from __future__ import print_function
from os.path import join, exists, abspath
from os import mkdir, environ
from socket import gethostname
from getpass import getuser
import time
import collections
import json
import hashlib
import logging
from six import string_types
import numpy
import pandas
import mhcnames
from .hyperparameters import HyperparameterDefaults
from .version import __version__
from .class1_neural_network import Class1NeuralNetwork, DEFAULT_PREDICT_BATCH_SIZE
from .encodable_sequences import EncodableSequences
from .regression_target import from_ic50, to_ic50
from .random_negative_peptides import RandomNegativePeptides
from .allele_encoding import MultipleAlleleEncoding, AlleleEncoding
from .auxiliary_input import AuxiliaryInputEncoder
from .batch_generator import MultiallelicMassSpecBatchGenerator
from .custom_loss import (
MSEWithInequalities,
MultiallelicMassSpecLoss,
ZeroLoss)
class Class1PresentationPredictor(object):
def __init__(
self,
class1_presentation_neural_networks,
allele_to_sequence,
manifest_df=None,
metadata_dataframes=None):
self.networks = class1_presentation_neural_networks
self.allele_to_sequence = allele_to_sequence
self._manifest_df = manifest_df
self.metadata_dataframes = (
dict(metadata_dataframes) if metadata_dataframes else {})
@property
def manifest_df(self):
"""
A pandas.DataFrame describing the models included in this predictor.
Returns
-------
pandas.DataFrame
"""
if self._manifest_df is None:
rows = []
for (i, model) in enumerate(self.networks):
rows.append((
self.model_name(i),
json.dumps(model.get_config()),
model
))
self._manifest_df = pandas.DataFrame(
rows,
columns=["model_name", "config_json", "model"])
return self._manifest_df
@property
def max_alleles(self):
max_alleles = self.networks[0].hyperparameters['max_alleles']
assert all(
n.hyperparameters['max_alleles'] == self.max_alleles
for n in self.networks)
return max_alleles
@staticmethod
def model_name(num):
"""
Generate a model name
Returns
-------
string
"""
random_string = hashlib.sha1(
str(time.time()).encode()).hexdigest()[:16]
return "LIGANDOME-CLASSI-%d-%s" % (
num,
random_string)
@staticmethod
def weights_path(models_dir, model_name):
"""
Generate the path to the weights file for a model
Parameters
----------
models_dir : string
model_name : string
Returns
-------
string
"""
return join(models_dir, "weights_%s.npz" % model_name)
def predict(self, peptides, alleles, batch_size=DEFAULT_PREDICT_BATCH_SIZE):
return self.predict_to_dataframe(
peptides=peptides,
alleles=alleles,
batch_size=batch_size).score.values
def predict_to_dataframe(
self,
peptides,
alleles,
include_details=False,
batch_size=DEFAULT_PREDICT_BATCH_SIZE):
if isinstance(peptides, string_types):
raise TypeError("peptides must be a list or array, not a string")
if isinstance(alleles, string_types):
raise TypeError(
"alleles must be an iterable or MultipleAlleleEncoding")
peptides = EncodableSequences.create(peptides)
if not isinstance(alleles, MultipleAlleleEncoding):
if len(alleles) > self.max_alleles:
raise ValueError(
"When alleles is a list, it must have at most %d elements. "
"These alleles are taken to be a genotype for an "
"individual, and the strongest prediction across alleles "
"will be taken for each peptide. Note that this differs "
"from Class1AffinityPredictor.predict(), where alleles "
"is expected to be the same length as peptides."
