From b5f9a292a07597f2b36d161a0f598dd6ad1a53be Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 5 Dec 2019 14:30:21 -0500
Subject: [PATCH] fixes
---
mhcflurry/batch_generator.py | 3 +
.../class1_presentation_neural_network.py | 64 +++++++++++--------
mhcflurry/class1_presentation_predictor.py | 11 +---
mhcflurry/custom_loss.py | 58 ++++++++---------
requirements.txt | 2 +-
test/test_class1_presentation_predictor.py | 22 +++++--
6 files changed, 86 insertions(+), 74 deletions(-)
diff --git a/mhcflurry/batch_generator.py b/mhcflurry/batch_generator.py
index 96c1a15d..4a0abb40 100644
--- a/mhcflurry/batch_generator.py
+++ b/mhcflurry/batch_generator.py
@@ -58,9 +58,12 @@ class BatchPlan(object):
for indices in self.batch_indices_generator(epochs=epochs):
batch_x_dict = {}
for (item, value) in x_dict.items():
+ assert not numpy.isnan(value[indices]).any(), (item, value)
batch_x_dict[item] = value[indices]
batch_y_list = []
for value in y_list:
+ assert not numpy.isnan(value[indices]).any(), (
+ len(batch_y_list), value)
batch_y_list.append(value[indices])
yield (batch_x_dict, batch_y_list)
diff --git a/mhcflurry/class1_presentation_neural_network.py b/mhcflurry/class1_presentation_neural_network.py
index 6ac7b1a2..5c33f1f3 100644
--- a/mhcflurry/class1_presentation_neural_network.py
+++ b/mhcflurry/class1_presentation_neural_network.py
@@ -17,10 +17,7 @@ from .random_negative_peptides import RandomNegativePeptides
from .allele_encoding import MultipleAlleleEncoding, AlleleEncoding
from .auxiliary_input import AuxiliaryInputEncoder
from .batch_generator import MultiallelicMassSpecBatchGenerator
-from .custom_loss import (
- MSEWithInequalities,
- MultiallelicMassSpecLoss,
- ZeroLoss)
+from .custom_loss import MSEWithInequalities, MultiallelicMassSpecLoss
class Class1PresentationNeuralNetwork(object):
@@ -97,6 +94,7 @@ class Class1PresentationNeuralNetwork(object):
Dense,
Flatten,
RepeatVector,
+ Reshape,
concatenate,
Activation,
Lambda,
@@ -178,13 +176,18 @@ class Class1PresentationNeuralNetwork(object):
node = layer(input_nodes)
layer_name_to_new_node[layer.name] = node
+ node = Reshape(
+ (self.hyperparameters['max_alleles'],),
+ name="unmasked_affinity_matrix_output")(node)
+
pre_mask_affinity_predictor_matrix_output = node
# Apply allele mask: zero out all outputs corresponding to alleles
# with the special index 0.
def alleles_to_mask(x):
import keras.backend as K
- return K.cast(K.expand_dims(K.not_equal(x, 0.0)), "float32")
+ result = K.cast(K.not_equal(x, 0), "float32")
+ return result
allele_mask = Lambda(alleles_to_mask, name="allele_mask")(input_alleles)
@@ -193,12 +196,10 @@ class Class1PresentationNeuralNetwork(object):
allele_mask,
pre_mask_affinity_predictor_matrix_output
])
-
- # First allele (i.e. the first column of the alleles matrix) is given
- # its own output. This is used for the affinity prediction loss.
- affinity_predictor_output = Lambda(
- lambda x: x[:, 0], name="affinity_output")(
- affinity_predictor_matrix_output)
+ node = Reshape(
+ (self.hyperparameters['max_alleles'], 1),
+ name="expand_dims_affinity_matrix_output")(
+ affinity_predictor_matrix_output)
auxiliary_input = None
if self.hyperparameters['auxiliary_input_features']:
@@ -228,13 +229,20 @@ class Class1PresentationNeuralNetwork(object):
bias_initializer=Zeros())
lifted = TimeDistributed(layer, name="presentation_adjustment")
presentation_adjustment = lifted(node)
+ presentation_adjustment = Reshape(
+ target_shape=(self.hyperparameters['max_alleles'],),
+ name="reshaped_presentation_adjustment")(presentation_adjustment)
+
def logit(x):
import tensorflow as tf
- return - tf.log(1. / x - 1.)
