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ten_models_revamped.py
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ten_models_revamped.py
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from threading import Timer
from tkinter.messagebox import *
from tkinter import *
from tkinter.ttk import *
import numpy as np
import pandas as pd
import tkinter as tk
from scipy.optimize import dual_annealing, minimize
from routines import *
# routine to load the dataset
def loadDataset(master):
pass
# routine to estimate model parameters
def paramEstimation():
# CHECK FOR NONE NULL MODELS, AND PRINT RESPECTIVE VALUES
computedModels = []
for model in modelMap:
param = modelMap[model]
if param:
computedModels.append(model)
if len(computedModels) == 0:
showinfo(title='Parameter Display', message='No parameters have been estimated')
else:
rootDisp = Toplevel(root)
rootDisp.title("Estimated Parameters")
for i in range(len(computedModels)):
model = computedModels[i]
labelParams = paramMap[model]
estimatedParams = modelMap[model]
estimatedMSE = mseMap[model]
paramString = ""
# CREATING THE STRING FOR PARAMETERS
for j in range(len(labelParams)):
# PARAMETER STRING = PARAMETER LABEL + PARAMETER VALUE
parameter = labelParams[j]
parameterValue = "{:.4e}".format(estimatedParams[j])
paramString += parameter + " : " + parameterValue + "\n"
# A LITTLE FORMATTING OF DATA
model = model + " "
# ADDING MSE TO ESTIMATED PARAMETERS
paramString += "MSE" + " : " + str(np.round_(estimatedMSE, decimals=4)) + "\n"
paramString = "\n" + paramString
# ADDING MODEL AND PARAMETERS FOR DISPLAY IN THE WINDOW
modelLabel = Label(rootDisp, text=model)
modelParams = Label(rootDisp, text=paramString)
modelLabel.grid(row=i, column=0, padx=25)
modelParams.grid(row=i, column=1, padx=25)
rootDisp.mainloop()
# routine to calculate ranks
def rankModels(path, loaded, genGraph = False):
# routine to get the weights
def getWeights(weightOp, cdata):
if weightOp.get() == 1:
n = cdata.shape[0] # number of models
m = cdata.shape[1] # number of criterias
# SO FAR SO GOOD
# CALCULATING THE WEIGHT MATRIX ----------------------------------------------
# normalizing the weight matrix
P = cdata / np.sum(cdata, axis=0)
# calculating the entropy vector
e = (-1) * np.sum(P * np.log(P), axis=0) / np.log(n)
# calculating the degree of diversification
d = 1-e
# calculating the weights
w = d / np.sum(d)
return w
elif weightOp.get() == 2:
n = cdata.shape[0] # number of models
m = cdata.shape[1] # number of criterias
# SO FAR SO GOOD
# CALCULATING THE WEIGHT MATRIX ----------------------------------------------
# normalizing the weight matrix
P = cdata / np.sum(cdata, axis=0)
# calculating the entropy vector
e = (-1) * np.sum(P * np.log(P), axis=0) / np.log(n)
# calculating the degree of diversification
d = 1-e
# calculating the weights
w = d / np.sum(d)
return w
elif weightOp.get() == 3:
n = cdata.shape[0] # number of models
m = cdata.shape[1] # number of criterias
# SO FAR SO GOOD
# CALCULATING THE WEIGHT MATRIX ----------------------------------------------
# normalizing the weight matrix
P = cdata / np.sum(cdata, axis=0)
# calculating the entropy vector
e = (-1) * np.sum(P * np.log(P), axis=0) / np.log(n)
# calculating the degree of diversification
d = 1-e
# calculating the weights
w = d / np.sum(d)
return w
# routine to get the ranks
def getRanks(rankOp, weightVec, cdata):
if rankOp.get() == 1:
n = cdata.shape[0]
y = cdata / np.sqrt(np.sum(cdata**2, axis=0))
v = weightVec * y
vpos = []
vneg = []
criteria_map = {
}
stringCriterias = ['mse', 'mae', 'r2', 'adjr2','aic', 'bic', 'pc', 'pp', 'meop', 'theil',]
for i in range(len(stringCriterias)):
cr = stringCriterias[i]
criteria_map[cr] = v[:, i]
for i in criteria_map:
print(i, criteria_map[i])
maximizer = ['r2', 'adjr2']
minimizer = ['mse', 'mae', 'pp', 'meop', 'aic', 'bic', 'pc', 'theil']
