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plot_fedsim_improvements.py
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# USED TO GENERATE THE compare_results_real.pdf GRAPH
import json
import os
import csv
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
color_avg ="#ff7f0e"
color_prox ="#13CA91"
color_sim ="#17becf"
color_avg ="#ff7f0e"
color_prox ="#fb99bc"
color_sim ="#17becf"
linewidth = 1.8
ROUNDS = 101
rounds = [i for i in range(ROUNDS)]
# RANDOM COMMPARISION GRAPH
# dataset = "logs/FINAL/random/"
# sim_rounds = []
# sim_test_acc = []
# random_rounds = []
# random_test_acc = []
# with open(dataset+'original.csv',mode='r') as csv_file:
# csv_reader = csv.DictReader(csv_file)
# line_count = 0
# for row in csv_reader:
# if line_count == 0:
# line_count += 1
# sim_rounds.append(float(row["round"]))
# sim_test_acc.append(float(row["test_acc"]))
# line_count += 1
# print(f'FedAvg log Processed {line_count} lines.')
#
# with open(dataset + 'random.csv', mode='r') as csv_file:
# csv_reader = csv.DictReader(csv_file)
# line_count = 0
# for row in csv_reader:
# if line_count == 0:
# line_count += 1
# random_rounds.append(float(row["round"]))
# random_test_acc.append(float(row["test_acc"]))
# line_count += 1
# print(f'FedAvg log Processed {line_count} lines.')
#
#
# fig, ax = plt.subplots(1, 1, figsize=[5, 3])
# # linewidth = 1.2
# linewidth = 1
#
# ax.plot(rounds, sim_test_acc, linewidth=linewidth, color=color_sim, label="FedSim")
# ax.plot(rounds, random_test_acc,"--", alpha=0.7, linewidth=linewidth, color="#000000", label="FedSim Random")
# # ax.set_title("FEMNIST Comparision ",fontweight='bold')
#
# plt.subplots_adjust(hspace=0.5)
# ax.set_xlabel("# Rounds")
# ax.set_ylabel('Test Accuracy')
#
# for i in range(1):
# # ax[i].set_xlabel("# Rounds")
#
# ax.spines['bottom'].set_color('#dddddd')
# ax.spines['top'].set_color('#dddddd')
# ax.spines['right'].set_color('#dddddd')
# ax.spines['left'].set_color('#dddddd')
# ax.tick_params(color='#dddddd')
# ax.set_xlim(0, ROUNDS)
# plt.legend(frameon=False, loc='lower right',
# prop=dict(weight='normal'),
# ) # note: different from plt.legend
#
# # print(all)
#
# # ax[0].set_title("Synthetic", fontsize=22)
# plt.tight_layout()
# # plt.subplots_adjust(bottom=0.2)
#
# plt.show()
# fig.savefig("random_compare.pdf")
#
# exit(0)
# datasets = ["IID","00", "2525","0505","7575" ,"11", "mnist","femnist", "mex"]
# datasets = ["IID","00","2525", "0505","7575", "11"]
datasets = ["mnist","femnist", "mex","goodreads"]
all = {}
for ds in datasets:
dataset = "docs/analysis/outputs/"+ds+".csv"
avg_rounds = []
avg_test_acc = []
prox_rounds = []
prox_test_acc = []
sim_rounds = []
sim_test_acc = []
with open(dataset,
mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
line_count = 0
for row in csv_reader:
if line_count == 0:
line_count += 1
avg_rounds.append(float(row["round"]))
avg_test_acc.append(float(row["avg"]))
prox_rounds.append(float(row["round"]))
prox_test_acc.append(float(row["prox"]))
sim_rounds.append(float(row["round"]))
sim_test_acc.append(float(row["sim"]))