+ return - tf.log(
+ tf.maximum(
+ tf.math.divide_no_nan(1., x) - 1.,
+ 0.0))
presentation_output_pre_sigmoid = Add()([
- Lambda(logit)(affinity_predictor_matrix_output),
+ Lambda(logit, name="logit")(affinity_predictor_matrix_output),
presentation_adjustment,
])
pre_mask_presentation_output = Activation(
@@ -254,9 +262,8 @@ class Class1PresentationNeuralNetwork(object):
input_alleles,
] + ([] if auxiliary_input is None else [auxiliary_input]),
outputs=[
- affinity_predictor_output,
+ affinity_predictor_matrix_output,
presentation_output,
- affinity_predictor_matrix_output
],
name="presentation",
)
@@ -405,7 +412,12 @@ class Class1PresentationNeuralNetwork(object):
y1,
])
- affinities_loss = MSEWithInequalities()
+ def keras_max(matrix):
+ import keras.backend as K
+ result = K.max(matrix, axis=1)
+ return result
+
+ affinities_loss = MSEWithInequalities(transform_function=keras_max)
encoded_y1 = affinities_loss.encode_y(
y1_with_random_negatives,
inequalities=adjusted_inequalities_with_random_negative)
@@ -426,9 +438,12 @@ class Class1PresentationNeuralNetwork(object):
allele_representations_hash = self.set_allele_representations(
allele_representations)
+ loss_reduction = "none"
self.network.compile(
- loss=[affinities_loss.keras_wrapped(), mms_loss.keras_wrapped(), ZeroLoss().keras_wrapped()],
- #loss_weights=[1.0, 1.0, 1.0],
+ loss=[
+ affinities_loss.get_keras_loss(reduction=loss_reduction),
+ mms_loss.get_keras_loss(reduction=loss_reduction),
+ ],
optimizer=self.hyperparameters['optimizer'])
if self.hyperparameters['learning_rate'] is not None:
K.set_value(
@@ -527,7 +542,7 @@ class Class1PresentationNeuralNetwork(object):
(train_generator, test_generator) = (
batch_generator.get_train_and_test_generators(
x_dict=x_dict_with_random_negatives,
- y_list=[encoded_y1, encoded_y2, encoded_y2],
+ y_list=[encoded_y1, encoded_y2],
epochs=1))
self.assert_allele_representations_hash(allele_representations_hash)
fit_history = self.network.fit_generator(
@@ -547,7 +562,6 @@ class Class1PresentationNeuralNetwork(object):
fit_info[key].extend(value)
if numpy.isnan(fit_info['loss'][-1]):
- import ipdb ; ipdb.set_trace()
raise ValueError("NaN loss")
# Print progress no more often than once every few seconds.
@@ -567,7 +581,6 @@ class Class1PresentationNeuralNetwork(object):
last_progress_print = time.time()
if batch_generator.num_test_batches:
- #import ipdb ; ipdb.set_trace()
val_loss = fit_info['val_loss'][-1]
if min_val_loss is None or (
val_loss < min_val_loss -
@@ -609,7 +622,7 @@ class Class1PresentationNeuralNetwork(object):
Predictions = collections.namedtuple(
"ligandone_neural_network_predictions",
- "score affinity")
+ "affinity score")
def predict(
self,
@@ -638,11 +651,8 @@ class Class1PresentationNeuralNetwork(object):
feature_parameters=self.hyperparameters[
'auxiliary_input_feature_parameters'])
- predictions = self.Predictions._make([
- numpy.squeeze(output)
- for output in self.network.predict(
- x_dict, batch_size=batch_size)[1:]
- ])
+ predictions = self.Predictions._make(
+ self.network.predict(x_dict, batch_size=batch_size))
return predictions
def set_allele_representations(self, allele_representations):
diff --git a/mhcflurry/class1_presentation_predictor.py b/mhcflurry/class1_presentation_predictor.py
index 42c317e9..4a4f3bc6 100644
--- a/mhcflurry/class1_presentation_predictor.py
+++ b/mhcflurry/class1_presentation_predictor.py
@@ -18,24 +18,15 @@ import pandas
import mhcnames
-from .hyperparameters import HyperparameterDefaults
from .version import __version__
from .class1_neural_network import Class1NeuralNetwork, DEFAULT_PREDICT_BATCH_SIZE
from .encodable_sequences import EncodableSequences
from .regression_target import from_ic50, to_ic50
-from .random_negative_peptides import RandomNegativePeptides
-from .allele_encoding import MultipleAlleleEncoding, AlleleEncoding
-from .auxiliary_input import AuxiliaryInputEncoder
-from .batch_generator import MultiallelicMassSpecBatchGenerator
-from .custom_loss import (
- MSEWithInequalities,
