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helpers.py
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helpers.py
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import os
import json
import numpy as np
import pandas as pd
from scipy.stats import wasserstein_distance
PEW_SURVEY_LIST = [26, 27, 29, 32, 34, 36, 41, 42, 43, 45, 49, 50, 54, 82, 92]
DEMOGRAPHIC_ATTRIBUTES = ['Overall',
'CREGION',
'AGE',
'SEX',
'EDUCATION',
'CITIZEN',
'MARITAL',
'RELIG',
'RELIGATTEND',
'POLPARTY',
'INCOME',
'POLIDEOLOGY',
'RACE']
MODEL_NAMES = {'human max': 'human (worst)',
'human mean': 'human (avg)',
'random': 'random',
'ai21_j1-grande': 'j1-grande',
'ai21_j1-jumbo': 'j1-jumbo',
'ai21_j1-grande-v2-beta': 'j1-grande-v2-beta',
'openai_ada': 'ada',
'openai_davinci': 'davinci',
'openai_text-ada-001': 'text-ada-001',
'openai_text-davinci-001': 'text-davinci-001',
'openai_text-davinci-002': 'text-davinci-002',
'openai_text-davinci-003': 'text-davinci-003',
}
MODEL_ORDER = {k: ki for ki, k in enumerate(MODEL_NAMES.keys())}
def get_probabilities(lps, references, mapping):
min_prob = np.exp(np.min(list(lps.values())))
remaining_prob = max(0, 1 - sum([np.exp(v) for v in lps.values()]))
dist, misses = [], []
for ref in references:
prefix = mapping[ref]
values = [lps[key] for key in [f" {prefix}", prefix] if key in lps]
misses.append(len(values) == 0)
dist.append(np.max(values) if len(values) else None)
Nmisses = sum(misses)
if Nmisses > 0:
miss_value = np.log(min(min_prob, remaining_prob / Nmisses))
dist = [d if d is not None else miss_value for d in dist]
probs_unnorm = np.array([np.exp(v) for v in dist])
res = {'logprobs': dist,
'probs_unnorm': probs_unnorm,
'probs_norm': probs_unnorm / np.sum(probs_unnorm),
'misses': misses}
return res
def extract_model_opinions(result_instance, context_type, info_df):
row = {}
input_id = result_instance['instance']['id']
question_raw = result_instance['instance']['input']['text']
references = [r['output']['text'] for r in result_instance['instance']['references']]
mapping = result_instance['output_mapping']
if context_type not in ['steer-portray', 'steer-bio']:
context = result_instance['request']['prompt'].split(f"Question: {question_raw}")[0].strip()
else:
context = question_raw.split('Question:')[0].strip() + '\n'
question_raw = question_raw.replace(context, "").strip().replace('Question: ', '')
question = question_raw + f" [{'/'.join(references)}]"
top_k_logprobs = result_instance['result']['completions'][0]['tokens'][0]['top_logprobs']
for k, v in zip(['input_id', 'question_raw', 'question', 'references',
'context', 'mapping', 'top_k_logprobs'],
[input_id, question_raw, question, references, context, mapping, top_k_logprobs]):
row[k] = v
## Get probability distribution
info_loc = np.where(np.logical_and(info_df['question'] == question_raw,
[set(r) == set(references) for r in info_df['references']]))[0]
assert len(info_loc) == 1
info = info_df.iloc[info_loc]
ordinal = info['option_ordinal'].values[0]
ordinal_refs = info['references'].values[0][:len(ordinal)]
refusal_refs = info['references'].values[0][len(ordinal):]
dist_info = get_probabilities(top_k_logprobs, info['references'].values[0], {v: k for k, v in mapping.items()})
dist_info['D_M'] = dist_info['probs_unnorm'][:len(ordinal)] / np.sum(dist_info['probs_unnorm'][:len(ordinal)])
dist_info['R_M'] = np.sum(dist_info['probs_norm'][len(ordinal):])
dist_info['ordinal'] = ordinal
dist_info['ordinal_refs'] = ordinal_refs
dist_info['refusal_refs'] = refusal_refs
dist_info['qkey'] = info['key'].values[0]
row.update(dist_info)
return row
def extract_human_opinions(hdf, model_df, md_df, demographic='Overall', wave=None):
assert wave is not None
question_keys = list(set(model_df['qkey']))
weight_key = [w for w in hdf.columns if w == f'WEIGHT_W{wave}']
assert len(weight_key) == 1
