diff --git a/mhcflurry/class1_neural_network.py b/mhcflurry/class1_neural_network.py
index 7bbdf4b55b8fbdcfbcbb7a48e20b7f01df58b8de..f23ca9773da75b80ec07304e431c4dfec47fa8e8 100644
--- a/mhcflurry/class1_neural_network.py
+++ b/mhcflurry/class1_neural_network.py
@@ -32,8 +32,7 @@ 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.
+ Both single allele and pan-allele prediction are supported.
Users will generally use Class1AffinityPredictor, which gives a higher-level
interface and supports ensembles.
@@ -269,12 +268,30 @@ class Class1NeuralNetwork(object):
return self._network
def update_network_description(self):
+ """
+ Update self.network_json and self.network_weights properties based on
+ this instances's neural network.
+ """
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):
+ """
+ Given a Keras JSON description of a neural network, return a key that
+ uniquely defines this network. Networks that share the same key should
+ have compatible weights matrices and give the same prediction outputs
+ when their weights are the same.
+
+ Parameters
+ ----------
+ network_json : string
+
+ Returns
+ -------
+ string
+ """
# As an optimization, we remove anything about regularization as these
# do not affect predictions.
def drop_properties(d):
@@ -427,6 +444,9 @@ class Class1NeuralNetwork(object):
Returns
-------
(numpy.array, numpy.array)
+
+ Indices and allele representations.
+
"""
return (
allele_encoding.indices,
@@ -444,11 +464,13 @@ class Class1NeuralNetwork(object):
Parameters
----------
- method
-
- Returns
- -------
-
+ network : keras.Model
+ x_dict : dict of string -> numpy.ndarray
+ Training data as would be passed keras.Model.fit().
+ method : string
+ Initialization method. Currently only "lsuv" is supported.
+ verbose : int
+ Status updates printed to stdout if verbose > 0
"""
if verbose:
print("Performing data-dependent init: ", method)
@@ -479,27 +501,32 @@ class Class1NeuralNetwork(object):
Fit using a generator. Does not support many of the features of fit(),
such as random negative peptides.
+ Fitting proceeds until early stopping is hit, using the peptides,
+ affinities, etc. given by the parameters starting with "validation_".
+
+ This is used for pre-training pan-allele models using data synthesized
+ by the allele-specific models.
+
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
- -------
-
+ validation_peptide_encoding : EncodableSequences
+ validation_affinities : list of float
+ validation_allele_encoding : AlleleEncoding
+ validation_inequalities : list of string
+ validation_output_indices : list of int
+ steps_per_epoch : int
+ epochs : int
+ min_epochs : int
+ patience : int
+ min_delta : float
+ verbose : int
+ progress_callback : thunk
+ progress_preamble : string
+ progress_print_interval : float
"""
- import keras
from keras import backend as K
fit_info = collections.defaultdict(list)
@@ -668,7 +695,7 @@ class Class1NeuralNetwork(object):
affinities : list of float
nM affinities. Must be same length of as peptides.
- allele_encoding : AlleleEncoding, optional
+ allele_encoding : AlleleEncoding
If not specified, the model will be a single-allele predictor.
inequalities : list of string, each element one of ">", "<", or "=".
@@ -676,21 +703,27 @@ class Class1NeuralNetwork(object):
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
+ mse_with_ineqalities). If None all inequalities are taken to be "=".
+
+ output_indices : list of int
+ For multi-output models only. Same length as affinities. Indicates
+ the index of the output (starting from 0) for each training example.
+
+ sample_weights : list of float
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
+ shuffle_permutation : list of int
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_callback : function
+ No-argument function to call after each epoch.
+
progress_preamble : string
Optional string of information to include in each progress update
@@ -752,8 +785,8 @@ class Class1NeuralNetwork(object):
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.
