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| import os | |
| import re | |
| import csv | |
| import json | |
| import math | |
| import openai | |
| import numpy as np | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| from tqdm import tqdm | |
| from scipy import stats | |
| from datetime import datetime | |
| from collections import defaultdict, Counter | |
| from matplotlib.ticker import FuncFormatter | |
| from matplotlib.colors import ListedColormap | |
| import panel as pn | |
| import altair as alt | |
| # def choices_to_df(choices, hue): | |
| # df = pd.DataFrame(choices, columns=['choices']) | |
| # df['hue'] = hue | |
| # df['hue'] = df['hue'].astype(str) | |
| # return df | |
| # binrange = (0, 100) | |
| # moves = [] | |
| # with open('dictator.csv', 'r') as f: | |
| # reader = csv.reader(f) | |
| # header = next(reader) | |
| # col2idx = {col: idx for idx, col in enumerate(header)} | |
| # for row in reader: | |
| # record = {col: row[idx] for col, idx in col2idx.items()} | |
| # if record['Role'] != 'first': continue | |
| # if int(record['Round']) > 1: continue | |
| # if int(record['Total']) != 100: continue | |
| # if record['move'] == 'None': continue | |
| # if record['gameType'] != 'dictator': continue | |
| # move = float(record['move']) | |
| # if move < binrange[0] or \ | |
| # move > binrange[1]: continue | |
| # moves.append(move) | |
| # df_dictator_human = choices_to_df(moves, 'Human') | |
| # choices = [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0] | |
| # df_dictator_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # choices = [25, 35, 70, 30, 20, 25, 40, 80, 30, 30, 40, 30, 30, 30, 30, 30, 40, 40, 30, 30, 40, 30, 60, 20, 40, 25, 30, 30, 30] | |
| # df_dictator_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # def extract_choices(recrods): | |
| # choices = [extract_amout( | |
| # messages[-1]['content'], | |
| # prefix='$', | |
| # print_except=True, | |
| # type=float) for messages in records['messages'] | |
| # ] | |
| # choices = [x for x in choices if x is not None] | |
| # # print(choices) | |
| # return choices | |
| # def extract_amout( | |
| # message, | |
| # prefix='', | |
| # print_except=True, | |
| # type=float, | |
| # brackets='[]' | |
| # ): | |
| # try: | |
| # matches = extract_brackets(message, brackets=brackets) | |
| # matches = [s[len(prefix):] \ | |
| # if s.startswith(prefix) \ | |
| # else s for s in matches] | |
| # invalid = False | |
| # if len(matches) == 0: | |
| # invalid = True | |
| # for i in range(len(matches)): | |
| # if matches[i] != matches[0]: | |
| # invalid = True | |
| # if invalid: | |
| # raise ValueError('Invalid answer: %s' % message) | |
| # return type(matches[0]) | |
| # except Exception as e: | |
| # if print_except: print(e) | |
| # return None | |
| # records = json.load(open('dictator_wo_ex_2023_03_13-11_24_07_PM.json', 'r')) | |
| # choices = extract_choices(records) | |
| # # Plot 1 - Dictator (altruism) | |
| # def plot_facet( | |
| # df_list, | |
| # x='choices', | |
| # hue='hue', | |
| # palette=None, | |
| # binrange=None, | |
| # bins=10, | |
| # # binwidth=10, | |
| # stat='count', | |
| # x_label='', | |
| # sharex=True, | |
| # sharey=False, | |
| # subplot=sns.histplot, | |
| # xticks_locs=None, | |
| # # kde=False, | |
| # **kwargs | |
| # ): | |
| # data = pd.concat(df_list) | |
| # if binrange is None: | |
| # binrange = (data[x].min(), data[x].max()) | |
| # g = sns.FacetGrid( | |
| # data, row=hue, hue=hue, | |
| # palette=palette, | |
| # aspect=2, height=2, | |
| # sharex=sharex, sharey=sharey, | |
| # despine=True, | |
| # ) | |
| # g.map_dataframe( | |
| # subplot, | |
| # x=x, | |
| # # kde=kde, | |
| # binrange=binrange, | |
| # bins=bins, | |
| # stat=stat, | |
| # **kwargs | |
| # ) | |
| # # g.add_legend(title='hue') | |
| # g.set_axis_labels(x_label, stat.title()) | |
| # g.set_titles(row_template="{row_name}") | |
| # for ax in g.axes.flat: | |
