Mike's Page

A colleague at Makerere is taking over teaching the Advanced Programming course. It needs some updating.

Yesterday I took part in the department of Development Informatics big data event at the University of Manchester.

Really interesting discussion. Made me think more carefully about what effect the blind-spots in my data will have, and how collecting data can make these blind spots worse.

At the end of the day we had a bit of a discussion. Our group was particularly concerned by the effect on power shifts or concentrations with increased data aggregation (similar to Neil Lawrence’s Digital Oligarchy).

We started with the question “What will be done with the data?” and then “How does it become information?”

This led to the obvious point that it depends on who can use it, which then reinforces the power-shift that we started from. The outcome of “How does it become information” leads to the question “How can using data actually foster development? (and avoid inequality)”.  We also had as concerns around the transformation from data to information that it is often bias or focused on inanimate or simple things (measuring the water pump rather than the people).

We finally looked at how to change or stop the shift or concentration in power. Two options presented themselves, either to stop using the data, and halt the path to large scale big-data analyses. This seems implausible, given the path we are on. A second option was “Can [the power shift] be mitigated by giving everyone access?”, in other words, will open data save us from the digital oligarchy?

This was again criticised; how can an illiterate farmer or boda-boda driver engage or use large data sets?

My own view is that we need layers of intermediary; from the machine learning/analysis experts who can combine and use the data, and visualise it in clear ways, to journalists and civil society who effectively ‘represent’ the citizen. Our concern is that the machine learning expert is a very particular part of society: usually white, highly educated, young and male. We can go some-way to mitigate that by investing in and supporting MSc and PhD level education in developing countries… however, I’m aware the students at Makerere (for example) were not a ‘typical’ sample of the Ugandan population. Most of the population is rural, with a good proportion unable to read or write. I suspect that the Ugandan students will represent their country-men and women little better than a muzungu. However, it is a start down the path, towards some form of democratic or universal access to the power provided by machine learning and big-data.

I presented my crash map project, and also submitted a paper on the topic.

Regarding our new paper:  Differentially Private Gaussian Processes.

In layman terms Gaussian Processes (GPs) are, usually, used to describe a dataset, to allow predictions to be made. E.g. given previous patient data, what is the best treatment a new patient should receive? It’s a nice framework as it incorporates assumptions clearly, and, because of its probabilistic underpinnings gives estimates of the confidence for a particular estimate.

Comparison of new ‘integral’ kernel with a normal RBF kernel. Note the underestimate the RBF kernel suffers between 20 and 40 years.

Differential Privacy is a method that’s recently started to go main-stream. I’ve written a brief introduction presentation here. The general idea is to add noise to any query result to mask the value of an individual row in a database, but still allow inference to be done on the whole database.

My research areas cover both Gaussian Processes and Differential Privacy, so it seemed to make sense to see if I could apply one to the other. In our latest paper we look at two ways to do this:

• Bin the data and add differential privacy noise to the bin totals. Then use a GP to do the inference.
• Use the raw non-private data to train a GP, then perturb the GP with structured noise to achieve DP.

For the former I developed a new kernel (a way of describing how data is correlated or structured) for binned or ‘histogram’ data. See this ipython notebook for examples. This hopefully is useful for many applications (outside the DP field). For example any inference using binned datasets. At the moment I’ve only applied it to the RBF kernel.

For the latter I used the results of [1] to determine the noise required to be added to the mean function of my GP. I found that we could considerably reduce the noise scale by using inducing (pseudo) inputs.

Malawi child dataset. Raw data and 2d histogram.

Both methods can be further improved, but it’s still early days for Differential Privacy. We need to look at how to apply DP to as many methods as possible, and start to incorporate it into more domains. I’ll be looking at how to apply it to the MND database. Finally, we need an easy “introduction to DP for practitioners”. Although I don’t know if the field is sufficiently mature for this yet.

[1] Hall, R., Rinaldo, A., & Wasserman, L. (2013). Differential privacy for functions and functional data. The Journal of Machine Learning Research, 14 (1), 703–727.

