BIO Machine learning

Machine Learning

This page is a placeholder, or under current development; it is here principally to establish the logical framework of the site. The material on this page is correct, but incomplete.

Overview of "classical" and current approaches to machine learning.

 

Introductory reading

Machine learning

 

Introduction

Paradigms

Neural Networks

Neural network

Deep learning

Hidden Markov Models

Hidden Markov model

Support Vector Machines

Support vector machine

Bayesian Networks

Bayesian network

Training sets

Gold standards as true positives and the problem of generating true negatives from non-observed data...

ROC and associated metrics

Receiver operating characteristic

Machine learning in R

CRAN task-view on machine learning

Joshua Reich has posted a self-contained R-script for a number of machine learning topics. See below.

#
# This file gives a quick demonstration of a few ML techniques that
# are available in R. The file is designed as a walk through, so instead
# of simply running the entire file at once, copy and paste the logical
# blocks and check the output as we go along.
#
# Joshua Reich (josh@i2pi.com) 	
# April 2, 2009
#


# First we need to load up some packages to support ML.
# If your system doesn't have the packages, check out
# the install.packages() command. 

library(rpart)
library(MASS)
library(class)
library(e1071)


rmulnorm <- function (n, mu, sigma)
{
	# A simple function for producing n random samples
	# from a multivariate normal distribution with mean mu
	# and covariance matrix sigma

	M <- t(chol(sigma))
	d <- nrow(sigma)
	Z <- matrix(rnorm(d*n),d,n)
	t(M %*% Z + mu)
}

cm <- function (actual, predicted)
{
	# Produce a confusion matrix

	t<-table(predicted,actual)
	# there is a potential bug in here if columns are tied for ordering
	t[apply(t,2,function(c) order(-c)[1]),] 
}

# Total number of observations
N <- 1000 * 3

# Number of training observations
Ntrain <- N * 0.7


# The data that we will be using for the demonstration consists
# of a mixture of 3 multivariate normal distributions. The goal
# is to come up with a classification system that can tell us,
# given a pair of coordinates, from which distribution the data 
# arises.

A <- rmulnorm (N/3, c(1,1), matrix(c(4,-6,-6,18), 2,2))
B <- rmulnorm (N/3, c(8,1), matrix(c(1,0,0,1), 2,2))
C <- rmulnorm (N/3, c(3,8), matrix(c(4,0.5,0.5,2), 2,2))

data <- data.frame(rbind (A,B,C))
colnames(data) <- c('x', 'y')
data$class <- c(rep('A', N/3), rep('B', N/3), rep('C', N/3))

# Lets have a look
plot_it <- function () {
	plot (data[,1:2], type='n')
	points(A, pch='A', col='red')
	points(B, pch='B', col='blue')
	points(C, pch='C', col='orange')
}
plot_it()

# Randomly arrange the data and divide it into a training
# and test set.
data <- data[sample(1:N),]
train <- data[1:Ntrain,]
test <- data[(Ntrain+1):N,]

# OK. Lets get to it

# K-Means

# kmeans(), built into the R base package, is an unsupervised
# learning technique. The goal is to cluster the observed data
# into groups. This is achieved by assuming a Euclidean distance
# metric, and finding points which lie at local centroids. All
# points are then assigned to their closest centroid and are
# thus clustered. The algorithmic approach to finding these
# centroids is to pick k points at random then assign all other 
# points to the centroids. The algorithm then chooses new
# centroids based on the mean point of the resulting clusters.
# Then with these new centroids, the remaining N-k points are
# reclustered. This repeats until some stopping condition is
# reached.
# This algorithm is quite simple to implement and quite often
# the Euclidean distance metric is inappropriate and I find 
# myself re-writing the algorithm using a different measure.
# Here we know, a priori, that there are 3 clusters, so we set 
# k = 3.

k <- kmeans(train[,1:2], 3)
plot(train[,1:2], type='n')
text(train[,1:2], as.character(k$cluster))

cm (train$class, k$cluster)