for criteria in criteria_map:
if criteria in maximizer:
best = np.amax(criteria_map[criteria])
worst = np.amin(criteria_map[criteria])
vpos.append(best)
vneg.append(worst)
if criteria in minimizer:
best = np.amin(criteria_map[criteria])
worst = np.amax(criteria_map[criteria])
vpos.append(best)
vneg.append(worst)
# converting ideal best and ideal worst data to usable form
vpos = np.array(vpos)
vneg = np.array(vneg)
spos = np.sqrt(np.sum( (v-vpos)**2 , axis=1))
sneg = np.sqrt(np.sum( (v-vneg)**2 , axis=1))
# final relative closeness results
c = sneg / (spos + sneg)
# ranking the models
ranked = {}
rankArr = [0 for _ in range(n)] # CHANGED HERE
initialRank = 1
for _ in range(n):
# populating rank list
index = np.argmax(c)
ranked[index] = initialRank
# discarding the considered index
c[index] = -1
initialRank += 1
for i in range(n):
rankArr[i] = ranked[i]
rankArr = [[i] for i in rankArr] # CHANGED HERE
# CALCULATED THE RANKS -----------------------------------------------
modelsWithRank = []
for i in range(len(rankArr)):
row = [data[i][0], rankArr[i][0]]
modelsWithRank.append(row)
return modelsWithRank
elif rankOp.get() == 2:
return
if loaded.get():
data = pd.read_csv(path.get())
X = data.Time
Y = data.CDF
# compute
data = []
# CHECKING IF MODEL PARAMETERS ARE EVEN ENTERED BY THE USER OR NOT, IF THEY ARE THE DATA LIST IS POPULATED
# WITH MODEL ALONG WITH THE ESTIMATED PARAMETERS
for model in modelMap:
param = modelMap[model]
if param:
data.append([model, param])
if len(data) >= 2:
if not genGraph:
rootOps = Toplevel(root)
rootOps.geometry("300x220")
weightOp = IntVar()
rankOp = IntVar()
modelRoutines = [
gomodel,
pnz,
delayedS,
inflectionS,
yamadaImperfect1,
yamadaImperfect2,
yamadaExponential,
vtub,
rmd,
changs
]
criteriaData = []
# VALIDATING THE DATABASE KEYS WITH MODEL NAMES
for selected in data:
criterias = []
for model in modelRoutines:
if selected[0] == model.name:
params = selected[1]
mse = model.mse(params, X, Y)
mae = model.mae(params, X, Y)
r2 = model.rsquare(params, X, Y)
adr = model.adrsquare(params, X, Y)
pp = model.PP(params, X, Y)
aic = model.aic(params, X, Y)
bic = model.bic(params, X, Y)
pc = model.pc(params, X, Y)
meop = model.meop(params, X, Y)
theil = model.theil(params, X, Y)
criterias = [mse, mae, r2, adr, aic, bic, pc, pp, meop, theil] # WHILE ADDING COLUMNS TO THE TREEVIEW MAKE SURE TO ENTER THE COLUMNS IN THIS ORDER OF CRITERIA ONLY
break
criterias.insert(0, selected[0])
criteriaData.append(criterias)
# CALCULATE TOPSIS OVER HERE--------------------------------
cdata = []
for modelData in criteriaData:
cdata.append(modelData[1:])
cdata = np.array(cdata)
# routine to return rank window
def rankCalc(weightOp, rankOp, cdata):
weights = getWeights(weightOp, cdata)
ranks = getRanks(rankOp, weights, cdata)
rootRank = Toplevel(rootOps)
rootRank.resizable(False, False)
columns = ('models', 'rank')
columnsText = ('Models', 'Rank')
tree = Treeview(rootRank, columns=columns, show='headings')
for i in range(len(columns)):
tree.heading(columns[i], text=columnsText[i])
tree.column(columns[i], anchor=CENTER)
for i in ranks:
# ADDING TO THE TREE VIEW
tree.insert('', END, values=tuple(i))
tree.grid(row=0, column=0)
rootRank.mainloop()
w1 = Radiobutton(rootOps, text='AHP', value=1, variable=weightOp)
w2 = Radiobutton(rootOps, text='EWM', value=2, variable=weightOp)
r1 = Radiobutton(rootOps, text='CODAS', value=1, variable=rankOp)
r2 = Radiobutton(rootOps, text='TOPSIS', value=2, variable=rankOp)
submit = Button(rootOps, text='Submit', command=lambda: rankCalc(weightOp, rankOp, cdata))
w1.grid(row=0, column=0)
w2.grid(row=1, column=0)
r1.grid(row=0, column=2)
r2.grid(row=1, column=2)
submit.grid(row=2, column=1)
rootOps.mainloop()
else:
pass
else:
showerror(title='Cannot rank', message="None or less models have been selected, ranking requires atleast 2 models.")