| # ax.yaxis.set_major_formatter( | |
| # FuncFormatter(lambda y, pos: '{:.2f}'.format(y)) | |
| # ) | |
| # binwidth = (binrange[1] - binrange[0]) / bins | |
| # if xticks_locs is None: | |
| # locs = np.linspace(binrange[0], binrange[1], bins//2+1) | |
| # locs = [loc + binwidth for loc in locs] | |
| # else: | |
| # locs = xticks_locs | |
| # labels = [str(int(loc)) for loc in locs] | |
| # locs = [loc + 0.5*binwidth for loc in locs] | |
| # plt.xticks(locs, labels) | |
| # g.set(xlim=binrange) | |
| # return g | |
| # df = df_dictator_human | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart1 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('steelblue'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_dictator_gpt4 | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart2 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('orange'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_dictator_turbo | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart3 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10)), | |
| # y='density:Q', | |
| # color=alt.value('green'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # final = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared') | |
| # #Plot 2 - - Ultimatum (Fairness) | |
| # df = pd.read_csv('ultimatum_strategy.csv') | |
| # df = df[df['gameType'] == 'ultimatum_strategy'] | |
| # df = df[df['Role'] == 'player'] | |
| # df = df[df['Round'] == 1] | |
| # df = df[df['Total'] == 100] | |
| # df = df[df['move'] != 'None'] | |
| # df['propose'] = df['move'].apply(lambda x: eval(x)[0]) | |
| # df['accept'] = df['move'].apply(lambda x: eval(x)[1]) | |
| # df = df[(df['propose'] >= 0) & (df['propose'] <= 100)] | |
| # df = df[(df['accept'] >= 0) & (df['accept'] <= 100)] | |
| # df_ultimatum_1_human = choices_to_df(list(df['propose']), 'Human') | |
| # df_ultimatum_2_human = choices_to_df(list(df['accept']), 'Human') | |
| # choices = [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0] | |
| # df_ultimatum_1_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # choices = [40, 40, 40, 30, 70, 70, 50, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 30, 30, 35, 50, 40, 70, 40, 60, 60, 70, 40, 50] | |
| # df_ultimatum_1_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # choices = [50.0, 50.0, 50.0, 1.0, 1.0, 1.0, 50.0, 25.0, 50.0, 1.0, 1.0, 20.0, 50.0, 50.0, 50.0, 20.0, 50.0, 1.0, 1.0, 1.0, 50.0, 50.0, 50.0, 1.0, 1.0, 1.0, 20.0, 1.0] + [0, 1] | |
| # df_ultimatum_2_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # choices = [None, 50, 50, 50, 50, 30, None, None, 30, 33.33, 40, None, 50, 40, None, 1, 30, None, 10, 50, 30, 10, 30, None, 30, None, 10, 30, 30, 30] | |
| # df_ultimatum_2_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # choices = [50.0, 50.0, 10.0, 40.0, 20.0, 50.0, 1.0, 1.0, 50.0, 1.0, 50.0, 50.0, 20.0, 10.0, 50.0, 20.0, 1.0, 1.0, 50.0, 1.0, 20.0, 1.0, 50.0, 50.0, 20.0, 20.0, 50.0, 20.0, 1.0, 50.0] | |
| # df_ultimatum_2_gpt4_female = choices_to_df(choices, hue='ChatGPT-4 Female') | |
| # choices = [1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 1.0, 1.0, 50.0, 50.0, 50.0, 20.0, 20.0, 1.0, 50.0, 1.0, 1.0, 1.0, 50.0, 20.0, 1.0, 50.0, 20.0, 20.0, 10.0, 50.0, 1.0, 1.0, 1.0] | |
| # df_ultimatum_2_gpt4_male = choices_to_df(choices, hue='ChatGPT-4 Male') | |
| # choices = [40.0, 1.0, 1.0, 20.0, 1.0, 20.0, 50.0, 50.0, 1.0, 1.0, 1.0, 50.0, 1.0, 20.0, 50.0, 10.0, 50.0, 1.0, 1.0, 20.0, 1.0, 50.0, 20.0, 20.0, 20.0, 1.0, 1.0, 1.0, 1.0, 40.0] | |
| # df_ultimatum_2_gpt4_US = choices_to_df(choices, hue='ChatGPT-4 US') | |
| # choices = [1.0, 1.0, 20.0, 50.0, 1.0, 1.0, 1.0, 1.0, 20.0, 20.0, 50.0, 20.0, 20.0, 50.0, 20.0, 1.0, 40.0, 50.0, 1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 50.0, 1.0, 1.0, 1.0, 1.0] | |
| # df_ultimatum_2_gpt4_Poland = choices_to_df(choices, hue='ChatGPT-4 Poland') | |
| # choices = [50.0, 1.0, 20.0, 50.0, 50.0, 50.0, 50.0, 1.0, 1.0, 50.0, 1.0, 50.0, 1.0, 50.0, 1.0, 20.0, 1.0, 1.0, 20.0, 50.0, 0.0, 20.0, 1.0, 1.0, 1.0, 1.0, 20.0, 20.0, 50.0, 20.0] | |