# K Nearest Neighbor

# In KNN we classify an unknown point by looking at k nearest
# neighbors of known classification. In the case of k=1, we
# find the closest point in our training set to a new point
# from our test set. We then assume that the new point has
# the same class as its closest neighbor in the test set.
# For k>1, we typically apply a voting mechanism to pick the
# modal class from the neighborhood of points in the training
# set. 

test$predicted_class <- knn(train[,1:2], test[,1:2], train$class, k=1)

(m<-cm(test$class, test$predicted_class))

# Here we demonstrate the bias-variance tradeoff as we increase k.

err <- matrix(nrow=N/100, ncol=2)
for (i in 1:nrow(err))
{
	k <- i * 4
	test$predicted_class <- knn(train[,1:2], test[,1:2], train$class, k=k)
	m<-cm(test$class, test$predicted_class)
	err[i,] <- c(k, 1 - sum(diag(m)) / sum(m))
}

plot (err)

# Kernel Methods

# Rather than using library functions, we will build our own here
# to demonstrate the mechanism, sacrificing efficiencies for 
# edification. This code is slow.

# Kernel methods follow on from KNN. Whereas KNN is by its nature
# highly local, and thus potentially non-smooth, kernel techniques
# apply a windowing function to the dataset to smooth the classifier.
# Here we use a Gaussian/radial function to weight the influence
# of the each point in the training set. We then take the average
# of the weighted distances to determine the most likely classification
# for new points in the test set.

kernel <- function (a, b)
{
	# Lets make a simple 'Gaussian' like kernel that
	# measures the distance between two points, a & b.
	exp(-sum((a-b)^2))
}

test$predicted_class <- NA
for (i in 1:nrow(test))
{
	print(i)
	# The weighted distances from each point in the training set to 
	# row i in the test set
	d<-apply(train[,1:2], 1, function(r) kernel(r, test[i,1:2]))

	# The class votes, based on the mean distance
	v<-aggregate(d, list(class=train$class), mean)

	# Predicted class = the class with the lowest distance
	test$predicted_class[i] <- v$class[order(-v$x)[1]]
}	

cm(test$class, test$predicted_class)


# Recursive Partitioning / Regression Trees

# rpart() implements an algorithm that attempts to recursively split
# the data such that each split best partitions the space according 
# to the classification. In a simple one-dimensional case with binary
# classification, the first split will occur at the point on the line
# where there is the biggest difference between the proportion of 
# cases on either side of that point. The algorithm continues to 
# split the space until a stopping condition is reached. Once the
# tree of splits is produced it can be pruned using regularization
# parameters that seek to ameliorate overfitting.

(r <- rpart(class ~ x + y, data = train))
plot(r)
text(r)

# Here we look at the confusion matrix and overall error rate from applying
# the tree rules to the training data.
predicted <- as.numeric(apply(predict(r), 1, function(r) order(-r)[1]))
(m <- cm (train$class, predicted))
1 - sum(diag(m)) / sum(m)

# And by comparison, against the test data.
predicted <- as.numeric(apply(predict(r, test[,1:2]), 1, function(r) order(-r)[1]))
(m <- cm (test$class, predicted))
1 - sum(diag(m)) / sum(m)

# PCA - Demonstrating that orthogonal bases are better for trees

# Recursive partitioning splits the space along orthogonal hyperplanes 
# that are parallel to the original feature coordinate axes. However,
# in our case, the clusters are not neatly split by such planes and
# better results can be found by transforming to another space. We
# use principle component analysis (PCA) to transform our space.
# PCA transforms the space by looking at the vectors along which the
# bulk of the variance in the data occur. The vector that embodies
# the greatest variance becomes the first principle component axis
# in the transformed space. The second axis then is formed along the
# vector that is orthogonal to the first but with the second most 
# variance in the data. And so on.
# It should be clear how this transform improves the performance of 
# recursive partitioning, but the cost is that the tree splits 
# no longer directly map to the feature space, which makes 
# interpretation much more difficult.

p<-princomp(train[,1:2])
train_pca <- data.frame(p$scores)
train_pca$class <- train$class