# routine to calculate criteria
def criteria(path):
data = []
# CHECKING IF MODEL PARAMETERS ARE EVEN ENTERED BY THE USER OR NOT, IF THEY ARE THE DATA LIST IS POPULATED
# WITH MODEL ALONG WITH THE ESTIMATED PARAMETERS
for model in modelMap:
param = modelMap[model]
if param:
data.append([model, param]) # THE FIRST ARG ---> STRING, SECOND ARG ---> PARAMETERS
if len(data) < 1:
showerror(title='No models selected', message="None or less models have been selected, select atleast 1 model to view the criterias.")
else:
# GETTING THE DATA FROM DATASET IN ORDER TO CALCULATE PERFORMANCE CRITERIA
dataset = pd.read_csv(path)
X = np.arange(1, len(dataset.Time)+1)
Y = dataset['CDF']
# FOR CREATING ENTRIES IN data:
rootCriteria = Toplevel(root)
rootCriteria.title("Criteria Results")
rootCriteria.geometry("1250x230")
rootCriteria.resizable(False, False)
modelRoutines = [
gomodel,
pnz,
delayedS,
inflectionS,
yamadaImperfect1,
yamadaImperfect2,
yamadaExponential,
vtub,
rmd,
changs
]
criteriaData = []
# VALIDATING THE DATABASE KEYS WITH MODEL NAMES
for selected in data:
criterias = []
for model in modelRoutines:
if selected[0] == model.name:
params = selected[1]
mse = model.mse(params, X, Y)
mae = model.mae(params, X, Y)
r2 = model.rsquare(params, X, Y)
adr = model.adrsquare(params, X, Y)
pp = model.PP(params, X, Y) # IF ANY PROBLEM OCCURS IT IS IN PP, AIC, MEOP or TS
aic = model.aic(params, X, Y)
bic = model.bic(params, X, Y)
pc = model.pc(params, X, Y)
meop = model.meop(params, X, Y)
theil = model.theil(params, X, Y)
criterias = [mse, mae, r2, adr, aic, bic, pc, pp, meop, theil] # WHILE ADDING COLUMNS TO THE TREEVIEW MAKE SURE TO ENTER THE COLUMNS IN THIS ORDER OF CRITERIA ONLY
break
criterias.insert(0, selected[0])
criteriaData.append(criterias)
# ADDING TO TABLE
columns = ('models', 'mse', 'mae', 'rsquare', 'adrsquare', 'aic', 'bic', 'pc', 'pp', 'meop', 'theil')
columnsText = ('Models', 'MSE', 'MAE', 'R\u00b2', 'Adj. R\u00b2', 'AIC', 'BIC', 'PC', 'PP', 'MEOP', 'TS')
tree = Treeview(rootCriteria, columns=columns, show='headings')
for i in range(len(columns)):
if i == 0:
tree.heading(columns[i], text=columnsText[i], anchor=CENTER)
tree.column(columns[i], minwidth=145, width=145, anchor=CENTER)
else:
tree.heading(columns[i], text=columnsText[i], anchor=CENTER)
tree.column(columns[i], minwidth=110, width=110, anchor=CENTER)
for i in criteriaData:
# SHORTENING THE NUMBERS TILL 5 DECIMAL PLACES
model = i[0]
temp = [np.round_(j, decimals=5) for j in i[1:]]
temp.insert(0, model)
# ADDING TO THE TREE VIEW
tree.insert('', END, values=tuple(temp))
tree.place(relx=0, rely=0)
rootCriteria.mainloop()
# routine to calculate reliability
def reliabilityCalc():
pass
# defining main routine
def main():
global root
root = Tk()
root.title("Sim. Annealing DSS")
root.geometry("700x500")
# some variables that are required
path = StringVar()
loaded = BooleanVar()
# hashmaps for essential data
global paramMap, modelMap, mseMap
modelMap = {
'GO model': None,
'Delayed S-Shaped model': None,
'Inflection S-Shaped model': None,
'PNZ model': None,
'Yamada Imperfect 1 model': None,
'Vtub-Shaped model': None,
'Yamada Imperfect 2 model': None,
'RMD model`': None,
'Yamada Exponential model': None,
'Chang et al\'s model': None
}
mseMap = {
'GO model': np.inf,
'Delayed S-Shaped model': np.inf,
'Inflection S-Shaped model': np.inf,
'PNZ model': np.inf,
'Yamada Imperfect 1 model': np.inf,
'Yamada Imperfect 2 model': np.inf,
'Yamada Exponential model': np.inf,
'Vtub-Shaped model': np.inf,
'RMD model': np.inf,
'Chang et al\'s model': np.inf
}
paramMap = {
'GO model': ['a', 'b'], # a, b
'Delayed S-Shaped model': ['a', 'b'], # a, b
