498 lines
16 KiB
Python
498 lines
16 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
import matplotlib as plt
|
|
import matplotlib.colors as pltcolors
|
|
import matplotlib.pyplot as plt
|
|
from mpl_toolkits.mplot3d import Axes3D
|
|
from sklearn.cross_validation import KFold
|
|
import Measures
|
|
from pyFTS.partitioners import Grid
|
|
from pyFTS.common import Membership,FuzzySet,FLR,Transformations
|
|
|
|
def Teste(par):
|
|
x = np.arange(1,par)
|
|
y = [ yy**yy for yyy in x]
|
|
plt.plot(x,y)
|
|
|
|
|
|
def getIntervalStatistics(original,models):
|
|
ret = "Model & RMSE & MAPE & Sharpness & Resolution & Coverage \\ \n"
|
|
for fts in models:
|
|
forecasts = fts.forecast(original)
|
|
ret = ret + fts.shortname + " & "
|
|
ret = ret + str( round(Measures.rmse_interval(original[fts.order-1 :],forecasts),2)) + " & "
|
|
ret = ret + str( round(Measures.mape_interval(original[fts.order-1 :],forecasts),2)) + " & "
|
|
ret = ret + str( round(Measures.sharpness(forecasts),2)) + " & "
|
|
ret = ret + str( round(Measures.resolution(forecasts),2)) + " & "
|
|
ret = ret + str( round(Measures.coverage(original[fts.order-1 :],forecasts),2)) + " \\ \n"
|
|
return ret
|
|
|
|
def plotDistribution(dist):
|
|
for k in dist.index:
|
|
alpha = np.array([dist[x][k] for x in dist])*100
|
|
x = [k for x in np.arange(0,len(alpha))]
|
|
y = dist.columns
|
|
plt.scatter(x,y,c=alpha,marker='s',linewidths=0,cmap='Oranges',norm=pltcolors.Normalize(vmin=0,vmax=1),vmin=0,vmax=1,edgecolors=None)
|
|
|
|
def plotComparedSeries(original,models, colors):
|
|
fig = plt.figure(figsize=[25,10])
|
|
ax = fig.add_subplot(111)
|
|
|
|
mi = []
|
|
ma = []
|
|
|
|
ax.plot(original,color='black',label="Original")
|
|
count = 0
|
|
for fts in models:
|
|
forecasted = fts.forecast(original)
|
|
|
|
if fts.isInterval:
|
|
lower = [kk[0] for kk in forecasted]
|
|
upper = [kk[1] for kk in forecasted]
|
|
mi.append(min(lower))
|
|
ma.append(max(upper))
|
|
for k in np.arange(0,fts.order):
|
|
lower.insert(0,None)
|
|
upper.insert(0,None)
|
|
ax.plot(lower,color=colors[count],label=fts.shortname)
|
|
ax.plot(upper,color=colors[count])
|
|
|
|
else:
|
|
mi.append(min(forecasted))
|
|
ma.append(max(forecasted))
|
|
forecasted.insert(0,None)
|
|
ax.plot(forecasted,color=colors[count],label=fts.shortname)
|
|
|
|
handles0, labels0 = ax.get_legend_handles_labels()
|
|
ax.legend(handles0,labels0)
|
|
count = count + 1
|
|
#ax.set_title(fts.name)
|
|
ax.set_ylim([min(mi),max(ma)])
|
|
ax.set_ylabel('F(T)')
|
|
ax.set_xlabel('T')
|
|
ax.set_xlim([0,len(original)])
|
|
|
|
|
|
def plotComparedIntervalsAhead(original,models, colors, distributions, time_from, time_to):
|
|
fig = plt.figure(figsize=[25,10])
|
|
ax = fig.add_subplot(111)
|
|
|
|
mi = []
|
|
ma = []
|
|
|
|
count = 0
|
|
for fts in models:
|
|
if fts.isDensity and distributions[count]:
|
|
density = fts.forecastDistributionAhead(original[:time_from],time_to,25)
