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Commit 053361ea authored by Tim O'Donnell's avatar Tim O'Donnell
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mhcflurry/__init__.py
+ 6
− 6
View file @ 053361ea
......@@ -12,15 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .class1_affinity_prediction.class1_binding_predictor import (
Class1BindingPredictor)
from .class1_affinity_prediction.multi_allele_predictor_ensemble import (
MultiAllelePredictorEnsemble)
from .class1_affinity_prediction.class1_neural_network import (
Class1NeuralNetwork)
from .class1_affinity_prediction.class1_affinity_predictor import (
Class1AffinityPredictor)
__version__ = "0.2.0"
__all__ = [
"Class1BindingPredictor",
"MultiAllelePredictorEnsemble",
"Class1NeuralNetwork",
"Class1AffinityPredictor",
"__version__",
]
mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_trained_on_hits.py
+ 2
− 2
View file @ 053361ea
......@@ -4,7 +4,7 @@ import pandas
from numpy import log, exp, nanmean
from ...affinity_measurement_dataset import AffinityMeasurementDataset
from ...class1_affinity_prediction import Class1BindingPredictor
from ...class1_affinity_prediction import Class1NeuralNetwork
from ...common import normalize_allele_name
from .mhc_binding_component_model_base import MHCBindingComponentModelBase
......@@ -99,7 +99,7 @@ class MHCflurryTrainedOnHits(MHCBindingComponentModelBase):
dataset = AffinityMeasurementDataset(
df.sample(frac=1)) # shuffle dataframe
print("Train data: ", dataset)
model = Class1BindingPredictor(
model = Class1NeuralNetwork(
**self.mhcflurry_hyperparameters)
model.fit_dataset(dataset, verbose=True)
self.allele_to_model[allele] = model
......
mhcflurry/class1_affinity_prediction/__init__.py
+ 4
− 2
View file @ 053361ea
from __future__ import absolute_import
from .class1_binding_predictor import Class1BindingPredictor
from .class1_neural_network import Class1NeuralNetwork
from .class1_affinity_predictor import Class1AffinityPredictor
__all__ = [
'Class1BindingPredictor',
'Class1NeuralNetwork',
'Class1AffinityPredictor',
]
mhcflurry/class1_affinity_prediction/multi_allele_predictor_ensemble.py → mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
+ 4
− 4
View file @ 053361ea
......@@ -11,10 +11,10 @@ import mhcnames
from ..encodable_sequences import EncodableSequences
from .class1_binding_predictor import Class1BindingPredictor
from .class1_neural_network import Class1NeuralNetwork
class MultiAllelePredictorEnsemble(object):
class Class1AffinityPredictor(object):
def __init__(
self,
allele_to_allele_specific_models={},
......@@ -117,7 +117,7 @@ class MultiAllelePredictorEnsemble(object):
for (allele, v)
in sorted(allele_to_allele_specific_models.items()))))
result = MultiAllelePredictorEnsemble(
result = Class1AffinityPredictor(
allele_to_allele_specific_models=allele_to_allele_specific_models,
class1_pan_allele_models=class1_pan_allele_models,
allele_to_pseudosequence=pseudosequences,
......@@ -226,7 +226,7 @@ class MultiAllelePredictorEnsemble(object):
output_assignments = ["output"] * len(encodable_peptides.sequences)
for i in range(n_models):
print("Training model %d / %d" % (i + 1, n_models))
model = Class1BindingPredictor(**architecture_hyperparameters)
model = Class1NeuralNetwork(**architecture_hyperparameters)
model.fit(
encodable_peptides,
affinities,
......
mhcflurry/class1_affinity_prediction/class1_binding_predictor.py → mhcflurry/class1_affinity_prediction/class1_neural_network.py
+ 1
− 1
View file @ 053361ea
......@@ -21,7 +21,7 @@ from ..regression_target import to_ic50, from_ic50
from ..common import random_peptides, amino_acid_distribution
class Class1BindingPredictor(object):
class Class1NeuralNetwork(object):
network_hyperparameter_defaults = HyperparameterDefaults(
kmer_size=15,
use_embedding=True,
......
mhcflurry/class1_affinity_prediction/cv_and_train_command.py deleted 100644 → 0
+ 0
− 400
View file @ d293a5ee
# Copyright (c) 2016. Mount Sinai School of Medicine
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
'''
Class1 allele-specific cross validation and training script.