% (
self.max_alleles))
alleles = MultipleAlleleEncoding(
experiment_names=numpy.tile("experiment", len(peptides)),
experiment_to_allele_list={
"experiment": [
mhcnames.normalize_allele_name(a) for a in alleles
],
},
allele_to_sequence=self.allele_to_sequence,
max_alleles_per_experiment=self.max_alleles)
score_array = []
affinity_array = []
for (i, network) in enumerate(self.networks):
predictions = network.predict(
peptides=peptides,
allele_encoding=alleles,
batch_size=batch_size)
score_array.append(predictions.score)
affinity_array.append(predictions.affinity)
score_array = numpy.array(score_array)
affinity_array = numpy.array(affinity_array)
ensemble_scores = numpy.mean(score_array, axis=0)
ensemble_affinity = numpy.mean(affinity_array, axis=0)
top_allele_index = numpy.argmax(ensemble_scores, axis=-1)
top_score = ensemble_scores[top_allele_index]
top_affinity = ensemble_affinity[top_allele_index]
result_df = pandas.DataFrame({"peptide": peptides.sequences})
result_df["allele"] = alleles.alleles[top_allele_index]
result_df["score"] = top_score
result_df["affinity"] = to_ic50(top_affinity)
if include_details:
for i in range(self.max_alleles):
result_df["allele%d" % (i + 1)] = alleles.allele[:, i]
result_df["allele%d score" % (i + 1)] = ensemble_scores[:, i]
result_df["allele%d score low" % (i + 1)] = numpy.percentile(
score_array[:, :, i], 5.0, axis=0)
result_df["allele%d score high" % (i + 1)] = numpy.percentile(
score_array[:, :, i], 95.0, axis=0)
result_df["allele%d affinity" % (i + 1)] = to_ic50(
ensemble_affinity[:, i])
result_df["allele%d affinity low" % (i + 1)] = to_ic50(
numpy.percentile(affinity_array[:, :, i], 95.0, axis=0))
result_df["allele%d affinity high" % (i + 1)] = to_ic50(
numpy.percentile(affinity_array[:, :, i], 5.0, axis=0))
return result_df
@staticmethod
def save_weights(weights_list, filename):
"""
Save the model weights to the given filename using numpy's ".npz"
format.
Parameters
----------
weights_list : list of array
filename : string
Should end in ".npz".
"""
numpy.savez(
filename,
**dict((("array_%d" % i), w) for (i, w) in enumerate(weights_list)))
def check_consistency(self):
"""
Verify that self.manifest_df is consistent with instance variables.
Currently only checks for agreement on the total number of models.
Throws AssertionError if inconsistent.
"""
assert len(self.manifest_df) == len(self.networks), (
"Manifest seems out of sync with models: %d vs %d entries: \n%s"% (
len(self.manifest_df),
len(self.networks),
str(self.manifest_df)))
def save(self, models_dir, model_names_to_write=None, write_metadata=True):
"""
Serialize the predictor to a directory on disk. If the directory does
not exist it will be created.
The serialization format consists of a file called "manifest.csv" with
the configurations of each Class1PresentationNeuralNetwork, along with
per-network files giving the model weights.
Parameters
----------
models_dir : string
Path to directory. It will be created if it doesn't exist.
"""
self.check_consistency()
if model_names_to_write is None:
# Write all models
model_names_to_write = self.manifest_df.model_name.values
if not exists(models_dir):
mkdir(models_dir)
sub_manifest_df = self.manifest_df.loc[
self.manifest_df.model_name.isin(model_names_to_write)
].copy()
