diff --git a/mhcflurry/class1_ligandome_predictor.py b/mhcflurry/class1_ligandome_predictor.py
index 2dc4350932844c8f3e7c395397fd21c0f438e7ec..685f2083ade3af8734e2bdd71a94515724943b1f 100644
--- a/mhcflurry/class1_ligandome_predictor.py
+++ b/mhcflurry/class1_ligandome_predictor.py
@@ -1,5 +1,8 @@
+from __future__ import print_function
+
import time
import collections
+from functools import partial
import numpy
@@ -43,7 +46,8 @@ class Class1LigandomePredictor(object):
"""
compile_hyperparameter_defaults = HyperparameterDefaults(
- loss="custom:mse_with_inequalities",
+ loss_delta=0.2,
+ loss_alpha=None,
optimizer="rmsprop",
learning_rate=None,
)
@@ -146,18 +150,26 @@ class Class1LigandomePredictor(object):
return network
@staticmethod
- def loss(y_true, y_pred, delta=0.2):
+ def loss(y_true, y_pred, delta=0.2, alpha=None):
"""
Loss function for ligandome prediction.
"""
import tensorflow as tf
+ import keras.backend as K
y_pred = tf.squeeze(y_pred, axis=-1)
y_true = tf.reshape(tf.cast(y_true, tf.bool), (-1,))
pos = tf.boolean_mask(y_pred, y_true)
- pos_max = tf.reduce_max(pos, axis=1)
+ if alpha is None:
+ pos_max = tf.reduce_max(pos, axis=1)
+ else:
+ # Smooth maximum
+ exp_alpha_x = tf.exp(alpha * pos)
+ numerator = tf.reduce_sum(tf.multiply(pos, exp_alpha_x), axis=1)
+ denominator = tf.reduce_sum(exp_alpha_x, axis=1)
+ pos_max = numerator / denominator
neg = tf.boolean_mask(y_pred, tf.logical_not(y_true))
result = tf.reduce_sum(
tf.maximum(0.0, tf.reshape(neg, (-1, 1)) - pos_max + delta) ** 2)
@@ -246,7 +258,10 @@ class Class1LigandomePredictor(object):
self.set_allele_representations(allele_representations)
self.network.compile(
- loss=self.loss,
+ loss=partial(
+ self.loss,
+ delta=self.hyperparameters['loss_delta'],
+ alpha=self.hyperparameters['loss_alpha']),
optimizer=self.hyperparameters['optimizer'])
if self.hyperparameters['learning_rate'] is not None:
K.set_value(
diff --git a/test/test_class1_ligandome_predictor.py b/test/test_class1_ligandome_predictor.py
index 7fb5cacf44a36c97e9ab62b1fc4973d6d4761a30..03f31081ebcf93c34edbbc071b39556d53562027 100644
--- a/test/test_class1_ligandome_predictor.py
+++ b/test/test_class1_ligandome_predictor.py
@@ -109,54 +109,74 @@ def evaluate_loss(loss, y_true, y_pred):
def test_loss():
- delta = 0.4
-
- # Hit labels
- y_true = [
- 1.0,
- 0.0,
- 1.0,
- 1.0,
- 0.0
- ]
- y_true = numpy.array(y_true)
- y_pred = [
- [0.3, 0.7, 0.5],
- [0.2, 0.4, 0.6],
- [0.1, 0.5, 0.3],
- [0.1, 0.7, 0.1],
- [0.8, 0.2, 0.4],
- ]
- y_pred = numpy.array(y_pred)
-
- # reference implementation 1
- contributions = []
- for i in range(len(y_true)):
- if y_true[i] == 1.0:
- for j in range(len(y_true)):
- if y_true[j] == 0.0:
- tightest_i = max(y_pred[i])
- contribution = sum(
- max(0, y_pred[j, k] - tightest_i + delta)**2
- for k in range(y_pred.shape[1])
- )
- contributions.append(contribution)
- contributions = numpy.array(contributions)
- expected1 = contributions.sum()
-
- # reference implementation 2: numpy
- pos = y_pred[y_true.astype(bool)].max(1)
- neg = y_pred[~y_true.astype(bool)]
- expected2 = (
- numpy.maximum(0, neg.reshape((-1, 1)) - pos + delta)**2).sum()
-
- numpy.testing.assert_almost_equal(expected1, expected2)
-
- computed = evaluate_loss(
- partial(Class1LigandomePredictor.loss, delta=delta),
- y_true,
- y_pred.reshape(y_pred.shape + (1,)))
- numpy.testing.assert_almost_equal(computed, expected1)
+ for delta in [0.0, 0.3]:
+ for alpha in [None, 1.0, 20.0]:
+ print("delta", delta)
+ print("alpha", alpha)
+ # Hit labels
+ y_true = [
+ 1.0,
+ 0.0,
+ 1.0,
+ 1.0,
+ 0.0
+ ]
+ y_true = numpy.array(y_true)
+ y_pred = [
+ [0.3, 0.7, 0.5],
+ [0.2, 0.4, 0.6],
+ [0.1, 0.5, 0.3],
+ [0.1, 0.7, 0.1],
+ [0.8, 0.2, 0.4],
+ ]
+ y_pred = numpy.array(y_pred)
+
+ # reference implementation 1
+
+ def smooth_max(x, alpha):
+ x = numpy.array(x)
+ alpha = numpy.array([alpha])
+ return (x * numpy.exp(x * alpha)).sum() / (
+ numpy.exp(x * alpha)).sum()
+
+ if alpha is None:
+ max_func = max
+ else:
+ max_func = partial(smooth_max, alpha=alpha)
+
+ contributions = []
+ for i in range(len(y_true)):
+ if y_true[i] == 1.0:
+ for j in range(len(y_true)):
+ if y_true[j] == 0.0:
+ tightest_i = max_func(y_pred[i])
+ contribution = sum(
+ max(0, y_pred[j, k] - tightest_i + delta)**2
+ for k in range(y_pred.shape[1])
+ )
+ contributions.append(contribution)
+ contributions = numpy.array(contributions)
+ expected1 = contributions.sum()
+
+ # reference implementation 2: numpy
+ pos = numpy.array([
+ max_func(y_pred[i])
+ for i in range(len(y_pred))
+ if y_true[i] == 1.0
+ ])
+
+ neg = y_pred[~y_true.astype(bool)]
+ expected2 = (
+ numpy.maximum(0, neg.reshape((-1, 1)) - pos + delta)**2).sum()
+
+ yield numpy.testing.assert_almost_equal, expected1, expected2, 4
+
+ computed = evaluate_loss(
+ partial(Class1LigandomePredictor.loss, delta=delta, alpha=alpha),
+ y_true,
+ y_pred.reshape(y_pred.shape + (1,)))
+
+ yield numpy.testing.assert_almost_equal, computed, expected1, 4
AA_DIST = pandas.Series(