import time
import collections
import logging
import json
import weakref
import itertools
import numpy
import pandas
from .hyperparameters import HyperparameterDefaults
from .encodable_sequences import EncodableSequences, EncodingError
from .regression_target import to_ic50, from_ic50
from .common import random_peptides, amino_acid_distribution
from .custom_loss import get_loss
from .data_dependent_weights_initialization import lsuv_init
class Class1NeuralNetwork(object):
"""
Low level class I predictor consisting of a single neural network.
Both single allele and pan-allele prediction are supported, but pan-allele
is in development and not yet well performing.
Users will generally use Class1AffinityPredictor, which gives a higher-level
interface and supports ensembles.
"""
network_hyperparameter_defaults = HyperparameterDefaults(
allele_amino_acid_encoding="BLOSUM62",
allele_dense_layer_sizes=[],
peptide_encoding={
'vector_encoding_name': 'BLOSUM62',
'alignment_method': 'pad_middle',
'left_edge': 4,
'right_edge': 4,
'max_length': 15,
},
peptide_dense_layer_sizes=[],
peptide_allele_merge_method="multiply",
peptide_allele_merge_activation="",
layer_sizes=[32],
dense_layer_l1_regularization=0.001,
dense_layer_l2_regularization=0.0,
activation="tanh",
init="glorot_uniform",
output_activation="sigmoid",
dropout_probability=0.0,
batch_normalization=False,
locally_connected_layers=[
{
"filters": 8,
"activation": "tanh",
"kernel_size": 3
}
],
num_outputs=1,
)
"""
Hyperparameters (and their default values) that affect the neural network
architecture.
"""
compile_hyperparameter_defaults = HyperparameterDefaults(
loss="custom:mse_with_inequalities",
optimizer="rmsprop",
learning_rate=None,
)
"""
Loss and optimizer hyperparameters. Any values supported by keras may be
used.
"""
fit_hyperparameter_defaults = HyperparameterDefaults(
max_epochs=500,
validation_split=0.1,
early_stopping=True,
minibatch_size=128,
data_dependent_initialization_method=None,
random_negative_rate=0.0,
random_negative_constant=25,
random_negative_affinity_min=20000.0,
random_negative_affinity_max=50000.0,
random_negative_match_distribution=True,
random_negative_distribution_smoothing=0.0,
random_negative_output_indices=None)
"""
Hyperparameters for neural network training.
"""
early_stopping_hyperparameter_defaults = HyperparameterDefaults(
patience=20,
min_delta=0.0,
)
"""
Hyperparameters for early stopping.
"""
miscelaneous_hyperparameter_defaults = HyperparameterDefaults(
train_data={},
)
"""
Miscelaneous hyperaparameters. These parameters are not used by this class
but may be interpreted by other code.
"""
hyperparameter_defaults = network_hyperparameter_defaults.extend(
compile_hyperparameter_defaults).extend(
fit_hyperparameter_defaults).extend(
early_stopping_hyperparameter_defaults).extend(
miscelaneous_hyperparameter_defaults
)
"""
Combined set of all supported hyperparameters and their default values.
"""
# Hyperparameter renames.
# These are updated from time to time as new versions are developed. It
# provides a primitive way to allow new code to work with models trained
# using older code.
# None indicates the hyperparameter has been dropped.
hyperparameter_renames = {
"use_embedding": None,
"pseudosequence_use_embedding": None,
"monitor": None,
"min_delta": None,
"verbose": None,
"mode": None,
"take_best_epoch": None,
'kmer_size': None,
'peptide_amino_acid_encoding': None,
'embedding_input_dim': None,
'embedding_output_dim': None,
'embedding_init_method': None,
'left_edge': None,
'right_edge': None,
}
@classmethod
def apply_hyperparameter_renames(cls, hyperparameters):
"""
Handle hyperparameter renames.
