data_describe.text.text_preprocessing¶
Text preprocessing module.
This module contains a number of methods by which text documents can be preprocessed. The individual preprocessing functions can be classified as “Bag of Words Functions” (to_lower, remove_punct, remove_digits, remove_single_char_and_spaces, remove_stopwords, lemmatize, stem) or “Document Functions” (tokenize, bag_of_words_to_docs). Each of the functions in these groups return generator objects, and when using them on their own, the internal function to_list can be utilized as depicted below.
Example
Individual Document Functions should be processed as such:
tokenized_docs = to_list(tokenize(original_docs), bow=False)
Individual Bag of Words Functions should be processed as such:
lower_case_docs_bow = to_list(to_lower(original_docs_bow))
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Turns list of documents into “bag of words” format. |
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Converts all letters in documents (“bag of words” format) to lowercase. |
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Removes all punctuation from documents (e.g. periods, question marks, etc.). |
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Removes all numbers and words containing numerical digits from documents. |
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Removes all words that contain only one character and blank spaces from documents. |
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Removes all “stop words” from documents. |
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Lemmatizes all words in documents. |
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Stems all words in documents. |
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Converts list of documents in “bag of words” format. |
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Creates a Term Frequency-Inverse Document Frequency matrix. |
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Creates a document-term matrix which gives wordcount per document. |
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Pre-process a text corpus. |
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Converts a generator expression from an individual preprocessing function into a list. |
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Generates frequency distribution of “n-grams” from all of the text documents. |
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Filters words outside specified frequency thresholds. |
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data_describe.text.text_preprocessing.nltk¶
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data_describe.text.text_preprocessing.tokenize(text_docs: Iterable[str]) → Iterable[Iterable[str]]¶ Turns list of documents into “bag of words” format.
- Parameters
text_docs – A list of text documents in string format
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.to_lower(text_docs_bow: Iterable[Iterable[str]]) → Iterable[Iterable[str]]¶ Converts all letters in documents (“bag of words” format) to lowercase.
- Parameters
text_docs_bow – A list of lists of words from a document
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.remove_punct(text_docs_bow: Iterable[Iterable[str]], replace_char: str = '', remove_all: bool = False) → Iterable[Iterable[str]]¶ Removes all punctuation from documents (e.g. periods, question marks, etc.).
- Parameters
text_docs_bow – A list of lists of words from a document
replace_char – Character to replace punctuation instances with. Default is space
remove_all – If True, removes all instances of punctuation from document. Default is False, which only removes leading and/or trailing instances.
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.remove_digits(text_docs_bow: Iterable[Iterable[str]]) → Iterable[Iterable[str]]¶ Removes all numbers and words containing numerical digits from documents.
- Parameters
text_docs_bow – A list of lists of words from a document
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.remove_single_char_and_spaces(text_docs_bow: Iterable[Iterable[str]]) → Iterable[Iterable[str]]¶ Removes all words that contain only one character and blank spaces from documents.
- Parameters
text_docs_bow – A list of lists of words from a document
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.remove_stopwords(text_docs_bow: Iterable[Iterable[str]], custom_stopwords: Optional[List[str]] = None) → Iterable[Iterable[str]]¶ Removes all “stop words” from documents.
“Stop words” can be defined as commonly used words which are typically useless for NLP.
- Parameters
text_docs_bow – A list of lists of words from a document
custom_stopwords – An optional list of words to remove along with the stop words. Defaults to nltk english stopwords.
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.lemmatize(text_docs_bow: Iterable[Iterable[str]]) → Iterable[Iterable[str]]¶ Lemmatizes all words in documents.
Lemmatization is grouping words together by their reducing them to their inflected forms so they can be analyzed as a single item.
- Parameters
text_docs_bow – A lists of list of words from a document
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.stem(text_docs_bow: Iterable[Iterable[str]]) → Iterable[Iterable[str]]¶ Stems all words in documents.
Stemming is grouping words together by taking the stems of their inflected forms so they can be analyzed as a single item.
