import collections
import hashlib
import json
import logging
import time
import warnings
from os.path import join, exists, abspath
from os import mkdir, environ
from socket import gethostname
from getpass import getuser
from functools import partial
from six import string_types
import numpy
from numpy.testing import assert_equal
import pandas
import mhcnames
from .class1_neural_network import Class1NeuralNetwork
from .common import random_peptides, positional_frequency_matrix
from .downloads import get_default_class1_models_dir
from .encodable_sequences import EncodableSequences
from .percent_rank_transform import PercentRankTransform
from .regression_target import to_ic50
from .version import __version__
from .ensemble_centrality import CENTRALITY_MEASURES
from .allele_encoding import AlleleEncoding
from .common import save_weights, load_weights
# Default function for combining predictions across models in an ensemble.
# See ensemble_centrality.py for other options.
DEFAULT_CENTRALITY_MEASURE = "mean"
# Any value > 0 will result in attempting to optimize models after loading.
OPTIMIZATION_LEVEL = int(environ.get("MHCFLURRY_OPTIMIZATION_LEVEL", 1))
[docs]class Class1AffinityPredictor(object):
"""
High-level interface for peptide/MHC I binding affinity prediction.
This class manages low-level `Class1NeuralNetwork` instances, each of which
wraps a single Keras network. The purpose of `Class1AffinityPredictor` is to
implement ensembles, handling of multiple alleles, and predictor loading and
saving. It also provides a place to keep track of metadata like prediction
histograms for percentile rank calibration.
"""
def __init__(
self,
allele_to_allele_specific_models=None,
class1_pan_allele_models=None,
allele_to_sequence=None,
manifest_df=None,
allele_to_percent_rank_transform=None,
metadata_dataframes=None,
provenance_string=None):
"""
Parameters
----------
allele_to_allele_specific_models : dict of string -> list of `Class1NeuralNetwork`
Ensemble of single-allele models to use for each allele.
class1_pan_allele_models : list of `Class1NeuralNetwork`
Ensemble of pan-allele models.
allele_to_sequence : dict of string -> string
MHC allele name to fixed-length amino acid sequence (sometimes
referred to as the pseudosequence). Required only if
class1_pan_allele_models is specified.
manifest_df : `pandas.DataFrame`, optional
Must have columns: model_name, allele, config_json, model.
Only required if you want to update an existing serialization of a
Class1AffinityPredictor. Otherwise this dataframe will be generated
automatically based on the supplied models.
allele_to_percent_rank_transform : dict of string -> `PercentRankTransform`, optional
`PercentRankTransform` instances to use for each allele
metadata_dataframes : dict of string -> pandas.DataFrame, optional
Optional additional dataframes to write to the models dir when
save() is called. Useful for tracking provenance.
provenance_string : string, optional
Optional info string to use in __str__.
"""
if allele_to_allele_specific_models is None:
allele_to_allele_specific_models = {}
if class1_pan_allele_models is None:
class1_pan_allele_models = []
self.allele_to_sequence = (
dict(allele_to_sequence)
if allele_to_sequence is not None else None) # make a copy
self._master_allele_encoding = None
if class1_pan_allele_models:
assert self.allele_to_sequence
self.allele_to_allele_specific_models = allele_to_allele_specific_models
self.class1_pan_allele_models = class1_pan_allele_models
self._manifest_df = manifest_df
if not allele_to_percent_rank_transform:
allele_to_percent_rank_transform = {}
self.allele_to_percent_rank_transform = allele_to_percent_rank_transform
self.metadata_dataframes = (
dict(metadata_dataframes) if metadata_dataframes else {})
self._cache = {}
self.optimization_info = {}
assert isinstance(self.allele_to_allele_specific_models, dict)
assert isinstance(self.class1_pan_allele_models, list)
self.provenance_string = provenance_string
@property
def manifest_df(self):
"""
A pandas.DataFrame describing the models included in this predictor.
Based on:
- self.class1_pan_allele_models
- self.allele_to_allele_specific_models
Returns
-------
pandas.DataFrame
"""
if self._manifest_df is None:
rows = []
for (i, model) in enumerate(self.class1_pan_allele_models):
rows.append((
self.model_name("pan-class1", i),
"pan-class1",
json.dumps(model.get_config()),
model
))
for (allele, models) in self.allele_to_allele_specific_models.items():
for (i, model) in enumerate(models):
rows.append((
self.model_name(allele, i),
allele,
json.dumps(model.get_config()),
model
))
self._manifest_df = pandas.DataFrame(
rows,
columns=["model_name", "allele", "config_json", "model"])
return self._manifest_df
[docs] def clear_cache(self):
"""
Clear values cached based on the neural networks in this predictor.
Users should call this after mutating any of the following:
- self.class1_pan_allele_models
- self.allele_to_allele_specific_models
- self.allele_to_sequence
Methods that mutate these instance variables will call this method on
their own if needed.
"""
self._cache.clear()
self.provenance_string = None
@property
def neural_networks(self):
"""
List of the neural networks in the ensemble.
