check_publish.py

import spacy
import lftk.lftk # check this version, not published version

# load a trained pipeline of your choice from spacy
# remember we already downloaed "en_core_web_sm" pipeline above?
nlp = spacy.load("en_core_web_sm")

# create a spaCy doc object
doc = nlp("I love research but my professor is strange.")

# initiate LFTK extractor by passing in doc
# you can pass in a list of multiple docs
LFTK = lftk.Extractor(docs = doc)

# optionally, you can customize how LFTK extractor calculates handcrafted linguistic features
# for example, include stop word? include puncutaion? maximum decimal digits?
LFTK.customize(stop_words=True, punctuations=False, round_decimal=3)

# now, extract the handcrafted linguistic features that you need
# refer to them as feature keys
extracted_features = LFTK.extract(features = ["a_word_ps", "a_kup_pw", "n_noun"])

# {'a_word_ps': 8.0, 'a_kup_pw': 5.754, 'n_noun': 2}
print(extracted_features)

# returns all available features as a list of dictionaries by default
searched_features = lftk.search_features()

# [{'key': 't_word', 'name': 'total_number_of_words', 'formulation': 'foundation', 'domain': 'surface', 'family': 'wordsent'}, {'key': 't_uword', 'name': 'total_number_of_unique_words', 'formulation': 'foundation', 'domain': 'surface', 'family': 'wordsent'}, {'key': 't_sent', 'name': 'total_number_of_sentences', 'formulation': 'foundation', 'domain': 'surface', 'family': 'wordsent'},...]
print(searched_features)

# specify attributes
searched_features = lftk.search_features(domain = "surface", family = "avgwordsent")

# [{'key': 'a_word_ps', 'name': 'average_number_of_words_per_sentence', 'formulation': 'derivation', 'domain': 'surface', 'family': 'avgwordsent'}, {'key': 'a_char_ps', 'name': 'average_number_of_characters_per_sentence', 'formulation': 'derivation', 'domain': 'surface', 'family': 'avgwordsent'}, {'key': 'a_char_pw', 'name': 'average_number_of_characters_per_word', 'formulation': 'derivation', 'domain': 'surface', 'family': 'avgwordsent'}]
print(searched_features)

# return pandas dataframe instead of list of dictionaries
searched_features = lftk.search_features(domain = 'surface', family = "avgwordsent", return_format = "pandas")

#   key                                             name formulation   domain       family
#4  a_word_ps       average_number_of_words_per_sentence  derivation  surface  avgwordsent
#5  a_char_ps  average_number_of_characters_per_sentence  derivation  surface  avgwordsent
#6  a_char_pw      average_number_of_characters_per_word  derivation  surface  avgwordsent
print(searched_features)

# specify attributes and set return_format to "list_key"
searched_features = lftk.search_features(family = "wordsent", language = "general", return_format = "list_key")

#['t_word', 't_stopword', 't_punct', 't_uword', 't_sent', 't_char']
print(searched_features)

# now, extract the handcrafted linguistic features that you need
extracted_features = LFTK.extract(features = searched_features)

# {'t_word': 8, 't_stopword': 4, 't_punct': 1, 't_uword': 9, 't_sent': 1, 't_char': 36}
print(extracted_features)