Parameters
----------
hyperparameters : dict
Returns
-------
dict : updated hyperparameters
"""
for (from_name, to_name) in cls.hyperparameter_renames.items():
if from_name in hyperparameters:
value = hyperparameters.pop(from_name)
if to_name:
hyperparameters[to_name] = value
return hyperparameters
def __init__(self, **hyperparameters):
self.hyperparameters = self.hyperparameter_defaults.with_defaults(
self.apply_hyperparameter_renames(hyperparameters))
self._network = None
self.network_json = None
self.network_weights = None
self.network_weights_loader = None
self.fit_info = []
self.prediction_cache = weakref.WeakKeyDictionary()
KERAS_MODELS_CACHE = {}
"""
Process-wide keras model cache, a map from: architecture JSON string to
(Keras model, existing network weights)
"""
@classmethod
def clear_model_cache(klass):
"""
Clear the Keras model cache.
"""
klass.KERAS_MODELS_CACHE.clear()
@classmethod
def borrow_cached_network(klass, network_json, network_weights):
"""
Return a keras Model with the specified architecture and weights.
As an optimization, when possible this will reuse architectures from a
process-wide cache.
The returned object is "borrowed" in the sense that its weights can
change later after subsequent calls to this method from other objects.
If you're using this from a parallel implementation you'll need to
hold a lock while using the returned object.
Parameters
----------
network_json : string of JSON
network_weights : list of numpy.array
Returns
-------
keras.models.Model
"""
assert network_weights is not None
key = klass.keras_network_cache_key(network_json)
if key not in klass.KERAS_MODELS_CACHE:
# Cache miss.
import keras.models
network = keras.models.model_from_json(network_json)
existing_weights = None
else:
# Cache hit.
(network, existing_weights) = klass.KERAS_MODELS_CACHE[key]
if existing_weights is not network_weights:
network.set_weights(network_weights)
klass.KERAS_MODELS_CACHE[key] = (network, network_weights)
# As an added safety check we overwrite the fit method on the returned
# model to throw an error if it is called.
def throw(*args, **kwargs):
raise NotImplementedError("Do not call fit on cached model.")
network.fit = throw
return network
def network(self, borrow=False):
"""
Return the keras model associated with this predictor.
Parameters
----------
borrow : bool
Whether to return a cached model if possible. See
borrow_cached_network for details
Returns
-------
keras.models.Model
"""
if self._network is None and self.network_json is not None:
self.load_weights()
if borrow:
return self.borrow_cached_network(
self.network_json,
self.network_weights)
else:
import keras.models
self._network = keras.models.model_from_json(self.network_json)
if self.network_weights is not None:
self._network.set_weights(self.network_weights)
self.network_json = None
self.network_weights = None
return self._network
def update_network_description(self):
if self._network is not None:
self.network_json = self._network.to_json()
self.network_weights = self._network.get_weights()
@staticmethod
def keras_network_cache_key(network_json):
# As an optimization, we remove anything about regularization as these
# do not affect predictions.
def drop_properties(d):
if 'kernel_regularizer' in d:
del d['kernel_regularizer']
return d
description = json.loads(
network_json,
object_hook=drop_properties)
return json.dumps(description)
def get_config(self):
"""
serialize to a dict all attributes except model weights
Returns
-------
dict
"""
self.update_network_description()
result = dict(self.__dict__)
result['_network'] = None
result['network_weights'] = None
result['network_weights_loader'] = None
result['prediction_cache'] = None
return result
@classmethod
def from_config(cls, config, weights=None, weights_loader=None):
"""
deserialize from a dict returned by get_config().
Parameters
----------
config : dict
weights : list of array, optional
Network weights to restore
weights_loader : callable, optional
Function to call (no arguments) to load weights when needed
Returns
-------
Class1NeuralNetwork
"""
config = dict(config)
instance = cls(**config.pop('hyperparameters'))
instance.__dict__.update(config)
instance.network_weights = weights
instance.network_weights_loader = weights_loader
instance.prediction_cache = weakref.WeakKeyDictionary()
return instance
def load_weights(self):
"""
Load weights by evaluating self.network_weights_loader, if needed.
After calling this, self.network_weights_loader will be None and
self.network_weights will be the weights list, if available.
"""
if self.network_weights_loader:
self.network_weights = self.network_weights_loader()
self.network_weights_loader = None
def get_weights(self):
"""
Get the network weights
Returns
-------
list of numpy.array giving weights for each layer or None if there is no
network
"""
self.update_network_description()
self.load_weights()
return self.network_weights
def __getstate__(self):
"""
serialize to a dict. Model weights are included. For pickle support.
