def setup():
global ALLELE_SPECIFIC_PREDICTOR, PAN_ALLELE_PREDICTOR
startup()
ALLELE_SPECIFIC_PREDICTOR = Class1AffinityPredictor.load(
get_path("models_class1", "models"))
PAN_ALLELE_PREDICTOR = Class1AffinityPredictor.load(
get_path("models_class1_pan", "models.with_mass_spec"))
def setup():
global PREDICTORS
startup()
PREDICTORS = {
'allele-specific':
Class1AffinityPredictor.load(get_path("models_class1", "models")),
'pan-allele':
Class1AffinityPredictor.load(
get_path("models_class1_pan", "models.combined"))
}
def test_cross_validation_with_imputation():
imputer = fancyimpute.MICE(
n_imputations=2, n_burn_in=1, n_nearest_columns=25)
train_data = (
mhcflurry.dataset.Dataset.from_csv(
get_path("data_kim2014" , "bdata.2009.mhci.public.1.txt"))
.get_alleles(["HLA-A0201", "HLA-A0202", "HLA-A0301"]))
folds = cross_validation_folds(
train_data,
n_folds=3,
imputer=imputer,
drop_similar_peptides=True,
alleles=["HLA-A0201", "HLA-A0202"])
eq_(set(x.allele for x in folds), {"HLA-A0201", "HLA-A0202"})
eq_(len(folds), 6)
for fold in folds:
eq_(fold.train.unique_alleles(), set([fold.allele]))
eq_(fold.imputed_train.unique_alleles(), set([fold.allele]))
eq_(fold.test.unique_alleles(), set([fold.allele]))
models = HYPERPARAMETER_DEFAULTS.models_grid(
activation=["tanh", "relu"],
layer_sizes=[[4]],
embedding_output_dim=[8],
n_training_epochs=[3])
print(models)
df = train_across_models_and_folds(folds, models)
print(df)
assert df.test_auc.mean() > 0.6
def test_cross_validation_with_imputation():
imputer = fancyimpute.MICE(n_imputations=2,
n_burn_in=1,
n_nearest_columns=25)
train_data = (mhcflurry.dataset.Dataset.from_csv(
get_path("data_kim2014", "bdata.2009.mhci.public.1.txt")).get_alleles(
["HLA-A0201", "HLA-A0202", "HLA-A0301"]))
folds = cross_validation_folds(train_data,
n_folds=3,
imputer=imputer,
drop_similar_peptides=True,
alleles=["HLA-A0201", "HLA-A0202"])
eq_(set(x.allele for x in folds), {"HLA-A0201", "HLA-A0202"})
eq_(len(folds), 6)
for fold in folds:
eq_(fold.train.unique_alleles(), set([fold.allele]))
eq_(fold.imputed_train.unique_alleles(), set([fold.allele]))
eq_(fold.test.unique_alleles(), set([fold.allele]))
models = HYPERPARAMETER_DEFAULTS.models_grid(activation=["tanh", "relu"],
layer_sizes=[[4]],
embedding_output_dim=[8],
n_training_epochs=[3])
print(models)
df = train_across_models_and_folds(folds, models)
print(df)
assert df.test_auc.mean() > 0.6
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)
def test_run():
try:
models_dir = tempfile.mkdtemp(prefix="mhcflurry-test-models")
hyperparameters_filename = os.path.join(models_dir,
"hyperparameters.json")
with open(hyperparameters_filename, "w") as fd:
json.dump(HYPERPARAMETERS, fd)
args = [
"--data",
get_path("data_curated", "curated_training_data.csv.bz2"),
"--hyperparameters",
hyperparameters_filename,
"--min-measurements-per-allele",
"9000",
"--out-models-dir",
models_dir,
]
print("Running with args: %s" % args)
train_allele_specific_models_command.run(args)
result = Class1AffinityPredictor.load(models_dir)
predictions = result.predict(peptides=["SLYNTVATL"],
alleles=["HLA-A*02:01"])
assert_equal(predictions.shape, (1, ))
assert_array_less(predictions, 500)
finally:
print("Deleting: %s" % models_dir)
shutil.rmtree(models_dir)
def test_class1_binding_predictor_A0205_training_accuracy():
dataset = Dataset.from_csv(get_path(
"data_combined_iedb_kim2014", "combined_human_class1_dataset.csv"))
dataset_a0205_all_lengths = dataset.get_allele("HLA-A0205")
dataset_a0205 = Dataset(
dataset_a0205_all_lengths._df.ix[
dataset_a0205_all_lengths._df.peptide.str.len() == 9])
predictor = Class1BindingPredictor(
name="A0205",
embedding_output_dim=32,
activation="tanh",
layer_sizes=[64],
optimizer="adam",
dropout_probability=0.0)
predictor.fit_dataset(dataset_a0205, n_training_epochs=1000)
peptides = dataset_a0205.peptides
ic50_pred = predictor.predict(peptides)
ic50_true = dataset_a0205.affinities
eq_(len(ic50_pred), len(ic50_true))
testing.assert_allclose(
np.log(ic50_pred),
np.log(ic50_true),
rtol=0.2,
atol=0.2)
def setup():
global PAN_ALLELE_PREDICTOR
startup()
PAN_ALLELE_PREDICTOR = Class1AffinityPredictor.load(
get_path("models_class1_pan", "models.combined"),
optimization_level=0,
)
def setup():
global AFFINITY_PREDICTOR
global CLEAVAGE_PREDICTOR
global CLEAVAGE_PREDICTOR_NO_FLANKING
global PRESENTATION_PREDICTOR
startup()
AFFINITY_PREDICTOR = Class1AffinityPredictor.load(get_path(
"models_class1_pan", "models.combined"),
optimization_level=0,
max_models=1)
CLEAVAGE_PREDICTOR = Class1ProcessingPredictor.load(get_path(
"models_class1_processing", "models.selected.with_flanks"),
max_models=1)
CLEAVAGE_PREDICTOR_NO_FLANKING = Class1ProcessingPredictor.load(
get_path("models_class1_processing", "models.selected.no_flank"),
max_models=1)
PRESENTATION_PREDICTOR = Class1PresentationPredictor.load()
def run_and_check(n_jobs=0, delete=True, additional_args=[]):
source_models_dir = get_path("models_class1_pan", "models.combined")
dest_models_dir = tempfile.mkdtemp(prefix="mhcflurry-test-models")