| # df_ultimatum_2_gpt4_China = choices_to_df(choices, hue='ChatGPT-4 China') | |
| # choices = [1.0, 1.0, 1.0, 50.0, 1.0, 1.0, 50.0, 40.0, 1.0, 1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 1.0, 50.0, 1.0, 20.0, 1.0, 20.0, 1.0, 50.0, 1.0, 50.0, 20.0, 1.0, 1.0, 50.0] | |
| # df_ultimatum_2_gpt4_UK = choices_to_df(choices, hue='ChatGPT-4 UK') | |
| # choices = [50.0, 1.0, 20.0, 50.0, 50.0, 50.0, 50.0, 10.0, 1.0, 40.0, 50.0, 20.0, 1.0, 1.0, 1.0, 50.0, 50.0, 20.0, 20.0, 1.0, 1.0, 50.0, 20.0, 50.0, 50.0, 20.0, 1.0, 20.0, 50.0, 1] | |
| # df_ultimatum_2_gpt4_Columbia = choices_to_df(choices, hue='ChatGPT-4 Columbia') | |
| # choices = [50.0, 1.0, 50.0, 20.0, 20.0, 20.0, 50.0, 20.0, 20.0, 1.0, 1.0, 1.0, 1.0, 20.0, 1.0, 50.0, 1.0, 20.0, 20.0, 50.0, 1.0, 50.0, 1.0, 40.0, 1.0, 20.0, 1.0, 20.0, 1.0, 1.0] | |
| # df_ultimatum_2_gpt4_under = choices_to_df(choices, hue='ChatGPT-4 Undergrad') | |
| # choices = [1.0, 20.0, 1.0, 40.0, 50.0, 1.0, 1.0, 1.0, 25.0, 20.0, 50.0, 20.0, 50.0, 50.0, 1.0, 50.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 50.0, 20.0, 1.0, 1.0, 1.0, 50.0, 20.0, 20.0] | |
| # df_ultimatum_2_gpt4_grad = choices_to_df(choices, hue='ChatGPT-4 Graduate') | |
| # df = df_ultimatum_1_human | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart1 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('steelblue'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_ultimatum_1_gpt4 | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart2 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('orange'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_ultimatum_1_turbo | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart3 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10)), | |
| # y='density:Q', | |
| # color=alt.value('green'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # final2 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared') | |
| # #Plot 3 - - Ultimatum (Responder) (spite) | |
| # df = df_ultimatum_2_human | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart1 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('steelblue'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_ultimatum_2_gpt4 | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart2 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('orange'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_ultimatum_2_turbo | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart3 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('green'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # final3 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared') | |
| # #Plot 4 - - Trust (as Investor) (trust) | |
| # binrange = (0, 100) | |
| # moves_1 = [] | |
| # moves_2 = defaultdict(list) | |
| # with open('trust_investment.csv', 'r') as f: | |
| # reader = csv.reader(f) | |
| # header = next(reader) | |
| # col2idx = {col: idx for idx, col in enumerate(header)} | |
| # for row in reader: | |
| # record = {col: row[idx] for col, idx in col2idx.items()} | |
| # # if record['Role'] != 'first': continue | |
| # if int(record['Round']) > 1: continue | |
| # # if int(record['Total']) != 100: continue | |
| # if record['move'] == 'None': continue | |
| # if record['gameType'] != 'trust_investment': continue | |
| # if record['Role'] == 'first': | |
| # move = float(record['move']) | |
| # if move < binrange[0] or \ | |
| # move > binrange[1]: continue | |
| # moves_1.append(move) | |
| # elif record['Role'] == 'second': | |
| # inv, ret = eval(record['roundResult']) | |
| # if ret < 0 or \ | |
| # ret > inv * 3: continue | |
| # moves_2[inv].append(ret) | |
| # else: continue | |
| # df_trust_1_human = choices_to_df(moves_1, 'Human') | |
| # df_trust_2_human = choices_to_df(moves_2[10], 'Human') | |
| # df_trust_3_human = choices_to_df(moves_2[50], 'Human') | |
| # df_trust_4_human = choices_to_df(moves_2[100], 'Human') | |