Returns
-------
dict
"""
self.update_network_description()
self.load_weights()
result = dict(self.__dict__)
result['_network'] = None
result['prediction_cache'] = None
return result
def __setstate__(self, state):
"""
Deserialize. For pickle support.
"""
self.__dict__.update(state)
self.prediction_cache = weakref.WeakKeyDictionary()
def peptides_to_network_input(self, peptides):
"""
Encode peptides to the fixed-length encoding expected by the neural
network (which depends on the architecture).
Parameters
----------
peptides : EncodableSequences or list of string
Returns
-------
numpy.array
"""
encoder = EncodableSequences.create(peptides)
encoded = encoder.variable_length_to_fixed_length_vector_encoding(
**self.hyperparameters['peptide_encoding'])
assert len(encoded) == len(peptides)
return encoded
@property
def supported_peptide_lengths(self):
"""
(minimum, maximum) lengths of peptides supported, inclusive.
Returns
-------
(int, int) tuple
"""
# We currently have an arbitrary hard floor of 5, even if the underlying
# peptide encoding supports smaller lengths.
#
# We empirically find the supported peptide lengths based on the
# lengths for which peptides_to_network_input throws ValueError.
try:
self.peptides_to_network_input([""])
except EncodingError as e:
return e.supported_peptide_lengths
raise RuntimeError("peptides_to_network_input did not raise")
def allele_encoding_to_network_input(self, allele_encoding):
"""
Encode alleles to the fixed-length encoding expected by the neural
network (which depends on the architecture).
Parameters
----------
allele_encoding : AlleleEncoding
Returns
-------
(numpy.array, numpy.array)
"""
return (
allele_encoding.indices,
allele_encoding.allele_representations(
self.hyperparameters['allele_amino_acid_encoding']))
@staticmethod
def data_dependent_weights_initialization(
network,
x_dict=None,
method="lsuv",
verbose=1):
"""
Data dependent weights initialization.
Parameters
----------
method
Returns
-------
"""
if verbose:
print("Performing data-dependent init: ", method)
if method == "lsuv":
assert x_dict is not None, "Data required for LSUV init"
lsuv_init(network, x_dict, verbose=verbose > 0)
else:
raise RuntimeError("Unsupported init method: ", method)
def fit_generator(
self,
generator,
validation_peptide_encoding,
validation_affinities,
validation_allele_encoding=None,
validation_inequalities=None,
validation_output_indices=None,
steps_per_epoch=10,
epochs=1000,
min_epochs=0,
patience=10,
min_delta=0.0,
verbose=1,
progress_callback=None,
progress_preamble="",
progress_print_interval=5.0):
"""
Fit using a generator. Does not support many of the features of fit(),
such as random negative peptides.
Parameters
----------
generator : generator yielding (alleles, peptides, affinities) tuples
where alleles and peptides are lists of strings, and affinities
is list of floats.
validation_peptide_encoding
validation_affinities
validation_allele_encoding
validation_inequalities
validation_output_indices
steps_per_epoch
epochs
patience
verbose
Returns
-------
"""
import keras
from keras import backend as K
fit_info = collections.defaultdict(list)
loss = get_loss(self.hyperparameters['loss'])
(validation_allele_input, allele_representations) = (
self.allele_encoding_to_network_input(validation_allele_encoding))
if self.network() is None:
self._network = self.make_network(
allele_representations=allele_representations,
**self.network_hyperparameter_defaults.subselect(
self.hyperparameters))
if verbose > 0:
self.network().summary()
network = self.network()
network.compile(
loss=loss.loss, optimizer=self.hyperparameters['optimizer'])
network._make_predict_function()
self.set_allele_representations(allele_representations)
if self.hyperparameters['learning_rate'] is not None:
K.set_value(
self.network().optimizer.lr,
self.hyperparameters['learning_rate'])
fit_info["learning_rate"] = float(
K.get_value(self.network().optimizer.lr))
validation_x_dict = {
'peptide': self.peptides_to_network_input(
validation_peptide_encoding),
'allele': validation_allele_input,
}
encode_y_kwargs = {}
if validation_inequalities is not None:
encode_y_kwargs["inequalities"] = validation_inequalities
if validation_output_indices is not None:
encode_y_kwargs["output_indices"] = validation_output_indices
output = loss.encode_y(
from_ic50(validation_affinities), **encode_y_kwargs)
validation_y_dict = {
'output': output,
}
mutable_generator_state = {
'yielded_values': 0 # total number of data points yielded
}
def wrapped_generator():
for (alleles, peptides, affinities) in generator:
(allele_encoding_input, _) = (
self.allele_encoding_to_network_input(alleles))
x_dict = {
'peptide': self.peptides_to_network_input(peptides),
'allele': allele_encoding_input,
}
y_dict = {
'output': from_ic50(affinities)
}
yield (x_dict, y_dict)
mutable_generator_state['yielded_values'] += len(affinities)
start = time.time()
iterator = wrapped_generator()