+ # 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]
@@ -810,7 +843,9 @@ class Class1NeuralNetwork(object):
if loss.supports_inequalities:
# Do not sample negative affinities: just use an inequality.
- random_negative_ic50 = self.hyperparameters['random_negative_affinity_min']
+ random_negative_ic50 = self.hyperparameters[
+ 'random_negative_affinity_min'
+ ]
random_negative_target = from_ic50(random_negative_ic50)
y_dict_with_random_negatives = {
@@ -1018,9 +1053,10 @@ class Class1NeuralNetwork(object):
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.
+ 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. The cache is used
+ only for allele-specific models (i.e. when allele_encoding is None).
Parameters
----------
@@ -1032,6 +1068,10 @@ class Class1NeuralNetwork(object):
batch_size : int
batch_size passed to Keras
+ output_index : int or None
+ For multi-output models. Gives the output index to return. If set to
+ None, then all outputs are returned as a samples x outputs matrix.
+
Returns
-------
numpy.array of nM affinity predictions
@@ -1070,7 +1110,7 @@ class Class1NeuralNetwork(object):
Merge multiple models at the tensorflow (or other backend) level.
Only certain neural network architectures support merging. Others will
- throw NotImplementedError.
+ result in a NotImplementedError.
Parameters
----------
@@ -1091,12 +1131,6 @@ class Class1NeuralNetwork(object):
if len(models) == 1:
return models[0]
-
- # Copy models since we are going to mutate their underlying networks
- models = [
- pickle.loads(pickle.dumps(model, protocol=pickle.HIGHEST_PROTOCOL))
- for model in models
- ]
assert len(models) > 1
result = Class1NeuralNetwork(**dict(models[0].hyperparameters))
@@ -1118,13 +1152,13 @@ class Class1NeuralNetwork(object):
for network in networks
]
- pan_allele_layer_names1 = [
+ pan_allele_layer_names = [
'allele', 'peptide', 'allele_representation', 'flattened_0',
'allele_flat', 'allele_peptide_merged', 'dense_0', 'dropout_0',
'dense_1', 'dropout_1', 'output',
]
- if all(names == pan_allele_layer_names1 for names in layer_names):
+ if all(names == pan_allele_layer_names for names in layer_names):
# Merging an ensemble of pan-allele architectures
network = networks[0]
peptide_input = Input(
@@ -1146,7 +1180,7 @@ class Class1NeuralNetwork(object):
sub_networks = []
for (i, network) in enumerate(networks):
layers = network.layers[
- pan_allele_layer_names1.index("allele_peptide_merged") + 1:
+ pan_allele_layer_names.index("allele_peptide_merged") + 1:
]
node = allele_peptide_merged
for layer in layers:
@@ -1176,7 +1210,6 @@ class Class1NeuralNetwork(object):
layer_names)
return result
-
def make_network(
self,
peptide_encoding,
@@ -1197,7 +1230,7 @@ class Class1NeuralNetwork(object):
num_outputs=1,
allele_representations=None):
"""
- Helper function to make a keras network for class1 affinity prediction.
+ Helper function to make a keras network for class 1 affinity prediction.
"""
# We import keras here to avoid tensorflow debug output, etc. unless we
@@ -1314,12 +1347,24 @@ class Class1NeuralNetwork(object):
def set_allele_representations(self, allele_representations):
"""
+ Set the allele representations in use by this model. This means mutating
+ the weights for the allele input embedding layer.
+
+ Rationale: instead of passing in the allele sequence for each data point
+ during model training or prediction (which is expensive in terms of
+ memory usage), we pass in an allele index between 0 and n-1 where n is
+ the number of alleles in some universe of possible alleles. This index
+ is used in the model to lookup the corresponding allele sequence. This
+ function sets the lookup table.
+
+ See also: AlleleEncoding.allele_representations()
Parameters
----------
- model
- allele_representations
-
+ allele_representations : numpy.ndarray of shape (a, l, m)
+ where a is the total number of alleles,
+ l is the allele sequence length,
+ m is the length of the vectors used to represent amino acids
"""
from keras.models import model_from_json
reshaped = allele_representations.reshape(