update requirements

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

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')

requirements.txt

@@ -6,8 +6,8 @@ keras
 scikit-learn
 h5py
 typechecks
-pepdata
 joblib
 cherrypy
 bottle
 six
+mhcnames

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