| 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}") | |