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FIMTDD_evaluator.py
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__author__='jautz'
import csv
import numpy as np
from mpl_toolkits.mplot3d import axes3d
#from pyFIMTDD import FIMTDD as FIMTGD
#from Greedy_FIMTDD_LS import FIMTDD as gFIMTLS
from umcFIMTDD_LS import FIMTDD as gFIMTLS
from pyFIMTDD import FIMTDD as FIMTGD
from FIMTDD_LS import FIMTDD as FIMTLS
import matplotlib.pyplot as plt
import itertools
import time
from multiprocessing import Pool
import multiprocessing as mp
import progressbar as pb
import os
import sys
from DataGenerator import *
from Legendre_Test import data_provider
sys.setrecursionlimit(100000)
def get_Kiel_data():
print("setting up data...")
data = list()
with open('KielClean.csv', 'r') as fp:
w_day = 1.0
prev_day = -1.0
prev_time = 0.0
r = csv.reader(fp, delimiter=',')
data = list()
for i in r:
row = list()
for j in i:
row.append(float(j))
if row == []:
continue
data.append(row)
data = np.array(data)
new_data = list()
# print len(data)
simulationSteps = len(data) - 100
print("data loaded in RAM")
# print simulationSteps
for idx in range(simulationSteps):
new_data.append(np.hstack((data[idx][5], data[idx + 48][5], data[idx + 72][5],
data[idx + 84][5], data[idx + 90][5], data[idx + 93][5],
data[idx + 94][5], data[idx + 95][5], data[idx + 96][3:])))
print("data prepared for processing")
data = np.array(new_data)
simulationSteps = len(data)
return data
# s#elf.bar = progressbar.ProgressBar(max_value=simulationSteps)
def Kiel_Test(paramlist,show,val):
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
if True:
data = get_Kiel_data()
c = 0
for i in range(100000):
c += 1
print(str(c)+'/'+str(100000))
input = data[i][8:10]
#target = data[1][i] + (np.random.uniform() - 0.5) * 0.2
target = data[i][10]
if i > -1:
cumLossgd.append(cumLossgd[-1] + np.fabs(target - fimtgd.eval_and_learn(np.array(input), target)))
cumLossls.append(cumLossls[-1] + np.fabs(target - fimtls.eval_and_learn(np.array(input), target)))
cumLossgls.append(cumLossgls[-1] + np.fabs(target - gfimtls.eval_and_learn(np.array(input), target)))
else:
#warm start
fimtgd.eval_and_learn(np.array(input), target)
fimtls.eval_and_learn(np.array(input), target)
gfimtls.eval_and_learn(np.array(input), target)
#plt.scatter(x=x,y=y)
#plt.show()
if show:
f=plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
#avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
#plt.plot(avglossgd,label="Average GD Loss")
#plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:"+str(min(cumLossgd[-1],cumLossls[-1])/max(cumLossgd[-1],cumLossls[-1])))
plt.legend()
figname="g"+str(paramlist[0])+"_nmin"+str(paramlist[1])+"_al"+str(paramlist[2])+"_thr"+str(paramlist[3])\
+ "_lr"+str(paramlist[4])+".png"
plt.savefig(figname)
#plt.show()
f.clear()
#print(i)
#print(fimtgd.count_leaves())
#print(fimtgd.count_nodes())
return [cumLossgd,cumLossls,cumLossgls,val,paramlist]
def test2d(paramlist,show,val):
#print(val)
#print(paramlist)
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
if True:
start = 0.0
end = 1.0
X = list()
Y = list()
x, y, z = axes3d.get_test_data(0.1)
num_d = len(x)**2
for i in range(len(x)):
for j in range(len(y)):
input = [x[i,j],y[i,j]]
target = z[i,j]
X.append(input)
Y.append(target)