|
|
for k in density.index:
|
|
alpha = np.array([density[x][k] for x in density])*100
|
|
x = [time_from + fts.order + k for x in np.arange(0,len(alpha))]
|
|
y = density.columns
|
|
ax.scatter(x,y,c=alpha,marker='s',linewidths=0,cmap='Oranges',
|
|
norm=pltcolors.Normalize(vmin=0,vmax=1),vmin=0,vmax=1,edgecolors=None)
|
|
|
|
if fts.isInterval:
|
|
forecasts = fts.forecastAhead(original[:time_from],time_to)
|
|
lower = [kk[0] for kk in forecasts]
|
|
upper = [kk[1] for kk in forecasts]
|
|
mi.append(min(lower))
|
|
ma.append(max(upper))
|
|
for k in np.arange(0,time_from):
|
|
lower.insert(0,None)
|
|
upper.insert(0,None)
|
|
ax.plot(lower,color=colors[count],label=fts.shortname)
|
|
ax.plot(upper,color=colors[count])
|
|
|
|
else:
|
|
forecasts = fts.forecast(original)
|
|
mi.append(min(forecasts))
|
|
ma.append(max(forecasts))
|
|
for k in np.arange(0,time_from):
|
|
forecasts.insert(0,None)
|
|
ax.plot(forecasts,color=colors[count],label=fts.shortname)
|
|
|
|
handles0, labels0 = ax.get_legend_handles_labels()
|
|
ax.legend(handles0,labels0)
|
|
count = count + 1
|
|
ax.plot(original,color='black',label="Original")
|
|
#ax.set_title(fts.name)
|
|
ax.set_ylim([min(mi),max(ma)])
|
|
ax.set_ylabel('F(T)')
|
|
ax.set_xlabel('T')
|
|
ax.set_xlim([0,len(original)])
|
|
|
|
|
|
def plotCompared(original,forecasts,labels,title):
|
|
fig = plt.figure(figsize=[13,6])
|
|
ax = fig.add_subplot(111)
|
|
ax.plot(original,color='k',label="Original")
|
|
for c in range(0,len(forecasted)):
|
|
ax.plot(forecasted[c],label=labels[c])
|
|
handles0, labels0 = ax.get_legend_handles_labels()
|
|
ax.legend(handles0,labels0)
|
|
ax.set_title(title)
|
|
ax.set_ylabel('F(T)')
|
|
ax.set_xlabel('T')
|
|
ax.set_xlim([0,len(original)])
|
|
ax.set_ylim([min(original),max(original)])
|
|
|
|
def SelecaoKFold_MenorRMSE(original,parameters,modelo):
|
|
nfolds = 5
|
|
ret = []
|
|
errors = np.array([[0 for k in parameters] for z in np.arange(0,nfolds)])
|
|
forecasted_best = []
|
|
print("Série Original")
|
|
fig = plt.figure(figsize=[18,10])
|
|
fig.suptitle("Comparação de modelos ")
|
|
ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) #left, bottom, width, height
|
|
ax0.set_xlim([0,len(original)])
|
|
ax0.set_ylim([min(original),max(original)])
|
|
ax0.set_title('Série Temporal')
|
|
ax0.set_ylabel('F(T)')
|
|
ax0.set_xlabel('T')
|
|
ax0.plot(original,label="Original")
|
|
min_rmse_fold = 100000.0
|
|
best = None
|
|
fc = 0 #Fold count
|
|
kf = KFold(len(original), n_folds=nfolds)
|
|
for train_ix, test_ix in kf:
|
|
train = original[train_ix]
|
|
test = original[test_ix]
|
|
min_rmse = 100000.0
|
|
best_fold = None
|
|
forecasted_best_fold = []
|
|
errors_fold = []
|
|
pc = 0 #Parameter count
|
|
for p in parameters:
|
|
sets = Grid.GridPartitionerTrimf(train,p)
|
|
fts = modelo(str(p)+ " particoes")
|
|
fts.train(train,sets)
|
|
forecasted = [fts.forecast(xx) for xx in test]
|
|
