File size: 8,809 Bytes
d8a1b62 4c4867c d8a1b62 4c4867c d8a1b62 4c4867c d8a1b62 4c4867c d8a1b62 4c4867c d8a1b62 4c4867c d8a1b62 4c4867c d8a1b62 4c4867c d8a1b62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
import os
import json
import pandas as pd
from collections import defaultdict, Counter
import altair as alt
import panel as pn
def choices_to_df(choices, hue):
df = pd.DataFrame(choices, columns=['choices'])
df['hue'] = hue
df['hue'] = df['hue'].astype(str)
return df
def arrange_data():
# Human Data
df = pd.read_csv('Project/2_scientific/ChatGPT-Behavioral-main/data/bomb_risk.csv')
df = df[df['Role'] == 'player']
df = df[df['gameType'] == 'bomb_risk']
df.sort_values(by=['UserID', 'Round'])
prefix_to_choices_human = defaultdict(list)
prefix_to_IPW = defaultdict(list)
prev_user = None
prev_move = None
prefix = ''
bad_user = False
for _, row in df.iterrows():
if bad_user: continue
if row['UserID'] != prev_user:
prev_user = row['UserID']
prefix = ''
bad_user = False
move = row['move']
if move < 0 or move > 100:
bad_users = True
continue
prefix_to_choices_human[prefix].append(move)
if len(prefix) == 0:
prefix_to_IPW[prefix].append(1)
elif prefix[-1] == '1':
prev_move = min(prev_move, 98)
prefix_to_IPW[prefix].append(1./(100 - prev_move))
elif prefix[-1] == '0':
prev_move = max(prev_move, 1)
prefix_to_IPW[prefix].append(1./(prev_move))
else: assert False
prev_move = move
prefix += '1' if row['roundResult'] == 'SAFE' else '0'
# Model Data
prefix_to_choices_model = defaultdict(lambda : defaultdict(list))
for model in ['ChatGPT-4', 'ChatGPT-3']:
if model == 'ChatGPT-4':
file_names = [
'bomb_gpt4_2023_05_15-12_13_51_AM.json'
]
elif model == 'ChatGPT-3':
file_names = [
'bomb_turbo_2023_05_14-10_45_50_PM.json'
]
choices = []
scenarios = []
for file_name in file_names:
with open(os.path.join('Project/2_scientific/ChatGPT-Behavioral-main/records', file_name), 'r') as f:
records = json.load(f)
choices += records['choices']
scenarios += records['scenarios']
assert len(scenarios) == len(choices)
print('loaded %i valid records' % len(scenarios))
prefix_to_choice = defaultdict(list)
prefix_to_result = defaultdict(list)
prefix_to_pattern = defaultdict(Counter)
wrong_sum = 0
for scenarios_tmp, choices_tmp in zip(scenarios, choices):
result = 0
for i, scenario in enumerate(scenarios_tmp):
prefix = tuple(scenarios_tmp[:i])
prefix = ''.join([str(x) for x in prefix])
choice = choices_tmp[i]
prefix_to_choice[prefix].append(choice)
prefix_to_pattern[prefix][tuple(choices_tmp[:-1])] += 1
prefix = tuple(scenarios_tmp[:i+1])
if scenario == 1:
result += choice
prefix_to_result[prefix].append(result)
print('# of wrong sum:', wrong_sum)
print('# of correct sum:', len(scenarios) - wrong_sum)
prefix_to_choices_model[model] = prefix_to_choice
# Arrange Data
round_dict = {'': [1, -1, -1],
'0': [2, 0, -1],
'1': [2, 1, -1],
'00': [3, 0, 0],
'01': [3, 0, 1],
'10': [3, 1, 0],
'11': [3, 1, 1]}
df_bomb_all = pd.DataFrame()
for prefix in round_dict:
df_bomb_human = choices_to_df(prefix_to_choices_human[prefix], hue='Human')
df_bomb_human['weight'] = prefix_to_IPW[prefix]
df_bomb_models = pd.concat([choices_to_df(
prefix_to_choices_model[model][prefix], hue=model
) for model in prefix_to_choices_model]
)
df_bomb_models['weight'] = 1
df_bomb_temp = pd.concat([df_bomb_human, df_bomb_models])
df_bomb_temp['prefix'] = prefix
df_bomb_all = pd.concat([df_bomb_all, df_bomb_temp])
df_density = df_bomb_all.groupby(['hue', 'prefix'])['choices'].value_counts(normalize=True).unstack(fill_value=0).stack().reset_index()
