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import os
import json
import pandas as pd
from statistics import mean
from datasets import load_dataset
from relbert import RelBERT
def cosine_similarity(a, b):
norm_a = sum(map(lambda x: x * x, a)) ** 0.5
norm_b = sum(map(lambda x: x * x, b)) ** 0.5
return sum(map(lambda x: x[0] * x[1], zip(a, b))) / (norm_a * norm_b)
# load dataset
data = load_dataset("cardiffnlp/relentless_full", split="test")
full_result = []
for lm in ['relbert-roberta-base-nce-t-rex', 'relbert-roberta-base-nce-nell']:
os.makedirs(f"./experiments/results/relbert/{lm}", exist_ok=True)
scorer = None
for d in data:
ppl_file = f"experiments/results/relbert/{lm}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl"
anchor_embeddings = [(a, b) for a, b in d['positive_examples']]
option_embeddings = [(x, y) for x, y in d['pairs']]
if not os.path.exists(ppl_file):
if scorer is None:
scorer = RelBERT(f"relbert/{lm}")
anchor_embeddings = scorer.get_embedding(d['positive_examples'])
option_embeddings = scorer.get_embedding(d['pairs'], batch_size=64)
similarity = [[cosine_similarity(a, b) for b in anchor_embeddings] for a in option_embeddings]
output = [{"similarity": s} for s in similarity]
with open(ppl_file, "w") as f:
f.write("\n".join([json.dumps(i) for i in output]))
with open(ppl_file) as f:
similarity = [json.loads(i)['similarity'] for i in f.read().split("\n") if len(i) > 0]
true_rank = d['ranks']
assert len(true_rank) == len(similarity), f"Mismatch in number of examples: {len(true_rank)} vs {len(similarity)}"
prediction = [max(s) for s in similarity]
rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
prediction_max = [rank_map[p] for p in prediction]
prediction = [min(s) for s in similarity]
rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
prediction_min = [rank_map[p] for p in prediction]
prediction = [mean(s) for s in similarity]
rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
prediction_mean = [rank_map[p] for p in prediction]
tmp = pd.DataFrame([true_rank, prediction_max, prediction_min, prediction_mean]).T
cor_max = tmp.corr("spearman").values[0, 1]
cor_min = tmp.corr("spearman").values[0, 2]
cor_mean = tmp.corr("spearman").values[0, 3]
full_result.append({"model": f"RelBERT\textsubscript{'{'}{lm.upper()}{'}'}", "relation_type": d['relation_type'], "correlation": cor_max})
df = pd.DataFrame(full_result)
df = df.pivot(columns="relation_type", index="model", values="correlation")
df['average'] = df.mean(1)
df.to_csv("experiments/results/relbert/relbert_misc.csv")
df = (100 * df).round()
print(df.to_markdown())
print(df.to_latex()) |