What it does:
* Run cross validation on a dataset over the specified model architectures
* Select the best architecture for each allele
* Re-train the best architecture on the full data for that allele
* Test "production" predictors on a held-out test set if available
Features:
* Supports imputation as a hyperparameter that can be searched over
* Parallelized with concurrent.futures
Note:
The parallelization is primary intended to be used with an
alternative concurrent.futures Executor such as dask-distributed that supports
multi-node parallelization. Theano in particular seems to have deadlocks
when running with single-node parallelization.
'''
from __future__ import (
print_function,
division,
absolute_import,
)
import sys
import argparse
import json
import logging
import time
import os
import socket
import hashlib
import pickle
import numpy
from .. import parallelism
from ..affinity_measurement_dataset import AffinityMeasurementDataset
from ..imputation_helpers import imputer_from_name
from .cross_validation import cross_validation_folds
from .train import (
impute_and_select_allele,
train_across_models_and_folds,
AlleleSpecificTrainTestFold)
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
"--train-data",
metavar="X.csv",
required=True,
help="Training data")
parser.add_argument(
"--test-data",
metavar="X.csv",
help="Optional test data")
parser.add_argument(
"--model-architectures",
metavar="X.json",
type=argparse.FileType('r'),
required=True,
help="JSON file giving model architectures to assess in cross validation."
" Can be - to read from stdin")
parser.add_argument(
"--imputer-description",
metavar="X.json",
type=argparse.FileType('r'),
help="JSON. Can be - to read from stdin")
parser.add_argument(
"--alleles",
metavar="ALLELE",
nargs="+",
default=None,
help="Use only the specified alleles")
parser.add_argument(
"--out-cv-results",
metavar="X.csv",
help="Write cross validation results to the given file")
parser.add_argument(
"--out-production-results",
metavar="X.csv",
help="Write production model information to the given file")
parser.add_argument(
"--out-models-dir",
metavar="DIR",
help="Write production models to files in this dir")
parser.add_argument(
"--max-models",
type=int,
metavar="N",
help="Use only the first N models")
parser.add_argument(
"--cv-num-folds",
type=int,
default=3,
metavar="N",
help="Number of cross validation folds. Default: %(default)s")
parser.add_argument(
"--cv-folds-per-task",
type=int,
default=10,
metavar="N",
help="When parallelizing cross validation, each task trains one model "
"architecture on N folds. Set to 1 for maximum potential parallelism. "
"This is less efficient if you have limited workers, however, since "
"the model must get compiled for each task. Default: %(default)s.")
parser.add_argument(
"--dask-scheduler",
metavar="HOST:PORT",
help="Host and port of dask distributed scheduler")
parser.add_argument(
"--num-local-processes",
metavar="N",
type=int,
help="Processes (exclusive with --dask-scheduler and --num-local-threads)")
parser.add_argument(
"--num-local-threads",
metavar="N",
type=int,
default=1,
help="Threads (exclusive with --dask-scheduler and --num-local-processes)")
parser.add_argument(
"--min-samples-per-allele",
default=100,
metavar="N",
help="Don't train predictors for alleles with fewer than N samples. "
"Set to 0 to disable filtering. Default: %(default)s",
type=int)
parser.add_argument(
"--quiet",
action="store_true",
default=False,
help="Output less info")
parser.add_argument(
"--verbose",
action="store_true",
default=False,
help="Output more info")
try:
import kubeface
kubeface.Client.add_args(parser)
except ImportError:
logging.error("Kubeface support disabled, not installed.")
