diff --git a/mhcflurry/class1_ligandome_predictor.py b/mhcflurry/class1_ligandome_predictor.py
index 5be86637fa093e5325db2bb82cbcf00e2955ccde..74a9e1db4715657448e3cfe4d1fc6f8eb5c139f2 100644
--- a/mhcflurry/class1_ligandome_predictor.py
+++ b/mhcflurry/class1_ligandome_predictor.py
@@ -140,88 +140,29 @@ class Class1LigandomePredictor(object):
name="ligandome",
)
#print('trainable', network.get_layer("td_dense_0").trainable)
- network.get_layer("td_dense_0").trainable = False
+ #network.get_layer("td_dense_0").trainable = False
#print('trainable', network.get_layer("td_dense_0").trainable)
return network
@staticmethod
- def loss(y_true, y_pred, lmbda=0.001):
- import keras.backend as K
+ def loss(y_true, y_pred, delta=0.2):
+ """
+ Loss function for ligandome prediction.
+ """
import tensorflow as tf
y_pred = tf.squeeze(y_pred, axis=-1)
-
- #y_pred = tf.Print(y_pred, [y_pred, tf.shape(y_pred)], "y_pred", summarize=20)
- #y_true = tf.Print(y_true, [y_true, tf.shape(y_true)], "y_true", summarize=20)
-
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)
- #pos_max = tf.reduce_logsumexp(tf.boolean_mask(y_pred, y_true), axis=1)
+ #pos_max = tf.reduce_max(pos, axis=1)
+
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)**2)
-
- term2 = tf.reduce_sum(
- tf.minimum(0.0, tf.reshape(neg, (-1, 1)) - pos_max))
- result = result + lmbda * term2
-
- #differences = tf.reshape(neg, (-1, 1)) - pos
-
- #result = tf.reduce_sum(tf.sign(differences) * differences**2)
- #result = tf.Print(result, [result], "result", summarize=20)
-
- #term2 = lmbda * tf.reduce_mean((1 - pos)**2)
- #result = result + term2
+ tf.maximum(0.0, tf.reshape(neg, (-1, 1)) - pos_max + delta) ** 2)
return result
- """
- pos = tf.boolean_mask(y_pred, y_true)
-
- pos = y_pred[y_true.astype(bool)].max(1)
- neg = y_pred[~y_true.astype(bool)]
- expected2 = (numpy.maximum(0,
- neg.flatten().reshape((-1, 1)) - pos) ** 2).sum()
- """
-
-
-
-
- @staticmethod
- def loss_old(y_true, y_pred):
- """Binary cross entropy after taking logsumexp over predictions"""
- import keras.backend as K
- import tensorflow as tf
- #y_pred_aggregated = K.logsumexp(y_pred, axis=1, keepdims=True)
- #y_pred_aggregated = K.sigmoid(y_pred_aggregated)
- #y_pred = tf.Print(y_pred, [y_pred], "y_pred", summarize=20)
- #y_true = tf.Print(y_true, [y_true], "y_true", summarize=20)
-
- y_pred_aggregated = K.max(y_pred, axis=1, keepdims=False)
- #y_pred_aggregated = tf.Print(y_pred_aggregated, [y_pred_aggregated], "y_pred_aggregated",
- # summarize=20)
-
- y_true = K.squeeze(K.cast(y_true, y_pred_aggregated.dtype), axis=-1)
- #print("SHAPES", y_pred, K.int_shape(y_pred), y_pred_aggregated, K.int_shape(y_pred_aggregated), y_true, K.int_shape(y_true))
- #K.print_tensor(y_pred_aggregated, "y_pred_aggregated")
- #K.print_tensor(y_true, "y_true")
-
- #y_pred_aggregated = K.print_tensor(y_pred_aggregated, "y_pred_aggregated")
-
-
- #y_true = K.print_tensor(y_true, "y_true")
-
- #return K.mean(
- # K.binary_crossentropy(y_true, y_pred_aggregated),
- # axis=-1)
- return K.mean(
- (y_true - y_pred_aggregated)**2,
- axis=-1
- )
-
def peptides_to_network_input(self, peptides):
"""
Encode peptides to the fixed-length encoding expected by the neural