# Compare the alignment of the clusters to the axis in the feature
# space versus the transformed space.
par(mfrow=c(1,2))
train_lda <- as.matrix(train[,1:2]) %*% l$scaling
plot_it()
plot(train_pca[,1:2], type='n')
text(train_pca[,1:2], train_pca$class)
par(mfrow=c(1,1))

r2 <- rpart(class ~ Comp.1 + Comp.2, data = train_pca)

predicted2 <- as.numeric(apply(predict(r2), 1, function(r) order(-r)[1]))
(m <- cm (train$class, predicted2))
1 - sum(diag(m)) / sum(m)

# LDA

# In linear discriminant analysis we no longer look for recursive 
# partitions, but rather for lines that go between the clusters.
# In some ways, this is similar to KNN. LDA makes the assumption
# that the clusters are drawn from multivariate normal distributions
# with different means, but identical covariances. LDA approaches
# the problem by applying a transform that applies the inverse of 
# the estimated covariance matrix to distributed the points 
# spherically. In this transformed space classification is simply
# a matter of finding the closest cluster mean. 
# The assumption of identical covariances doesn't hold for our
# dataset, but still provides an improvement. Quadratic DA drops
# this assumption at the cost of greater complexity. 

l <- lda(class ~ x + y, data = train)

(m <- cm(train$class, predict(l)$class))
1 - sum(diag(m)) / sum(m)

par(mfrow=c(1,2))
train_lda <- as.matrix(train[,1:2]) %*% l$scaling
plot_it()
plot(train_lda, type='n')
text(train_lda, train$class)
par(mfrow=c(1,1))

# SVM

# Support vector machines take the next step from LDA/QDA. However
# instead of making linear voronoi boundaries between the cluster
# means, we concern ourselves primarily with the points on the 
# boundaries between the clusters. These boundary points define
# the 'support vector'. Between two completely separable clusters
# there are two support vectors and a margin of empty space
# between them. The SVM optimization technique seeks to maximize
# the margin by choosing a hyperplane between the support vectors
# of the opposing clusters. For non-separable clusters, a slack
# constraint is added to allow for a small number of points to
# lie inside the margin space. The Cost parameter defines how
# to choose the optimal classifier given the presence of points
# inside the margin. Using the kernel trick (see Mercer's theorem)
# we can get around the requirement for linear separation 
# by representing the mapping from the linear feature space to
# some other non-linear space that maximizes separation. Normally
# a kernel would be used to define this mapping, but with the
# kernel trick, we can represent this kernel as a dot product.
# In the end, we don't even have to define the transform between
# spaces, only the dot product distance metric. This leaves
# this algorithm much the same, but with the addition of
# parameters that define this metric. The default kernel used
# is a radial kernel, similar to the kernel defined in my 
# kernel method example. The addition is a term, gamma, to
# add a regularization term to weight the importance of distance.

s <- svm( I(factor(class)) ~ x + y, data = train, cost = 100, gama = 1)

(m <- cm(train$class, predict(s)))
1 - sum(diag(m)) / sum(m)

(m <- cm(test$class, predict(s, test[,1:2])))
1 - sum(diag(m)) / sum(m)

 

 

Further reading and resources

Salakhutdinov & Hinton (2012) An efficient learning procedure for deep Boltzmann machines. Neural Comput 24:1967-2006. (pmid: 22509963)

[ PubMed ] [ DOI ] We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.

Hinton et al. (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527-54. (pmid: 16764513)

[ PubMed ] [ DOI ] We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.

Lodhi, H. (2012) Computational biology perspective: kernel methods and deep learning. WIREs: Computational Statistics 4(5):455-465.

(pmid: None) [ Source URL ][ DOI ] The field of machine learning provides useful means and tools for finding accurate solutions to complex and challenging biological problems. In recent years a class of learning algorithms namely kernel methods has been successfully applied to various tasks in computational biology. In this article we present an overview of kernel methods and support vector machines and focus on their applications to biological sequences. We also describe a new class of approaches that is termed as deep learning. These techniques have desirable characteristics and their use can be highly effective within the field of computational biology.