MetaModelling and Deploying ML Software
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Abstract
Data is not so much the new oil, it is the new software. Data driven algorithms are increasingly present in continuously deployed production software. What challenges does this present and how can the mathematical sciences help?
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Introduction
Peppercorns [edit]
Asking Siri “What is a trillion to the power of a thousand minus one?” leads to a 30 minute response^{1} consisting of only 9s. I found this out because my nine year old grabbed my phone and did it. The only way to stop Siri was to force closure. This is an interesting example of a system feature that’s not a bug, in fact it requires clever processing from Wolfram Alpha. But it’s an unexpected result from the system performing correctly.
This challenge of facing a circumstance that was unenvisaged in design but has consequences in deployment becomes far larger when the environment is uncontrolled. Or in the extreme case, where actions of the intelligent system effect the wider environment and change it.
These unforseen circumstances are likely to lead to need for much more efficient turnaround and update for our intelligent systems. Whether we are correcting for security flaws (which are bugs) or unenvisaged circumstantial challenges: an issue I’m referring to as peppercorns. Rapid deployment of system updates is required. For example, Apple have “fixed” the problem of Siri returning long numbers.
The challenge is particularly acute because of the scale at which we can deploy AI solutions. This means when something does go wrong, it may be going wrong in billions of households simultaneously.
You can also check my blog post on “Decision Making and Diversity” You can also check my blog post on “Natural vs Artifical Intelligence”
Deep Learning [edit]
DeepFace [edit]
The DeepFace architecture (Taigman et al. 2014) consists of layers that deal with translation and rotational invariances. These layers are followed by three locallyconnected layers and two fullyconnected layers. Color illustrates feature maps produced at each layer. The neural network includes more than 120 million parameters, where more than 95% come from the local and fully connected layers.
Deep Learning as Pinball [edit]
Sometimes deep learning models are described as being like the brain, or too complex to understand, but one analogy I find useful to help the gist of these models is to think of them as being similar to early pin ball machines.
In a deep neural network, we input a number (or numbers), whereas in pinball, we input a ball.
Think of the location of the ball on the leftright axis as a single number. Our simple pinball machine can only take one number at a time. As the ball falls through the machine, each layer of pins can be thought of as a different layer of ‘neurons’. Each layer acts to move the ball from left to right.
In a pinball machine, when the ball gets to the bottom it might fall into a hole defining a score, in a neural network, that is equivalent to the decision: a classification of the input object.
An image has more than one number associated with it, so it is like playing pinball in a hyperspace.
pods.notebook.display_plots('pinball{sample:0>3}.svg',
'../slides/diagrams',
sample=IntSlider(1, 1, 2, 1))
Learning involves moving all the pins to be in the correct position, so that the ball ends up in the right place when it’s fallen through the machine. But moving all these pins in hyperspace can be difficult.
In a hyperspace you have to put a lot of data through the machine for to explore the positions of all the pins. Even when you feed many millions of data points through the machine, there are likely to be regions in the hyperspace where no ball has passed. When future test data passes through the machine in a new route unusual things can happen.
Adversarial examples exploit this high dimensional space. If you have access to the pinball machine, you can use gradient methods to find a position for the ball in the hyper space where the image looks like one thing, but will be classified as another.
Probabilistic methods explore more of the space by considering a range of possible paths for the ball through the machine. This helps to make them more data efficient and gives some robustness to adversarial examples.
Containerization [edit]
Containerization has had a dramatic effect on global economics, placing many people in the developing world at the end of the supply chain.


For example, you can buy Wild Alaskan Cod fished from Alaska, processed in China, sold in North America. This is driven by the low cost of transport for frozen cod vs the higher relative cost of cod processing in the US versus China. Similarly, Scottish prawns are also processed in China for sale in the UK.
This effect on cost of transport vs cost of processing is the main driver of the topology of the modern supply chain and the associated effect of globalization. If transport is much cheaper than processing, then processing will tend to agglomerate in places where processing costs can be minimized.
Solve Supply Chain, then solve everything else.
Emulation [edit]
In many real world systems, decisions are made through simulating the environment. Simulations may operate at different granularities. For example, simulations are used in weather forecasts and climate forecasts. The UK Met office uses the same code for both, but operates climate simulations one at greater spatial and temporal resolutions.