| # choices = [50.0, 50.0, 40.0, 30.0, 50.0, 50.0, 40.0, 50.0, 50.0, 50.0, 50.0, 50.0, 30.0, 30.0, 50.0, 50.0, 50.0, 40.0, 40.0, 50.0, 50.0, 50.0, 50.0, 40.0, 50.0, 50.0, 50.0, 50.0] | |
| # df_trust_1_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # choices = [50.0, 50.0, 30.0, 30.0, 30.0, 60.0, 50.0, 40.0, 20.0, 20.0, 50.0, 40.0, 30.0, 20.0, 30.0, 20.0, 30.0, 60.0, 50.0, 30.0, 50.0, 20.0, 20.0, 30.0, 50.0, 30.0, 30.0, 50.0, 40.0] + [30] | |
| # df_trust_1_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # choices = [20.0, 20.0, 20.0, 20.0, 15.0, 15.0, 15.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 15.0, 20.0, 15.0, 15.0, 15.0, 15.0, 15.0, 20.0, 20.0, 15.0] | |
| # df_trust_2_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # choices = [20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 25.0, 30.0, 30.0, 20.0, 25.0, 30.0, 20.0, 20.0, 18.0] + [20, 20, 20, 25, 25, 25, 30] | |
| # df_trust_2_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # choices = [100.0, 75.0, 75.0, 75.0, 75.0, 75.0, 100.0, 75.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 75.0, 100.0, 75.0, 75.0, 75.0, 100.0, 100.0, 100.0, 75.0, 100.0, 100.0, 100.0, 100.0, 75.0, 100.0, 75.0] | |
| # df_trust_3_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # choices = [150.0, 100.0, 150.0, 150.0, 50.0, 150.0, 100.0, 150.0, 100.0, 100.0, 100.0, 150.0] + [100, 100, 100, 100, 100, 100, 100, 100] | |
| # df_trust_3_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # choices = [200.0, 200.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 200.0, 200.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0] | |
| # df_trust_4_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # choices = [225.0, 225.0, 300.0, 300.0, 220.0, 300.0, 250.0] + [200, 200, 250, 200, 200] | |
| # df_trust_4_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # df = df_trust_1_human | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart1 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('steelblue'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_trust_1_gpt4 | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart2 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('orange'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_trust_1_turbo | |
| # bin_ranges = [0, 10, 30, 50, 70] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart3 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('green'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # final4 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared') | |
| # #Plot 5 - Trust (as Banker) (fairness, altruism, reciprocity) | |
| # df = df_trust_3_human | |
| # bin_ranges = [0, 25, 50, 75, 100, 125, 150] | |
| # custom_ticks = [2, 6, 10, 14, 18] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart1 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('steelblue') | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_trust_3_gpt4 | |
| # bin_ranges = [0, 25, 50, 75, 100, 125, 150] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart2 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(step=10), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('orange') | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_trust_3_turbo | |
| # bin_ranges = [0, 25, 50, 75, 100, 125, 150] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart3 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(step=10)), | |
| # y='density:Q', | |
| # color=alt.value('green') | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # # chart1 | |
| # final5 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared') | |
| # #Plot 6 - Public Goods (Free-Riding, altruism, cooperation) | |
| # df = pd.read_csv('public_goods_linear_water.csv') | |
| # df = df[df['Role'] == 'contributor'] | |
| # df = df[df['Round'] <= 3] | |
| # df = df[df['Total'] == 20] | |
| # df = df[df['groupSize'] == 4] | |
| # df = df[df['move'] != None] | |
