# textmodel Performance Comparisons

#### Kenneth Benoit

    library("quanteda")
    ## Package version: 4.1.0
    ## 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 53.48532 53.83255 58.29222 55.66174 63.86882  66.14026    20
    ##   bern 61.52206 69.89108 78.93381 71.93561 74.12189 154.88578    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 61.13498 61.60338  67.75137 70.58433  71.86597  74.30065    20
    ##   bern 88.72966 96.26019 102.02488 98.10093 100.94958 181.06506    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 61.03092  62.42500  66.26935  63.94071  71.24055  77.9166    20
    ##  fastNaiveBayes 92.28674 100.67160 106.88356 103.47129 104.79010 184.0025    20
    ##      naivebayes 73.57864  76.28413  88.55177  82.82295  85.40671 225.7032    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 87.96353  96.28247  99.24511  98.77507 102.97835 112.9129    20
    ##  fastNaiveBayes 98.78708 105.73265 113.79462 112.13176 113.73429 190.6067    20
    ##      naivebayes 79.12381  81.19800  91.04372  87.51110  91.56138 176.1217    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.