textmodel Performance Comparisons

library("quanteda")
## Package version: 4.1.0
## Unicode version: 15.1
## ICU version: 74.2
## Parallel computing: disabled
## 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.

# 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 79.08463 79.80335  87.26348  86.5047  94.44121  98.97308    20
##   bern 85.81136 87.40202 103.65923 100.3969 101.27728 248.54457    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  89.66024  90.0256  99.05364  96.9222 105.1076 137.0219    20
##   bern 117.36796 130.4211 137.90381 131.1845 132.1390 279.6771    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  89.56055  89.82281  94.94477  90.35571 103.2956 107.3613    20
##  fastNaiveBayes 128.57287 140.70646 149.81367 143.11938 144.5183 290.7424    20
##      naivebayes 102.80552 114.56167 122.81710 116.50983 117.3888 268.0316    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 117.3816 131.2258 147.8742 132.4213 140.7163 290.4510    20
##  fastNaiveBayes 139.5544 151.0667 155.4482 155.1305 160.7934 175.1964    20
##      naivebayes 111.4806 114.5395 123.7796 125.9621 128.0498 134.9978    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