| # df = df[(df['move'] >= 0) & (df['move'] <= 20)] | |
| # df = df[df['gameType'] == 'public_goods_linear_water'] | |
| # round_1 = df[df['Round'] == 1]['move'] | |
| # round_2 = df[df['Round'] == 2]['move'] | |
| # round_3 = df[df['Round'] == 3]['move'] | |
| # print(len(round_1), len(round_2), len(round_3)) | |
| # df_PG_human = pd.DataFrame({ | |
| # 'choices': list(round_1) | |
| # }) | |
| # df_PG_human['hue'] = 'Human' | |
| # # df_PG_human | |
| # file_names = [ | |
| # 'PG_basic_turbo_2023_05_09-02_49_09_AM.json', | |
| # 'PG_basic_turbo_loss_2023_05_09-03_59_49_AM.json', | |
| # 'PG_basic_gpt4_2023_05_09-11_15_42_PM.json', | |
| # 'PG_basic_gpt4_loss_2023_05_09-10_44_38_PM.json', | |
| # ] | |
| # choices = [] | |
| # for file_name in file_names: | |
| # with open(file_name, 'r') as f: | |
| # choices += json.load(f)['choices'] | |
| # choices_baseline = choices | |
| # choices = [tuple(x)[0] for x in choices] | |
| # df_PG_turbo = choices_to_df(choices, hue=str('ChatGPT-3')) | |
| # # df_PG_turbo.head() | |
| # df_PG_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4')) | |
| # # df_PG_gpt4.head() | |
| # df = df_PG_human | |
| # bin_ranges = [0, 25, 50, 75, 100, 125, 150] | |
| # custom_ticks = [2, 6, 10, 14, 18] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart1 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(step=2), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('steelblue'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_PG_gpt4 | |
| # bin_ranges = [0, 25, 50, 75, 100, 125, 150] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart2 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(step=2), axis=None), | |
| # y='density:Q', | |
| # color=alt.value('orange'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # df = df_PG_turbo | |
| # bin_ranges = [0, 25, 50, 75, 100, 125, 150] | |
| # # Calculate density as a percentage | |
| # density_percentage = df['choices'].value_counts(normalize=True) | |
| # # Create a DataFrame with the density percentages | |
| # density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values}) | |
| # # Create the bar chart using Altair | |
| # chart3 = alt.Chart(density_df).mark_bar().encode( | |
| # x=alt.X('choices:O', bin=alt.Bin(step=2)), | |
| # y='density:Q', | |
| # color=alt.value('green'), | |
| # tooltip=['density'] | |
| # ).properties( | |
| # width=500, | |
| # title='Density of Choices' | |
| # ).interactive() | |
| # # chart1 | |
| # final6 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared') | |
| # #Final_Final | |
| # final_final = (final | final2 | final3 ) & (final4 | final5 | final6) | |
| # # we want to use bootstrap/template, tell Panel to load up what we need | |
| # pn.extension(design='bootstrap') | |
| # # we want to use vega, tell Panel to load up what we need | |
| # pn.extension('vega') | |
| # # create a basic template using bootstrap | |
| # template = pn.template.BootstrapTemplate( | |
| # title='SI649 project2test', | |
| # ) | |
| # maincol = pn.Column() | |
| # maincol.append("TEST") | |
| # template.main.append(maincol) | |
| # template.servable(title="SI649 Project2Test") | |
| #Dashboard | |
| import panel as pn | |
| import vega_datasets | |
| # Enable Panel extensions | |
| pn.extension(design='bootstrap') | |
| pn.extension('vega') | |
| template = pn.template.BootstrapTemplate( | |
| title='SI649 Project2', | |
| ) | |
| # the main column will hold our key content | |
| maincol = pn.Column() | |
| options1 = ['ALL', 'Choose Your Own', 'Based On Category'] | |
| select0 = pn.widgets.Select(options=options1, name='Choose what to compare') | |
| # maincol.append(select0) | |
| # Charts | |
| charts = [] | |
| charts.append(final) | |
| charts.append(final2) | |
| charts.append(final3) | |
| charts.append(final4) | |
| charts.append(final5) | |
| charts.append(final6) | |
| # Define options for dropdown | |
| options = [f'Chart {i+1}' for i in range(len(charts))] | |
| # Panel widgets | |
