diff --git a/candlegp/models/__init__.py b/candlegp/models/__init__.py index 2f2a9bc..3015489 100644 --- a/candlegp/models/__init__.py +++ b/candlegp/models/__init__.py @@ -1,2 +1,3 @@ from .gpr import GPR +from .sgpr import SGPR diff --git a/candlegp/models/gpr.py b/candlegp/models/gpr.py index 77f7eb8..ab22fc5 100644 --- a/candlegp/models/gpr.py +++ b/candlegp/models/gpr.py @@ -41,13 +41,9 @@ def __init__(self, X, Y, kern, mean_function=None, **kwargs): kern, mean_function are appropriate GPflow objects """ likelihood = likelihoods.Gaussian(ttype=type(X.data)) - #X = DataHolder(X) - #Y = DataHolder(Y) super(GPR,self).__init__(X, Y, kern, likelihood, mean_function, **kwargs) self.num_latent = Y.size(1) - #@name_scope('likelihood') - #@params_as_tensors def compute_log_likelihood(self): """ Construct a tensorflow function to compute the likelihood. @@ -60,8 +56,6 @@ def compute_log_likelihood(self): m = self.mean_function(self.X) return densities.multivariate_normal(self.Y, m, L) - #@name_scope('predict') - #@params_as_tensors def predict_f(self, Xnew, full_cov=False): """ Xnew is a data matrix, point at which we want to predict diff --git a/candlegp/models/model.py b/candlegp/models/model.py index a61fea8..6dbe748 100644 --- a/candlegp/models/model.py +++ b/candlegp/models/model.py @@ -111,7 +111,7 @@ def predict_f_samples(self, Xnew, num_samples): Xnew. """ mu, var = self.predict_f(Xnew, full_cov=True) - jitter = Variable(torch.eye(mu.size(0), out=mu.data.new())) * self.jitter_level # TV-Todo: GPU-friendly + jitter = Variable(torch.eye(mu.size(0), out=mu.data.new())) * self.jitter_level samples = [] for i in range(self.num_latent): # TV-Todo: batch?? L = torch.potrf(var[:, :, i] + jitter, upper=False) diff --git a/candlegp/models/sgpr.py b/candlegp/models/sgpr.py new file mode 100644 index 0000000..0f3bc95 --- /dev/null +++ b/candlegp/models/sgpr.py @@ -0,0 +1,309 @@ +# Copyright 2016 James Hensman, alexggmatthews, Mark van der Wilk +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy +import torch +from torch.autograd import Variable + +from .. import likelihoods +from .. import densities +from .. import parameter + +from .model import GPModel + + +class SGPRUpperMixin(object): + """ + Upper bound for the GP regression marginal likelihood. + It is implemented here as a Mixin class which works with SGPR and GPRFITC. + Note that the same inducing points are used for calculating the upper bound, + as are used for computing the likelihood approximation. This may not lead to + the best upper bound. The upper bound can be tightened by optimising Z, just + as just like the lower bound. This is especially important in FITC, as FITC + is known to produce poor inducing point locations. An optimisable upper bound + can be found in https://github.com/markvdw/gp_upper. + + The key reference is + + :: + + @misc{titsias_2014, + title={Variational Inference for Gaussian and Determinantal Point Processes}, + url={http://www2.aueb.gr/users/mtitsias/papers/titsiasNipsVar14.pdf}, + publisher={Workshop on Advances in Variational Inference (NIPS 2014)}, + author={Titsias, Michalis K.}, + year={2014}, + month={Dec} + } + """ + + def compute_upper_bound(self): + num_inducing = self.Z.size(0) + num_data = self.Y.size(0) + + Kdiag = self.kern.Kdiag(self.X) + Kuu = self.kern.K(self.Z) + tf.eye(num_inducing, dtype=settings.tf_float) * settings.numerics.jitter_level + Kuf = self.kern.K(self.Z, self.X) + + L = tf.cholesky(Kuu, upper=False) + LB = tf.cholesky(Kuu + self.likelihood.variance ** -1.0 * tf.matmul(Kuf, Kuf, transpose_b=True), upper=False) + + LinvKuf = tf.matrix_triangular_solve(L, Kuf, lower=True) + # Using the Trace bound, from Titsias' presentation + c = tf.reduce_sum(Kdiag) - tf.reduce_sum(LinvKuf ** 2.0) + # Kff = self.kern.K(self.X) + # Qff = tf.matmul(Kuf, LinvKuf, transpose_a=True) + + # Alternative bound on max eigenval: + # c = tf.reduce_max(tf.reduce_sum(tf.abs(Kff - Qff), 0)) + corrected_noise = self.likelihood.variance + c + + const = -0.5 * num_data * tf.log(2 * np.pi * self.likelihood.variance) + logdet = tf.reduce_sum(tf.log(tf.diag_part(L))) - tf.reduce_sum(tf.log(tf.diag_part(LB))) + + LC = tf.cholesky(Kuu + corrected_noise ** -1.0 * tf.matmul(Kuf, Kuf, transpose_b=True), upper=True) + v = tf.matrix_triangular_solve(LC, corrected_noise ** -1.0 * tf.matmul(Kuf, self.Y), lower=True) + quad = -0.5 * corrected_noise ** -1.0 * tf.reduce_sum(self.Y ** 2.0) + 0.5 * tf.reduce_sum(v ** 2.0) + + return const + logdet + quad + + +class SGPR(GPModel, SGPRUpperMixin): + """ + Sparse Variational GP regression. The key reference is + + :: + + @inproceedings{titsias2009variational, + title={Variational learning of inducing variables in + sparse Gaussian processes}, + author={Titsias, Michalis K}, + booktitle={International Conference on + Artificial Intelligence and Statistics}, + pages={567--574}, + year={2009} + } + + + + """ + + def __init__(self, X, Y, kern, Z, mean_function=None, **kwargs): + """ + X is a data matrix, size N x D + Y is a data matrix, size N x R + Z is a matrix of pseudo inputs, size M x D + kern, mean_function are appropriate GPflow objects + + This method only works with a Gaussian likelihood. + """ + likelihood = likelihoods.Gaussian(ttype=type(X.data)) + super(SGPR,self).__init__(X, Y, kern, likelihood, mean_function, **kwargs) + self.Z = parameter.Param(Z) + self.num_data = X.size(0) + self.num_latent = Y.size(1) + + def compute_log_likelihood(self): + """ + For a derivation of the terms in here, see the associated + SGPR notebook. + """ + + num_inducing = self.Z.size(0) + num_data = self.Y.size(0) + output_dim = self.Y.size(1) + + err = self.Y - self.mean_function(self.X) + Kdiag = self.kern.Kdiag(self.X) + Kuf = self.kern.K(self.Z.get(), self.X) + jitter = Variable(torch.eye(self.Z.get().size(0), out=self.Z.data.new())) * self.jitter_level + Kuu = self.kern.K(self.Z.get()) + jitter + L = torch.potrf(Kuu, upper=False) + sigma = self.likelihood.variance.get()**0.5 + + # Compute intermediate matrices + A = torch.gesv(Kuf, L)[0] / sigma # could use triangular solve + AAT = torch.matmul(A, A.t()) + B = AAT + Variable(torch.eye(num_inducing, out=AAT.data.new())) + LB = torch.potrf(B, upper=False) + Aerr = torch.matmul(A, err) + c = torch.gesv(Aerr, LB)[0] / sigma # could use triangular solve + + # compute log marginal bound + bound = -0.5 * num_data * output_dim * float(numpy.log(2 * numpy.pi)) + bound += -output_dim * torch.sum(torch.log(torch.diag(LB))) + bound -= 0.5 * num_data * output_dim * torch.log(self.likelihood.variance.get()) + bound += -0.5 * torch.sum(err**2) / self.likelihood.variance.get() + bound += 0.5 * torch.sum(c**2) + bound += -0.5 * output_dim * torch.sum(Kdiag) / self.likelihood.variance.get() + bound += 0.5 * output_dim * torch.sum(torch.diag(AAT)) + + return bound + + def predict_f(self, Xnew, full_cov=False): + """ + Compute the mean and variance of the latent function at some new points + Xnew. For a derivation of the terms in here, see the associated SGPR + notebook. + """ + num_inducing = self.Z.size(0) + err = self.Y - self.mean_function(self.X) + Kuf = self.kern.K(self.Z.get(), self.X) + jitter = Variable(torch.eye(self.Z.get().size(0), out=self.Z.data.new())) * self.jitter_level + Kuu = self.kern.K(self.Z.get()) + jitter + Kus = self.kern.K(self.Z.get(), Xnew) + sigma = self.likelihood.variance.get()**0.5 + L = torch.potrf(Kuu, upper=False) + A = torch.gesv(Kuf, L)[0] / sigma # could use triangular solve here and below + B = torch.matmul(A,A.t()) + Variable(torch.eye(num_inducing, out=A.data.new())) + LB = torch.potrf(B, upper=False) + Aerr = torch.matmul(A, err) + c = torch.gesv(Aerr, LB)[0] / sigma + tmp1,_ = torch.gesv(Kus, L) + tmp2,_ = torch.gesv(tmp1,LB) + mean = torch.matmul(tmp2.t(), c) + if full_cov: + var = self.kern.K(Xnew) + torch.matmul(tmp2.t(), tmp2) - torch.matmul(tmp1.t(), tmp1) + var = var.unsqueeze(2).expand(var.size(0),var.size(0), self.Y.size(1)) + else: + var = self.kern.Kdiag(Xnew) + (tmp2**2).sum(0) - (tmp1**2).sum(0) + var = var.unsqueeze(1).expand(var.size(0), self.Y.size(1)) + return mean + self.mean_function(Xnew), var + + +class GPRFITC(GPModel, SGPRUpperMixin): + def __init__(self, X, Y, kern, Z, mean_function=None, **kwargs): # was mean_function = Zero() + """ + This implements GP regression with the FITC approximation. + The key reference is + + @inproceedings{Snelson06sparsegaussian, + author = {Edward Snelson and Zoubin Ghahramani}, + title = {Sparse Gaussian Processes using Pseudo-inputs}, + booktitle = {Advances In Neural Information Processing Systems }, + year = {2006}, + pages = {1257--1264}, + publisher = {MIT press} + } + + Implementation loosely based on code from GPML matlab library although + obviously gradients are automatic in GPflow. + + X is a data matrix, size N x D + Y is a data matrix, size N x R + Z is a matrix of pseudo inputs, size M x D + kern, mean_function are appropriate GPflow objects + + This method only works with a Gaussian likelihood. + + """ + X = DataHolder(X) + Y = DataHolder(Y) + likelihood = likelihoods.Gaussian() + GPModel.