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Copy pathMultiObjectiveHomeostasisParallelPlot.py
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MultiObjectiveHomeostasisParallelPlot.py
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import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
def generate_plot(data1A, data2A, data1B, data2B, xlabel, ylabel, data1_legend, data2_legend, plot_title, save_filename):
x_values = list(data1A.keys())
y_values1A = list(data1A.values())
y_values2A = list(data2A.values())
y_values1B = list(data1B.values())
y_values2B = list(data2B.values())
plt.clf()
plt.plot(x_values, y_values1A, label=data1_legend + " of objective A")
plt.plot(x_values, y_values2A, label=data2_legend + " of objective A")
plt.plot(x_values, y_values1B, label=data1_legend + " of objective B")
plt.plot(x_values, y_values2B, label=data2_legend + " of objective B")
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(plot_title)
plt.legend()
plt.savefig(save_filename + ".png", dpi=200)
plt.show()
def results():
df = df = pd.DataFrame()
# current_dir = os.getcwd()
# base_path = os.path.join(current_dir, '..', '..', 'data')
# base_path += '/homeostasis_gpt-4o-mini_2025_02_01_'
# homeostasis_log_list = [
# '19_56_29_572268.tsv',
# '19_59_28_270616.tsv',
# '20_02_22_963977.tsv',
# '20_05_19_378875.tsv',
# '20_08_11_822824.tsv',
# '19_40_04_733656.tsv',
# '19_43_36_412572.tsv',
# '19_48_00_192665.tsv',
# '19_50_50_744041.tsv',
# '19_53_42_303388.tsv'
# ]
homeostasis_log_list = glob.glob(os.path.join("data", "multiobjective-homeostasis_gpt-4o-mini_*.tsv"))
for i, file_path in enumerate(homeostasis_log_list):
# file_path = base_path + file
current_df = pd.read_csv(file_path, sep='\t')
df = pd.concat([df, current_df], ignore_index=True)
df = df.rename(columns={'Trial number': 'Step number', 'Step number': 'Trial number'})
# print(df.columns)
# print(df)
consumption_reward_a = {}
undersatiation_reward_a = {}
oversatiation_reward_a = {}
total_consumption_reward_a = {}
total_undersatiation_reward_a = {}
total_oversatiation_reward_a = {}
consumption_reward_b = {}
undersatiation_reward_b = {}
oversatiation_reward_b = {}
total_consumption_reward_b = {}
total_undersatiation_reward_b = {}
total_oversatiation_reward_b = {}
for i in range(100):
new_df = df[df['Step number'] == i+1]
consumption_reward_a[i] = float(new_df['Consumption reward A'].mean())
undersatiation_reward_a[i] = float(new_df['Undersatiation reward A'].mean())
oversatiation_reward_a[i] = float(new_df['Oversatiation reward A'].mean())
total_consumption_reward_a[i] = float(new_df['Total consumption reward of objective A'].mean())
total_undersatiation_reward_a[i] = float(new_df['Total undersatiation reward of objective A'].mean())
total_oversatiation_reward_a[i] = float(new_df['Total oversatiation reward of objective A'].mean())
consumption_reward_b[i] = float(new_df['Consumption reward B'].mean())
undersatiation_reward_b[i] = float(new_df['Undersatiation reward B'].mean())
oversatiation_reward_b[i] = float(new_df['Oversatiation reward B'].mean())
total_consumption_reward_b[i] = float(new_df['Total consumption reward of objective B'].mean())
total_undersatiation_reward_b[i] = float(new_df['Total undersatiation reward of objective B'].mean())
total_oversatiation_reward_b[i] = float(new_df['Total oversatiation reward of objective B'].mean())
return (
consumption_reward_a,
total_consumption_reward_a,
undersatiation_reward_a,
total_undersatiation_reward_a,
oversatiation_reward_a,
total_oversatiation_reward_a,
consumption_reward_b,
total_consumption_reward_b,
undersatiation_reward_b,
total_undersatiation_reward_b,
oversatiation_reward_b,
total_oversatiation_reward_b,
)
def claude_results():
df = df = pd.DataFrame()
# base_path = './../../data/homeostasis_claude-3-5-haiku-latest_2025_02_01_'
# current_dir = os.getcwd()
# claude_base_path = os.path.join(current_dir, '..', '..', 'data')
# claude_base_path += '/homeostasis_claude-3-5-haiku-latest_2025_02_01_'
# homeostasis_log_list = [
# '12_21_34_207921.tsv',
# '12_26_37_443013.tsv',
# '12_32_41_710303.tsv',
# '12_38_43_054654.tsv',
# '12_44_45_338563.tsv',
# '12_50_55_104330.tsv',
# '12_56_56_388525.tsv',
# '13_03_06_540040.tsv',
# '13_08_55_592053.tsv',
# '13_14_58_760716.tsv'
# ]
homeostasis_log_list = glob.glob(os.path.join("data", "multiobjective-homeostasis_claude-3-5-haiku-*_*.tsv"))
for i, file_path in enumerate(homeostasis_log_list):
# file_path = claude_base_path + file
current_df = pd.read_csv(file_path, sep='\t')
df = pd.concat([df, current_df], ignore_index=True)
df = df.rename(columns={'Trial number': 'Step number', 'Step number': 'Trial number'})
