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Naïve forecaster; Theil's U Statistic in Measures

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Petrônio Cândido de Lima e Silva 2017-01-22 18:41:42 -02:00
parent 9a90c4d4e4
commit 72610e9dc3
3 changed files with 129 additions and 36 deletions
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12
benchmarks/Measures.py
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@ -24,6 +24,18 @@ def mape_interval(targets, forecasts):
return np.mean(abs(fmean - targets) / fmean) * 100
# Theil's U Statistic
def U(targets, forecasts):
#forecasts.insert(0,None)
l = len(targets)
naive = []
y = []
for k in np.arange(0,l-1):
y.append(((targets[k+1]-forecasts[k])/targets[k]) ** 2)
naive.append(((targets[k + 1] - targets[k]) / targets[k]) ** 2)
return np.sqrt(sum(y)/sum(naive))
# Sharpness - Mean size of the intervals
def sharpness(forecasts):
tmp = [i[1] - i[0] for i in forecasts]

137
benchmarks/benchmarks.py
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@ -7,10 +7,11 @@ 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
# from sklearn.cross_validation import KFold
from pyFTS.benchmarks import Measures
from pyFTS.partitioners import Grid
from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
from pyFTS import pfts
def getIntervalStatistics(original, models):
@ -35,20 +36,21 @@ def plotDistribution(dist):
vmin=0, vmax=1, edgecolors=None)
def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, file=None,tam=[20, 5],intervals=True):
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",linewidth=1.5)
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)*0.95)
ma.append(max(forecasted)*1.05)
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
@ -59,15 +61,15 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
forecasted = fts.forecastInterval(original)
lower = [kk[0] for kk in forecasted]
upper = [kk[1] for kk in forecasted]
mi.append(min(lower)*0.95)
ma.append(max(upper)*1.05)
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)
lbl = fts.shortname
if typeonlegend: lbl += " (Interval)"
ax.plot(lower, color=colors[count], label=lbl,ls="--")
ax.plot(upper, color=colors[count],ls="--")
ax.plot(lower, color=colors[count], label=lbl, ls="--")
ax.plot(upper, color=colors[count], ls="--")
handles0, labels0 = ax.get_legend_handles_labels()
ax.legend(handles0, labels0, loc=2)
@ -78,17 +80,15 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
ax.set_xlabel('T')
ax.set_xlim([0, len(original)])
Util.showAndSaveImage(fig,file,save)
Util.showAndSaveImage(fig, file, save)
def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to,
interpol=False, save=False, file=None,tam=[20, 5], resolution=None):
interpol=False, save=False, file=None, tam=[20, 5], resolution=None):
fig = plt.figure(figsize=tam)
ax = fig.add_subplot(111)
if resolution is None: resolution = (max(original) - min(original))/100
if resolution is None: resolution = (max(original) - min(original)) / 100
mi = []
ma = []
@ -96,7 +96,8 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
count = 0
for fts in models:
if fts.hasDistributionForecasting and distributions[count]:
density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to, resolution, parameters=None)
density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to, resolution,
parameters=None)
y = density.columns
t = len(y)
@ -106,13 +107,14 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
x = [time_from + k for x in np.arange(0, t)]
for cc in np.arange(0,resolution,5):
ax.scatter(x, y+cc, c=alpha, marker='s', linewidths=0, cmap='Oranges', edgecolors=None)
for cc in np.arange(0, resolution, 5):
ax.scatter(x, y + cc, c=alpha, marker='s', linewidths=0, cmap='Oranges', edgecolors=None)
if interpol and k < max(density.index):
diffs = [(density[q][k + 1] - density[q][k])/50 for q in density]
for p in np.arange(0,50):
xx = [time_from + k + 0.02*p for q in np.arange(0, t)]
alpha2 = np.array([density[density.columns[q]][k] + diffs[q]*p for q in np.arange(0, t)]) * 100
diffs = [(density[q][k + 1] - density[q][k]) / 50 for q in density]
for p in np.arange(0, 50):
xx = [time_from + k + 0.02 * p for q in np.arange(0, t)]
alpha2 = np.array(
[density[density.columns[q]][k] + diffs[q] * p for q in np.arange(0, t)]) * 100
ax.scatter(xx, y, c=alpha2, marker='s', linewidths=0, cmap='Oranges',
norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
@ -122,7 +124,7 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
upper = [kk[1] for kk in forecasts]
mi.append(min(lower))