data = [X,Y]
data = np.array(data)
data = data.transpose()
np.random.shuffle(data)
data = data.transpose()
for i in range(num_d):
input = data[0][i]
target = data[1][i] + (np.random.uniform() - 0.5) * 0.2
o_target = data[1][i]
if num_d/2 < i:
target += 1.0
o_target += 1.0
cumLossgd.append(cumLossgd[-1] + np.fabs(o_target - fimtgd.eval_and_learn(np.array(input), target)))
cumLossls.append(cumLossls[-1] + np.fabs(o_target - fimtls.eval_and_learn(np.array(input), target)))
cumLossgls.append(cumLossgls[-1] + np.fabs(o_target - gfimtls.eval_and_learn(np.array(input), target)))
#plt.scatter(x=x,y=y)
#plt.show()
if show:
f=plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
#avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
#plt.plot(avglossgd,label="Average GD Loss")
#plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:"+str(min(cumLossgd[-1],cumLossls[-1])/max(cumLossgd[-1],cumLossls[-1])))
plt.legend()
figname="g"+str(paramlist[0])+"_nmin"+str(paramlist[1])+"_al"+str(paramlist[2])+"_thr"+str(paramlist[3])\
+ "_lr"+str(paramlist[4])+".png"
plt.savefig(figname)
#plt.show()
f.clear()
#print(i)
#print(fimtgd.count_leaves())
#print(fimtgd.count_nodes())
return [cumLossgd,cumLossls,cumLossgls,val,paramlist]
def legendre_test(paramlist,show,val):
#print(val)
#print(paramlist)
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
if True:
start = 0.0
end = 1.0
i = 0
for input,target,o_target in data_provider([9,9,32,32,4],[0.05,0.05,0.05,0.05,0.05],[1000,1000,3000,2000,2000],5):
#print(i,'/',2000)
i+=1
#cumLossgd.append(cumLossgd[-1] + np.sqrt(np.fabs(o_target - fimtgd.eval_and_learn(np.array(input), target))**2))
#cumLossls.append(cumLossls[-1] + np.sqrt(np.fabs(o_target - fimtls.eval_and_learn(np.array(input), target))**2))
#cumLossgls.append(cumLossgls[-1] + np.sqrt(np.fabs(o_target - gfimtls.eval_and_learn(np.array(input), target))**2))
cumLossgd.append(cumLossgd[-1] + np.sqrt(np.fabs(o_target - fimtgd.eval_and_learn(np.array(input), target))**2))
cumLossls.append(cumLossls[-1] + np.sqrt(np.fabs(o_target - fimtls.eval_and_learn(np.array(input), target))**2))
cumLossgls.append(cumLossgls[-1] + np.sqrt(np.fabs(o_target - gfimtls.eval_and_learn(np.array(input), target))**2))
#plt.scatter(x=x,y=y)
#plt.show()
if show:
f=plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
#avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
#plt.plot(avglossgd,label="Average GD Loss")
#plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:"+str(min(cumLossgd[-1],cumLossls[-1])/max(cumLossgd[-1],cumLossls[-1])))
plt.legend()
figname="g"+str(paramlist[0])+"_nmin"+str(paramlist[1])+"_al"+str(paramlist[2])+"_thr"+str(paramlist[3])\
+ "_lr"+str(paramlist[4])+".png"
plt.savefig(figname)
#plt.show()
f.clear()
#print(i)
# print("legendre_test FIMTDD_LS: ", fimtls)
# print('FIMTDD_LS count_leaves: ', fimtls.count_leaves())
# print('FIMTDD_LS count_nodes: ', fimtls.count_nodes())
return [cumLossgd,cumLossls,cumLossgls,val,paramlist]
def sine_test(paramlist,show,val):
#print(val)
#print(paramlist)
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
if True:
start = 0.0
end = 1.0
x = list()
y = list()
for i in range(4000):
input = np.random.uniform(0.0,1.0)*2*np.pi
target = np.sin(input)
if i > 2000:
target += 1.0
o_target = target
