted from
   https://github.com/WebValley2022/python-programming
   

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3. View Slide

4. Introduction
   The Programming Game
   The Program
   👩💻
   🧑💼
   The Programmer
   The User
   creates
   uses generates
   Results
   

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5. Introduction
   The Programming Game
   The Program
   👩💻
   🧑💼
   The Programmer
   The User
   uses
   

   (i.e. coding)
   provides generates
   Output data
   The WebValley Team
   Input data
   • So a Program:
   
   
   • asks the user for some input data
   
   
   • makes some computations
   
   
   • returns a value or a series of values
   passed to
   Programming
   Language
   to create
   

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6. Quick Check and Recap
   • Q: What is Python?
   
   
   • A: Programming language
   
   
   • Q: What is a program?
   
   
   • A: A program is a software that resolves a particular task/problem
   
   
   • Key Components:
   
   
   • User
   
   
   • Input data
   
   
   • Output data
   
   
   • (soon) Algorithm!
   

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7. The Problem
   What we’ll work on for the next hours
   • We will implement an innovative
   Pokémon Search Engine
   
   
   • iow, our software program will look for
   the K top Pokémon that are most
   similar to some input Pokémon
   characteristics (i.e. the search query)
   
   
   • The search query (i.e. input data to our
   program) will be some characteristics
   (numbers) denoting a Pokémon
   

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8. How?
   

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9. Let’s clarify a bit!
   

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10. The Problem
    • Q: What do we mean by "most similar to the search query” ?
    
    
    • Q: What kind of data can a user provide to our program ?
    
    
    • Q: (once we’ve clari
    f
    ied this)
    

    what are the instructions to actually solve the problem?
    • The problem is:
    

    “(somehow) determine what Pokémons are most similar to a speci
    f
    ied set of
    characteristics denoting a Pokémon provided by the user.”
    • There's a lot of ambiguity in what we just said
    

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11. From Problem to Coding
    Through Algorithm
    

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12. The Algorithm
    • The algorithm is the heart of our software
    
    
    • Input Data (search query given by the user). A series of numbers representing a Pokémon:
    
    
    • HP (Hit Points)
    
    
    • Speed
    
    
    • Attack
    
    
    • Defense
    
    
    • Special Attack
    
    
    • Special Defence
    
    
    • The search query then will be a series of numbers
    
    
    • Based on those numbers, the program will return (output data) A list of K Pokémons
    
    
    • K is a parameter of the algorithm
    

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13. The Program
    Pokémon Search Engine
    K
    

    (Parameter)
    HP (Hit Points)
    
    
    Speed
    
    
    Attack
    
    
    Defense
    
    
    Special Attack
    
    
    Special Defence
    Input Data // Search Query // Pokémon
    The Program
    

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14. How to Proceed?
    • Breaking down a problem into many small sub-problems (i.e. Tasks)
    
    
    • For example:
    

    Problem: “start from a bunch of numbers and extract K Pokémons according to
    certain similarity measure”
    
    
    • What will our algorithm do?
    
    
    • Search for the K Pokèmon with the highest “similarity”
    
    
    • Breakdown into Tasks
    
    
    • Compare the data inserted by the user and those of existing Pokémon
    
    
    • Generalisation -> compare any pair of Pokémon
    
    
    • Sort the results of the comparison
    
    
    • Return the top K
    

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15. How to Proceed?
    • Task 1:
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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16. Let’s get cracking!
    • Task 1:
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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17. T1 - Ask the user for Pokémon data and store them accordingly
    

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18. Let’s get cracking!
    • Task 1: ✅
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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19. Task 2
    2 Similarity Function
    • What can we use to compare two series of numbers?
    
