import json from random import uniform, seed from statistics import mean import pandas as pd from datasets import load_dataset from scipy.stats import spearmanr with pd.option_context("max_colwidth", 1000): # baselines target = { "flan-t5-xxl": "Flan-T5\textsubscript{XXL}", "opt-13b": "OPT\textsubscript{13B}", "davinci": "GPT-3\textsubscript{davinci}" } pretty_name = { 'is competitor/rival of': "Rival", 'is friend/ally of': "Ally", 'is influenced by': "Inf", 'is known for': "Know", 'is similar to': "Sim", 'average': "Avg", } # def get_iaa(scores_all): # avg = [[mean(__s for _m, __s in enumerate(_s) if _m != _n) for _s in scores_all] for _n in range(7)] # single = [[_s[_n] for _s in scores_all] for _n in range(7)] # tmptmp = [] # ps = [] # for a, s in zip(avg, single): # c = round(pd.DataFrame([a, s]).T.corr("spearman").values[0][1] * 100, 1) # ps.append(spearmanr(a, s)[1] < 0.05) # # if str(c) == "nan": # # seed(0) # # c_tmp = [] # # for _ in range(1000): # # s_tmp = [_s + uniform(-0.5, 0.5) for _s in s] # # c_tmp.append(round(pd.DataFrame([a, s_tmp]).T.corr("spearman").values[0][1] * 100, 1)) # # c = mean(c_tmp) # tmptmp.append(c) # list(zip(tmptmp, ps)) # return mean(tmptmp) def format_text(_x, _y, _z): bf = max(_x, _y, _z) wf = str(min(_x, _y, _z)) _x = "\textcolor{blue}{" + str(_x) + "}" if _x == bf else str(_x) _y = "\textcolor{blue}{" + str(_y) + "}" if _y == bf else str(_y) _z = "\textcolor{blue}{" + str(_z) + "}" if _z == bf else str(_z) _x = "\textcolor{red}{" + str(_x) + "}" if _x == wf else str(_x) _y = "\textcolor{red}{" + str(_y) + "}" if _y == wf else str(_y) _z = "\textcolor{red}{" + str(_z) + "}" if _z == wf else str(_z) return f"{_x} / {_y} / {_z}" data = load_dataset("cardiffnlp/relentless_full", split="test") table_full = [] for prompt in ['qa', 'lc']: output = [] for d in data: for i in target.keys(): with open(f"experiments/results/lm_{prompt}/{i}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl") as f: ppl = [json.loads(x)['perplexity'] for x in f.read().split("\n") if len(x) > 0] rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)} prediction = [rank_map[p] for p in ppl] # get index total_n = len(d['ranks']) p = int(total_n/3) top_n = [0, int(total_n * p / 100) + 1] top_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[top_n[0]: top_n[1]]] bottom_n = [total_n - int(total_n * p / 100), total_n] bottom_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]] mid_n = [top_n[1], bottom_n[0]] mid_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[mid_n[0]: mid_n[1]]] # top top_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[top_n[0]: top_n[1]]] top_acc = len(set(top_pred).intersection(set(top_label))) / len(top_label) * 100 # middle mid_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[mid_n[0]: mid_n[1]]] mid_acc = len(set(mid_pred).intersection(set(mid_label))) / len(mid_label) * 100 # top bottom_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]] bottom_acc = len(set(bottom_pred).intersection(set(bottom_label))) / len(bottom_label) * 100 output.append({"model": i, "relation_type": d['relation_type'], "top": round(top_acc, 1), "bottom": round(bottom_acc, 1), "middle": round(mid_acc, 1)}) for i in target.keys(): output.append({ "model": i, "relation_type": "average", "top": round(mean([o['top'] for o in output if o['model'] == i]), 0), "bottom": round(mean([o['bottom'] for o in output if o['model'] == i]), 0), "middle": round(mean([o['middle'] for o in output if o['model'] == i]), 0) }) df = pd.DataFrame(output) df['accuracy'] = [format_text(x, y, z) for x, y, z in zip(df['top'], df['middle'], df['bottom'])] table = df.pivot(index="relation_type", columns="model", values="accuracy") table.columns.name = None table.index.name = None table = table[target.keys()] table.columns = [target[i] for i in table.columns] table.index = [pretty_name[i] for i in table.index] table = table.T[list(pretty_name.values())] table = table.T table = table.to_latex(escape=False) table = table.split(r"\midrule")[1].split(r"\bottomrule")[0] table = r"\multicolumn{4}{l}{\emph{" + prompt.upper() + r" template}} \\ " + table table_full.append(table) table_full = "\midrule".join(table_full) # # output = [] # top_all = [] # mid_all = [] # bottom_all = [] # # for d in data: # if d['relation_type'] == "is influenced by": # break # total_n = len(d['ranks']) # p = int(total_n / 3) # top_n = [0, int(total_n * p / 100) + 1] # top_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[top_n[0]: top_n[1]]] # bottom_n = [total_n - int(total_n * p / 100), total_n] # bottom_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]] # mid_n = [top_n[1], bottom_n[0]] # mid_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[mid_n[0]: mid_n[1]]] # # output.append({ # "model": "IAA", "relation_type": d['relation_type'], # "top": round(get_iaa([d['scores_all'][_i] for _i in top_label]), 1), # "bottom": round(get_iaa([d['scores_all'][_i] for _i in mid_label]), 1), # "middle": round(get_iaa([d['scores_all'][_i] for _i in bottom_label]), 1) # }) # top_all += [d['scores_all'][_i] for _i in top_label] # mid_all += [d['scores_all'][_i] for _i in mid_label] # bottom_all += [d['scores_all'][_i] for _i in bottom_label] # output.append({ # "model": "IAA", "relation_type": "average", # "top": round(get_iaa(top_all), 1), # "bottom": round(get_iaa(mid_all), 1), # "middle": round(get_iaa(bottom_all), 1) # }) # # df = pd.DataFrame(output) # df['accuracy'] = [format_text(x, y, z) for x, y, z in zip(df['top'], df['middle'], df['bottom'])] # table = df.pivot(index="relation_type", columns="model", values="accuracy") # table.columns.name = None # table.index.name = None # table.index = [pretty_name[i] for i in table.index] # table = table.T[list(pretty_name.values())] # table = table.to_latex(escape=False) # table = table.split(r"\midrule")[1].split(r"\bottomrule")[0] # # table = r"\multicolumn{4}{l}{\emph{" + prompt.upper() + r" template}} \\ " + table # table_full = table_full + table print() print() print(table_full)