A statistical emulator is a datadriven model that learns about the underlying simulation. Importantly, learns with uncertainty, so it ‘knows what it doesn’t know’. In practice, we can call the emulator in place of the simulator. If the emulator ‘doesn’t know’, it can call the simulator for the answer.
As well as reconstructing an individual simulator, the emulator can calibrate the simulation to the real world, by monitoring differences between the simulator and real data. This allows the emulator to characterise where the simulation can be relied on, i.e. we can validate the simulator.
Similarly, the emulator can adjudicate between simulations. This is known as multifidelity emulation. The emulator characterizes which emulations perform well where.
If all this modelling is done with judiscious handling of the uncertainty, the computational doubt, then the emulator can assist in desciding what experiment should be run next to aid a decision: should we run a simulator, in which case which one, or should we attempt to acquire data from a real world intervention.
Uncertainty Quantification [edit]
Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
We will to illustrate different concepts of Uncertainty Quantification (UQ) and the role that Gaussian processes play in this field. Based on a simple simulator of a car moving between a valley and a mountain, we are going to illustrate the following concepts:
Systems emulation. Many real world decisions are based on simulations that can be computationally very demanding. We will show how simulators can be replaced by emulators: Gaussian process models fitted on a few simulations that can be used to replace the simulator. Emulators are cheap to compute, fast to run, and always provide ways to quantify the uncertainty of how precise they are compared the original simulator.
Emulators in optimization problems. We will show how emulators can be used to optimize blackbox functions that are expensive to evaluate. This field is also called Bayesian Optimization and has gained an increasing relevance in machine learning as emulators can be used to optimize computer simulations (and machine learning algorithms) quite efficiently.
Multifidelity emulation methods. In many scenarios we have simulators of different quality about the same measure of interest. In these cases the goal is to merge all sources of information under the same model so the final emulator is cheaper and more accurate than an emulator fitted only using data from the most accurate and expensive simulator.
Mountain Car Simulator [edit]
To illustrate the above mentioned concepts we we use the mountain car simulator. This simulator is widely used in machine learning to test reinforcement learning algorithms. The goal is to define a control policy on a car whose objective is to climb a mountain. Graphically, the problem looks as follows:
The goal is to define a sequence of actions (push the car right or left with certain intensity) to make the car reach the flag after a number T of time steps.
At each time step t, the car is characterized by a vector $\inputVector_{t} = (p_t,v_t)$ of states which are respectively the the position and velocity of the car at time t. For a sequence of states (an episode), the dynamics of the car is given by
$$\inputVector_{t+1} = \mappingFunction(\inputVector_{t},\textbf{u}_{t})$$
where u_{t} is the value of an action force, which in this example corresponds to push car to the left (negative value) or to the right (positive value). The actions across a full episode are represented in a policy $\textbf{u}_{t} = \pi(\inputVector_{t},\theta)$ that acts according to the current state of the car and some parameters θ. In the following examples we will assume that the policy is linear which allows us to write $\pi(\inputVector_{t},\theta)$ as
$$\pi(\inputVector,\theta)= \theta_0 + \theta_p p + \theta_vv.$$
For t = 1, …, T now given some initial state $\inputVector_{0}$ and some some values of each u_{t}, we can simulate the full dynamics of the car for a full episode using Gym. The values of u_{t} are fully determined by the parameters of the linear controller.
After each episode of length T is complete, a reward function R_{T}(θ) is computed. In the mountain car example the reward is computed as 100 for reaching the target of the hill on the right hand side, minus the squared sum of actions (a real negative to push to the left and a real positive to push to the right) from start to goal. Note that our reward depend on θ as we make it dependent on the parameters of the linear controller.
Emulate the Mountain Car
Our goal in this section is to find the parameters θ of the linear controller such that
θ^{*} = argmax_{θ}R_{T}(θ).
In this section, we directly use Bayesian optimization to solve this problem. We will use GPyOpt so we first define the objective function:
obj_func = lambda x: mc.run_simulation(env, x)[0]
objective = GPyOpt.core.task.SingleObjective(obj_func)
For each set of parameter values of the linear controller we can run an episode of the simulator (that we fix to have a horizon of T = 500) to generate the reward. Using as input the parameters of the controller and as outputs the rewards we can build a Gaussian process emulator of the reward.
