sam/pyFTS
1
0
Fork 0
Code Issues 3 Pull Requests Actions Packages Projects Releases Wiki Activity

Correções de bugs e pequenas otimizações diversas:

Browse Source 
- Otimização do GridPartitioner
 - Correção na geração de PFLRG's em PFTS
 - Métodos de __str__ mais intuitivos
This commit is contained in:
Petrônio Cândido de Lima e Silva 2017-01-10 18:05:51 -02:00
parent dba1919a18
commit ba1b4fbae6
10 changed files with 506 additions and 465 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

851
benchmarks/benchmarks.py
View File

@ -5,337 +5,345 @@ import matplotlib.colors as pltcolors
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D from mpl_toolkits.mplot3d import Axes3D
from sklearn.cross_validation import KFold from sklearn.cross_validation import KFold
import Measures from pyFTS.benchmarks import Measures
from pyFTS.partitioners import Grid from pyFTS.partitioners import Grid
from pyFTS.common import Membership,FuzzySet,FLR,Transformations from pyFTS.common import Membership, FuzzySet, FLR, Transformations
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 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): def plotDistribution(dist):
for k in dist.index: for k in dist.index:
alpha = np.array([dist[x][k] for x in dist])*100 alpha = np.array([dist[x][k] for x in dist]) * 100
x = [k for x in np.arange(0,len(alpha))] x = [k for x in np.arange(0, len(alpha))]
y = dist.columns 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) 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): def plotComparedSeries(original, models, colors):
fig = plt.figure(figsize=[25,10]) fig = plt.figure(figsize=[15, 5])
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
mi = [] mi = []
ma = [] ma = []
count = 0 ax.plot(original, color='black', label="Original")
for fts in models: count = 0
if fts.isDensity and distributions[count]: for fts in models:
density = fts.forecastDistributionAhead(original[:time_from],time_to,25) if fts.hasPointForecasting:
for k in density.index: forecasted = fts.forecast(original)
alpha = np.array([density[x][k] for x in density])*100 mi.append(min(forecasted))
x = [time_from + fts.order + k for x in np.arange(0,len(alpha))] ma.append(max(forecasted))
y = density.columns for k in np.arange(0, fts.order):
ax.scatter(x,y,c=alpha,marker='s',linewidths=0,cmap='Oranges', forecasted.insert(0, None)
norm=pltcolors.Normalize(vmin=0,vmax=1),vmin=0,vmax=1,edgecolors=None) ax.plot(forecasted, color=colors[count], label=fts.shortname, ls="-")
if fts.isInterval: if fts.hasIntervalForecasting:
forecasts = fts.forecastAhead(original[:time_from],time_to) forecasted = fts.forecastInterval(original)
lower = [kk[0] for kk in forecasts] lower = [kk[0] for kk in forecasted]
upper = [kk[1] for kk in forecasts] upper = [kk[1] for kk in forecasted]
mi.append(min(lower)) mi.append(min(lower))
ma.append(max(upper)) ma.append(max(upper))
for k in np.arange(0,time_from): for k in np.arange(0, fts.order):
lower.insert(0,None) lower.insert(0, None)
upper.insert(0,None) upper.insert(0, None)
ax.plot(lower,color=colors[count],label=fts.shortname) ax.plot(lower, color=colors[count], label=fts.shortname,ls="--")
ax.plot(upper,color=colors[count]) ax.plot(upper, color=colors[count],ls="--")
else: handles0, labels0 = ax.get_legend_handles_labels()
forecasts = fts.forecast(original) ax.legend(handles0, labels0, loc=2)
mi.append(min(forecasts)) count = count + 1
ma.append(max(forecasts)) # ax.set_title(fts.name)
for k in np.arange(0,time_from): ax.set_ylim([min(mi), max(ma)])
forecasts.insert(0,None) ax.set_ylabel('F(T)')