def run(argv=sys.argv[1:]):
args = parser.parse_args(argv)
if args.verbose:
logging.root.setLevel(level="DEBUG")
elif not args.quiet:
logging.root.setLevel(level="INFO")
logging.info("Running with arguments: %s" % args)
# Set parallel backend
if args.dask_scheduler:
backend = parallelism.DaskDistributedParallelBackend(
args.dask_scheduler)
elif hasattr(args, 'storage_prefix') and args.storage_prefix:
backend = parallelism.KubefaceParallelBackend(args)
else:
if args.num_local_processes:
backend = parallelism.ConcurrentFuturesParallelBackend(
args.num_local_processes,
processes=True)
else:
backend = parallelism.ConcurrentFuturesParallelBackend(
args.num_local_threads,
processes=False)
parallelism.set_default_backend(backend)
logging.info("Using parallel backend: %s" % backend)
go(args)
def go(args):
backend = parallelism.get_default_backend()
model_architectures = json.loads(args.model_architectures.read())
logging.info("Read %d model architectures" % len(model_architectures))
if args.max_models:
model_architectures = model_architectures[:args.max_models]
logging.info(
"Subselected to %d model architectures" % len(model_architectures))
train_data = AffinityMeasurementDataset.from_csv(args.train_data)
logging.info("Loaded training dataset: %s" % train_data)
test_data = None
if args.test_data:
test_data = AffinityMeasurementDataset.from_csv(args.test_data)
logging.info("Loaded testing dataset: %s" % test_data)
if args.min_samples_per_allele:
train_data = train_data.filter_alleles_by_count(
args.min_samples_per_allele)
logging.info(
"Filtered training dataset to alleles with >= %d observations: %s"
% (args.min_samples_per_allele, train_data))
if any(x['impute'] for x in model_architectures):
if not args.imputer_description:
parser.error(
"--imputer-description is required when any models "
"use imputation")
imputer_description = json.load(args.imputer_description)
logging.info("Loaded imputer description: %s" % imputer_description)
imputer_kwargs_defaults = {
'min_observations_per_peptide': 2,
'min_observations_per_allele': 10,
}
impute_kwargs = dict(
(key, imputer_description.pop(key, default))
for (key, default) in imputer_kwargs_defaults.items())
imputer = imputer_from_name(**imputer_description)
else:
imputer = None
impute_kwargs = {}
logging.info(
"Generating cross validation folds. Imputation: %s" %
("yes" if imputer else "no"))
cv_folds = cross_validation_folds(
train_data,
n_folds=args.cv_num_folds,
imputer=imputer,
impute_kwargs=impute_kwargs,
drop_similar_peptides=True,
alleles=args.alleles)
logging.info(
"Training %d model architectures across %d folds = %d models"
% (
len(model_architectures),
len(cv_folds),
len(model_architectures) * len(cv_folds)))
start = time.time()
cv_results = train_across_models_and_folds(
cv_folds,
model_architectures,
folds_per_task=args.cv_folds_per_task)
logging.info(
"Completed cross validation in %0.2f seconds" % (time.time() - start))
cv_results["summary_score"] = (
cv_results.test_auc.fillna(0) +
cv_results.test_tau.fillna(0) +
cv_results.test_f1.fillna(0))
allele_and_model_to_ranks = {}
for allele in cv_results.allele.unique():
model_ranks = (
cv_results.ix[cv_results.allele == allele]
.groupby("model_num")
.summary_score
.mean()
.rank(method='first', ascending=False, na_option="top")
.astype(int))
allele_and_model_to_ranks[allele] = model_ranks.to_dict()
cv_results["summary_rank"] = [
allele_and_model_to_ranks[row.allele][row.model_num]
for (_, row) in cv_results.iterrows()
]
if args.out_cv_results:
cv_results.to_csv(args.out_cv_results, index=False)
print("Wrote: %s" % args.out_cv_results)
numpy.testing.assert_equal(
set(cv_results.summary_rank),
set(1 + numpy.arange(len(model_architectures))))
best_architectures_by_allele = (
cv_results.ix[cv_results.summary_rank == 1]
.set_index("allele")
.model_num
.to_dict())
logging.info("")
train_folds = []
train_models = []
imputation_args_list = []
best_architectures = []
for (allele_num, allele) in enumerate(cv_results.allele.unique()):
best_index = best_architectures_by_allele[allele]
architecture = model_architectures[best_index]
best_architectures.append(architecture)
train_models.append(architecture)
logging.info(
"Allele: %s best architecture is index %d: %s" %
(allele, best_index, architecture))
if architecture['impute']:
imputation_args = dict(impute_kwargs)
imputation_args.update(dict(
dataset=train_data,
imputer=imputer,
allele=allele))
imputation_args_list.append(imputation_args)
test_data_this_allele = None
if test_data is not None:
test_data_this_allele = test_data.get_allele(allele)
fold = AlleleSpecificTrainTestFold(
allele=allele,
train=train_data.get_allele(allele),
imputed_train=None,
test=test_data_this_allele)
train_folds.append(fold)
if imputation_args_list:
imputation_results = list(backend.map(
lambda kwargs: impute_and_select_allele(**kwargs),
imputation_args_list))
new_train_folds = []
for (best_architecture, train_fold) in zip(
best_architectures, train_folds):
imputed_train = None
if best_architecture['impute']:
imputed_train = imputation_results.pop(0)
new_train_folds.append(
train_fold._replace(imputed_train=imputed_train))
assert not imputation_results
train_folds = new_train_folds
logging.info("Training %d production models" % len(train_folds))
start = time.time()
train_results = train_across_models_and_folds(
train_folds,
train_models,
cartesian_product_of_folds_and_models=False,
return_predictors=args.out_models_dir is not None)
logging.info(
"Completed production training in %0.2f seconds"
% (time.time() - start))
if args.out_models_dir:
predictor_names = []
run_name = (hashlib.sha1(
("%s-%f" % (socket.gethostname(), time.time())).encode())
.hexdigest()[:8])
for (_, row) in train_results.iterrows():
predictor_name = "-".join(str(x) for x in [
row.allele,
"impute" if row.model_impute else "noimpute",
"then".join(str(s) for s in row.model_layer_sizes),
"dropout%g" % row.model_dropout_probability,
"fracneg%g" % row.model_fraction_negative,
run_name,
]).replace(".", "_")
predictor_names.append(predictor_name)
out_path = os.path.join(
args.out_models_dir, predictor_name + ".pickle")
with open(out_path, "wb") as fd:
# Use this protocol so we have Python 2 compatability.
pickle.dump(row.predictor, fd, protocol=2)
print("Wrote: %s" % out_path)
del train_results["predictor"]
train_results["predictor_name"] = predictor_names
if args.out_production_results:
train_results.to_csv(args.out_production_results, index=False)
print("Wrote: %s" % args.out_production_results)
mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
+ 2
− 2
View file @ 053361ea
......@@ -13,7 +13,7 @@ import pandas
import mhcnames
from .class1_binding_predictor import Class1BindingPredictor
from .class1_neural_network import Class1NeuralNetwork
from ..common import configure_logging
def normalize_allele_name(s):
......@@ -122,7 +122,7 @@ def run():
train_data = df.ix[df.allele == allele].dropna().sample(
frac=1.0)
model = Class1BindingPredictor(
model = Class1NeuralNetwork(
verbose=args.verbosity,
**hyperparameters)
......
mhcflurry/predict_command.py
+ 12
− 40
View file @ 053361ea
......@@ -46,7 +46,8 @@ import pandas
import itertools
from .downloads import get_path
from . import class1_affinity_prediction, class1_allele_specific_ensemble
from .class1_affinity_prediction import Class1AffinityPredictor
parser = argparse.ArgumentParser(
description=__doc__,
......@@ -94,24 +95,11 @@ parser.add_argument(
help="Output column name for predictions. Default: '%(default)s'")
parser.add_argument(
"--predictor",
choices=("class1-allele-specific-single", "class1-allele-specific-ensemble"),
default="class1-allele-specific-ensemble",
help="Predictor to use. Default: %(default)s.")
parser.add_argument(
"--models-class1-allele-specific-ensemble",
metavar="DIR",
default=None,
help="Directory containing class1 allele specific ensemble models. "
"Default: %s" % get_path("models_class1_allele_specific_ensemble", test_exists=False))
parser.add_argument(
"--models-class1-allele-specific-single",
"--models",
metavar="DIR",
default=None,
help="Directory containing class1 allele specific single models. "
"Default: %s" % get_path("models_class1_allele_specific_single", test_exists=False))
help="Directory containing models. "
"Default: %s" % get_path("models_class1", test_exists=False))
def run(argv=sys.argv[1:]):
......@@ -155,29 +143,13 @@ def run(argv=sys.argv[1:]):
print("Predicting for %d alleles and %d peptides = %d predictions" % (
len(args.alleles), len(args.peptides), len(df)))
if args.predictor == "class1-allele-specific-single":
models_dir = args.models_class1_allele_specific_single
if models_dir is None:
# The reason we set the default here instead of in the argument parser is that
# we want to test_exists at this point, so the user gets a message instructing
# them to download the models if needed.
models_dir = get_path("models_class1_allele_specific_single")
predictor = (
class1_affinity_prediction
.class1_single_model_multi_allele_predictor
.Class1SingleModelMultiAllelePredictor
).load_from_download_directory(models_dir)
elif args.predictor == "class1-allele-specific-ensemble":
models_dir = args.models_class1_allele_specific_ensemble
if models_dir is None:
models_dir = get_path("models_class1_allele_specific_ensemble")
predictor = (
class1_allele_specific_ensemble
.class1_ensemble_multi_allele_predictor
.Class1EnsembleMultiAllelePredictor
).load_from_download_directory(models_dir)
else:
assert False
models_dir = args.models
if models_dir is None:
# The reason we set the default here instead of in the argument parser is that
# we want to test_exists at this point, so the user gets a message instructing
# them to download the models if needed.
models_dir = get_path("models_class1")
predictor = Class1AffinityPredictor.load(models_dir)
predictions = {} # allele -> peptide -> value
for (allele, sub_df) in df.groupby(args.allele_column):
......