We start defining the input space, which is threedimensional:
##  We define the input space of the emulator
space= [{'name':'postion_parameter', 'type':'continuous', 'domain':(1.2, +1)},
{'name':'velocity_parameter', 'type':'continuous', 'domain':(1/0.07, +1/0.07)},
{'name':'constant', 'type':'continuous', 'domain':(1, +1)}]
design_space = GPyOpt.Design_space(space=space)
Now we initizialize a Gaussian process emulator.
In Bayesian optimization an acquisition function is used to balance exploration and exploitation to evaluate new locations close to the optimum of the objective. In this notebook we select the expected improvement (EI). For further details have a look to the review paper of Shahriari et al (2015).
aquisition_optimizer = GPyOpt.optimization.AcquisitionOptimizer(design_space)
acquisition = GPyOpt.acquisitions.AcquisitionEI(model, design_space, optimizer=aquisition_optimizer)
evaluator = GPyOpt.core.evaluators.Sequential(acquisition) # Collect points sequentially, no parallelization.
To initalize the model we start sampling some initial points (25) for the linear controler randomly.
n_initial_points = 25
random_design = RandomDesign(design_space)
initial_design = random_design.get_samples(n_initial_points)
Before we start any optimization, lets have a look to the behavior of the car with the first of these initial points that we have selected randomly.
random_controller = initial_design[0,:]
_, _, _, frames = mc.run_simulation(env, np.atleast_2d(random_controller), render=True)
anim=mc.animate_frames(frames, 'Random linear controller')
As we can see the random linear controller does not manage to push the car to the top of the mountain. Now, let’s optimize the regret using Bayesian optimization and the emulator for the reward. We try 50 new parameters chosen by the EI.
max_iter = 50
bo = GPyOpt.methods.ModularBayesianOptimization(model, design_space, objective, acquisition, evaluator, initial_design)
bo.run_optimization(max_iter = max_iter )
Now we visualize the result for the best controller that we have found with Bayesian optimization.
_, _, _, frames = mc.run_simulation(env, np.atleast_2d(bo.x_opt), render=True)
anim=mc.animate_frames(frames, 'Best controller after 50 iterations of Bayesian optimization')
he car can now make it to the top of the mountain! Emulating the reward function and using the EI helped as to find a linear controller that solves the problem.
Data Efficient Emulation [edit]
In the previous section we solved the mountain car problem by directly emulating the reward but no considerations about the dynamics $\inputVector_{t+1} = \mappingFunction(\inputVector_{t},\textbf{u}_{t})$ of the system were made. Note that we had to run 75 episodes of 500 steps each to solve the problem, which required to call the simulator 500 × 75 = 37500 times. In this section we will show how it is possible to reduce this number by building an emulator for f that can later be used to directly optimize the control.
The inputs of the model for the dynamics are the velocity, the position and the value of the control so create this space accordingly.
space_dynamics = [{'name':'position', 'type':'continuous', 'domain':[1.2, +0.6]},
{'name':'velocity', 'type':'continuous', 'domain':[0.07, +0.07]},
{'name':'action', 'type':'continuous', 'domain':[1, +1]}]
design_space_dynamics = GPyOpt.Design_space(space=space_dynamics)
The outputs are the velocity and the position. Indeed our model will capture the change in position and velocity on time. That is, we will model
Δv_{t + 1} = v_{t + 1} − v_{t}
Δx_{t + 1} = p_{t + 1} − p_{t}
with Gaussian processes with prior mean v_{t} and p_{t} respectively. As a covariance function, we use a Matern52. We need therefore two models to capture the full dynamics of the system.
position_model = GPyOpt.models.GPModel(optimize_restarts=5, verbose=False, exact_feval=True, ARD=True)
velocity_model = GPyOpt.models.GPModel(optimize_restarts=5, verbose=False, exact_feval=True, ARD=True)
Next, we sample some input parameters and use the simulator to compute the outputs. Note that in this case we are not running the full episodes, we are just using the simulator to compute $\inputVector_{t+1}$ given $\inputVector_{t}$ and u_{t}.