ax.plot(forecasts,color=colors[count],label=fts.shortname) ax.set_xlabel('T')
ax.set_xlim([0, len(original)])
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): def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to):
fig = plt.figure(figsize=[13,6]) fig = plt.figure(figsize=[25, 10])
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
ax.plot(original,color='k',label="Original")
for c in range(0,len(forecasts)):
ax.plot(forecasts[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): mi = []
nfolds = 5 ma = []
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 count = 0
print("\nSérie Diferencial") for fts in models:
errors = np.array([[0 for k in parameters] for z in np.arange(0,nfolds)]) if fts.hasDistributionForecasting and distributions[count]:
forecastedd_best = [] density = fts.forecastDistributionAhead(original[:time_from], time_to, 25)
ax2 = fig.add_axes([0, 0, 0.65, 0.45]) #left, bottom, width, height for k in density.index:
ax2.set_xlim([0,len(original)]) alpha = np.array([density[x][k] for x in density]) * 100
ax2.set_ylim([min(original),max(original)]) x = [time_from + fts.order + k for x in np.arange(0, len(alpha))]
ax2.set_title('Série Temporal') y = density.columns
ax2.set_ylabel('F(T)') ax.scatter(x, y, c=alpha, marker='s', linewidths=0, cmap='Oranges',
ax2.set_xlabel('T') norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
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): if fts.hasIntervalForecasting:
ret = [] forecasts = fts.forecastAhead(original[:time_from], time_to)
errors = [] lower = [kk[0] for kk in forecasts]
forecasted_best = [] upper = [kk[1] for kk in forecasts]
print("Série Original") mi.append(min(lower))
fig = plt.figure(figsize=[20,12]) ma.append(max(upper))
fig.suptitle("Comparação de modelos ") for k in np.arange(0, time_from):
ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) #left, bottom, width, height lower.insert(0, None)
ax0.set_xlim([0,len(original)]) upper.insert(0, None)
ax0.set_ylim([min(original),max(original)]) ax.plot(lower, color=colors[count], label=fts.shortname)
ax0.set_title('Série Temporal') ax.plot(upper, color=colors[count])
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): else:
fig = plt.figure(figsize=[13,6]) 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(forecasts)):
ax.plot(forecasts[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 ") fig.suptitle("Comparação de modelos ")
ax0 = fig.add_axes([0, 0, 1, 1]) #left, bottom, width, height 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 = [] rows = []
for model in models_fo: for model in models_fo:
fts = model["model"] fts = model["model"]
@ -346,28 +354,29 @@ def compareModelsPlot(original,models_fo,models_ho):
handles0, labels0 = ax0.get_legend_handles_labels() handles0, labels0 = ax0.get_legend_handles_labels()
ax0.legend(handles0, labels0) ax0.legend(handles0, labels0)
def compareModelsTable(original,models_fo,models_ho):
fig = plt.figure(figsize=[12,4]) def compareModelsTable(original, models_fo, models_ho):
fig = plt.figure(figsize=[12, 4])
fig.suptitle("Comparação de modelos ") fig.suptitle("Comparação de modelos ")
columns = ['Modelo','Ordem','Partições','RMSE','MAPE (%)'] columns = ['Modelo', 'Ordem', 'Partições', 'RMSE', 'MAPE (%)']
rows = [] rows = []
for model in models_fo: for model in models_fo:
fts = model["model"] fts = model["model"]
error_r = Measures.rmse(model["forecasted"],original) error_r = Measures.rmse(model["forecasted"], original)
error_m = round(Measures.mape(model["forecasted"],original)*100,2) error_m = round(Measures.mape(model["forecasted"], original) * 100, 2)
