# textmodel Performance Comparisons

#### Kenneth Benoit

    library("quanteda")
    ## Package version: 4.2.0
    ## Unicode version: 14.0
    ## ICU version: 71.1
    ## Parallel computing: 10 of 10 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.

    # 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
    )
    ## Warning in microbenchmark(multi = textmodel_nb(dfmat_train,
    ## dfmat_train$polarity, : less accurate nanosecond times to avoid potential
    ## integer overflows
    ## Unit: milliseconds
    ##   expr      min       lq     mean   median       uq      max neval
    ##  multi 51.45061 52.28271 60.59049 54.32045 61.75426 136.8729    20
    ##   bern 58.58859 61.19004 70.56123 68.16055 70.70653 141.3598    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 59.05677 59.87156 65.00725 64.46526 70.04485  72.35036    20
    ##   bern 85.30874 92.33995 99.03242 96.51304 98.96039 171.86191    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 58.57506 60.06543  64.85441  62.21453  69.41526  73.30976    20
    ##  fastNaiveBayes 88.26291 96.98521 100.10928 100.05087 103.10463 111.23460    20
    ##      naivebayes 70.81520 71.47522  89.52677  78.18112  82.76117 251.39519    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 84.96885  94.91449 101.09729  97.39626  99.39509 176.4982    20
    ##  fastNaiveBayes 95.70810 107.45432 114.73058 112.01850 116.23123 193.6231    20
    ##      naivebayes 76.38817  80.72086  87.82437  82.17314  92.54163 129.7558    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.