Returns
-------
list of `Class1NeuralNetwork`
"""
result = []
for models in self.allele_to_allele_specific_models.values():
result.extend(models)
result.extend(self.class1_pan_allele_models)
return result
[docs] @classmethod
def merge(cls, predictors):
"""
Merge the ensembles of two or more `Class1AffinityPredictor` instances.
Note: the resulting merged predictor will NOT have calibrated percentile
ranks. Call `calibrate_percentile_ranks` on it if these are needed.
Parameters
----------
predictors : sequence of `Class1AffinityPredictor`
Returns
-------
`Class1AffinityPredictor` instance
"""
assert len(predictors) > 0
if len(predictors) == 1:
return predictors[0]
allele_to_allele_specific_models = collections.defaultdict(list)
class1_pan_allele_models = []
allele_to_sequence = predictors[0].allele_to_sequence
for predictor in predictors:
for (allele, networks) in (
predictor.allele_to_allele_specific_models.items()):
allele_to_allele_specific_models[allele].extend(networks)
class1_pan_allele_models.extend(
predictor.class1_pan_allele_models)
return 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
)
[docs] def merge_in_place(self, others):
"""
Add the models present in other predictors into the current predictor.
Parameters
----------
others : list of Class1AffinityPredictor
Other predictors to merge into the current predictor.
Returns
-------
list of string : names of newly added models
"""
new_model_names = []
original_manifest = self.manifest_df
new_manifest_rows = []
for predictor in others:
for model in predictor.class1_pan_allele_models:
model_name = self.model_name(
"pan-class1",
len(self.class1_pan_allele_models))
row = pandas.Series(collections.OrderedDict([
("model_name", model_name),
("allele", "pan-class1"),
("config_json", json.dumps(model.get_config())),
("model", model),
])).to_frame().T
new_manifest_rows.append(row)
self.class1_pan_allele_models.append(model)
new_model_names.append(model_name)
for allele in predictor.allele_to_allele_specific_models:
if allele not in self.allele_to_allele_specific_models:
self.allele_to_allele_specific_models[allele] = []
current_models = self.allele_to_allele_specific_models[allele]
for model in predictor.allele_to_allele_specific_models[allele]:
model_name = self.model_name(allele, len(current_models))
row = pandas.Series(collections.OrderedDict([
("model_name", model_name),
("allele", allele),
("config_json", json.dumps(model.get_config())),
("model", model),
])).to_frame().T
new_manifest_rows.append(row)
current_models.append(model)
new_model_names.append(model_name)
self._manifest_df = pandas.concat(
[original_manifest] + new_manifest_rows,
ignore_index=True)
self.clear_cache()
self.check_consistency()
return new_model_names
@property
def supported_alleles(self):
"""
Alleles for which predictions can be made.
Returns
-------
list of string
"""
if 'supported_alleles' not in self._cache:
result = set(self.allele_to_allele_specific_models)
if self.allele_to_sequence:
result = result.union(self.allele_to_sequence)
self._cache["supported_alleles"] = sorted(result)
return self._cache["supported_alleles"]
@property
def supported_peptide_lengths(self):
"""
(minimum, maximum) lengths of peptides supported by *all models*,
inclusive.
Returns
-------
(int, int) tuple
"""
if 'supported_peptide_lengths' not in self._cache:
length_ranges = set(
network.supported_peptide_lengths
for network in self.neural_networks)
result = (
max(lower for (lower, upper) in length_ranges),
min(upper for (lower, upper) in length_ranges))
self._cache["supported_peptide_lengths"] = result
return self._cache["supported_peptide_lengths"]
[docs] def check_consistency(self):
"""
Verify that self.manifest_df is consistent with:
- self.class1_pan_allele_models
- self.allele_to_allele_specific_models
Currently only checks for agreement on the total number of models.
Throws AssertionError if inconsistent.
"""
num_models = len(self.class1_pan_allele_models) + sum(
len(v) for v in self.allele_to_allele_specific_models.values())
assert len(self.manifest_df) == num_models, (
"Manifest seems out of sync with models: %d vs %d entries: "
"\n%s\npan-allele: %s\nallele-specific: %s"% (
len(self.manifest_df),
num_models,
str(self.manifest_df),
str(self.class1_pan_allele_models),
str(self.allele_to_allele_specific_models)))
[docs] 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 Class1NeuralNetwork, along with per-network
files giving the model weights. If there are pan-allele predictors in
the ensemble, the allele sequences are also stored in the
directory. There is also a small file "index.txt" with basic metadata:
when the models were trained, by whom, on what host.