rows.append([model["name"],fts.order,len(fts.sets),error_r,error_m]) rows.append([model["name"], fts.order, len(fts.sets), error_r, error_m])
for model in models_ho: for model in models_ho:
fts = model["model"] fts = model["model"]
error_r = Measures.rmse(model["forecasted"][fts.order:],original[fts.order:]) 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) 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]) 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 = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height
ax1.set_xticks([]) ax1.set_xticks([])
ax1.set_yticks([]) ax1.set_yticks([])
ax1.table(cellText=rows, ax1.table(cellText=rows,
colLabels=columns, colLabels=columns,
cellLoc='center', cellLoc='center',
bbox=[0,0,1,1]) bbox=[0, 0, 1, 1])
sup = "\\begin{tabular}{" sup = "\\begin{tabular}{"
header = "" header = ""
body = "" body = ""
@ -392,100 +401,102 @@ def compareModelsTable(original,models_fo,models_ho):
return sup + header + body + "\\end{tabular}" return sup + header + body + "\\end{tabular}"
from pyFTS import hwang from pyFTS import hwang
def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
ret = [] def HOSelecaoSimples_MenorRMSE(original, parameters, orders):
errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))]) ret = []
forecasted_best = [] errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
print("Série Original") forecasted_best = []
fig = plt.figure(figsize=[20,12]) print("Série Original")
fig.suptitle("Comparação de modelos ") fig = plt.figure(figsize=[20, 12])
ax0 = fig.add_axes([0, 0.5, 0.6, 0.45]) #left, bottom, width, height fig.suptitle("Comparação de modelos ")
ax0.set_xlim([0,len(original)]) ax0 = fig.add_axes([0, 0.5, 0.6, 0.45]) # left, bottom, width, height
ax0.set_ylim([min(original),max(original)]) ax0.set_xlim([0, len(original)])
ax0.set_title('Série Temporal') ax0.set_ylim([min(original), max(original)])
ax0.set_ylabel('F(T)') ax0.set_title('Série Temporal')
ax0.set_xlabel('T') ax0.set_ylabel('F(T)')
ax0.plot(original,label="Original") ax0.set_xlabel('T')
min_rmse = 100000.0 ax0.plot(original, label="Original")
best = None min_rmse = 100000.0
pc = 0 best = None
for p in parameters: pc = 0
oc = 0 for p in parameters:
for o in orders: oc = 0
sets = Grid.GridPartitionerTrimf(original,p) for o in orders:
fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o)) sets = Grid.GridPartitionerTrimf(original, p)
fts.train(original,sets) fts = hwang.HighOrderFTS(o, "k = " + str(p) + " w = " + str(o))
forecasted = [fts.forecast(original, xx) for xx in range(o,len(original))] fts.train(original, sets)
error = Measures.rmse(np.array(forecasted),np.array(original[o:])) forecasted = [fts.forecast(original, xx) for xx in range(o, len(original))]
for kk in range(o): error = Measures.rmse(np.array(forecasted), np.array(original[o:]))
forecasted.insert(0,None) for kk in range(o):
ax0.plot(forecasted,label=fts.name) forecasted.insert(0, None)
print(o,p,error) ax0.plot(forecasted, label=fts.name)
errors[oc,pc] = error print(o, p, error)
if error < min_rmse: errors[oc, pc] = error
min_rmse = error if error < min_rmse:
best = fts min_rmse = error
forecasted_best = forecasted best = fts
oc = oc + 1 forecasted_best = forecasted
pc = pc + 1 oc = oc + 1
handles0, labels0 = ax0.get_legend_handles_labels() pc = pc + 1
ax0.legend(handles0, labels0) handles0, labels0 = ax0.get_legend_handles_labels()
ax1 = Axes3D(fig, rect=[0.6, 0.5, 0.45, 0.45], elev=30, azim=144) ax0.legend(handles0, labels0)
#ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d') ax1 = Axes3D(fig, rect=[0.6, 0.5, 0.45, 0.45], elev=30, azim=144)
