textmodel Performance Comparisons

library("quanteda")
## Package version: 4.5.1
## Unicode version: 17.0
## ICU version: 78.2
## Parallel computing: 4 of 4 threads used.
## See https://quanteda.io for tutorials and examples.
library("quanteda.textmodels")

Naive Bayes

quanteda.textmodels implements fast methods for fitting and predicting Naive Bayes textmodels built especially for sparse document-feature matrices from textual data. It implements two models: multinomial and Bernoulli. (See Manning, Raghavan, and Schütze 2008, Chapter 13.)

Here, we compare performance for the two models, and then to the performance from two other packages for fitting these models.

For these tests, we will choose the dataset of 50,000 movie reviews from Maas et. al. (2011). We will use their partition into test and training sets for training and fitting our models.

## Error in load(url("https://quanteda.org/data/data_corpus_LMRD.rda")) : 
##   cannot open the connection to 'https://quanteda.org/data/data_corpus_LMRD.rda'

# large movie review database of 50,000 movie reviews
load(url("https://quanteda.org/data/data_corpus_LMRD.rda"))

dfmat <- tokens(data_corpus_LMRD) %>%
  dfm()
dfmat_train <- dfm_subset(dfmat, set == "train")
dfmat_test <- dfm_subset(dfmat, set == "test")

Comparing the performance of fitting the model:

library("microbenchmark")
microbenchmark(
    multi = textmodel_nb(dfmat_train, dfmat_train$polarity, distribution = "multinomial"),
    bern = textmodel_nb(dfmat_train, dfmat_train$polarity, distribution = "Bernoulli"),
    times = 20
)
## Unit: milliseconds
##   expr      min       lq     mean   median       uq      max neval
##  multi 17.34228 18.25558 19.61589 18.59600 21.98941 23.34993    20
##   bern 19.07082 19.60250 21.67953 20.20188 24.37336 25.46585    20

And for prediction:

microbenchmark(
    multi = predict(textmodel_nb(dfmat_train, dfmat_train$polarity, distribution = "multinomial"),
                    newdata = dfmat_test),
    bern = predict(textmodel_nb(dfmat_train, dfmat_train$polarity, distribution = "Bernoulli"),
                   newdata = dfmat_test),
    times = 20
)
## Unit: milliseconds
##   expr      min       lq     mean   median       uq       max neval
##  multi 19.46192 19.60194 27.74975 20.10417 23.71974 154.40133    20
##   bern 24.62757 25.18223 27.68735 29.08259 29.90426  30.92961    20

Now let’s see how textmodel_nb() compares to equivalent functions from other packages. Multinomial:

library("fastNaiveBayes")
library("naivebayes")
## naivebayes 1.0.0 loaded
## For more information please visit:
## https://majkamichal.github.io/naivebayes/

microbenchmark(
    textmodels = {
      tmod <-  textmodel_nb(dfmat_train, dfmat_train$polarity, smooth = 1, distribution = "multinomial")
      pred <- predict(tmod, newdata = dfmat_test)
    },
    fastNaiveBayes = { 
      tmod <- fnb.multinomial(as(dfmat_train, "dgCMatrix"), y = dfmat_train$polarity, laplace = 1, sparse = TRUE)
      pred <- predict(tmod, newdata = as(dfmat_test, "dgCMatrix"))
    },
    naivebayes = {
      tmod = multinomial_naive_bayes(as(dfmat_train, "dgCMatrix"), dfmat_train$polarity, laplace = 1)
      pred <- predict(tmod, newdata = as(dfmat_test, "dgCMatrix"))
    },
    times = 20
)
## Unit: milliseconds
##            expr      min       lq     mean   median       uq      max neval
##      textmodels 19.53707 19.87151 20.75813 20.21271 20.48463 24.94757    20
##  fastNaiveBayes 21.14390 23.51752 25.00508 25.79616 26.11771 28.29275    20
##      naivebayes 20.33773 20.53154 24.13106 21.00064 25.18010 57.92771    20

And Bernoulli. Note here that while we are supplying the Boolean matrix to textmodel_nb(), this re-weighting from the count matrix would have been performed automatically within the function had we not done so in advance - it’s done here just for comparison.

dfmat_train_bern <- dfm_weight(dfmat_train, scheme = "boolean")
dfmat_test_bern <- dfm_weight(dfmat_test, scheme = "boolean")

microbenchmark(
    textmodel_nb = {
      tmod <-  textmodel_nb(dfmat_train_bern, dfmat_train$polarity, smooth = 1, distribution = "Bernoulli")
      pred <- predict(tmod, newdata = dfmat_test)
    },
    fastNaiveBayes = { 
      tmod <- fnb.bernoulli(as(dfmat_train_bern, "dgCMatrix"), y = dfmat_train$polarity, laplace = 1, sparse = TRUE)
      pred <- predict(tmod, newdata = as(dfmat_test_bern, "dgCMatrix"))
    },
    naivebayes = {
      tmod = bernoulli_naive_bayes(as(dfmat_train_bern, "dgCMatrix"), dfmat_train$polarity, laplace = 1)
      pred <- predict(tmod, newdata = as(dfmat_test_bern, "dgCMatrix"))
    },
    times = 20
)
## Unit: milliseconds
##            expr      min       lq     mean   median       uq       max neval
##    textmodel_nb 24.86683 25.29759 28.04719 29.18314 29.54875  30.72266    20
##  fastNaiveBayes 23.57191 25.75930 33.81470 27.96343 28.23410 158.91459    20
##      naivebayes 21.98665 22.53168 24.59505 24.25621 26.71482  27.62090    20

References

Maas, Andrew L., Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts (2011). “Learning Word Vectors for Sentiment Analysis”. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).

Majka M (2020). naivebayes: High Performance Implementation of the Naive Bayes Algorithm in R. R package version 0.9.7, <URL: https://CRAN.R-project.org/package=naivebayes>. Date: 2020-03-08.

Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze (2008). Introduction to Information Retrieval. Cambridge University Press.

Skogholt, Martin (2020). fastNaiveBayes: Extremely Fast Implementation of a Naive Bayes Classifier. R package version 2.2.1. https://github.com/mskogholt/fastNaiveBayes. Date: 2020-05-04.

- Naive Bayes
- References