Parameters
----------
models_dir : string
Path to directory. It will be created if it doesn't exist.
model_names_to_write : list of string, optional
Only write the weights for the specified models. Useful for
incremental updates during training.
write_metadata : boolean, optional
Whether to write optional metadata
"""
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)
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"))
if self.allele_to_percent_rank_transform:
percent_ranks_df = None
for (allele, transform) in self.allele_to_percent_rank_transform.items():
series = transform.to_series()
if percent_ranks_df is None:
percent_ranks_df = pandas.DataFrame(index=series.index)
numpy.testing.assert_array_almost_equal(
series.index.values,
percent_ranks_df.index.values)
percent_ranks_df[allele] = series.values
percent_ranks_path = join(models_dir, "percent_ranks.csv")
percent_ranks_df.to_csv(
percent_ranks_path,
index=True,
index_label="bin")
logging.info("Wrote: %s", percent_ranks_path)
[docs] @staticmethod
def load(models_dir=None, max_models=None, optimization_level=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 `Class1NeuralNetwork` instances to load
optimization_level : int
If >0, model optimization will be attempted. Defaults to value of
environment variable MHCFLURRY_OPTIMIZATION_LEVEL.
Returns
-------
`Class1AffinityPredictor` instance
"""
if models_dir is None:
try:
models_dir = get_default_class1_models_dir()
except RuntimeError as e:
# Fall back to the affinity predictor included in presentation
# predictor if possible.
from mhcflurry.class1_presentation_predictor import (
Class1PresentationPredictor)
try:
presentation_predictor = Class1PresentationPredictor.load()
return presentation_predictor.affinity_predictor
except RuntimeError:
raise e
if optimization_level is None:
optimization_level = OPTIMIZATION_LEVEL
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(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())))
provenance_string = None
try:
info_path = join(models_dir, "info.txt")
info = pandas.read_csv(
info_path, sep="\t", header=None, index_col=0).iloc[
:, 0
].to_dict()
provenance_string = "generated on %s" % info["trained on"]
except OSError:
pass
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,
provenance_string=provenance_string
)
if optimization_level >= 1:
optimized = result.optimize()
logging.info(
"Model optimization %s",
"succeeded" if optimized else "not supported for these models")
return result
def __repr__(self):
pieces = ["at 0x%0x" % id(self), "[mhcflurry %s]" % __version__]
pan_models = len(self.class1_pan_allele_models)
total_models = len(self.neural_networks)
if total_models == 0:
pieces.append("[empty]")
elif pan_models == total_models:
pieces.append("[pan]")
elif pan_models == 0:
pieces.append("[allele-specific]")
else:
pieces.append("[pan+allele-specific]")
if self.provenance_string:
pieces.append(self.provenance_string)
return "<Class1AffinityPredictor %s>" % " ".join(pieces)
[docs] def optimize(self, warn=True):
"""
EXPERIMENTAL: Optimize the predictor for faster predictions.
Currently the only optimization implemented is to merge multiple pan-
allele predictors at the tensorflow level.
The optimization is performed in-place, mutating the instance.
Returns
----------
bool
Whether optimization was performed
"""
num_class1_pan_allele_models = len(self.class1_pan_allele_models)
if num_class1_pan_allele_models > 1:
provenance_string = self.provenance_string
try:
self.class1_pan_allele_models = [
Class1NeuralNetwork.merge(
self.class1_pan_allele_models,
merge_method="concatenate")
]
except NotImplementedError as e:
if warn:
logging.warning("Optimization failed: %s", str(e))
return False
self._manifest_df = None
self.clear_cache()
self.optimization_info["pan_models_merged"] = True
self.optimization_info["num_pan_models_merged"] = (
num_class1_pan_allele_models)
self.provenance_string = provenance_string
else:
return False
return True
[docs] @staticmethod
def model_name(allele, num):
"""
Generate a model name
Parameters
----------
allele : string
num : int
Returns
-------
string
"""
random_string = hashlib.sha1(
str(time.time()).encode()).hexdigest()[:16]
return "%s-%d-%s" % (
allele.upper().replace("*", "_").replace(":", "_"),
num,
random_string)
[docs] @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)
@property
def master_allele_encoding(self):
"""
An AlleleEncoding containing the universe of alleles specified by
self.allele_to_sequence.
Returns
-------
AlleleEncoding
"""
if (self._master_allele_encoding is None or
self._master_allele_encoding.allele_to_sequence !=
self.allele_to_sequence):
self._master_allele_encoding = AlleleEncoding(
allele_to_sequence=self.allele_to_sequence)
return self._master_allele_encoding
[docs] def fit_allele_specific_predictors(
self,
n_models,
architecture_hyperparameters_list,
allele,
peptides,
affinities,
inequalities=None,
train_rounds=None,
models_dir_for_save=None,
verbose=0,
progress_preamble="",
progress_print_interval=5.0):
"""
Fit one or more allele specific predictors for a single allele using one
or more neural network architectures.
The new predictors are saved in the Class1AffinityPredictor instance
and will be used on subsequent calls to `predict`.