noise = (np.random.uniform() - 0.5) * 0.8
target += noise
x.append(input)
y.append(target)
cumLossgd.append(cumLossgd[-1] + np.sqrt(np.fabs(o_target - fimtgd.eval_and_learn(np.array(input), target))**2))
cumLossls.append(cumLossls[-1] + np.sqrt(np.fabs(o_target - fimtls.eval_and_learn(np.array(input), target))**2))
cumLossgls.append(cumLossgls[-1] + np.sqrt(np.fabs(o_target - gfimtls.eval_and_learn(np.array(input), target))**2))
#plt.scatter(x=x,y=y)
#plt.show()
if show:
f=plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
#avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
#plt.plot(avglossgd,label="Average GD Loss")
#plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:"+str(min(cumLossgd[-1],cumLossls[-1])/max(cumLossgd[-1],cumLossls[-1])))
plt.legend()
figname="g"+str(paramlist[0])+"_nmin"+str(paramlist[1])+"_al"+str(paramlist[2])+"_thr"+str(paramlist[3])\
+ "_lr"+str(paramlist[4])+".png"
plt.savefig(figname)
#plt.show()
f.clear()
#print(i)
#print(fimtgd.count_leaves())
#print(fimtgd.count_nodes())
return [cumLossgd,cumLossls,cumLossgls,val,paramlist]
#counter = 0
def abalone_test(paramlist,show,val):
#print(val)
#print(paramlist)
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
with open( "abalone.data", 'rt') as abalonefile:
i = 0
for row in abalonefile:
i += 1
row=row.rstrip().split(',')
target=float(row[-1])
if row[0]=="M":
numgender=1.
if row[0]=="I":
numgender=0.5
if row[0]=="F":
numgender=0.
input=[numgender]
for item in row[1:-1]:
input.append(float(item))
cumLossgd.append(cumLossgd[-1] + np.fabs(target - fimtgd.eval_and_learn(np.array(input), target)))
cumLossls.append(cumLossls[-1] + np.fabs(target - fimtls.eval_and_learn(np.array(input), target)))
cumLossgls.append(cumLossgls[-1] + np.fabs(target - gfimtls.eval_and_learn(np.array(input), target)))
if show:
f=plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
#avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
#plt.plot(avglossgd,label="Average GD Loss")
#plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:"+str(min(cumLossgd[-1],cumLossls[-1])/max(cumLossgd[-1],cumLossls[-1])))
plt.legend()
figname="g"+str(paramlist[0])+"_nmin"+str(paramlist[1])+"_al"+str(paramlist[2])+"_thr"+str(paramlist[3])\
+ "_lr"+str(paramlist[4])+".png"
plt.savefig(figname)
#plt.show()
f.clear()
#print(i)
#print(fimtgd.count_leaves())
#print(fimtgd.count_nodes())
return [cumLossgd,cumLossls,cumLossgls,val,paramlist]
def flightdata_test(paramlist,show,val):
fimtgd = FIMTGD(gamma=paramlist[0], n_min=paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls = FIMTLS(gamma=paramlist[0], n_min=paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
cumLossgd = [0]
cumLossls = [0]
symbolDict={}
symbolCounter=0.
directory=os.fsencode("Flight Data")
for root, dirs, files in os.walk(directory):
for file in files:
with open(os.path.join(root,file), 'rt') as flightdata:
try:
next(flightdata)
for row in flightdata:
row=row.rstrip().split(',')
#input=row[0:3,5,7,8,9,11,16,17,18,23]
if not "NA" in row[0:4]+[row[5]]+row[7:10]+[row[11]]+row[16:19]+ [row[23]] and not "NA" in row [14]:
target = float(row[14])
input=[float(x) for x in row[0:4]+[row[5]]+[row[7]]]
if not row[8] in symbolDict.keys():
symbolDict[row[8]]=symbolCounter
symbolCounter=symbolCounter+1.
