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🧪 Adds goodness of fit test

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This commit is contained in:
Daniel Svitan 2025-05-21 19:18:30 +02:00
parent bd60a9aa3f
commit c3651fb62e
1 changed files with 42 additions and 15 deletions
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57
analysis.py
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@ -36,6 +36,9 @@ categories = [
] ]
categories_c = 17 # how many categories categories_c = 17 # how many categories
# from how many years do we have data
years = 9
def map_counties(arr: List[str]) -> List[int]: def map_counties(arr: List[str]) -> List[int]:
ret = [] ret = []
@ -57,7 +60,6 @@ with open("dataset.txt") as stream:
raw_data.append([year, category, *map_counties(wins_raw)]) raw_data.append([year, category, *map_counties(wins_raw)])
# 0 - year # 0 - year
# 1 - abteilung (category) idx (starts at 1) # 1 - abteilung (category) idx (starts at 1)
# 2-7 - first to last place county idxs # 2-7 - first to last place county idxs
@ -69,28 +71,53 @@ data_original = np.array(raw_data)
# ZA | 9 | 8 | ... # ZA | 9 | 8 | ...
# KE | 4 | 6 | ... # KE | 4 | 6 | ...
# as a row-first 2d numpy array (first dimension will represent counties, second counts of placements) # as a row-first 2d numpy array (first dimension will represent counties, second counts of placements)
data = np.zeros((counties_c, 5)) # 5 because top five # data = np.zeros((counties_c, 5)) # 5 because top five
# for sample in data_original:
# results = sample[2:7]
# for placement_idx, county_idx in enumerate(results):
# data[county_idx, placement_idx] += 1
# data is table where rows represent placement and columns county index
# 1st | 5 | 1 | 2 | ...
# 2nd | 3 | 0 | 7 | ...
# 3rd ...
# data = np.zeros((5, years * categories_c)) # same as (5, len(data_original))
# for i, sample in enumerate(data_original):
# results = sample[2:7]
# for j in range(5):
# data[j][i] = results[j]
# wins per county
# goodness-of-fit problem using Chi Square
# based on observed vs expected frequency
observed = np.zeros(counties_c)
for sample in data_original: for sample in data_original:
results = sample[2:7] results = sample[2:7]
for placement_idx, county_idx in enumerate(results): for i in results:
data[county_idx, placement_idx] += 1 observed[i] += 1
expected = np.ones_like(observed) * (sum(observed) / len(observed))
print("Data:") print("Data:")
print(data) print(observed)
print(expected)
chi2, p = stats.chisquare(f_obs=observed, f_exp=expected)
print(f"Chi-square = {chi2:.2f}, p-value = {p:.4f}")
# H0: county and placement are independent # H0: county and placement are independent
# H1: county and placement are not independent # H1: county and placement are not independent
print("\nAttempting Chi-Square test")
chi2, p, dof, expected = stats.chi2_contingency(data)
print(f"Chi-Square Statistic: {chi2}") # print("\nAttempting Chi-Square test")
print(f"p-value: {p}") # chi2, p, dof, expected = stats.chi2_contingency(data)
print(f"Degrees of Freedom: {dof}")
#print("Expected Frequencies:\n", expected)
print("\nAttempting Fisher's Exact test") # print(f"Chi-Square Statistic: {chi2}")
oddsratio, p_value = stats.fisher_exact(data) # print(f"p-value: {p}")
# print(f"Degrees of Freedom: {dof}")
# print("Expected Frequencies:\n", expected)
print(f"Odds Ratio: {oddsratio}") # print("\nAttempting Fisher's Exact test")
print(f"p-value: {p_value}") # oddsratio, p_value = stats.fisher_exact(data)
# print(f"Odds Ratio: {oddsratio}")
# print(f"p-value: {p_value}")