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

Gaussian Process Regression benchmark method

Browse Source
This commit is contained in:
Petrônio Cândido 2019-05-31 10:13:46 -03:00
parent 18c0fed021
commit 9b90853f6b
2 changed files with 54 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

80
pyFTS/benchmarks/gaussianproc.py
View File

@ -27,6 +27,7 @@ class GPR(fts.FTS):
self.benchmark_only = True
self.min_order = 1
self.alpha = kwargs.get("alpha", 0.05)
self.data = None
self.lscale = kwargs.get('length_scale', 1)
@ -34,10 +35,18 @@ class GPR(fts.FTS):
self.model = GaussianProcessRegressor(kernel=self.kernel, alpha=.05,
n_restarts_optimizer=10,
normalize_y=False)
self.model_fit = None
#self.model_fit = None
def _prepare_x(self, data):
l = len(data)
X = []
def train(self, data, **kwargs):
for t in np.arange(self.order, l):
X.append([data[t - k - 1] for k in np.arange(self.order)])
return X
def _prepare_xy(self, data):
l = len(data)
X = []
Y = []
@ -46,16 +55,37 @@ class GPR(fts.FTS):
X.append([data[t - k - 1] for k in np.arange(self.order)])
Y.append(data[t])
self.model_fit = self.model.fit(X, Y)
return (X,Y)
def _extend(self, data):
if not isinstance(data, list):
data = data.tolist()
tmp = self.data
tmp.extend(data)
return tmp
def train(self, data, **kwargs):
if not isinstance(data, list):
data = data.tolist()
X,Y = self._prepare_xy(data)
self.data = data
self.model.fit(X, Y)
def forecast(self, data, **kwargs):
X = []
l = len(data)
for t in np.arange(self.order, l):
X.append([data[t - k - 1] for k in np.arange(self.order)])
data = self._extend(data)
X = self._prepare_x(data)
return self.model.predict(X)
return self.model_fit.predict(X)
def forecast_ahead(self, data, steps, **kwargs):
data = self._extend(data)
for k in np.arange(steps):
X = self._prepare_x(data)
Y, sigma = self.model.predict(X, return_std=True)
data.append(Y[-1])
return data[-steps:]
def forecast_interval(self, data, **kwargs):
@ -64,12 +94,9 @@ class GPR(fts.FTS):
else:
alpha = self.alpha
X = []
l = len(data)
for t in np.arange(self.order, l):
X.append([data[t - k - 1] for k in np.arange(self.order)])
X = self._prepare_x(data)
Y, sigma = self.model_fit.predict(X, return_cov=True)
Y, sigma = self.model.predict(X, return_cov=True)
uncertainty = st.norm.ppf(alpha) * np.sqrt(np.diag(sigma))
@ -87,16 +114,18 @@ class GPR(fts.FTS):
smoothing = kwargs.get("smoothing", 0.5)
X = [data[t] for t in np.arange(self.order+10)]
if not isinstance(data, list):
data = data.tolist()
S = []
for k in np.arange(self.order, steps+self.order):
sample = [[X[k-t-1] for t in np.arange(self.order)] for p in range(5)]
Y, sigma = self.model_fit.predict([sample], return_std=True)
X.append(Y[0])
S.append(sigma[0])
X = self._prepare_x(data)
Y, sigma = self.model.predict(X, return_std=True)
data.append(Y[-1])
S.append(sigma[-1])
X = X[-steps:]
X = data[-steps:]
intervals = []
for k in range(steps):
@ -116,7 +145,7 @@ class GPR(fts.FTS):
for t in np.arange(self.order, l):
X.append([data[t - k - 1] for k in np.arange(self.order)])
Y, sigma = self.model_fit.predict(X, return_std=True)
Y, sigma = self.model.predict(X, return_std=True)
for k in len(Y):
@ -144,9 +173,9 @@ class GPR(fts.FTS):
for k in np.arange(self.order, steps+self.order):
sample = [X[k-t-1] for t in np.arange(self.order)]
Y, sigma = self.model_fit.predict([sample], return_std=True)
Y, sigma = self.model.predict([sample], return_std=True)
X.append(Y[0])
S.append(S[0])
S.append(sigma[0])
X = X[-steps:]
#uncertainty = st.norm.ppf(alpha) * np.sqrt(np.diag(sigma))
@ -167,10 +196,3 @@ class GPR(fts.FTS):
return ret

6
pyFTS/tests/general.py
View File

@ -35,17 +35,17 @@ horizon=5
#points = model.predict(test[:10], type='point', steps_ahead=horizon)
intervals = model.predict(test[:10], type='interval', alpha=.05, steps_ahead=horizon)
intervals = model.predict(test[:10], type='point', alpha=.05, steps_ahead=horizon)
print(test[:10])
print(intervals)
distributions = model.predict(test[:10], type='distribution', steps_ahead=horizon, num_bins=100)
#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_interval2(intervals, test[:10], start_at=model.order, ax=ax)
cUtil.plot_distribution2(distributions, test[:10], start_at=model.order, ax=ax, cmap="Blues")
#cUtil.plot_distribution2(distributions, test[:10], start_at=model.order, ax=ax, cmap="Blues")
print("")