- MultiallelicMassSpecLoss,
- ZeroLoss)
+from .allele_encoding import MultipleAlleleEncoding
from .downloads import get_default_class1_presentation_models_dir
from .class1_presentation_neural_network import Class1PresentationNeuralNetwork
from .common import save_weights, load_weights
-
class Class1PresentationPredictor(object):
def __init__(
self,
diff --git a/mhcflurry/custom_loss.py b/mhcflurry/custom_loss.py
index e6f72264..6132ddf6 100644
--- a/mhcflurry/custom_loss.py
+++ b/mhcflurry/custom_loss.py
@@ -57,13 +57,15 @@ class Loss(object):
def __str__(self):
return "<Loss: %s>" % self.name
- def keras_wrapped(self, reduction="sum_over_batch_size"):
+ def loss(self, y_true, y_pred):
+ raise NotImplementedError()
+
+ def get_keras_loss(self, reduction="sum_over_batch_size"):
from keras.losses import LossFunctionWrapper
return LossFunctionWrapper(
self.loss, reduction=reduction, name=self.name)
-
class StandardKerasLoss(Loss):
"""
A loss function supported by Keras, such as MSE.
@@ -109,6 +111,9 @@ class MSEWithInequalities(Loss):
supports_inequalities = True
supports_multiple_outputs = False
+ def __init__(self, transform_function=None):
+ self.transform_function = transform_function
+
@staticmethod
def encode_y(y, inequalities=None):
y = array(y, dtype="float32")
@@ -133,13 +138,17 @@ class MSEWithInequalities(Loss):
assert not isnan(encoded).any()
return encoded
- @staticmethod
- def loss(y_true, y_pred):
+ def loss(self, y_true, y_pred):
# We always delay import of Keras so that mhcflurry can be imported
# initially without tensorflow debug output, etc.
from keras import backend as K
import tensorflow as tf
+ if self.transform_function:
+ y_pred = self.transform_function(y_pred)
+
+ y_true = K.squeeze(y_true, axis=-1)
+
# Handle (=) inequalities
diff1 = y_pred - y_true
diff1 *= K.cast(y_true >= 0.0, "float32")
@@ -160,7 +169,10 @@ class MSEWithInequalities(Loss):
K.sum(K.square(diff1)) +
K.sum(K.square(diff2)) +
K.sum(K.square(diff3))) / K.cast(K.shape(y_pred)[0], "float32")
- return tf.where(tf.is_nan(result), tf.zeros_like(result), result)
+
+ return result
+
+ #return tf.where(tf.is_nan(result), tf.zeros_like(result), result)
class MSEWithInequalitiesAndMultipleOutputs(Loss):
@@ -188,6 +200,9 @@ class MSEWithInequalitiesAndMultipleOutputs(Loss):
supports_inequalities = True
supports_multiple_outputs = True
+ def __init__(self, transform_function=None):
+ self.transform_function = transform_function
+
@staticmethod
def encode_y(y, inequalities=None, output_indices=None):
y = array(y, dtype="float32")
@@ -211,10 +226,12 @@ class MSEWithInequalitiesAndMultipleOutputs(Loss):
return encoded
- @staticmethod
- def loss(y_true, y_pred):
+ def loss(self, y_true, y_pred):
from keras import backend as K
+ if self.transform_function:
+ y_pred = self.transform_function(y_pred)
+
y_true = K.flatten(y_true)
output_indices = y_true // 10
@@ -236,7 +253,7 @@ class MSEWithInequalitiesAndMultipleOutputs(Loss):
# ], axis=-1)
#updated_y_pred = tf.gather_nd(y_pred, indexer)
- return MSEWithInequalities.loss(updated_y_true, updated_y_pred)
+ return MSEWithInequalities().loss(updated_y_true, updated_y_pred)
class MultiallelicMassSpecLoss(Loss):
@@ -261,10 +278,7 @@ class MultiallelicMassSpecLoss(Loss):
def loss(self, y_true, y_pred):
import tensorflow as tf
-
- y_pred = tf.squeeze(y_pred, axis=-1)
y_true = tf.reshape(y_true, (-1,))
-
pos = tf.boolean_mask(y_pred, tf.math.equal(y_true, 1.0))
pos_max = tf.reduce_max(pos, axis=1)
neg = tf.boolean_mask(y_pred, tf.math.equal(y_true, 0.0))
@@ -274,23 +288,6 @@ class MultiallelicMassSpecLoss(Loss):
return tf.where(tf.is_nan(result), 0.0, result)
-class ZeroLoss(Loss):
- """
- """
- name = "zero_loss"
- supports_inequalities = False
- supports_multiple_outputs = False
-
- @staticmethod
- def encode_y(y):
- return y
-
- @staticmethod
- def loss(y_true, y_pred):
- import keras.backend as K
- return K.constant(0.0)
-
-
def check_shape(name, arr, expected_shape):
"""
Raise ValueError if arr.shape != expected_shape.
