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"""
Tests for training and predicting using Class1 pan-allele models.
"""
import json
import os
import shutil
import tempfile
import subprocess
from copy import deepcopy
import pandas
from numpy.testing import assert_array_less, assert_equal
from mhcflurry import Class1AffinityPredictor,Class1NeuralNetwork
from mhcflurry.allele_encoding import AlleleEncoding
from mhcflurry.downloads import get_path
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': [64],
'learning_rate': None,
'locally_connected_layers': [],
'loss': 'custom:mse_with_inequalities',
'max_epochs': 5000,
'minibatch_size': 128,
'optimizer': 'rmsprop',
'output_activation': 'sigmoid',
'patience': 20,
'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': 25,
'random_negative_distribution_smoothing': 0.0,
'random_negative_match_distribution': True,
'random_negative_rate': 0.2,
'train_data': {},
'validation_split': 0.1,
}
ALLELE_TO_SEQUENCE = pandas.read_csv(
get_path(
"allele_sequences", "allele_sequences.csv"),
index_col=0).sequence.to_dict()
TRAIN_DF = pandas.read_csv(
get_path(
"data_curated", "curated_training_data.no_mass_spec.csv.bz2"))
TRAIN_DF = TRAIN_DF.loc[TRAIN_DF.allele.isin(ALLELE_TO_SEQUENCE)]
TRAIN_DF = TRAIN_DF.loc[TRAIN_DF.peptide.str.len() >= 8]
TRAIN_DF = TRAIN_DF.loc[TRAIN_DF.peptide.str.len() <= 15]
def test_train_simple():
network = Class1NeuralNetwork(**HYPERPARAMETERS)
allele_encoding = AlleleEncoding(
TRAIN_DF.allele.values,
allele_to_sequence=ALLELE_TO_SEQUENCE)
network.fit(
TRAIN_DF.peptide.values,
affinities=TRAIN_DF.measurement_value.values,
allele_encoding=allele_encoding,
inequalities=TRAIN_DF.measurement_inequality.values)
predictions = network.predict(
peptides=TRAIN_DF.peptide.values,
allele_encoding=allele_encoding)
print(pandas.Series(predictions).describe())