'Inflection S-Shaped model': ['a', 'b', '\u03B2'], # a, b, beta
'PNZ model': ['a', 'b', '\u03B1', '\u03B2'], # a, b, alpha, beta
'Yamada Imperfect 1 model': ['a', 'b', '\u03B1'], # a, b, alpha
'Yamada Imperfect 2 model': ['a', 'b', '\u03B1'], # a, b, alpha
'Yamada Exponential model': ['a', '\u03B1', '\u03B2', '\u03B3'], # a, alpha, beta, gamma
'Vtub-Shaped model': ['a', 'b', '\u03B1', '\u03B2', 'N'], # a, b, alpha, beta, N
'RMD model': ['a', 'b', '\u03B1', '\u03B2'], # a, b, alpha, beta
'Chang et al\'s model':['a', 'b', '\u03B1', '\u03B2', 'N'] # a, b, alpha, beta, N
}
# creating tab controller
tabController = Notebook(root)
# defining the tabs
global datasetTab, paramTab, rankingTab, reliabilityTab
datasetTab = Frame(tabController)
paramTab = Frame(tabController)
rankingTab = Frame(tabController)
reliabilityTab = Frame(tabController)
# tabs of DSS
# using a map to get the tab names and the tabs themselves
tabs = {
datasetTab: "Data Set Handling",
paramTab: "Parameter Estimation & Model Selection",
rankingTab: "Model Ranking",
reliabilityTab: "Model Reliability"
}
# adding the tabs
for i in tabs:
tabController.add(i, text=tabs[i])
# subroutine to view the dataset
def viewDataset(stringvar):
data = pd.read_csv(stringvar.get())
# defining the treeview
tree = Treeview(datasetTab, show='headings', height=9, selectmode="extended")
# define columns
tree['columns'] = ('time', 'cdf')
tree.column('time', anchor=CENTER, width=350)
tree.column('cdf', anchor=CENTER, width=350)
# define headings
tree.heading('time', text='Time', anchor=CENTER)
tree.heading('cdf', text='CDF', anchor=CENTER)
# add data to the treeview
for i in range(len(data)):
tree.insert('', END, values=(data.loc[i].Time, data.loc[i].CDF))
# placing the tree on the canvas and removing the
# surrogate dataset canvas
tree.place(relx=0, rely=0.55)
datasetCanvas.destroy()
# subroutine to load the dataset
def loadDataset(path, stringvar, loaded):
try:
data = pd.read_csv(path)
X = np.arange(1, len(data.Time)+1)
Y = data['CDF']
stringvar.set(path)
loaded.set(True)
viewDataset(stringvar)
showinfo(title='Load successful', message='The dataset has been loaded successfuly.')
return X, Y
except FileNotFoundError or PermissionError:
return showerror(title="Incorrect path", message='The path entered does not exist. Please make sure to enter correct path.')
# dataset tab elements
pathEntryLabel = Label(datasetTab, text="Enter path of dataset")
pathEntry = Entry(datasetTab, width=72)
loadButton = Button(datasetTab, text='Load Dataset', command=lambda: loadDataset(pathEntry.get(), path, loaded))
datasetCanvas = Canvas(datasetTab, width=700, height=225, bg='#ffdac9')
datasetMessage = tk.Label(datasetCanvas, text='Dataset Not Loaded.', bg='#ffdac9').place(relx=0.5, rely=0.5, anchor=CENTER)
# placing dataset elements
pathEntryLabel.place(relx=0.1, rely=0.1)
pathEntry.place(relx=0.3, rely=0.1)
loadButton.place(relx=0.3, rely=0.3, relwidth=0.25, relheight=0.1)
# placing the dataset window
datasetCanvas.place(relx=0, rely=0.55)
tabController.pack(expand=1, fill='both')
# parameter estimation and model selection
style = Style()
(
style.configure('GO.TButton', background = 'red'),
style.configure('Delayed.TButton', background = 'red'),
style.configure('Inflection.TButton', background = 'red'),
style.configure('YamadaRay.TButton', background = 'red'),
style.configure('YamadaIm1.TButton', background = 'red'),
style.configure('YamadaIm2.TButton', background = 'red'),
style.configure('YamadaExpo.TButton', background = 'red'),
style.configure('Vtub.TButton', background = 'red'),
style.configure('RMD.TButton', background = 'red'),
style.configure('Changs.TButton', background = 'red')
)