# Initialization required if a data_dependent_initialization_method
# is set and this is our first time fitting (i.e. fit_info is empty).
data_dependent_init = self.hyperparameters[
'data_dependent_initialization_method'
]
if data_dependent_init and not self.fit_info:
first_chunk = next(iterator)
self.data_dependent_weights_initialization(
network,
first_chunk[0], # x_dict
method=data_dependent_init,
verbose=verbose)
iterator = itertools.chain([first_chunk], iterator)
min_val_loss_iteration = None
min_val_loss = None
last_progress_print = 0
epoch = 1
while True:
epoch_start_time = time.time()
fit_history = network.fit_generator(
iterator,
steps_per_epoch=steps_per_epoch,
initial_epoch=epoch - 1,
epochs=epoch,
use_multiprocessing=False,
workers=1,
validation_data=(validation_x_dict, validation_y_dict),
verbose=verbose,
)
epoch_time = time.time() - epoch_start_time
for (key, value) in fit_history.history.items():
fit_info[key].extend(value)
val_loss = fit_info['val_loss'][-1]
if min_val_loss is None or val_loss < min_val_loss - min_delta:
min_val_loss = val_loss
min_val_loss_iteration = epoch
patience_epoch_threshold = min(
epochs, max(min_val_loss_iteration + patience, min_epochs))
progress_message = (
"epoch %3d/%3d [%0.2f sec.]: loss=%g val_loss=%g. Min val "
"loss %g at epoch %s. Cum. points: %d. Stop at epoch %d." % (
epoch,
epochs,
epoch_time,
fit_info['loss'][-1],
val_loss,
min_val_loss,
min_val_loss_iteration,
mutable_generator_state['yielded_values'],
patience_epoch_threshold,
)).strip()
# Print progress no more often than once every few seconds.
if progress_print_interval is not None and (
time.time() - last_progress_print > progress_print_interval):
print(progress_preamble, progress_message)
last_progress_print = time.time()
if progress_callback:
progress_callback()
if epoch >= patience_epoch_threshold:
if progress_print_interval is not None:
print(progress_preamble, "STOPPING", progress_message)
break
epoch += 1
fit_info["time"] = time.time() - start
fit_info["num_points"] = mutable_generator_state["yielded_values"]
self.fit_info.append(dict(fit_info))
def fit(
self,
peptides,
affinities,
allele_encoding=None,
inequalities=None,
output_indices=None,
sample_weights=None,
shuffle_permutation=None,
verbose=1,
progress_callback=None,
progress_preamble="",
progress_print_interval=5.0):
"""
Fit the neural network.
Parameters
----------
peptides : EncodableSequences or list of string
affinities : list of float
nM affinities. Must be same length of as peptides.
allele_encoding : AlleleEncoding, optional
If not specified, the model will be a single-allele predictor.
inequalities : list of string, each element one of ">", "<", or "=".
Inequalities to use for fitting. Same length as affinities.
Each element must be one of ">", "<", or "=". For example, a ">"
will train on y_pred > y_true for that element in the training set.
Requires using a custom losses that support inequalities (e.g.
mse_with_ineqalities).