diff --git a/test/test_class1_ligandome_predictor.py b/test/test_class1_ligandome_predictor.py
index 411d9c066898c63373ca89439810f657fa5dfbb6..7fb5cacf44a36c97e9ab62b1fc4973d6d4761a30 100644
--- a/test/test_class1_ligandome_predictor.py
+++ b/test/test_class1_ligandome_predictor.py
@@ -21,6 +21,7 @@ logging.getLogger('matplotlib').disabled = True
import pandas
import argparse
import sys
+from functools import partial
from numpy.testing import assert_, assert_equal, assert_allclose
import numpy
@@ -107,7 +108,9 @@ def evaluate_loss(loss, y_true, y_pred):
raise ValueError("Unsupported backend: %s" % K.backend())
-def Xtest_loss():
+def test_loss():
+ delta = 0.4
+
# Hit labels
y_true = [
1.0,
@@ -134,7 +137,7 @@ def Xtest_loss():
if y_true[j] == 0.0:
tightest_i = max(y_pred[i])
contribution = sum(
- max(0, y_pred[j, k] - tightest_i)**2
+ max(0, y_pred[j, k] - tightest_i + delta)**2
for k in range(y_pred.shape[1])
)
contributions.append(contribution)
@@ -145,12 +148,12 @@ def Xtest_loss():
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)**2).sum()
+ numpy.maximum(0, neg.reshape((-1, 1)) - pos + delta)**2).sum()
numpy.testing.assert_almost_equal(expected1, expected2)
computed = evaluate_loss(
- Class1LigandomePredictor.loss,
+ partial(Class1LigandomePredictor.loss, delta=delta),
y_true,
y_pred.reshape(y_pred.shape + (1,)))
numpy.testing.assert_almost_equal(computed, expected1)
@@ -197,20 +200,16 @@ def make_motif(allele, peptides, frac=0.01):
peptides=peptides,
allele=allele,
)
-
random_predictions_df = pandas.DataFrame({"peptide": peptides.sequences})
random_predictions_df["prediction"] = predictions
random_predictions_df = random_predictions_df.sort_values(
"prediction", ascending=True)
- #print("Random peptide predictions", allele)
- #print(random_predictions_df)
top = random_predictions_df.iloc[:int(len(random_predictions_df) * frac)]
matrix = positional_frequency_matrix(top.peptide.values)
- #print("Matrix")
return matrix
-def test_synthetic_allele_refinement():
+def test_synthetic_allele_refinement(max_epochs=10):
refine_allele = "HLA-C*01:02"
alleles = [
"HLA-A*02:01", "HLA-B*27:01", "HLA-C*07:01",
@@ -266,8 +265,8 @@ def test_synthetic_allele_refinement():
predictor = Class1LigandomePredictor(
PAN_ALLELE_PREDICTOR_NO_MASS_SPEC,
max_ensemble_size=1,
- max_epochs=10,
- learning_rate=0.00001,
+ max_epochs=max_epochs,
+ learning_rate=0.0001,
patience=5,
min_delta=0.0)
@@ -295,8 +294,6 @@ def test_synthetic_allele_refinement():
pre_auc = roc_auc_score(train_df.hit.values, train_df.pre_max_prediction.values)
print("PRE_AUC", pre_auc)
- #import ipdb ; ipdb.set_trace()
-
assert_allclose(pre_predictions, expected_pre_predictions)
motifs_history = []
@@ -396,13 +393,19 @@ parser.add_argument(
"--out-motifs-pickle",
default=None,
help="Metrics output")
+parser.add_argument(
+ "--max-epochs",
+ default=100,
+ type=int,
+ help="Max epochs")
if __name__ == '__main__':
# If run directly from python, leave the user in a shell to explore results.
setup()
args = parser.parse_args(sys.argv[1:])
- (predictor, predictions, metrics, motifs) = test_synthetic_allele_refinement()
+ (predictor, predictions, metrics, motifs) = (
+ test_synthetic_allele_refinement(max_epochs=args.max_epochs))
if args.out_metrics_csv:
metrics.to_csv(args.out_metrics_csv)