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@ankur-tutlani
ankur-tutlani authored Apr 13, 2024
1 parent 0fa278a commit 63070cf
Showing 1 changed file with 90 additions and 32 deletions.
122 changes: 90 additions & 32 deletions multi_agent_coordination/simulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,6 @@
import random
import string



# 1.iteration_name: iteration name. String
# 2.path_to_save_output: path to save output files. String
# 3.num_neighbors: number of neighbours. Integer
Expand Down Expand Up @@ -48,17 +46,15 @@
# grid2d: Return the 2d grid graph of mxn nodes, each connected to its nearest neighbors.




def perturbed_response1(AGENT_NO,data_to_look,perturb_ratio,name_len,prob_new_name):

try:
count_pd = data_to_look.loc[data_to_look["agent"]==AGENT_NO]["name_offered_by_opponent"].value_counts().reset_index()
count_pd["tot_sum"] = count_pd["name_offered_by_opponent"] / sum(count_pd["name_offered_by_opponent"])
best_response = count_pd.loc[count_pd["tot_sum"] == max(count_pd["tot_sum"])]['index'].tolist()
count_pd["tot_sum"] = count_pd["count"] / sum(count_pd["count"])
best_response = count_pd.loc[count_pd["tot_sum"] == max(count_pd["tot_sum"])]['name_offered_by_opponent'].tolist()
draw1= random.choices(population=best_response,k=1)

Nonbest_response = count_pd.loc[count_pd["tot_sum"] != max(count_pd["tot_sum"])]['index'].tolist()
Nonbest_response = count_pd.loc[count_pd["tot_sum"] != max(count_pd["tot_sum"])]['name_offered_by_opponent'].tolist()
if len(Nonbest_response) > 0:
draw2= random.choices(population=Nonbest_response,k=1)
best_response = random.choices(population=[draw1[0],draw2[0]],weights=[1-perturb_ratio,perturb_ratio],k=1)
Expand All @@ -81,12 +77,11 @@ def perturbed_response1(AGENT_NO,data_to_look,perturb_ratio,name_len,prob_new_na




def perturbed_response2(AGENT_NO,data_to_look,name_len,prob_new_name):
try:
count_pd = data_to_look.loc[data_to_look["agent"]==AGENT_NO]["name_offered_by_opponent"].value_counts().reset_index()
count_pd["tot_sum"] = count_pd["name_offered_by_opponent"] / sum(count_pd["name_offered_by_opponent"])
names_list = count_pd['index'].tolist()
count_pd["tot_sum"] = count_pd["count"] / sum(count_pd["count"])
names_list = count_pd['name_offered_by_opponent'].tolist()
share_list = count_pd['tot_sum'].tolist()
draw1= random.choices(population=names_list,weights=share_list,k=1)
best_response = draw1[0]
Expand All @@ -106,15 +101,14 @@ def perturbed_response2(AGENT_NO,data_to_look,name_len,prob_new_name):




def perturbed_response3(AGENT_NO,data_to_look,perturb_ratio,name_len,prob_new_name):
try:
count_pd = data_to_look.loc[data_to_look["agent"]==AGENT_NO]["name_offered_by_opponent"].value_counts().reset_index()
count_pd["tot_sum"] = count_pd["name_offered_by_opponent"] / sum(count_pd["name_offered_by_opponent"])
best_response = count_pd.loc[count_pd["tot_sum"] == max(count_pd["tot_sum"])]['index'].tolist()
count_pd["tot_sum"] = count_pd["count"] / sum(count_pd["count"])
best_response = count_pd.loc[count_pd["tot_sum"] == max(count_pd["tot_sum"])]['name_offered_by_opponent'].tolist()
draw1= random.choices(population=best_response,k=1)

Nonbest_response = count_pd['index'].tolist()
Nonbest_response = count_pd['name_offered_by_opponent'].tolist()
draw2= random.choices(population=Nonbest_response,k=1)
best_response = random.choices(population=[draw1[0],draw2[0]],weights=[1-perturb_ratio,perturb_ratio],k=1)
best_response = best_response[0]
Expand All @@ -132,12 +126,11 @@ def perturbed_response3(AGENT_NO,data_to_look,perturb_ratio,name_len,prob_new_na




def perturbed_response4(AGENT_NO,data_to_look,name_len,prob_new_name):
try:
count_pd = data_to_look.loc[data_to_look["agent"]==AGENT_NO]["name_offered_by_opponent"].value_counts().reset_index()
count_pd["tot_sum"] = count_pd["name_offered_by_opponent"] / sum(count_pd["name_offered_by_opponent"])
best_response = count_pd.loc[count_pd["tot_sum"] == max(count_pd["tot_sum"])]['index'].tolist()
count_pd["tot_sum"] = count_pd["count"] / sum(count_pd["count"])
best_response = count_pd.loc[count_pd["tot_sum"] == max(count_pd["tot_sum"])]['name_offered_by_opponent'].tolist()
draw1= random.choices(population=best_response,k=1)
best_response = draw1[0]