If None all inequalities are taken to be "=".
sample_weights : list of float, optional
If not specified, all samples (including random negatives added
during training) will have equal weight. If specified, the random
negatives will be assigned weight=1.0.
shuffle_permutation : list of int, optional
Permutation (integer list) of same length as peptides and affinities
If None, then a random permutation will be generated.
verbose : int
Keras verbosity level
progress_preamble : string
Optional string of information to include in each progress update
progress_print_interval : float
How often (in seconds) to print progress update. Set to None to
disable.
"""
from keras import backend as K
encodable_peptides = EncodableSequences.create(peptides)
peptide_encoding = self.peptides_to_network_input(encodable_peptides)
fit_info = collections.defaultdict(list)
length_counts = (
pandas.Series(encodable_peptides.sequences)
.str.len().value_counts().to_dict())
num_random_negative = {}
for length in range(8, 16):
num_random_negative[length] = int(
length_counts.get(length, 0) *
self.hyperparameters['random_negative_rate'] +
self.hyperparameters['random_negative_constant'])
num_random_negative = pandas.Series(num_random_negative)
logging.info("Random negative counts per length:\n%s" % (
str(num_random_negative.to_dict())))
aa_distribution = None
if self.hyperparameters['random_negative_match_distribution']:
aa_distribution = amino_acid_distribution(
encodable_peptides.sequences,
smoothing=self.hyperparameters[
'random_negative_distribution_smoothing'])
logging.info(
"Using amino acid distribution for random negative:\n%s" % (
str(aa_distribution.to_dict())))
y_values = from_ic50(numpy.array(affinities, copy=False))
assert numpy.isnan(y_values).sum() == 0, y_values
if inequalities is not None:
# Reverse inequalities because from_ic50() flips the direction
# (i.e. lower affinity results in higher y values).
adjusted_inequalities = pandas.Series(inequalities).map({
"=": "=",
">": "<",
"<": ">",
}).values
else:
adjusted_inequalities = numpy.tile("=", len(y_values))
if len(adjusted_inequalities) != len(y_values):
raise ValueError("Inequalities and y_values must have same length")
x_dict_without_random_negatives = {
'peptide': peptide_encoding,
}
allele_representations = None
if allele_encoding is not None:
(allele_encoding_input, allele_representations) = (
self.allele_encoding_to_network_input(allele_encoding))
x_dict_without_random_negatives['allele'] = allele_encoding_input
# Shuffle y_values and the contents of x_dict_without_random_negatives
# This ensures different data is used for the test set for early stopping
# when multiple models are trained.
if shuffle_permutation is None:
shuffle_permutation = numpy.random.permutation(len(y_values))
y_values = y_values[shuffle_permutation]
assert numpy.isnan(y_values).sum() == 0, y_values
peptide_encoding = peptide_encoding[shuffle_permutation]
adjusted_inequalities = adjusted_inequalities[shuffle_permutation]
for key in x_dict_without_random_negatives:
x_dict_without_random_negatives[key] = (
x_dict_without_random_negatives[key][shuffle_permutation])
if sample_weights is not None:
sample_weights = numpy.array(sample_weights, copy=False)[
shuffle_permutation
]
if output_indices is not None:
output_indices = numpy.array(output_indices, copy=False)[
shuffle_permutation
]
loss = get_loss(self.hyperparameters['loss'])
if not loss.supports_inequalities and (
any(inequality != "=" for inequality in adjusted_inequalities)):
raise ValueError("Loss %s does not support inequalities" % loss)
if (not loss.supports_multiple_outputs and output_indices is not None
and (output_indices != 0).any()):
raise ValueError("Loss %s does not support multiple outputs" % loss)
if self.hyperparameters['num_outputs'] != 1:
if output_indices is None:
raise ValueError(
"Must supply output_indices for multi-output predictor")
if self.network() is None:
self._network = self.make_network(
allele_representations=allele_representations,
**self.network_hyperparameter_defaults.subselect(
self.hyperparameters))
if verbose > 0:
self.network().summary()
if allele_representations is not None:
self.set_allele_representations(allele_representations)
self.network().compile(
loss=loss.loss, optimizer=self.hyperparameters['optimizer'])
if self.hyperparameters['learning_rate'] is not None:
K.set_value(
self.network().optimizer.lr,
self.hyperparameters['learning_rate'])
fit_info["learning_rate"] = float(
K.get_value(self.network().optimizer.lr))
if loss.supports_inequalities:
