TfIdfVectorizer: TfIDF(Term Frequency Inverse Document Frequency) Vectorizer
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TfIdfVectorizer R Documentation
TfIDF(Term Frequency Inverse Document Frequency) Vectorizer
Description
Creates a tf-idf matrix
Details
Given a list of text, it creates a sparse matrix consisting of tf-idf score for tokens from the text.
Super class
superml::CountVectorizer -> TfIdfVectorizer
Public fields
sentencesa list containing sentences
max_dfWhen building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold, value lies between 0 and 1.
min_dfWhen building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold, value lies between 0 and 1.
max_featuresuse top features sorted by count to be used in bag of words matrix.
ngram_rangeThe lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ngram_range of c(1, 1) means only unigrams, c(1, 2) means unigrams and bigrams, and c(2, 2) means only bigrams.
splitsplitting criteria for strings, default: " "
lowercaseconvert all characters to lowercase before tokenizing
regexregex expression to use for text cleaning.
remove_stopwordsa list of stopwords to use, by default it uses its inbuilt list of standard stopwords
smooth_idflogical, to prevent zero division, adds one to document frequencies, as if an extra document was seen containing every term in the collection exactly once
normlogical, if TRUE, each output row will have unit norm ‘l2’: Sum of squares of vector elements is 1. if FALSE returns non-normalized vectors, default: TRUE
Methods
Public methods
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Method new()
Usage
TfIdfVectorizer$new(
min_df,
max_df,
max_features,
ngram_range,
regex,
remove_stopwords,
split,
lowercase,
smooth_idf,
norm
)
Arguments
min_dfnumeric, When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold, value lies between 0 and 1.
max_dfnumeric, When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold, value lies between 0 and 1.
max_featuresinteger, Build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.
ngram_rangevector, The lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ngram_range of c(1, 1) means only unigrams, c(1, 2) means unigrams and bigrams, and c(2, 2) means only bigrams.
regexcharacter, regex expression to use for text cleaning.
remove_stopwordslist, a list of stopwords to use, by default it uses its inbuilt list of standard english stopwords
splitcharacter, splitting criteria for strings, default: " "
lowercaselogical, convert all characters to lowercase before tokenizing, default: TRUE
smooth_idflogical, to prevent zero division, adds one to document frequencies, as if an extra document was seen containing every term in the collection exactly once
normlogical, if TRUE, each output row will have unit norm ‘l2’: Sum of squares of vector elements is 1. if FALSE returns non-normalized vectors, default: TRUE
parallellogical, speeds up ngrams computation using n-1 cores, defaults: TRUE
Details
Create a new 'TfIdfVectorizer' object.
Returns
A 'TfIdfVectorizer' object.
Examples
TfIdfVectorizer$new()
Method fit()
Usage
TfIdfVectorizer$fit(sentences)
Arguments
sentencesa list of text sentences
Details
Fits the TfIdfVectorizer model on sentences
Returns
NULL
Examples
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf = TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.3)
tf$fit(sents)
Method fit_transform()
Usage
TfIdfVectorizer$fit_transform(sentences)
Arguments
sentencesa list of text sentences
Details
Fits the TfIdfVectorizer model and returns a sparse matrix of count of tokens
Returns
a sparse matrix containing tf-idf score for tokens in each given sentence
Examples
\dontrun{
sents <- c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf <- TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.1)
tf_matrix <- tf$fit_transform(sents)
}
Method transform()
Usage
TfIdfVectorizer$transform(sentences)
Arguments
sentencesa list of new text sentences
Details
Returns a matrix of tf-idf score of tokens
Returns
a sparse matrix containing tf-idf score for tokens in each given sentence
Examples
\dontrun{
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
new_sents <- c("dark at night",'mothers day')
tf = TfIdfVectorizer$new(min_df=0.1)
tf$fit(sents)
tf_matrix <- tf$transform(new_sents)
}
Method clone()
The objects of this class are cloneable with this method.
Usage
TfIdfVectorizer$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `TfIdfVectorizer$new`
## ------------------------------------------------
TfIdfVectorizer$new()
## ------------------------------------------------
## Method `TfIdfVectorizer$fit`
## ------------------------------------------------
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf = TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.3)
tf$fit(sents)
## ------------------------------------------------
## Method `TfIdfVectorizer$fit_transform`
## ------------------------------------------------
## Not run:
sents <- c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf <- TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.1)
tf_matrix <- tf$fit_transform(sents)
## End(Not run)
## ------------------------------------------------
## Method `TfIdfVectorizer$transform`
## ------------------------------------------------
## Not run:
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
new_sents <- c("dark at night",'mothers day')
tf = TfIdfVectorizer$new(min_df=0.1)
tf$fit(sents)
tf_matrix <- tf$transform(new_sents)
## End(Not run)
superml documentation built on Nov. 14, 2022, 9:05 a.m.
