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summarise_sequencelevel.py
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summarise_sequencelevel.py
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import pandas as pd
import os
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
model_name_dict = { 'gpt2':'GPT2 354M', 'gpt2_xl': 'GPT2 1.5B', 'gpt6b': 'GPT-J 6B', \
'OPT350M': 'OPT 350M', 'OPT1B':'OPT 1.3B', 'OPT6B':'OPT 6.7B', \
'GradxEmb':'Grad x Emb', }
def get_one_line_for_one_FA(model_name, FA_name, task_name):
print(' ====> ', FA_name)
eva_output_dir=f"evaluation_results/benchmark/{model_name}_{FA_name}/{task_name}/"
directory = os.fsencode(eva_output_dir)
suff_mean = 0
comp_mean = 0
random_suff_mean = 0
random_comp_mean = 0
if FA_name == 'norm': lis =['Grad norms']
elif FA_name == 'input_x_gradient': lis =['GradxEmb']
elif FA_name == 'integrated_gradients': lis =['Integrated Grad']
else: lis = [FA_name.replace('_', ' ').title()]
len = 0
for file in os.listdir(directory):
filename = os.fsdecode(file)
if filename.endswith("mean.csv"):
len +=1
faithful_results = pd.read_csv(eva_output_dir+filename) # one data
suff_mean += faithful_results['norm_suff_mean'][0]
random_suff_mean += faithful_results['random_suff_mean'][0]
comp_mean += faithful_results['norm_comp_mean'][0]
random_comp_mean += faithful_results['random_comp_mean'][0]
continue
else:continue
# print(suff_mean-random_suff_mean, comp_mean-random_comp_mean)
# lis.append((suff_mean-random_suff_mean)/len)
# lis.append((comp_mean-random_comp_mean)/len)
suff = math.log(suff_mean/random_suff_mean)
comp = math.log(comp_mean/random_comp_mean)
lis.append(suff)
lis.append(comp)
return lis
all_results = []
for model_name in ["gpt6b", "OPT350M", "gpt2", "gpt2_xl", "OPT1B", "OPT6B"]: # "gpt2","gpt2_xl", "OPT1B", "OPT6B"
# "gpt6b", "OPT350M",
for dataset in ['tellmewhy', 'wikitext']: #
print()
print()
print(f' ============== {model_name}, {dataset} ============== ')
# try: norm = get_one_line_for_one_FA(model_name, "norm", dataset)
# except: norm = None
signed = get_one_line_for_one_FA(model_name, "input_x_gradient", dataset)
integrated = get_one_line_for_one_FA(model_name, "integrated_gradients", dataset)
gradient_shap = get_one_line_for_one_FA(model_name, "gradient_shap", dataset)
rollout_attention = get_one_line_for_one_FA(model_name, "attention_rollout", dataset)
last_attention = get_one_line_for_one_FA(model_name, "attention_last", dataset)
attention = get_one_line_for_one_FA(model_name, "attention", dataset)
ours = get_one_line_for_one_FA(model_name, "ours", dataset)
df = pd.DataFrame([signed, integrated, gradient_shap,\
rollout_attention, last_attention, attention, ours], columns=['FAs', 'Soft Suff', 'Soft Comp'])
print(df)
os.makedirs(f'evaluation_results/summary/benchmark/{dataset}/', exist_ok=True)
df.to_csv(f'evaluation_results/summary/benchmark/{dataset}/{model_name}_{dataset}.csv')
df['Model'] = model_name
df['Data'] = dataset
all_results.append(df)
df = pd.concat(all_results)
df.to_csv(f'evaluation_results/summary/benchmark/ALL.csv')
df.replace(model_name_dict,inplace=True)
#dataset = 'wikitext' # tellmewhy wikitext
for dataset in ['wikitext', 'tellmewhy']: # wikitext
# "FAs","Soft Suff","Soft Comp","Model","Data"
select_data = df.loc[df['Data'] == dataset]
suff = select_data[["FAs","Soft Suff","Model"]]
comp = select_data[["FAs","Soft Comp","Model"]]
#sns.set(style="darkgrid")
plt.figure(figsize=(22, 22))
fig, axs = plt.subplots(nrows=3, ncols=1, sharex=False, ) #squeeze=True,
#fig.title('Wikitext sentence-level faithfulness')
plt.subplot(3,1,3) # row colum
axs[2].set_visible(False)
plt.subplot(3,1,1) # row colum
sns.barplot(x="Model", y="Soft Comp", hue="FAs", data=comp, #errorbar=None, #width= 0.6,
order=['OPT 350M','OPT 1.3B', 'OPT 6.7B','GPT2 354M', 'GPT2 1.5B', 'GPT-J 6B'])
plt.xlabel('Models', fontweight='bold')
plt.subplot(3,1,2) # row colum
sns.barplot(x="Model", y="Soft Suff", hue="FAs", data=suff, #errorbar=None, width= 0.6,
order=['OPT 350M','OPT 1.3B', 'OPT 6.7B','GPT2 354M', 'GPT2 1.5B', 'GPT-J 6B']) # , height=8
plt.xlabel('Models', fontweight='bold')
handles, labels = axs[0].get_legend_handles_labels()
fig.legend(handles[:8], labels[:8], ncol=4, loc='center', bbox_to_anchor=(0.5, 0.21), fontsize=9) # 00 0.4 middle 0.8 top
# fig.legend(nrow=1, loc='lower right', bbox_to_anchor=(1.19, 0.1)) #
plt.legend()
axs[0].get_legend().remove()
axs[1].get_legend().remove()
# Add xticks on the middle of the group bars
fig.suptitle(f' {dataset.capitalize()} (sequence-level)', fontsize=15)
fig.tight_layout()
plt.show()
plt.savefig(f"./evaluation_results/summary/benchmark/{dataset}_sentence.png",bbox_inches='tight')
print(f"saving at ===> ./evaluation_results/summary/benchmark/{dataset}_sentence.png")