line_count += 1
# print(f'FedAvg log Processed {line_count} lines.')
all[ds] = {"avg": [], "sim": [], "prox": []}
all[ds]["avg"] = avg_test_acc
all[ds]["sim"] = sim_test_acc
all[ds]["prox"] = prox_test_acc
all[ds]["diff_avg"] = (np.array(sim_test_acc) - np.array(avg_test_acc)) *100
all[ds]["diff_prox"] = (np.array(sim_test_acc) - np.array(prox_test_acc)) *100
# PRINT ALL SYNTHETIC
if(False):
# fig, ax = plt.subplots(2, 3, figsize=[7.8,8.5])
fig, ax = plt.subplots(2, 3, figsize=[10,6])
ax[0,0].plot(rounds, all["IID"]["sim"], linewidth=linewidth, color=color_sim, label="FedSim")
ax[0,0].plot(rounds, all["IID"]["avg"],":", linewidth=linewidth, color=color_avg, label="FedAvg")
ax[0,0].plot(rounds, all["IID"]["prox"],"-.", linewidth=linewidth, color=color_prox, label="FedProx")
ax[0,0].set_title("Synthetic IID", fontweight='bold')
ax[0,1].plot(rounds, all["00"]["sim"], linewidth=linewidth, color=color_sim)
ax[0,1].plot(rounds, all["00"]["avg"],":", linewidth=linewidth, color=color_avg)
ax[0,1].plot(rounds, all["00"]["prox"],"-.", linewidth=linewidth, color=color_prox)
ax[0,1].set_title("Synthetic(0,0)", fontweight='bold')
ax[0,2].plot(rounds, all["2525"]["sim"], linewidth=linewidth, color=color_sim)
ax[0,2].plot(rounds, all["2525"]["avg"], ":", linewidth=linewidth, color=color_avg)
ax[0,2].plot(rounds, all["2525"]["prox"],"-.", linewidth=linewidth, color=color_prox)
ax[0,2].set_title("Synthetic(0.25,0.25)", fontweight='bold')
ax[1,0].plot(rounds, all["0505"]["sim"], linewidth=linewidth, color=color_sim)
ax[1,0].plot(rounds, all["0505"]["avg"],":", linewidth=linewidth, color=color_avg)
ax[1,0].plot(rounds, all["0505"]["prox"],"-.", linewidth=linewidth, color=color_prox)
ax[1,0].set_title("Synthetic(0.5,0.5)", fontweight='bold')
#
ax[1,1].plot(rounds, all["7575"]["sim"], linewidth=linewidth, color=color_sim)
ax[1,1].plot(rounds, all["7575"]["avg"],":", linewidth=linewidth, color=color_avg)
ax[1,1].plot(rounds, all["7575"]["prox"],"-.", linewidth=linewidth, color=color_prox)
ax[1,1].set_title("Synthetic(0.75,0.75)", fontweight='bold')
ax[1,2].plot(rounds, all["11"]["sim"], linewidth=linewidth, color=color_sim)
ax[1,2].plot(rounds, all["11"]["avg"], ":", linewidth=linewidth, color=color_avg)
ax[1,2].plot(rounds, all["11"]["prox"],"-.", linewidth=linewidth, color=color_prox)
ax[1,2].set_title("Synthetic(1,1)", fontweight='bold')
# ax[0,2].plot(rounds, all["mnist"]["sim"], linewidth=linewidth, color=color_sim)
# ax[0,2].plot(rounds, all["mnist"]["avg"], alpha=0.6, linewidth=linewidth, color=color_avg)
# ax[0,2].plot(rounds, all["mnist"]["prox"], alpha=0.6, linewidth=linewidth, color=color_prox)
# ax[0,2].set_title("MNIST", fontweight='bold')
#
# ax[1,2].plot(rounds, all["femnist"]["sim"], linewidth=linewidth, color=color_sim)
# ax[1,2].plot(rounds, all["femnist"]["avg"], alpha=0.6, linewidth=linewidth, color=color_avg)
# ax[1,2].plot(rounds, all["femnist"]["prox"], alpha=0.6, linewidth=linewidth, color=color_prox)
# ax[1,2].set_title("FEMNIST", fontweight='bold')
# plt.subplots_adjust(hspace=0.2)
for i in range(3):
for j in range(2):
if(False):
ax[j,i].set_xlabel("# Rounds")
ax[j,i].set_ylabel('Test Accuracy')
# ax[j,i].set_xlabel("# Rounds")
# ax[j,i].set_ylabel('Test Accuracy')
ax[j, i].spines['bottom'].set_color('#dddddd')
ax[j, i].spines['top'].set_color('#dddddd')
ax[j, i].spines['right'].set_color('#dddddd')
ax[j, i].spines['left'].set_color('#dddddd')
ax[j, i].tick_params(color='#dddddd')
ax[j, i].set_xlim(0, ROUNDS)
fig.legend(frameon=False, loc='lower center',
bbox_to_anchor=(0.53, 0),
ncol=3, prop=dict(weight='normal'), borderaxespad=-0.3) # note: different from plt.legend