# Save a new predictor that has no percent rank calibration data.
original_predictor = Class1AffinityPredictor.load(source_models_dir)
print("Loaded predictor", source_models_dir)
new_predictor = Class1AffinityPredictor(
class1_pan_allele_models=original_predictor.class1_pan_allele_models,
allele_to_sequence=original_predictor.allele_to_sequence,
)
new_predictor.save(dest_models_dir)
print("Saved predictor to", dest_models_dir)
new_predictor = Class1AffinityPredictor.load(dest_models_dir)
assert_equal(len(new_predictor.allele_to_percent_rank_transform), 0)
args = [
"mhcflurry-calibrate-percentile-ranks",
"--models-dir",
dest_models_dir,
"--match-amino-acid-distribution-data",
get_path("data_curated", "curated_training_data.affinity.csv.bz2"),
"--motif-summary",
"--num-peptides-per-length",
"1000",
"--allele",
"HLA-A*02:01",
"HLA-B*07:02",
"--verbosity",
"1",
"--num-jobs",
str(n_jobs),
] + additional_args
print("Running with args: %s" % args)
subprocess.check_call(args)
new_predictor = Class1AffinityPredictor.load(dest_models_dir)
assert_equal(len(new_predictor.allele_to_percent_rank_transform), 2)
if delete:
print("Deleting: %s" % dest_models_dir)
shutil.rmtree(dest_models_dir)
else:
print("Not deleting: %s" % dest_models_dir)
def test_class1_neural_network_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=[{
"filters": 8,
"activation": "tanh",
"kernel_size": 3
}],
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.affinity.csv.bz2"))
df = df.loc[df.allele == allele]
df = df.loc[df.peptide.str.len() == 9]
df = df.loc[df.measurement_type == "quantitative"]
df = df.loc[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)
# Test that a second predictor has the same architecture json.
# This is important for an optimization we use to re-use predictors of the
# same architecture at prediction time.
hyperparameters2 = dict(activation="tanh",
layer_sizes=[16],
max_epochs=1,
early_stopping=False,
validation_split=0.0,
locally_connected_layers=[{
"filters": 8,
"activation": "tanh",
"kernel_size": 3
}],
dense_layer_l1_regularization=0.0,
dropout_probability=0.0)
predictor2 = Class1NeuralNetwork(**hyperparameters2)
predictor2.fit(df.peptide.values, df.measurement_value.values, verbose=0)
eq_(predictor.network().to_json(), predictor2.network().to_json())
def run_and_check(n_jobs=0):
models_dir = tempfile.mkdtemp(prefix="mhcflurry-test-models")
hyperparameters_filename = os.path.join(models_dir, "hyperparameters.yaml")
with open(hyperparameters_filename, "w") as fd:
json.dump(HYPERPARAMETERS, fd)
args = [
"mhcflurry-class1-train-allele-specific-models",
"--data",
get_path("data_curated", "curated_training_data.affinity.csv.bz2"),
"--hyperparameters",
hyperparameters_filename,
"--allele",
"HLA-A*02:01",
"HLA-A*03:01",
"--out-models-dir",
models_dir,
"--num-jobs",
str(n_jobs),
]
print("Running with args: %s" % args)
subprocess.check_call(args)
# Calibrate percentile ranks
args = [
"mhcflurry-calibrate-percentile-ranks",
"--models-dir",
models_dir,
"--num-peptides-per-length",
"10000",
"--num-jobs",
str(n_jobs),
]
print("Running with args: %s" % args)
subprocess.check_call(args)
result = Class1AffinityPredictor.load(models_dir)
predictions = result.predict(peptides=["SLYNTVATL"],
alleles=["HLA-A*02:01"])
assert_equal(predictions.shape, (1, ))
assert_array_less(predictions, 1000)
df = result.predict_to_dataframe(peptides=["SLYNTVATL"],
alleles=["HLA-A*02:01"])
print(df)
assert "prediction_percentile" in df.columns
print("Deleting: %s" % models_dir)
shutil.rmtree(models_dir)
def test_imputation():
imputer = fancyimpute.MICE(n_imputations=2,
n_burn_in=1,
n_nearest_columns=25)
train_data = (mhcflurry.dataset.Dataset.from_csv(
get_path("data_kim2014", "bdata.2009.mhci.public.1.txt")).get_alleles(
["HLA-A0201", "HLA-A0202", "HLA-A0301"]))
folds = cross_validation_folds(train_data,
n_folds=3,
imputer=imputer,
drop_similar_peptides=True,
alleles=["HLA-A0201", "HLA-A0202"])
eq_(set(x.allele for x in folds), {"HLA-A0201", "HLA-A0202"})
eq_(len(folds), 6)
for fold in folds:
eq_(fold.train.unique_alleles(), set([fold.allele]))
eq_(fold.imputed_train.unique_alleles(), set([fold.allele]))
eq_(fold.test.unique_alleles(), set([fold.allele]))
def test_imputation():
imputer = fancyimpute.MICE(
n_imputations=2, n_burn_in=1, n_nearest_columns=25)
train_data = (
mhcflurry.dataset.Dataset.from_csv(
get_path("data_kim2014", "bdata.2009.mhci.public.1.txt"))
.get_alleles(["HLA-A0201", "HLA-A0202", "HLA-A0301"]))
folds = cross_validation_folds(
train_data,
n_folds=3,
imputer=imputer,
drop_similar_peptides=True,
alleles=["HLA-A0201", "HLA-A0202"])
eq_(set(x.allele for x in folds), {"HLA-A0201", "HLA-A0202"})
eq_(len(folds), 6)
for fold in folds:
eq_(fold.train.unique_alleles(), set([fold.allele]))
eq_(fold.imputed_train.unique_alleles(), set([fold.allele]))
eq_(fold.test.unique_alleles(), set([fold.allele]))
def test_class1_binding_predictor_A0205_training_accuracy():
dataset = Dataset.from_csv(
get_path("data_combined_iedb_kim2014",
"combined_human_class1_dataset.csv"))
dataset_a0205_all_lengths = dataset.get_allele("HLA-A0205")
dataset_a0205 = Dataset(dataset_a0205_all_lengths._df.ix[
dataset_a0205_all_lengths._df.peptide.str.len() == 9])
predictor = Class1BindingPredictor(name="A0205",
embedding_output_dim=32,
activation="tanh",
layer_sizes=[64],
optimizer="adam",
dropout_probability=0.0)
predictor.fit_dataset(dataset_a0205, n_training_epochs=1000)
peptides = dataset_a0205.peptides
ic50_pred = predictor.predict(peptides)
ic50_true = dataset_a0205.affinities
eq_(len(ic50_pred), len(ic50_true))
testing.assert_allclose(np.log(ic50_pred),
np.log(ic50_true),
rtol=0.2,
atol=0.2)
def run():
from mhcflurry.amino_acid import COMMON_AMINO_ACIDS
args = parser.parse_args(sys.argv[1:])
configure_logging()
serial_run = not args.cluster_parallelism and args.num_jobs == 0
if not args.affinity_predictor:
args.affinity_predictor = get_path(
"models_class1_pan", "models.combined")
print("Using downloaded affinity predictor: ", args.affinity_predictor)
if not args.frequency_matrices:
args.frequency_matrices = os.path.join(
args.affinity_predictor, "frequency_matrices.csv.bz2")
if not args.length_distributions:
args.length_distributions = os.path.join(args.affinity_predictor,
"length_distributions.csv.bz2")
if not args.train_data:
args.train_data = os.path.join(args.affinity_predictor,
"train_data.csv.bz2")
frequency_matrices_df = pandas.read_csv(args.frequency_matrices)
length_distributions = pandas.read_csv(args.length_distributions)
train_data = pandas.read_csv(args.train_data)
alleles = args.alleles
if alleles:
print("Using specified alleles, ", *alleles)
else:
alleles = frequency_matrices_df.allele.unique()
if args.max_alleles:
alleles = alleles[:args.max_alleles]
print("Using %d alleles" % len(alleles), alleles)
amino_acids = sorted(COMMON_AMINO_ACIDS)
distribution = frequency_matrices_df.loc[
(frequency_matrices_df.cutoff_fraction == 1.0), amino_acids
].mean(0)
normalized_frequency_matrices = frequency_matrices_df.copy()
normalized_frequency_matrices.loc[:, amino_acids] = (
normalized_frequency_matrices[amino_acids] / distribution)
GLOBAL_DATA["args"] = args
GLOBAL_DATA["normalized_frequency_matrices"] = normalized_frequency_matrices
GLOBAL_DATA["length_distributions"] = length_distributions
GLOBAL_DATA["train_data"] = train_data
artifacts_out = os.path.join(args.out, "artifacts")
if not os.path.exists(args.out):
os.mkdir(args.out)
if not os.path.exists(artifacts_out):
os.mkdir(artifacts_out)
tasks = [
{
"task_num": i,
"allele": allele,
"out_dir": artifacts_out,
}
for (i, allele) in enumerate(alleles)
]
jobs = []
for task in tasks:
if not jobs or len(jobs[-1]['tasks']) >= args.chunk_size:
jobs.append({'tasks': []})
jobs[-1]['tasks'].append(task)
print("Generated %d tasks, packed into %d jobs" % (len(tasks), len(jobs)))
worker_pool = None
start = time.time()
if serial_run:
# Serial run
print("Running in serial.")
results = (
do_job(**job) for job in jobs)
elif args.cluster_parallelism:
# Run using separate processes HPC cluster.
print("Running on cluster.")