weight_key = weight_key[0]
res = {'qkey': [], 'attribute': [], 'group': [], 'D_H': [], 'R_H': []}
for qkey in question_keys:
col_names = [qkey, demographic] if demographic != 'Overall' else [qkey]
cdf = hdf[[weight_key] + col_names]
cdf = cdf[[type(v) == str for v in cdf[qkey]]]
cdf = cdf.groupby(col_names, as_index=False).agg({weight_key: sum})
if demographic == 'Overall':
dist_all = {'Overall': {k: v for k, v in zip(cdf[qkey], cdf[weight_key])}}
else:
options = md_df[md_df['key'] == demographic]['options'].values[0]
def chain(row):
dist = {k: v for k, v in zip(row[qkey], row[weight_key])}
row['dist'] = dist
return row
cdf = cdf[cdf[demographic].isin(options)]
cdf = cdf.groupby([demographic], as_index=False).agg(list).apply(chain, axis=1)
dist_all = {k: v for k, v in zip(cdf[demographic], cdf['dist'])}
vdf = model_df[model_df['qkey'] == qkey][['ordinal_refs', 'refusal_refs', 'ordinal']].iloc[:1]
for group_name, dist in dist_all.items():
opinion_dist = np.array([dist[v] if v in dist else 0 for v in vdf['ordinal_refs'].values[0]])
if np.sum(opinion_dist) == 0: continue
opinion_dist /= np.sum(opinion_dist)
refusal_prob = np.sum([dist[v] if v in dist else 0 for v in vdf['refusal_refs'].values[0]])
refusal_prob /= np.sum(list(dist.values()))
for kk, vv in zip(['qkey', 'attribute', 'group', 'D_H', 'R_H'],
[qkey, demographic, group_name, opinion_dist, refusal_prob]):
res[kk].append(vv)
return pd.DataFrame(res)
def get_max_wd(ordered_ref_weights):
d0, d1 = np.zeros(len(ordered_ref_weights)), np.zeros(len(ordered_ref_weights))
d0[np.argmax(ordered_ref_weights)] = 1
d1[np.argmin(ordered_ref_weights)] = 1
max_wd = wasserstein_distance(ordered_ref_weights, ordered_ref_weights, d0, d1)
return max_wd
def get_model_opinions(result_dir, result_files, info_df):
model_df = []
for f in result_files:
context_type = f.split('context=')[1].split(',')[0]
model_name = f.split('model=')[1].split(',')[0]
print(f)
print(model_name, context_type)
results_json = json.load(open(os.path.join(result_dir, f, 'scenario_state.json'), 'rb'))['request_states']
mdf = pd.DataFrame([extract_model_opinions(r, context_type, info_df) for r in results_json])
mdf['results_path'] = f
mdf['context_type'] = context_type
mdf['model_name'] = MODEL_NAMES[model_name]
mdf['model_order'] = MODEL_ORDER[model_name]
model_df.append(mdf)
print('-' * 100)
model_df = pd.concat(model_df)
return model_df
def get_steering_group(steer_type, steer_df, contexts):
steer_dict = {}
for context in contexts:
if steer_type == 'steer-qa':
question = context.split('\n')[0].replace('Question: ', '')
answer_dict = context.split('\n')[1:-1]
answer_dict = {l.split('. ')[0]: l.split('. ')[1] for l in answer_dict}
answer = answer_dict[context.split('Answer: ')[1]]
assert question in steer_df['question'].values
assert answer in steer_df[steer_df['question'] == question]['correct'].values
rel = steer_df[np.logical_and(steer_df['question'] == question,
steer_df['correct'] == answer)]
else:
rel = steer_df[steer_df['question'] == context]
assert len(rel) == 1
steer_dict[context] = {'attribute': rel['md'].values[0],
'group': rel['subgroup'].values[0]}
return steer_dict
VIS_STYLES = [dict(selector="th", props=[('width', '90px'), ("font-size", "95%"),
('border-left', '1px solid black'),
('border-bottom', '1px solid black'),
('border-right', '1px solid black'),
('border-top', '1px solid black')]),
dict(selector="td", props=[('text-align', 'center'),
('border-left', '1px solid black'),
('border-bottom', '1px solid black'),
('border-right', '1px solid black'),
('border-top', '1px solid black')]),
dict(selector="th.row_heading", props=[('text-align', 'center'), ("font-size", "100%")]),
dict(selector="th.col_heading",
props=[('text-align', 'center'),
('width', '100px'),
('vertical-align', 'top'),
("transform", "translate(0%,10%)"),
("font-size", "70%")
])]