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Commit b09ed3b4 authored by Tim O'Donnell's avatar Tim O'Donnell
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cleanup hyperparameters

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mhcflurry/class1_neural_network.py
+ 34
− 58
View file @ b09ed3b4
......@@ -9,7 +9,7 @@ import pandas
from .hyperparameters import HyperparameterDefaults
from .encodable_sequences import EncodableSequences
from .encodable_sequences import EncodableSequences, EncodingError
from .amino_acid import available_vector_encodings, vector_encoding_length
from .regression_target import to_ic50, from_ic50
from .common import random_peptides, amino_acid_distribution
......@@ -28,12 +28,15 @@ class Class1NeuralNetwork(object):
"""
network_hyperparameter_defaults = HyperparameterDefaults(
kmer_size=15,
peptide_amino_acid_encoding="BLOSUM62",
allele_amino_acid_encoding="BLOSUM62",
embedding_input_dim=21,
embedding_output_dim=8,
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="",
......@@ -45,7 +48,6 @@ class Class1NeuralNetwork(object):
output_activation="sigmoid",
dropout_probability=0.0,
batch_normalization=False,
embedding_init_method="glorot_uniform",
locally_connected_layers=[
{
"filters": 8,
......@@ -69,15 +71,6 @@ class Class1NeuralNetwork(object):
used.
"""
input_encoding_hyperparameter_defaults = HyperparameterDefaults(
alignment_method="pad_middle",
left_edge=4,
right_edge=4)
"""
Number of amino acid residues that are given fixed positions on the each
side in the variable length encoding.
"""
fit_hyperparameter_defaults = HyperparameterDefaults(
max_epochs=500,
validation_split=0.1,
......@@ -110,7 +103,6 @@ class Class1NeuralNetwork(object):
hyperparameter_defaults = network_hyperparameter_defaults.extend(
compile_hyperparameter_defaults).extend(
input_encoding_hyperparameter_defaults).extend(
fit_hyperparameter_defaults).extend(
early_stopping_hyperparameter_defaults).extend(
miscelaneous_hyperparameter_defaults
......@@ -132,6 +124,13 @@ class Class1NeuralNetwork(object):
"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
......@@ -375,22 +374,8 @@ class Class1NeuralNetwork(object):
numpy.array
"""
encoder = EncodableSequences.create(peptides)
if (self.hyperparameters['peptide_amino_acid_encoding'] == "embedding"):
encoded = encoder.variable_length_to_fixed_length_categorical(
max_length=self.hyperparameters['kmer_size'],
**self.input_encoding_hyperparameter_defaults.subselect(
self.hyperparameters))
elif (
self.hyperparameters['peptide_amino_acid_encoding'] in
available_vector_encodings()):
encoded = encoder.variable_length_to_fixed_length_vector_encoding(
self.hyperparameters['peptide_amino_acid_encoding'],
max_length=self.hyperparameters['kmer_size'],
**self.input_encoding_hyperparameter_defaults.subselect(
self.hyperparameters))
else:
raise ValueError("Unsupported peptide_amino_acid_encoding: %s" %
self.hyperparameters['peptide_amino_acid_encoding'])
encoded = encoder.variable_length_to_fixed_length_vector_encoding(
**self.hyperparameters['peptide_encoding'])
assert len(encoded) == len(peptides)
return encoded
......@@ -404,10 +389,16 @@ class Class1NeuralNetwork(object):
(int, int) tuple
"""
return (
self.hyperparameters['left_edge'] +
self.hyperparameters['right_edge'],
self.hyperparameters['kmer_size'])
# 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):
"""
......@@ -799,11 +790,8 @@ class Class1NeuralNetwork(object):
def make_network(
self,
kmer_size,
peptide_encoding,
allele_amino_acid_encoding,
peptide_amino_acid_encoding,
embedding_input_dim,
embedding_output_dim,
allele_dense_layer_sizes,
peptide_dense_layer_sizes,
peptide_allele_merge_method,
......@@ -816,7 +804,6 @@ class Class1NeuralNetwork(object):
output_activation,
dropout_probability,
batch_normalization,
embedding_init_method,
locally_connected_layers,
allele_representations=None):
"""
......@@ -832,23 +819,12 @@ class Class1NeuralNetwork(object):
from keras.layers.embeddings import Embedding
from keras.layers.normalization import BatchNormalization
if peptide_amino_acid_encoding == "embedding":
peptide_input = Input(
shape=(kmer_size,), dtype='int32', name='peptide')
current_layer = Embedding(
input_dim=embedding_input_dim,
output_dim=embedding_output_dim,
input_length=kmer_size,
embeddings_initializer=embedding_init_method,
name="peptide_embedding")(peptide_input)
else:
peptide_input = Input(
shape=(
kmer_size,
vector_encoding_length(peptide_amino_acid_encoding)),
dtype='float32',
name='peptide')
current_layer = peptide_input
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]
......