results = cluster_results_from_args(
args,
work_function=do_job,
work_items=jobs,
constant_data=GLOBAL_DATA,
input_serialization_method="dill",
result_serialization_method="pickle",
clear_constant_data=False)
else:
worker_pool = worker_pool_with_gpu_assignments_from_args(args)
print("Worker pool", worker_pool)
assert worker_pool is not None
for task in tasks:
task['constant_data'] = GLOBAL_DATA
results = worker_pool.imap_unordered(
partial(call_wrapped_kwargs, do_job),
jobs,
chunksize=1)
print("Reading results")
task_results = {}
for job_result in tqdm.tqdm(results, total=len(jobs)):
for task_result in job_result:
task_results[task_result['task_num']] = task_result
print("Received all results in %0.2f sec" % (time.time() - start))
artifacts_df = pandas.DataFrame(task_results).T.set_index("task_num")
length_distributions_out = os.path.join(args.out,
"length_distributions.csv")
length_distributions.to_csv(length_distributions_out,
index=False)
print("Wrote: ", length_distributions_out)
artifacts_summary_out = os.path.join(args.out, "artifacts.csv")
artifacts_df.to_csv(artifacts_summary_out)
print("Wrote: ", artifacts_summary_out)
if worker_pool:
worker_pool.close()
worker_pool.join()
def test_small_run():
base_temp_dir = tempfile.mkdtemp()
temp_dir = join(base_temp_dir, "models_class1_allele_specific_single")
mkdir(temp_dir)
def write_json(payload, filename):
path = join(temp_dir, filename)
with open(path, 'w') as fd:
json.dump(payload, fd)
return path
models = HYPERPARAMETER_DEFAULTS.models_grid(impute=[False, True],
activation=["tanh"],
layer_sizes=[[4], [8]],
embedding_output_dim=[16],
dropout_probability=[.25],
n_training_epochs=[20])
imputer_args = {
"imputation_method_name": "mice",
"n_burn_in": 2,
"n_imputations": 10,
"n_nearest_columns": 10,
"min_observations_per_peptide": 5,
"min_observations_per_allele": 1000, # limit the number of alleles
}
bdata2009 = downloads.get_path("data_kim2014",
"bdata.2009.mhci.public.1.txt")
bdata_blind = downloads.get_path("data_kim2014",
"bdata.2013.mhci.public.blind.1.txt")
mkdir(join(temp_dir, "models"))
args = [
"--model-architectures",
write_json(models, "models.json"),
"--imputer-description",
write_json(imputer_args, "imputer.json"),
"--train-data",
bdata2009,
"--test-data",
bdata_blind,
"--out-cv-results",
join(temp_dir, "cv.csv"),
"--out-production-results",
join(temp_dir, "production.csv"),
"--out-models",
join(temp_dir, "models"),
"--cv-num-folds",
"2",
"--alleles",
"HLA-A0201",
"HLA-A0301",
"--verbose",
"--num-local-threads",
"1",
]
print("Running cv_and_train_command with args: %s " % str(args))
cv_and_train_command.run(args)
verify_trained_models(base_temp_dir)
'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,
'random_negative_method': 'by_allele',
'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]
TRAIN_DF = TRAIN_DF.loc[
TRAIN_DF.allele.isin(TRAIN_DF.allele.value_counts().iloc[:3].index)
]
from os.path import join
import pypandoc
import pandas
from keras.utils.vis_utils import plot_model
from tabulate import tabulate
from mhcflurry import __version__
from mhcflurry.downloads import get_path
from mhcflurry.class1_affinity_predictor import Class1AffinityPredictor
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
"--cv-summary-csv",
metavar="FILE.csv",
default=get_path(
"cross_validation_class1", "summary.all.csv", test_exists=False),
help="Cross validation scores summary. Default: %(default)s",
)
parser.add_argument(
"--class1-models-dir",
metavar="DIR",
default=get_path(
"models_class1", "models", test_exists=False),
help="Class1 models. Default: %(default)s",
)
parser.add_argument(
"--class1-unselected-models-dir",
metavar="DIR",
default=get_path(
"models_class1_unselected", "models", test_exists=False),
help="Class1 unselected models. Default: %(default)s",
def run_and_check(n_jobs=0, delete=True, additional_args=[]):
models_dir = tempfile.mkdtemp(prefix="mhcflurry-test-models")
hyperparameters_filename = os.path.join(models_dir, "hyperparameters.yaml")
with open(hyperparameters_filename, "w") as fd:
json.dump(HYPERPARAMETERS_LIST, fd)
data_df = pandas.read_csv(
get_path("data_curated", "curated_training_data.no_mass_spec.csv.bz2"))
selected_data_df = data_df.loc[data_df.allele.str.startswith("HLA-A")]
selected_data_df.to_csv(os.path.join(models_dir, "_train_data.csv"),
index=False)
args = [
"mhcflurry-class1-train-pan-allele-models",
"--data",
os.path.join(models_dir, "_train_data.csv"),
"--allele-sequences",
get_path("allele_sequences", "allele_sequences.csv"),
"--hyperparameters",
hyperparameters_filename,
"--out-models-dir",
models_dir,
"--num-jobs",
str(n_jobs),
"--num-folds",
"2",
"--verbosity",
"1",
] + additional_args
print("Running with args: %s" % args)
subprocess.check_call(args)
# Run model selection
models_dir_selected = tempfile.mkdtemp(
prefix="mhcflurry-test-models-selected")
