old_contents: import numpy as np
  from sklearn.base import BaseEstimator
  from sklearn.base import ClassifierMixin
  from sklearn.utils.validation import check_array
  from sklearn.utils.validation import check_X_y
  
  
  class TopTermsClassifier(BaseEstimator, ClassifierMixin):
  
      def __init__(self, n_terms=10):
          # Parameters
          self.n_terms = n_terms
  
          # Attributes
          self.top_terms_per_class_ = None
  
      def fit(self, X, y=None, **fit_params):
  
          # scikit-learn checks
          X, y = check_X_y(X, y)
  
          n_terms = min(self.n_terms, X.shape[1])
  
          # Get a list of unique labels from y
          labels = np.unique(y)
  
          # Determine the n top terms per class
          self.top_terms_per_class_ = {
              c: set(np.argpartition(np.sum(X[y == c], axis=0), -n_terms)[-n_terms:])
              for c in labels
          }
  
          # Return the classifier
          return self
  
      def _classify(self, x):
  
          # Find the terms in the document
          terms = set(np.where(x > 0)[0])
  
          # Find the class that has the most top words in common with the document
          return max(
              self.top_terms_per_class_.keys(),
              key=lambda c: len(set.intersection(terms, self.top_terms_per_class_[c]))
          )
  
      def predict(self, X):
  
          # scikit-learn checks
          X = check_array(X)
  
          return np.array([self._classify(x) for x in X])
  
new_contents: import numpy as np
  from sklearn.base import BaseEstimator
  from sklearn.base import ClassifierMixin
  from sklearn.utils.validation import check_array
  from sklearn.utils.validation import check_X_y
  
  
  class TopTermsClassifier(BaseEstimator, ClassifierMixin):
  
      def __init__(self, n_terms=10):
          # Parameters
          self.n_terms = n_terms
  
          # Attributes
          self.top_terms_per_class_ = None
  
      def fit(self, X, y=None, **fit_params):
  
          # scikit-learn checks
          X, y = check_X_y(X, y)
  
          n_terms = min(self.n_terms, X.shape[1])
  
          # Get a list of unique labels from y
          labels = np.unique(y)
  
          # Determine the n top terms per class
          self.top_terms_per_class_ = {
              c: set(np.argpartition(np.sum(X[y == c], axis=0), -n_terms)[-n_terms:])
              for c in labels
          }
  
          # Return the classifier
          return self
  
      def _predict(self, x):
  
          # Find the terms in the document
          terms = set(np.where(x > 0)[0])
  
          # Find the class that has the most top words in common with the document
          return max(
              self.top_terms_per_class_.keys(),
              key=lambda c: len(set.intersection(terms, self.top_terms_per_class_[c]))
          )
  
      def predict(self, X):
  
          # scikit-learn checks
          X = check_array(X)
  
          return np.array([self._predict(x) for x in X])
  
subject: Rename _classify to _predict