import numpy as np
from GPyOpt.experiment_design.random_design import RandomDesign
import mountain_car as mc
###  Random locations of the inputs
n_initial_points = 500
random_design_dynamics = RandomDesign(design_space_dynamics)
initial_design_dynamics = random_design_dynamics.get_samples(n_initial_points)
###  Simulation of the (normalized) outputs
y = np.zeros((initial_design_dynamics.shape[0], 2))
for i in range(initial_design_dynamics.shape[0]):
y[i, :] = mc.simulation(initial_design_dynamics[i, :])
# Normalize the data from the simulation
y_normalisation = np.std(y, axis=0)
y_normalised = y/y_normalisation
In general we might use much smarter strategies to design our emulation of the simulator. For example, we could use the variance of the predictive distributions of the models to collect points using uncertainty sampling, which will give us a better coverage of the space. For simplicity, we move ahead with the 500 randomly selected points.
Now that we have a data set, we can update the emulators for the location and the velocity.
position_model.updateModel(initial_design_dynamics, y[:, [0]], None, None)
velocity_model.updateModel(initial_design_dynamics, y[:, [1]], None, None)
We can now have a look to how the emulator and the simulator match. First, we show a contour plot of the car aceleration for each pair of can position and velocity. You can use the bar bellow to play with the values of the controler to compare the emulator and the simulator.
We can see how the emulator is doing a fairly good job approximating the simulator. On the edges, however, it struggles to captures the dynamics of the simulator.
Given some input parameters of the linear controlling, how do the dynamics of the emulator and simulator match? In the following figure we show the position and velocity of the car for the 500 time steps of an episode in which the parameters of the linear controller have been fixed beforehand. The value of the input control is also shown.
We now make explicit use of the emulator, using it to replace the simulator and optimize the linear controller. Note that in this optimization, we don’t need to query the simulator anymore as we can reproduce the full dynamics of an episode using the emulator. For illustrative purposes, in this example we fix the initial location of the car.
We define the objective reward function in terms of the simulator.
###  Optimize control parameters with emulator
car_initial_location = np.asarray([0.58912799, 0])
###  Reward objective function using the emulator
obj_func_emulator = lambda x: mc.run_emulation([position_model, velocity_model], x, car_initial_location)[0]
objective_emulator = GPyOpt.core.task.SingleObjective(obj_func_emulator)
And as before, we use Bayesian optimization to find the best possible linear controller.
###  Elements of the optimization that will use the multifidelity emulator
model = GPyOpt.models.GPModel(optimize_restarts=5, verbose=False, exact_feval=True, ARD=True)
The design space is the three continuous variables that make up the linear controller.
space= [{'name':'linear_1', 'type':'continuous', 'domain':(1/1.2, +1)},
{'name':'linear_2', 'type':'continuous', 'domain':(1/0.07, +1/0.07)},
{'name':'constant', 'type':'continuous', 'domain':(1, +1)}]
design_space = GPyOpt.Design_space(space=space)
aquisition_optimizer = GPyOpt.optimization.AcquisitionOptimizer(design_space)
random_design = RandomDesign(design_space)
initial_design = random_design.get_samples(25)
We set the acquisition function to be expected improvement using GPyOpt
.
acquisition = GPyOpt.acquisitions.AcquisitionEI(model, design_space, optimizer=aquisition_optimizer)
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
bo_emulator = GPyOpt.methods.ModularBayesianOptimization(model, design_space, objective_emulator, acquisition, evaluator, initial_design)
bo_emulator.run_optimization(max_iter=50)
_, _, _, frames = mc.run_simulation(env, np.atleast_2d(bo_emulator.x_opt), render=True)
anim=mc.animate_frames(frames, 'Best controller using the emulator of the dynamics')
And the problem is again solved, but in this case we have replaced the simulator of the car dynamics by a Gaussian process emulator that we learned by calling the simulator only 500 times. Compared to the 37500 calls that we needed when applying Bayesian optimization directly on the simulator this is a great gain.
