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Bugfixes and improvements on benchmark methods

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This commit is contained in:
Petrônio Cândido 2019-05-30 10:07:32 -03:00
parent ac71ee34c6
commit 985e91f2ee
5 changed files with 95 additions and 17 deletions
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1
pyFTS/benchmarks/BSTS.py
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@ -21,6 +21,7 @@ class ARIMA(fts.FTS):
self.has_point_forecasting = True
self.has_interval_forecasting = True
self.has_probability_forecasting = True
self.uod_clip = False
self.model = None
self.model_fit = None
self.trained_data = None

22
pyFTS/benchmarks/arima.py
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@ -21,6 +21,7 @@ class ARIMA(fts.FTS):
self.has_point_forecasting = True
self.has_interval_forecasting = True
self.has_probability_forecasting = True
self.uod_clip = False
self.model = None
self.model_fit = None
self.trained_data = None
@ -96,6 +97,11 @@ class ARIMA(fts.FTS):
if self.model_fit is None:
return np.nan
if 'alpha' in kwargs:
alpha = kwargs.get('alpha',0.05)
else:
alpha = self.alpha
sigma = np.sqrt(self.model_fit.sigma2)
l = len(data)
@ -112,8 +118,8 @@ class ARIMA(fts.FTS):
if isinstance(mean,(list, np.ndarray)):
mean = mean[0]
tmp.append(mean + st.norm.ppf(self.alpha) * sigma)
tmp.append(mean + st.norm.ppf(1 - self.alpha) * sigma)
tmp.append(mean + st.norm.ppf(alpha) * sigma)
tmp.append(mean + st.norm.ppf(1 - alpha) * sigma)
ret.append(tmp)
@ -123,6 +129,11 @@ class ARIMA(fts.FTS):
if self.model_fit is None:
return np.nan
if 'alpha' in kwargs:
alpha = kwargs.get('alpha',0.05)
else:
alpha = self.alpha
smoothing = kwargs.get("smoothing",0.5)
sigma = np.sqrt(self.model_fit.sigma2)
@ -138,8 +149,8 @@ class ARIMA(fts.FTS):
hsigma = (1 + k*smoothing)*sigma
tmp.append(nmeans[k] + st.norm.ppf(self.alpha) * hsigma)
tmp.append(nmeans[k] + st.norm.ppf(1 - self.alpha) * hsigma)
tmp.append(nmeans[k] + st.norm.ppf(alpha) * hsigma)
tmp.append(nmeans[k] + st.norm.ppf(1 - alpha) * hsigma)
ret.append(tmp)
@ -206,4 +217,5 @@ class ARIMA(fts.FTS):
ret.append(dist)
return ret
return ret

12
pyFTS/benchmarks/quantreg.py
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@ -19,6 +19,7 @@ class QuantileRegression(fts.FTS):
self.has_point_forecasting = True
self.has_interval_forecasting = True
self.has_probability_forecasting = True
self.uod_clip = False
self.benchmark_only = True
self.min_order = 1
self.alpha = kwargs.get("alpha", 0.05)
@ -96,7 +97,7 @@ class QuantileRegression(fts.FTS):
def forecast_ahead_interval(self, ndata, steps, **kwargs):
smoothing = kwargs.get("smoothing", 0.9)
smoothing = kwargs.get("smoothing", 0.1)
l = len(ndata)
@ -110,7 +111,9 @@ class QuantileRegression(fts.FTS):
for k in np.arange(self.order, steps+self.order):
intl = self.point_to_interval(nmeans[k - self.order: k], self.lower_qt, self.upper_qt)
ret.append([intl[0]*(1 + k*smoothing), intl[1]*(1 + k*smoothing)])
tmpk = k-self.order
ret.append([intl[0]*(1 + (tmpk*smoothing)), intl[1]*(1 + (tmpk*smoothing))])
return ret[-steps:]
@ -136,7 +139,7 @@ class QuantileRegression(fts.FTS):
return ret
def forecast_ahead_distribution(self, ndata, steps, **kwargs):
smoothing = kwargs.get("smoothing", 0.9)
smoothing = kwargs.get("smoothing", 0.1)
l = len(ndata)
@ -154,7 +157,8 @@ class QuantileRegression(fts.FTS):
intervals = [[nmeans[self.order], nmeans[self.order]]]
for qt in self.dist_qt:
intl1 = self.point_to_interval(nmeans[k - self.order: k], qt[0], qt[1])
intl2 = [intl1[0] * (1 + k * smoothing), intl1[1] * (1 + k * smoothing)]
tmpk = k - self.order
intl2 = [intl1[0] * (1 + (tmpk * smoothing)), intl1[1] * (1 + (tmpk * smoothing))]
intervals.append(intl2)
dist.append_interval(intervals)

