| import json | |
| from itertools import permutations | |
| from string import ascii_letters | |
| from statistics import mean | |
| import numpy as np | |
| import pandas as pd | |
| with open("data/data_processed.new.test.jsonl") as f: | |
| data = [json.loads(line) for line in f] | |
| with open("data/data_processed.new.validation.jsonl") as f: | |
| data += [json.loads(line) for line in f] | |
| tmp = {} | |
| for i in data: | |
| if i['relation_type'] not in tmp: | |
| tmp[i['relation_type']] = i['scores_all'] | |
| else: | |
| tmp[i['relation_type']] = i['scores_all'] + tmp[i['relation_type']] | |
| num_annotators = len(list(tmp.values())[0][0]) | |
| df = None | |
| for r, scores in tmp.items(): | |
| corr_matrix = np.ones((num_annotators, num_annotators)) * 100 | |
| for a, b in permutations(range(num_annotators), 2): | |
| score_a = [s[a] for s in scores] | |
| score_b = [s[b] for s in scores] | |
| corr_matrix[a][b] = pd.DataFrame([score_a, score_b]).T.corr("spearman").values[0][1] * 100 | |
| corr_df = pd.DataFrame(corr_matrix, columns=[ascii_letters[i].upper() for i in range(num_annotators)], | |
| index=[ascii_letters[i].upper() for i in range(num_annotators)]) | |
| corr_df['Others'] = [pd.DataFrame([ | |
| [s[a] for s in scores], | |
| [mean(_s for _n, _s in enumerate(s) if _n != a) for s in scores] | |
| ]).T.corr("spearman").values[0][1] * 100 for a in range(num_annotators)] | |
| corr_df = corr_df.T | |
| corr_df['Avg'] = corr_df.mean(1) | |
| corr_df = corr_df.T | |
| print(r) | |
| print(corr_df.round(0).astype(int).to_latex()) | |
| print() | |
| if df is None: | |
| df = corr_df | |
| else: | |
| df += corr_df | |
| df = df/5 | |
| df = df.T | |
| df.pop("Avg") | |
| df['Avg'] = df.mean(1) | |
| df = df.T | |
| print("ALL") | |
| print(df.round(0).astype(int).to_latex()) | |