MultiFidelity Emulation [edit]
In some scenarios we have simulators of the same environment that have different fidelities, that is that reflect with different level of accuracy the dynamics of the real world. Running simulations of the different fidelities also have a different cost: hight fidelity simulations are more expensive the cheaper ones. If we have access to these simulators we can combine high and low fidelity simulations under the same model.
So let’s assume that we have two simulators of the mountain car dynamics, one of high fidelity (the one we have used) and another one of low fidelity. The traditional approach to this form of multifidelity emulation is to assume that
$$\mappingFunction_i\left(\inputVector\right) = \rho\mappingFunction_{i1}\left(\inputVector\right) + \delta_i\left(\inputVector \right)$$
where $\mappingFunction_{i1}\left(\inputVector\right)$ is a low fidelity simulation of the problem of interest and $\mappingFunction_i\left(\inputVector\right)$ is a higher fidelity simulation. The function $\delta_i\left(\inputVector \right)$ represents the difference between the lower and higher fidelity simulation, which is considered additive. The additive form of this covariance means that if $\mappingFunction_{0}\left(\inputVector\right)$ and $\left\{\delta_i\left(\inputVector \right)\right\}_{i=1}^m$ are all Gaussian processes, then the process over all fidelities of simuation will be a joint Gaussian process.
But with Deep Gaussian processes we can consider the form
$$\mappingFunction_i\left(\inputVector\right) = \mappingFunctionTwo_{i}\left(\mappingFunction_{i1}\left(\inputVector\right)\right) + \delta_i\left(\inputVector \right),$$
where the low fidelity representation is non linearly transformed by $\mappingFunctionTwo(\cdot)$ before use in the process. This is the approach taken in Perdikaris et al. (2017). But once we accept that these models can be composed, a highly flexible framework can emerge. A key point is that the data enters the model at different levels, and represents different aspects. For example these correspond to the two fidelities of the mountain car simulator.
We start by sampling both of them at 250 random input locations.
###  Collect points from low and high fidelity simulator  ###
space = GPyOpt.Design_space([
{'name':'position', 'type':'continuous', 'domain':(1.2, +1)},
{'name':'velocity', 'type':'continuous', 'domain':(0.07, +0.07)},
{'name':'action', 'type':'continuous', 'domain':(1, +1)}])
n_points = 250
random_design = GPyOpt.experiment_design.RandomDesign(space)
x_random = random_design.get_samples(n_points)
Next, we evaluate the high and low fidelity simualtors at those locations.
d_position_hf = np.zeros((n_points, 1))
d_velocity_hf = np.zeros((n_points, 1))
d_position_lf = np.zeros((n_points, 1))
d_velocity_lf = np.zeros((n_points, 1))
#  Collect high fidelity points
for i in range(0, n_points):
d_position_hf[i], d_velocity_hf[i] = mc.simulation(x_random[i, :])
#  Collect low fidelity points
for i in range(0, n_points):
d_position_lf[i], d_velocity_lf[i] = mc.low_cost_simulation(x_random[i, :])
It is time to build the multifidelity model for both the position and the velocity.
As we did in the previous section we use the emulator to optimize the simulator. In this case we use the high fidelity output of the emulator.
###  Optimize controller parameters
obj_func = lambda x: mc.run_simulation(env, x)[0]
obj_func_emulator = lambda x: mc.run_emulation([position_model, velocity_model], x, car_initial_location)[0]
objective_multifidelity = GPyOpt.core.task.SingleObjective(obj_func)
And we optimize using Bayesian optimzation.
model = GPyOpt.models.GPModel(optimize_restarts=5, verbose=False, exact_feval=True, ARD=True)
space= [{'name':'linear_1', 'type':'continuous', 'domain':(1/1.2, +1)},
{'name':'linear_2', 'type':'continuous', 'domain':(1/0.07, +1/0.07)},
{'name':'constant', 'type':'continuous', 'domain':(1, +1)}]
design_space = GPyOpt.Design_space(space=space)
aquisition_optimizer = GPyOpt.optimization.AcquisitionOptimizer(design_space)
n_initial_points = 25
random_design = RandomDesign(design_space)
initial_design = random_design.get_samples(n_initial_points)
acquisition = GPyOpt.acquisitions.AcquisitionEI(model, design_space, optimizer=aquisition_optimizer)
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
bo_multifidelity = GPyOpt.methods.ModularBayesianOptimization(model, design_space, objective_multifidelity, acquisition, evaluator, initial_design)
bo_multifidelity.run_optimization(max_iter=50)
_, _, _, frames = mc.run_simulation(env, np.atleast_2d(bo_multifidelity.x_opt), render=True)
anim=mc.animate_frames(frames, 'Best controller with multifidelity emulator')
Best Controller with MultiFidelity Emulator
And problem solved! We see how the problem is also solved with 250 observations of the high fidelity simulator and 250 of the low fidelity simulator.