input.append(symbolDict[row[8]])
if not row[9] in symbolDict.keys():
symbolDict[row[9]]=symbolCounter
symboprilCounter=symbolCounter+1.
input.append(symbolDict[row[9]])
input.append(float(row[11]))
if not row[16] in symbolDict.keys():
symbolDict[row[16]]=symbolCounter
symbolCounter=symbolCounter+1.
input.append(symbolDict[row[16]])
if not row[17] in symbolDict.keys():
symbolDict[row[17]]=symbolCounter
symbolCounter=symbolCounter+1.
input.append(symbolDict[row[17]])
input.append(float(18))
input.append(float(row[23]))
#print ("Input: " +str(input))
except(UnicodeDecodeError):
continue
cumLossgd.append(cumLossgd[-1] + np.fabs(target - fimtgd.eval_and_learn(np.array(input), target)))
cumLossls.append(cumLossls[-1] + np.fabs(target - fimtls.eval_and_learn(np.array(input), target)))
if show:
f=plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
#avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
#plt.plot(avglossgd,label="Average GD Loss")
#plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:"+str(min(cumLossgd[-1],cumLossls[-1])/max(cumLossgd[-1],cumLossls[-1])))
plt.legend()
figname="g"+str(paramlist[0])+"_nmin"+str(paramlist[1])+"_al"+str(paramlist[2])+"_thr"+str(paramlist[3])\
+ "_lr"+str(paramlist[4])+".png"
plt.savefig(figname)
#plt.show()
f.clear()
print(fimtgd.count_leaves(),fimtgd.count_nodes())
return [cumLossgd,cumLossls,val,paramlist]
def line_test(paramlist,show,val):
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
data=generate_Line(4000)
data=np.array(sorted(data,key=lambda x:x[0]))
o_target=data[:,-1]
input=data[:,1:-1]
for counter in range(len(data)):
noise = (np.random.uniform() - 0.5) * 0.8
target= o_target[counter] + noise
cumLossgd.append(cumLossgd[-1] + np.fabs(o_target[counter] - fimtgd.eval_and_learn(np.array(input[counter]), target)))
cumLossls.append(cumLossls[-1] + np.fabs(o_target[counter] - fimtls.eval_and_learn(np.array(input[counter]), target)))
cumLossgls.append(cumLossgls[-1] + np.fabs(o_target[counter] - gfimtls.eval_and_learn(np.array(input[counter]), target)))
if show:
f = plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
# avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
# plt.plot(avglossgd,label="Average GD Loss")
# plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:" + str(min(cumLossgd[-1], cumLossls[-1]) / max(cumLossgd[-1], cumLossls[-1])))
plt.legend()
figname = "g" + str(paramlist[0]) + "_nmin" + str(paramlist[1]) + "_al" + str(paramlist[2]) + "_thr" + str(
paramlist[3]) \
+ "_lr" + str(paramlist[4]) + ".png"
plt.savefig(figname)
# plt.show()
f.clear()
return [cumLossgd, cumLossls, cumLossgls, val, paramlist]
def lexp_test(paramlist,show,val):
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
data=generate_Lexp(4000)
data=np.array(sorted(data,key=lambda x:x[0]))
o_target=data[:,-1]
input=data[:,1:-1]
for counter in range(len(data)):
noise = (np.random.uniform() - 0.5) * 0.8
target= o_target[counter] + noise
cumLossgd.append(cumLossgd[-1] + np.fabs(o_target[counter] - fimtgd.eval_and_learn(np.array(input[counter]), target)))
cumLossls.append(cumLossls[-1] + np.fabs(o_target[counter] - fimtls.eval_and_learn(np.array(input[counter]), target)))
cumLossgls.append(cumLossgls[-1] + np.fabs(o_target[counter] - gfimtls.eval_and_learn(np.array(input[counter]), target)))