args = [
"mhcflurry-class1-select-pan-allele-models",
"--data",
os.path.join(models_dir, "train_data.csv.bz2"),
"--models-dir",
models_dir,
"--out-models-dir",
models_dir_selected,
"--max-models",
"1",
"--num-jobs",
str(n_jobs),
] + additional_args
print("Running with args: %s" % args)
subprocess.check_call(args)
result = Class1AffinityPredictor.load(models_dir_selected,
optimization_level=0)
assert_equal(len(result.neural_networks), 2)
predictions = result.predict(peptides=["SLYNTVATL"],
alleles=["HLA-A*02:01"])
assert_equal(predictions.shape, (1, ))
assert_array_less(predictions, 1000)
if delete:
print("Deleting: %s" % models_dir)
shutil.rmtree(models_dir)
shutil.rmtree(models_dir_selected)
def test_performance_improves_for_A0205_with_pretraining():
# test to make sure that imputation improves predictive accuracy after a
# small number of training iterations (5 epochs)
dataset = Dataset.from_csv(
get_path("data_combined_iedb_kim2014", "combined_human_class1_dataset.csv"))
print("Full dataset: %d pMHC entries" % len(dataset))
limited_alleles = ["HLA-A0205", "HLA-A0201", "HLA-A0101", "HLA-B0702"]
# restrict to just five alleles
dataset = dataset.get_alleles(limited_alleles)
print("After filtering to %s, # entries: %d" % (
limited_alleles, len(dataset)))
a0205_data_without_imputation = dataset.get_allele("HLA-A0205")
print("Dataset with only A0205, # entries: %d" % (
len(a0205_data_without_imputation)))
predictor_without_imputation = Class1BindingPredictor(
name="A0205-no-impute")
X_index, ic50_true, sample_weights, _ = (
a0205_data_without_imputation.kmer_index_encoding())
assert sample_weights.min() >= 0, sample_weights.min()
assert sample_weights.max() <= 1, sample_weights.max()
assert ic50_true.min() >= 0, ic50_true.min()
predictor_without_imputation.fit_kmer_encoded_arrays(
X=X_index,
ic50=ic50_true,
sample_weights=sample_weights,
n_training_epochs=10)
ic50_pred_without_imputation = (
predictor_without_imputation
.predict_ic50_for_kmer_encoded_array(X_index))
diff_squared = (ic50_true - ic50_pred_without_imputation) ** 2
ic50_true_label = ic50_true <= 500
ic50_pred_label_without_imputation = ic50_pred_without_imputation <= 500
ic50_label_same_without_imputation = (
ic50_true_label == ic50_pred_label_without_imputation)
mse_without_imputation = (
(diff_squared * sample_weights).sum() / sample_weights.sum())
accuracy_without_imputation = ((
ic50_label_same_without_imputation * sample_weights).sum() /
sample_weights.sum())
imputed_datset = dataset.impute_missing_values(MICE(n_imputations=25))
print("After imputation, dataset for %s has %d entries" % (
limited_alleles, len(imputed_datset)))
a0205_data_with_imputation = imputed_datset.get_allele("HLA-A0205")
print("Limited to just A0205, # entries: %d" % (
len(a0205_data_with_imputation)))
X_index_imputed, ic50_imputed, sample_weights_imputed, _ = \
a0205_data_with_imputation.kmer_index_encoding()
assert sample_weights_imputed.min() >= 0, sample_weights_imputed.min()
assert sample_weights_imputed.max() <= 1, sample_weights_imputed.max()
assert ic50_imputed.min() >= 0, ic50_imputed.min()
predictor_with_imputation = Class1BindingPredictor(name="A0205-impute")
predictor_with_imputation.fit_kmer_encoded_arrays(
X=X_index,
ic50=ic50_true,
sample_weights=sample_weights,
X_pretrain=X_index_imputed,
ic50_pretrain=ic50_imputed,
sample_weights_pretrain=sample_weights_imputed,
n_training_epochs=10)
ic50_pred_with_imputation = \
predictor_with_imputation.predict_ic50_for_kmer_encoded_array(X_index)
diff_squared = (ic50_true - ic50_pred_with_imputation) ** 2
mse_with_imputation = (
(diff_squared * sample_weights).sum() / sample_weights.sum())
ic50_pred_label_with_imputation = ic50_pred_with_imputation <= 500
ic50_label_same_with_imputation = (
ic50_true_label == ic50_pred_label_with_imputation)
accuracy_with_imputation = ((
ic50_label_same_with_imputation * sample_weights).sum() /
sample_weights.sum())
print("RMS w/out imputation: %f" % (np.sqrt(mse_without_imputation),))
print("RMS w/ imputation: %f" % (np.sqrt(mse_with_imputation),))
assert mse_with_imputation < mse_without_imputation, \
"Expected MSE with imputation (%f) to be < w/o imputation (%f)" % (
mse_with_imputation, mse_without_imputation)
print("IC50 <= 500nM accuracy w/out imputation: %f" % (
accuracy_without_imputation,))
print("IC50 <= 500nM accuracy w/ imputation: %f" % (
accuracy_with_imputation,))
assert accuracy_with_imputation > accuracy_without_imputation
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
"data",
metavar="CSV",
help="Model selection data")
parser.add_argument(
"--proteome-peptides",
metavar="CSV",
required=True,
help="Proteome peptides")
parser.add_argument(
"--protein-data",
metavar="CSV",
default=get_path("data_references", "uniprot_proteins.csv.bz2", test_exists=False),
help="Proteome data. Default: %(default)s.")