A few months ago I took part in the NBN R hack-evenings at the University of Sheffield. Unlike everyone else in the room I was coding in python.

There is no python wrapper for the API, so I created one. Feel free to clone and use (and pull requests happily accepted)! An example to illustrate how to use the API is here:

import pynbn
c = pynbn.connect('lionfish_pynbn','password');

sp = c.get_tvk(query='Bombus terrestris') #get the tvk (the "taxon version key" for buff tails)
keys = []
for res in sp['results']:
k = res['ptaxonVersionKey']
keys.append(str(k))
print "%d species match this query string" % len(keys)
print keys
tvk = keys[0]
print "We'll use the first key (%s)" % tvk
#we usually take the first item from this list (advice from the NBN hackday)
obs = c.get_observations(tvks=[tvk], start_year=1990, end_year=2010) #get observations
print "There are %d records for B. terrestris between 1990 and 2010" % len(obs)

I’ll shortly be adding this to pip, so it can be installed with,

pip install pynbn

When I lived in Uganda, I used to cycle to work every day, and was aware of the pollution building as I cycled down the hill into the city centre.

Air pollution monitor and example output. The vertical lines mark when we lit a match in the same room as the sensor.

I wanted to measure this pollution, so using the (very cheap) shinyei sensor and a mobile phone investigated what we could measure, and with what accuracy. I’ve not got very far yet, but this funding app gives and idea about what we’ve done and what we hope to achieve.

Air pollution monitoring in Kampala

Example of trilateration on the output areas of Sheffield

In the last post, we looked at methods to find the optimum landmark. In this post we look at how to find one’s location given a set of landmarks.

Previously I’ve naively found the probability of each location on a grid, given the reported distances to the landmarks, then sampled from this grid to find the probability for each output area.

In this notebook we approach the problem differently, and look for the probability of the set of distances to the landmarks given the output area. By swapping the order, we are able to use the node in the Bayesian network.

Read more (ipynb)

Combining two estimated distances to find your location.

Trilateration is like triangulation, but uses the distances to landmarks, rather than their angles, to determine one’s location. GPS is probably the most common example of trilateration in use at the moment.

In our problem we have a set of landmarks. We know the distance (with some uncertainty) to one, and we want to know which of the remaining landmarks we should select next to maximise the amount of information we gain about our location.

For our particular example, we ask people to estimate the distance of various landmarks from their house.

We look at how to find a good landmark quickly, by using Bayes’ rule to rearrange the expression for the entropy in the probability distribution.

Read more.

The online psychic is running happily on my local machine, but I needed to get it onto this webserver.

Unfortunately the server doesn’t have pandas, numpy, etc installed.

I tried downloading virtualenv, which when untarred and run generates its own module collection. But I found its version of pip didn’t work, “shared host SystemError: Cannot compile ‘Python.h’. “.

So the next option: Anaconda? (I’ve only 1Gb of space, turns out that’s not enough).

Back to virtualenv:

Install my own version of python: https://my.justhost.com/cgi/help/python-install

Then following the instructions here: http://stackoverflow.com/questions/24748084/installing-numpy-without-sudo

Combined with the help here:

http://docs.python-guide.org/en/latest/dev/virtualenvs/

(download virtualenv here: https://pypi.python.org/pypi/virtualenv#downloads )

It still didn’t work – there’s a problem with the configuration of virtualenv’s python. It might be better to scrap virtualenv and download all the modules etc that I’ll need and compile them. The only advantage of virtualenv was that it would provide pip etc.

New python executable in venv/bin/python
ERROR: The executable venv/bin/python is not functioning
ERROR: It thinks sys.prefix is u'/home/.sites/81/site18/.users/89/mts-michael/python' (should be u'/home/.sites/81/site18/.users/89/mts-michael/venv')

These ‘lessons’ are really notes for myself, as I become familiar with python, pandas, matrix factorisation methods, etc. I’ll add a bit of an introduction at some point, and organise them nicely, but for now you can read them in my ipython folder.

Gibbs sampling a 2d multivariate Gaussian