if show:
f = plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
# avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
# plt.plot(avglossgd,label="Average GD Loss")
# plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:" + str(min(cumLossgd[-1], cumLossls[-1]) / max(cumLossgd[-1], cumLossls[-1])))
plt.legend()
figname = "g" + str(paramlist[0]) + "_nmin" + str(paramlist[1]) + "_al" + str(paramlist[2]) + "_thr" + str(
paramlist[3]) \
+ "_lr" + str(paramlist[4]) + ".png"
plt.savefig(figname)
# plt.show()
f.clear()
return [cumLossgd, cumLossls, cumLossgls, val, paramlist]
def losc_test(paramlist,show,val):
fimtgd=FIMTGD(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
fimtls=FIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[4])
gfimtls=gFIMTLS(gamma=paramlist[0], n_min = paramlist[1], alpha=paramlist[2], threshold=paramlist[3], learn=paramlist[5])
cumLossgd =[0]
cumLossls =[0]
cumLossgls =[0]
data=generate_Losc(4000)
data=np.array(sorted(data,key=lambda x:x[0]))
o_target=data[:,-1]
input=data[:,1:-1]
for counter in range(len(data)):
noise = (np.random.uniform() - 0.5) * 0.8
target= o_target[counter] + noise
cumLossgd.append(cumLossgd[-1] + np.fabs(o_target[counter] - fimtgd.eval_and_learn(np.array(input[counter]), target)))
cumLossls.append(cumLossls[-1] + np.fabs(o_target[counter] - fimtls.eval_and_learn(np.array(input[counter]), target)))
cumLossgls.append(cumLossgls[-1] + np.fabs(o_target[counter] - gfimtls.eval_and_learn(np.array(input[counter]), target)))
if show:
f = plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
# avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
# plt.plot(avglossgd,label="Average GD Loss")
# plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:" + str(min(cumLossgd[-1], cumLossls[-1]) / max(cumLossgd[-1], cumLossls[-1])))
plt.legend()
figname = "g" + str(paramlist[0]) + "_nmin" + str(paramlist[1]) + "_al" + str(paramlist[2]) + "_thr" + str(
paramlist[3]) \
+ "_lr" + str(paramlist[4]) + ".png"
plt.savefig(figname)
# plt.show()
f.clear()
return [cumLossgd, cumLossls, cumLossgls, val, paramlist]
def callback_func(list):
global result_list
global numberoftests
global counter
global bar
#print("[Thread "+str(list[2])+' ('+str(counter)+'/'+str(numberoftests)+')]: process finished')
bar.update(counter)
counter += 1
result_list[list[3]] = list
def callback_err(argv=None):
print("Error, process killed")
def find_max(result_list):
global gammalist
global n_minlist
global alphalist
global thresholdlist
global learnlist
global minparamgd
global minvalgd
global minparamls
global minvalls
global counter
global numberoftests
global minvalgls
global minparamgls
global c_loss_ls
global c_loss_gd
global c_loss_gls
for gdls in result_list:
if gdls[0][-1]<minvalgd:
minvalgd=gdls[0][-1]
minparamgd=gdls[-1]
c_loss_gd = gdls[0]
if gdls[1][-1]<minvalls:
minvalls=gdls[1][-1]
minparamls=gdls[-1]
c_loss_ls = gdls[1]
if gdls[2][-1]<minvalgls:
minvalgls=gdls[2][-1]
minparamgls=gdls[-1]
c_loss_gls = gdls[2]
return minvalgd,minparamgd,minvalls,minparamls,minvalgls,minparamgls
global gammalist
global n_minlist
global alphalist
global thresholdlist
global learnlist
global minparamgd
global minvalgd
global minparamls
global minvalls
global counter