parser.add_argument(
"--out",
metavar="CSV",
required=True,
help="File to write")
def run():
args = parser.parse_args(sys.argv[1:])
data_df = pandas.read_csv(args.data)
print("Read", args.data, len(data_df))
print(data_df)
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 3 07:26:12 2020
Author: Ruby Li
This script includes MHCflurry model
"""
from mhcflurry import Class1AffinityPredictor
from mhcflurry.downloads import get_path
import pandas as pd
import numpy as np
data_path = get_path('data_curated', 'curated_training_data.no_mass_spec.csv.bz2')
df = pandas.read_csv(data_path)
df = df.loc[(df.peptide.str.len() >= 8) & (df.peptide.str.len() <= 11)]
models = {}
for hla in HLAs:
new_predictor = Class1AffinityPredictor()
if(df.loc[df.allele == hla].shape[0]>0):
single_allele_train_data = df.loc[df.allele == hla].sample(21, replace = True)
else:
models[hla] = ''
continue
model = new_predictor.fit_allele_specific_predictors(n_models=1,architecture_hyperparameters_list=[{"layer_sizes": [16],"max_epochs": 5,"random_negative_constant": 5,}],peptides=single_allele_train_data.peptide.values,affinities=single_allele_train_data.measurement_value.values,allele="HLA-B*57:01")
models[hla] = model
binding_affinity = []
for i in range(len(test_peptide)):
def run():
args = parser.parse_args(sys.argv[1:])
df = pandas.read_csv(args.benchmark)
peptides = df.peptide.unique()
alleles = set()
for some in df.hla.unique():
alleles.update(some.split())
precomputed_dfs = {}
if 'netmhcpan4.ba' in args.predictors:
precomputed_dfs['netmhcpan4.ba'] = load_results(
get_path("data_mass_spec_benchmark",
"predictions/all.netmhcpan4.ba"),
result_df=pandas.DataFrame(
dtype=numpy.float32,
index=peptides,
columns=[
"%s affinity" % a for a in alleles
])).rename(columns=lambda s: s.replace("affinity", "").strip())
precomputed_dfs['netmhcpan4.ba'] *= -1 # flip since it's affinities
if 'netmhcpan4.el' in args.predictors:
precomputed_dfs['netmhcpan4.el'] = load_results(
get_path("data_mass_spec_benchmark",
"predictions/all.netmhcpan4.el"),
result_df=pandas.DataFrame(
dtype=numpy.float32,
index=peptides,
columns=[
"%s score" % a for a in alleles
])).rename(columns=lambda s: s.replace("score", "").strip())
if 'mixmhcpred' in args.predictors:
precomputed_dfs['mixmhcpred'] = load_results(
get_path("data_mass_spec_benchmark", "predictions/all.mixmhcpred"),
result_df=pandas.DataFrame(
dtype=numpy.float32,
index=peptides,
columns=[
"%s score" % a for a in alleles
])).rename(columns=lambda s: s.replace("score", "").strip())
skip_experiments = set()
for hla_text, sub_df in tqdm.tqdm(df.groupby("hla"),
total=df.hla.nunique()):
hla = hla_text.split()
for (name, precomputed_df) in precomputed_dfs.items():
df.loc[sub_df.index, name] = numpy.nan
prediction_df = pandas.DataFrame(index=sub_df.peptide, dtype=float)
for allele in hla:
if allele not in precomputed_df.columns or precomputed_df[
allele].isnull().all():
print(sub_df.sample_id.unique(), hla)
skip_experiments.update(sub_df.sample_id.unique())
prediction_df[allele] = precomputed_df.loc[prediction_df.index,
allele]
df.loc[sub_df.index, name] = prediction_df.max(1,
skipna=False).values
df.loc[sub_df.index, name + "_best_allele"] = prediction_df.idxmax(
1, skipna=False).values
if 'netmhcpan4.ba' in args.predictors:
# unflip the values
df['netmhcpan4.ba'] *= -1
print("Skip experiments", skip_experiments)
print("results")
print(df)
df.to_csv(args.out, index=False)
print("Wrote", args.out)
def run_and_check_with_model_selection(n_jobs=1):
models_dir1 = tempfile.mkdtemp(prefix="mhcflurry-test-models")
hyperparameters_filename = os.path.join(models_dir1,
"hyperparameters.yaml")
# Include one architecture that has max_epochs = 0. We check that it never
# gets selected in model selection.
hyperparameters = [
deepcopy(HYPERPARAMETERS[0]),
deepcopy(HYPERPARAMETERS[0]),
]
hyperparameters[-1]["max_epochs"] = 0
with open(hyperparameters_filename, "w") as fd:
json.dump(hyperparameters, fd)
args = [
"mhcflurry-class1-train-allele-specific-models",
"--data",
get_path("data_curated", "curated_training_data.affinity.csv.bz2"),
"--hyperparameters",
hyperparameters_filename,
"--allele",
"HLA-A*02:01",
"HLA-A*03:01",
"--out-models-dir",
models_dir1,
"--num-jobs",
str(n_jobs),
"--held-out-fraction-reciprocal",
"10",
"--n-models",
"1",
]
print("Running with args: %s" % args)
subprocess.check_call(args)
result = Class1AffinityPredictor.load(models_dir1)
assert_equal(len(result.neural_networks), 4)
models_dir2 = tempfile.mkdtemp(prefix="mhcflurry-test-models")
args = [
"mhcflurry-class1-select-allele-specific-models",
"--data",
get_path("data_curated", "curated_training_data.affinity.csv.bz2"),
"--exclude-data",
models_dir1 + "/train_data.csv.bz2",
"--out-models-dir",
models_dir2,
"--models-dir",
models_dir1,
"--num-jobs",
str(n_jobs),
"--mse-max-models",
"1",
"--unselected-accuracy-scorer",
"combined:mass-spec,mse",
"--unselected-accuracy-percentile-threshold",
"95",
]
print("Running with args: %s" % args)
subprocess.check_call(args)
result = Class1AffinityPredictor.load(models_dir2)
assert_equal(len(result.neural_networks), 2)
assert_equal(len(result.allele_to_allele_specific_models["HLA-A*02:01"]),
1)
assert_equal(len(result.allele_to_allele_specific_models["HLA-A*03:01"]),
1)
assert_equal(
result.allele_to_allele_specific_models["HLA-A*02:01"]
[0].hyperparameters["max_epochs"], 500)
assert_equal(
result.allele_to_allele_specific_models["HLA-A*03:01"]
[0].hyperparameters["max_epochs"], 500)
print("Deleting: %s" % models_dir1)
print("Deleting: %s" % models_dir2)
shutil.rmtree(models_dir1)
