update plot for mnist and femnist

Author: CharlieDinh

plot_femnist.py

@@ -13,3 +13,16 @@
 import torch
 torch.manual_seed(0)
 
+algorithms_list =  ["FEDL","FedAvg","FEDL", "FEDL","FedAvg","FEDL", "FEDL","FedAvg","FEDL"]
+rho = [0,0,0,0,0,0,0,0,0,0,0,0]
+lamb_value = [0, 0, 0, 0, 0, 0, 0, 0, 0]
+learning_rate = [0.003, 0.003, 0.015, 0.003, 0.003, 0.015, 0.003, 0.003, 0.015]
+hyper_learning_rate = [0.2, 0, 0.5, 0.2, 0, 0.5, 0.2, 0, 0.5]
+local_ep = [10, 10, 10, 20, 20, 20, 40, 40, 40]
+batch_size = [20, 20, 0, 20, 20, 0, 20, 20, 0]
+DATA_SET = "Femnist"
+number_users = 10
+
+plot_summary_nist(num_users=number_users, loc_ep1=local_ep, Numb_Glob_Iters=800, lamb=lamb_value,
+                               learning_rate=learning_rate, hyper_learning_rate = hyper_learning_rate, algorithms_list=algorithms_list, batch_size=batch_size, rho = rho, dataset=DATA_SET)
+print("-- FINISH -- :",)

plot_mnist.py

@@ -13,5 +13,14 @@
 import torch
 torch.manual_seed(0)
 
-def main(dataset, algorithm, model, batch_size, learning_rate, hyper_learning_rate, L, num_glob_iters,
-         local_epochs, optimizer, clients_per_round, times):
+algorithms_list = ["FEDL","FedAvg","FEDL","FedAvg","FEDL","FedAvg","FEDL","FEDL"]
+rho = [0,0,0,0,0,0,0,0,0,0,0,0,0]
+lamb_value = [0, 0, 0, 0, 0, 0,0, 0, 0, 0]
+learning_rate = [0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003]
+hyper_learning_rate = [0.2,0,0.2,0,0.2,0,2.0,4.0]
+local_ep = [20, 20, 20, 20, 20, 20, 20, 20]
+batch_size = [20,20,40,40,0,0,0,0]
+DATA_SET = "Mnist"
+number_users = 10
+plot_summary_mnist(num_users=number_users, loc_ep1=local_ep, Numb_Glob_Iters=800, lamb=lamb_value,learning_rate=learning_rate, hyper_learning_rate = hyper_learning_rate, algorithms_list=algorithms_list, batch_size=batch_size, rho = rho, dataset=DATA_SET)
+print("-- FINISH -- :",)

utils/plot_utils.py

@@ -350,7 +350,7 @@ def plot_summary_nist(num_users=100, loc_ep1=[], Numb_Glob_Iters=10, lamb=[], le
         ax1.plot(glob_acc[i, 1:], linestyle=linestyles[i],
                  label=algs_lbl[i] + " : " + '$B = $' + stringbatch + ', $\eta = $' + str(hyper_learning_rate[i]) + ', $K_l = $' + str(loc_ep1[i]))
         ax1.set_ylim([min, max])
-        ax1.legend(loc='lower right')
+        ax1.legend(loc='lower right', prop={'size': 10})
 
     for i in range(num_al):
         stringbatch = str(batch_size[i+num_al])
@@ -359,7 +359,7 @@ def plot_summary_nist(num_users=100, loc_ep1=[], Numb_Glob_Iters=10, lamb=[], le
         ax2.plot(glob_acc[i+num_al, 1:], linestyle=linestyles[i],
                  label=algs_lbl[i + num_al] + " : " + '$B = $' + stringbatch + ', $\eta = $' + str(hyper_learning_rate[i+num_al*1]) + ', $K_l = $' + str(loc_ep1[i + num_al]))
         ax2.set_ylim([min, max])
-        ax2.legend(loc='lower right')
+        ax2.legend(loc='lower right', prop={'size': 10})
 
     for i in range(num_al):
         stringbatch = str(batch_size[i+num_al*2])
@@ -368,7 +368,7 @@ def plot_summary_nist(num_users=100, loc_ep1=[], Numb_Glob_Iters=10, lamb=[], le
         ax3.plot(glob_acc[i+num_al*2, 1:], linestyle=linestyles[i],
                  label=algs_lbl[i + num_al*2] + " : " + '$B = $' + stringbatch + ', $\eta = $' + str(hyper_learning_rate[i+num_al*2]) + ', $K_l = $' + str(loc_ep1[i+ 2*num_al]))
         ax3.set_ylim([min, max])
-        ax3.legend(loc='lower right')
+        ax3.legend(loc='lower right', prop={'size': 10})
 
     ax.set_title('FEMNIST', y=1.02)
     ax.set_xlabel('Global rounds ' + '$K_g$')