def goModelParamEst(modelObject, path, loaded):
if loaded.get():
# getting the data
data = pd.read_csv(path.get())
X = data.Time
Y = data.CDF
# rest compute
rootPar = Toplevel(paramTab)
rootPar.title("GO Model Initial Parameters")
rootPar.geometry("550x275")
def _sub():
optimMessage['text'] = 'optimizing...\ndo not close this window'
def optim():
bounds = [
(float(param1lb.get()), float(param1ub.get())),
(float(param2lb.get()), float(param2ub.get()))
]
minimizationResults = dual_annealing(func=modelObject.OLS, args=(X, Y), bounds=bounds, maxiter=int(iterEntry.get()))
estimatesdParams = list(minimizationResults.x)
mse = np.round_(modelObject.mse(estimatesdParams, X, Y), decimals=4)
# CHECKING WETHER CALCULATED MSE IS SMALLER THAN THE STORED MSE PREVIOUS MSE
if modelObject.estAgain:
paramStringNew = ''
paramStringOld = ''
oldParams = modelMap[modelObject.name]
subParamMap = paramMap[modelObject.name]
numParams = len(bounds)
for i in range(numParams):
parNew = subParamMap[i] + "=" + "{:.4e}".format(estimatesdParams[i]) + ", "
parOld = subParamMap[i] + "=" + "{:.4e}".format(oldParams[i]) + ", "
paramStringNew += parNew
paramStringOld += parOld
res = askyesno(
title='MSE changed',
message='Old parameters are {}, new parameters are {}. Previous MSE is {}, new MSE is {}. Do you want to continue?'.format(
paramStringOld[:-2],
paramStringNew[:-2],
mseMap[modelObject.name],
mse
)
)
if res == True:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
rootPar.destroy()
if res == False:
rootPar.destroy()
else:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
modelObject.estAgain = True
rootPar.destroy()
# CHANGING BUTTON COLOR
style.configure('GO.TButton', background='green')
#
rootPar.destroy()
t = Timer(1, optim)
t.start()
def _removeModel():
try:
mseMap[modelObject.name] = np.inf
modelMap[modelObject.name] = None
modelObject.estAgain = False
# CHANGING BUTTON COLOR
style.configure('GO.TButton', background='red')
#
rootPar.destroy()
showinfo(title='Model Removal', message="Model has been successfuly removed")
except:
showinfo(title='Model Removal', message="Error occured while removing model")
#
optimFrame = Canvas(rootPar, width=250, height=50, bg='#ffdac9')
optimMessage = tk.Label(optimFrame, text='Enter the bounds and click submit', bg='#ffdac9')
#
param1Label = Label(rootPar, text="a (cummulative faults)")
param2Label = Label(rootPar, text="b (detection rate)")
param1lb = Entry(rootPar, width=15) # THIS IS PARAMETER 'a' FOR GO MODEL
param2lb = Entry(rootPar, width=15) # THIS IS PARAMETER 'b' FOR GO MODEL
param1ub = Entry(rootPar, width=15) # THIS IS PARAMETER 'a' FOR GO MODEL
param2ub = Entry(rootPar, width=15) # THIS IS PARAMETER 'b' FOR GO MODEL
submitAndEstimate = Button(rootPar, text="Submit", command=_sub)
removeModel = Button(rootPar, text="Remove", command=_removeModel)
# LABELS PLACING
param1Label.place(relx=0, rely=0)
param2Label.place(relx=0, rely=0.15)
# ENTRY PLACING
param1lb.place(relx=0.4, rely=0)
param2lb.place(relx=0.4, rely=0.15)
param1ub.place(relx=0.6, rely=0)
param2ub.place(relx=0.6, rely=0.15)
submitAndEstimate.place(relx=0.3, rely=0.5, relwidth=0.35, relheight=0.2)
removeModel.place(relx=0.3, rely=0.7, relwidth=0.35, relheight=0.2)
# OPTIM FRAME PLACING
optimFrame.place(relx=0.3, rely=0)
optimMessage.place(relx=0.15, rely=0.2)
# LABELS PLACING
param1Label.place(relx=0, rely=0.25)
param2Label.place(relx=0, rely=0.35)
# ENTRY PLACING
param1lb.place(relx=0.4, rely=0.25)
param2lb.place(relx=0.4, rely=0.35)
param1ub.place(relx=0.6, rely=0.25)
param2ub.place(relx=0.6, rely=0.35)
submitAndEstimate.place(relx=0.3, rely=0.5, relwidth=0.35, relheight=0.15)
