Mike's Page

Informatics, Development, Cycling, Data, Travel ...

Month: May 2018

Coregionalised Air

Air pollution coregionalised between the US embassy and Makerere campus

Air pollution coregionalised between the US embassy and Makerere campus

Next week I’ll be presenting at Manchester’s Advances in Data Science conference, on the air pollution project. I’ve written an extended abstract on the topic.

We use a custom-crafted kernel for coregionalising between multiple sensors, to allow us to make probabilistic predictions at the level of the high-quality reference sensor, across the whole city, using the low-quality noisy sensors. We estimate the coregionalision parameters using training data we’ve collect – which ideally should include close or colocated measurements from pairings of sensors.

In future we hope to:

  1. Include the uncertainty in the coregionalisation (e.g. by integrating over the distribution of the coregionalisation parameters, e.g. using CCD.
  2. Allow this coregionalisation to vary over time. This will require non-stationarity, and is probably best achieved using a more flexible, non-analytic solution. E.g. writing the model in STAN
  3. .

  4. Updating the model in real time. I think another advantage of using a STAN or similar framework would be the gradual inclusion of new MC steps incorporting new data, as we throw out old data, this allows the gradual change of coregionalisation to be incorporated.

Update

Building flat coregionalisation kernel

Building flat coregionalisation kernel

We can’t just use the standard coregionalisation kernel, as we’re not just kronecker-product multiplying a coregionalisation matrix with a repeating covariance matrix. Instead we want to element-wise multiply a matrix that expresses the coregionalisation with another matrix that expresses the covariance due to space and time proximity (see above figure).

Here is the GPy kernel code to do this;

import GPy
import numpy as np
from GPy.kern import Kern
from GPy.core.parameterization import Param
#from paramz.transformations import Logexp
#import math
class FlatCoreg(Kern): 
    """
    """

    def __init__(self, input_dim, active_dims=0, rank=1, output_dim=None, name='flatcoreg'):
        super(FlatCoreg, self).__init__(input_dim, active_dims, name)

        assert isinstance(active_dims,int), "Can only use one dimension"
        
        
        W = 0.5*np.random.randn(rank,output_dim)/np.sqrt(rank)
        self.W = Param('W', W)
        self.link_parameters(self.W) #this just takes a list of parameters we need to optimise.

    def update_gradients_full(self, dL_dK, X, X2=None):
        
        if X2 is None:
            X2 = X.copy()
            
        dK_dW = np.zeros([self.W.shape[1],X.shape[0],X2.shape[0]])
        for i,x in enumerate(X):
            for j,x2 in enumerate(X2):
                wi = int(x[0])
                wj = int(x2[0])
                dK_dW[wi,i,j] = self.W[0,wj]
                dK_dW[wj,i,j] += self.W[0,wi]
        self.W.gradient = np.sum(dK_dW * dL_dK,(1,2))
       

    def k_xx(X,X2,W,l_time=2.0,l_dist=0.1):
        #k_time = np.exp(-(X[0]-X2[0])**2/(2*l_time))
        #k_dist = np.exp(-(X[1]-X2[1])**2/(2*l_dist))
        k_coreg = coregmat[int(X[2]),int(X2[2])]
        return k_coreg #k_time * k_dist * k_coreg 
        
    def K(self, X, X2=None):
        coregmat = np.array(self.W.T @ self.W)
        if X2 is None:
            X2 = X
        K_xx = np.zeros([X.shape[0],X2.shape[0]])
        for i,x in enumerate(X):
            for j,x2 in enumerate(X2):
                K_xx[i,j] = coregmat[int(x[0]),int(x2[0])]
        return K_xx
    

    def Kdiag(self, X):
        return np.diag(self.K(X))

k = (GPy.kern.RBF(1,active_dims=[0],name='time')*GPy.kern.RBF(1,active_dims=[1],name='space'))*FlatCoreg(1,output_dim=3,active_dims=2,rank=1)
#k = FlatCoreg(1,output_dim=3,active_dims=2,rank=1)
#k.coregion.kappa.fix(0)   

This allows us to make predictions over the whole space in the region of the high quality sensor, with automatic calibration via the W vector.

DASK and ec2 – use daskec2lite

I’ve started having the same problem as in this issue. I think something else has been updated which has caused the new error. As it says on the dask-ec2 readme, dask-ec2’s project is now deprecated – and so I didn’t try fixing the new bug. I tried for a while using kubernetes (kops, terraform, etc), but it’s quite a pain to set up (not well documented yet maybe) and is serious overkill for what I want (and probably what a lot of people want…). So instead…

I’ve written a replacement for dask-ec2, I’ve called daskec2lite.

It needs a little bit more work but is nearly finished. I’ll hopefully have some time later in the year to get it to a more ‘release’ state, but feel free to use it.

daskec2lite --help

usage: daskec2lite [-h] [--pathtokeyfile [PATHTOKEYFILE]]
[--keyname [KEYNAME]] [--username [USERNAME]]
[--numinstances [NUM_INSTANCES]]
[--instancetype [INSTANCE_TYPE]] [--imageid [IMAGEID]]
[--spotprice [SPOTPRICE]] [--region [REGION_NAME]]
[--wpi [WORKERS_PER_INSTANCE]] [--sgid [SGID]] [--destroy]

Create an EC2 spot-price cluster, populate with a dask scheduler and workers.
Example: daskec2lite --pathtokeyfile '/home/mike/.ssh/research.pem' --keyname
'research' --username 'mike' --imageid ami-19a58760 --sgid sg-9146afe9

optional arguments:
-h, --help show this help message and exit
--pathtokeyfile [PATHTOKEYFILE]
path to keyfile [required]
--keyname [KEYNAME] key name to use to access instances [required]
--username [USERNAME]
user to log into remote instances as [required]
--numinstances [NUM_INSTANCES]
number of instances to start
--instancetype [INSTANCE_TYPE]
type of instance to request
--imageid [IMAGEID] AWS image to use [required]
--spotprice [SPOTPRICE]
Spot price limit ($/hour/instance)
--region [REGION_NAME]
Region to use
--wpi [WORKERS_PER_INSTANCE]
Workers per instance
--sgid [SGID] Security Group ID [required]
--destroy Destroy the cluster

© 2018 Mike's Page

Theme by Anders NorenUp ↑