ax1.set_title('Comparação dos Erros Quadráticos Médios por tamanho da janela') # ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
ax1.set_ylabel('RMSE') ax1.set_title('Comparação dos Erros Quadráticos Médios por tamanho da janela')
ax1.set_xlabel('Quantidade de Partições') ax1.set_ylabel('RMSE')
ax1.set_zlabel('W') ax1.set_xlabel('Quantidade de Partições')
X,Y = np.meshgrid(parameters,orders) ax1.set_zlabel('W')
surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True) X, Y = np.meshgrid(parameters, orders)
ret.append(best) surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
ret.append(forecasted_best) ret.append(best)
ret.append(forecasted_best)
# Modelo diferencial # Modelo diferencial
print("\nSérie Diferencial") print("\nSérie Diferencial")
errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))]) errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
forecastedd_best = [] forecastedd_best = []
ax2 = fig.add_axes([0, 0, 0.6, 0.45]) #left, bottom, width, height ax2 = fig.add_axes([0, 0, 0.6, 0.45]) # left, bottom, width, height
ax2.set_xlim([0,len(original)]) ax2.set_xlim([0, len(original)])
ax2.set_ylim([min(original),max(original)]) ax2.set_ylim([min(original), max(original)])
ax2.set_title('Série Temporal') ax2.set_title('Série Temporal')
ax2.set_ylabel('F(T)') ax2.set_ylabel('F(T)')
ax2.set_xlabel('T') ax2.set_xlabel('T')
ax2.plot(original,label="Original") ax2.plot(original, label="Original")
min_rmse = 100000.0 min_rmse = 100000.0
bestd = None bestd = None
pc = 0 pc = 0
for p in parameters: for p in parameters:
oc = 0 oc = 0
for o in orders: for o in orders:
sets = Grid.GridPartitionerTrimf(Transformations.differential(original),p) sets = Grid.GridPartitionerTrimf(Transformations.differential(original), p)
fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o)) fts = hwang.HighOrderFTS(o, "k = " + str(p) + " w = " + str(o))
fts.train(original,sets) fts.train(original, sets)
forecasted = [fts.forecastDiff(original, xx) for xx in range(o,len(original))] forecasted = [fts.forecastDiff(original, xx) for xx in range(o, len(original))]
error = Measures.rmse(np.array(forecasted),np.array(original[o:])) error = Measures.rmse(np.array(forecasted), np.array(original[o:]))
for kk in range(o): for kk in range(o):
forecasted.insert(0,None) forecasted.insert(0, None)
ax2.plot(forecasted,label=fts.name) ax2.plot(forecasted, label=fts.name)
print(o,p,error) print(o, p, error)
errors[oc,pc] = error errors[oc, pc] = error
if error < min_rmse: if error < min_rmse:
min_rmse = error min_rmse = error
bestd = fts bestd = fts
forecastedd_best = forecasted forecastedd_best = forecasted
oc = oc + 1 oc = oc + 1
pc = pc + 1 pc = pc + 1
handles0, labels0 = ax2.get_legend_handles_labels() handles0, labels0 = ax2.get_legend_handles_labels()
ax2.legend(handles0, labels0) ax2.legend(handles0, labels0)
ax3 = Axes3D(fig, rect=[0.6, 0.0, 0.45, 0.45], elev=30, azim=144) 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 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
ax3.set_title('Comparação dos Erros Quadráticos Médios') ax3.set_title('Comparação dos Erros Quadráticos Médios')
ax3.set_ylabel('RMSE') ax3.set_ylabel('RMSE')
ax3.set_xlabel('Quantidade de Partições') ax3.set_xlabel('Quantidade de Partições')
ax3.set_zlabel('W') ax3.set_zlabel('W')
X,Y = np.meshgrid(parameters,orders) X, Y = np.meshgrid(parameters, orders)
surf = ax3.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True) surf = ax3.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
ret.append(bestd) ret.append(bestd)
ret.append(forecastedd_best) ret.append(forecastedd_best)
return ret return ret

