scicloj.ml.smile.nlp — scicloj/scicloj.ml.smile 7.4

->vocabulary-top-n clj

(->vocabulary-top-n bows n)

Takes top-n most frequent tokens as vocabulary

bow->something-sparse clj

(bow->something-sparse ds bow-col indices-col bow->sparse-fn options)

Converts a bag-of-word column bow-col to a sparse data column indices-col. The exact transformation to the sparse representtaion is given by bow->sparse-fn

bow->sparse clj

(bow->sparse ds bow-col indices-col bow->sparse-fn vocabulary)

Generic function to convert a colmn to something sparse, using the given vocabulary.

bow->sparse-and-vocab clj

(bow->sparse-and-vocab ds
                       bow-col
                       indices-col
                       bow->sparse-fn
                       {:keys [create-vocab-fn]
                        :or {create-vocab-fn create-vocab-all}})

Converts a bag-of-word column bow-col to a sparse data column indices-col. The exact transformation to the sparse representtaion is given by bow->sparse-fn

bow->sparse-indices clj

(bow->sparse-indices bow vocab->index-map)

Converts the token-frequencies to the sparse vectors needed by Maxent

bow->tfidf clj

(bow->tfidf ds bow-column tfidf-column)

(bow->tfidf ds bow-column tfidf-column options)

Calculates the tfidf score from bag-of-words (as token frequency maps) in column bow-column and stores them in a new column tfid-column as maps of token->tfidf-score. Possible options:

• :tf-map-handler-fn : If present, it gets applied to the global term-frequency map after creating it. Fn need to take map of terms to frequencies and return such map. Typical use is to prune less frequent terms. Defaults to identity, so all terms are retained.
• :tf-weighting-scheme See function [[tf-idf]]
• :idf-weighting-scheme See function [[tf-idf]]

count-vectorize clj

(count-vectorize ds text-col bow-col)

(count-vectorize
  ds
  text-col
  bow-col
  {:keys [text->bow-fn] :or {text->bow-fn default-text->bow} :as options})

Converts text column text-col to bag-of-words representation in the form of a frequency-count map. The default text->bow function is default-text-bow. All options are passed to it.

create-vocab-all clj

(create-vocab-all bow)

Uses all tokens as the vocabulary

default-text->bow clj

(default-text->bow text)

(default-text->bow text options)

Converts text to token counts (a map token -> count). Takes options: stopwords being either a keyword naming a default Smile dictionary (:default :google :comprehensive :mysql) or a seq of stop words. The stopwords get normalized in the same way as the text itself, so it should contain full words (non stemmed) As default, no stopwords are used. stemmer being either :none or :porter for selecting the porter stemmer. freq-handler-fn A function taking a term-frequency map, and can further manipulate it. Defaults to identity

default-tokenize clj

(default-tokenize text)

(default-tokenize text options)

Tokenizes text. The usage of a stemmer can be configured by options :stemmer

default-word-normalize clj

(default-word-normalize word)

freqs->SparseArray clj

(freqs->SparseArray freq-map vocab->index-map)

Converts the token-frequency map to s smile SparseArray

idf clj

(idf terms bows)

(idf term bows options)

resolve-stemmer clj

(resolve-stemmer options)

resolve-stopwords clj

(resolve-stopwords stopwords-option)

simple-normalizer clj

tf clj

(tf term bow)

(tf term bow options)

tf-map clj

(tf-map bows)

tf-map-handler-top-n clj

(tf-map-handler-top-n n freqs)

Keeps the n most frequent terms in teh term-frequency table

tfidf clj

(tfidf term bow bows)

(tfidf term bow bows options)

Calculates tfidf. term : The term for which to calculate the tfidf value bow : bag-of-words representation of the document (= term-frequency map) bows : list of bag-of-words representing the corpus (= list of term-frequency maps)

options supported : - :tf-weighting-scheme The term-frequency weighting scheme with supported values :raw-count , :term-frequency Default is: :raw-count see here: https://en.wikipedia.org/wiki/Tf%E2%80%93idf#Term_frequency_2 - :idf-weighting-scheme The inverse term-frequency weighting scheme with supported values :smooth-sklearn, :idf, :smooth Default is: smooth-sklearn https://en.wikipedia.org/wiki/Tf%E2%80%93idf#Inverse_document_frequency_2 https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer

tfidf->dense-array clj

(tfidf->dense-array ds tfidf-column array-column)

Converts the sparse tfidf map based representation into dense double arrays

word-process clj

(word-process stemmer word-normalizer-fn word options)