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Updated analysis.py
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@dagenaik
dagenaik committed Jul 29, 2024
1 parent c4ac5c5 commit b0aa9f6
Showing 1 changed file with 40 additions and 19 deletions.
59 changes: 40 additions & 19 deletions src/analysis.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import pandas as pd
import seaborn as sns
import numpy as np
Expand Down Expand Up @@ -36,38 +37,38 @@ class ExperimentKind(Enum):


def loadSyntheticData(experiment_kind, episode_number, data_kind):
df_random = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/random/{filename}.csv', header=None)
df_no_advice = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/noadvice/{filename}.csv', header=None)
df_advice_00 = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-synthetic-{experiment_kind}/{filename}-u-0.01.csv', header=None)
df_advice_02 = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-synthetic-{experiment_kind}/{filename}-u-0.2.csv', header=None)
df_advice_04 = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-synthetic-{experiment_kind}/{filename}-u-0.4.csv', header=None)
df_advice_06 = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-synthetic-{experiment_kind}/{filename}-u-0.6.csv', header=None)
df_advice_08 = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-synthetic-{experiment_kind}/{filename}-u-0.8.csv', header=None)
#df_advice_10 = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice/{filename}-u-1.0.csv', header=None)
df_random = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/random/{filename}.csv', header=None)
df_no_advice = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/noadvice/{filename}.csv', header=None)

return {
'random': df_random,
'no_advice': df_no_advice,
'advice_00': df_advice_00,
'advice_02': df_advice_02,
'advice_04': df_advice_04,
'advice_06': df_advice_06,
'advice_08': df_advice_08,
#'advice_10': df_advice_10
'random': df_random,
'no_advice': df_no_advice
}


def loadHumanData(experiment_kind, episode_number, data_kind):
def loadCoopData(experiment_kind, episode_number, data_kind):
df_advice_coop_sequential = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-{experiment_kind}-topleft-bottomright/{filename}.csv', header=None)
df_advice_coop_parallel = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-{experiment_kind}-topright-bottomleft/{filename}.csv', header=None)
df_random = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/random/{filename}.csv', header=None)
df_no_advice = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/noadvice/{filename}.csv', header=None)
df_advice_coop_topleft_bottomright = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-{experiment_kind}-topleft-bottomright/{filename}.csv', header=None)
df_advice_coop_topright_bottomleft = pd.read_csv(f'{inputFolder}/{episode_number}/{data_kind.value}_data/advice-{experiment_kind}-topright-bottomleft/{filename}.csv', header=None)

return {
'coop_sequential': df_advice_coop_sequential,
'coop_parallel': df_advice_coop_parallel,
'random': df_random,
'no_advice': df_no_advice,
'coop_topleft_bottomright': df_advice_coop_topleft_bottomright,
'coop_topright_bottomleft':df_advice_coop_topright_bottomleft
'no_advice': df_no_advice
}


Expand All @@ -87,7 +88,7 @@ def print_rewards():
dfs = loadSyntheticData(experiment_kind, episode_number, DataKind.REWARD)

else:
dfs = loadHumanData(experiment_kind, episode_number, DataKind.REWARD)
dfs = loadCoopData(experiment_kind, episode_number, DataKind.REWARD)

for df_name, df in dfs.items():
mean = df.mean()
Expand All @@ -114,15 +115,35 @@ def cumulative_reward():
dfs = loadSyntheticData(experiment_kind, episode_number, DataKind.REWARD)

else:
dfs = loadHumanData(experiment_kind, episode_number, DataKind.REWARD)
dfs = loadCoopData(experiment_kind, episode_number, DataKind.REWARD)

fig = plt.figure()
ax = plt.gca()

for df_name, df in dfs.items():

mean = df.mean()
x = np.arange(len(mean))
plt.plot(x, mean, label=df_name)
if df_name == 'coop_sequential':
plt.plot(x, mean, label=df_name, color='tomato')
elif df_name == 'coop_parallel':
plt.plot(x, mean, label=df_name, color='lightseagreen')
elif df_name == 'advice_00':
plt.plot(x, mean, label=df_name, color='orange')
elif df_name == 'advice_02':
plt.plot(x, mean, label=df_name, color='yellowgreen')
elif df_name == 'advice_04':
plt.plot(x, mean, label=df_name, color='dodgerblue')
elif df_name == 'advice_06':
plt.plot(x, mean, label=df_name, color='darkviolet')
elif df_name == 'advice_08':
plt.plot(x, mean, label=df_name, color='hotpink')
elif df_name == 'no_advice':
plt.plot(x, mean, label=df_name, color='black', linestyle='dashed')
elif df_name == 'random':
plt.plot(x, mean, label=df_name, color='black', linestyle='dotted')
else:
plt.plot(x, mean, label=df_name)
# Used to plot standard deviation
#if df_name=='no_advice':
# std = df.std()
Expand All @@ -138,20 +159,20 @@ def cumulative_reward():

if experiment_kind in ['all', 'holes', 'human5', 'human10']:
#legend_labels = ['Random', 'No advice', 'No advice sigma', 'No advice max', 'No advice min', 'Advice@u=0.0', 'Advice@u=0.2', 'Advice@u=0.4', 'Advice@u=0.6', 'Advice@u=0.8']
legend_labels = ['Random', 'No advice', 'Advice@u=0.0', 'Advice@u=0.2', 'Advice@u=0.4', 'Advice@u=0.6', 'Advice@u=0.8']
legend_labels = ['Advice@u=0.0', 'Advice@u=0.2', 'Advice@u=0.4',
'Advice@u=0.6', 'Advice@u=0.8', 'No advice', 'Random']
else:
#legend_labels = ['Random', 'No advice', 'No advice sigma', 'No advice max', 'No advice min', 'Top Left Bottom Right', 'Top Right Bottom Left']
legend_labels = ['Random', 'No advice', 'Top Left Bottom Right', 'Top Right Bottom Left']

legend_labels = ['Coop - Sequential', 'Coop - Parallel', 'No advice', 'Random']

plt.legend(labels = legend_labels, fontsize='14', loc = 'upper left')
plt.legend(labels=legend_labels, fontsize='14', loc='upper left')
#plt.show()

logging.info('\tSave linear plot')
savefig(f'{folder_name}/{experiment_kind}/cumulative_reward-{experiment_kind}-{episode_number}-linear')

logging.info('\tSave log plot')
plt.legend(labels = legend_labels, fontsize='14', loc = 'lower right')
plt.legend(labels=legend_labels, fontsize='14', loc='lower right')
plt.yscale('log')
ax.autoscale()
savefig(f'{folder_name}/{experiment_kind}/cumulative_reward-{experiment_kind}-{episode_number}-log')
Expand All @@ -175,7 +196,7 @@ def heatmap():
dfs = loadSyntheticData(experiment_kind, episode_number, DataKind.POLICY)

else:
dfs = loadHumanData(experiment_kind, episode_number, DataKind.POLICY)
dfs = loadCoopData(experiment_kind, episode_number, DataKind.POLICY)

#logging.debug(dfs['advice_00'])

Expand Down

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