diff --git a/mhcflurry/class1_ligandome_neural_network.py b/mhcflurry/class1_presentation_neural_network.py
similarity index 99%
rename from mhcflurry/class1_ligandome_neural_network.py
rename to mhcflurry/class1_presentation_neural_network.py
index b61a0a56371ffebf2f47c812c1752df35ebacbe6..db3a25350476eabf937b8b29cceaeb6a65e188fa 100644
--- a/mhcflurry/class1_ligandome_neural_network.py
+++ b/mhcflurry/class1_presentation_neural_network.py
@@ -23,7 +23,7 @@ from .custom_loss import (
ZeroLoss)
-class Class1LigandomeNeuralNetwork(object):
+class Class1PresentationNeuralNetwork(object):
network_hyperparameter_defaults = HyperparameterDefaults(
allele_amino_acid_encoding="BLOSUM62",
peptide_encoding={
diff --git a/mhcflurry/class1_ligandome_predictor.py b/mhcflurry/class1_presentation_predictor.py
similarity index 98%
rename from mhcflurry/class1_ligandome_predictor.py
rename to mhcflurry/class1_presentation_predictor.py
index 7e36266831901d03d773c659c743dd7a65bf13c3..1471ad974dba121e5a6b07264c99f33febf32fcf 100644
--- a/mhcflurry/class1_ligandome_predictor.py
+++ b/mhcflurry/class1_presentation_predictor.py
@@ -33,7 +33,7 @@ from .custom_loss import (
ZeroLoss)
-class Class1LigandomePredictor(object):
+class Class1PresentationPredictor(object):
def __init__(
self,
class1_ligandome_neural_networks,
@@ -229,7 +229,7 @@ class Class1LigandomePredictor(object):
not exist it will be created.
The serialization format consists of a file called "manifest.csv" with
- the configurations of each Class1LigandomeNeuralNetwork, along with
+ the configurations of each Class1PresentationNeuralNetwork, along with
per-network files giving the model weights.
Parameters
@@ -321,8 +321,6 @@ class Class1LigandomePredictor(object):
"""
if models_dir is None:
models_dir = get_default_class1_models_dir()
- if optimization_level is None:
- optimization_level = OPTIMIZATION_LEVEL
manifest_path = join(models_dir, "manifest.csv")
manifest_df = pandas.read_csv(manifest_path, nrows=max_models)
diff --git a/test/test_class1_ligandome_predictor.py b/test/test_class1_presentation_predictor.py
similarity index 96%
rename from test/test_class1_ligandome_predictor.py
rename to test/test_class1_presentation_predictor.py
index 214a91a8e690f2a66580f0ee1fabb7f5bf393613..768d270a42df15df2fbdfa0341b5eb6b14951a03 100644
--- a/test/test_class1_ligandome_predictor.py
+++ b/test/test_class1_presentation_predictor.py
@@ -6,7 +6,7 @@ Idea:
possiblility is DLA-88*501:01 but human would be better
- generate synethetic multi-allele MS by combining single-allele MS for differnet
alleles, including the selected allele
-- train ligandome predictor based on the no-ms pan-allele models on theis
+- train presentation predictor based on the no-ms pan-allele models on theis
synthetic dataset
- see if the pan-allele predictor learns the "correct" motif for the selected
allele, i.e. updates to become more similar to the with-ms pan allele predictor.
@@ -33,7 +33,7 @@ from sklearn.metrics import roc_auc_score
from mhcflurry import Class1AffinityPredictor, Class1NeuralNetwork
from mhcflurry.allele_encoding import MultipleAlleleEncoding
-from mhcflurry.class1_ligandome_neural_network import Class1LigandomeNeuralNetwork
+from mhcflurry.class1_presentation_neural_network import Class1PresentationNeuralNetwork
from mhcflurry.encodable_sequences import EncodableSequences
from mhcflurry.downloads import get_path
from mhcflurry.regression_target import from_ic50
@@ -300,7 +300,7 @@ def test_real_data_multiallelic_refinement(max_epochs=10):
combined_train_df = pandas.concat([multi_train_df, pan_sub_train_df])
- ligandome_predictor = Class1LigandomeNeuralNetwork(
+ presentation_predictor = Class1PresentationNeuralNetwork(
pan_predictor,
auxiliary_input_features=[],
max_ensemble_size=1,
@@ -311,7 +311,7 @@ def test_real_data_multiallelic_refinement(max_epochs=10):
random_negative_rate=1.0)
pre_predictions = from_ic50(
- ligandome_predictor.predict(
+ presentation_predictor.predict(
output="affinities",
peptides=combined_train_df.peptide.values,
alleles=allele_encoding))
@@ -338,7 +338,7 @@ def test_real_data_multiallelic_refinement(max_epochs=10):
update_motifs()
print("Fitting...")