@@ -308,7 +305,10 @@ def check_shape(name, arr, expected_shape):
# Register custom losses.
-for cls in [MSEWithInequalities, MSEWithInequalitiesAndMultipleOutputs, MultiallelicMassSpecLoss, ZeroLoss]:
+for cls in [
+ MSEWithInequalities,
+ MSEWithInequalitiesAndMultipleOutputs,
+ MultiallelicMassSpecLoss]:
CUSTOM_LOSSES[cls.name] = cls()
diff --git a/requirements.txt b/requirements.txt
index 62b6d89b..c5ad8719 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,7 @@ six
numpy>=1.11
pandas>=0.20.3
Keras>=2.2.5
-tensorflow>=1.1.0,<2.0.0
+tensorflow>=1.15.0,<2.0.0
appdirs
scikit-learn
mhcnames
diff --git a/test/test_class1_presentation_predictor.py b/test/test_class1_presentation_predictor.py
index 7cf4480c..499b7bd0 100644
--- a/test/test_class1_presentation_predictor.py
+++ b/test/test_class1_presentation_predictor.py
@@ -91,13 +91,16 @@ def teardown():
cleanup()
-def Xtest_basic():
+def test_basic():
affinity_predictor = PAN_ALLELE_PREDICTOR_NO_MASS_SPEC
models = []
for affinity_network in affinity_predictor.class1_pan_allele_models:
presentation_network = Class1PresentationNeuralNetwork(
- max_epochs=1,
- learning_rate=0.0001,
+ optimizer="adam",
+ random_negative_rate=0.0,
+ random_negative_constant=0,
+ max_epochs=5,
+ learning_rate=0.001,
batch_generator_batch_size=256)
presentation_network.load_from_class1_neural_network(affinity_network)
print(presentation_network.network.get_config())
@@ -124,6 +127,8 @@ def Xtest_basic():
merged_df = pandas.merge(
df, df_predictor.set_index("peptide"), left_index=True, right_index=True)
+ print(merged_df)
+
assert_allclose(
merged_df["tightest_affinity"], merged_df["affinity"], rtol=1e-5)
assert_allclose(
@@ -157,7 +162,10 @@ def Xtest_basic():
# Test that fitting a model changes the predictor but not the original model
train_df = pandas.DataFrame({
- "peptide": random_peptides(256),
+ "peptide": numpy.concatenate([
+ random_peptides(256, length=length)
+ for length in range(8,16)
+ ]),
})
train_df["allele"] = "HLA-A*02:20"
train_df["experiment"] = "experiment1"
@@ -167,7 +175,7 @@ def Xtest_basic():
experiment_to_allele_list={
"experiment1": ["HLA-A*02:20", "HLA-A*02:01"],
},
- allele_to_sequence=predictor4.allele_to_sequence)
+ allele_to_sequence=predictor.allele_to_sequence)
model = predictor4.models[0]
new_predictor = Class1PresentationPredictor(
models=[model],
@@ -182,7 +190,7 @@ def Xtest_basic():
train_df.peptide.values,
alleles=["HLA-A*02:20"])
print(train_df)
- import ipdb ; ipdb.set_trace()
+ #import ipdb ; ipdb.set_trace()
def scramble_peptide(peptide):
@@ -447,7 +455,7 @@ def Xtest_real_data_multiallelic_refinement(max_epochs=10):
import ipdb ; ipdb.set_trace()
-def test_synthetic_allele_refinement_with_affinity_data(
+def Xtest_synthetic_allele_refinement_with_affinity_data(
max_epochs=10, include_affinities=False):
refine_allele = "HLA-C*01:02"
alleles = [
--
GitLab