    
    • A Pokémon with these scores (user's inputs) should be compared with
    all the other existing Pokémons
    
    
    • So.. time to acquire some Pokémon Data
    
    
    • CSV
    f
    ile (Comma Separated Values) with all data from all generations of
    Pokemon
    
    
    • Task 2.a.1: Read and Store Pokémon Data
    

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20. Get Pokemon Dataset
    • I am going to add the data to the repository
    
    
    • Q: What’s the git command I am going to type in ?
    • Once done, here is how to get a copy of the data
    
    
    • git pull origin main
    

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21. Task 2
    2.a Pokémon Dataset
    

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22. Let’s get cracking!
    • Task 1: ✅
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • (+1) Read all Pokémon data and store accordingly
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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23. Let’s get cracking!
    • Task 1: ✅
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • (+1) Read all Pokémon data and store accordingly
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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24. T2.a - Read all Pokémon Data and Store them accordingly
    

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25. Let’s get cracking!
    • Task 1: ✅
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • (+1) Read all Pokémon data and store accordingly
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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26. Task 2
    2.b Similarity Function
    30 43 55 68 78
    39 45 58 60 80
    Bulbasaur Ivysaur Venusaur
    Chameleon Chameleon Charizard
    • What function to get a similarity degree?
    
    
    • Hypothesis: let's start from just 1 feature (e.g., hit points)
    

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27. Task 2.b
    Introducing Tuxemon
    30 43 55 68 78
    39 45 58 60 80
    Bulbasaur Ivysaur Venusaur
    Charmeleon Chameleon Charizard
    Tuxemon
    64
    K=1: Ivysaur
    K=2: Ivysaur, Charmeleon
    K=3: Ivysaur, Charmeleon, Charizard
    

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28. Task 2.b
    Introducing Tuxemon
    64
    64
    64
    64
    64
    64
    19
    4
    16
    25
    6
    14
    

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29. Task 2
    2.b Similarity Function
    • Let's add another attribute
    (attack)
    
    
    • We still have to compute a
    distance
    
    
    • In this case we calculate
    the distance on a 2D plane
    40
    53
    65
    78
    90
    30 43 55 68 80
    54, 55
    78, 84
    58, 64
    39, 52
    80, 82
    60, 62
    45, 49
    

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30. Task 2
    2.b Similarity Function
    • Euclidean distance
    
    
    • We use Pythagoras Theorem
    
    
    • The distance is the green line
    (hypotenuse) of the triangle in
    f
    igure
    

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31. Task 2
    2.b Similarity Function
    

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32. Task 2
    2.b Similarity Function
    Adding more axes on our
    chart makes our formula
    slightly more complex
    3 Dimensions (HP, Attack, Defence):
    4 Dimensions (HP, Attack, Defence, Special Attack):
    n Dimensions:
    

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33. T2.b - De
    f
    ine Similarity Function
    

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34. Let’s get cracking!
    • Task 1: ✅
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • (+1) Read all Pokémon data and store accordingly
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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35. Similarity Data Structure
    

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36. T2.c - Compute the Similarity score of search query and each Pokémon
    

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37. Let’s get cracking!
    • Task 1: ✅
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2:
    
    
    • (+1) Read all Pokémon data and store accordingly
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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38. Top K Results
    

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39. T2.d - Return the top K most similar Pokémon
    

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40. Let’s get cracking!
    • Task 1: ✅
    
    
    • Ask the user for Pokémon data and store them accordingly
    
    
    • Task 2: ✅
    
    
    • (+1) Read all Pokémon data and store accordingly
    
    
    • De
    f
    ine the Pokémon Similarity Function
    
    
    • Compute similarity between the search query and existing Pokémon
    
    
    • Return the top K most similar Pokémon
    
    
    • Task 3:
    
    
    • Return and Show algorithm’s output
    
    
    • (what will be the algorithm output?)
    

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41. Bonus Track
    • Visualise Results:
    
    
    • Using Jupyter we can show images of the K nearest Pokémon!
    
    
    • Visual feedback
    
    
    • Images taken from https://veekun.com/dex/downloads and rescaled
    

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42. Any Question?
    Valerio Maggio (he/him/his)
    valerio.maggio@gmail.com
    @leriomaggio
    

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