- ligandome_predictor.fit(
+ presentation_predictor.fit(
peptides=combined_train_df.peptide.values,
labels=combined_train_df.label.values,
allele_encoding=allele_encoding,
@@ -416,7 +416,7 @@ def test_synthetic_allele_refinement_with_affinity_data(max_epochs=10):
del affinity_train_df["measurement_value"]
affinity_train_df["is_affinity"] = True
- predictor = Class1LigandomeNeuralNetwork(
+ predictor = Class1PresentationNeuralNetwork(
PAN_ALLELE_PREDICTOR_NO_MASS_SPEC,
auxiliary_input_features=["gene"],
max_ensemble_size=1,
@@ -475,7 +475,7 @@ def test_synthetic_allele_refinement_with_affinity_data(max_epochs=10):
metric_rows = []
def progress():
- (_, ligandome_prediction, affinities_predictions) = (
+ (_, presentation_prediction, affinities_predictions) = (
predictor.predict(
output="all",
peptides=mms_train_df.peptide.values,
@@ -483,7 +483,7 @@ def test_synthetic_allele_refinement_with_affinity_data(max_epochs=10):
affinities_predictions = from_ic50(affinities_predictions)
for (kind, predictions) in [
("affinities", affinities_predictions),
- ("ligandome", ligandome_prediction)]:
+ ("presentation", presentation_prediction)]:
mms_train_df["max_prediction"] = predictions.max(1)
mms_train_df["predicted_allele"] = pandas.Series(alleles).loc[
@@ -520,7 +520,7 @@ def test_synthetic_allele_refinement_with_affinity_data(max_epochs=10):
update_motifs()
- return (ligandome_prediction, auc)
+ return (presentation_prediction, auc)
print("Pre fitting:")
progress()
@@ -616,7 +616,7 @@ def test_synthetic_allele_refinement(max_epochs=10):
train_df = pandas.concat([hits_df, decoys_df], ignore_index=True)
- predictor = Class1LigandomeNeuralNetwork(
+ predictor = Class1PresentationNeuralNetwork(
PAN_ALLELE_PREDICTOR_NO_MASS_SPEC,
max_ensemble_size=1,
max_epochs=max_epochs,
@@ -668,7 +668,7 @@ def test_synthetic_allele_refinement(max_epochs=10):
metric_rows = []
def progress():
- (_, ligandome_prediction, affinities_predictions) = (
+ (_, presentation_prediction, affinities_predictions) = (
predictor.predict(
output="all",
peptides=train_df.peptide.values,
@@ -676,7 +676,7 @@ def test_synthetic_allele_refinement(max_epochs=10):
affinities_predictions = from_ic50(affinities_predictions)
for (kind, predictions) in [
("affinities", affinities_predictions),
- ("ligandome", ligandome_prediction)]:
+ ("presentation", presentation_prediction)]:
train_df["max_prediction"] = predictions.max(1)
train_df["predicted_allele"] = pandas.Series(alleles).loc[
@@ -713,7 +713,7 @@ def test_synthetic_allele_refinement(max_epochs=10):
update_motifs()
- return (ligandome_prediction, auc)
+ return (presentation_prediction, auc)
print("Pre fitting:")
progress()
@@ -798,7 +798,7 @@ def test_batch_generator(sample_rate=0.1):
combined_train_df = pandas.concat(
[multi_train_df, pan_sub_train_df], ignore_index=True, sort=True)
- ligandome_predictor = Class1LigandomeNeuralNetwork(
+ presentation_predictor = Class1PresentationNeuralNetwork(
pan_predictor,
auxiliary_input_features=[],
max_ensemble_size=1,
@@ -809,7 +809,7 @@ def test_batch_generator(sample_rate=0.1):
min_delta=0.0,
random_negative_rate=1.0)
- fit_results = ligandome_predictor.fit(
+ fit_results = presentation_predictor.fit(
peptides=combined_train_df.peptide.values,
labels=combined_train_df.label.values,
allele_encoding=allele_encoding,