ax[0, 0].set_xlabel("# Rounds")
ax[0, 0].set_ylabel('Test Accuracy')
ax[1, 0].set_xlabel("# Rounds")
ax[1, 0].set_ylabel('Test Accuracy')
# print(all)
# ax[0].set_title("Synthetic", fontsize=22)
plt.tight_layout()
plt.subplots_adjust(bottom=0.09)
plt.subplots_adjust(left=0.055, wspace=0.13)
plt.show()
fig.savefig("full_results_synthetic.pdf")
else:
fig, ax = plt.subplots(2, 2, figsize=[10, 6])
# linewidth = 1.2
# ax[0,0].plot([i for i in range(31)], all["mnist"]["diff_avg"], linewidth=linewidth, color=color_sim, label="FedSim")
ax[0,0].plot([i for i in range(31)], all["mnist"]["diff_avg"],":", alpha=1, linewidth=linewidth, color=color_avg, label="(FedSim - FedAvg)")
ax[0,0].plot([i for i in range(31)], all["mnist"]["diff_prox"], "-.", alpha=1, linewidth=linewidth, color=color_prox, label="(FedSim - FedProx)")
ax[0,0].set_title("MNIST",fontweight='bold')
# ax[0,0].axvspan(0, 1, alpha=0.2, color='#787878')
ax[0,0].set_xlim(0, 31)
# ax[0,0].set_ylim(0.2, 0.92)
ax[0,0].axvspan(0, 0, alpha=0.2, color='#787878')
# ax[0,1].plot([i for i in range(501)], all["femnist"]["sim"], linewidth=linewidth, color=color_sim)
ax[0,1].plot([i for i in range(501)], all["femnist"]["diff_avg"],":", alpha=1, linewidth=linewidth, color=color_avg)
ax[0,1].plot([i for i in range(501)], all["femnist"]["diff_prox"], "-.", alpha=1, linewidth=linewidth, color=color_prox)
ax[0,1].set_title("FEMNIST", fontweight='bold')
ax[0,1].set_xlim(0, 501)
ax[0,1].axvspan(0, 1, alpha=0.2, color='#787878')
# ax[0,1].set_ylim(0.3, 0.83)
# ax[1,0].plot([i for i in range(201)], all["mex"]["sim"], linewidth=linewidth, color=color_sim)
ax[1,0].plot([i for i in range(201)], all["mex"]["diff_avg"],":", alpha=1, linewidth=linewidth, color=color_avg)
ax[1,0].plot([i for i in range(201)], all["mex"]["diff_prox"], "-.", alpha=1, linewidth=linewidth, color=color_prox)
ax[1,0].set_title("Fed-MEx", fontweight='bold')
ax[1,0].set_xlim(0, 201)
ax[1,0].axvspan(0, 37, alpha=0.2, color='#787878')
# ax[1,0].set_ylim(0.6, 0.96)
# ax[1,1].plot([i for i in range(251)], all["goodreads"]["sim"], linewidth=linewidth, color=color_sim)
ax[1,1].plot([i for i in range(251)], all["goodreads"]["diff_avg"],":", alpha=1, linewidth=linewidth, color=color_avg)
ax[1,1].plot([i for i in range(251)], all["goodreads"]["diff_prox"], "-.", alpha=1, linewidth=linewidth, color=color_prox)
ax[1,1].set_title("Fed-Goodreads", fontweight='bold')
ax[1,1].set_xlim(0, 251)
# [0, 1, 2, 3, 4, 5, 12, 15, 16, 17, 24, 25, 40, 106, 122, 140]
ax[1,1].axvspan(0, 5, alpha=0.2, color='#787878')
ax[1,1].axvspan(12, 12, alpha=0.2, color='#787878')
ax[1,1].axvspan(15, 17, alpha=0.2, color='#787878')
ax[1,1].axvspan(24, 25, alpha=0.2, color='#787878')
ax[1,1].axvspan(40, 40, alpha=0.2, color='#787878')
ax[1,1].axvspan(106, 106, alpha=0.2, color='#787878')
ax[1,1].axvspan(122, 122, alpha=0.2, color='#787878')
ax[1,1].axvspan(140, 140, alpha=0.2, color='#787878')
# ax[1,1].set_ylim(0.53, 0.63)
plt.subplots_adjust(hspace=0.5)
ax[0,0].set_xlabel("# Rounds")
ax[0,0].set_ylabel('Test Acc. Improvement (%)')
ax[1,0].set_xlabel("# Rounds")
ax[1,0].set_ylabel('Test Acc. Improvement (%)')
for i in range(2):
for j in range(2):
ax[j, i].spines['bottom'].set_color('#dddddd')
ax[j, i].spines['top'].set_color('#dddddd')
ax[j, i].spines['right'].set_color('#dddddd')
ax[j, i].spines['left'].set_color('#dddddd')
ax[j, i].tick_params(color='#dddddd')
ax[j, i].axhline(y=0, color='r', linestyle='-')