Parameters
----------
n_models : int
Number of neural networks to fit
architecture_hyperparameters_list : list of dict
List of hyperparameter sets.
allele : string
peptides : `EncodableSequences` or list of string
affinities : list of float
nM affinities
inequalities : list of string, each element one of ">", "<", or "="
See `Class1NeuralNetwork.fit` for details.
train_rounds : sequence of int
Each training point i will be used on training rounds r for which
train_rounds[i] > r, r >= 0.
models_dir_for_save : string, optional
If specified, the Class1AffinityPredictor is (incrementally) written
to the given models dir after each neural network is fit.
verbose : int
Keras verbosity
progress_preamble : string
Optional string of information to include in each progress update
progress_print_interval : float
How often (in seconds) to print progress. Set to None to disable.
Returns
-------
list of `Class1NeuralNetwork`
"""
allele = mhcnames.normalize_allele_name(allele)
if allele not in self.allele_to_allele_specific_models:
self.allele_to_allele_specific_models[allele] = []
encodable_peptides = EncodableSequences.create(peptides)
peptides_affinities_inequalities_per_round = [
(encodable_peptides, affinities, inequalities)
]
if train_rounds is not None:
for round in sorted(set(train_rounds)):
round_mask = train_rounds > round
if round_mask.any():
sub_encodable_peptides = EncodableSequences.create(
encodable_peptides.sequences[round_mask])
peptides_affinities_inequalities_per_round.append((
sub_encodable_peptides,
affinities[round_mask],
None if inequalities is None else inequalities[round_mask]))
n_rounds = len(peptides_affinities_inequalities_per_round)
n_architectures = len(architecture_hyperparameters_list)
# Adjust progress info to indicate number of models and
# architectures.
pieces = []
if n_models > 1:
pieces.append("Model {model_num:2d} / {n_models:2d}")
if n_architectures > 1:
pieces.append(
"Architecture {architecture_num:2d} / {n_architectures:2d}")
if len(peptides_affinities_inequalities_per_round) > 1:
pieces.append("Round {round:2d} / {n_rounds:2d}")
pieces.append("{n_peptides:4d} peptides")
progress_preamble_template = "[ %s ] {user_progress_preamble}" % (
", ".join(pieces))
models = []
for model_num in range(n_models):
for (architecture_num, architecture_hyperparameters) in enumerate(
architecture_hyperparameters_list):
model = Class1NeuralNetwork(**architecture_hyperparameters)
for round_num in range(n_rounds):
(round_peptides, round_affinities, round_inequalities) = (
peptides_affinities_inequalities_per_round[round_num]
)
model.fit(
round_peptides,
round_affinities,
inequalities=round_inequalities,
verbose=verbose,
progress_preamble=progress_preamble_template.format(
n_peptides=len(round_peptides),
round=round_num,
n_rounds=n_rounds,
user_progress_preamble=progress_preamble,
model_num=model_num + 1,
n_models=n_models,
architecture_num=architecture_num + 1,
n_architectures=n_architectures),
progress_print_interval=progress_print_interval)
model_name = self.model_name(allele, model_num)
row = pandas.Series(collections.OrderedDict([
("model_name", model_name),
("allele", allele),
("config_json", json.dumps(model.get_config())),
("model", model),
])).to_frame().T
self._manifest_df = pandas.concat(
[self.manifest_df, row], ignore_index=True)
self.allele_to_allele_specific_models[allele].append(model)
if models_dir_for_save:
self.save(
models_dir_for_save, model_names_to_write=[model_name])
models.append(model)
self.clear_cache()
return models
[docs] def fit_class1_pan_allele_models(
self,
n_models,
architecture_hyperparameters,
alleles,
peptides,
affinities,
inequalities,
models_dir_for_save=None,
verbose=1,
progress_preamble="",
progress_print_interval=5.0):
"""
Fit one or more pan-allele predictors using a single neural network
architecture.
The new predictors are saved in the Class1AffinityPredictor instance
and will be used on subsequent calls to `predict`.
Parameters
----------
n_models : int
Number of neural networks to fit
architecture_hyperparameters : dict
alleles : list of string
Allele names (not sequences) corresponding to each peptide
peptides : `EncodableSequences` or list of string
affinities : list of float
nM affinities
inequalities : list of string, each element one of ">", "<", or "="
See Class1NeuralNetwork.fit for details.
models_dir_for_save : string, optional
If specified, the Class1AffinityPredictor is (incrementally) written
to the given models dir after each neural network is fit.
verbose : int
Keras verbosity
progress_preamble : string
Optional string of information to include in each progress update
progress_print_interval : float
How often (in seconds) to print progress. Set to None to disable.