- Parameters
text_docs_bow – A list of lists of words from a document
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.bag_of_words_to_docs(text_docs_bow: Iterable[Iterable[str]]) → Iterable[str]¶ Converts list of documents in “bag of words” format.
This converts back into form of document being stored in one string.
- Parameters
text_docs_bow – A list of lists of words from a document
- Returns
A generator expression for all of the processed documents
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data_describe.text.text_preprocessing.create_tfidf_matrix(text_docs: Iterable[str], **kwargs) → pd.DataFrame¶ Creates a Term Frequency-Inverse Document Frequency matrix.
- Parameters
text_docs – A list of strings of text documents
**kwargs – Other arguments to be passed to sklearn.feature_extraction.text.TfidfVectorizer
- Returns
- Pandas DataFrame of TF-IDF matrix with documents as rows and words
as columns
- Return type
matrix_df
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data_describe.text.text_preprocessing.create_doc_term_matrix(text_docs: Iterable[str], **kwargs) → pd.DataFrame¶ Creates a document-term matrix which gives wordcount per document.
- Parameters
text_docs – A list of strings of text documents
**kwargs – Other arguments to be passed to sklearn.feature_extraction.text.CountVectorizer
- Returns
- Pandas DataFrame of document-term matrix with documents as rows
and words as columns
- Return type
matrix_df
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data_describe.text.text_preprocessing.preprocess_texts(text_docs: Iterable[str], lem: bool = False, stem: bool = False, custom_pipeline: List = None) → Iterable[Any]¶ Pre-process a text corpus.
Cleans list of documents by running through a customizable text-preprocessing pipeline.
- Parameters
text_docs – A list of strings of text documents (also accepts arrays and Pandas series)
lem – If True, lemmatization becomes part of the pre-processing. Recommended to set as False and run user-created lemmatization function if pipeline is customized. Default is False.
stem – If True, stemming becomes part of the pre-processing. Recommended to set as False and run user-created stemming function if pipeline is customized. Default is False.
custom_pipeline – A custom list of strings and/or function objects which are the function names that text_docs_bow will run through. Default is None, which uses the pipeline: [‘tokenize’, ‘to_lower’, ‘remove_punct’, ‘remove_digits’, ‘remove_single_char_and_spaces’, ‘remove_stopwords’]
- Returns
- List of lists of words for each document which have undergone a
pre-processing pipeline
- Return type
text_docs
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data_describe.text.text_preprocessing.to_list(text_docs_gen) → List[Any]¶ Converts a generator expression from an individual preprocessing function into a list.
- Parameters
text_docs_gen – A generator expression for the processed text documents
- Returns
- A list of processed text documents or a list of tokens (list of strings)
for each document
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data_describe.text.text_preprocessing.ngram_freq(text_docs_bow: Iterable[Iterable[str]], n: int = 3, only_n: bool = False) → ’nltk.FreqDist’¶ Generates frequency distribution of “n-grams” from all of the text documents.
- Parameters
text_docs_bow – A list of lists of words from a document
n – Highest n for n-gram sequence to include. Default is 3
only_n – If True, will only include n-grams for specified value of n. Default is False, which also includes n-grams for all numbers leading up to n
- Raises
ValueError – n must be >= 2.
- Returns
- Dictionary which contains all identified n-grams as keys and their
respective counts as their values
- Return type
freq
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data_describe.text.text_preprocessing.filter_dictionary(text_docs: List[str], no_below: int = 10, no_above: float = 0.2, **kwargs)¶ Filters words outside specified frequency thresholds.
- Parameters
text_docs – A list of list of words from a document, can include n-grams up to 3.
no_below – Keep tokens which are contained in at least no_below documents. Default is 10.
no_above – Keep tokens which are contained in no more than no_above portion of documents (fraction of total corpus size). Default is 0.2.
**kwargs – Other arguments to be passed to gensim.corpora.Dictionary.filter_extremes
- Returns
- Gensim Dictionary encapsulates the mapping between normalized words
and their integer ids.
corpus: Bag of Words (BoW) representation of documents (token_id, token_count).
- Return type
dictionary