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("")