Returns
-------
list of `Class1NeuralNetwork`
"""
alleles = pandas.Series(alleles).map(mhcnames.normalize_allele_name)
allele_encoding = AlleleEncoding(
alleles,
borrow_from=self.master_allele_encoding)
encodable_peptides = EncodableSequences.create(peptides)
models = []
for i in range(n_models):
logging.info("Training model %d / %d", i + 1, n_models)
model = Class1NeuralNetwork(**architecture_hyperparameters)
model.fit(
encodable_peptides,
affinities,
inequalities=inequalities,
allele_encoding=allele_encoding,
verbose=verbose,
progress_preamble=progress_preamble,
progress_print_interval=progress_print_interval)
model_name = self.model_name("pan-class1", i)
row = pandas.Series(collections.OrderedDict([
("model_name", model_name),
("allele", "pan-class1"),
("config_json", json.dumps(model.get_config())),
("model", model),
])).to_frame().T
self._manifest_df = pandas.concat(
[self.manifest_df, row], ignore_index=True)
self.class1_pan_allele_models.append(model)
if models_dir_for_save:
self.save(
models_dir_for_save, model_names_to_write=[model_name])
models.append(model)
self.clear_cache()
return models
[docs] def add_pan_allele_model(self, model, models_dir_for_save=None):
"""
Add a pan-allele model to the ensemble and optionally do an incremental
save.
Parameters
----------
model : Class1NeuralNetwork
models_dir_for_save : string
Directory to save resulting ensemble to
"""
model_name = self.model_name("pan-class1", 1)
row = pandas.Series(collections.OrderedDict([
("model_name", model_name),
("allele", "pan-class1"),
("config_json", json.dumps(model.get_config())),
("model", model),
])).to_frame().T
self._manifest_df = pandas.concat(
[self.manifest_df, row], ignore_index=True)
self.class1_pan_allele_models.append(model)
self.clear_cache()
self.check_consistency()
if models_dir_for_save:
self.save(
models_dir_for_save, model_names_to_write=[model_name])
[docs] def percentile_ranks(self, affinities, allele=None, alleles=None, throw=True):
"""
Return percentile ranks for the given ic50 affinities and alleles.
The 'allele' and 'alleles' argument are as in the `predict` method.
Specify one of these.
Parameters
----------
affinities : sequence of float
nM affinities
allele : string
alleles : sequence of string
throw : boolean
If True, a ValueError will be raised in the case of unsupported
alleles. If False, a warning will be logged and NaN will be returned
for those percentile ranks.
Returns
-------
numpy.array of float
"""
if allele is not None:
normalized_allele = mhcnames.normalize_allele_name(allele)
try:
transform = self.allele_to_percent_rank_transform[normalized_allele]
return transform.transform(affinities)
except KeyError:
if self.allele_to_sequence:
# See if we have information for an equivalent allele
sequence = self.allele_to_sequence[normalized_allele]
other_alleles = [
other_allele for (other_allele, other_sequence)
in self.allele_to_sequence.items()
if other_sequence == sequence
]
for other_allele in other_alleles:
if other_allele in self.allele_to_percent_rank_transform:
transform = self.allele_to_percent_rank_transform[
other_allele]
return transform.transform(affinities)
msg = "Allele %s has no percentile rank information" % (
allele + (
"" if allele == normalized_allele
else " (normalized to %s)" % normalized_allele))
if throw:
raise ValueError(msg)
warnings.warn(msg)
return numpy.ones(len(affinities)) * numpy.nan # Return NaNs
if alleles is None:
raise ValueError("Specify allele or alleles")
df = pandas.DataFrame({"affinity": affinities})
df["allele"] = alleles
df["result"] = numpy.nan
for (allele, sub_df) in df.groupby("allele"):
df.loc[sub_df.index, "result"] = self.percentile_ranks(
sub_df.affinity, allele=allele, throw=throw)
return df.result.values
[docs] def predict(
self,
peptides,
alleles=None,
allele=None,
throw=True,
centrality_measure=DEFAULT_CENTRALITY_MEASURE,
model_kwargs={}):
"""
Predict nM binding affinities.
If multiple predictors are available for an allele, the predictions are
the geometric means of the individual model (nM) predictions.
One of 'allele' or 'alleles' must be specified. If 'allele' is specified
all predictions will be for the given allele. If 'alleles' is specified
it must be the same length as 'peptides' and give the allele
corresponding to each peptide.
Parameters
----------
peptides : `EncodableSequences` or list of string
alleles : list of string
allele : string
throw : boolean
If True, a ValueError will be raised in the case of unsupported
alleles or peptide lengths. If False, a warning will be logged and
the predictions for the unsupported alleles or peptides will be NaN.
centrality_measure : string or callable
Measure of central tendency to use to combine predictions in the
ensemble. Options include: mean, median, robust_mean.
model_kwargs : dict
Additional keyword arguments to pass to Class1NeuralNetwork.predict
Returns
-------
numpy.array of predictions
"""
df = self.predict_to_dataframe(
peptides=peptides,
alleles=alleles,
allele=allele,
throw=throw,
include_percentile_ranks=False,
include_confidence_intervals=False,
centrality_measure=centrality_measure,
model_kwargs=model_kwargs
)
return df.prediction.values
[docs] def predict_to_dataframe(
self,
peptides,
alleles=None,
allele=None,
throw=True,
include_individual_model_predictions=False,
include_percentile_ranks=True,
include_confidence_intervals=True,
centrality_measure=DEFAULT_CENTRALITY_MEASURE,
model_kwargs={}):
"""
Predict nM binding affinities. Gives more detailed output than `predict`
method, including 5-95% prediction intervals.