6
common/FLR.py
View File

@ -1,10 +1,14 @@
import numpy as np
class FLR: class FLR:
def __init__(self, LHS, RHS): def __init__(self, LHS, RHS):
self.LHS = LHS self.LHS = LHS
self.RHS = RHS self.RHS = RHS
def __str__(self): def __str__(self):
return str(self.LHS) + " -> " + str(self.RHS) return self.LHS.name + " -> " + self.RHS.name
def generateNonRecurrentFLRs(fuzzyData): def generateNonRecurrentFLRs(fuzzyData):
flrs = {} flrs = {}

2
common/FuzzySet.py
View File

@ -20,7 +20,7 @@ class FuzzySet:
return self.mf(x, self.parameters) return self.mf(x, self.parameters)
def __str__(self): def __str__(self):
return self.name + ": " + str(self.mf) + "(" + str(self.parameters) + ")" return self.name + ": " + str(self.mf.__name__) + "(" + str(self.parameters) + ")"
def fuzzyInstance(inst, fuzzySets): def fuzzyInstance(inst, fuzzySets):

23
fts.py
View File

@ -10,9 +10,11 @@ class FTS:
self.shortname = name self.shortname = name
self.name = name self.name = name
self.detail = name self.detail = name
self.isSeasonal = False self.hasSeasonality = False
self.isInterval = False self.hasPointForecasting = True
self.isDensity = False self.hasIntervalForecasting = False
self.hasDistributionForecasting = False
self.dump = False
def fuzzy(self, data): def fuzzy(self, data):
best = {"fuzzyset": "", "membership": 0.0} best = {"fuzzyset": "", "membership": 0.0}
@ -28,6 +30,21 @@ class FTS:
def forecast(self, data): def forecast(self, data):
pass pass
def forecastInterval(self, data):
pass
def forecastDistribution(self, data):
pass
def forecastAhead(self, data, steps):
pass
def forecastAheadInterval(self, data, steps):
pass
def forecastAheadDistribution(self, data, steps):
pass
def train(self, data, sets): def train(self, data, sets):
pass pass

6
hofts.py
View File

@ -1,6 +1,6 @@
import numpy as np import numpy as np
from pyFTS.common import FuzzySet,FLR from pyFTS.common import FuzzySet,FLR
import fts from pyFTS import fts
class HighOrderFLRG: class HighOrderFLRG:
@ -18,7 +18,7 @@ class HighOrderFLRG:
if len(self.strlhs) == 0: if len(self.strlhs) == 0:
for c in self.LHS: for c in self.LHS:
if len(self.strlhs) > 0: if len(self.strlhs) > 0:
self.strlhs = self.strlhs + ", " self.strlhs += ", "
self.strlhs = self.strlhs + c.name self.strlhs = self.strlhs + c.name
return self.strlhs return self.strlhs
@ -63,7 +63,7 @@ class HighOrderFTS(fts.FTS):
self.sets = sets self.sets = sets
for s in self.sets: self.setsDict[s.name] = s for s in self.sets: self.setsDict[s.name] = s
tmpdata = FuzzySet.fuzzySeries(data, sets) tmpdata = FuzzySet.fuzzySeries(data, sets)
flrs = FuzzySet.generateRecurrentFLRs(tmpdata) flrs = FLR.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs) self.flrgs = self.generateFLRG(flrs)
def getMidpoints(self, flrg): def getMidpoints(self, flrg):

2
hwang.py
View File

@ -1,6 +1,6 @@
import numpy as np import numpy as np
from pyFTS.common import FuzzySet,FLR,Transformations from pyFTS.common import FuzzySet,FLR,Transformations
import fts from pyFTS import fts
class HighOrderFTS(fts.FTS): class HighOrderFTS(fts.FTS):

5
ifts.py
View File

@ -1,6 +1,6 @@
import numpy as np import numpy as np
from pyFTS.common import FuzzySet,FLR from pyFTS.common import FuzzySet,FLR
import hofts, fts, tree from pyFTS import hofts, fts, tree
class IntervalFTS(hofts.HighOrderFTS): class IntervalFTS(hofts.HighOrderFTS):
@ -10,7 +10,8 @@ class IntervalFTS(hofts.HighOrderFTS):
self.name = "Interval FTS" self.name = "Interval FTS"
self.detail = "Silva, P.; Guimarães, F.; Sadaei, H. (2016)" self.detail = "Silva, P.; Guimarães, F.; Sadaei, H. (2016)"
self.flrgs = {} self.flrgs = {}
self.isInterval = True self.hasPointForecasting = False
self.hasIntervalForecasting = True
def getUpper(self, flrg): def getUpper(self, flrg):
if flrg.strLHS() in self.flrgs: if flrg.strLHS() in self.flrgs:

18
partitioners/Grid.py
View File

@ -10,17 +10,21 @@ from pyFTS.common import FuzzySet, Membership
def GridPartitionerTrimf(data, npart, names=None, prefix="A"): def GridPartitionerTrimf(data, npart, names=None, prefix="A"):
sets = [] sets = []
dmax = max(data) dmax = max(data)
dmax += dmax * 0.10 dmax += dmax * 0.1
print(dmax)
dmin = min(data) dmin = min(data)
dmin -= dmin * 0.10 dmin -= dmin * 0.1
print(dmin)
dlen = dmax - dmin dlen = dmax - dmin
partlen = math.ceil(dlen / npart) partlen = math.ceil(dlen / npart)
partition = math.ceil(dmin) #p2 = partlen / 2
for c in range(npart): #partition = dmin #+ partlen
count = 0
for c in np.arange(dmin, dmax, partlen):
sets.append( sets.append(
FuzzySet.FuzzySet(prefix + str(c), Membership.trimf, [round(partition - partlen, 3), partition, partition + partlen], FuzzySet.FuzzySet(prefix + str(count), Membership.trimf, [c - partlen, c, c + partlen],c))
partition)) count += 1
partition += partlen #partition += partlen
return sets return sets

