# How to use TfidfVectorizer in R ?

#### Manish Saraswat

#### 2024-02-18

In this tutorial, we’ll look at how to create tfidf feature matrix in R in two simple steps with superml. Superml borrows speed gains using parallel computation and optimised functions from data.table R package. Tfidf matrix can be used to as features for a machine learning model. Also, we can use tdidf features as an embedding to represent the given texts.

## Install

You can install latest cran version using (recommended):

    install.packages("superml")

You can install the developmemt version directly from github using:

    devtools::install_github("saraswatmks/superml")

## Caveats on superml installation

For machine learning, superml is based on the existing R packages. Hence, while installing the package, we don’t install all the dependencies. However, while training any model, superml will automatically install the package if its not found. Still, if you want to install all dependencies at once, you can simply do:

    install.packages("superml", dependencies=TRUE)

## Sample Data

First, we’ll create a sample data. Feel free to run it alongside in your laptop and check the results.

    library(superml)

    # should be a vector of texts
    sents <-  c('i am going home and home',
              'where are you going.? //// ',
              'how does it work',
              'transform your work and go work again',
              'home is where you go from to work')

    # generate more sentences
    n <- 10
    sents <- rep(sents, n)
    length(sents)
    #> [1] 50

For sample, we’ve generated 50 documents. Let’s create the features now. For ease, superml uses the similar API layout as python scikit-learn.

    # initialise the class
    tfv <- TfIdfVectorizer$new(max_features = 10, remove_stopwords = FALSE)

    # generate the matrix
    tf_mat <- tfv$fit_transform(sents)

    head(tf_mat, 3)
    #>      work      home       you     where     going go       and your transform
    #> [1,]    0 0.8164966 0.0000000 0.0000000 0.4082483  0 0.4082483    0         0
    #> [2,]    0 0.0000000 0.5773503 0.5773503 0.5773503  0 0.0000000    0         0
    #> [3,]    1 0.0000000 0.0000000 0.0000000 0.0000000  0 0.0000000    0         0
    #>      to
    #> [1,]  0
    #> [2,]  0
    #> [3,]  0

Few observations:

- `remove_stopwords = FALSE` defaults to `TRUE`. We set it to `FALSE` since most of the words in our dummy `sents` are stopwords.
- `max_features = 10` select the top 10 features (tokens) based on frequency.
- The returned matrix is normalised by default, `norm = TRUE` is set by default.

Now, let’s generate the matrix using its `ngram_range` features.

    # initialise the class
    tfv <- TfIdfVectorizer$new(min_df = 0.4, remove_stopwords = FALSE, ngram_range = c(1, 3))

    # generate the matrix
    tf_mat <- tfv$fit_transform(sents)

    head(tf_mat, 3)
    #>      work      home       you     where     going go       and
    #> [1,]    0 0.8164966 0.0000000 0.0000000 0.4082483  0 0.4082483
    #> [2,]    0 0.0000000 0.5773503 0.5773503 0.5773503  0 0.0000000
    #> [3,]    1 0.0000000 0.0000000 0.0000000 0.0000000  0 0.0000000

Few observations:

- `ngram_range = c(1,3)` set the lower and higher range respectively of the resulting ngram tokens.
- `min_df = 0.4` says to keep the tokens which occurs in atleast 40% & above of the documents.

## Usage for a Machine Learning Model

In order to use Tfidf Vectorizer for a machine learning model, sometimes it gets confusing as to which method `fit_transform`, `fit`, `transform` should be used to generate tfidf features for the given data. Here’s a way to do:

    library(data.table)
    library(superml)

    # use sents from above
    sents <-  c('i am going home and home',
              'where are you going.? //// ',
              'how does it work',
              'transform your work and go work again',
              'home is where you go from to work',
              'how does it work')

    # create dummy data
    train <- data.table(text = sents, target = rep(c(0,1), 3))
    test <- data.table(text = sample(sents), target = rep(c(0,1), 3))