__init__(self, X, Y, kern, likelihood, mean_function, **kwargs) + self.Z = Parameter(Z) + self.num_data = X.shape[0] + self.num_latent = Y.shape[1] + + def _build_common_terms(self): + num_inducing = tf.shape(self.Z)[0] + err = self.Y - self.mean_function(self.X) # size N x R + Kdiag = self.kern.Kdiag(self.X) + Kuf = self.kern.K(self.Z, self.X) + Kuu = self.kern.K(self.Z) + tf.eye(num_inducing, dtype=settings.tf_float) * settings.jitter + + Luu = tf.cholesky(Kuu) # => Luu Luu^T = Kuu + V = tf.matrix_triangular_solve(Luu, Kuf) # => V^T V = Qff = Kuf^T Kuu^-1 Kuf + + diagQff = tf.reduce_sum(tf.square(V), 0) + nu = Kdiag - diagQff + self.likelihood.variance + + B = tf.eye(num_inducing, dtype=settings.tf_float) + tf.matmul(V / nu, V, transpose_b=True) + L = tf.cholesky(B) + beta = err / tf.expand_dims(nu, 1) # size N x R + alpha = tf.matmul(V, beta) # size N x R + + gamma = tf.matrix_triangular_solve(L, alpha, lower=True) # size N x R + + return err, nu, Luu, L, alpha, beta, gamma + + def _build_likelihood(self): + """ + Construct a tensorflow function to compute the bound on the marginal + likelihood. + """ + + # FITC approximation to the log marginal likelihood is + # log ( normal( y | mean, K_fitc ) ) + # where K_fitc = Qff + diag( \nu ) + # where Qff = Kfu Kuu^{-1} Kuf + # with \nu_i = Kff_{i,i} - Qff_{i,i} + \sigma^2 + + # We need to compute the Mahalanobis term -0.5* err^T K_fitc^{-1} err + # (summed over functions). + + # We need to deal with the matrix inverse term. + # K_fitc^{-1} = ( Qff + \diag( \nu ) )^{-1} + # = ( V^T V + \diag( \nu ) )^{-1} + # Applying the Woodbury identity we obtain + # = \diag( \nu^{-1} ) - \diag( \nu^{-1} ) V^T ( I + V \diag( \nu^{-1} ) V^T )^{-1) V \diag(\nu^{-1} ) + # Let \beta = \diag( \nu^{-1} ) err + # and let \alpha = V \beta + # then Mahalanobis term = -0.5* ( \beta^T err - \alpha^T Solve( I + V \diag( \nu^{-1} ) V^T, alpha ) ) + + err, nu, Luu, L, alpha, beta, gamma = self._build_common_terms() + + mahalanobisTerm = -0.5 * tf.reduce_sum(tf.square(err) / tf.expand_dims(nu, 1)) \ + + 0.5 * tf.reduce_sum(tf.square(gamma)) + + # We need to compute the log normalizing term -N/2 \log 2 pi - 0.5 \log \det( K_fitc ) + + # We need to deal with the log determinant term. + # \log \det( K_fitc ) = \log \det( Qff + \diag( \nu ) ) + # = \log \det( V^T V + \diag( \nu ) ) + # Applying the determinant lemma we obtain + # = \log [ \det \diag( \nu ) \det( I + V \diag( \nu^{-1} ) V^T ) ] + # = \log [ \det \diag( \nu ) ] + \log [ \det( I + V \diag( \nu^{-1} ) V^T ) ] + + constantTerm = -0.5 * self.num_data * tf.log(tf.constant(2. * np.pi, settings.tf_float)) + logDeterminantTerm = -0.5 * tf.reduce_sum(tf.log(nu)) - tf.reduce_sum(tf.log(tf.matrix_diag_part(L))) + logNormalizingTerm = constantTerm + logDeterminantTerm + + return mahalanobisTerm + logNormalizingTerm * self.num_latent + + def _build_predict(self, Xnew, full_cov=False): + """ + Compute the mean and variance of the latent function at some new points + Xnew. + """ + _, _, Luu, L, _, _, gamma = self._build_common_terms() + Kus = self.kern.K(self.Z, Xnew) # size M x Xnew + + w = tf.matrix_triangular_solve(Luu, Kus, lower=True) # size M x Xnew + + tmp = tf.matrix_triangular_solve(tf.transpose(L), gamma, lower=False) + mean = tf.matmul(w, tmp, transpose_a=True) + self.mean_function(Xnew) + intermediateA = tf.matrix_triangular_solve(L, w, lower=True) + + if full_cov: + var = self.kern.K(Xnew) - tf.matmul(w, w, transpose_a=True) \ + + tf.matmul(intermediateA, intermediateA, transpose_a=True) + var = tf.tile(tf.expand_dims(var, 2), tf.stack([1, 1, tf.shape(self.Y)[1]])) + else: + var = self.kern.Kdiag(Xnew) - tf.reduce_sum(tf.square(w), 0) \ + + tf.reduce_sum(tf.square(intermediateA), 0) # size Xnew, + var = tf.tile(tf.expand_dims(var, 1), tf.stack([1, tf.shape(self.Y)[1]])) + + return mean, var diff --git a/notebooks/gp_regression.ipynb b/notebooks/gp_regression.ipynb index 1beac54..89b4728 100644 --- a/notebooks/gp_regression.ipynb +++ b/notebooks/gp_regression.