Author: Kenneth Benoit

# textmodel Performance Comparisons

#### Kenneth Benoit

    library("quanteda.textmodels")
    library("quanteda")
    ## Package version: 2.1.2
    ## Parallel computing: 2 of 12 threads used.
    ## See https://quanteda.io for tutorials and examples.
    ##
    ## Attaching package: 'quanteda'
    ## The following object is masked from 'package:utils':
    ##
    ##     View

## 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://www.dropbox.com/s/sjdfmx8ggwfda5o/data_corpus_LMRD.rda?dl=1"))

    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  99.86278 102.5719 110.8320 105.7324 111.2497 155.0494    20
    ##   bern 107.91838 115.9724 136.8044 121.4117 159.3917 204.5792    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 101.1713 103.3825 114.5249 114.6708 121.6393 142.9834    20
    ##   bern 145.7240 166.0975 186.4209 181.0711 201.1558 251.0727    20

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

    library("fastNaiveBayes")
    library("naivebayes")
    ## naivebayes 0.9.7 loaded

    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 115.8562 118.4777 141.4019 121.2114 165.2528 214.0449    20
    ##  fastNaiveBayes 209.9275 215.7662 237.9394 221.7094 258.1063 301.8511    20
    ##      naivebayes 160.2207 165.8272 206.1408 172.3713 216.3462 510.7100    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(
        textmodels = {
          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
    ##      textmodels 156.0294 180.9418 200.7879 195.2821 226.0766 253.7442    20
    ##  fastNaiveBayes 236.7125 257.0343 300.2694 284.4215 329.0159 537.7433    20
    ##      naivebayes 164.2927 190.8325 210.0139 202.9607 211.6752 301.4993    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 2.2.0. https://github.com/mskogholt/fastNaiveBayes. Date: 2020-02-23.