Add test that ISM scores nonzero

    pssm_scores = get_pssm_scores(sequences, pssm)

    self.assertEqual(pssm_scores.shape, (3, 5))

  def test_in_silico_mutagenesis(self):

  def test_in_silico_mutagenesis_shape(self):

    """Test in-silico mutagenesis returns correct shape."""

    # Construct and train SequenceDNN model

    sequences = np.array(["ACGTA", "GATAG", "CGCGC"])

    # Call in-silico mutagenesis

    mutagenesis_scores = in_silico_mutagenesis(model, sequences)

    self.assertEqual(mutagenesis_scores.shape, (1, 3, 4, 5, 1))

  def test_in_silico_mutagenesis_nonzero(self):

    """Test in-silico mutagenesis returns nonzero output."""

    # Construct and train SequenceDNN model

    sequences = np.array(["ACGTA", "GATAG", "CGCGC"])

    sequences = dc.utils.save.seq_one_hot_encode(sequences, letters=LETTERS)

    labels = np.array([1, 0, 0])

    labels = np.reshape(labels, (3, 1))

    self.assertEqual(sequences.shape, (3, 4, 5, 1))

    #X = np.random.rand(10, 1, 4, 50)

    #y = np.random.randint(0, 2, size=(10, 1))

    #dataset = dc.data.NumpyDataset(X, y)

    dataset = dc.data.NumpyDataset(sequences, labels)

    model = dc.models.SequenceDNN(

        5, "binary_crossentropy", num_filters=[1, 1], kernel_size=[15, 15])

    model.fit(dataset, nb_epoch=1)

    # Call in-silico mutagenesis

    mutagenesis_scores = in_silico_mutagenesis(model, sequences)

    self.assertEqual(mutagenesis_scores.shape, (1, 3, 4, 5, 1))

    # Check nonzero elements exist

    assert np.count_nonzero(mutagenesis_scores) > 0