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import os |
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import json |
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import pandas as pd |
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from datasets import load_dataset |
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pd.set_option('display.max_rows', None) |
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pd.set_option('display.max_columns', None) |
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os.makedirs("experiments/analysis/qualitative", exist_ok=True) |
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target = { |
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"flan-t5-xxl": "Flan-T5\textsubscript{XXL}", |
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"opt-13b": "OPT\textsubscript{13B}", |
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"davinci": "GPT-3\textsubscript{davinci}" |
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} |
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pretty_name = { |
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'average': "Avg", |
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'is competitor/rival of': "Rival", |
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'is friend/ally of': "Ally", |
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'is influenced by': "Inf", |
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'is known for': "Know", |
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'is similar to': "Sim" |
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} |
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p = 30 |
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data = load_dataset("cardiffnlp/relentless_full", split="test") |
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for prompt in ['qa', 'lc']: |
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output = [] |
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for d in data: |
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for i in target.keys(): |
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with open(f"experiments/results/lm_{prompt}/{i}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl") as f: |
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ppl = [json.loads(x)['perplexity'] for x in f.read().split("\n") if len(x) > 0] |
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rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)} |
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prediction = [rank_map[p] for p in ppl] |
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total_n = len(d['ranks']) |
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p = int(total_n / 3) |
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top_n = [0, int(total_n * p / 100) + 1] |
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top_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[top_n[0]: top_n[1]]] |
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bottom_n = [total_n - int(total_n * p / 100), total_n] |
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bottom_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]] |
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mid_n = [top_n[1], bottom_n[0]] |
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mid_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[mid_n[0]: mid_n[1]]] |
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top_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[top_n[0]: top_n[1]]] |
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top_acc = len(set(top_pred).intersection(set(top_label))) / len(top_label) * 100 |
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mid_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[mid_n[0]: mid_n[1]]] |
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mid_acc = len(set(mid_pred).intersection(set(mid_label))) / len(mid_label) * 100 |
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bottom_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]] |
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bottom_acc = len(set(bottom_pred).intersection(set(bottom_label))) / len(bottom_label) * 100 |
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output.append({ |
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"relation_type": d['relation_type'], |
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"model": i, |
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"top_pred_and_bottom_gold": [" : ".join(d['pairs'][x]) for x in set(top_pred).intersection(bottom_label)], |
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"bottom_pred_and_top_gold": [" : ".join(d['pairs'][x]) for x in set(bottom_pred).intersection(top_label)], |
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}) |
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df = pd.DataFrame(output) |
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df.to_csv(f"experiments/analysis/qualitative/{prompt}.{p}.csv", index=False) |
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df['relation_type'] = [pretty_name[i] for i in df['relation_type']] |
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print(df) |
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new_df = [] |
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for _, i in df.iterrows(): |
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top_pred_and_bottom_gold = i['top_pred_and_bottom_gold'][:min(len(i['top_pred_and_bottom_gold']), 4)] |
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bottom_pred_and_top_gold = i['bottom_pred_and_top_gold'][:min(len(i['bottom_pred_and_top_gold']), 4)] |
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for x in range(max(len(bottom_pred_and_top_gold), len(top_pred_and_bottom_gold))): |
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if len(top_pred_and_bottom_gold) >= x + 1: |
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t = ", ".join(top_pred_and_bottom_gold[x * 1:min(len(top_pred_and_bottom_gold) + 1, (x + 1)*1)]) |
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else: |
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t = "" |
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if len(bottom_pred_and_top_gold) >= x + 1: |
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b = ", ".join(bottom_pred_and_top_gold[x*1:min(len(bottom_pred_and_top_gold) + 1, (x + 1)*1)]) |
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else: |
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b = "" |
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new_df.append({"relation_type": i['relation_type'], "model": i['model'], "top": t, "bottom": b}) |
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df_new = pd.DataFrame(new_df) |
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df_new['model'] = [target[i] for i in df_new['model']] |
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df_new = df_new[['model', 'relation_type', 'top', 'bottom']] |
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df_new = df_new.sort_values(by=['model', 'relation_type']) |
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df_new.to_csv(f"experiments/analysis/qualitative/{prompt}.{p}.format.csv", index=False) |
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with pd.option_context("max_colwidth", 1000): |
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table = df_new.to_latex(index=False, escape=False) |
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table = table.split(r"\midrule")[1].split(r"\bottomrule")[0] |
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print(table) |
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