How to use TfidfVectorizer in R ?

knitr::opts_chunk$set(
  collapse = TRUE,
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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)

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)

Few observations:

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)

Few observations:

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)
head(test, 3)

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)
dim(test_tf_features)

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

head(train_tf_features, 3)
head(test_tf_features, 3)

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")

predictions <- xgb$predict(x_test)
predictions

Summary

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



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superml documentation built on April 28, 2020, 9:05 a.m.