def plot_gp_pred(sigma, **fillargs):
# pdb.set_trace()
nugget = (sigma ** 2 / (0.1 + d.astype('float') ** 2))
gp = GaussianProcess(corr='squared_exponential', nugget=nugget)
gp.fit(np.atleast_2d(range(n)).T, np.atleast_2d(d).T)
x = np.atleast_2d(np.linspace(0, n - 1)).T
y_pred, MSE = gp.predict(x, eval_MSE=True)
pylab.plot(x, y_pred)
pylab.fill_between(x.T[0], y_pred + MSE, y_pred - MSE, **fillargs)
def _calculate(self):
from sklearn.gaussian_process.gaussian_process import GaussianProcess
X = self.xs.reshape((self.xs.shape[0], 1))
y = self.ys
yserr = self.yserr
nugget = (yserr / y) ** 2
gp = GaussianProcess(
# nugget=nugget
)
gp.fit(X, y)
return gp
def function(self):
params = {
'reduce_dim': self.reductionMethodComboBox.currentText(),
'n_components': self.numOfComponenetsSpinBox.value(),
'regr': self.regrComboBox.currentText(),
'corr': self.corrComboBox.currentText(),
'storage_mode': self.storageModeComboBox.currentText(),
'verbose': self.verboseCheckBox.isChecked(),
'theta0': self.theta0DoubleSpinBox.value(),
'normalize': self.normalizeCheckBox.isChecked(),
'optimizer': self.optimizerComboBox.currentText(),
'random_start': self.randomStartSpinBox.value(),
}
return params, self.getChangedValues(params, GaussianProcess())
'DictionaryLearning':DictionaryLearning(), 'ElasticNet':ElasticNet(), 'ElasticNetCV':ElasticNetCV(), 'EmpiricalCovariance':EmpiricalCovariance(), 'ExtraTreeClassifier':ExtraTreeClassifier(), 'ExtraTreeRegressor':ExtraTreeRegressor(), 'ExtraTreesClassifier':ExtraTreesClassifier(), 'ExtraTreesRegressor':ExtraTreesRegressor(), 'FactorAnalysis':FactorAnalysis(), 'FastICA':FastICA(), 'FeatureAgglomeration':FeatureAgglomeration(), 'FunctionTransformer':FunctionTransformer(), 'GMM':GMM(), 'GaussianMixture':GaussianMixture(), 'GaussianNB':GaussianNB(), 'GaussianProcess':GaussianProcess(), 'GaussianProcessClassifier':GaussianProcessClassifier(), 'GaussianProcessRegressor':GaussianProcessRegressor(), 'GaussianRandomProjection':GaussianRandomProjection(), 'GenericUnivariateSelect':GenericUnivariateSelect(), 'GradientBoostingClassifier':GradientBoostingClassifier(), 'GradientBoostingRegressor':GradientBoostingRegressor(), 'GraphLasso':GraphLasso(), 'GraphLassoCV':GraphLassoCV(), 'HuberRegressor':HuberRegressor(), 'Imputer':Imputer(), 'IncrementalPCA':IncrementalPCA(), 'IsolationForest':IsolationForest(), 'Isomap':Isomap(), 'KMeans':KMeans(), 'KNeighborsClassifier':KNeighborsClassifier(),
print("Sklearn RT")
t0 = time.time()
rt_sklearn = DecisionTreeRegressor(max_depth=7,
max_features="sqrt",
random_state=2016).fit(
X_train, y_train)
y_pred = rt_sklearn.predict(X_test)
print("Time taken: %0.3f" % (time.time() - t0))
score = mean_absolute_error(y_test, y_pred)
print("Error: %0.3f" % score)
print("")
print("Skearn GP")
gp = GaussianProcess(regr="constant",
corr='absolute_exponential',
beta0=None,
storage_mode='full',
verbose=False,
theta0=0.1,
thetaL=None,
thetaU=None,
optimizer='fmin_cobyla',
random_start=1,
normalize=True,
nugget=0.05,
random_state=2016).fit(X_train, y_train)
y_pred = gp.predict(X_test)
print("Time taken: %0.3f" % (time.time() - t0))
score = mean_absolute_error(y_test, y_pred)
print("Error: %0.3f" % score)
print("")
