docstr: Ensure model training does not error out
  Ensure model returns predictions of the right type
  Test model loss at least outperforms naive baseline
docstr_tok: ["Ensure","model","training","does","not","error","out","Ensure","model","returns","predictions","of","the","right","type","Test","model","loss","at","least","outperforms","naive","baseline"]
code: def test_reasonable_predictions(self):
      """
      Ensure model training does not error out
      Ensure model returns predictions of the right type
      Test model loss at least outperforms naive baseline
      """
      model = ComparisonRegressor(**self.default_config())
  
      # fake dataset generation
      animals = [
          "dog",
          "cat",
          "horse",
          "cow",
          "pig",
          "sheep",
          "goat",
          "chicken",
          "guinea pig",
          "donkey",
          "turkey",
          "duck",
          "camel",
          "goose",
          "llama",
          "rabbit",
          "fox",
      ]
      numbers = [
          "one",
          "two",
          "three",
          "four",
          "five",
          "six",
          "seven",
          "eight",
          "nine",
          "ten",
          "eleven",
          "twelve",
          "thirteen",
          "fourteen",
          "fifteen",
          "sixteen",
      ]
  
      n_per = 150
      similar = []
      different = []
      for dataset in [animals, numbers]:
          for i in range(n_per // 2):
              similar.append(
                  [random.choice(dataset), random.choice(dataset)]
              )
      for i in range(n_per):
          different.append(
              [random.choice(animals), random.choice(numbers)]
          )
  
      targets = np.asarray([1] * len(similar) + [0] * len(different))
      data = similar + different
  
      x_tr, x_te, t_tr, t_te = train_test_split(
          data, targets, test_size=0.3, random_state=42
      )
      model.finetune(x_tr, t_tr)
  
      predictions = model.predict(x_te)
      mse = np.mean(
          [(pred - true) ** 2 for pred, true in zip(predictions, t_te)]
      )
      naive_baseline = max(np.mean(targets == 1), np.mean(targets == 0))
      naive_baseline_mse = np.mean(
          [(naive_baseline - true) ** 2 for true in t_te]
      )
      self.assertIsInstance(predictions, np.ndarray)
      self.assertIsInstance(predictions[0], np.floating)
      # whether it is float32 or float64 depends on whether it is run on cpu or gpu.
  
      self.assertGreater(naive_baseline_mse, mse)
  
file_path: finetune/tests/test_comparison_regressor.py
imports: import os
  import unittest
  import warnings
  import numpy
  import random
  from sklearn.model_selection import train_test_split
  from finetune import ComparisonRegressor
label_desc: Use image node_id 3 for calling the TestComparisonRegression obj's underlying member method code with example usage: obj.test_reasonable_predictions() without return types