# Expensive test - not run by nose.
from mhcflurry import train_pan_allele_models_command
from mhcflurry.downloads import get_path
from mhcflurry.allele_encoding import AlleleEncoding
import pandas
import numpy
PRETRAIN_DATA_PATH = get_path("random_peptide_predictions",
"predictions.csv.bz2")
FULL_TRAIN_DF = pandas.read_csv(
get_path("data_curated", "curated_training_data.no_mass_spec.csv.bz2"))
TRAIN_DF = FULL_TRAIN_DF.loc[(FULL_TRAIN_DF.peptide.str.len() >= 8)
& (FULL_TRAIN_DF.peptide.str.len() <= 15)]
ALLELE_SEQUENCES = pandas.read_csv(get_path("allele_sequences",
"allele_sequences.csv"),
index_col=0).sequence
ALLELE_SEQUENCES = ALLELE_SEQUENCES.loc[ALLELE_SEQUENCES.index.isin(
TRAIN_DF.allele)]
TRAIN_DF = TRAIN_DF.loc[TRAIN_DF.allele.isin(ALLELE_SEQUENCES.index)]
FOLDS_DF = pandas.DataFrame(index=TRAIN_DF.index)
FOLDS_DF["fold_0"] = True
HYPERPARAMTERS = {
'activation': 'tanh',
'allele_dense_layer_sizes': [],
'batch_normalization': False,
'dense_layer_l1_regularization': 0.0,
'dense_layer_l2_regularization': 0.0,
def test_performance_improves_for_A0205_with_pretraining():
# test to make sure that imputation improves predictive accuracy after a
# small number of training iterations (5 epochs)
dataset = Dataset.from_csv(
get_path("data_combined_iedb_kim2014",
"combined_human_class1_dataset.csv"))
print("Full dataset: %d pMHC entries" % len(dataset))
limited_alleles = ["HLA-A0205", "HLA-A0201", "HLA-A0101", "HLA-B0702"]
# restrict to just five alleles
dataset = dataset.get_alleles(limited_alleles)
print("After filtering to %s, # entries: %d" %
(limited_alleles, len(dataset)))
a0205_data_without_imputation = dataset.get_allele("HLA-A0205")
print("Dataset with only A0205, # entries: %d" %
(len(a0205_data_without_imputation)))
predictor_without_imputation = Class1BindingPredictor(
name="A0205-no-impute")
X_index, ic50_true, sample_weights, _ = (
a0205_data_without_imputation.kmer_index_encoding())
assert sample_weights.min() >= 0, sample_weights.min()
assert sample_weights.max() <= 1, sample_weights.max()
assert ic50_true.min() >= 0, ic50_true.min()
predictor_without_imputation.fit_kmer_encoded_arrays(
X=X_index,
ic50=ic50_true,
sample_weights=sample_weights,
n_training_epochs=10)
ic50_pred_without_imputation = (
predictor_without_imputation.predict_ic50_for_kmer_encoded_array(
X_index))
diff_squared = (ic50_true - ic50_pred_without_imputation)**2
ic50_true_label = ic50_true <= 500
ic50_pred_label_without_imputation = ic50_pred_without_imputation <= 500
ic50_label_same_without_imputation = (
ic50_true_label == ic50_pred_label_without_imputation)
mse_without_imputation = ((diff_squared * sample_weights).sum() /
sample_weights.sum())
accuracy_without_imputation = (
(ic50_label_same_without_imputation * sample_weights).sum() /
sample_weights.sum())
imputed_datset = dataset.impute_missing_values(MICE(n_imputations=25))
print("After imputation, dataset for %s has %d entries" %
(limited_alleles, len(imputed_datset)))
a0205_data_with_imputation = imputed_datset.get_allele("HLA-A0205")
print("Limited to just A0205, # entries: %d" %
(len(a0205_data_with_imputation)))
X_index_imputed, ic50_imputed, sample_weights_imputed, _ = \
a0205_data_with_imputation.kmer_index_encoding()
assert sample_weights_imputed.min() >= 0, sample_weights_imputed.min()
assert sample_weights_imputed.max() <= 1, sample_weights_imputed.max()
assert ic50_imputed.min() >= 0, ic50_imputed.min()
predictor_with_imputation = Class1BindingPredictor(name="A0205-impute")
predictor_with_imputation.fit_kmer_encoded_arrays(
X=X_index,
ic50=ic50_true,
sample_weights=sample_weights,
X_pretrain=X_index_imputed,
ic50_pretrain=ic50_imputed,
sample_weights_pretrain=sample_weights_imputed,
n_training_epochs=10)
ic50_pred_with_imputation = \
predictor_with_imputation.predict_ic50_for_kmer_encoded_array(X_index)
diff_squared = (ic50_true - ic50_pred_with_imputation)**2
mse_with_imputation = ((diff_squared * sample_weights).sum() /
sample_weights.sum())
ic50_pred_label_with_imputation = ic50_pred_with_imputation <= 500
ic50_label_same_with_imputation = (
ic50_true_label == ic50_pred_label_with_imputation)
accuracy_with_imputation = (
(ic50_label_same_with_imputation * sample_weights).sum() /
sample_weights.sum())
print("RMS w/out imputation: %f" % (np.sqrt(mse_without_imputation), ))
print("RMS w/ imputation: %f" % (np.sqrt(mse_with_imputation), ))
assert mse_with_imputation < mse_without_imputation, \
"Expected MSE with imputation (%f) to be < w/o imputation (%f)" % (
mse_with_imputation, mse_without_imputation)
print("IC50 <= 500nM accuracy w/out imputation: %f" %
(accuracy_without_imputation, ))
print("IC50 <= 500nM accuracy w/ imputation: %f" %
(accuracy_with_imputation, ))
assert accuracy_with_imputation > accuracy_without_imputation
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 cleanup, startup
teardown = cleanup
setup = startup
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',
def test_class1_affinity_predictor_a0205_memorize_training_data():
