class_docstr: Tie-in to incorporate sckit-learn's various vectorizers into Scattertext
  >>> from sklearn.datasets import fetch_20newsgroups
  >>> from sklearn.feature_extraction.text import CountVectorizer
  >>> from scattertext.CorpusFromScikit import CorpusFromScikit
  >>> newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
  >>> count_vectorizer = CountVectorizer()
  >>> X_counts = count_vectorizer.fit_transform(newsgroups_train.data)
  >>> corpus = CorpusFromScikit(
  ...     X=X_counts,
  ...     y=newsgroups_train.target,
  ...     feature_vocabulary=count_vectorizer.vocabulary_,
  ...     category_names=newsgroups_train.target_names,
  ...     raw_texts=newsgroups_train.data
  ... ).build()
docstr: Parameters
  ----------
  X: sparse matrix integer, giving term-document-matrix counts
  y: list, integer categories
  feature_vocabulary: dict (feat_name -> idx)
  category_names: list of category names (len of y)
  raw_texts: array-like of raw texts
  unigram_frequency_path: str (see TermDocMatrix)
docstr_tok: ["Parameters","--","--","--","--","--","X",":","sparse","matrix","integer",",","giving","term-document-matrix","counts","y",":","list",",","integer","categories","feature_vocabulary",":","dict","(","feat_name","-",">","idx",")","category_names",":","list","of","category","names","(","len","of","y",")","raw_texts",":","array-like","of","raw","texts","unigram_frequency_path",":","str","(","see","TermDocMatrix",")"]
code: def __init__(
      self,
      X,
      y,
      feature_vocabulary,
      category_names,
      raw_texts,
      unigram_frequency_path=None,
  ):
      """
      Parameters
      ----------
      X: sparse matrix integer, giving term-document-matrix counts
      y: list, integer categories
      feature_vocabulary: dict (feat_name -> idx)
      category_names: list of category names (len of y)
      raw_texts: array-like of raw texts
      unigram_frequency_path: str (see TermDocMatrix)
  
      """
      TermDocMatrixFromScikit.__init__(
          self,
          X,
          y,
          feature_vocabulary,
          category_names,
          unigram_frequency_path,
      )
      self.raw_texts = raw_texts
  
imports: import numpy
  from scattertext.Corpus import Corpus
  from scattertext.TermDocMatrixFromScikit import TermDocMatrixFromScikit
label_desc: Use image node_id 1 to create a new CorpusFromScikit object from inherited base classes: TermDocMatrixFromScikit with example: obj = CorpusFromScikit(X, y, feature_vocabulary, category_names, raw_texts, unigram_frequency_path)