# Parametric non Parametric inference¶

Suppose you have a physical model of an output variable, which takes the form of a parametric model. You now want to model the random effects of the data by a non-parametric (better: infinite parametric) model, such as a Gaussian Process as described in BayesianLinearRegression. We can do inference in both worlds, the parameteric and infinite parametric one, by extending the features to a mix between

\begin{align} p(\mathbf{y}|\boldsymbol{\Phi}, \alpha, \sigma) &= \int p(\mathbf{y}|\boldsymbol{\Phi}, \mathbf{w}, \sigma)p(\mathbf{w}|\alpha) \,\mathrm{d}\mathbf{w}\\ &= \langle\mathcal{N}(\mathbf{y}|\boldsymbol{\Phi}\mathbf{w}, \sigma^2\mathbf{I})\rangle_{\mathcal{N}(\mathbf{0}, \alpha\mathbf{I})}\\ &= \mathcal{N}(\mathbf{y}|\mathbf{0}, \alpha\boldsymbol{\Phi}\boldsymbol{\Phi}^\top + \sigma^2\mathbf{I}) \end{align}

Thus, we can maximize this marginal likelihood w.r.t. the hyperparameters $\alpha, \sigma$ by log transforming and maximizing:

\begin{align} \hat\alpha, \hat\sigma = \mathop{\arg\max}_{\alpha, \sigma}\log p(\mathbf{y}|\boldsymbol{\Phi}, \alpha, \sigma) \end{align}

So we will define a mixed inference model mixing parametric and non-parametric models together. One part is described by a paramtric feature space mapping $\boldsymbol{\Phi}\mathbf{w}$ and the other part is a non-parametric function $\mathbf{f}_\text{n}$. For this we define the underlying function $\mathbf{f}$ as

\begin{align} p(\mathbf{f}) &= p\left( \underbrace{ \begin{bmatrix} \delta(t-T)\\ \boldsymbol{\Phi} \end{bmatrix} }_{=:\mathbf{A}} \left. \begin{bmatrix} \mathbf{f}_{\text{n}}\\ \mathbf{w} \end{bmatrix} \right| \mathbf{0}, \mathbf{A} \underbrace{ \begin{bmatrix} \mathbf{K}_{\mathbf{f}} & \\ & \mathbf{K}_{\mathbf{w}} \end{bmatrix} }_{=:\boldsymbol{\Sigma}} \mathbf{A}^\top \right)\enspace, \end{align}

where $\mathbf{K}_{\mathbf{f}}$ is the covariance describing the non-parametric part $\mathbf{f}_\text{n}\sim\mathcal{N}(\mathbf{0}, \mathbf{K}_\mathbf{f})$ and $\mathbf{K}_{\mathbf{w}}$ is the covariance of the prior over $\mathbf{w}\sim\mathcal{N}(\mathbf{w}|\mathbf{0}, \mathbf{K}_{\mathbf{w}})$.

Thus we can now predict the different parts and even the paramters $\mathbf{w}$ themselves using (Note: If someone is willing to write down the proper path to this, here a welcome and thank you very much. Thanks to Philipp Hennig for his ideas in this.)

\begin{align} p(\mathbf{f}|\mathbf{y}) &= \mathcal{N}(\mathbf{f} | \boldsymbol{\Sigma}\mathbf{A}^\top \underbrace{ (\mathbf{A}\boldsymbol{\Sigma}\mathbf{A}^\top + \sigma^2\mathbf{I})^{-1}}_{=:\mathbf{K}^{-1}}\mathbf{y}, \boldsymbol{\Sigma}-\boldsymbol{\Sigma}\mathbf{A}^\top\mathbf{K}^{-1}\mathbf{A}\boldsymbol{\Sigma}) \\ p(\mathbf{w}|\mathbf{y}) &= \mathcal{N}(\mathbf{w} | \mathbf{K}_\mathbf{w}\boldsymbol{\Phi}^\top\mathbf{K}^{-1}\mathbf{y}, \mathbf{K}_{\mathbf{w}}-\mathbf{K}_{\mathbf{w}}\boldsymbol{\Phi}^\top\mathbf{K}^{-1}\boldsymbol{\Phi}\mathbf{K}_{\mathbf{w}})) \\ p(\mathbf{f}_\text{n}|\mathbf{y}) &= \mathcal{N}(\mathbf{f}_\text{n}| \mathbf{K}_\mathbf{f}\mathbf{K}^{-1}\mathbf{y}, \mathbf{K}_{\mathbf{f}}-\mathbf{K}_{\mathbf{f}}\mathbf{K}^{-1}\mathbf{K}_{\mathbf{f}})) \end{align}
In [1]:
import GPy, numpy as np, pandas as pd
from GPy.kern import LinearSlopeBasisFuncKernel, DomainKernel, ChangePointBasisFuncKernel
%matplotlib inline
from matplotlib import pyplot as plt