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| TfIdfVectorizer | R Documentation |
TfIDF(Term Frequency Inverse Document Frequency) Vectorizer
Description
Creates a tf-idf matrix
Details
Given a list of text, it creates a sparse matrix consisting of tf-idf score for tokens from the text.
Super class
superml::CountVectorizer -> TfIdfVectorizer
Public fields
sentencesa list containing sentences
max_dfWhen building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold, value lies between 0 and 1.
min_dfWhen building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold, value lies between 0 and 1.
max_featuresuse top features sorted by count to be used in bag of words matrix.
ngram_rangeThe lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ngram_range of c(1, 1) means only unigrams, c(1, 2) means unigrams and bigrams, and c(2, 2) means only bigrams.
splitsplitting criteria for strings, default: " "
lowercaseconvert all characters to lowercase before tokenizing
regexregex expression to use for text cleaning.
remove_stopwordsa list of stopwords to use, by default it uses its inbuilt list of standard stopwords
smooth_idflogical, to prevent zero division, adds one to document frequencies, as if an extra document was seen containing every term in the collection exactly once
normlogical, if TRUE, each output row will have unit norm ‘l2’: Sum of squares of vector elements is 1. if FALSE returns non-normalized vectors, default: TRUE
Methods
Public methods
Method new()
Usage
TfIdfVectorizer$new( min_df, max_df, max_features, ngram_range, regex, remove_stopwords, split, lowercase, smooth_idf, norm )
Arguments
min_dfnumeric, When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold, value lies between 0 and 1.
max_dfnumeric, When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold, value lies between 0 and 1.
max_featuresinteger, Build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.
ngram_rangevector, The lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ngram_range of c(1, 1) means only unigrams, c(1, 2) means unigrams and bigrams, and c(2, 2) means only bigrams.
regexcharacter, regex expression to use for text cleaning.
remove_stopwordslist, a list of stopwords to use, by default it uses its inbuilt list of standard english stopwords
splitcharacter, splitting criteria for strings, default: " "
lowercaselogical, convert all characters to lowercase before tokenizing, default: TRUE
smooth_idflogical, to prevent zero division, adds one to document frequencies, as if an extra document was seen containing every term in the collection exactly once
normlogical, if TRUE, each output row will have unit norm ‘l2’: Sum of squares of vector elements is 1. if FALSE returns non-normalized vectors, default: TRUE
parallellogical, speeds up ngrams computation using n-1 cores, defaults: TRUE
Details
Create a new 'TfIdfVectorizer' object.
Returns
A 'TfIdfVectorizer' object.
Examples
TfIdfVectorizer$new()
Method fit()
Usage
TfIdfVectorizer$fit(sentences)
Arguments
sentencesa list of text sentences
Details
Fits the TfIdfVectorizer model on sentences
Returns
NULL
Examples
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf = TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.3)
tf$fit(sents)
Method fit_transform()
Usage
TfIdfVectorizer$fit_transform(sentences)
Arguments
sentencesa list of text sentences
Details
Fits the TfIdfVectorizer model and returns a sparse matrix of count of tokens
Returns
a sparse matrix containing tf-idf score for tokens in each given sentence
Examples
\dontrun{
sents <- c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf <- TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.1)
tf_matrix <- tf$fit_transform(sents)
}
Method transform()
Usage
TfIdfVectorizer$transform(sentences)
Arguments
sentencesa list of new text sentences
Details
Returns a matrix of tf-idf score of tokens
Returns
a sparse matrix containing tf-idf score for tokens in each given sentence
Examples
\dontrun{
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
new_sents <- c("dark at night",'mothers day')
tf = TfIdfVectorizer$new(min_df=0.1)
tf$fit(sents)
tf_matrix <- tf$transform(new_sents)
}
Method clone()
The objects of this class are cloneable with this method.
Usage
TfIdfVectorizer$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `TfIdfVectorizer$new`
## ------------------------------------------------
TfIdfVectorizer$new()
## ------------------------------------------------
## Method `TfIdfVectorizer$fit`
## ------------------------------------------------
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf = TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.3)
tf$fit(sents)
## ------------------------------------------------
## Method `TfIdfVectorizer$fit_transform`
## ------------------------------------------------
## Not run:
sents <- c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
tf <- TfIdfVectorizer$new(smooth_idf = TRUE, min_df = 0.1)
tf_matrix <- tf$fit_transform(sents)
## End(Not run)
## ------------------------------------------------
## Method `TfIdfVectorizer$transform`
## ------------------------------------------------
## Not run:
sents = c('i am alone in dark.','mother_mary a lot',
'alone in the dark?', 'many mothers in the lot....')
new_sents <- c("dark at night",'mothers day')
tf = TfIdfVectorizer$new(min_df=0.1)
tf$fit(sents)
tf_matrix <- tf$transform(new_sents)
## End(Not run)
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