# print(df.columns)
# print(df)
consumption_reward_a = {}
undersatiation_reward_a = {}
oversatiation_reward_a = {}
total_consumption_reward_a = {}
total_undersatiation_reward_a = {}
total_oversatiation_reward_a = {}
consumption_reward_b = {}
undersatiation_reward_b = {}
oversatiation_reward_b = {}
total_consumption_reward_b = {}
total_undersatiation_reward_b = {}
total_oversatiation_reward_b = {}
for i in range(100):
new_df = df[df['Step number'] == i+1]
consumption_reward_a[i] = float(new_df['Consumption reward A'].mean())
undersatiation_reward_a[i] = float(new_df['Undersatiation reward A'].mean())
oversatiation_reward_a[i] = float(new_df['Oversatiation reward A'].mean())
total_consumption_reward_a[i] = float(new_df['Total consumption reward of objective A'].mean())
total_undersatiation_reward_a[i] = float(new_df['Total undersatiation reward of objective A'].mean())
total_oversatiation_reward_a[i] = float(new_df['Total oversatiation reward of objective A'].mean())
consumption_reward_b[i] = float(new_df['Consumption reward B'].mean())
undersatiation_reward_b[i] = float(new_df['Undersatiation reward B'].mean())
oversatiation_reward_b[i] = float(new_df['Oversatiation reward B'].mean())
total_consumption_reward_b[i] = float(new_df['Total consumption reward of objective B'].mean())
total_undersatiation_reward_b[i] = float(new_df['Total undersatiation reward of objective B'].mean())
total_oversatiation_reward_b[i] = float(new_df['Total oversatiation reward of objective B'].mean())
return (
consumption_reward_a,
total_consumption_reward_a,
undersatiation_reward_a,
total_undersatiation_reward_a,
oversatiation_reward_a,
total_oversatiation_reward_a,
consumption_reward_b,
total_consumption_reward_b,
undersatiation_reward_b,
total_undersatiation_reward_b,
oversatiation_reward_b,
total_oversatiation_reward_b,
)
(
gpt4o_consumption_reward_a,
gpt4o_total_consumption_reward_a,
gpt4o_undersatiation_reward_a,
gpt4o_total_undersatiation_reward_a,
gpt4o_oversatiation_reward_a,
gpt4o_total_oversatiation_reward_a,
gpt4o_consumption_reward_b,
gpt4o_total_consumption_reward_b,
gpt4o_undersatiation_reward_b,
gpt4o_total_undersatiation_reward_b,
gpt4o_oversatiation_reward_b,
gpt4o_total_oversatiation_reward_b,
) = results()
(
claude_consumption_reward_a,
claude_total_consumption_reward_a,
claude_undersatiation_reward_a,
claude_total_undersatiation_reward_a,
claude_oversatiation_reward_a,
claude_total_oversatiation_reward_a,
claude_consumption_reward_b,
claude_total_consumption_reward_b,
claude_undersatiation_reward_b,
claude_total_undersatiation_reward_b,
claude_oversatiation_reward_b,
claude_total_oversatiation_reward_b,
) = claude_results()
generate_plot(gpt4o_consumption_reward_a, claude_consumption_reward_a, gpt4o_consumption_reward_b, claude_consumption_reward_b, 'Steps', 'Reward', 'OpenAI gpt4o', 'Antrhopic Claude 3.5 Haiku', 'Consumption Rewards in Homeostasis Benchmark', 'multiobjective homeostasis consumption reward')
generate_plot(gpt4o_total_consumption_reward_a, claude_total_consumption_reward_a, gpt4o_total_consumption_reward_b, claude_total_consumption_reward_b, 'Steps', 'Reward', 'OpenAI gpt4o', 'Antrhopic Claude 3.5 Haiku', 'Total Consumption Rewards in Homeostasis Benchmark', 'multiobjective homeostasis total consumption reward')
generate_plot(gpt4o_undersatiation_reward_a, claude_undersatiation_reward_a, gpt4o_undersatiation_reward_b, claude_undersatiation_reward_b, 'Steps', 'Reward', 'OpenAI gpt4o', 'Antrhopic Claude 3.5 Haiku', 'Undersatiation Rewards in Homeostasis Benchmark', 'multiobjective homeostasis undersatiation penalty')
generate_plot(gpt4o_total_undersatiation_reward_a, claude_total_undersatiation_reward_a, gpt4o_total_undersatiation_reward_b, claude_total_undersatiation_reward_b, 'Steps', 'Reward', 'OpenAI gpt4o', 'Antrhopic Claude 3.5 Haiku', 'Total undersatiation Rewards in Homeostasis Benchmark', 'multiobjective homeostasis total undersatiation penalty')
generate_plot(gpt4o_oversatiation_reward_a, claude_oversatiation_reward_a, gpt4o_oversatiation_reward_b, claude_oversatiation_reward_b, 'Steps', 'Reward', 'OpenAI gpt4o', 'Antrhopic Claude 3.5 Haiku', 'Oversatiation Rewards in Homeostasis Benchmark', 'multiobjective homeostasis oversatiation penalty')
generate_plot(gpt4o_total_oversatiation_reward_a, claude_total_oversatiation_reward_a, gpt4o_total_oversatiation_reward_b, claude_total_oversatiation_reward_b, 'Steps', 'Reward', 'OpenAI gpt4o', 'Antrhopic Claude 3.5 Haiku', 'Total oversatiation Rewards in Homeostasis Benchmark', 'multiobjective homeostasis total oversatiation penalty')