49
pyFTS/common/Util.py
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@ -244,7 +244,7 @@ def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False
upper = [kk[1] for kk in intervals]
mi = min(lower) * 0.95
ma = max(upper) * 1.05
for k in np.arange(0, order):
for k in np.arange(0, order+1):
lower.insert(0, None)
upper.insert(0, None)
if typeonlegend: label += " (Interval)"
@ -253,6 +253,53 @@ def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False
return [mi, ma]
def plot_interval2(intervals, data, **kwargs):
'''
Plot forecasted intervals on matplotlib
:param intervals: list of forecasted intervals
:param data: the original test sample
:keyword start_at: the time index (inside of data) to start to plot the intervals
:keyword label: figure label
:keyword color: matplotlib color name
:keyword typeonlegend:
:keyword ls: matplotlib line style
:keyword linewidth: matplotlib width
:return: a list [lower, upper] with the minimum and maximum bounds of the intervals
'''
l = len(intervals)
start_at = kwargs.get('start_at', 1)
ax = kwargs.get('ax', None)
if ax is None:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
for k in np.arange(0, start_at-1):
intervals.insert(0, [None,None])
intervals.insert(start_at, [data[start_at], data[start_at]])
lower = [kk[0] for kk in intervals]
upper = [kk[1] for kk in intervals]
mi = min(lower) * 0.95
ma = max(upper) * 1.05
typeonlegend = kwargs.get('typeonlegend', False)
color = kwargs.get('color', 'red')
label = kwargs.get('label','')
linewidth = kwargs.get('linewidth', 1)
ls = kwargs.get('ls','-')
if typeonlegend: label += " (Interval)"
ax.plot(lower, color=color, label=label, ls=ls,linewidth=linewidth)
ax.plot(upper, color=color, ls=ls,linewidth=linewidth)
return [mi, ma]
def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1):
'''
Plot the FLRG rules of a FTS model on a matplotlib axis

28
pyFTS/tests/general.py
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@ -13,20 +13,34 @@ from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple
from pyFTS.benchmarks import benchmarks as bchmk, Measures
from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
from pyFTS.common import Transformations, Membership
from pyFTS.benchmarks import arima, quantreg, BSTS
from pyFTS.fcm import fts, common, GA
from pyFTS.data import Enrollments, TAIEX
x = [k for k in np.arange(-2*np.pi, 2*np.pi, 0.15)]
y = [np.sin(k) for k in x]
data = TAIEX.get_data()
from pyFTS.probabilistic import ProbabilityDistribution
train = data[:500]
test = data[500:1000]
pd = ProbabilityDistribution.ProbabilityDistribution(type='histogram', data=y, num_bins=30)
model = quantreg.QuantileRegression(order=1, dist=True)
model.fit(train)
print(pd.quantile([.5]))
print(pd.cdf)
horizon=5
#points = model.predict(test[:10], type='point', steps_ahead=horizon)
intervals = model.predict(test[:10], type='interval', alpha=.05, smoothing=0.01, steps_ahead=horizon)
print(test[:10])
print(intervals)
distributions = model.predict(test[:10], type='distribution', steps_ahead=horizon, num_bins=100)
fig, ax = plt.subplots(nrows=1, ncols=1,figsize=[15,5])
ax.plot(test[:10], label='Original',color='black')
cUtil.plot_interval(ax, intervals, model.order, label='ensemble')
cUtil.plot_distribution2(distributions, test[:10], start_at=2, ax=ax, cmap="Blues")
'''
model = fts.FCM_FTS(partitioner=fs, order=1)