mhcflurry/encodable_sequences.py
+ 35
− 17
View file @ b09ed3b4
......@@ -13,6 +13,14 @@ import pandas
from . import amino_acid
class EncodingError(ValueError):
def __init__(self, message, supported_peptide_lengths):
self.supported_peptide_lengths = supported_peptide_lengths
ValueError.__init__(
self,
message + " Supported lengths: %s - %s." % supported_peptide_lengths)
class EncodableSequences(object):
"""
Sequences of amino acids.
......@@ -36,7 +44,7 @@ class EncodableSequences(object):
if not all(isinstance(obj, string_types) for obj in sequences):
raise ValueError("Sequence of strings is required")
self.sequences = numpy.array(sequences)
lengths = pandas.Series(self.sequences).str.len()
lengths = pandas.Series(self.sequences, dtype=numpy.object_).str.len()
self.min_length = lengths.min()
self.max_length = lengths.max()
......@@ -187,26 +195,23 @@ class EncodableSequences(object):
shape=(len(sequences), max_length),
dtype="int32")
df = pandas.DataFrame({"peptide": sequences})
df = pandas.DataFrame({"peptide": sequences}, dtype=numpy.object_)
df["length"] = df.peptide.str.len()
middle_length = max_length - left_edge - right_edge
min_length = left_edge + right_edge
# For efficiency we handle each supported peptide length using bulk
# array operations.
for (length, sub_df) in df.groupby("length"):
if length < left_edge + right_edge:
raise ValueError(
"Sequence '%s' (length %d) unsupported: length must be at "
"least %d. There are %d total peptides with this length." % (
sub_df.iloc[0].peptide, length, left_edge + right_edge,
len(sub_df)))
if length > max_length:
raise ValueError(
"Sequence '%s' (length %d) unsupported: length must be at "
"most %d. There are %d total peptides with this length." % (
sub_df.iloc[0].peptide, length, max_length,
len(sub_df)))
if length < min_length or length > max_length:
raise EncodingError(
"Sequence '%s' (length %d) unsupported. There are %d "
"total peptides with this length." % (
sub_df.iloc[0].peptide,
length,
len(sub_df)), supported_peptide_lengths=(
min_length, max_length))
# Array of shape (num peptides, length) giving fixed-length amino
# acid encoding each peptide of the current length.
......@@ -240,17 +245,30 @@ class EncodableSequences(object):
-right_edge:
] = fixed_length_sequences[:, -right_edge:]
elif alignment_method == "left_pad_right_pad":
# We arbitrarily set a minimum length of 5, although this encoding
# could handle smaller peptides.
min_length = 5
# Result array is int32, filled with X (null amino acid) value.
result = numpy.full(fill_value=amino_acid.AMINO_ACID_INDEX['X'],
shape=(len(sequences), max_length * 2), dtype="int32")
df = pandas.DataFrame({"peptide": sequences})
df = pandas.DataFrame({"peptide": sequences}, dtype=numpy.object_)
# For efficiency we handle each supported peptide length using bulk
# array operations.
for (length, sub_df) in df.groupby(df.peptide.str.len()):
# Array of shape (num peptides, length) giving fixed-length amino
# acid encoding each peptide of the current length.
if length < min_length or length > max_length:
raise EncodingError(
"Sequence '%s' (length %d) unsupported. There are %d "
"total peptides with this length." % (
sub_df.iloc[0].peptide,
length,
len(sub_df)), supported_peptide_lengths=(
min_length, max_length))
# Array of shape (num peptides, length) giving fixed-length
# amino acid encoding each peptide of the current length.
fixed_length_sequences = numpy.stack(sub_df.peptide.map(
lambda s: numpy.array(
[amino_acid.AMINO_ACID_INDEX[char] for char in
......
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