error = Measures.rmse(np.array(forecasted),np.array(test))
|
|
errors_fold.append(error)
|
|
print(fc, p, error)
|
|
errors[fc,pc] = error
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
best_fold = fts
|
|
forecasted_best_fold = forecasted
|
|
pc = pc + 1
|
|
forecasted_best_fold = [best_fold.forecast(xx) for xx in original]
|
|
ax0.plot(forecasted_best_fold,label=best_fold.name)
|
|
if np.mean(errors_fold) < min_rmse_fold:
|
|
min_rmse_fold = np.mean(errors)
|
|
best = best_fold
|
|
forecasted_best = forecasted_best_fold
|
|
fc = fc + 1
|
|
handles0, labels0 = ax0.get_legend_handles_labels()
|
|
ax0.legend(handles0, labels0)
|
|
ax1 = Axes3D(fig, rect=[0.7, 0.5, 0.3, 0.45], elev=30, azim=144)
|
|
#ax1 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
|
|
ax1.set_title('Comparação dos Erros Quadráticos Médios')
|
|
ax1.set_zlabel('RMSE')
|
|
ax1.set_xlabel('K-fold')
|
|
ax1.set_ylabel('Partições')
|
|
X,Y = np.meshgrid(np.arange(0,nfolds),parameters)
|
|
surf = ax1.plot_surface(X, Y, errors.T, rstride=1, cstride=1, antialiased=True)
|
|
ret.append(best)
|
|
ret.append(forecasted_best)
|
|
|
|
# Modelo diferencial
|
|
print("\nSérie Diferencial")
|
|
errors = np.array([[0 for k in parameters] for z in np.arange(0,nfolds)])
|
|
forecastedd_best = []
|
|
ax2 = fig.add_axes([0, 0, 0.65, 0.45]) #left, bottom, width, height
|
|
ax2.set_xlim([0,len(original)])
|
|
ax2.set_ylim([min(original),max(original)])
|
|
ax2.set_title('Série Temporal')
|
|
ax2.set_ylabel('F(T)')
|
|
ax2.set_xlabel('T')
|
|
ax2.plot(original,label="Original")
|
|
min_rmse = 100000.0
|
|
min_rmse_fold = 100000.0
|
|
bestd = None
|
|
fc = 0
|
|
diff = Transformations.differential(original)
|
|
kf = KFold(len(original), n_folds=nfolds)
|
|
for train_ix, test_ix in kf:
|
|
train = diff[train_ix]
|
|
test = diff[test_ix]
|
|
min_rmse = 100000.0
|
|
best_fold = None
|
|
forecasted_best_fold = []
|
|
errors_fold = []
|
|
pc = 0
|
|
for p in parameters:
|
|
sets = Grid.GridPartitionerTrimf(train,p)
|
|
fts = modelo(str(p)+ " particoes")
|
|
fts.train(train,sets)
|
|
forecasted = [fts.forecastDiff(test,xx) for xx in np.arange(len(test))]
|
|
error = Measures.rmse(np.array(forecasted),np.array(test))
|
|
print(fc, p,error)
|
|
errors[fc,pc] = error
|
|
errors_fold.append(error)
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
best_fold = fts
|
|
pc = pc + 1
|
|
forecasted_best_fold = [best_fold.forecastDiff(original, xx) for xx in np.arange(len(original))]
|
|
ax2.plot(forecasted_best_fold,label=best_fold.name)
|
|
if np.mean(errors_fold) < min_rmse_fold:
|
|
min_rmse_fold = np.mean(errors)
|
|
best = best_fold
|
|
forecasted_best = forecasted_best_fold
|
|
fc = fc + 1
|
|
handles0, labels0 = ax2.get_legend_handles_labels()
|
|
ax2.legend(handles0, labels0)
|
|
ax3 = Axes3D(fig, rect=[0.7, 0, 0.3, 0.45], elev=30, azim=144)
|
|
#ax1 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
|
|