Emukit Playground [edit]
Emukit playground is a software toolkit for exploring the use of statistical emulation as a tool. It was built by Adam Hirst, during his software engineering internship at Amazon and supervised by Cliff McCollum.
You can explore Bayesian optimization of a taxi simulation.
Emukit [edit]
The aim is to provide a suite where different approaches to emulation are assimilated under one roof. The current version of Emukit includes multifidelity emulation for build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost; Bayesian optimisation for optimising physical experiments and tune parameters of machine learning algorithms or other computational simulations; experimental design and active learning: design the most informative experiments and perform active learning with machine learning models; sensitivity analysis: analyse the influence of inputs on the outputs of a given system; and Bayesian quadrature: efficiently compute the integrals of functions that are expensive to evaluate.
{For monitoring systems in production, emulation needn’t just be about simulator models. What we envisage, is that even data driven models could be emulated. This is important for understanding system behaviour, how the different components are interconnected. This drives the notion of the information dynamics of the machine learning system. What is the effect of one particular intervention in the wider system? One way of answering this is through emulation. But it requires that our machine learning models (and our simulators) are deployed in an environment where emulation can be automatically deployed. The resulting system would allow us to monitor the downstream effects of indivdiual decision making on the wider system.
You can also check my blog post on “New Directions in Kernels and Gaussian Processes”
Deep Gaussian Processes
One challenge is developing flexible enough models to perform the emulation that also propagate uncertainty through the model. One candidate set of models for this challenge is deep Gaussian processes (DGPs). For the remainder of these notes we introduce the theory behind DGPs.
While there are some difficulties in algorithmically implementing these algorithms at scale, they are mathematically far simpler than the equivalent neural network models, and perhaps as a result offer greater promise for theoretical understanding of deep learning (see e.g. Dunlop et al., n.d.).
Including the low rank decomposition of $\mappingMatrix$ in the neural network, we obtain a new mathematical form. Effectively, we are adding additional latent layers, $\latentVector$, in between each of the existing hidden layers. In a neural network these are sometimes known as bottleneck layers. The network can now be written mathematically as
$$
\begin{align}
\latentVector_{1} &= \eigenvectwoMatrix^\top_1 \inputVector\\
\hiddenVector_{1} &= \basisFunction\left(\eigenvectorMatrix_1 \latentVector_{1}\right)\\
\latentVector_{2} &= \eigenvectwoMatrix^\top_2 \hiddenVector_{1}\\
\hiddenVector_{2} &= \basisFunction\left(\eigenvectorMatrix_2 \latentVector_{2}\right)\\
\latentVector_{3} &= \eigenvectwoMatrix^\top_3 \hiddenVector_{2}\\
\hiddenVector_{3} &= \basisFunction\left(\eigenvectorMatrix_3 \latentVector_{3}\right)\\
\dataVector &= \mappingVector_4^\top\hiddenVector_{3}.
\end{align}
$$
$$
\begin{align}
\latentVector_{1} &= \eigenvectwoMatrix^\top_1 \inputVector\\
\latentVector_{2} &= \eigenvectwoMatrix^\top_2 \basisFunction\left(\eigenvectorMatrix_1 \latentVector_{1}\right)\\
\latentVector_{3} &= \eigenvectwoMatrix^\top_3 \basisFunction\left(\eigenvectorMatrix_2 \latentVector_{2}\right)\\
\dataVector &= \mappingVector_4 ^\top \latentVector_{3}
\end{align}
$$
Now if we replace each of these neural networks with a Gaussian process. This is equivalent to taking the limit as the width of each layer goes to infinity, while appropriately scaling down the outputs.