# Do not sample negative affinities: just use an inequality.
random_negative_ic50 = self.hyperparameters['random_negative_affinity_min']
random_negative_target = from_ic50(random_negative_ic50)
y_dict_with_random_negatives = {
"output": numpy.concatenate([
numpy.tile(
random_negative_target, int(num_random_negative.sum())),
y_values,
]),
}
# Note: we are using "<" here not ">" because the inequalities are
# now in target-space (0-1) not affinity-space.
adjusted_inequalities_with_random_negatives = (
["<"] * int(num_random_negative.sum()) +
list(adjusted_inequalities))
else:
# Randomly sample random negative affinities
y_dict_with_random_negatives = {
"output": numpy.concatenate([
from_ic50(
numpy.random.uniform(
self.hyperparameters[
'random_negative_affinity_min'],
self.hyperparameters[
'random_negative_affinity_max'],
int(num_random_negative.sum()))),
y_values,
]),
}
adjusted_inequalities_with_random_negatives = None
assert numpy.isnan(y_dict_with_random_negatives['output']).sum() == 0, (
y_dict_with_random_negatives)
if sample_weights is not None:
sample_weights_with_random_negatives = numpy.concatenate([
numpy.ones(int(num_random_negative.sum())),
sample_weights])
else:
sample_weights_with_random_negatives = None
if output_indices is not None:
random_negative_output_indices = (
self.hyperparameters['random_negative_output_indices']
if self.hyperparameters['random_negative_output_indices']
else list(range(0, self.hyperparameters['num_outputs'])))
output_indices_with_random_negatives = numpy.concatenate([
pandas.Series(random_negative_output_indices, dtype=int).sample(
n=int(num_random_negative.sum()), replace=True).values,
output_indices
])
else:
output_indices_with_random_negatives = None
encode_y_kwargs = {}
if adjusted_inequalities_with_random_negatives is not None:
encode_y_kwargs["inequalities"] = (
adjusted_inequalities_with_random_negatives)
if output_indices_with_random_negatives is not None:
encode_y_kwargs["output_indices"] = (
output_indices_with_random_negatives)
y_dict_with_random_negatives['output'] = loss.encode_y(
y_dict_with_random_negatives['output'],
**encode_y_kwargs)
min_val_loss_iteration = None
min_val_loss = None
# Initialization required if a data_dependent_initialization_method
# is set and this is our first time fitting (i.e. fit_info is empty).
needs_initialization = self.hyperparameters[
'data_dependent_initialization_method'
] is not None and not self.fit_info
start = time.time()
last_progress_print = None
x_dict_with_random_negatives = {}
for i in range(self.hyperparameters['max_epochs']):
random_negative_peptides_list = []
for (length, count) in num_random_negative.iteritems():
random_negative_peptides_list.extend(
random_peptides(
count,
length=length,
distribution=aa_distribution))
random_negative_peptides = EncodableSequences.create(
random_negative_peptides_list)
random_negative_peptides_encoding = (
self.peptides_to_network_input(random_negative_peptides))
if not x_dict_with_random_negatives:
if len(random_negative_peptides) > 0:
x_dict_with_random_negatives["peptide"] = numpy.concatenate([
random_negative_peptides_encoding,
peptide_encoding,
])
if 'allele' in x_dict_without_random_negatives:
x_dict_with_random_negatives['allele'] = numpy.concatenate([
x_dict_without_random_negatives['allele'][
numpy.random.choice(
x_dict_without_random_negatives[
'allele'].shape[0],
size=len(random_negative_peptides_list))],
x_dict_without_random_negatives['allele']
])
else:
x_dict_with_random_negatives = (
x_dict_without_random_negatives)
else:
# Update x_dict_with_random_negatives in place.
# This is more memory efficient than recreating it as above.
if len(random_negative_peptides) > 0:
x_dict_with_random_negatives["peptide"][:len(random_negative_peptides)] = (
random_negative_peptides_encoding
)
if 'allele' in x_dict_with_random_negatives:
x_dict_with_random_negatives['allele'][:len(random_negative_peptides)] = (
x_dict_with_random_negatives['allele'][
len(random_negative_peptides) + numpy.random.choice(
x_dict_with_random_negatives['allele'].shape[0] -
len(random_negative_peptides),
size=len(random_negative_peptides))
]
)
if needs_initialization:
self.data_dependent_weights_initialization(
self.network(),
x_dict_with_random_negatives,
method=self.hyperparameters[
'data_dependent_initialization_method'],
verbose=verbose)
needs_initialization = False
epoch_start = time.time()
fit_history = self.network().fit(
x_dict_with_random_negatives,
y_dict_with_random_negatives,
shuffle=True,
batch_size=self.hyperparameters['minibatch_size'],
verbose=verbose,
epochs=i + 1,
initial_epoch=i,
validation_split=self.hyperparameters['validation_split'],
sample_weight=sample_weights_with_random_negatives)
epoch_time = time.time() - epoch_start
for (key, value) in fit_history.history.items():
fit_info[key].extend(value)
# Print progress no more often than once every few seconds.
if progress_print_interval is not None and (
not last_progress_print or (
time.time() - last_progress_print
> progress_print_interval)):
print((progress_preamble + " " +
"Epoch %3d / %3d [%0.2f sec]: loss=%g. "
"Min val loss (%s) at epoch %s" % (
i,
self.hyperparameters['max_epochs'],
epoch_time,
fit_info['loss'][-1],
str(min_val_loss),
min_val_loss_iteration)).strip())
last_progress_print = time.time()
if self.hyperparameters['validation_split']:
val_loss = fit_info['val_loss'][-1]
if min_val_loss is None or (
val_loss < min_val_loss - self.hyperparameters['min_delta']):
min_val_loss = val_loss
min_val_loss_iteration = i
if self.hyperparameters['early_stopping']:
threshold = (
min_val_loss_iteration +
self.hyperparameters['patience'])
if i > threshold:
if progress_print_interval is not None:
print((progress_preamble + " " +
"Stopping at epoch %3d / %3d: loss=%g. "
"Min val loss (%g) at epoch %s" % (
i,
self.hyperparameters['max_epochs'],
fit_info['loss'][-1],
(
min_val_loss if min_val_loss is not None
else numpy.nan),
min_val_loss_iteration)).strip())
break
if progress_callback:
progress_callback()
fit_info["time"] = time.time() - start
fit_info["num_points"] = len(peptides)
self.fit_info.append(dict(fit_info))
def predict(
self,
peptides,
allele_encoding=None,
batch_size=4096,
output_index=0):
"""
Predict affinities.
If peptides are specified as EncodableSequences, then the predictions
will be cached for this predictor as long as the EncodableSequences object
remains in memory. The cache is keyed in the object identity of the
EncodableSequences, not the sequences themselves.
Parameters
----------
peptides : EncodableSequences or list of string
allele_encoding : AlleleEncoding, optional
Only required when this model is a pan-allele model
batch_size : int
batch_size passed to Keras
Returns
-------
numpy.array of nM affinity predictions
"""
assert self.prediction_cache is not None
use_cache = (
allele_encoding is None and
isinstance(peptides, EncodableSequences))
if use_cache and peptides in self.prediction_cache:
return self.prediction_cache[peptides].copy()
x_dict = {
'peptide': self.peptides_to_network_input(peptides)
}
if allele_encoding is not None:
(allele_encoding_input, allele_representations) = (
self.allele_encoding_to_network_input(allele_encoding))
x_dict['allele'] = allele_encoding_input
self.set_allele_representations(allele_representations)
network = self.network()
else:
network = self.network(borrow=True)
raw_predictions = network.predict(x_dict, batch_size=batch_size)
predictions = numpy.array(raw_predictions, dtype = "float64")
if output_index is not None:
predictions = predictions[:,output_index]
result = to_ic50(predictions)
if use_cache:
self.prediction_cache[peptides] = result
return result
def make_network(
self,
peptide_encoding,
allele_amino_acid_encoding,
allele_dense_layer_sizes,
peptide_dense_layer_sizes,
peptide_allele_merge_method,
peptide_allele_merge_activation,
layer_sizes,
dense_layer_l1_regularization,
dense_layer_l2_regularization,
activation,
init,
output_activation,
dropout_probability,
batch_normalization,
locally_connected_layers,
num_outputs=1,
allele_representations=None):
"""
Helper function to make a keras network for class1 affinity prediction.