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": { "cell_id": "C56FA1DC11504021833EAF55BEF3E07D" }, @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": { "cell_id": "0503F0A2DAD644419AC97031797D4301" }, @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": { "cell_id": "7D9AA2647C2C4AD6A42AA374EFF07F28" }, @@ -62,18 +62,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 3, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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6k1wh9CftcL9+/boOHjyo7du3q6KiIuEx27anjHc4HFN+5/V65fV64z9bljWTWuNcLlfG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\ncAcAAxHuAGAgwh0ADPR/YbRnK7hFHeoAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": { "cell_id": "65D558A994034F1880BE4EA866570627" }, @@ -122,7 +122,7 @@ ")" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": { "cell_id": "F4419330180D453E8DD2183657756C8E" }, @@ -145,18 +145,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 5, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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SH5/P9+nvMCenCTffXI+//CUDJ08Gvv5IS0sLPb2CArdPVCoVJBKJ059Nm/bT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\nAABJRU5ErkJggg==\n", 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Wvvyye+72tra2lM9r1B8U2JOUb9PjQFPHhg5txWOPHcWpU9Kge1SmWnD3er3Q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\nNBohlUpTstVOLXYy4BiG6co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IIRGQmZkZ6yJ0ocBOCCER0JupjwONAjshhERIvLTaKbAT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\nJPP/fQ4BLitxuoMAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": { "cell_id": "AA735CF7DD064499A5C55594A5FAA2C8" }, @@ -196,22 +196,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 : 85.64975977703897\n", - "5 : 28.21004707164614\n", - "10 : 20.952411660923964\n", - "15 : 20.862138912288145\n", - "20 : 20.86005754176822\n", - "25 : 20.860018649777768\n", - "30 : 20.860017982341702\n", - "35 : 20.860017977805835\n", - "40 : 20.860017973703307\n", - "45 : 20.860017969992807\n" + "0 : 172.1448193985431\n", + "5 : 28.572146360670075\n", + "10 : 22.051259186540303\n", + "15 : 21.659851715671604\n", + "20 : 21.652461448540507\n", + "25 : 21.652325343400133\n", + "30 : 21.652322894623428\n", + "35 : 21.652322889975387\n", + "40 : 21.652322885771532\n", + "45 : 21.652322881969678\n" ] }, { "data": { "text/html": [ - "Model GPR
  • mean_function: Linear
  • kern: RBF
  • likelihood: Gaussian
ParameterValuePriorParamType
mean_function.A[[-0.02798468]]NoneParam
mean_function.b[ 3.11907971]NoneParam
kern.variance[ 0.73385206]NonePositiveParam
kern.lengthscales[ 0.07378214]NonePositiveParam
likelihood.variance[ 0.03258215]NonePositiveParam
" + "Model GPR
  • mean_function: Linear
  • kern: RBF
  • likelihood: Gaussian
ParameterValuePriorParamType
mean_function.A[[-1.61672576]]NoneParam
mean_function.b[ 3.63185399]NoneParam
kern.variance[ 0.69043957]NonePositiveParam
kern.lengthscales[ 0.0694677]NonePositiveParam
likelihood.variance[ 0.01671995]NonePositiveParam
" ], "text/plain": [ "GPR (\n", @@ -224,7 +224,7 @@ ")" ] }, - "execution_count": 6, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": { "cell_id": "ED272B8B122E49D29D44A170B4E5D44E" }, @@ -257,18 +257,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 7, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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NWCxGeXk5ent7Q966R0skGhmmufzyAWzfXo2dO8vw1luluPLKfnznO52YMiV+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\nU1i0qB+FhU7wPPD552q8+WYZ/va3Qlx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gXYeEeJ+tViuCMaaVGzc2orNTidWrP4NcLsx7XF1dDZZlo14XH9dbWVmJ3t7o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\nXTbGRwSUbLVAlSqALVsqIgK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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -299,39 +299,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "cell_id": "CB39B19CBCD540E78EC33A0B169C85FD" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "likelihood with priors 33.06474343184192\n" - ] - }, - { - "data": { - "text/html": [ - "Model GPR
  • mean_function: Linear
  • kern: RBF
  • likelihood: Gaussian
ParameterValuePriorParamType
mean_function.A[[-0.02798468]]N([ 0.],[ 10.])Param
mean_function.b[ 3.11907971]N([ 0.],[ 10.])Param
kern.variance[ 0.73385206]Ga([ 1.],[ 1.])PositiveParam
kern.lengthscales[ 0.07378214]Ga([ 1.],[ 1.])PositiveParam
likelihood.variance[ 0.03258215]Ga([ 1.],[ 1.])PositiveParam