We will create some data with a non-linear function, strongly driven by piecewise linear trends:

In [2]:
np.random.seed(12345)

In [3]:
x = np.random.uniform(0, 10, 40)[:,None]
x.sort(0)
starts, stops = np.arange(0, 10, 3), np.arange(1, 11, 3)

k_lin = LinearSlopeBasisFuncKernel(1, starts, stops, variance=1., ARD=1)
Phi = k_lin.phi(x)
_ = plt.plot(x, Phi)


We will assume the prior over $w_i\sim\mathcal{N}(0, 3)$ and a Matern32 structure in the non-parametric part. Additionally, we add a half parametric part, which is a periodic effect only active between $x\in[3,8]$:

In [4]:
k = GPy.kern.Matern32(1, .3)
Kf = k.K(x)

k_per = GPy.kern.PeriodicMatern32(1, variance=100, period=1)
k_per.period.fix()
k_dom = DomainKernel(1, 1., 5.)
k_perdom = k_per * k_dom
Kpd = k_perdom.K(x)

In [5]:
np.random.seed(1234)
alpha = np.random.gamma(3, 1, Phi.shape[1])
w = np.random.normal(0, alpha)[:,None]

f_SE = np.random.multivariate_normal(np.zeros(x.shape[0]), Kf)[:, None]
f_perdom = np.random.multivariate_normal(np.zeros(x.shape[0]), Kpd)[:, None]
f_w = Phi.dot(w)

f = f_SE + f_w + f_perdom

y = f + np.random.normal(0, .1, f.shape)

In [6]:
plt.plot(x, f_w)
_ = plt.plot(x, y)
# Make sure the function is driven by the linear trend, as there can be a difficulty in identifiability.


With this data, we can fit a model using the basis functions as paramtric part. If you want to implement your own basis function kernel, see GPy.kern._src.basis_funcs.BasisFuncKernel and implement the necessary parts. Usually it is enough to implement the phi(X) method, returning the higher dimensional mapping of inputs X.

In [7]:
k = (GPy.kern.Bias(1)
+ GPy.kern.Matern52(1)
+ LinearSlopeBasisFuncKernel(1, ARD=1, start=starts, stop=stops, variance=.1, name='linear_slopes')
+ k_perdom.copy()
)

k.randomize()
m = GPy.models.GPRegression(x, y, k)

In [8]:
m.checkgrad()

Out[8]:
True
In [9]:
m.optimize()

In [10]:
m.plot()

Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f67951eecd0>
In [11]:
x_pred = np.linspace(0, 10, 500)[:,None]
pred_SE, var_SE = m._raw_predict(x_pred, kern=m.kern.Mat52)
pred_per, var_per = m._raw_predict(x_pred, kern=m.kern.mul)
pred_bias, var_bias = m._raw_predict(x_pred, kern=m.kern.bias)
pred_lin, var_lin = m._raw_predict(x_pred, kern=m.kern.linear_slopes)

In [12]:
m.plot_f(resolution=500, predict_kw=dict(kern=m.kern.Mat52), plot_data=False)
plt.plot(x, f_SE)

Out[12]:
[<matplotlib.lines.Line2D at 0x7f67951f7c10>]
In [13]:
m.plot_f(resolution=500, predict_kw=dict(kern=m.kern.mul), plot_data=False)
plt.plot(x, f_perdom)

Out[13]:
[<matplotlib.lines.Line2D at 0x7f6794fa1ad0>]
In [14]:
m.plot_f(resolution=500, predict_kw=dict(kern=m.kern.linear_slopes), plot_data=False)
plt.plot(x, f_w)

Out[14]:
[<matplotlib.lines.Line2D at 0x7f6794ee4190>]
In [15]:
w_pred, w_var = m.kern.linear_slopes.posterior_inf()

In [16]:
df = pd.DataFrame(w, columns=['truth'], index=np.arange(Phi.shape[1]))
df['mean'] = w_pred
df['std'] = np.sqrt(w_var.diagonal())

In [17]:
np.round(df, 2)

Out[17]:
truth mean std
0 0.06 0.00 0.00
1 -2.59 -2.34 0.63
2 1.90 1.42 0.51
3 4.36 4.46 0.51