NLP Word Frequency Algorithms (TheAlgorithms#2142)

* NLP Word Frequency Algorithms

* Added type hints and Wikipedia link to tf-idf

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Fix line length for flake8

* Fix line length for flake8 V2

* Add line escapes and change int to float

* Corrected doctests

* Fix for TravisCI

* Fix for TravisCI V2

* Tests passing locally

* Tests passing locally

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Update machine_learning/word_frequency_functions.py

Co-authored-by: Christian Clauss <[email protected]>

* Add doctest examples and clean up docstrings

Co-authored-by: Christian Clauss <[email protected]>

machine_learning/word_frequency_functions.py

@@ -0,0 +1,133 @@
+import string
+from math import log10
+
+"""
+    tf-idf Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf
+    tf-idf and other word frequency algorithms are often used
+    as a weighting factor in information retrieval and text
+    mining. 83% of text-based recommender systems use
+    tf-idf for term weighting. In Layman's terms, tf-idf
+    is a statistic intended to reflect how important a word
+    is to a document in a corpus (a collection of documents)
+
+
+    Here I've implemented several word frequency algorithms
+    that are commonly used in information retrieval: Term Frequency,
+    Document Frequency, and TF-IDF (Term-Frequency*Inverse-Document-Frequency)
+    are included.
+
+    Term Frequency is a statistical function that
+    returns a number representing how frequently
+    an expression occurs in a document. This
+    indicates how significant a particular term is in
+    a given document.
+
+    Document Frequency is a statistical function that returns
+    an integer representing the number of documents in a
+    corpus that a term occurs in (where the max number returned
+    would be the number of documents in the corpus).
+
+    Inverse Document Frequency is mathematically written as
+    log10(N/df), where N is the number of documents in your
+    corpus and df is the Document Frequency. If df is 0, a
+    ZeroDivisionError will be thrown.
+
+    Term-Frequency*Inverse-Document-Frequency is a measure
+    of the originality of a term. It is mathematically written
+    as tf*log10(N/df). It compares the number of times
+    a term appears in a document with the number of documents
+    the term appears in. If df is 0, a ZeroDivisionError will be thrown.
+"""
+
+
+def term_frequency(term : str, document : str) -> int:
+    """
+    Return the number of times a term occurs within
+    a given document.
+    @params: term, the term to search a document for, and document,
+            the document to search within
+    @returns: an integer representing the number of times a term is
+            found within the document
+
+    @examples:
+    >>> term_frequency("to", "To be, or not to be")
+    2
+    """
+    # strip all punctuation and newlines and replace it with ''
+    document_without_punctuation = document.translate(
+        str.maketrans("", "", string.punctuation)
+    ).replace("\n", "")
+    tokenize_document = document_without_punctuation.split(" ")  # word tokenization
+    return len(
+        [word for word in tokenize_document if word.lower() == term.lower()]
+    )
+
+
+def document_frequency(term: str, corpus: str) -> int:
+    """
+    Calculate the number of documents in a corpus that contain a
+    given term
+    @params : term, the term to search each document for, and corpus, a collection of
+             documents. Each document should be separated by a newline.
+    @returns : the number of documents in the corpus that contain the term you are
+               searching for and the number of documents in the corpus
+    @examples :
+    >>> document_frequency("first", "This is the first document in the corpus.\\nThIs\
+is the second document in the corpus.\\nTHIS is \
+the third document in the corpus.")
+    (1, 3)
+    """
+    corpus_without_punctuation = corpus.translate(
+        str.maketrans("", "", string.punctuation)
+    )  # strip all punctuation and replace it with ''
+    documents = corpus_without_punctuation.split("\n")
+    lowercase_documents = [document.lower() for document in documents]
+    return len(
+        [document for document in lowercase_documents if term.lower() in document]
+    ), len(documents)
+
+
+def inverse_document_frequency(df : int, N: int) -> float:
+    """
+    Return an integer denoting the importance
+    of a word. This measure of importance is
+    calculated by log10(N/df), where N is the
+    number of documents and df is
+    the Document Frequency.
+    @params : df, the Document Frequency, and N,
+    the number of documents in the corpus.
+    @returns : log10(N/df)
+    @examples :
+    >>> inverse_document_frequency(3, 0)
+    Traceback (most recent call last):
+     ...
+    ValueError: log10(0) is undefined.
+    >>> inverse_document_frequency(1, 3)
+    0.477
+    >>> inverse_document_frequency(0, 3)
+    Traceback (most recent call last):
+     ...
+    ZeroDivisionError: df must be > 0
+    """
+    if df == 0:
+        raise ZeroDivisionError("df must be > 0")
+    elif N == 0:
+        raise ValueError("log10(0) is undefined.")
+    return round(log10(N / df), 3)
+
+
+def tf_idf(tf : int, idf: int) -> float:
+    """
+    Combine the term frequency
+    and inverse document frequency functions to
+    calculate the originality of a term. This
+    'originality' is calculated by multiplying
+    the term frequency and the inverse document
+    frequency : tf-idf = TF * IDF
+    @params : tf, the term frequency, and idf, the inverse document
+    frequency
+    @examples :
+    >>> tf_idf(2, 0.477)
+    0.954
+    """
+    return round(tf * idf, 3)