HashCode2020/visualise.py at master · JustCheckingHow/HashCode2020 · GitHub

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from parser_books import parse_data
from tqdm import tqdm
from collections import Counter


def visualise_basic(filename):
    libraries, book_values_dict, days = parse_data(filename)
    total_books_value = sum(book_values_dict.values())
    total_books = len(book_values_dict)

    lib_scores = {}
    lib_days = {}
    lib_unique = {}
    lib_weighted = {}
    lib_scans = {}
    for lib_id, lib in tqdm(enumerate(libraries), total=len(libraries)):
        lib_score = sum([book_values_dict[b] for b in lib.books])
        lib_scores[lib_id] = lib_score
        lib_days[lib_id] = lib.signup_time
        lib_scans[lib_id] = lib.number_of_scans
        # check unique books
        my_unique_set = set(lib.books)
        for j in range(0, len(libraries)):
            if lib_id == j:
                continue
            my_unique_set -= set(libraries[j].books)
        lib_unique[lib_id] = list(my_unique_set)
        my_unique_score = 0
        if my_unique_set:
            for b in my_unique_set:
                my_unique_score += book_values_dict[b]
        lib_weighted[lib_id] = my_unique_score

    libs = list(lib_scores.keys())

    # total score potential
    fig1, ax1 = plt.subplots()
    lib_scores = Counter(lib_scores)
    scores = [l[1] for l in lib_scores.most_common()]
    ax1.bar(libs, scores, color="r")
    ax1.set_xticks(libs, libs)
    ax1.set_title("Library score potential")

    # number of unique books
    fig2, ax2 = plt.subplots()
    unique_books = list(lib_unique.values())
    unique_books = [len(subset) for subset in unique_books]
    unique_books = sorted(unique_books, reverse=True)
    ax2.bar(libs, unique_books, color="g")
    ax2.set_xticks(libs, libs)
    ax2.set_title("Library unique books")

    # weighted score of unique books
    fig3, ax3 = plt.subplots()
    unqiue_scores = list(lib_weighted.values())
    unqiue_scores = sorted(unqiue_scores)
    ax3.bar(libs, unqiue_scores, color="b")
    ax3.set_xticks(libs, libs)
    ax3.set_title("Library unique score")

    fig4, ax4 = plt.subplots()
    days_lib = list(lib_days.values())
    days_lib = sorted(days_lib, reverse=True)
    ax4.bar(libs, days_lib, color="orange")
    ax4.set_xticks(libs, libs)
    ax4.set_title("Library signup days")

    fig5, ax5 = plt.subplots()
    multi = list(lib_scans.values())
    multi = sorted(multi, reverse=True)
    ax5.bar(libs, multi, color="black")
    ax5.set_xticks(libs, libs)
    ax5.set_title("Library multi books")

    plt.show()


def visualise_time(filename):
    libraries, book_values_dict, days = parse_data(filename)
    print(f"DAYS {days}")
    book_count = Counter(book_values_dict)
    total_books_value = sum(book_values_dict.values())
    total_books = len(book_values_dict)
    times, scans, books = [], [], []
    libs = {}
    for i in tqdm(range(len(libraries))):
        times.append(libraries[i].signup_time)
        scans.append(libraries[i].number_of_scans)
        books.append(len(libraries[i].books))
        libs[i] = {"signup": libraries[i].signup_time}
    libs = [i for i in range(len(libraries))]
    times = sorted(times)
    scans = sorted(scans)

    fig4, ax4 = plt.subplots()
    ax4.bar(libs, times, color="orange")
    ax4.set_xticks(libs, libs)
    ax4.set_title("Library signup days")

    fig5, ax5 = plt.subplots()
    ax5.bar(libs, scans, color="g")
    ax5.set_xticks(libs, libs)
    ax5.set_title("Library multi scans")

    fig1, ax1 = plt.subplots()
    book_values = [x[1] for x in book_count.most_common()]
    ax1.hist(book_values, color="r")
    ax1.set_title("Book values")

    fig2, ax2 = plt.subplots()
    ax2.hist(books, color="b")
    ax2.set_title("Books")

    plt.show()


# <<<<<<< HEAD
# # visualise_time('data/b_read_on.txt')
# visualise_time('data/d_tough_choices.txt')
# # visualise_time('data/e_so_many_books.txt')
# visualise_time('data/c_incunabula.txt')
# =======
# # visualise_basic('data/b_read_on.txt')
# <<<<<<< HEAD
# visualise_basic('data/c_incunabula.txt')
# =======
# # visualise_basic('data/d_tough_choices.txt')
visualise_time("data/e_so_many_books.txt")