removeModel.place(relx=0.3, rely=0.65, relwidth=0.35, relheight=0.15)
rootPar.mainloop()
else:
showinfo(title='Dataset not loaded yet', message='Please load the dataset before model selection')
# THIS IS FOR MODEL 2 ---> DELAYED S
def delayedSParamEst(modelObject, path, loaded):
if loaded.get():
# getting the data
data = pd.read_csv(path.get())
X = data.Time
Y = data.CDF
# rest compute
rootPar = Toplevel(paramTab)
rootPar.title("Delayed S Shaped Model Initial Parameters")
rootPar.geometry("550x275")
def _sub():
optimMessage['text'] = 'optimizing...\ndo not close this window'
def optim():
bounds = [
(float(param1lb.get()), float(param1ub.get())),
(float(param2lb.get()), float(param2ub.get()))
]
minimizationResults = dual_annealing(func=modelObject.OLS, args=(X, Y), bounds=bounds, maxiter=int(iterEntry.get()))
estimatesdParams = list(minimizationResults.x)
mse = np.round_(modelObject.mse(estimatesdParams, X, Y), decimals=4)
# CHECKING WETHER CALCULATED MSE IS SMALLER THAN THE STORED MSE/ PREVIOUS MSE
if modelObject.estAgain:
paramStringNew = ''
paramStringOld = ''
oldParams = modelMap[modelObject.name]
subParamMap = paramMap[modelObject.name]
numParams = len(bounds)
for i in range(numParams):
parNew = subParamMap[i] + "=" + "{:.4e}".format(estimatesdParams[i]) + ", "
parOld = subParamMap[i] + "=" + "{:.4e}".format(oldParams[i]) + ", "
paramStringNew += parNew
paramStringOld += parOld
res = askyesno(
title='MSE changed',
message='Old parameters are {}, new parameters are {}. Previous MSE is {}, new MSE is {}. Do you want to continue?'.format(
paramStringOld[:-2],
paramStringNew[:-2],
mseMap[modelObject.name],
mse
)
)
if res == True:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
rootPar.destroy()
if res == False:
rootPar.destroy()
else:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
modelObject.estAgain = True
rootPar.destroy()
# CHANGING BUTTON COLOR
style.configure('Delayed.TButton', background='green')
#
rootPar.destroy()
t = Timer(1, optim)
t.start()
def _removeModel():
try:
mseMap[modelObject.name] = np.inf
modelMap[modelObject.name] = None
modelObject.estAgain = False
# CHANGING BUTTON COLOR
style.configure('Delayed.TButton', background='red')
#
rootPar.destroy()
showinfo(title='Model Removal', message="Model has been successfuly removed")
except:
showinfo(title='Model Removal', message="Error occured while removing model")
param1Label = Label(rootPar, text="a (cummulative faults)")
param2Label = Label(rootPar, text="b (detection rate)")
param1lb = Entry(rootPar, width=15) # THIS IS PARAMETER 'a' FOR GO MODEL
param2lb = Entry(rootPar, width=15) # THIS IS PARAMETER 'b' FOR GO MODEL
param1ub = Entry(rootPar, width=15) # THIS IS PARAMETER 'a' FOR GO MODEL
param2ub = Entry(rootPar, width=15) # THIS IS PARAMETER 'b' FOR GO MODEL
submitAndEstimate = Button(rootPar, text="Submit", command=_sub)
removeModel = Button(rootPar, text="Remove", command=_removeModel)
optimFrame = Canvas(rootPar, width=250, height=50, bg='#ffdac9')
optimMessage = tk.Label(optimFrame, text='Enter the bounds and click submit', bg='#ffdac9')
# LABELS PLACING
param1Label.place(relx=0, rely=0)
param2Label.place(relx=0, rely=0.15)
# ENTRY PLACING
param1lb.place(relx=0.4, rely=0)
param2lb.place(relx=0.4, rely=0.15)
param1ub.place(relx=0.6, rely=0)
param2ub.place(relx=0.6, rely=0.15)
submitAndEstimate.place(relx=0.3, rely=0.5, relwidth=0.35, relheight=0.2)
removeModel.place(relx=0.3, rely=0.7, relwidth=0.35, relheight=0.2)
# OPTIM FRAME PLACING
optimFrame.place(relx=0.3, rely=0)
optimMessage.place(relx=0.15, rely=0.2)
# LABELS PLACING
param1Label.place(relx=0, rely=0.25)
param2Label.place(relx=0, rely=0.35)
# ENTRY PLACING
param1lb.place(relx=0.4, rely=0.25)
