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import time
import pandas
from mhcflurry.allele_encoding import AlleleEncoding
from mhcflurry.amino_acid import BLOSUM62_MATRIX
from mhcflurry.class1_affinity_predictor import Class1AffinityPredictor
from mhcflurry.downloads import get_path
from numpy.testing import assert_equal
from mhcflurry.testing_utils import module_cleanup
teardown = module_cleanup
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ALLELE_TO_SEQUENCE = pandas.read_csv(
get_path(
"allele_sequences", "allele_sequences.csv"),
index_col=0).sequence.to_dict()
HYPERPARAMETERS = {
'activation': 'tanh',
'allele_dense_layer_sizes': [],
'batch_normalization': False,
'dense_layer_l1_regularization': 0.0,
'dense_layer_l2_regularization': 0.0,
'dropout_probability': 0.5,
'early_stopping': True,
'init': 'glorot_uniform',
'layer_sizes': [4],
'learning_rate': None,
'locally_connected_layers': [],
'loss': 'custom:mse_with_inequalities',
'max_epochs': 40,
'minibatch_size': 128,
'optimizer': 'rmsprop',
'output_activation': 'sigmoid',
'patience': 2,
'peptide_allele_merge_activation': '',
'peptide_allele_merge_method': 'concatenate',
'peptide_amino_acid_encoding': 'BLOSUM62',
'peptide_dense_layer_sizes': [],
'peptide_encoding': {
'alignment_method': 'left_pad_centered_right_pad',
'max_length': 15,
'vector_encoding_name': 'BLOSUM62',
},
'random_negative_affinity_max': 50000.0,
'random_negative_affinity_min': 20000.0,
'random_negative_constant': 0,
'random_negative_distribution_smoothing': 0.0,
'random_negative_match_distribution': True,
'random_negative_rate': 0.0,
'train_data': {},
'validation_split': 0.1,
}
def test_changing_allele_representations():
allele1 = "HLA-A*02:01"
allele2 = "HLA-C*03:04"
allele3 = "HLA-B*07:01"
peptide = "SIINFEKL"
allele_to_sequence = {}
for allele in [allele1, allele2]:
allele_to_sequence[allele] = ALLELE_TO_SEQUENCE[allele]
data1 = []
for i in range(5000):
data1.append((allele1, peptide, 0, "="))
data1.append((allele2, peptide, 50000, "="))
data1 = pandas.DataFrame(
data1, columns=["allele", "peptide", "affinity", "inequality"])
predictor = Class1AffinityPredictor(allele_to_sequence=allele_to_sequence)
predictor.fit_class1_pan_allele_models(
n_models=1,
architecture_hyperparameters=HYPERPARAMETERS,
alleles=data1.allele.values,
peptides=data1.peptide.values,
affinities=data1.affinity.values,
inequalities=data1.inequality.values)
(value1, value2) = predictor.predict([peptide, peptide], alleles=[allele1, allele2])
assert value1 < 100, value1
assert value2 > 4000, value2
allele_to_sequence[allele3] = ALLELE_TO_SEQUENCE[allele3]
predictor.allele_to_sequence = allele_to_sequence
predictor.clear_cache()
(value1, value2, value3) = predictor.predict(
[peptide, peptide, peptide],
alleles=[allele1, allele2, allele3])
assert value1 < 100, value1
assert value2 > 4000, value2