ma.append(max(upper))
for k in np.arange(0, time_from-fts.order):
for k in np.arange(0, time_from - fts.order):
lower.insert(0, None)
upper.insert(0, None)
ax.plot(lower, color=colors[count], label=fts.shortname)
@ -430,16 +432,17 @@ def compareModelsTable(original, models_fo, models_ho):
return sup + header + body + "\\end{tabular}"
def simpleSearch_RMSE(original, model, partitions, orders, save=False, file=None,tam=[10, 15],plotforecasts=False,elev=30, azim=144):
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(partitions))] for kk in range(len(orders))])
forecasted_best = []
fig = plt.figure(figsize=tam)
#fig.suptitle("Comparação de modelos ")
# fig.suptitle("Comparação de modelos ")
if plotforecasts:
ax0 = fig.add_axes([0, 0.5, 0.9, 0.45]) # left, bottom, width, height
ax0 = fig.add_axes([0, 0.4, 0.9, 0.5]) # left, bottom, width, height
ax0.set_xlim([0, len(original)])
ax0.set_ylim([min(original)*0.9, max(original)*1.1])
ax0.set_ylim([min(original) * 0.9, max(original) * 1.1])
ax0.set_title('Forecasts')
ax0.set_ylabel('F(T)')
ax0.set_xlabel('T')
@ -453,14 +456,14 @@ def simpleSearch_RMSE(original, model, partitions, orders, save=False, file=None
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]))
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])
# print(original[o:])
# print(forecasted[-1])
for kk in range(o):
forecasted.insert(0, None)
if plotforecasts: ax0.plot(forecasted, label=fts.name)
#print(o, p, mape)
# print(o, p, mape)
errors[oc, pc] = error
if error < min_rmse:
min_rmse = error
@ -468,12 +471,12 @@ def simpleSearch_RMSE(original, model, partitions, orders, save=False, file=None
forecasted_best = forecasted
oc += 1
pc += 1
#print(min_rmse)
# print(min_rmse)
if plotforecasts:
#handles0, labels0 = ax0.get_legend_handles_labels()
#ax0.legend(handles0, labels0)
# 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)
ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], 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('Error Surface')
@ -485,6 +488,70 @@ def simpleSearch_RMSE(original, model, partitions, orders, save=False, file=None
ret.append(best)
ret.append(forecasted_best)
Util.showAndSaveImage(fig,file,save)
# plt.tight_layout()
Util.showAndSaveImage(fig, file, save)
return ret
def pftsExploreOrderAndPartitions(data,save=False, file=None):
fig, axes = plt.subplots(nrows=4, ncols=1, figsize=[6, 8])
data_fs1 = Grid.GridPartitionerTrimf(data, 10)
mi = []
ma = []
axes[0].set_title('Point Forecasts by Order')
axes[2].set_title('Interval Forecasts by Order')
for order in np.arange(1, 6):
fts = pfts.ProbabilisticFTS("")
fts.shortname = "n = " + str(order)
fts.train(data, data_fs1, order=order)
point_forecasts = fts.forecast(data)
interval_forecasts = fts.forecastInterval(data)
lower = [kk[0] for kk in interval_forecasts]
upper = [kk[1] for kk in interval_forecasts]
mi.append(min(lower) * 0.95)
ma.append(max(upper) * 1.05)
for k in np.arange(0, order):
point_forecasts.insert(0, None)
lower.insert(0, None)
upper.insert(0, None)
axes[0].plot(point_forecasts, label=fts.shortname)
axes[2].plot(lower, label=fts.shortname)
axes[2].plot(upper)
axes[1].set_title('Point Forecasts by Number of Partitions')
axes[3].set_title('Interval Forecasts by Number of Partitions')
for partitions in np.arange(5, 11):
data_fs = Grid.GridPartitionerTrimf(data, partitions)
fts = pfts.ProbabilisticFTS("")
fts.shortname = "q = " + str(partitions)
fts.train(data, data_fs, 1)
point_forecasts = fts.forecast(data)
interval_forecasts = fts.forecastInterval(data)
lower = [kk[0] for kk in interval_forecasts]
upper = [kk[1] for kk in interval_forecasts]
mi.append(min(lower) * 0.95)
ma.append(max(upper) * 1.05)
point_forecasts.insert(0, None)
lower.insert(0, None)
upper.insert(0, None)
axes[1].plot(point_forecasts, label=fts.shortname)
axes[3].plot(lower, label=fts.shortname)
axes[3].plot(upper)
for ax in axes:
ax.set_ylabel('F(T)')
ax.set_xlabel('T')
ax.plot(data, label="Original", color="black", linewidth=1.5)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1))
ax.set_ylim([min(mi), max(ma)])
ax.set_xlim([0, len(data)])
plt.tight_layout()
Util.showAndSaveImage(fig, file, save)

14
benchmarks/naive.py Normal file
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@ -0,0 +1,14 @@
#!/usr/bin/python
# -*- coding: utf8 -*-
from pyFTS import fts
class Naive(fts.FTS):
def __init__(self, name):
super(Naive, self).__init__(1, "Naïve" + name)
self.name = "Naïve Model"
self.detail = "Naïve Model"
def forecast(self, data):
return data