| select1 = pn.widgets.Select(options=options, name='Select Chart 1') | |
| select2 = pn.widgets.Select(options=options, name='Select Chart 2') | |
| options = ['Altruism', 'Fairness', 'spite', 'trust', 'reciprocity', 'free-riding', 'cooperation'] | |
| select_widget = pn.widgets.Select(options=options, name='Select a category') | |
| # # Define function to update chart | |
| # def update_chart(value): | |
| # if value: | |
| # index = int(value.split()[-1]) - 1 | |
| # return charts[index] | |
| # else: | |
| # return None | |
| # # Combine dropdown and chart | |
| # @pn.depends(select1.param.value, select2.param.value) | |
| # def update_plots(value1, value2): | |
| # selected_chart1 = update_chart(value1) | |
| # selected_chart2 = update_chart(value2) | |
| # if selected_chart1 and selected_chart2: | |
| # return pn.Row(selected_chart1, selected_chart2) | |
| # elif selected_chart1: | |
| # return selected_chart1 | |
| # elif selected_chart2: | |
| # return selected_chart2 | |
| # else: | |
| # return None | |
| # # Define functions for each category | |
| # def update_plots_altruism(): | |
| # return pn.Row(final, final5) | |
| # def update_plots_fairness(): | |
| # return pn.Row(final2, final5) | |
| # def update_plots_spite(): | |
| # return final | |
| # def update_plots_trust(): | |
| # return final4 | |
| # def update_plots_reciprocity(): | |
| # return final5 | |
| # def update_plots_freeriding(): | |
| # return final6 | |
| # def update_plots_cooperation(): | |
| # return final6 | |
| # # Define a dictionary to map categories to update functions | |
| # update_functions = { | |
| # 'Altruism': update_plots_altruism, | |
| # 'Fairness': update_plots_fairness, | |
| # 'spite': update_plots_spite, | |
| # 'trust': update_plots_trust, | |
| # 'reciprocity': update_plots_reciprocity, | |
| # 'freeriding': update_plots_freeriding, | |
| # 'cooperation': update_plots_cooperation | |
| # } | |
| # # # Define function to update chart based on selected category | |
| # # def update_plots_category(event): | |
| # # selected_category = event.new | |
| # # maincol.clear() # Clear existing content in main column | |
| # # if selected_category in update_functions: | |
| # # update_function = update_functions[selected_category] | |
| # # maincol.append(update_function()) | |
| # # else: | |
| # # maincol.append(pn.pane.Markdown(f"No update function found for category: {selected_category}")) | |
| # # Define function to update chart based on selected category | |
| # def update_plots_category(event): | |
| # selected_category = event.new | |
| # maincol.clear() # Clear existing content in main column | |
| # if selected_category in update_functions: | |
| # update_function = update_functions[selected_category] | |
| # maincol.append(update_function()) | |
| # else: | |
| # maincol.append(pn.pane.Markdown(f"No update function found for category: {selected_category}")) | |
| # # Append select_widget again to allow changing the category selection | |
| # maincol.append(select_widget) | |
| # # Callback function to handle select widget events | |
| # def select_callback(event): | |
| # selected_value = event.new | |
| # maincol.clear() # Clear existing content in main column | |
| # if selected_value == 'Choose Your Own': | |
| # maincol.append(select1) | |
| # maincol.append(select2) | |
| # maincol.append(update_plots) | |
| # elif selected_value == 'Based On Category': | |
| # maincol.append(select_widget) | |
| # select_widget.param.watch(update_plots_category, 'value') | |
| # # # Bind the update_plots_category function to the select widget's 'value' parameter | |
| # # select.param.watch | |
| # # maincol.append(update_plots_category()) | |
| # # Bind the callback function to the select widget's 'value' parameter | |
| # select0.param.watch(select_callback, 'value') | |
| # maincol.append(select0) | |
| maincol.append(select1) | |
| template.main.append(maincol) | |
| template.servable(title='SI649 Project2') |