code: def get_coefficient_df(self, corpus, document_scores):
      from statsmodels.regression.linear_model import OLS
  
      """
  
          :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(),)
      if any(" " in t for t in self._get_terms(corpus)):
          logging.warning(
              "UngerCoefficients is currently designed for only unigram terms. "
              "Run corpus.get_unigram_corpus() before using this."
          )
  
      X = ungar_transform(self._get_tdm(corpus))
      model = OLS(document_scores, X.T).fit()
      df = pd.DataFrame(
          {
              "Word": self._get_terms(corpus),
              "Beta": model.params,
              "Tstat": model.tvalues,
              "Frequency": corpus.get_term_doc_mat().sum(axis=0)[0].A1,
          }
      ).set_index("Word")
  
      return df
  
imports: import logging
  import numpy
  import pandas
  from scattertext.continuous.coefficientbase import CoefficientBase
label_desc: Use image node_id 1 for calling the UngarCoefficients obj's underlying member method code with example usage: obj.get_coefficient_df(corpus, document_scores) and returns: df