# Memorize the dataset.
hyperparameters = dict(
activation="tanh",
layer_sizes=[64],
max_epochs=100,
early_stopping=False,
validation_split=0.0,
locally_connected_layers=[],
dense_layer_l1_regularization=0.0,
dropout_probability=0.0)
allele = "HLA-A*02:05"
df = pandas.read_csv(
get_path(
"data_curated", "curated_training_data.affinity.csv.bz2"))
df = df.loc[
df.allele == allele
]
df = df.loc[
df.peptide.str.len() == 9
]
df = df.loc[
df.measurement_type == "quantitative"
]
df = df.loc[
df.measurement_source == "kim2014"
]
predictor = Class1AffinityPredictor()
predictor.fit_allele_specific_predictors(
n_models=2,
architecture_hyperparameters_list=[hyperparameters],
allele=allele,
peptides=df.peptide.values,
affinities=df.measurement_value.values,
verbose=0,
)
predictor.calibrate_percentile_ranks(num_peptides_per_length=1000)
ic50_pred = predictor.predict(df.peptide.values, allele=allele)
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)
ic50_pred_df = predictor.predict_to_dataframe(
df.peptide.values, allele=allele)
print(ic50_pred_df)
assert 'prediction_percentile' in ic50_pred_df.columns
assert ic50_pred_df.prediction_percentile.isnull().sum() == 0
ic50_pred_df2 = predictor.predict_to_dataframe(
df.peptide.values,
allele=allele,
include_individual_model_predictions=True)
print(ic50_pred_df2)
# Test an unknown allele
print("Starting unknown allele check")
eq_(predictor.supported_alleles, [allele])
ic50_pred = predictor.predict(
df.peptide.values,
allele="HLA-A*02:01",
throw=False)
assert numpy.isnan(ic50_pred).all()
assert_raises(
ValueError,
predictor.predict,
df.peptide.values,
allele="HLA-A*02:01")
eq_(predictor.supported_alleles, [allele])
assert_raises(
ValueError,
predictor.predict,
["AAAAA"], # too short
allele=allele)
assert_raises(
ValueError,
predictor.predict,
["AAAAAAAAAAAAAAAAAAAA"], # too long
allele=allele)
ic50_pred = predictor.predict(
["AAAAA", "AAAAAAAAA", "AAAAAAAAAAAAAAAAAAAA"],
allele=allele,
throw=False)
assert numpy.isnan(ic50_pred[0])
assert not numpy.isnan(ic50_pred[1])
assert numpy.isnan(ic50_pred[2])
def test_a1_known_epitopes_in_newly_trained_model():
allele = "HLA-A*01:01"
df = pandas.read_csv(
get_path(
"data_curated", "curated_training_data.affinity.csv.bz2"))
df = df.loc[
(df.allele == allele) &
(df.peptide.str.len() >= 8) &
(df.peptide.str.len() <= 15)
]
hyperparameters = {
"max_epochs": 100,
"patience": 10,
"early_stopping": True,
"validation_split": 0.2,
"random_negative_rate": 0.0,
"random_negative_constant": 25,
"peptide_amino_acid_encoding": "BLOSUM62",
"use_embedding": False,
"kmer_size": 15,
"batch_normalization": False,
"locally_connected_layers": [
{
"filters": 8,
"activation": "tanh",
"kernel_size": 3
}
],
"activation": "relu",
"output_activation": "sigmoid",
"layer_sizes": [
32
],
"random_negative_affinity_min": 20000.0,
"random_negative_affinity_max": 50000.0,
"dense_layer_l1_regularization": 0.001,
"dropout_probability": 0.0
}
predictor = Class1AffinityPredictor()
predictor.fit_allele_specific_predictors(
n_models=2,
architecture_hyperparameters_list=[hyperparameters],
allele=allele,
peptides=df.peptide.values,
affinities=df.measurement_value.values,
verbose=0,
)
predict_and_check("HLA-A*01:01", "EVDPIGHLY", predictor=predictor)
models_dir = tempfile.mkdtemp("_models")
print(models_dir)
predictor.save(models_dir)
predictor2 = Class1AffinityPredictor.load(models_dir)
predict_and_check("HLA-A*01:01", "EVDPIGHLY", predictor=predictor2)
shutil.rmtree(models_dir)
predictor3 = Class1AffinityPredictor(
allele_to_allele_specific_models={
allele: [predictor.allele_to_allele_specific_models[allele][0]]
})
predict_and_check("HLA-A*01:01", "EVDPIGHLY", predictor=predictor3)
models_dir = tempfile.mkdtemp("_models")
print(models_dir)
predictor3.save(models_dir)
predictor4 = Class1AffinityPredictor.load(models_dir)
predict_and_check("HLA-A*01:01", "EVDPIGHLY", predictor=predictor4)
shutil.rmtree(models_dir)
import pandas
import logomaker
from matplotlib import pyplot
from mhcflurry.downloads import get_path
from mhcflurry.amino_acid import COMMON_AMINO_ACIDS
AMINO_ACIDS = sorted(COMMON_AMINO_ACIDS)
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
"--class1-models-dir-with-ms",
metavar="DIR",
default=get_path(
"models_class1_pan", "models.with_mass_spec", test_exists=False),
help="Class1 models. Default: %(default)s",
)
parser.add_argument(
"--class1-models-dir-no-ms",
metavar="DIR",
default=get_path(
"models_class1_pan", "models.no_mass_spec", test_exists=False),
help="Class1 models. Default: %(default)s",
)
parser.add_argument(
"--logo-cutoff",
default=0.01,
type=float,
help="Fraction of top to use for motifs",
)