" - ], - "text/plain": [ - "GPR (\n", - " (mean_function): Linear (\n", - " )\n", - " (kern): RBF (\n", - " )\n", - " (likelihood): Gaussian (\n", - " )\n", - ")" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "k2 = candlegp.kernels.RBF(1, lengthscales=torch.DoubleTensor([0.3]),variance=torch.DoubleTensor([1.0]))\n", "mean2 = candlegp.mean_functions.Linear(torch.DoubleTensor([1]), torch.DoubleTensor([0]))\n", @@ -348,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "cell_id": "2C025E9BC16D44FB8915B71B2140A1CC" }, @@ -359,84 +331,209 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "cell_id": "52D204E2251B4B8F88F9D2AA2D506F6B" }, + "outputs": [], + "source": [ + "pyplot.plot(res[0]); pyplot.title(\"likelihood\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "9181466484404420BA416155CEDF151D" + }, + "outputs": [], + "source": [ + "for (n,p0),p,c in zip(m.named_parameters(),res[1:],['r','g','b','y','b']):\n", + " pyplot.plot(torch.stack(p).squeeze().numpy(), c=c, label=n)\n", + " pyplot.plot((0,len(p)),(p0.data.view(-1)[0],p0.data.view(-1)[0]), c=c)\n", + "pyplot.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "ED2420783A214D6C8C8F35E27E498C4F" + }, + "source": [ + "## Plotting simulated functions\n", + "\n", + "(Note that the simulations are for the de-noised functions - i.e. without the noise contribution of the likelihood.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cell_id": "9819F0FD9C9244E59BB41A4F18EA5F21" + }, + "outputs": [], + "source": [ + "xstar = torch.linspace(0,1,100).double()\n", + "mc_params = torch.stack([torch.cat(p, dim=0) for p in res[1:]],dim=1)\n", + "\n", + "allsims = []\n", + "for ps in mc_params[:50]:\n", + " for mp,p in zip(m2.parameters(),ps):\n", + " mp.set(p)\n", + " allsims.append(m2.predict_f_samples(Variable(xstar.unsqueeze(1)), 1).squeeze(0).t())\n", + "allsims = torch.cat(allsims, dim=0)\n", + "\n", + "pyplot.plot(xstar.numpy(),allsims.data.numpy().T, 'b', lw=2, alpha=0.1)\n", + "\n", + "mu, var = m.predict_y(Variable(xstar.unsqueeze(1)))\n", + "cred_size = (var**0.5*2).squeeze(1)\n", + "mu = mu.squeeze(1)\n", + "pyplot.plot(xstar.numpy(),mu.data.numpy(),'b')\n", + "pyplot.fill_between(xstar.numpy(),mu.data.numpy()+cred_size.data.numpy(), mu.data.numpy()-cred_size.data.numpy(),facecolor='0.75')\n", + "pyplot.plot(X.numpy(), Y.numpy(), 'kx', mew=2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_id": "5F353474524442BAB744D7D1BED20CDE" + }, + "source": [ + "# Sparse Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "cell_id": "8F1FB7E83FFD40CB99764C93D488590F" + }, "outputs": [ { "data": { - "image/png": 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9990BABs3bgxmmcsvvzy45vLLL8cRRxwxfsKeUrjfuwB45on0i/nw6+3RHBv0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\n0EzXeBujdNKugfc8SZRORg0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YDb864F+KuLGDZYPssjvsH98Mcsj84DA58i3hNTKBn7SU\nY7l0cvmoxpQGFe0jo3QcQTNTCU5Kswv8wEtHg9I5/OhodVtekt/DYAtL8EopHZHDlyVOk7m1MjDP\nHMbLdm8BHvqT93eGFU6kHnGl5jpRj5BYeSkafoIWGfjPyyY6DQ6fHHaU9/8bjvQPcG2UKRpAssCP\nBDPZUXsKTTPachq+ZUW/N5WxPs3t0RIjbUscpcNr+BkoHb4tGTX8RCjbYAR+JhBRw4/kEtFYRgWU\nDsc9qz4mIPTDz+XSl4o8VBp+jEvljLZB9kzFvUEKWVm5KYFXGkZGMt+fGNu9jcPJ4hPk9zDYgkDJ\nGnglPmckedqQnNLhPySew+XBBP4gl9FRMamTtIC1mMCXuGVGC4z+5nO9AMm5dNhOSLLnVkaPcgJ/\nn/09Zec1r/UOsDa2dSAmaPiVkrJBAqXDQ9zOUkSsf7h6VGkx+LEtE6jEio4ZtleA2FZdYWzb0XZl\nEvjleukk31YtTBmBH6V0St5LCEKv5S+MSAQ+4TUORRQjAD+1AuPwa0HpWPGluMoLiLrxjUBU5QrH\n4zs0xe8LuqmhEdhrHsjRb5PfwyB6VWho+NYX14QVcUIjEiULP/BKSulw75LlahfhC1fav5Nra5Uo\nneHhFEpHEXjFFIyEMWS1+jnms2j4CalCiGWBfGQlrK9+O66s6Aj8CIcv9F9Woy1fTyzgzh/vkc1c\nZBq+aLRVafiawljctzgtRkfchCaxjvH13plCAp+ndIoezRJ4oqSHG5BjjgNmdoEs4rRXcSctHq7v\nmpdVw9eldHK5uLFN9TG5CZSOcoBxPsfS9ki0FrahigaHH5ZjJQsPANhtrsd1yigdXluDL6wbJbn7\neVrNKUr7KggG6+vl2qrIbqlMVMcmpeh7pEP9Ee+V+H3C8VFRw0/w0mGOBjIFpAyBDwDWguNAZu+m\nNtqmcfg8380jbTUn9EOE49aidGQavvAMTrkcPvdN8E2RKYxKt0zjh18XRDTVUtH7gNJ2uuFR2A32\nxT8Ddt09PJa05Vg5Gj4hcSqAP8eD9wMOjLYqDZ9mN75moHQC4ecLfGkWSR6xfVYThMCe+8D6ysX+\npTKB78TTL/Mbbsjaq9Tw/X7s28G1NauG79dTFN7j4IC+0dayQkqHafhJqRWYwJesDpUru5SsldJ2\neQe8/1j/Hfqm+D2WFU58WTVTMYjptfuBLPc3olfttpZqtBXelSskT9P1w+cDtCJpwVMmMdFmlYQs\nDh41wJQR+DEvHcsOtaYWRXRt5H7JEjVpZeCnRya5fDrHx0CIPqXDC/xUDd/NLPADm4eOwGffiSz3\njAzifqp8u8V9CHbdw4ve5M+rPC4Y9t43Xif/MftZDmPIyzT8jCk1VBz+4EDUmJnUPjsnEfgJHD5L\nNT06Ei+/TA1f2i4AGOzz/vcFJjn8GGDOntFrLAvBoPC/MfL+M/Tqk2XTDLKkKjR8O4UyEfvO4ZUE\nbkUqjt1ddgdmdMbLFtuYJXo4TVEf5/TIFaVWuPHGG7Fx40YQQjBjxgysWLECnZ1eBz7++OO4+uqr\n4TgO2tvbceGFF1alwUqIgVd8B8q2jRMhc1NMNNr6fvj5fLrmwGBZag1ffOG8gS7wv1bcSxNSBKRp\nqhocfjCKmethGmIcvhP97agngFiqW4HDBxDP4S6211Fo+P46ne7kBH7WtNgBhy+8i6F+L4ZDh8O3\n7DDil73nRA2f2zRb7D+l7UZXCRHq3emvfpRbNkIw2nrnyWFHgd70s+z1AWFZ4sTulKHhM4VBxuEL\nY9f+5n9Fy2F2LWJFszykGKKJnQuvT9Xw62SdVaAigX/yySdj2bJlAIDbbrsNN910E5YvX47BwUFc\nddVVOPfcc1EoFLBz586UkipHxDWsVPQ+JP8FEy2BL5ndEykd30uH32AhAdZ//hTueZ9Sc/jiQIlQ\nOmwHLJXAp8qBJt0yDxA8EmTnJceTNkXnyiXiB8hrbjG/b+F+IrrYuXHNTxY8F6N0uDLe+GZg05/D\nunU0fBWHzXY3GxYyhA4OAs2tyv6O9Ek+FybmYmMsScNnzxsIfO6kQikhlqXakDIKQQDRQOD7Gn4u\nFy+HDzDTpUsYVBMIIDHaStJHpHH4+Ryo44CwVvObtWi7ZQqOBjINP+K2KVkNp9Whe7zKqGgd0dIS\nfnijo6PBoL733ntx1FFHoVAoAABmzJhRSTV6iHH4nNG2vUwNP2mAZNTwSWehckpHIfApn0I2rVzx\nuBalw2lgaRqMSC1ZAoevyr/O/47sshXn8FPdE0tRo611ysdBzvg8yMFHeAcG+9XtTfliCUu9MSxs\n1q1K7CVDTrJ6SxhrVpNHedHRkXgdKg0/bf+HAFx5MzthnfBe7+8gMRgXhBTs/Srx0smyyo01QUHp\nyBLESSkdrky2WVAQaRty+ETXaJvLgfCxJ2k5skQFJ/HajCvxKqPibJk33HAD7r77brS0tOD8888H\nAIFGwPkAACAASURBVGzevBmlUgkXXHABhoeHceKJJ2Lx4sXS+zds2IANGzYAANauXRtMElkxyA+K\nYhF2Lgd3eAgUQOvuc9CqKHer/39XoQCrvQOUUrzqH8vnclDo1Ghva0NfyUFzeweaOjvRk9K+QqGA\nV20bcJ2IxsSe14GLbu7YzvZ2sDRf7TM70QfAcl3IFpeNuTxauwrSNrTPmIEmybPvbGnBCICZnV3I\nS867w83Yxsro6MBOAIRS5Boa0FkoBP0mguRykXfY29SMEiHBsVetHCjCwLjG5mbM4K7falloyNlg\nYUmdM2ZioKEBfG7Jrt12g8Vv0uKj+O2fomfVR9He0ow+SoN+7pozF9b+B2F005/RC8BySkE/tnXM\nQAvf3sYGjPrPLOu30dm7ohdAKyEQRD7sfB4Frm/EscyO201NgZLe2NqGUQCt7R3KMWq1tWJnx0zM\nXPYx9H7jrMj4mVmYLX3vM2bvCmaaTvqmSqND2O7/vevPbgmOd1sWHAAdM2dhwLZRAmB1zIC7Yzsa\nGhvhjo2gBKClvR1thQIctxiMXxF8/W5TQzCu2PHRGTPQC2DmjI5gLOZyOczq6MB2AG0zZ4FN0Y2N\njZgpPE9PviH4Tq2GRuRsG42tLegH0NXVhdH2dvQBaGhqit3Lo7epCaMAmlrb0P7Rz2GgIY+hddeh\nubEB7bF3GQroxsbGYHwWCrNBZF5k7PmHh4Ln55HPNyAnfDu1QKrAX7NmDXp7e2PHly1bhiOPPBKn\nnnoqTj31VKxbtw7r16/H0qVL4TgOnnvuOaxevRpjY2M477zzsO+++2LOnDmxcpYsWYIlS5YEv7u7\nVcMmGU28puI6cEAA39960G7AcEq523f0gIxGI3SLTBuXbGvXt3MnaGkMw8USRvr6kYbu7m5QEC/v\ninAcAGjvjsgxfnXb72/64YqbZvgYHRlGsa9Peq5/YBADkmd3/Xb09veDSM7zm33393vPRx0HRcdJ\nfEfUsiPn3WIRtFgMn1N0Rx8bFcojGOXokp7ubTH6ZHtfP8iwEKkKgPrvr29HT2RD+e07d4KMjoEO\neCkWXO7ZBoaHMcTV7/hl9Pf1SfuNjnj3Dna/GjvnuDTyLKp+cjgtb6zkif7B4WHlGC0UCrC+83P0\nARCSP6N3QJx2POwcDsdK4vvi7Bn8dY4/9vsGh+D6Gq7b1AJgu9dmn9ceGhnBSHc36A7O80kAX26w\nJzF3nPrjq3dHbzAWC4UCdmz3pqKBkfB9jZZKsedxOMrFtXMYGx5G0S9z+45e0D5PDoyNFRP7wh3z\npo2RkoOxnh64DZ7tZHhwEKOx+8IPdJQbT90926ObMYl3iRHWPoqlEkqSZ9OFTLbKkCrwV69erVXQ\nwoULsXbtWixduhRdXV1ob29HU1MTmpqacOCBB+KFF17QblRZiKUXtoBD5gN/+wvIrE75PTySlmK2\nBZQkUYBBaoUMy9kxTUqH38Ivn/eGl4oOolTTvZJDmh++LaF0dDh8sTxxslS5ATLIjLaiMU+ZToDb\nC9iVGDZZ24rcR5d1pzNG6QwNxs/pjgP2bvkYDl1vIVUqBBENai0zAtW4lxltWVCiLPBK99llaUhU\nG9/I3DJVkbYMjNLhd7xi2lMahy/atdjv1LTnPKVTbuBVShVVQkUc/ubNm4O/N27cGAj0+fPn48kn\nn4TjOBgdHcUzzzyDPfbYo7KWpkH8YFpaYX36K7DOPA/Y/w3p9ye9KNlHFeTZ1zPaBnWoBo/4IUQ4\nfLZnboLRVrWMTEutkMVLR0vgixy+lczhx/pO6CPHiRnzlHEATDCIRlvWJps7H5xTCVqFybOhGgLf\nn5jsPIK+zeLay0M19nQ5fFWbg03Zuf2Jg6hgzmgbKAa6RtsMHD7bqJ0T+NJ3HzHa+ntH8Dte8R47\nSeA4fADhO5F5fKV5v6XVkfW+KqEiDv+6667D5s2bQXyOdvny5QCAuXPn4rDDDsOqVatgWRbe9ra3\nYc8990wprUKIA3/GLG+gyAJHZEjU8CUfI1uaZcmWmZgRMSnwyhcQAh0UgLognbO9AJanHwO987b0\nOtP88Pn+YE3TMtpKVlpJGn7Ma4fEg16oH2eQtt9AkIKiFHGlI6I2ylcXi7RNroJp+HRYIvB1xwFb\noeRyUc8QHehq+Ak8cgQaGr71yS+B3vcHYGQY9Mm/RTV8pqgo3A0J2+IxaK+kvsAPX5FLx7LDlWKa\nW2Yu768KuXPBZiiawthfKZLD3gS67hcgx56YfF+kjJTzMacFou/uXAVUJPBXrVqlPHfyySfj5JNP\nrqT4TBDd4ciMWdkKkA3YpDB7xn22dejPzuJ17TPU53gNTbUtI4P/YVhHLoS7+cXkOsXjOm6Eqr9l\nSNXwUwS+RWK5zb1t7jQEPqt7dER+rWzizhp4FWj4Eu5cdxwwSof3gNFeJQrXqeIvtCkdxXFu+z7S\nORvkpGVwf+dv6sGn10jQ8MmJH4B19LHCwQRKRpVLh0XLOgqFQ/TSKY5xQp7zEtPtY39MkJldsFUb\nmUTaz/+d0UvHstPHdRUxZfa0jQmajowCP0mQyaiUnZ5Biczo1B9IXNpm6zs/j6aGTdLw2+IeKRFE\n/NzFAVWmH34EfNCQJg8a3EpS/PAlbpoCh5/odsqDafgyYQwoBH7GT6CxipROLpedu5Ul2ZOhYg6f\nc8tkCLK2OpzCkKDhS9qWSMmoIm0tO4w/kCpmXD0NDd5mR3y2TF1KJ20XMQ2kpR2Jnbf955oMfvgT\nChJKJxOSZmYmgJpbgSOOAQDQHT1hPbovixdELa1hMi8gzuHneYHfkVxuJHVBip87QyDwNQZ3RIOp\nsYYv2DnoP58ERkb02smukQljoCoaPrFtT4scHoqf1Jz4CaN07BwyS/xYfykywZZrE2AIKB2u39mq\n0ymFgjcIvEqhWhLbkGK05fPhpCUyyzf4NhxOyPOJ1BIQbH2ZVQmoBFmN3hViymj4YppfMlPDMydy\nQ5KG7w0E8t7TQQ47Cu5f/wTs8N2nZnSGUZNZIPMq4sFPBo2N3kAWN3ZhSNKgyw28UiGrl44o8GWB\nVuJvzuBKr/+h98esdP9kQghg50AHFW6ysmdVRtomVNTUJF9F6K70Ai8dzv6TllFUVUfabkyp5aUZ\nbXkN3x+TpVJcw5dNXFmcGYAEDd8K65G9L64e0tDo7UTHC3mq6aXjZNDw+bZqvjopjMAvE+LLbMsY\n3SvtcDGqkcuM2dvjaQJt7VE3P93qUjRd0tAYzc/R0hbuYyqWNbNLWU66l07CR7DnPiBHH4tIP6Rx\nlOLHIiZPS9PwS0XgxWfTy02qf1BF6UjKiLmRanx4jc3AgGRSyeylw9WtK/B1J3RdlEXplOI5gKQT\nh667ssJoy1Z6jNLh64tUwx1jGj5P47DxlzZ2ZasaHVQirO2ECbMGmDqUjsQtMwsi3FouB7DdgXjw\nPvfDg8CMmd59msY661PnJJxUGG2Jn45Y8TzkjJUgp3+aOyAKhJT0vwkavr36e7CWvDtSZiJHuftr\nQMRMlqLXTdqEJPN+AfQ/QjtJ4EveUznLd271ReYvDI+z3E3LvxSkfJYiMNrmODpDV8MXV1AZV2gi\nlO8zTcMnwvkKBJZgtKVDgxi69b8FDT9hRcpPNoFbJs/haxpt2QSTheabdxDI296pf72IwOffaPiZ\nEOMsZQm2NGFd8WsAgPv/vhKtIy/kvmdpa3W1rAQX0ZjGz4QK+7DYrkfifW9aHM2JnpXSycrhJ+V8\nWfoxkNcfHq8nE4dvAbIEEroafj7vZa6UQUPDJ7kGT9QlvVNe4H/wU6APP+CtTPyxYR25UHWnB8bh\nNzWHfasr8EVUrOGrKJ24kAwDAMNnJYmURMZVi98H9Pofov+Bu0BO/EDYhiQng8BF1PL6Vqnhpxlt\nWaCX5l4CAKyPfr4yL5skSqwGmDoafkzgZ9PweRCi2OQjl4/OxBn5N+VmFTIwocIGeotc4KcnI1P4\nR+97sGeA1mqTplumyoMiC4evEhK6H2GShi/l8KPPT079BMgJ7wPYht/Scrg22yl0gww+NULaOrj+\n1BSOYubGWmn4vIbMwFadJZnRVlKO6GapbEPUaEs3/8v7vcPP8sP88Nnf4u28i3Eu53nVcRw+OWIB\n0NgE8pbjk9uRxUuHXyFXEjRlGw6/PIh++EmpjXUhvgNxdysxBLuaYMt+pkm1tIUioWsXYPurkfMB\nxDar/Oz3PwT2/ofotUXXD1/lI00zaPjc5EAWHAf6wrPAS8/pL7NzuXC/WBEaXjqkrQPkfR9OrkMU\n+Fk/WvZuW1oRvDBdDV/cG6BWHP6c1wBPPR6dEANKpxgXwBk0fOuTX/I2HwkOCH3ge0DRV1/xftu8\nhp/gDWRZXtAU0/CZ4ta1C+wf/Er+nDz8yVS5z7MMtgU4VaCz6oQpI/AzvaRykRc1/IyaXRaIGj77\nfch8WEtOgnuJl5k0thLR9cMvF1kHKLGiml5MMIgpccPf5JjjQLdt8X7ovt8kiqoafvhiObyGp903\nfh80t4SeV7qMTgb6gJz+GfXkF1wkF1bWinOBF56O7kYW8cMXAq9k5SieKWL34O+lLiiloWa/1Rf4\nqRw+14ZczovduOWX2cdqwOFnUBYtW3+ilyFr4F2FmDICv+KlrQ5EDT/JB7lScEZbANxGGRYS+T5d\nP/ws0NXwpWHzVvAhS2myxM2yLc6Wofkc3HLcuuTaaO78JP43C8RVXlbD24jvxtvUwuX10ZT4imyL\nMliL/i39ItXGOa1twEFvjB5kGr5TiguqSjj8IFcT9SZAtu/DgJ8BlqREJIuUTub6fZQTeGVXKvDL\nv7UcTCEOvw6PImbGrCWlIwq6YPMJkkKrVNltTywzSajJ2hX4mfsfExPwBx4a/S2DZYWpDLLmqbEs\noLUdhEXGAnLhVo6xlKNwCK996k5KLEVwc0tEu9VCBoGvhSzeITyHL3p5VcLhB22gYUAbn1qEp3GS\nAq8sK7piy/pugz10MyiPlXL4JGnCrD6mjsCvAaVjHXdS9IDopaOzdVpDI8hb35G9ctHPWWNnJK9N\nNRD4un74SXlO2MdPKXD40SAH+9pj0jdJSBiNTCxg99eg/aMrk5vKPvjGptiKggVmVQyRXtDdQo+B\nuZ42twD77O8VscfeeveqUmSXiyyChq0yS0U9ga/tpcO5pvp907Tw7eF50WYigjcgV2K7k+2hm4ZK\nKR0GY7TNhoqNV7Iyj1gA+8c3w1nxfm+pqdLweT/1d53iZav0oz3ty/87WujMTi9oS1bfohNADjrM\nL5J4Wg4rmw1CSpMFi27gVbnIsrrgj/EafiR2IYXSYVkfGxthf/1ytBQKkQ1LYmD9pNqEguUuOWS+\n5yXTNVtdVlK7gPD9ZzTaknd8APTlF0AOOwqktR10n/1BCrtmb4cPa+1VcM/5uFf26Ss8rxRtZBH4\nskhb9h5ldJ3mqsXvN+pSED8tRn7/12Pkj7f6dQg2ExH8xFvJhB4EXmXh8K3KfOgDl1Ij8LOBn/nT\ncs9kBdOq+FB4IK7hAbDefRroyR+Eu/zd0qKsNVcol+XW6SuiBxoa4xq+6yL5I60FpcP/nWGy4et3\nQ4FPQDiqJ43S8YQM6dpFr61M4KvcOP3cM2TvfWGdfKpemSJEQZ/IY0uasPtc2Ku/F/6uQNgD0b4h\n7TNBJNs/KlEOpUNdEOJvE55kv9Cm8Dnaz6d0bL5PLIuL/E3x0qkg8VkWSoccsQD0wbv99lRB4NeJ\nzJ86Ap8J31kFWF/7XvK1WcFmfnHDcs5Lh7zleJA3vxWAxHOGA2lqCQO20pBvjBuSnFK2sVEVg3IV\nOPzAnZBp+EQ4riiPGfA6NTVxO0XgB9dVQAGKlI5VQ1tOVmRtQ5bxwfepitbikTVdBEfpWJ1c7iRe\n4Cdx+JVq+BmMtuSMlSBLPwZi2aBV0PDTsmxWC1NG4Acdtsvu3lK9Fsjl/VQKJLKtICEE5D/OrH59\nDY2hW10g8B0kS3zhI9Pd+SgJlfrhAxylw8rQCDiyrDBnTcdMvbYGlI5Kw/f/r0DgE8sWtFs/VqLO\nPtVSZBT4WQQNu5a8cynA3GWDZ6+Aw+dy6dAhb7xbfErwNEqHo5dILl9+LjPZlooKkFw+zMhb5nsn\ny5YDI0OgLz1vjLaZUY8OYxqO6INcKzQ0yCkdDY2CvPUdsL59TbblvbIw/u+EZ07KVR5QOv42iRry\nHsQC9V3zdJ8jmno4AZVogjENv76eFomosXuy/eObYb3nQ3GjLRB//qyUjht66RA+lUiE0knww6+U\n0uGTtWVBme/dOu5dAKOujMAvE+XmJNGBHV2+auccLxcNjdzkoqnhB89Psu/6pYSuhq/H4UeMtkmG\nPYvAWux7OO01T6+paRp+uWmhI2WoOPwJ8DnVi1YKDNd8faLAz2a0BaXeXgbE8qhPBstGMHtkccvM\nCn7DlSyYCBO9JqYMpZM5J0kWtM8A+neGy9Z6RcfxRlsmwFwneYCF8r56iFA6Gd0yYxq+wOGnBF6R\nI46B/eOb9dvKPCxUHH6+EcBgZaszMZGXJodvXXpD7YVD3QS+5BuwiOcBxVCO0XZkCGhqjlJElhWW\nJTXachNuJW6ZbhnZMoHM79Ra/b0wGaIsZ1ENMXUEftacJBlgnfddYPNL3AHJcrYWaGgMVxN2zhvz\njpN4S4gaCZZEo63sesEbJ4vAL8cYxjR8labX0JB8XgcqQZ/y4ZOMKbvLQj0izgHFNyA+fxlG21Ip\nTsvwAlhqJ6qWhs/tsJUFGYU12XOf8Ecg8LNVWS4mwBq0Sqhhh5HO2WGgEBC49tUi2CtS74xOoN03\nQPNG23p7g1RitBW9cSgFQPTeVzlaTxqlE+QoquDdiX74iZuA1Bn1pnQSOfzsRlu4jmRTmvCZpDmz\nlKkVsoEs8IO9+L2mtW6s4L3r7rdbJUwhDd9HDSn8AGJq2BqBfOAMEOaWmOMoHR0vnWoOIG1KR8bh\n+x9okNaXev0nUj0ylNO/gVumYmgHm49U8O5sQeALtp1awvr6FcDwINyLzlZcUGYbshr3ZZROuQKf\nN9o6jnxf5CS3TPY9VuiWSd73YZB3n5Y9025F35oR+BMfMg+FWlTT1AzA1zaYAHOdFHlf4wFUqVum\nyzR8HQ6/fEqHqKIl85pePEkQ/e+zplaoAGT3uV5GSRXKGJPWF9cAu83N2BBh0gPC99Xa7kWaz95N\nsyxuLLjxFawXRZ/gpcP3fwWULiGkPBtANTR8E3iVEbU02sbqkgz2WoNp+E6K0bYmz1+Jhs9pbwBi\ngVdpkbZZESSZU5z30wNU5GEVM9rW1y0z0Xe+jD4jLJFdFsg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+ "text/html": [ + "Model SGPR
  • mean_function: Linear
  • kern: RBF
  • likelihood: Gaussian
ParameterValuePriorParamType
Z[[ 0.59475344]\n", + " [ 0.42028281]\n", + " [ 0.00093972]\n", + " [ 0.8433978 ]\n", + " [ 0.04595426]\n", + " [ 0.64909101]\n", + " [ 0.37004566]]NoneParam
mean_function.A[[ 1.]]NoneParam
mean_function.b[ 0.]NoneParam
kern.variance[ 1.]NonePositiveParam
kern.lengthscales[ 0.3]NonePositiveParam
likelihood.variance[ 0.01]NonePositiveParam
" + ], "text/plain": [ - "" + "SGPR (\n", + " (mean_function): Linear (\n", + " )\n", + " (kern): RBF (\n", + " )\n", + " (likelihood): Gaussian (\n", + " )\n", + ")" ] }, + "execution_count": 28, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "pyplot.plot(res[0]); pyplot.title(\"likelihood\");" + "k3 = candlegp.kernels.RBF(1, lengthscales=torch.DoubleTensor([0.3]),variance=torch.DoubleTensor([1.0]))\n", + "mean3 = candlegp.mean_functions.Linear(torch.DoubleTensor([1]), torch.DoubleTensor([0]))\n", + "m3 = candlegp.models.SGPR(Variable(X), Variable(Y.unsqueeze(1)), k3, X[:7].clone(), mean_function=mean3)\n", + "m3.likelihood.variance.set(0.01)\n", + "m3\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 