param2lb.place(relx=0.4, rely=0.35)
param1ub.place(relx=0.6, rely=0.25)
param2ub.place(relx=0.6, rely=0.35)
submitAndEstimate.place(relx=0.3, rely=0.5, relwidth=0.35, relheight=0.15)
removeModel.place(relx=0.3, rely=0.65, relwidth=0.35, relheight=0.15)
rootPar.mainloop()
else:
showinfo(title='Dataset not loaded', message='load dataset')
# THIS IS FOR MODEL 3 ---> INFLECTION S
def inflectionSParamEst(modelObject, path, loaded):
if loaded.get():
data = pd.read_csv(path.get())
X = data.Time
Y = data.CDF
rootPar = Toplevel(paramTab)
rootPar.title("Inflection S Shaped Initial Parameters")
rootPar.geometry("550x200")
def _sub():
def optim():
bounds = [
(float(param1lb.get()), float(param1ub.get())),
(float(param2lb.get()), float(param2ub.get())),
(float(param3lb.get()), float(param3ub.get()))
]
minimizationResults = dual_annealing(func=modelObject.OLS, args=(X, Y), bounds=bounds, maxiter=int(iterEntry.get()))
estimatesdParams = list(minimizationResults.x)
mse = np.round_(modelObject.mse(estimatesdParams, X, Y), decimals=4)
# CHECKING WETHER CALCULATED MSE IS SMALLER THAN THE STORED MSE/ PREVIOUS MSE
if modelObject.estAgain:
paramStringNew = ''
paramStringOld = ''
oldParams = modelMap[modelObject.name]
subParamMap = paramMap[modelObject.name]
numParams = len(arrToOptimize)
for i in range(numParams):
parNew = subParamMap[i] + "=" + "{:.4e}".format(estimatesdParams[i]) + ", "
parOld = subParamMap[i] + "=" + "{:.4e}".format(oldParams[i]) + ", "
paramStringNew += parNew
paramStringOld += parOld
res = askyesno(
title='MSE changed',
message='Old parameters are {}, new parameters are {}. Previous MSE is {}, new MSE is {}. Do you want to continue?'.format(
paramStringOld[:-2],
paramStringNew[:-2],
mseMap[modelObject.name],
mse
)
)
if res == True:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
rootPar.destroy()
if res == False:
rootPar.destroy()
else:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
modelObject.estAgain = True
rootPar.destroy()
# CHANGING BUTTON COLOR
style.configure('Inflection.TButton', background='green')
#
rootPar.destroy()
t = Timer(1, optim)
t.start()
def _removeModel():
try:
mseMap[modelObject.name] = np.inf
modelMap[modelObject.name] = None
# CHANGING BUTTON COLOR
style.configure('Inflection.TButton', background='red')
#
rootPar.destroy()
showinfo(title='Model Removal', message="Model has been successfuly removed")
except:
showinfo(title='Model Removal', message="Error occured while removing model")
param1Label = Label(rootPar, text="a (cummulative faults)") # THIS IS PARAMETER 'a' FOR INFLECTION S MODEL
param2Label = Label(rootPar, text="b (detection rate)") # THIS IS PARAMETER 'b' FOR INFLECTION S MODEL
param3Label = Label(rootPar, text="\u03B2 (scale parameter)") #beta
param1lb = Entry(rootPar, width=15)
param2lb = Entry(rootPar, width=15)
param3lb = Entry(rootPar, width=15)
param1ub = Entry(rootPar, width=15)
param2ub = Entry(rootPar, width=15)
param3ub = Entry(rootPar, width=15)
submitAndEstimate = Button(rootPar, text="Submit", command=_sub)
removeModel = Button(rootPar, text="Remove", command=_removeModel)
# LABELS PLACING
param1Label.place(relx=0, rely=0)
param2Label.place(relx=0, rely=0.125)
param3Label.place(relx=0, rely=0.25)
# ENTRY PLACING
param1lb.place(relx=0.4, rely=0)
param2lb.place(relx=0.4, rely=0.125)
param3lb.place(relx=0.4, rely=0.25)
param1ub.place(relx=0.6, rely=0)
param2ub.place(relx=0.6, rely=0.125)
param3ub.place(relx=0.6, rely=0.25)
submitAndEstimate.place(relx=0.3, rely=0.50, relwidth=0.35, relheight=0.2)
removeModel.place(relx=0.3, rely=0.70, relwidth=0.35, relheight=0.2)
rootPar.mainloop()
else:
showinfo()