If multiple predictors are available for an allele, the predictions are
the geometric means of the individual model predictions.
One of 'allele' or 'alleles' must be specified. If 'allele' is specified
all predictions will be for the given allele. If 'alleles' is specified
it must be the same length as 'peptides' and give the allele
corresponding to each peptide.
Parameters
----------
peptides : `EncodableSequences` or list of string
alleles : list of string
allele : string
throw : boolean
If True, a ValueError will be raised in the case of unsupported
alleles or peptide lengths. If False, a warning will be logged and
the predictions for the unsupported alleles or peptides will be NaN.
include_individual_model_predictions : boolean
If True, the predictions of each individual model are included as
columns in the result DataFrame.
include_percentile_ranks : boolean, default True
If True, a "prediction_percentile" column will be included giving
the percentile ranks. If no percentile rank info is available,
this will be ignored with a warning.
centrality_measure : string or callable
Measure of central tendency to use to combine predictions in the
ensemble. Options include: mean, median, robust_mean.
model_kwargs : dict
Additional keyword arguments to pass to Class1NeuralNetwork.predict
Returns
-------
`pandas.DataFrame` of predictions
"""
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 a list or array, not a string")
if allele is None and alleles is None:
raise ValueError("Must specify 'allele' or 'alleles'.")
peptides = EncodableSequences.create(peptides)
df = pandas.DataFrame({
'peptide': peptides.sequences
}, copy=False)
if allele is not None:
if alleles is not None:
raise ValueError("Specify exactly one of allele or alleles")
df["allele"] = allele
normalized_allele = mhcnames.normalize_allele_name(allele)
df["normalized_allele"] = normalized_allele
unique_alleles = [normalized_allele]
else:
df["allele"] = numpy.array(alleles)
df["normalized_allele"] = df.allele.map(
mhcnames.normalize_allele_name)
unique_alleles = df.normalized_allele.unique()
if len(df) == 0:
# No predictions.
logging.warning("Predicting for 0 peptides.")
empty_result = pandas.DataFrame(
columns=[
'peptide',
'allele',
'prediction',
'prediction_low',
'prediction_high'
])
return empty_result
(min_peptide_length, max_peptide_length) = (
self.supported_peptide_lengths)
if (peptides.min_length < min_peptide_length or
peptides.max_length > max_peptide_length):
# Only compute this if needed
all_peptide_lengths_supported = False
sequence_length = df.peptide.str.len()
df["supported_peptide_length"] = (
(sequence_length >= min_peptide_length) &
(sequence_length <= max_peptide_length))
if (~df.supported_peptide_length).any():
msg = (
"%d peptides have lengths outside of supported range [%d, %d]: "
"%s" % (
(~df.supported_peptide_length).sum(),
min_peptide_length,
max_peptide_length,
str(df.loc[~df.supported_peptide_length].peptide.unique())))
logging.warning(msg)
if throw:
raise ValueError(msg)
else:
# Handle common case efficiently.
df["supported_peptide_length"] = True
all_peptide_lengths_supported = True
num_pan_models = (
len(self.class1_pan_allele_models)
if not self.optimization_info.get("pan_models_merged", False)
else self.optimization_info["num_pan_models_merged"])
max_single_allele_models = max(
len(self.allele_to_allele_specific_models.get(allele, []))
for allele in unique_alleles
)
predictions_array = numpy.zeros(
shape=(df.shape[0], num_pan_models + max_single_allele_models),
dtype="float64")
predictions_array[:] = numpy.nan
if self.class1_pan_allele_models:
master_allele_encoding = self.master_allele_encoding
unsupported_alleles = [
allele for allele in
df.normalized_allele.unique()
if allele not in self.allele_to_sequence
]
if unsupported_alleles:
truncate_at = 100
allele_string = " ".join(
sorted(self.allele_to_sequence)[:truncate_at])
if len(self.allele_to_sequence) > truncate_at:
allele_string += " + %d more alleles" % (
len(self.allele_to_sequence) - truncate_at)
msg = (
"No sequences for allele(s): %s.\n"
"Supported alleles: %s" % (
" ".join(unsupported_alleles), allele_string))
logging.warning(msg)
if throw:
raise ValueError(msg)
mask = df.supported_peptide_length & (
~df.normalized_allele.isin(unsupported_alleles))
row_slice = None
if mask is None or mask.all():
row_slice = slice(None, None, None) # all rows
masked_allele_encoding = AlleleEncoding(
df.normalized_allele,
borrow_from=master_allele_encoding)
masked_peptides = EncodableSequences.create(peptides)
elif mask.sum() > 0:
row_slice = mask
masked_allele_encoding = AlleleEncoding(
df.loc[mask].normalized_allele,
borrow_from=master_allele_encoding)
masked_peptides = EncodableSequences.create(
peptides.sequences[mask])
if row_slice is not None:
# The following line is a performance optimization that may be
# revisited. It causes the neural network to set to include
# only the alleles actually being predicted for. This makes
# the network much smaller. However, subsequent calls to
# predict will need to reset these weights, so there is a
# tradeoff.
masked_allele_encoding = masked_allele_encoding.compact()
if self.optimization_info.get("pan_models_merged"):
# Multiple pan-allele models have been merged into one
# at the tensorflow level.
assert len(self.class1_pan_allele_models) == 1
predictions = self.class1_pan_allele_models[0].predict(
masked_peptides,
allele_encoding=masked_allele_encoding,
output_index=None,
**model_kwargs)
predictions_array[row_slice, :num_pan_models] = predictions
else:
for (i, model) in enumerate(self.class1_pan_allele_models):
predictions_array[row_slice, i] = model.predict(
masked_peptides,
allele_encoding=masked_allele_encoding,
**model_kwargs)
if self.allele_to_allele_specific_models:
unsupported_alleles = [
allele for allele in unique_alleles
if not self.allele_to_allele_specific_models.get(allele)
]
if unsupported_alleles:
msg = (
"No single-allele models for allele(s): %s.\n"
"Supported alleles are: %s" % (
" ".join(unsupported_alleles),
" ".join(sorted(self.allele_to_allele_specific_models))))
logging.warning(msg)
if throw:
raise ValueError(msg)
for allele in unique_alleles:
models = self.allele_to_allele_specific_models.get(allele, [])
if len(unique_alleles) == 1 and all_peptide_lengths_supported:
mask = None
else:
mask = (
(df.normalized_allele == allele) &
df.supported_peptide_length).values
row_slice = None
if mask is None or mask.all():
peptides_for_allele = peptides
row_slice = slice(None, None, None)
elif mask.sum() > 0:
peptides_for_allele = EncodableSequences.create(
df.loc[mask].peptide.values)
row_slice = mask
if row_slice is not None:
for (i, model) in enumerate(models):
predictions_array[
row_slice,
num_pan_models + i,
] = model.predict(peptides_for_allele, **model_kwargs)
if callable(centrality_measure):
centrality_function = centrality_measure
else:
centrality_function = CENTRALITY_MEASURES[centrality_measure]
logs = numpy.log(predictions_array)
log_centers = centrality_function(logs)
df["prediction"] = numpy.exp(log_centers)
if include_confidence_intervals:
df["prediction_low"] = numpy.exp(
numpy.nanpercentile(logs, 5.0, axis=1))
df["prediction_high"] = numpy.exp(
numpy.nanpercentile(logs, 95.0, axis=1))
if include_individual_model_predictions:
for i in range(num_pan_models):
df["model_pan_%d" % i] = predictions_array[:, i]
for i in range(max_single_allele_models):
df["model_single_%d" % i] = predictions_array[
:, num_pan_models + i
]
if include_percentile_ranks:
if self.allele_to_percent_rank_transform:
df["prediction_percentile"] = self.percentile_ranks(
df.prediction,
alleles=df.normalized_allele.values,
throw=throw)
else:
warnings.warn("No percentile rank information available.")
del df["supported_peptide_length"]
del df["normalized_allele"]
return df
[docs] def calibrate_percentile_ranks(
self,
peptides=None,
num_peptides_per_length=int(1e5),
alleles=None,
bins=None,
motif_summary=False,
summary_top_peptide_fractions=[0.001],
verbose=False,
model_kwargs={}):
"""
Compute the cumulative distribution of ic50 values for a set of alleles
over a large universe of random peptides, to enable taking quantiles
of this distribution later.
Parameters
----------
peptides : sequence of string or EncodableSequences, optional
Peptides to use
num_peptides_per_length : int, optional
If peptides argument is not specified, then num_peptides_per_length
peptides are randomly sampled from a uniform distribution for each
supported length
alleles : sequence of string, optional
Alleles to perform calibration for. If not specified all supported
alleles will be calibrated.
bins : object
Anything that can be passed to numpy.histogram's "bins" argument
can be used here, i.e. either an integer or a sequence giving bin
edges. This is in ic50 space.
motif_summary : bool
If True, the length distribution and per-position amino acid
frequencies are also calculated for the top x fraction of tightest-
binding peptides, where each value of x is given in the
summary_top_peptide_fractions list.
summary_top_peptide_fractions : list of float
Only used if motif_summary is True
verbose : boolean
Whether to print status updates to stdout
model_kwargs : dict
Additional low-level Class1NeuralNetwork.predict() kwargs.
Returns
----------
dict of string -> pandas.DataFrame
If motif_summary is True, this will have keys "frequency_matrices" and
"length_distributions". Otherwise it will be empty.
