old_contents: import numpy
  import pandas
  numpy.random.seed(0)
  
  from mhcflurry import Class1NeuralNetwork
  
  from nose.tools import eq_
  from numpy import testing
  
  from mhcflurry.downloads import get_path
  
  
  def test_class1_affinity_predictor_a0205_training_accuracy():
      # Memorize the dataset.
      hyperparameters = dict(
          activation="tanh",
          layer_sizes=[16],
          max_epochs=500,
          early_stopping=False,
          validation_split=0.0,
          locally_connected_layers=[],
          dense_layer_l1_regularization=0.0,
          dropout_probability=0.0)
  
      # First test a Class1NeuralNetwork, then a Class1AffinityPredictor.
      allele = "HLA-A*02:05"
  
      df = pandas.read_csv(
          get_path(
              "data_curated", "curated_training_data.csv.bz2"))
      df = df.ix[
          df.allele == df
      ]
      df = df.ix[
          df.peptide.str.len() == 9
      ]
      df = df.ix[
          df.measurement_type == "quantitative"
      ]
      df = df.ix[
          df.measurement_source == "kim2014"
      ]
  
      predictor = Class1NeuralNetwork(**hyperparameters)
      predictor.fit(df.peptide.values, df.measurement_value.values)
      ic50_pred = predictor.predict(df.peptide.values)
      ic50_true = df.measurement_value.values
      eq_(len(ic50_pred), len(ic50_true))
      testing.assert_allclose(
          numpy.log(ic50_pred),
          numpy.log(ic50_true),
          rtol=0.2,
          atol=0.2)
new_contents: import numpy
  import pandas
  numpy.random.seed(0)
  
  from mhcflurry import Class1NeuralNetwork
  
  from nose.tools import eq_
  from numpy import testing
  
  from mhcflurry.downloads import get_path
  
  
  def test_class1_affinity_predictor_a0205_training_accuracy():
      # Memorize the dataset.
      hyperparameters = dict(
          activation="tanh",
          layer_sizes=[16],
          max_epochs=500,
          early_stopping=False,
          validation_split=0.0,
          locally_connected_layers=[],
          dense_layer_l1_regularization=0.0,
          dropout_probability=0.0)
  
      # First test a Class1NeuralNetwork, then a Class1AffinityPredictor.
      allele = "HLA-A*02:05"
  
      df = pandas.read_csv(
          get_path(
              "data_curated", "curated_training_data.csv.bz2"))
      df = df.ix[
          df.allele == allele
      ]
      df = df.ix[
          df.peptide.str.len() == 9
      ]
      df = df.ix[
          df.measurement_type == "quantitative"
      ]
      df = df.ix[
          df.measurement_source == "kim2014"
      ]
  
      predictor = Class1NeuralNetwork(**hyperparameters)
      predictor.fit(df.peptide.values, df.measurement_value.values)
      ic50_pred = predictor.predict(df.peptide.values)
      ic50_true = df.measurement_value.values
      eq_(len(ic50_pred), len(ic50_true))
      testing.assert_allclose(
          numpy.log(ic50_pred),
          numpy.log(ic50_true),
          rtol=0.2,
          atol=0.2)
subject: fix test