old_contents: import numpy as np
  import pandas as pd
  
  import tagnews
  
  
  class Test_GeoCoder():
      @classmethod
      def setup_class(cls):
          cls.model = tagnews.GeoCoder()
  
      def test_extract_geostrings(self):
          self.model.extract_geostrings(
              ('This is example article text with a location of'
               ' 55th and Woodlawn where something happened.')
          )
  
      def test_extract_geostring_probs(self):
          article = ('This is example article text with a location of'
                     ' 55th and Woodlawn where something happened.')
          words, probs = self.model.extract_geostring_probs(article)
          max_prob = probs.max()
          max_word = words[np.argmax(probs)]
          geostrings = self.model.extract_geostrings(article,
                                                     prob_thresh=max_prob-0.001)
          assert max_word in [word for geostring in geostrings for word in geostring]
  
      def test_extract_geostring_probs_word_not_in_glove(self):
          """
          Regression test for issue #105.
          """
          article = '___1234567890nonexistent0987654321___'
          words, probs = self.model.extract_geostring_probs(article)
  
      def test_lat_longs_from_geostring_lists(self):
          geostring_lists = [['5500', 'S', 'Woodlawn'], ['100', 'N.', 'Wacker'], ['thigh']]
          coords, scores = self.model.lat_longs_from_geostring_lists(
              geostring_lists, sleep_secs=0.0
          )
  
          assert coords.shape[0] == len(geostring_lists) == len(scores) == len(num_found)
  
      def test_community_areas(self):
          # Approximately 55th and Woodlawn, which is in Hyde Park.
          coords = pd.DataFrame([[41.793465, -87.596930]],
                                columns=['lat', 'long'])
          com_area = self.model.community_area_from_coords(coords)
          assert com_area == ['HYDE PARK']
  
new_contents: import numpy as np
  import pandas as pd
  
  import tagnews
  
  
  class Test_GeoCoder():
      @classmethod
      def setup_class(cls):
          cls.model = tagnews.GeoCoder()
  
      def test_extract_geostrings(self):
          self.model.extract_geostrings(
              ('This is example article text with a location of'
               ' 55th and Woodlawn where something happened.')
          )
  
      def test_extract_geostring_probs(self):
          article = ('This is example article text with a location of'
                     ' 55th and Woodlawn where something happened.')
          words, probs = self.model.extract_geostring_probs(article)
          max_prob = probs.max()
          max_word = words[np.argmax(probs)]
          geostrings = self.model.extract_geostrings(article,
                                                     prob_thresh=max_prob-0.001)
          assert max_word in [word for geostring in geostrings for word in geostring]
  
      def test_extract_geostring_probs_word_not_in_glove(self):
          """
          Regression test for issue #105.
          """
          article = '___1234567890nonexistent0987654321___'
          words, probs = self.model.extract_geostring_probs(article)
  
      def test_lat_longs_from_geostring_lists(self):
          geostring_lists = [['5500', 'S', 'Woodlawn'], ['100', 'N.', 'Wacker'], ['thigh']]
          coords, scores = self.model.lat_longs_from_geostring_lists(
              geostring_lists, sleep_secs=0.0
          )
  
          assert coords.shape[0] == len(geostring_lists) == len(scores)
  
      def test_community_areas(self):
          # Approximately 55th and Woodlawn, which is in Hyde Park.
          coords = pd.DataFrame([[41.793465, -87.596930]],
                                columns=['lat', 'long'])
          com_area = self.model.community_area_from_coords(coords)
          assert com_area == ['HYDE PARK']
  
subject: remove num_found from tests