"""
# We import keras here to avoid tensorflow debug output, etc. unless we
# are actually about to use Keras.
from keras.layers import Input
import keras.layers
from keras.layers.core import Dense, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.layers.normalization import BatchNormalization
peptide_encoding_shape = self.peptides_to_network_input([]).shape[1:]
peptide_input = Input(
shape=peptide_encoding_shape,
dtype='float32',
name='peptide')
current_layer = peptide_input
inputs = [peptide_input]
kernel_regularizer = None
l1 = dense_layer_l1_regularization
l2 = dense_layer_l2_regularization
if l1 > 0 or l2 > 0:
kernel_regularizer = keras.regularizers.l1_l2(l1, l2)
for (i, locally_connected_params) in enumerate(locally_connected_layers):
current_layer = keras.layers.LocallyConnected1D(
name="lc_%d" % i,
**locally_connected_params)(current_layer)
current_layer = Flatten(name="flattened_0")(current_layer)
for (i, layer_size) in enumerate(peptide_dense_layer_sizes):
current_layer = Dense(
layer_size,
name="peptide_dense_%d" % i,
kernel_regularizer=kernel_regularizer,
activation=activation)(current_layer)
if batch_normalization:
current_layer = BatchNormalization(name="batch_norm_early")(
current_layer)
if allele_representations is not None:
allele_input = Input(
shape=(1,),
dtype='float32',
name='allele')
inputs.append(allele_input)
allele_layer = Embedding(
name="allele_representation",
input_dim=allele_representations.shape[0],
output_dim=numpy.product(allele_representations.shape[1:], dtype=int),
input_length=1,
trainable=False)(allele_input)
for (i, layer_size) in enumerate(allele_dense_layer_sizes):
allele_layer = Dense(
layer_size,
name="allele_dense_%d" % i,
kernel_regularizer=kernel_regularizer,
activation=activation)(allele_layer)
allele_layer = Flatten(name="allele_flat")(allele_layer)
if peptide_allele_merge_method == 'concatenate':
current_layer = keras.layers.concatenate([
current_layer, allele_layer
], name="allele_peptide_merged")
elif peptide_allele_merge_method == 'multiply':
current_layer = keras.layers.multiply([
current_layer, allele_layer
], name="allele_peptide_merged")
else:
raise ValueError(
"Unsupported peptide_allele_encoding_merge_method: %s"
% peptide_allele_merge_method)
if peptide_allele_merge_activation:
current_layer = keras.layers.Activation(
peptide_allele_merge_activation,
name="alelle_peptide_merged_%s" %
peptide_allele_merge_activation)(current_layer)
for (i, layer_size) in enumerate(layer_sizes):
current_layer = Dense(
layer_size,
activation=activation,
kernel_regularizer=kernel_regularizer,
name="dense_%d" % i)(current_layer)
if batch_normalization:
current_layer = BatchNormalization(
name="batch_norm_%d" % i)(current_layer)
if dropout_probability > 0:
current_layer = Dropout(
rate=1 - dropout_probability,
name="dropout_%d" % i)(current_layer)
output = Dense(
num_outputs,
kernel_initializer=init,
activation=output_activation,
name="output")(current_layer)
model = keras.models.Model(
inputs=inputs,
outputs=[output],
name="predictor")
return model
def set_allele_representations(self, allele_representations):
"""
Parameters
----------
model
allele_representations
"""
reshaped = allele_representations.reshape((allele_representations.shape[0], -1))
layer = self.network().get_layer("allele_representation")
(existing,) = layer.get_weights()
if existing.shape == reshaped.shape:
layer.set_weights([reshaped])
else:
raise NotImplementedError(
"Network surgery required: %s != %s" % (
str(existing.shape), str(reshaped.shape)))