28
pfts.py
View File

@ -2,21 +2,21 @@ import numpy as np
import pandas as pd import pandas as pd
import math import math
from pyFTS.common import FuzzySet, FLR from pyFTS.common import FuzzySet, FLR
import hofts, ifts, tree from pyFTS import hofts, ifts, tree
class ProbabilisticFLRG(hofts.HighOrderFLRG): class ProbabilisticFLRG(hofts.HighOrderFLRG):
def __init__(self, order): def __init__(self, order):
super(ProbabilisticFLRG, self).__init__(order) super(ProbabilisticFLRG, self).__init__(order)
self.RHS = {} self.RHS = {}
self.frequencyCount = 0 self.frequencyCount = 0.0
def appendRHS(self, c): def appendRHS(self, c):
self.frequencyCount = self.frequencyCount + 1 self.frequencyCount += 1
if c.name in self.RHS: if c.name in self.RHS:
self.RHS[c.name] = self.RHS[c.name] + 1 self.RHS[c.name] += 1
else: else:
self.RHS[c.name] = 1 self.RHS[c.name] = 1.0
def getProbability(self, c): def getProbability(self, c):
return self.RHS[c] / self.frequencyCount return self.RHS[c] / self.frequencyCount
@ -38,23 +38,27 @@ class ProbabilisticFTS(ifts.IntervalFTS):
self.detail = "Silva, P.; Guimarães, F.; Sadaei, H." self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
self.flrgs = {} self.flrgs = {}
self.globalFrequency = 0 self.globalFrequency = 0
self.isInterval = True self.hasPointForecasting = True
self.isDensity = True self.hasIntervalForecasting = True
self.hasDistributionForecasting = True
def generateFLRG(self, flrs): def generateFLRG(self, flrs):
flrgs = {} flrgs = {}
l = len(flrs) l = len(flrs)
for k in np.arange(self.order + 1, l): for k in np.arange(self.order, l+1):
if self.dump: print("FLR: " + str(k))
flrg = ProbabilisticFLRG(self.order) flrg = ProbabilisticFLRG(self.order)
for kk in np.arange(k - self.order, k): for kk in np.arange(k - self.order, k):
flrg.appendLHS(flrs[kk].LHS) flrg.appendLHS(flrs[kk].LHS)
if self.dump: print("LHS: " + str(flrs[kk]))
if flrg.strLHS() in flrgs: if flrg.strLHS() in flrgs:
flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS) flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
else: else:
flrgs[flrg.strLHS()] = flrg; flrgs[flrg.strLHS()] = flrg;
flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS) flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
if self.dump: print("RHS: " + str(flrs[k-1]))
self.globalFrequency = self.globalFrequency + 1 self.globalFrequency = self.globalFrequency + 1
return (flrgs) return (flrgs)
@ -68,9 +72,9 @@ class ProbabilisticFTS(ifts.IntervalFTS):
def getMidpoints(self, flrg): def getMidpoints(self, flrg):
if flrg.strLHS() in self.flrgs: if flrg.strLHS() in self.flrgs:
tmp = self.flrgs[flrg.strLHS()] tmp = self.flrgs[flrg.strLHS()]
ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].midpoint for s in tmp.RHS])) ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
else: else:
ret = sum(np.array([0.33 * s.midpoint for s in flrg.LHS])) ret = sum(np.array([0.33 * s.centroid for s in flrg.LHS]))
return ret return ret
def getUpper(self, flrg): def getUpper(self, flrg):

2
sfts.py
View File

@ -27,7 +27,7 @@ class SeasonalFTS(fts.FTS):
self.name = "Seasonal FTS" self.name = "Seasonal FTS"
self.detail = "Chen" self.detail = "Chen"
self.seasonality = 1 self.seasonality = 1
self.isSeasonal = True self.hasSeasonality = True
def generateFLRG(self, flrs): def generateFLRG(self, flrs):
flrgs = [] flrgs = []