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main_plot.py
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main_plot.py
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#!/usr/bin/env python
import h5py
import matplotlib.pyplot as plt
import numpy as np
import argparse
import importlib
import random
import os
from utils.plot_utils import *
import torch
torch.manual_seed(0)
#------------------------------------------------------Human Activity------------------------------------------------------
if(0): # plot for Human Activity
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "human_activity"
local_ep = [5,5,5,5,5,5,5]
L_k = [0.001,0.01,0.1,1.0,0.01,0.01,100.0]
learning_rate = [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,0,0,0,0,0,0,0]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU", "Local", "Global", "Mocha"]#,"PerAvg_p","FedAvg"]
plot_summary_human_activity_eta(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0): # plot for Human Activity
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "human_activity"
local_ep = [5,5,5]
L_k = [0.1,0.1,0.1]
learning_rate = [0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,1,2]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU"]#,"PerAvg_p","FedAvg"]
plot_summary_human_activity_akl(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0): # plot for Human Activity
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "human_activity"
local_ep = [5,5,5,5,5,5,5]
L_k = [0.001,0.01,0.1,1.0,0.01,0.01,100.0]
learning_rate = [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,0,0,0,0,0,0,0]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU", "Local", "Global", "Mocha"]#,"PerAvg_p","FedAvg"]
plot_summary_human_activity_eta_non(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0): # plot for Human Activity
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "human_activity"
local_ep = [5,5,5]
L_k = [0.1,0.1,0.1]
learning_rate = [0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,1,2]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU"]#,"PerAvg_p","FedAvg"]
plot_summary_human_activity_akl_non(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
#------------------------------------------------------vehicle_sensor------------------------------------------------------
if(0): # plot for vehicle_sensor
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "vehicle_sensor"
local_ep = [5,5,5,5,5,5,5]
L_k = [0.001,0.01,0.1,1.0,0.01,0.01,100.0]
learning_rate = [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,0,0,0,0,0,0]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU", "Local", "Global", "Mocha"]#,"PerAvg_p","FedAvg"]
plot_summary_vehicle_eta(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(1): # plot for vehicle_sensor
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "vehicle_sensor"
local_ep = [5,5,5,5,5,5,5]
L_k = [0.01,0.01,0.01]
learning_rate = [0.05, 0.05, 0.05, 0.05, 0.05]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,1,2]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU"]#,"PerAvg_p","FedAvg"]
plot_summary_vehicle_akl(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0): # plot for vehicle_sensor
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "vehicle_sensor"
local_ep = [5,5,5,5,5,5,5]
L_k = [0.001,0.01,0.1,1.0,0.01,0.01,100.0]
learning_rate = [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,0,0,0,0,0,0]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU", "Local", "Global", "Mocha"]#,"PerAvg_p","FedAvg"]
plot_summary_vehicle_eta_non(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0): # plot for vehicle_sensor
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "vehicle_sensor"
local_ep = [5,5,5,5,5]
L_k = [0.01,0.01,0.01]
learning_rate = [0.05, 0.05, 0.05, 0.05, 0.05]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,1,2]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU"]#,"PerAvg_p","FedAvg"]
plot_summary_vehicle_akl_non(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
#------------------------------------------------------Mnist------------------------------------------------------
if(0):
numusers = 1.0 # Mnist
num_glob_iters = 200
dataset = "Mnist"
local_ep = [5,5,5,5,5,5,5]
L_k = [0.001,0.005,0.01,0.05,0.01,0.01,100.0]
learning_rate = [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,0,0,0,0,0,0]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU", "Local", "Global", "Mocha"]#,"PerAvg_p","FedAvg"]
plot_summary_mnist_eta(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0): # plot for Mnist
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "Mnist"
local_ep = [5,5,5,5,5]
L_k = [0.005,0.005,0.005,0.005]
learning_rate = [0.03, 0.03, 0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,1,2,3]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU"]#,"PerAvg_p","FedAvg"]
plot_summary_mnist_akl(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0):
numusers = 1.0 # Mnist
num_glob_iters = 200
dataset = "Mnist"
local_ep = [5,5,5,5,5,5,5]
L_k = [0.001,0.005,0.01,0.05,0.01,0.01,100.0]
learning_rate = [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0,0,0,0,0,0,0]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU", "Local", "Global", "Mocha"]#,"PerAvg_p","FedAvg"]
plot_summary_mnist_eta_non(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)
if(0): # plot for Mnist
numusers = 1.0 # full user
num_glob_iters = 200
dataset = "Mnist"
local_ep = [5,5,5,5,5]
L_k = [0.005,0.005,0.005,0.005]
learning_rate = [0.03, 0.03, 0.03, 0.03, 0.03]
beta = [1, 1, 1, 1, 1, 1, 1]
batch_size = [20,20,20,20,20,20,20]
K = [0, 1, 2, 3]
personal_learning_rate = [1,1,1,1,1,1,1,1]
algorithms = [ "FedU","FedU","FedU","FedU"]#,"PerAvg_p","FedAvg"]
plot_summary_mnist_akl_non(num_users=numusers, loc_ep1=local_ep, Numb_Glob_Iters=num_glob_iters, lamb=L_k,
learning_rate=learning_rate, beta = beta, algorithms_list=algorithms, batch_size=batch_size, dataset=dataset, k = K, personal_learning_rate = personal_learning_rate)