$$
\begin{align}
\latentVector_{1} &= \mappingFunctionVector_1\left(\inputVector\right)\\
\latentVector_{2} &= \mappingFunctionVector_2\left(\latentVector_{1}\right)\\
\latentVector_{3} &= \mappingFunctionVector_3\left(\latentVector_{2}\right)\\
\dataVector &= \mappingFunctionVector_4\left(\latentVector_{3}\right)
\end{align}
$$
Olympic Marathon Data [edit]


The first thing we will do is load a standard data set for regression modelling. The data consists of the pace of Olympic Gold Medal Marathon winners for the Olympics from 1896 to present. First we load in the data and plot.
data = pods.datasets.olympic_marathon_men()
x = data['X']
y = data['Y']
offset = y.mean()
scale = np.sqrt(y.var())
xlim = (1875,2030)
ylim = (2.5, 6.5)
yhat = (yoffset)/scale
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
_ = ax.plot(x, y, 'r.',markersize=10)
ax.set_xlabel('year', fontsize=20)
ax.set_ylabel('pace min/km', fontsize=20)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
mlai.write_figure(figure=fig,
filename='../slides/diagrams/datasets/olympicmarathon.svg',
transparent=True,
frameon=True)
Things to notice about the data include the outlier in 1904, in this year, the olympics was in St Louis, USA. Organizational problems and challenges with dust kicked up by the cars following the race meant that participants got lost, and only very few participants completed.
More recent years see more consistently quick marathons.
Alan Turing [edit]


If we had to summarise the objectives of machine learning in one word, a very good candidate for that word would be generalization. What is generalization? From a human perspective it might be summarised as the ability to take lessons learned in one domain and apply them to another domain. If we accept the definition given in the first session for machine learning,
$$
\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}
$$
then we see that without a model we can’t generalise: we only have data. Data is fine for answering very specific questions, like “Who won the Olympic Marathon in 2012?”, because we have that answer stored, however, we are not given the answer to many other questions. For example, Alan Turing was a formidable marathon runner, in 1946 he ran a time 2 hours 46 minutes (just under four minutes per kilometer, faster than I and most of the other Endcliffe Park Run runners can do 5 km). What is the probability he would have won an Olympics if one had been held in 1946?
To answer this question we need to generalize, but before we formalize the concept of generalization let’s introduce some formal representation of what it means to generalize in machine learning.
Our first objective will be to perform a Gaussian process fit to the data, we’ll do this using the GPy software.
m_full = GPy.models.GPRegression(x,yhat)
_ = m_full.optimize() # Optimize parameters of covariance function
The first command sets up the model, then m_full.optimize()
optimizes the parameters of the covariance function and the noise level of the model. Once the fit is complete, we’ll try creating some test points, and computing the output of the GP model in terms of the mean and standard deviation of the posterior functions between 1870 and 2030. We plot the mean function and the standard deviation at 200 locations. We can obtain the predictions using y_mean, y_var = m_full.predict(xt)
xt = np.linspace(1870,2030,200)[:,np.newaxis]
yt_mean, yt_var = m_full.predict(xt)
yt_sd=np.sqrt(yt_var)
Now we plot the results using the helper function in teaching_plots
.
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
plot.model_output(m_full, scale=scale, offset=offset, ax=ax, xlabel='year', ylabel='pace min/km', fontsize=20, portion=0.2)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
mlai.write_figure(figure=fig,
filename='../slides/diagrams/gp/olympicmarathongp.svg',
transparent=True, frameon=True)
Fit Quality
In the fit we see that the error bars (coming mainly from the noise variance) are quite large. This is likely due to the outlier point in 1904, ignoring that point we can see that a tighter fit is obtained. To see this making a version of the model, m_clean
, where that point is removed.
x_clean=np.vstack((x[0:2, :], x[3:, :]))
y_clean=np.vstack((y[0:2, :], y[3:, :]))
m_clean = GPy.models.GPRegression(x_clean,y_clean)
_ = m_clean.optimize()
Deep GP Fit [edit]
Let’s see if a deep Gaussian process can help here. We will construct a deep Gaussian process with one hidden layer (i.e. one Gaussian process feeding into another).
