code: def _regress_terms(
      self, X, cat, categories, category_idx_store, neg_mask, terms
  ):
      pos_mask = categories.isin(
          category_idx_store.getidxstrictbatch([cat])
      ).values
      catX = X[neg_mask | pos_mask, :]
      catY = np.zeros(catX.shape[0]).astype(bool)
      catY[pos_mask[neg_mask | pos_mask]] = True
      scores = pd.Series(
          self._get_model().fit(catX, catY).coef_[0], index=terms
      ).sort_values(ascending=False)
      return scores
  
file_path: scattertext/scattertext/diachronic/DiachronicTermMiner.py
imports: import numpy
  import pandas
  from sklearn.linear_model import LogisticRegression
  from scattertext.diachronic.BubbleDiachronicVisualization import BubbleDiachronicVisualization
  from scattertext.diachronic.DiachronicVisualizer import DiachronicVisualizer
label_desc: Use image node_id 4 for calling the DiachronicTermMiner obj's underlying member method code with example usage: obj._regress_terms(X, cat, categories, category_idx_store, neg_mask, terms) and returns: scores