From 94f4c43719192f08047b433be735086d803f5620 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Mon, 22 May 2017 21:56:21 -0400
Subject: [PATCH] update requirements
---
.../class1_affinity_predictor.py | 74 +++++++++----------
.../class1_neural_network.py | 17 ++++-
requirements.txt | 2 +-
setup.py | 3 +-
4 files changed, 52 insertions(+), 44 deletions(-)
diff --git a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
index 69615774..a3861cb2 100644
--- a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
@@ -16,42 +16,6 @@ from ..downloads import get_path
from .class1_neural_network import Class1NeuralNetwork
-class LazyLoadingClass1NeuralNetwork(object):
- @classmethod
- def wrap(cls, instance):
- if isinstance(instance, cls):
- return instance
- elif isinstance(instance, Class1NeuralNetwork):
- return cls(model=instance)
- raise TypeError("Unsupported type: %s" % instance)
-
- @classmethod
- def wrap_list(cls, lst):
- return [
- cls.wrap(instance)
- for instance in lst
- ]
-
- def __init__(self, model=None, config=None, weights_filename=None):
- if model is None:
- assert config is not None
- assert weights_filename is not None
- else:
- assert config is None
- assert weights_filename is None
-
- self.model = model
- self.config = config
- self.weights_filename = weights_filename
-
- @property
- def instance(self):
- if self.model is None:
- self.model = Class1NeuralNetwork.from_config(self.config)
- self.model.restore_weights(self.weights_filename)
- return self.model
-
-
class Class1AffinityPredictor(object):
def __init__(
self,
@@ -339,4 +303,40 @@ class Class1AffinityPredictor(object):
columns = [
c for c in df.columns if c not in df_predictions.columns
]
- return df[columns]
\ No newline at end of file
+ return df[columns]
+
+
+class LazyLoadingClass1NeuralNetwork(object):
+ @classmethod
+ def wrap(cls, instance):
+ if isinstance(instance, cls):
+ return instance
+ elif isinstance(instance, Class1NeuralNetwork):
+ return cls(model=instance)
+ raise TypeError("Unsupported type: %s" % instance)
+
+ @classmethod
+ def wrap_list(cls, lst):
+ return [
+ cls.wrap(instance)
+ for instance in lst
+ ]
+
+ def __init__(self, model=None, config=None, weights_filename=None):
+ if model is None:
+ assert config is not None
+ assert weights_filename is not None
+ else:
+ assert config is None
+ assert weights_filename is None
+
+ self.model = model
+ self.config = config
+ self.weights_filename = weights_filename
+
+ @property
+ def instance(self):
+ if self.model is None:
+ self.model = Class1NeuralNetwork.from_config(self.config)
+ self.model.restore_weights(self.weights_filename)
+ return self.model
\ No newline at end of file
diff --git a/mhcflurry/class1_affinity_prediction/class1_neural_network.py b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
index 336e20b9..8738ba02 100644
--- a/mhcflurry/class1_affinity_prediction/class1_neural_network.py
+++ b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
@@ -257,6 +257,7 @@ class Class1NeuralNetwork(object):
peptides : EncodableSequences or list of string
affinities : list of float
+ nM affinities. Must be same length of as peptides.
allele_pseudosequences : EncodableSequences or list of string, optional
If not specified, the model will be a single-allele predictor.
@@ -407,15 +408,18 @@ class Class1NeuralNetwork(object):
def predict(self, peptides, allele_pseudosequences=None):
"""
+ Predict affinities
Parameters
----------
- peptides
- allele_pseudosequences
+ peptides : EncodableSequences or list of string
+
+ allele_pseudosequences : EncodableSequences or list of string, optional
+ Only required when this model is a pan-allele model
Returns
-------
-
+ numpy.array of nM affinity predictions
"""
x_dict = {
'peptide': self.peptides_to_network_input(peptides)
@@ -428,6 +432,9 @@ class Class1NeuralNetwork(object):
return to_ic50(predictions)
def compile(self):
+ """
+ Compile the keras model. Used internally.
+ """
self.network.compile(
**self.compile_hyperparameter_defaults.subselect(
self.hyperparameters))
@@ -450,7 +457,9 @@ class Class1NeuralNetwork(object):
batch_normalization,
embedding_init_method,
locally_connected_layers):
-
+ """
+ Helper function to make a keras network for class1 affinity prediction.
+ """
if use_embedding:
peptide_input = Input(
shape=(kmer_size,), dtype='int32', name='peptide')
diff --git a/requirements.txt b/requirements.txt
index 757af426..d1773b9a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,8 +6,8 @@ keras
scikit-learn
h5py
typechecks
-pepdata
joblib
cherrypy
bottle
six
+mhcnames
diff --git a/setup.py b/setup.py
index 42f50f45..1770e536 100644
--- a/setup.py
+++ b/setup.py
@@ -57,9 +57,8 @@ if __name__ == '__main__':
'scikit-learn',
'h5py',
'typechecks',
- 'pepdata',
- 'bottle',
'six',
+ 'mhcnames',
]
if PY2:
# concurrent.futures is a standard library in Py3 but Py2
--
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