ax3.set_title('Comparação dos Erros Quadráticos Médios')
|
|
ax3.set_zlabel('RMSE')
|
|
ax3.set_xlabel('K-fold')
|
|
ax3.set_ylabel('Partições')
|
|
X,Y = np.meshgrid(np.arange(0,nfolds),parameters)
|
|
surf = ax3.plot_surface(X, Y, errors.T, rstride=1, cstride=1, antialiased=True)
|
|
ret.append(best)
|
|
ret.append(forecasted_best)
|
|
return ret
|
|
|
|
def SelecaoSimples_MenorRMSE(original,parameters,modelo):
|
|
ret = []
|
|
errors = []
|
|
forecasted_best = []
|
|
print("Série Original")
|
|
fig = plt.figure(figsize=[20,12])
|
|
fig.suptitle("Comparação de modelos ")
|
|
ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) #left, bottom, width, height
|
|
ax0.set_xlim([0,len(original)])
|
|
ax0.set_ylim([min(original),max(original)])
|
|
ax0.set_title('Série Temporal')
|
|
ax0.set_ylabel('F(T)')
|
|
ax0.set_xlabel('T')
|
|
ax0.plot(original,label="Original")
|
|
min_rmse = 100000.0
|
|
best = None
|
|
for p in parameters:
|
|
sets = Grid.GridPartitionerTrimf(original,p)
|
|
fts = modelo(str(p)+ " particoes")
|
|
fts.train(original,sets)
|
|
#print(original)
|
|
forecasted = fts.forecast(original)
|
|
forecasted.insert(0,original[0])
|
|
#print(forecasted)
|
|
ax0.plot(forecasted,label=fts.name)
|
|
error = Measures.rmse(np.array(forecasted),np.array(original))
|
|
print(p,error)
|
|
errors.append(error)
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
best = fts
|
|
forecasted_best = forecasted
|
|
handles0, labels0 = ax0.get_legend_handles_labels()
|
|
ax0.legend(handles0, labels0)
|
|
ax1 = fig.add_axes([0.7, 0.5, 0.3, 0.45]) #left, bottom, width, height
|
|
ax1.set_title('Comparação dos Erros Quadráticos Médios')
|
|
ax1.set_ylabel('RMSE')
|
|
ax1.set_xlabel('Quantidade de Partições')
|
|
ax1.set_xlim([min(parameters),max(parameters)])
|
|
ax1.plot(parameters,errors)
|
|
ret.append(best)
|
|
ret.append(forecasted_best)
|
|
# Modelo diferencial
|
|
print("\nSérie Diferencial")
|
|
difffts = Transformations.differential(original)
|
|
errors = []
|
|
forecastedd_best = []
|
|
ax2 = fig.add_axes([0, 0, 0.65, 0.45]) #left, bottom, width, height
|
|
ax2.set_xlim([0,len(difffts)])
|
|
ax2.set_ylim([min(difffts),max(difffts)])
|
|
ax2.set_title('Série Temporal')
|
|
ax2.set_ylabel('F(T)')
|
|
ax2.set_xlabel('T')
|
|
ax2.plot(difffts,label="Original")
|
|
min_rmse = 100000.0
|
|
bestd = None
|
|
for p in parameters:
|
|
sets = Grid.GridPartitionerTrimf(difffts,p)
|
|
fts = modelo(str(p)+ " particoes")
|
|
fts.train(difffts,sets)
|
|
forecasted = fts.forecast(difffts)
|
|
forecasted.insert(0,difffts[0])
|
|
ax2.plot(forecasted,label=fts.name)
|
|
error = Measures.rmse(np.array(forecasted),np.array(difffts))
|
|
print(p,error)
|
|
errors.append(error)
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
bestd = fts
|
|
forecastedd_best = forecasted
|
|
handles0, labels0 = ax2.get_legend_handles_labels()
|
|
ax2.legend(handles0, labels0)
|
|
ax3 = fig.add_axes([0.7, 0, 0.3, 0.45]) #left, bottom, width, height