Expand All @@ -154,7 +147,6 @@ def perturbed_response4(AGENT_NO,data_to_look,name_len,prob_new_name):




def network_simulations(iteration_name,
path_to_save_output,
num_neighbors,
Expand Down Expand Up @@ -237,6 +229,11 @@ def network_simulations(iteration_name,
norms_db_to_fill['timeperiod'] = ''


agents_in_edges = list(set([i[0] for i in potential_edges]+[i[1] for i in potential_edges]))
agents_in_network = list(range(num_agents))
delta_agents_hardcoded = [i for i in agents_in_network if i not in agents_in_edges]


for timeperiod in range(1,num_of_trials+1):
empty_df_to_fill_temp = empty_df_to_fill_trial[0:0]
for v in range(num_of_rounds):
Expand All @@ -256,33 +253,33 @@ def network_simulations(iteration_name,
elif function_to_use == 'perturbed_response2':
if len(empty_df_to_fill_temp) > len(empty_df_to_fill_trial):

name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response1(AGENT_NO = agent2,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill1 = perturbed_response2(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response2(AGENT_NO = agent2,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
else:
name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response1(AGENT_NO = agent2,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill1 = perturbed_response2(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response2(AGENT_NO = agent2,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)



elif function_to_use == 'perturbed_response3':
if len(empty_df_to_fill_temp) > len(empty_df_to_fill_trial):

name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response1(AGENT_NO = agent2,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill1 = perturbed_response3(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response3(AGENT_NO = agent2,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
else:
name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response1(AGENT_NO = agent2,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill1 = perturbed_response3(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response3(AGENT_NO = agent2,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)



elif function_to_use == 'perturbed_response4':
if len(empty_df_to_fill_temp) > len(empty_df_to_fill_trial):

name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response1(AGENT_NO = agent2,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill1 = perturbed_response4(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response4(AGENT_NO = agent2,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
else:
name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response1(AGENT_NO = agent2,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill1 = perturbed_response4(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
name_to_fill2 = perturbed_response4(AGENT_NO = agent2,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)



Expand Down Expand Up @@ -329,6 +326,56 @@ def network_simulations(iteration_name,


empty_df_to_fill_temp = pd.concat([empty_df_to_fill_temp,data_to_append,data_to_append2],ignore_index=True,sort=False)

if len(delta_agents_hardcoded) > 0:
for agent1 in delta_agents_hardcoded:

if function_to_use == 'perturbed_response1':
if len(empty_df_to_fill_temp) > len(empty_df_to_fill_trial):
name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
else:
name_to_fill1 = perturbed_response1(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)

elif function_to_use == 'perturbed_response2':
if len(empty_df_to_fill_temp) > len(empty_df_to_fill_trial):
name_to_fill1 = perturbed_response2(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
else:
name_to_fill1 = perturbed_response2(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)

elif function_to_use == 'perturbed_response3':
if len(empty_df_to_fill_temp) > len(empty_df_to_fill_trial):
name_to_fill1 = perturbed_response3(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
else:
name_to_fill1 = perturbed_response3(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)

elif function_to_use == 'perturbed_response4':
if len(empty_df_to_fill_temp) > len(empty_df_to_fill_trial):
name_to_fill1 = perturbed_response4(AGENT_NO = agent1,data_to_look = empty_df_to_fill_temp,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)
else:
name_to_fill1 = perturbed_response4(AGENT_NO = agent1,data_to_look = empty_df_to_fill_trial,perturb_ratio=perturb_ratio,name_len=name_len,prob_new_name=prob_new_name)

if (agent1 in fixed_agents) and len(fixed_values_to_use) < len(fixed_agents):
for jj in fixed_agents:
if jj not in list(fixed_values_to_use.keys()):
xx = empty_df_to_fill_temp.loc[empty_df_to_fill_temp["agent"]==jj].sort_values(["timeperiod"],ascending=True).head(1)
if len(xx) > 0:
fixed_values_to_use[xx["agent"].values[0]]=xx["name_offered"].values[0]

if agent1 in fixed_agents:
try:
name_offered = fixed_values_to_use[agent1]
except:
name_offered = name_to_fill1
else:
name_offered = name_to_fill1


data_to_append = pd.DataFrame({'agent':agent1,
'name_offered':name_offered,
'timeperiod':timeperiod
},index=[0])

empty_df_to_fill_temp = pd.concat([empty_df_to_fill_temp,data_to_append],ignore_index=True,sort=False)


empty_df_to_fill_trial = pd.concat([empty_df_to_fill_trial,empty_df_to_fill_temp],ignore_index=True,sort=False)
Expand Down Expand Up @@ -430,9 +477,19 @@ def network_simulations(iteration_name,
data_for_trend_plot = data_for_trend_plot.reset_index(drop=True)
for label,grp in data_for_trend_plot.groupby('name'):
grp.plot(x='timeperiod',y='percent_count',ax=ax,label=label)


x_values11 = data_for_trend_plot['timeperiod'].unique()
total_periods11 = len(x_values11)
max_display_labels = 5
interval11 = max(1, total_periods11 // max_display_labels)

ax.set_xticks(np.arange(len(x_values11))[::interval11])
ax.set_xticklabels(x_values11[::interval11])


ax.set_xlabel('Timeperiod')
ax.set_ylabel('Count %')
# plt.show()
plt.savefig(path_to_save_output+"top_names_"+iteration_name+"_"+today+".png")
plt.clf()

Expand Down Expand Up @@ -477,4 +534,5 @@ def network_simulations(iteration_name,
parameters_pd[["parameter","parameter_values"]].to_excel(path_to_save_output+"parameters_"+iteration_name+"_"+today+".xlsx",index=None)


return(print("done"))
return(print("done"))

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