144 lines
4.0 KiB
Python
144 lines
4.0 KiB
Python
from typing import List
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import scipy.stats as stats
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import numpy as np
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import matplotlib.pyplot as plt
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counties = [
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"BA",
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"TN",
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"TT",
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"NR",
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"BB",
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"ZA",
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"PO",
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"KE"
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]
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counties_c = len(counties) # how many counties
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counties_population = [
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736_385, # BA
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565_900, # TN
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565_572, # TT
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665_600, # NR
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611_124, # BB
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686_063, # ZA
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810_008, # PO
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778_799 # KE
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] # source: https://sk.wikipedia.org/wiki/Zoznam_krajov_na_Slovensku
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total_population = sum(counties_population)
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categories = [
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"Problematika voľného času",
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"Matematika, fyzika",
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"Chémia, potravinárstvo",
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"Biológia",
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"Životné prostredie, geografia, geológia",
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"Zdravotníctvo, farmakológia",
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"Pôdohospodárstvo (poľnohospodárstvo, lesné a vodné hospodárstvo)",
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"Cestovný ruch, hotelierstvo, gastronómia",
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"Strojárstvo, hutníctvo, doprava",
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"Stavebníctvo, geodézia, kartografia",
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"Informatika",
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"Elektrotechnika, hardware, mechatronika",
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"História, filozofia, právne vedy",
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"Tvorba učebných pomôcok, didaktické technológie",
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"Ekonomika a riadenie",
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"Teória kultúry, umenie, umelecká, odevná tvorba",
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"Pedagogika, psychológia, sociológia"
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]
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categories_c = 17 # how many categories
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# from how many years do we have data
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years = 9
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def map_counties(arr: List[str]) -> List[int]:
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ret = []
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for county in arr:
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ret.append(counties.index(county))
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return ret
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raw_data = []
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with open("dataset.txt") as stream:
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for line in stream.readlines():
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if not line:
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continue
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split = line.strip().split(" ")
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year = int(split[0])
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category = int(split[1])
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wins_raw = split[2].split(",")
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raw_data.append([year, category, *map_counties(wins_raw)])
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# 0 - year
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# 1 - abteilung (category) idx (starts at 1)
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# 2-7 - first to last place county idxs
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data_original = np.array(raw_data)
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# table where counties are rows and counts of placements are columnes
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# #1 | #2 | ...
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# BA | 5 | 4 | ...
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# ZA | 9 | 8 | ...
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# KE | 4 | 6 | ...
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# as a row-first 2d numpy array (first dimension will represent counties, second counts of placements)
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# data = np.zeros((counties_c, 5)) # 5 because top five
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# for sample in data_original:
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# results = sample[2:7]
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# for placement_idx, county_idx in enumerate(results):
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# data[county_idx, placement_idx] += 1
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# data is table where rows represent placement and columns county index
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# 1st | 5 | 1 | 2 | ...
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# 2nd | 3 | 0 | 7 | ...
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# 3rd ...
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# data = np.zeros((5, years * categories_c)) # same as (5, len(data_original))
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# for i, sample in enumerate(data_original):
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# results = sample[2:7]
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# for j in range(5):
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# data[j][i] = results[j]
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# wins per county
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# goodness-of-fit problem using Chi Square
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# based on observed vs expected frequency
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observed = np.zeros(counties_c)
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for sample in data_original:
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results = sample[2:7]
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for i in results:
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observed[i] += 1
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print("Observed before adjusting for population:")
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print(observed)
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# micro-wins per capita (because wins would be a tiny number)
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for i in range(len(observed)):
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observed[i] = observed[i] / counties_population[i] * 1_000_000
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print("Observed after adjusting for population:")
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print(observed)
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expected = np.ones_like(observed) * (sum(observed) / len(observed))
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print("Expected after adjusting for population:")
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print(expected)
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chi2, p = stats.chisquare(f_obs=observed, f_exp=expected)
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print(f"Chi-square = {chi2:.2f}, p-value = {p:.4f}")
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# H0: county and placement are independent
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# H1: county and placement are not independent
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# print("\nAttempting Chi-Square test")
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# chi2, p, dof, expected = stats.chi2_contingency(data)
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# print(f"Chi-Square Statistic: {chi2}")
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# print(f"p-value: {p}")
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# print(f"Degrees of Freedom: {dof}")
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# print("Expected Frequencies:\n", expected)
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# print("\nAttempting Fisher's Exact test")
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# oddsratio, p_value = stats.fisher_exact(data)
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# print(f"Odds Ratio: {oddsratio}")
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# print(f"p-value: {p_value}")
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