|
|
ax3.set_title('Comparação dos Erros Quadráticos Médios')
|
|
ax3.set_ylabel('RMSE')
|
|
ax3.set_xlabel('Quantidade de Partições')
|
|
ax3.set_xlim([min(parameters),max(parameters)])
|
|
ax3.plot(parameters,errors)
|
|
ret.append(bestd)
|
|
ret.append(forecastedd_best)
|
|
return ret
|
|
|
|
def compareModelsPlot(original,models_fo,models_ho):
|
|
fig = plt.figure(figsize=[13,6])
|
|
fig.suptitle("Comparação de modelos ")
|
|
ax0 = fig.add_axes([0, 0, 1, 1]) #left, bottom, width, height
|
|
rows = []
|
|
for model in models_fo:
|
|
fts = model["model"]
|
|
ax0.plot(model["forecasted"], label=model["name"])
|
|
for model in models_ho:
|
|
fts = model["model"]
|
|
ax0.plot(model["forecasted"], label=model["name"])
|
|
handles0, labels0 = ax0.get_legend_handles_labels()
|
|
ax0.legend(handles0, labels0)
|
|
|
|
def compareModelsTable(original,models_fo,models_ho):
|
|
fig = plt.figure(figsize=[12,4])
|
|
fig.suptitle("Comparação de modelos ")
|
|
columns = ['Modelo','Ordem','Partições','RMSE','MAPE (%)']
|
|
rows = []
|
|
for model in models_fo:
|
|
fts = model["model"]
|
|
error_r = Measures.rmse(model["forecasted"],original)
|
|
error_m = round(Measures.mape(model["forecasted"],original)*100,2)
|
|
rows.append([model["name"],fts.order,len(fts.sets),error_r,error_m])
|
|
for model in models_ho:
|
|
fts = model["model"]
|
|
error_r = Measures.rmse(model["forecasted"][fts.order:],original[fts.order:])
|
|
error_m = round(Measures.mape(model["forecasted"][fts.order:],original[fts.order:])*100,2)
|
|
rows.append([model["name"],fts.order,len(fts.sets),error_r,error_m])
|
|
ax1 = fig.add_axes([0, 0, 1, 1]) #left, bottom, width, height
|
|
ax1.set_xticks([])
|
|
ax1.set_yticks([])
|
|
ax1.table(cellText=rows,
|
|
colLabels=columns,
|
|
cellLoc='center',
|
|
bbox=[0,0,1,1])
|
|
sup = "\\begin{tabular}{"
|
|
header = ""
|
|
body = ""
|
|
footer = ""
|
|
|
|
for c in columns:
|
|
sup = sup + "|c"
|
|
if len(header) > 0:
|
|
header = header + " & "
|
|
header = header + "\\textbf{" + c + "} "
|
|
sup = sup + "|} \\hline\n"
|
|
header = header + "\\\\ \\hline \n"
|
|
|
|
for r in rows:
|
|
lin = ""
|
|
for c in r:
|
|
if len(lin) > 0:
|
|
lin = lin + " & "
|
|
lin = lin + str(c)
|
|
|
|
body = body + lin + "\\\\ \\hline \n"
|
|
|
|
return sup + header + body + "\\end{tabular}"
|
|
|
|
from pyFTS import hwang
|
|
|
|
def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
|
|
ret = []
|
|
errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
|
|
forecasted_best = []
|
|
print("Série Original")
|
|
fig = plt.figure(figsize=[20,12])
|
|
fig.suptitle("Comparação de modelos ")
|
|
ax0 = fig.add_axes([0, 0.5, 0.6, 0.45]) #left, bottom, width, height
|
|
ax0.set_xlim([0,len(original)])
|
|
ax0.set_ylim([min(original),max(original)])
|
|
ax0.set_title('Série Temporal')
|
|
ax0.set_ylabel('F(T)')
|
|
ax0.set_xlabel('T')
|
|
ax0.plot(original,label="Original")
