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import pandas as pd
import numpy as np
from statsmodels.stats.inter_rater import fleiss_kappa
df = pd.read_csv("Movies - Annotation (3 Annotators).csv")
df.columns = df.columns.str.strip()
ratings = df[['A1', 'A2', 'A3']]
ratings = ratings.apply(pd.to_numeric, errors='coerce').dropna().astype(int)
all_categories = sorted(ratings.stack().unique())
def to_fleiss_matrix(df, categories):
matrix = []
for _, row in df.iterrows():
counts = [list(row).count(cat) for cat in categories]
matrix.append(counts)
return np.array(matrix)
fleiss_matrix = to_fleiss_matrix(ratings, all_categories)
kappa = fleiss_kappa(fleiss_matrix, method='fleiss')
print(f"Fleiss' Kappa: {kappa:.4f}")
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