df_density = df_density.rename(columns={'hue': 'Subject', 'choices': 'Boxes', 0: 'Density'})
df_density['Round'] = df_density['prefix'].apply(lambda x: round_dict[x][0])
return df_density
df_density = arrange_data()
alt.data_transformers.disable_max_rows()
# Enable Panel extensions
pn.extension(design='bootstrap')
pn.extension('vega')
template = pn.template.BootstrapTemplate(
title='Nan-Hsin Lin | SI649 Scientific Viz Project',
)
# Define a function to create and return a plot
def create_plot(bomb_1, bomb_2):
bomb_1 = int(not bomb_1)
bomb_2 = int(not bomb_2)
selection = alt.selection_single(encodings=['color'], empty='none', value=3)
opacityCondition = alt.condition(selection, alt.value(1), alt.value(0.3))
range_ = ['#009FB7', '#FED766', '#FE4A49']
plot = alt.Chart(df_density).transform_filter(
(alt.datum.prefix == '') | (alt.datum.prefix == str(bomb_1)) | (alt.datum.prefix == str(bomb_1) + str(bomb_2))
).mark_bar(opacity=0.5).encode(
x=alt.X('Boxes:Q',
bin=alt.Bin(maxbins=10),
title='Number of boxes opened',
axis=alt.Axis(ticks=False,
labelFontSize=11,
labelColor='#AAA7AD',
titleFontSize=12,
titleColor='#AAA7AD',
domain=False)),
y=alt.Y('Density:Q',
stack=None,
scale=alt.Scale(domain=[0, 1]),
axis=alt.Axis(format='.0%',
ticks=False,
tickCount=5,
labelFontSize=11,
labelColor='#AAA7AD',
titleFontSize=12,
titleColor='#AAA7AD',
domain=False,
grid=False)),
color=alt.Color('Round:N',
scale=alt.Scale(domain=[1, 2, 3], range=range_)),
row=alt.Row('Subject:N',
header=alt.Header(title=None, orient='top', labelFontSize=16),
sort='descending'),
tooltip=['Subject:N', 'Round:N', 'Boxes:Q', alt.Tooltip('Density:Q', format='.0%')]
).properties(width=400, height=150
).configure_view(strokeWidth=3, stroke='lightgrey'
).configure_legend(
titleFontSize=12,
titleColor='#AAA7AD',
titleAnchor='middle',
titlePadding=8,
labelFontSize=12,
labelColor='#AAA7AD',
labelFontWeight='bold',
symbolOffset=20,
orient='none',
direction='horizontal',
legendX=120,
legendY=-90,
symbolSize=200
).add_selection(selection).encode(
opacity=opacityCondition
)
return plot
# Create widgets
switch_1 = pn.widgets.Switch(name='Bomb in Round 1', value=True)
switch_2 = pn.widgets.Switch(name='Bomb in Round 2', value=True)
plot_widgets = pn.bind(create_plot, switch_1, switch_2)
# Combine everything in a Panel Column to create an app
maincol = pn.Column()
maincol.append(pn.Row(pn.layout.HSpacer(),
"### A Turing test of whether AI chatbots are behaviorally similar to humans",
pn.layout.HSpacer()))
maincol.append(pn.Row(pn.Spacer(width=100)))
maincol.append(pn.Row(pn.layout.HSpacer(),
"#### Bomb Risk Game: Human vs. ChatGPT-4 vs. ChatGPT-3",
pn.layout.HSpacer()))
maincol.append(pn.Row(pn.layout.HSpacer(),
"Bomb in Round 1", switch_1,
pn.Spacer(width=50),
"Bomb in Round 2", switch_2,
pn.layout.HSpacer()))
maincol.append(pn.Row(pn.layout.HSpacer(),
plot_widgets,
pn.layout.HSpacer()))
maincol.append(pn.Row(pn.layout.HSpacer(),
"**Fig 5.** ChatGPT-4 and ChatGPT-3 act as if they have particular risk preferences. Both have the same mode as human distribution in the first round or when experiencing favorable outcomes in the Bomb Risk Game. When experiencing negative outcomes, ChatGPT-4 remains consistent and risk-neutral, while ChatGPT-3 acts as if it becomes more risk-averse.",
pn.layout.HSpacer()))
template.main.append(maincol)
# set the app to be servable
template.servable(title="SI649 Scientific Viz Project") |