# Network JSON configs may have changed since the models were added,
# for example due to changes to the allele representation layer.
# So we update the JSON configs here also.
updated_network_config_jsons = []
for (_, row) in sub_manifest_df.iterrows():
updated_network_config_jsons.append(
json.dumps(row.model.get_config()))
weights_path = self.weights_path(models_dir, row.model_name)
self.save_weights(
row.model.get_weights(), weights_path)
logging.info("Wrote: %s", weights_path)
sub_manifest_df["config_json"] = updated_network_config_jsons
self.manifest_df.loc[
sub_manifest_df.index,
"config_json"
] = updated_network_config_jsons
write_manifest_df = self.manifest_df[[
c for c in self.manifest_df.columns if c != "model"
]]
manifest_path = join(models_dir, "manifest.csv")
write_manifest_df.to_csv(manifest_path, index=False)
logging.info("Wrote: %s", manifest_path)
if write_metadata:
# Write "info.txt"
info_path = join(models_dir, "info.txt")
rows = [
("trained on", time.asctime()),
("package ", "mhcflurry %s" % __version__),
("hostname ", gethostname()),
("user ", getuser()),
]
pandas.DataFrame(rows).to_csv(
info_path, sep="\t", header=False, index=False)
if self.metadata_dataframes:
for (name, df) in self.metadata_dataframes.items():
metadata_df_path = join(models_dir, "%s.csv.bz2" % name)
df.to_csv(metadata_df_path, index=False, compression="bz2")
# Save allele sequences
if self.allele_to_sequence is not None:
allele_to_sequence_df = pandas.DataFrame(
list(self.allele_to_sequence.items()),
columns=['allele', 'sequence']
)
allele_to_sequence_df.to_csv(
join(models_dir, "allele_sequences.csv"), index=False)
logging.info("Wrote: %s", join(models_dir, "allele_sequences.csv"))
@staticmethod
def load(models_dir=None, max_models=None):
"""
Deserialize a predictor from a directory on disk.
Parameters
----------
models_dir : string
Path to directory. If unspecified the default downloaded models are
used.
max_models : int, optional
Maximum number of models to load
Returns
-------
`Class1AffinityPredictor` instance
"""
if models_dir is None:
models_dir = get_default_class1_models_dir()
manifest_path = join(models_dir, "manifest.csv")
manifest_df = pandas.read_csv(manifest_path, nrows=max_models)
allele_to_allele_specific_models = collections.defaultdict(list)
class1_pan_allele_models = []
all_models = []
for (_, row) in manifest_df.iterrows():
weights_filename = Class1AffinityPredictor.weights_path(
models_dir, row.model_name)
config = json.loads(row.config_json)
# We will lazy-load weights when the network is used.
model = Class1NeuralNetwork.from_config(
config,
weights_loader=partial(
Class1AffinityPredictor.load_weights,
abspath(weights_filename)))
if row.allele == "pan-class1":
class1_pan_allele_models.append(model)
else:
allele_to_allele_specific_models[row.allele].append(model)
all_models.append(model)
manifest_df["model"] = all_models
# Load allele sequences
allele_to_sequence = None
if exists(join(models_dir, "allele_sequences.csv")):
allele_to_sequence = pandas.read_csv(
join(models_dir, "allele_sequences.csv"),
index_col=0).iloc[:, 0].to_dict()
allele_to_percent_rank_transform = {}
percent_ranks_path = join(models_dir, "percent_ranks.csv")
if exists(percent_ranks_path):
percent_ranks_df = pandas.read_csv(percent_ranks_path, index_col=0)
for allele in percent_ranks_df.columns:
allele_to_percent_rank_transform[allele] = (
PercentRankTransform.from_series(percent_ranks_df[allele]))
logging.info(
"Loaded %d class1 pan allele predictors, %d allele sequences, "
"%d percent rank distributions, and %d allele specific models: %s",
len(class1_pan_allele_models),
len(allele_to_sequence) if allele_to_sequence else 0,
len(allele_to_percent_rank_transform),
sum(len(v) for v in allele_to_allele_specific_models.values()),
", ".join(
"%s (%d)" % (allele, len(v))
for (allele, v)
in sorted(allele_to_allele_specific_models.items())))
result = Class1AffinityPredictor(
allele_to_allele_specific_models=allele_to_allele_specific_models,
class1_pan_allele_models=class1_pan_allele_models,
allele_to_sequence=allele_to_sequence,
manifest_df=manifest_df,
allele_to_percent_rank_transform=allele_to_percent_rank_transform,
)
if optimization_level >= 1:
optimized = result.optimize()
logging.info(
"Model optimization %s",
"succeeded" if optimized else "not supported for these models")
return result
# TODO: implement saving and loading