package owl

  1. Overview
  2. Docs
package owl
Legend:
Library
Module
Module type
Parameter
Class
Class type

NLP: TFIDF module

Type definition
type tf_typ =
  1. | Binary
  2. | Count
  3. | Frequency
  4. | Log_norm
    (*

    Type of term frequency.

    *)
type df_typ =
  1. | Unary
  2. | Idf
  3. | Idf_Smooth
    (*

    Type of inverse document frequency.

    *)
type t

Type of a TFIDF model

Query model
val length : t -> int

Size of Tfidf model, i.e. number of documents contained.

val term_freq : tf_typ -> float -> float -> float

term_freq term_count num_words calculates the term frequency weight.

val doc_freq : df_typ -> float -> float -> float

doc_freq doc_count num_docs calculates the document frequency weight.

val get_uri : t -> string

Return the path of the TFIDF model.

val get_corpus : t -> Owl_nlp_corpus.t

Return the corpus contained in TFIDF model

val vocab_len : t -> int

Return the size of the vocabulary contained in the TFIDF model.

val get_handle : t -> in_channel

Get the file handle associated with TFIDF model.

val doc_count_of : t -> string -> float

doc_count_of tfidf w calculate document frequency for a given word w.

val doc_count : Owl_nlp_vocabulary.t -> string -> float array * int

doc_count vocab fname count occurrency in all documents contained in the raw text corpus of file fname, for all words

val term_count : ('a, float) Hashtbl.t -> 'a array -> unit

term_count count doc counts the term occurrency in a document, and saves the result in count hashtbl.

val density : t -> float

Return the percentage of non-zero elements in doc-term matrix.

val doc_to_vec : (float, 'a) Bigarray.kind -> t -> (int * float) array -> (float, 'a) Owl_dense.Ndarray.Generic.t

doc_to_vec kind tfidf vec converts a TFIDF vector from its sparse represents to dense ndarray vector whose length equals the vocabulary size.

Iteration functions
val get : t -> int -> (int * float) array

Return the ith TFIDF vector in the model. The format of return is (vocabulary index, weight) tuple array of a document.

val next : t -> (int * float) array

Return the next document vector in the model. The format of return is (vocabulary index, weight) tuple array of a document.

val next_batch : ?size:int -> t -> (int * float) array array

Return the next batch of document vectors in the model, the default size is 100.

val iteri : (int -> (int * float) array -> unit) -> t -> unit

Iterate all the document vectors in a TFIDF model. The format of document vector is (vocabulary index, weight) tuple array of a document.

val mapi : (int -> (int * float) array -> 'a) -> t -> 'a array

Map all the document vectors in a TFIDF model. The format of document vector is (vocabulary index, weight) tuple array of a document.

val reset_iterators : t -> unit

Reset the iterator to the beginning of the TFIDF model.

Core functions
val build : ?norm:bool -> ?sort:bool -> ?tf:tf_typ -> ?df:df_typ -> Owl_nlp_corpus.t -> t

This function builds up a TFIDF model according to the passed in parameters.

Parameters: * norm: whether to normalise the vectors in the TFIDF model, default is false. * sort: whether to sort the terms in a TFIDF vector in increasing order w.r.t their vocabulary indices. The default is false. * tf: type of term frequency used in building TFIDF. The default is Count. * df: type of document frequency used in building TFIDF. The default is Idf. * corpus: the corpus built by Owl_nlp_corpus model atop of which TFIDF will be built.

I/O functions
val save : t -> string -> unit

save tfidf fname saves the TFIDF to a file of given file name fname.

val load : string -> t

load fname loads a TFIDF from a file of name fname.

val to_string : t -> string

Convert a TFIDF to its string representation, contains summary information.

val print : t -> unit

Pretty print out the summary information of a TFIDF model.

Helper functions
val tf_typ_string : tf_typ -> string

Convert term frequency type into string.

val df_typ_string : df_typ -> string

Convert document frequency type into string.

val apply : t -> string -> (int * float) array

Convert a single document according to a given model

val normalise : ('a * float) array -> ('a * float) array

normalise x makes x a unit vector by dividing its l2norm.

val create : tf_typ -> df_typ -> Owl_nlp_corpus.t -> t

Wrap up a TFIDF model type. Low-level function and you are not supposed to use it.

val all_pairwise_distance : Owl_nlp_similarity.t -> t -> ('a * float) array -> (int * float) array

Calculate pairwise distance for the whole model, return format is (id,dist) array.

val nearest : ?typ:Owl_nlp_similarity.t -> t -> ('a * float) array -> int -> (int * float) array

Return K-nearest neighbours, it is very slow due to linear search.

OCaml

Innovation. Community. Security.

GitHub Discord Twitter Peertube RSS
About
Changelog Releases Industrial Users Academic Users Why OCaml
Ecosystem
Packages Community Events OCaml Planet Jobs
Resources
Install OCaml Get Started Platform Tools Language Manual Standard Library API Books Exercises Papers OCaml Playground Logo
Policies
Carbon Footprint Governance Privacy Code of Conduct