|
|
min_rmse = 100000.0
|
|
best = None
|
|
pc = 0
|
|
for p in parameters:
|
|
oc = 0
|
|
for o in orders:
|
|
sets = Grid.GridPartitionerTrimf(original,p)
|
|
fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
|
|
fts.train(original,sets)
|
|
forecasted = [fts.forecast(original, xx) for xx in range(o,len(original))]
|
|
error = Measures.rmse(np.array(forecasted),np.array(original[o:]))
|
|
for kk in range(o):
|
|
forecasted.insert(0,None)
|
|
ax0.plot(forecasted,label=fts.name)
|
|
print(o,p,error)
|
|
errors[oc,pc] = error
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
best = fts
|
|
forecasted_best = forecasted
|
|
oc = oc + 1
|
|
pc = pc + 1
|
|
handles0, labels0 = ax0.get_legend_handles_labels()
|
|
ax0.legend(handles0, labels0)
|
|
ax1 = Axes3D(fig, rect=[0.6, 0.5, 0.45, 0.45], elev=30, azim=144)
|
|
#ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
|
|
ax1.set_title('Comparação dos Erros Quadráticos Médios por tamanho da janela')
|
|
ax1.set_ylabel('RMSE')
|
|
ax1.set_xlabel('Quantidade de Partições')
|
|
ax1.set_zlabel('W')
|
|
X,Y = np.meshgrid(parameters,orders)
|
|
surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
|
|
ret.append(best)
|
|
ret.append(forecasted_best)
|
|
|
|
# Modelo diferencial
|
|
print("\nSérie Diferencial")
|
|
errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
|
|
forecastedd_best = []
|
|
ax2 = fig.add_axes([0, 0, 0.6, 0.45]) #left, bottom, width, height
|
|
ax2.set_xlim([0,len(original)])
|
|
ax2.set_ylim([min(original),max(original)])
|
|
ax2.set_title('Série Temporal')
|
|
ax2.set_ylabel('F(T)')
|
|
ax2.set_xlabel('T')
|
|
ax2.plot(original,label="Original")
|
|
min_rmse = 100000.0
|
|
bestd = None
|
|
pc = 0
|
|
for p in parameters:
|
|
oc = 0
|
|
for o in orders:
|
|
sets = Grid.GridPartitionerTrimf(Transformations.differential(original),p)
|
|
fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
|
|
fts.train(original,sets)
|
|
forecasted = [fts.forecastDiff(original, xx) for xx in range(o,len(original))]
|
|
error = Measures.rmse(np.array(forecasted),np.array(original[o:]))
|
|
for kk in range(o):
|
|
forecasted.insert(0,None)
|
|
ax2.plot(forecasted,label=fts.name)
|
|
print(o,p,error)
|
|
errors[oc,pc] = error
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
bestd = fts
|
|
forecastedd_best = forecasted
|
|
oc = oc + 1
|
|
pc = pc + 1
|
|
handles0, labels0 = ax2.get_legend_handles_labels()
|
|
ax2.legend(handles0, labels0)
|
|
ax3 = Axes3D(fig, rect=[0.6, 0.0, 0.45, 0.45], elev=30, azim=144)
|
|
#ax3 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
|
|
ax3.set_title('Comparação dos Erros Quadráticos Médios')
|
|
ax3.set_ylabel('RMSE')
|
|
ax3.set_xlabel('Quantidade de Partições')
|
|
ax3.set_zlabel('W')
|
|
X,Y = np.meshgrid(parameters,orders)
|
|
surf = ax3.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
|
|
ret.append(bestd)
|
|
ret.append(forecastedd_best)
|
|
return ret
|