Novos benchmarks
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
3cc0e0ebda
commit
9a90c4d4e4
@ -35,32 +35,32 @@ def plotDistribution(dist):
|
||||
vmin=0, vmax=1, edgecolors=None)
|
||||
|
||||
|
||||
def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, file=None,tam=[20, 5]):
|
||||
def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, file=None,tam=[20, 5],intervals=True):
|
||||
fig = plt.figure(figsize=tam)
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
mi = []
|
||||
ma = []
|
||||
|
||||
ax.plot(original, color='black', label="Original")
|
||||
ax.plot(original, color='black', label="Original",linewidth=1.5)
|
||||
count = 0
|
||||
for fts in models:
|
||||
if fts.hasPointForecasting:
|
||||
forecasted = fts.forecast(original)
|
||||
mi.append(min(forecasted))
|
||||
ma.append(max(forecasted))
|
||||
mi.append(min(forecasted)*0.95)
|
||||
ma.append(max(forecasted)*1.05)
|
||||
for k in np.arange(0, fts.order):
|
||||
forecasted.insert(0, None)
|
||||
lbl = fts.shortname
|
||||
if typeonlegend: lbl += " (Point)"
|
||||
ax.plot(forecasted, color=colors[count], label=lbl, ls="-")
|
||||
|
||||
if fts.hasIntervalForecasting:
|
||||
if fts.hasIntervalForecasting and intervals:
|
||||
forecasted = fts.forecastInterval(original)
|
||||
lower = [kk[0] for kk in forecasted]
|
||||
upper = [kk[1] for kk in forecasted]
|
||||
mi.append(min(lower))
|
||||
ma.append(max(upper))
|
||||
mi.append(min(lower)*0.95)
|
||||
ma.append(max(upper)*1.05)
|
||||
for k in np.arange(0, fts.order):
|
||||
lower.insert(0, None)
|
||||
upper.insert(0, None)
|
||||
@ -430,101 +430,61 @@ def compareModelsTable(original, models_fo, models_ho):
|
||||
return sup + header + body + "\\end{tabular}"
|
||||
|
||||
|
||||
from pyFTS import hwang
|
||||
|
||||
|
||||
def HOSelecaoSimples_MenorRMSE(original, parameters, orders):
|
||||
def simpleSearch_RMSE(original, model, partitions, orders, save=False, file=None,tam=[10, 15],plotforecasts=False,elev=30, azim=144):
|
||||
ret = []
|
||||
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(partitions))] 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
|
||||
fig = plt.figure(figsize=tam)
|
||||
#fig.suptitle("Comparação de modelos ")
|
||||
if plotforecasts:
|
||||
ax0 = fig.add_axes([0, 0.5, 0.9, 0.45]) # left, bottom, width, height
|
||||
ax0.set_xlim([0, len(original)])
|
||||
ax0.set_ylim([min(original)*0.9, max(original)*1.1])
|
||||
ax0.set_title('Forecasts')
|
||||
ax0.set_ylabel('F(T)')
|
||||
ax0.set_xlabel('T')
|
||||
min_rmse = 1000000.0
|
||||
best = None
|
||||
pc = 0
|
||||
for p in parameters:
|
||||
for p in partitions:
|
||||
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:]))
|
||||
fts = model("q = " + str(p) + " n = " + str(o))
|
||||
fts.train(original, sets, o)
|
||||
forecasted = fts.forecast(original)
|
||||
error = Measures.rmse(np.array(original[o:]),np.array(forecasted[:-1]))
|
||||
mape = Measures.mape(np.array(original[o:]), np.array(forecasted[:-1]))
|
||||
#print(original[o:])
|
||||
#print(forecasted[-1])
|
||||
for kk in range(o):
|
||||
forecasted.insert(0, None)
|
||||
ax0.plot(forecasted, label=fts.name)
|
||||