global numberoftests
global result_list
global bar
global minvalgls
global minparamgls
global c_loss_ls
global c_loss_gd
global c_loss_gls
if __name__ == '__main__':
global gammalist
global n_minlist
global alphalist
global thresholdlist
global learnlist
global minparamgd
global minvalgd
global minparamls
global minvalls
global counter
global numberoftests
global result_list
global bar
global minvalgls
global minparamgls
global c_loss_ls
global c_loss_gd
global c_loss_gls
#pool = #()
if(True): #For plot testing purposes, set this to false
gammalist=[0.5,0.6,0.6,0.4]
n_minlist=[75,100,125,150,200]#np.arange(1,1000,50)
alphalist=[0.0005,0.001,0.01]
thresholdlist= [50,100,125]
learnlist=[0.05,0.75,0.1]
greedlist=[2,5,50]
#gammalist=[0.5,0.75,1.0,2.0,4.0]
#n_minlist=[48,96,96*2,96*4]#np.arange(1,1000,50)
#alphalist=[0.001,0.01,0.1]
#thresholdlist= [1,7,20]
#learnlist=[0.75,0.1,0.125,0.15]
#greedlist=[1,5,25,50]
#learnlist = [0.05]
#greedlist = [5]
else:
gammalist = [1.0]
n_minlist = [96]
alphalist = [0.001]
thresholdlist = [15]
learnlist = [0.05]
greedlist = [5]
minparamgd=[]
minvalgd=np.inf
minparamls=[]
minvalls=np.inf
minparamgls = []
minvalgls = np.inf
pool = Pool(processes=mp.cpu_count()-1)
counter=0
numberoftests=len(gammalist)*len(n_minlist)*len(alphalist)*len(thresholdlist)*len(learnlist)
result_list = [None]*numberoftests
results = np.zeros(numberoftests)
c = 0
# bar = pb.ProgressBar(max_value=numberoftests)
bar = pb.ProgressBar(maxval=numberoftests)
if(False): #for non pool test, set this to true
# if(True): #for non pool test, set this to true
for paramlist in itertools.product(gammalist, n_minlist, alphalist, thresholdlist, learnlist):
paramlist = list(paramlist)
idx = learnlist.index(paramlist[-1])
paramlist.append(greedlist[idx])
# line_test(paramlist,False,12)
Kiel_Test(paramlist,False,12)
c=c+1
else:
for paramlist in itertools.product(gammalist, n_minlist, alphalist, thresholdlist, learnlist):
paramlist = list(paramlist)
idx = learnlist.index(paramlist[-1])
paramlist.append(greedlist[idx])
# pool.apply_async(func=legendre_test,args=(paramlist,False,c),callback=callback_func)
pool.apply_async(func=Kiel_Test,args=(paramlist,False,c),callback=callback_func)
#callback_func(legendre_test(paramlist,False,c))
c = c+1
pool.close()
pool.join()
s0 = ('Proceses Finished for Legendre Dataset:')
#print(result_list)
minvalgd, minparamgd, minvalls, minparamls,minvalgls,minparamgls = find_max(result_list)
s1 = ("Optimal GD: "+ str(minparamgd)+ " with " + str(minvalgd))
s2 = ("Optimal LS: "+ str(minparamls)+ " with " + str(minvalls))
s3 = ("Optimal umvcLS: " + str(minparamgls) + " with " + str(minvalgls))
with open('results.txt','a+') as fp:
fp.write(s0+'\n'+s1+'\n'+s2+'\n'+s3+'\n\n')
print(s0+'\n'+s1+'\n'+s2+'\n'+s3+'\n\n')
# f=plt.figure()
# plt.plot(c_loss_gd[1:], label="FIMTGD")
# f.hold(True)
# plt.plot(c_loss_ls[1:], label="FIMTLS")
# f.hold(True)
# plt.plot(c_loss_gls[1:], label="umcFIMTLS")
# #avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
# #plt.plot(avglossgd,label="Average GD Loss")
# #plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
# plt.legend()
# plt.savefig('optimal_performance.png')
# plt.show()
# f.clear()
#abalone_test(minparamgd,True,0)
#abalone_test(minparamls,True,0)