docstr: :param corpus: TermDocMatrix, should just have unigrams
  :param document_scores: np.array, continuous value for each document score
  :return: pd.DataFrame
docstr_tok: [":","param","corpus",":","TermDocMatrix",",","should","just","have","unigrams",":","param","document_scores",":","np.array",",","continuous","value","for","each","document","score",":","return",":","pd.DataFrame"]
code: def get_coefficient_df(self, corpus, document_scores, pipeline=None):
      """
  
      :param corpus: TermDocMatrix, should just have unigrams
      :param document_scores: np.array, continuous value for each document score
      :return: pd.DataFrame
      """
      assert document_scores.shape == (corpus.get_num_docs(),)
      tdm = self._get_tdm(corpus)
  
      model = self._get_default_pipeline(pipeline)
      model.fit(X=tdm.toarray(), y=document_scores)
  
      df = pd.DataFrame(
          {
              "Word": self._get_terms(corpus),
              "Beta": model.named_steps["lr"].coef_,
              "Frequency": self._get_tdm(corpus).sum(axis=0)[0].A1,
          }
      ).set_index("Word")
  
      return df
  
file_path: scattertext/scattertext/continuous/sklearnpipeline.py
imports: import pandas
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.linear_model import RidgeCV, ElasticNetCV, LassoCV
  from sklearn.pipeline import Pipeline
  from scattertext.continuous.coefficientbase import CoefficientBase
label_desc: Use image node_id 1 for calling the RidgeCoefficients obj's underlying member method code with example usage: obj.get_coefficient_df(corpus, document_scores, pipeline) and returns: df