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.
You can install latest cran version using (recommended):
You can install the developmemt version directly from github using:
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:
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] 50For 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 and go going where you again am
#> [1,] 0 0.7159943 0.3579971 0 0.3579971 0.0000000 0.0000000 0 0.480654
#> [2,] 0 0.0000000 0.0000000 0 0.4563106 0.4563106 0.4563106 0 0.000000
#> [3,] 1 0.0000000 0.0000000 0 0.0000000 0.0000000 0.0000000 0 0.000000
#> are
#> [1,] 0.0000000
#> [2,] 0.6126516
#> [3,] 0.0000000Few 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.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 and go going where you
#> [1,] 0 0.8164966 0.4082483 0 0.4082483 0.0000000 0.0000000
#> [2,] 0 0.0000000 0.0000000 0 0.5773503 0.5773503 0.5773503
#> [3,] 1 0.0000000 0.0000000 0 0.0000000 0.0000000 0.0000000Few 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.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 0head(test, 3)
#> text target
#> 1: home is where you go from to work 0
#> 2: how does it work 1
#> 3: how does it work 0Now, 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$text)
dim(train_tf_features)
#> [1] 6 15
dim(test_tf_features)
#> [1] 6 15We generate 15 features for each of the given data. Let’s see how they look:
head(train_tf_features, 3)
#> work home and does does it does it work go
#> [1,] 0.0000000 0.8164966 0.4082483 0.0000000 0.0000000 0.0000000 0
#> [2,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0
#> [3,] 0.2478085 0.0000000 0.0000000 0.3425257 0.3425257 0.3425257 0
#> going how how does how does it it it work where
#> [1,] 0.4082483 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> [2,] 0.5773503 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5773503
#> [3,] 0.0000000 0.3425257 0.3425257 0.3425257 0.3425257 0.3425257 0.0000000
#> you
#> [1,] 0.0000000
#> [2,] 0.5773503
#> [3,] 0.0000000head(test_tf_features, 3)
#> work home and does does it does it work go going
#> [1,] 0.3401651 0.4701829 0 0.0000000 0.0000000 0.0000000 0.4701829 0
#> [2,] 0.2478085 0.0000000 0 0.3425257 0.3425257 0.3425257 0.0000000 0
#> [3,] 0.2478085 0.0000000 0 0.3425257 0.3425257 0.3425257 0.0000000 0
#> how how does how does it it it work where you
#> [1,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4701829 0.4701829
#> [2,] 0.3425257 0.3425257 0.3425257 0.3425257 0.3425257 0.0000000 0.0000000
#> [3,] 0.3425257 0.3425257 0.3425257 0.3425257 0.3425257 0.0000000 0.0000000Finally, to train a machine learning model on this, you can simply do:
# ensure the input to classifier is a data.table or data.frame object
x_train <- data.table(cbind(train_tf_features, target = train$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...
#> [1] train-error:0.500000
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [10] train-error:0.500000
predictions <- xgb$predict(x_test)
predictions
#> [1] 0.5 0.5 0.5 0.5 0.5 0.5In this tutorial, we discussed how to use superml’s tfidfvectorizer to create tfidf matrix and train a machine learning model on it.