"""
if bins is None:
bins = to_ic50(numpy.linspace(1, 0, 1000))
if alleles is None:
alleles = self.supported_alleles
if peptides is None:
peptides = []
lengths = range(
self.supported_peptide_lengths[0],
self.supported_peptide_lengths[1] + 1)
for length in lengths:
peptides.extend(
random_peptides(num_peptides_per_length, length))
encoded_peptides = EncodableSequences.create(peptides)
if motif_summary:
frequency_matrices = []
length_distributions = []
else:
frequency_matrices = None
length_distributions = None
for allele in alleles:
start = time.time()
predictions = self.predict(
encoded_peptides, allele=allele, model_kwargs=model_kwargs)
if verbose:
elapsed = time.time() - start
print(
"Generated %d predictions for allele %s in %0.2f sec: "
"%0.2f predictions / sec" % (
len(encoded_peptides.sequences),
allele,
elapsed,
len(encoded_peptides.sequences) / elapsed))
transform = PercentRankTransform()
transform.fit(predictions, bins=bins)
self.allele_to_percent_rank_transform[allele] = transform
if frequency_matrices is not None:
predictions_df = pandas.DataFrame({
'peptide': encoded_peptides.sequences,
'prediction': predictions
}).drop_duplicates('peptide').set_index("peptide")
predictions_df["length"] = predictions_df.index.str.len()
for (length, sub_df) in predictions_df.groupby("length"):
for cutoff_fraction in summary_top_peptide_fractions:
selected = sub_df.prediction.nsmallest(
max(
int(len(sub_df) * cutoff_fraction),
1)).index.values
matrix = positional_frequency_matrix(selected).reset_index()
original_columns = list(matrix.columns)
matrix["allele"] = allele
matrix["length"] = length
matrix["cutoff_fraction"] = cutoff_fraction
matrix["cutoff_count"] = len(selected)
matrix = matrix[
["allele", "length", "cutoff_fraction", "cutoff_count"]
+ original_columns
]
frequency_matrices.append(matrix)
# Length distribution
for cutoff_fraction in summary_top_peptide_fractions:
cutoff_count = max(
int(len(predictions_df) * cutoff_fraction), 1)
length_distribution = predictions_df.prediction.nsmallest(
cutoff_count).index.str.len().value_counts()
length_distribution.index.name = "length"
length_distribution /= length_distribution.sum()
length_distribution = length_distribution.to_frame()
length_distribution.columns = ["fraction"]
length_distribution = length_distribution.reset_index()
length_distribution["allele"] = allele
length_distribution["cutoff_fraction"] = cutoff_fraction
length_distribution["cutoff_count"] = cutoff_count
length_distribution = length_distribution[[
"allele",
"cutoff_fraction",
"cutoff_count",
"length",
"fraction"
]].sort_values(["cutoff_fraction", "length"])
length_distributions.append(length_distribution)
if frequency_matrices is not None:
frequency_matrices = pandas.concat(
frequency_matrices, ignore_index=True)
if length_distributions is not None:
length_distributions = pandas.concat(
length_distributions, ignore_index=True)
if motif_summary:
return {
'frequency_matrices': frequency_matrices,
'length_distributions': length_distributions,
}
return {}
[docs] def model_select(
self,
score_function,
alleles=None,
min_models=1,
max_models=10000):
"""
Perform model selection using a user-specified scoring function.
This works only with allele-specific models, not pan-allele models.
Model selection is done using a "step up" variable selection procedure,
in which models are repeatedly added to an ensemble until the score
stops improving.
Parameters
----------
score_function : Class1AffinityPredictor -> float function
Scoring function
alleles : list of string, optional
If not specified, model selection is performed for all alleles.
min_models : int, optional
Min models to select per allele
max_models : int, optional
Max models to select per allele
Returns
-------
Class1AffinityPredictor : predictor containing the selected models
"""
if alleles is None:
alleles = self.supported_alleles
dfs = []
allele_to_allele_specific_models = {}
for allele in alleles:
df = pandas.DataFrame({
'model': self.allele_to_allele_specific_models[allele]
})
df["model_num"] = df.index
df["allele"] = allele
df["selected"] = False
round_num = 1
while not df.selected.all() and sum(df.selected) < max_models:
score_col = "score_%2d" % round_num
prev_score_col = "score_%2d" % (round_num - 1)
existing_selected = list(df[df.selected].model)
df[score_col] = [
numpy.nan if row.selected else
score_function(
Class1AffinityPredictor(
allele_to_allele_specific_models={
allele: [row.model] + existing_selected
}
)
)
for (_, row) in df.iterrows()
]
if round_num > min_models and (
df[score_col].max() < df[prev_score_col].max()):
break
# In case of a tie, pick a model at random.
(best_model_index,) = df.loc[
(df[score_col] == df[score_col].max())
].sample(1).index
df.loc[best_model_index, "selected"] = True
round_num += 1
dfs.append(df)
allele_to_allele_specific_models[allele] = list(
df.loc[df.selected].model)
df = pandas.concat(dfs, ignore_index=True)
new_predictor = Class1AffinityPredictor(
allele_to_allele_specific_models,
metadata_dataframes={
"model_selection": df,
})
return new_predictor