29, "metadata": { - "cell_id": "9181466484404420BA416155CEDF151D" + "cell_id": "6EECFA90549A41C186352FD6EEDA084F" }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 : 151.118466812541\n", + "5 : 15.269211836567024\n", + "10 : 8.870809890310468\n", + "15 : 8.464744596637871\n", + "20 : 8.460658622843269\n", + "25 : 8.460599191888335\n", + "30 : 8.460598355187699\n", + "35 : 8.460598350641789\n", + "40 : 8.46059834653309\n", + "45 : 8.460598342817946\n" + ] + }, { "data": { - "image/png": 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VLUFyTEVRLO+LvmcAaO5sht/hB5/iEUlEBux9A4Bz506Uq5/lHTtgB6CIYq/P2RbUf7en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H46u8uIRKQXhtNmk+5yqoq6dy9qQYEldYIzFj6b7P6p1EEdib0QjTKWtYxlFQCh8CgGu3ZhixpG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\nEGIpVc7OziYiIgKADRs2lOpYO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+ "text/html": [ + "Model SGPR
  • mean_function: Linear
  • kern: RBF
  • likelihood: Gaussian
ParameterValuePriorParamType
Z[[ 0.4704737 ]\n", + " [ 0.38097791]\n", + " [ 0.00898348]\n", + " [ 0.84370832]\n", + " [ 0.07373433]\n", + " [ 0.62807518]\n", + " [ 0.24009045]]NoneParam
mean_function.A[[-1.72983099]]NoneParam
mean_function.b[ 3.70241307]NoneParam
kern.variance[ 0.47886879]NonePositiveParam
kern.lengthscales[ 0.0886398]NonePositiveParam
likelihood.variance[ 0.03231634]NonePositiveParam
" + ], "text/plain": [ - "" + "SGPR (\n", + " (mean_function): Linear (\n", + " )\n", + " (kern): RBF (\n", + " )\n", + " (likelihood): Gaussian (\n", + " )\n", + ")" ] }, + "execution_count": 29, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "for (n,p0),p,c in zip(m.named_parameters(),res[1:],['r','g','b','y','b']):\n", - " pyplot.plot(torch.stack(p).squeeze().numpy(), c=c, label=n)\n", - " pyplot.plot((0,len(p)),(p0.data.view(-1)[0],p0.data.view(-1)[0]), c=c)\n", - "pyplot.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "cell_id": "ED2420783A214D6C8C8F35E27E498C4F" - }, - "source": [ - "## Plotting simulated functions\n", + "opt = torch.optim.LBFGS(m3.parameters(), lr=1e-2, max_iter=40)\n", + "def eval_model():\n", + " obj = m3()\n", + " opt.zero_grad()\n", + " obj.backward()\n", + " return obj\n", "\n", - "(Note that the simulations are for the de-noised functions - i.e. without the noise contribution of the likelihood.)" + "for i in range(50):\n", + " obj = m3()\n", + " opt.zero_grad()\n", + " obj.backward()\n", + " opt.step(eval_model)\n", + " if i%5==0:\n", + " print(i,':',obj.data[0])\n", + "m3\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "metadata": { - "cell_id": "9819F0FD9C9244E59BB41A4F18EA5F21" + "cell_id": "9D4C2F52C55E4C9ABDAC2697C683013C" }, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 12, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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1pjqOUPQFjf578cUVtrYS9PsBJhMb06mDixcTbGwk6HRyuK4wBmKO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zwQajWafTkOf4bhMMXeHr//ZMKd32xGWF/3lC1CkgAXL1axs2NhNLLwwgsNOE6ClZUZVlYinDnj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\n66FQ79xZGGa1MTJCz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/ig7HcXG/8NLJ6XQOBO36+vpjPq5bLBasXfs+3n23HDNmtKc8aEskEowfPx4l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\nCCFZ5v8HuV/QhSKZh8UAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -445,30 +542,21 @@ ], "source": [ "xstar = torch.linspace(0,1,100).double()\n", - "mc_params = torch.stack([torch.cat(p, dim=0) for p in res[1:]],dim=1)\n", - "\n", - "allsims = []\n", - "for ps in mc_params[:50]:\n", - " for mp,p in zip(m2.parameters(),ps):\n", - " mp.set(p)\n", - " allsims.append(m2.predict_f_samples(Variable(xstar.unsqueeze(1)), 1).squeeze(0).t())\n", - "allsims = torch.cat(allsims, dim=0)\n", - "\n", - "pyplot.plot(xstar.numpy(),allsims.data.numpy().T, 'b', lw=2, alpha=0.1)\n", - "\n", - "mu, var = m.predict_y(Variable(xstar.unsqueeze(1)))\n", + "mu, var = m3.predict_y(Variable(xstar.unsqueeze(1)))\n", "cred_size = (var**0.5*2).squeeze(1)\n", + "\n", "mu = mu.squeeze(1)\n", "pyplot.plot(xstar.numpy(),mu.data.numpy(),'b')\n", "pyplot.fill_between(xstar.numpy(),mu.data.numpy()+cred_size.data.numpy(), mu.data.numpy()-cred_size.data.numpy(),facecolor='0.75')\n", - "pyplot.plot(X.numpy(), Y.numpy(), 'kx', mew=2)\n" + "pyplot.plot(X.numpy(), Y.numpy(), 'kx', mew=2)\n", + "pyplot.plot(m3.Z.data.numpy(), torch.zeros(m3.Z.size(0)).numpy(),'o')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "cell_id": "C4403DE3573B429F88364E2CA2A0EC11" + "cell_id": "D675114ABCC945AA8694C5C74046259E" }, "outputs": [], "source": []