print(o, p, error)
|
||||
if plotforecasts: ax0.plot(forecasted, label=fts.name)
|
||||
#print(o, p, mape)
|
||||
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)
|
||||
oc += 1
|
||||
pc += 1
|
||||
#print(min_rmse)
|
||||
if plotforecasts:
|
||||
#handles0, labels0 = ax0.get_legend_handles_labels()
|
||||
#ax0.legend(handles0, labels0)
|
||||
ax0.plot(original, label="Original", linewidth=3.0, color="black")
|
||||
ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.45], elev=elev, azim=azim)
|
||||
if not plotforecasts: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
|
||||
# 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)
|
||||
ax1.set_title('Error Surface')
|
||||
ax1.set_ylabel('Model order')
|
||||
ax1.set_xlabel('Number of partitions')
|
||||
ax1.set_zlabel('RMSE')
|
||||
X, Y = np.meshgrid(partitions, 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)
|
||||
Util.showAndSaveImage(fig,file,save)
|
||||
|
||||
return ret
|
||||
|
||||
2
fts.py
2
fts.py
@ -45,7 +45,7 @@ class FTS:
|
||||
def forecastAheadDistribution(self, data, steps):
|
||||
pass
|
||||
|
||||
def train(self, data, sets):
|
||||
def train(self, data, sets, order=1):
|
||||
pass
|
||||
|
||||
def getMidpoints(self, flrg):
|
||||
|
||||
@ -9,21 +9,26 @@ from pyFTS.common import Membership, Util
|
||||
|
||||
def plotSets(data, sets, titles, tam=[12, 10], save=False, file=None):
|
||||
num = len(sets)
|
||||
fig = plt.figure(figsize=tam)
|
||||
#fig = plt.figure(figsize=tam)
|
||||
maxx = max(data)
|
||||
minx = min(data)
|
||||
h = 1/num
|
||||
for k in range(num):
|
||||
ax0 = fig.add_axes([0, (k+1)*h, 0.65, h*0.7]) # left, bottom, width, height
|
||||
ax0.set_title(titles[k])
|
||||
ax0.set_ylim([0, 1])
|
||||
ax0.set_xlim([minx, maxx])
|
||||
#h = 1/num
|
||||
#print(h)
|
||||
fig, axes = plt.subplots(nrows=num, ncols=1,figsize=tam)
|
||||
for k in np.arange(0,num):
|
||||
#ax = fig.add_axes([0.05, 1-(k*h), 0.9, h*0.7]) # left, bottom, width, height
|
||||
ax = axes[k]
|
||||
ax.set_title(titles[k])
|
||||
ax.set_ylim([0, 1])
|
||||
ax.set_xlim([minx, maxx])
|
||||
for s in sets[k]:
|
||||
if s.mf == Membership.trimf:
|
||||
ax0.plot([s.parameters[0],s.parameters[1],s.parameters[2]],[0,1,0])
|
||||
ax.plot([s.parameters[0],s.parameters[1],s.parameters[2]],[0,1,0])
|
||||
elif s.mf == Membership.gaussmf:
|
||||
tmpx = [ kk for kk in np.arange(s.lower, s.upper)]
|
||||
tmpy = [s.membership(kk) for kk in np.arange(s.lower, s.upper)]
|
||||
ax0.plot(tmpx, tmpy)
|
||||
ax.plot(tmpx, tmpy)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
Util.showAndSaveImage(fig, file, save)
|
||||
4
pfts.py
4
pfts.py
@ -37,8 +37,8 @@ class ProbabilisticFLRG(hofts.HighOrderFLRG):
|
||||
|
||||
class ProbabilisticFTS(ifts.IntervalFTS):
|
||||
def __init__(self, name):
|
||||
super(ProbabilisticFTS, self).__init__("PIFTS")
|
||||
self.shortname = "PIFTS " + name
|
||||
super(ProbabilisticFTS, self).__init__("PFTS")
|
||||
self.shortname = "PFTS " + name
|
||||
self.name = "Probabilistic FTS"
|
||||
self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
|
||||
self.flrgs = {}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user