add experiment for model with peptide and allele input

experiments/train-single-class1-model.py

+#!/usr/bin/env python
+
+# Copyright (c) 2015. Mount Sinai School of Medicine
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from __future__ import (
+    print_function,
+    division,
+    absolute_import,
+    unicode_literals
+)
+
+from os.path import join
+import argparse
+
+import numpy as np
+
+import keras
+from keras.models import Graph
+from keras.layers.core import Dense, Flatten
+from keras.layers.noise import GaussianDropout
+from keras.layers.embeddings import Embedding
+from keras.utils import np_utils
+
+from mhcflurry.data_helpers import load_allele_datasets
+from mhcflurry.class1_allele_specific_hyperparameters import (
+    EMBEDDING_DIM,
+    HIDDEN_LAYER_SIZE,
+    MAX_IC50
+)
+from mhcflurry.paths import (
+    CLASS1_DATA_DIRECTORY
+)
+CSV_FILENAME = "combined_human_class1_dataset.csv"
+CSV_PATH = join(CLASS1_DATA_DIRECTORY, CSV_FILENAME)
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument(
+    "--binding-data-csv-path",
+    default=CSV_PATH,
+    help="CSV file with 'mhc', 'peptide', 'peptide_length', 'meas' columns")
+
+parser.add_argument(
+    "--min-samples-per-allele",
+    default=5,
+    help="Don't train predictors for alleles with fewer samples than this",
+    type=int)
+
+def build_graph_model(peptide_length=9,
+                      amino_acid_vocabulary_size=20,
+                      num_alleles=123,
+                      init="he_uniform",
+                      hidden_layer_size=HIDDEN_LAYER_SIZE,
+                      output_activation="sigmoid",
+                      activation="relu",
+                      embedding_output_dim=EMBEDDING_DIM,
+                      dropout_probability=0.1,
+                      ):
+    graph = Graph()
+    graph.add_input(name='peptide', input_shape=(peptide_length,), dtype='int')
+
+    graph.add_node(Embedding(input_dim=amino_acid_vocabulary_size, 
+                             output_dim=embedding_output_dim, 
+                             init=init,
+                             input_length=peptide_length),
+                   input='peptide', 
+                   name='peptide_embedding')
+
+    graph.add_node(Flatten(), 
+                   input='peptide_embedding', 
+                   name='flatten_peptide_embedding')
+
+    graph.add_node(Dense(output_dim=hidden_layer_size,
+                         activation=activation,
+                         init=init), 
+                   input='flatten_peptide_embedding', 
+                   name='hidden_peptide')
+
+    graph.add_node(GaussianDropout(dropout_probability), 
+                   name='dropout_peptide',
+                   input='hidden_peptide')
+
+    graph.add_input(name='allele', input_shape=(num_alleles,))
+    graph.add_node(Dense(
+                         output_dim=hidden_layer_size,
+                         activation=activation,
+                         init=init), 
+                   input='allele', 
+                   name='hidden_allele')
+    graph.add_node(GaussianDropout(dropout_probability), 
+                   name='dropout_allele',
+                   input='hidden_allele')
+
+    graph.add_node(Dense(output_dim=hidden_layer_size, activation=activation,),
+                   name='combined_dense', 
+                   inputs=['dropout_peptide', 'dropout_allele'], 
+                   merge_mode='concat')
+
+    graph.add_node(Dense(output_dim=1, activation=output_activation),
+                   name='output_dense', 
+                   input='combined_dense')
+    graph.add_output(name='output', input='output_dense')
+
+    return graph
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    allele_groups = load_allele_datasets(
+        args.binding_data_csv_path,
+        peptide_length=9,
+        binary_encoding=False,
+        max_ic50=MAX_IC50,
+        sep=",",
+        peptide_column_name="peptide")
+    # concatenate datasets from all alleles to use for pre-training of
+    # allele-specific predictors
+    X_peptide = np.vstack([group.X for group in allele_groups.values()])
+    Y_all = np.concatenate([group.Y for group in allele_groups.values()])
+    print("Total Dataset size = %d" % len(Y_all))
+
+    # Build 1-hot encoding of alleles
+    allele_to_index = \
+      dict((allele, i) 
+        for (i, allele) in enumerate(allele_groups.keys()))
+
+    X_allele_vector = np.array(
+        [allele_to_index[allele] 
+          for (allele, group) in allele_groups.items() 
+          for x in range(group.Y.shape[0])])
+
+    X_allele = np_utils.to_categorical(X_allele_vector)
+
+    optimizer = keras.optimizers.RMSprop(
+      lr=0.001,
+      rho=0.9,
+      epsilon=1e-6)
+
+    graph = build_graph_model()
+
+    print("Compiling model...")
+    graph.compile(optimizer=optimizer, loss={'output': 'mse'})
+
+    print("Fitting model...")
+    graph.fit({
+        'peptide': X_peptide, 
+        'allele': X_allele, 
+        'output': Y_all}, 
+        nb_epoch=20,
+        validation_split=0.1,
+        shuffle=True)
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