Let’s see how the data looks like:

    head(train, 3)
    #>                           text target
    #> 1:    i am going home and home      0
    #> 2: where are you going.? ////       1
    #> 3:            how does it work      0

    head(test, 3)
    #>                                 text target
    #> 1:                  how does it work      0
    #> 2: home is where you go from to work      1
    #> 3:          i am going home and home      0

Now, we generate features for train-test data:

    # initialise the class
    tfv <- TfIdfVectorizer$new(min_df = 0.3, remove_stopwords = FALSE, ngram_range = c(1,3))

    # we fit on train data
    tfv$fit(train$text)

    train_tf_features <- tfv$transform(train$text)
    test_tf_features <- tfv$transform(test$text)

    dim(train_tf_features)
    #> [1]  6 15
    dim(test_tf_features)
    #> [1]  6 15

We generate 15 features for each of the given data. Let’s see how they look:

    head(train_tf_features, 3)
    #>           work      home       you     where   it work        it how does it
    #> [1,] 0.0000000 0.8164966 0.0000000 0.0000000 0.0000000 0.0000000   0.0000000
    #> [2,] 0.0000000 0.0000000 0.5773503 0.5773503 0.0000000 0.0000000   0.0000000
    #> [3,] 0.2478085 0.0000000 0.0000000 0.0000000 0.3425257 0.3425257   0.3425257
    #>       how does       how     going go does it work   does it      does
    #> [1,] 0.0000000 0.0000000 0.4082483  0    0.0000000 0.0000000 0.0000000
    #> [2,] 0.0000000 0.0000000 0.5773503  0    0.0000000 0.0000000 0.0000000
    #> [3,] 0.3425257 0.3425257 0.0000000  0    0.3425257 0.3425257 0.3425257
    #>            and
    #> [1,] 0.4082483
    #> [2,] 0.0000000
    #> [3,] 0.0000000

    head(test_tf_features, 3)
    #>           work      home       you     where   it work        it how does it
    #> [1,] 0.2478085 0.0000000 0.0000000 0.0000000 0.3425257 0.3425257   0.3425257
    #> [2,] 0.3401651 0.4701829 0.4701829 0.4701829 0.0000000 0.0000000   0.0000000
    #> [3,] 0.0000000 0.8164966 0.0000000 0.0000000 0.0000000 0.0000000   0.0000000
    #>       how does       how     going        go does it work   does it      does
    #> [1,] 0.3425257 0.3425257 0.0000000 0.0000000    0.3425257 0.3425257 0.3425257
    #> [2,] 0.0000000 0.0000000 0.0000000 0.4701829    0.0000000 0.0000000 0.0000000
    #> [3,] 0.0000000 0.0000000 0.4082483 0.0000000    0.0000000 0.0000000 0.0000000
    #>            and
    #> [1,] 0.0000000
    #> [2,] 0.0000000
    #> [3,] 0.4082483

Finally, to train a machine learning model on this, you can simply do:

    # ensure the input to classifier is a data.table or data.frame object
    x_train <- data.table(cbind(train_tf_features, target = train$target))
    x_test <- data.table(test_tf_features)

    xgb <- XGBTrainer$new(n_estimators = 10, objective = "binary:logistic")
    xgb$fit(x_train, "target")
    #> converting the data into xgboost format..
    #> starting with training...
    #> [15:11:24] WARNING: src/learner.cc:767:
    #> Parameters: { "nrounds" } are not used.
    #>
    #> [1]  train-logloss:0.693147
    #> Will train until train_logloss hasn't improved in 50 rounds.
    #>
    #> [10] train-logloss:0.693147

    predictions <- xgb$predict(x_test)
    predictions
    #> [1] 0.5 0.5 0.5 0.5 0.5 0.5

## Summary

In this tutorial, we discussed how to use superml’s tfidfvectorizer to create tfidf matrix and train a machine learning model on it.