# THIS IS FOR MODEL 4 ---> PHAM NORDMANN ZHANG
def pnzParamEst(modelObject, path, loaded):
rootPar = Toplevel(paramTab)
rootPar.title("PNZ Model Initial Parameters")
rootPar.geometry("550x250")
def _sub():
arrToOptimize = [
float(param1.get()),
float(param2.get()),
float(param3.get()),
float(param4.get())
]
minimizationResults = minimize(fun=modelObject.OLS, x0=arrToOptimize, args=(X, Y), method='Nelder-Mead')
estimatesdParams = list(minimizationResults.x)
mse = np.round_(modelObject.mse(estimatesdParams, X, Y), decimals=4)
# CHECKING WETHER CALCULATED MSE IS SMALLER THAN THE STORED MSE/ PREVIOUS MSE
if modelObject.estAgain:
paramStringNew = ''
paramStringOld = ''
oldParams = modelMap[modelObject.name]
subParamMap = paramMap[modelObject.name]
numParams = len(arrToOptimize)
for i in range(numParams):
parNew = subParamMap[i] + "=" + "{:.4e}".format(estimatesdParams[i]) + ", "
parOld = subParamMap[i] + "=" + "{:.4e}".format(oldParams[i]) + ", "
paramStringNew += parNew
paramStringOld += parOld
res = askyesno(
title='MSE changed',
message='Old parameters are {}, new parameters are {}. Previous MSE is {}, new MSE is {}. Do you want to continue?'.format(
paramStringOld[:-2],
paramStringNew[:-2],
mseMap[modelObject.name],
mse
)
)
if res == True:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
rootPar.destroy()
if res == False:
rootPar.destroy()
else:
mseMap[modelObject.name] = mse
modelMap[modelObject.name] = list(minimizationResults.x)
modelObject.estAgain = True
rootPar.destroy()
# CHANGING BUTTON COLOR
style.configure('YamadaRay.TButton', background='green')
#
rootPar.destroy()
def _removeModel():
try:
mseMap[modelObject.name] = np.inf
modelMap[modelObject.name] = None
# CHANGING BUTTON COLOR
style.configure('YamadaRay.TButton', background='red')
#
rootPar.destroy()
showinfo(title='Model Removal', message="Model has been successfuly removed")
except:
showinfo(title='Model Removal', message="Error occured while removing model")
param1Label = Label(rootPar, text="a (cummulative faults)") # THIS IS PARAMETER 'a' FOR YAMADA RAYLEIGH MODEL
param2Label = Label(rootPar, text="b (detection rate)") # THIS IS PARAMETER 'alpha' FOR YAMADA RAYLEIGH MODEL
param3Label = Label(rootPar, text="\u03B1 (total expenditure)") # THIS IS PARAMETER 'beta' FOR YAMADA RAYLEIGH MODEL
param4Label = Label(rootPar, text="\u03B2 (scale parameter)") # THIS IS PARAMETER 'gamma' FOR YAMADA RAYLEIGH MODEL
param1lb = Entry(rootPar, width=15)
param2lb = Entry(rootPar, width=15)
param3lb = Entry(rootPar, width=15)
param4lb = Entry(rootPar, width=15)
param1ub = Entry(rootPar, width=15)
param2ub = Entry(rootPar, width=15)
param3ub = Entry(rootPar, width=15)
param4ub = Entry(rootPar, width=15)
submitAndEstimate = Button(rootPar, text="Submit", command=_sub)
removeModel = Button(rootPar, text="Remove", command=_removeModel)
# LABELS PLACING
param1Label.place(relx=0, rely=0)
param2Label.place(relx=0, rely=0.1)
param3Label.place(relx=0, rely=0.2)
param4Label.place(relx=0, rely=0.3)
# ENTRY PLACING
param1lb.place(relx=0.4, rely=0)
param2lb.place(relx=0.4, rely=0.1)
param3lb.place(relx=0.4, rely=0.2)
param4lb.place(relx=0.4, rely=0.3)
param1ub.place(relx=0.6, rely=0)
param2ub.place(relx=0.6, rely=0.1)
param3ub.place(relx=0.6, rely=0.2)
param4ub.place(relx=0.6, rely=0.3)
submitAndEstimate.place(relx=0.3, rely=0.50, relwidth=0.35, relheight=0.2)
removeModel.place(relx=0.3, rely=0.70, relwidth=0.35, relheight=0.2)
rootPar.mainloop()
# THIS IS FOR MODEL 5 ---> YAMADA IMPERFECT 1
def yamadaImperfect1ParamEst(modelObject, path, loaded):
rootPar = Toplevel(paramTab)
rootPar.title("Yamada Imperfect1 Model Initial Parameters")
rootPar.geometry("550x200")
def _sub():