# ax[j, i].set_xlim(0, ROUNDS)
# for i in range(3):
# # ax[i].set_xlabel("# Rounds")
#
# ax[ i].spines['bottom'].set_color('#dddddd')
# ax[ i].spines['top'].set_color('#dddddd')
# ax[ i].spines['right'].set_color('#dddddd')
# ax[ i].spines['left'].set_color('#dddddd')
# ax[i].tick_params(color='#dddddd')
# ax[ i].set_xlim(0, ROUNDS)
fig.legend(frameon=False, loc='lower center', ncol=3, prop=dict(weight='normal', size=13),
borderaxespad=-0.3) # note: different from plt.legend
# print(all)
# ax[0].set_title("Synthetic", fontsize=22)
plt.tight_layout()
plt.subplots_adjust(bottom=0.13)
plt.subplots_adjust(left=0.062, wspace=0.08)
plt.show()
fig.savefig("compare_results_real.pdf")
exit(0)
def graph_print(method, params, num_groups = 1, name="non"):
sim_rounds = []
sim_test_acc = []
avg_rounds = []
avg_test_acc = []
prox_rounds = []
prox_test_acc = []
groups = []
logdir = "logs/"+name
with open(logdir + '/fed_'+method + '_' + str(num_groups) + '.csv', mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
line_count = 0
for row in csv_reader:
if line_count == 0:
line_count += 1
sim_rounds.append(float(row["round"]))
sim_test_acc.append(float(row["test_acc"]))
line_count += 1
print(f'Fed Processed {line_count} lines.')
if method == "sim":
for i in range(num_groups):
group_data = []
with open(logdir + '/fed_sim_g_'+ str(i)+'.csv', mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
line_count = 0
for row in csv_reader:
if line_count == 0:
line_count += 1
group_data.append(float(row["test_acc"]))
line_count += 1
groups.append(group_data)
print(f'FedSim Groups {line_count} lines.')
with open('fedavg_original/'+str(params["dataset"])+'_'+str(params["clients_per_round"])+'.csv', mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
line_count = 0
for row in csv_reader:
if line_count == 0:
line_count += 1
avg_rounds.append(float(row["round"]))
avg_test_acc.append(float(row["test_acc"]))
line_count += 1
print(f'FedAvg log Processed {line_count} lines.')
with open('fedavg_original/'+str(params["dataset"])+'_'+str(params["clients_per_round"])+'_prox.csv', mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
line_count = 0
for row in csv_reader:
if line_count == 0:
line_count += 1
prox_rounds.append(float(row["round"]))
prox_test_acc.append(float(row["test_acc"]))
line_count += 1
# print(f'FedProx log Processed {line_count} lines.')
fig, ax = plt.subplots(2,1, figsize=[12, 16])
ax[0].plot(sim_rounds, sim_test_acc, linewidth=3.0, color="#17becf", label="FedSim - G - "+str(num_groups))
ax[0].plot(avg_rounds, avg_test_acc, ":",alpha=0.6, linewidth=3.0, color="#ff7f0e", label="FedAvg")
ax[0].plot(prox_rounds, prox_test_acc, "-",alpha=0.6, linewidth=3.0, color="#90C978", label="FedProx")
ax[1].plot(sim_rounds, sim_test_acc, "-",linewidth=1.0, alpha=0.8, color="#0000ff", label="FedSim - G - "+str(num_groups))
ax[1].plot(avg_rounds, avg_test_acc, "-",linewidth=1.0, alpha=0.8, color="#ff0000", label="Fed Avg")
for idx,g in enumerate(groups):
ax[1].plot(sim_rounds, g, linewidth=1.5, alpha=0.3, label="Group - " + str(idx))
ax[0].set_xlabel("# Rounds", fontsize=22)
ax[0].set_ylabel('Testing Accuracy', fontsize=22)
ax[0].set_title("FedSim comparision - Data:"+str(params["dataset"])
+ " Clients/round: "+ str(params["clients_per_round"])
+ " E: " + str(params["num_epochs"])
+ " Groups: " + str(params["num_groups"]), fontsize=18)
ax[0].legend(fontsize=22, loc='lower center')
ax[0].grid()
ax[1].set_xlabel("# Rounds", fontsize=22)
ax[1].set_ylabel('Testing Accuracy', fontsize=22)
ax[1].set_title("Group Accuracies", fontsize=22)
ax[1].legend(fontsize=22, loc='lower right')
ax[1].grid()
# plt.xticks(fontsize=17)
# plt.yticks(fontsize=17)
# ax.tick_params(color='#dddddd')
# fig.showfig.show()()
fig.savefig(logdir+"/fed"+method+"_acc_"+str(num_groups)+".pdf")
plt.close(fig)