test/dummy_models.py
+ 5
− 5
View file @ 053361ea
......@@ -13,7 +13,7 @@
# limitations under the License.
import numpy as np
from mhcflurry import Class1BindingPredictor
from mhcflurry import Class1NeuralNetwork
class Dummy9merIndexEncodingModel(object):
"""
......@@ -30,19 +30,19 @@ class Dummy9merIndexEncodingModel(object):
n_cols,)
return np.ones(n_rows, dtype=float) * self.constant_output_value
always_zero_predictor_with_unknown_AAs = Class1BindingPredictor(
always_zero_predictor_with_unknown_AAs = Class1NeuralNetwork(
model=Dummy9merIndexEncodingModel(0),
allow_unknown_amino_acids=True)
always_zero_predictor_without_unknown_AAs = Class1BindingPredictor(
always_zero_predictor_without_unknown_AAs = Class1NeuralNetwork(
model=Dummy9merIndexEncodingModel(0),
allow_unknown_amino_acids=False)
always_one_predictor_with_unknown_AAs = Class1BindingPredictor(
always_one_predictor_with_unknown_AAs = Class1NeuralNetwork(
model=Dummy9merIndexEncodingModel(1),
allow_unknown_amino_acids=True)
always_one_predictor_without_unknown_AAs = Class1BindingPredictor(
always_one_predictor_without_unknown_AAs = Class1NeuralNetwork(
model=Dummy9merIndexEncodingModel(1),
allow_unknown_amino_acids=False)
test/test_class1_binding_predictor_A0205.py
+ 2
− 2
View file @ 053361ea
......@@ -2,7 +2,7 @@ import numpy
import pandas
numpy.random.seed(0)
from mhcflurry import Class1BindingPredictor
from mhcflurry import Class1NeuralNetwork
from nose.tools import eq_
from numpy import testing
......@@ -25,7 +25,7 @@ def test_class1_binding_predictor_A0205_training_accuracy():
df.measurement_source == "kim2014"
]
predictor = Class1BindingPredictor(
predictor = Class1NeuralNetwork(
activation="tanh",
layer_sizes=[64],
max_epochs=1000, # Memorize the dataset.
......
test/test_hyperparameters.py
+ 2
− 2
View file @ 053361ea
from numpy.testing import assert_equal
from mhcflurry.class1_affinity_prediction import Class1BindingPredictor
from mhcflurry.class1_affinity_prediction import Class1NeuralNetwork
def test_all_combinations_of_hyperparameters():
......@@ -8,7 +8,7 @@ def test_all_combinations_of_hyperparameters():
activation=["tanh", "sigmoid"],
fraction_negative=[0, 0.2])
results = (
Class1BindingPredictor
Class1NeuralNetwork
.hyperparameter_defaults
.models_grid(**combinations_dict))
assert_equal(len(results), 4)
......
test/test_imputation.py
+ 3
− 3
View file @ 053361ea
......@@ -3,7 +3,7 @@ np.random.seed(0)
from mhcflurry.imputation_helpers import imputer_from_name
from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
from mhcflurry import Class1BindingPredictor
from mhcflurry import Class1NeuralNetwork
from fancyimpute import MICE, KNN, SoftImpute, IterativeSVD
from nose.tools import eq_
......@@ -54,7 +54,7 @@ def test_performance_improves_for_A0205_with_pretraining():
print("AffinityMeasurementDataset with only A0205, # entries: %d" % (
len(a0205_data_without_imputation)))
predictor_without_imputation = Class1BindingPredictor(
predictor_without_imputation = Class1NeuralNetwork(
name="A0205-no-impute")
X_index, ic50_true, sample_weights, _ = (
......@@ -97,7 +97,7 @@ def test_performance_improves_for_A0205_with_pretraining():
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 = Class1NeuralNetwork(name="A0205-impute")
predictor_with_imputation.fit_kmer_encoded_arrays(
X=X_index,
......
test/test_serialization.py
+ 2
− 2
View file @ 053361ea
import pickle
import numpy as np
from mhcflurry.class1_affinity_prediction import Class1BindingPredictor
from mhcflurry.class1_affinity_prediction import Class1NeuralNetwork
def test_predict_after_saving_model_to_disk():
# don't even bother fitting the model, just save its random weights
# and check we get the same predictions back afterward
model = Class1BindingPredictor(name="rando")
model = Class1NeuralNetwork(name="rando")
peptides = ["A" * 9, "C" * 9]
original_predictions = model.predict(peptides)
......
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