predict: Predict Methods for Poisson NMF and Multinomial Topic Model

In fastTopics: Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

Predict Methods for Poisson NMF and Multinomial Topic Model

Description

Predict loadings based on previously fit Poisson NMF, or predict topic proportions based on previously fit multinomial topic model. This can be thought of as projecting data points onto a previously estimated set of factors fit$F.

Usage

## S3 method for class 'poisson_nmf_fit'
predict(object, newdata, numiter = 20, ...)

## S3 method for class 'multinom_topic_model_fit'
predict(object, newdata, numiter = 20, ...)

Arguments

object	An object of class “poisson_nmf_fit” or “multinom_topic_model_fit”.
newdata	An optional counts matrix. If omitted, the loadings estimated in the original data are returned.
numiter	The number of updates to perform.
...	Additional arguments passed to fit_poisson_nmf.

Value

A loadings matrix with one row for each data point and one column for each topic or factor. For predict.multinom_topic_model_fit, the output can also be interpreted as a matrix of estimated topic proportions, in which L[i,j] is the proportional contribution of topic j to data point i.

See Also

fit_poisson_nmf

Examples

# Simulate a 175 x 1,200 counts matrix.
set.seed(1)
dat <- simulate_count_data(175,1200,k = 3)

# Split the data into training and test sets.
train <- dat$X[1:100,]
test <- dat$X[101:175,]

# Fit a Poisson non-negative matrix factorization using the
# training data.
fit <- init_poisson_nmf(train,F = dat$F,init.method = "random")
fit <- fit_poisson_nmf(train,fit0 = fit)

# Compare the estimated loadings in the training data against the
# loadings used to simulate these data.
Ltrain <- predict(fit)
plot(dat$L[1:100,],Ltrain,pch = 20,col = "darkblue")
abline(a = 0,b = 1,col = "magenta",lty = "dotted",
       xlab = "true",ylab = "estimated")

# Next, predict loadings in unseen (test) data points, and compare
# these predictions against the loadings that were used to simulate
# the test data.
Ltest <- predict(fit,test)
plot(dat$L[101:175,],Ltest,pch = 20,col = "darkblue",
     xlab = "true",ylab = "estimated")
abline(a = 0,b = 1,col = "magenta",lty = "dotted")

# Simulate a 175 x 1,200 counts matrix.
set.seed(1)
dat <- simulate_multinom_gene_data(175,1200,k = 3)

# Split the data into training and test sets.
train <- dat$X[1:100,]
test <- dat$X[101:175,]

# Fit a topic model using the training data.
fit <- init_poisson_nmf(train,F = dat$F,init.method = "random")
fit <- fit_poisson_nmf(train,fit0 = fit)
fit <- poisson2multinom(fit)

# Compare the estimated topic proportions in the training data against
# the topic proportions used to simulate these data.
Ltrain <- predict(fit)
plot(dat$L[1:100,],Ltrain,pch = 20,col = "darkblue")
abline(a = 0,b = 1,col = "magenta",lty = "dotted",
       xlab = "true",ylab = "estimated")

# Next, predict loadings in unseen (test) data points, and compare
# these predictions against the loadings that were used to simulate
# the test data.
Ltest <- predict(fit,test)
plot(dat$L[101:175,],Ltest,pch = 20,col = "darkblue",
     xlab = "true",ylab = "estimated")
abline(a = 0,b = 1,col = "magenta",lty = "dotted")

fastTopics documentation built on Sept. 11, 2024, 5:14 p.m.