Build a Deep GP with an additional hidden layer (one dimensional) to fit the model.
hidden = 1
m = deepgp.DeepGP([y.shape[1],hidden,x.shape[1]],Y=yhat, X=x, inits=['PCA','PCA'],
kernels=[GPy.kern.RBF(hidden,ARD=True),
GPy.kern.RBF(x.shape[1],ARD=True)], # the kernels for each layer
num_inducing=50, back_constraint=False)
Now optimize the model.
for layer in m.layers:
layer.likelihood.variance.constrain_positive(warning=False)
m.optimize(messages=True,max_iters=10000)
Olympic Marathon Data Deep GP
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
plot.model_sample(m, scale=scale, offset=offset, samps=10, ax=ax,
xlabel='year', ylabel='pace min/km', portion = 0.225)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
mlai.write_figure(figure=fig, filename='../slides/diagrams/deepgp/olympicmarathondeepgpsamples.svg',
transparent=True, frameon=True)
Olympic Marathon Data Deep GP
Fitted GP for each layer
Now we explore the GPs the model has used to fit each layer. First of all, we look at the hidden layer.
m.visualize(scale=scale, offset=offset, xlabel='year',
ylabel='pace min/km',xlim=xlim, ylim=ylim,
dataset='olympicmarathon',
diagrams='../slides/diagrams/deepgp')
pods.notebook.display_plots('olympicmarathondeepgplayer{sample:0>1}.svg',
'../slides/diagrams/deepgp', sample=(0,1))
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
m.visualize_pinball(ax=ax, scale=scale, offset=offset, points=30, portion=0.1,
xlabel='year', ylabel='pace km/min', vertical=True)
mlai.write_figure(figure=fig, filename='../slides/diagrams/deepgp/olympicmarathondeepgppinball.svg',
transparent=True, frameon=True)
Olympic Marathon Pinball Plot
The pinball plot shows the flow of any input ball through the deep Gaussian process. In a pinball plot a series of vertical parallel lines would indicate a purely linear function. For the olypmic marathon data we can see the first layer begins to shift from input towards the right. Note it also does so with some uncertainty (indicated by the shaded backgrounds). The second layer has less uncertainty, but bunches the inputs more strongly to the right. This input layer of uncertainty, followed by a layer that pushes inputs to the right is what gives the heteroschedastic noise.
MXFusion: Modular Probabilistic Programming on MXNet [edit]
One challenge for practitioners in Gaussian processes, is flexible software that allows the construction of the relevant GP modle. With this in mind, the Amazon Cambridge team has developed MXFusion. It is a modular probabilistic programming language focussed on efficient implementation of hybrid GPneural network models, but with additional probabilistic programming capabilities.
We developed the framework for greater ease of transitioning models from ‘science’ to ‘production’, our aim was to have code that could be created by scientists, but deployed in our systems through solutions such as AWS SageMaker.


Conclusion
Machine learning models are deployed as components in an interacting system to achieve modern AI. Some of those components are inspired by a mechanistic understanding of the world around us (e.g. economic or physical understanding).
Meta modelling involves fiting machine learning models to existing systems to improve speed and interpretability.
Deep Gaussian processes are a flexible approach to meta modelling, which provide the necessary uncertainty estimates and the potential for being more mathematically tractable.
References
Dunlop, Matthew M., Mark A. Girolami, Andrew M. Stuart, and Aretha L. Teckentrup. n.d. “How Deep Are Deep Gaussian Processes?” Journal of Machine Learning Research 19 (54): 1–46. http://jmlr.org/papers/v19/18015.html.
Perdikaris, P., M. Raissi, A. Damianou, N. D. Lawrence, and G. E. Karniadakis. 2017. “Nonlinear Information Fusion Algorithms for DataEfficient MultiFidelity Modelling.” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 473 (2198). The Royal Society. https://doi.org/10.1098/rspa.2016.0751.
Taigman, Yaniv, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf. 2014. “DeepFace: Closing the Gap to HumanLevel Performance in Face Verification.” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2014.220.
Apple has fixed this issue so that Siri no longer does this.↩