diff --git a/README.md b/README.md
index 99945648ed109e901bdd426a32a45bbefd061409..bf02a88b45a5e4d4a3de740685a082dfc4c8105c 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ If you find MHCflurry useful in your research please cite:
> O'Donnell, T. et al., 2017. MHCflurry: open-source class I MHC binding affinity prediction. bioRxiv. Available at: http://www.biorxiv.org/content/early/2017/08/09/174243.
-## Setup
+## Setup (pip)
Install the package:
@@ -56,6 +56,32 @@ export KERAS_BACKEND=theano
You may also needs to `pip install theano`.
+## Setup (conda)
+
+You can alternatively get up and running with a [conda](https://conda.io/docs/)
+environment as follows:
+
+```
+conda create -q -n mhcflurry-env python=3.6 'tensorflow>=1.1.0'
+source activate mhcflurry-env
+```
+
+Then continue as above:
+
+```
+pip install mhcflurry
+mhcflurry-downloads fetch
+```
+
+If you wish to test your installation, you can install `nose` and run the tests
+from a checkout:
+
+```
+pip install nose
+nosetests .
+```
+
+
## Making predictions from the command-line
@@ -103,7 +129,7 @@ notebook for an overview of the Python API, including fitting your own predictor
An ensemble of eight single-allele models was trained for each allele with at least
100 measurements in the training set (118 alleles). The models were trained on a
random 80% sample of the data for the allele and the remaining 20% was used for
-early stopping. All models use the same [architecture](downloads-generation/models_class1/hyperparameters.json). The
+early stopping. All models use the same [architecture](downloads-generation/models_class1/hyperparameters.yaml). The
predictions are taken to be the geometric mean of the nM binding affinity
predictions of the individual models. The training script is [here](downloads-generation/models_class1/GENERATE.sh).
diff --git a/downloads-generation/models_class1/GENERATE.sh b/downloads-generation/models_class1/GENERATE.sh
index ff06a043083a585eacad7c0ef8425f9cee9f9686..ae98f51ebe9a09381c3dd497d49dfcdd2643dfdb 100755
--- a/downloads-generation/models_class1/GENERATE.sh
+++ b/downloads-generation/models_class1/GENERATE.sh
@@ -25,11 +25,11 @@ cd $SCRATCH_DIR/$DOWNLOAD_NAME
mkdir models
-cp $SCRIPT_DIR/hyperparameters.json .
+cp $SCRIPT_DIR/hyperparameters.yaml .
time mhcflurry-class1-train-allele-specific-models \
--data "$(mhcflurry-downloads path data_curated)/curated_training_data.csv.bz2" \
- --hyperparameters hyperparameters.json \
+ --hyperparameters hyperparameters.yaml \
--out-models-dir models \
--min-measurements-per-allele 200
diff --git a/downloads-generation/models_class1/hyperparameters.json b/downloads-generation/models_class1/hyperparameters.json
deleted file mode 100644
index 75fd5d646d05fd3a655aea0b42527f18892c44cc..0000000000000000000000000000000000000000
--- a/downloads-generation/models_class1/hyperparameters.json
+++ /dev/null
@@ -1,37 +0,0 @@
-[
- {
- "n_models": 12,
- "max_epochs": 500,
- "patience": 10,
- "early_stopping": true,
- "validation_split": 0.2,
-
- "random_negative_rate": 0.0,
- "random_negative_constant": 25,
-
- "use_embedding": false,
- "kmer_size": 15,
- "batch_normalization": false,
- "locally_connected_layers": [
- {
- "filters": 8,
- "activation": "tanh",
- "kernel_size": 3
- },
- {
- "filters": 8,
- "activation": "tanh",
- "kernel_size": 3
- }
- ],
- "activation": "relu",
- "output_activation": "sigmoid",
- "layer_sizes": [
- 32
- ],
- "random_negative_affinity_min": 20000.0,
- "random_negative_affinity_max": 50000.0,
- "dense_layer_l1_regularization": 0.001,
- "dropout_probability": 0.0
- }
-]
diff --git a/downloads-generation/models_class1/hyperparameters.yaml b/downloads-generation/models_class1/hyperparameters.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0e114b320231a585d8ef78df925caff74dbf43c6
--- /dev/null
+++ b/downloads-generation/models_class1/hyperparameters.yaml
@@ -0,0 +1,54 @@
+[{
+##########################################
+# ENSEMBLE SIZE
+##########################################
+"n_models": 12,
+
+##########################################
+# OPTIMIZATION
+##########################################
+"max_epochs": 500,
+"patience": 10,
+"early_stopping": true,
+"validation_split": 0.2,
+
+##########################################
+# RANDOM NEGATIVE PEPTIDES
+##########################################
+"random_negative_rate": 0.0,
+"random_negative_constant": 25,
+"random_negative_affinity_min": 20000.0,
+"random_negative_affinity_max": 50000.0,
+
+##########################################
+# PEPTIDE REPRESENTATION
+##########################################
+# One of "one-hot", "embedding", or "BLOSUM62".
+"peptide_amino_acid_encoding": "BLOSUM62",
+"use_embedding": false, # maintained for backward compatability
+"kmer_size": 15,
+
+##########################################
+# NEURAL NETWORK ARCHITECTURE
+##########################################
+"locally_connected_layers": [
+ {
+ "filters": 8,
+ "activation": "tanh",
+ "kernel_size": 3
+ },
+ {
+ "filters": 8,
+ "activation": "tanh",
+ "kernel_size": 3
+ }
+],
+"activation": "relu",
+"output_activation": "sigmoid",
+"layer_sizes": [
+ 32
+],
+"dense_layer_l1_regularization": 0.001,
+"batch_normalization": false,
+"dropout_probability": 0.0,
+}]
diff --git a/mhcflurry/__init__.py b/mhcflurry/__init__.py
index ab21634ef51b18b023376f6919e7a15382aa44f2..cf13e1e6339778da3c658e5d9b64afddfac31ecb 100644
--- a/mhcflurry/__init__.py
+++ b/mhcflurry/__init__.py
@@ -17,7 +17,7 @@ from .class1_affinity_prediction.class1_neural_network import (
from .class1_affinity_prediction.class1_affinity_predictor import (
Class1AffinityPredictor)
-__version__ = "0.9.2"
+__version__ = "0.9.3"
__all__ = [
"Class1NeuralNetwork",
diff --git a/mhcflurry/class1_affinity_prediction/class1_neural_network.py b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
index fe8de016b2de9009bdcb3a0e087471760e969ea5..13a7f55c8250255a3fdc43a4c5017505801bfe6a 100644
--- a/mhcflurry/class1_affinity_prediction/class1_neural_network.py
+++ b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
@@ -9,14 +9,17 @@ import keras.models
import keras.layers.pooling
import keras.regularizers
from keras.layers import Input
-import keras.layers.merge
+import keras.layers
from keras.layers.core import Dense, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.layers.normalization import BatchNormalization
from mhcflurry.hyperparameters import HyperparameterDefaults
-from ..encodable_sequences import EncodableSequences
+from ..encodable_sequences import (
+ EncodableSequences,
+ available_vector_encodings,
+ vector_encoding_length)
from ..regression_target import to_ic50, from_ic50
from ..common import random_peptides, amino_acid_distribution
@@ -35,6 +38,7 @@ class Class1NeuralNetwork(object):
network_hyperparameter_defaults = HyperparameterDefaults(
kmer_size=15,
use_embedding=False,
+ peptide_amino_acid_encoding="one-hot",
embedding_input_dim=21,
embedding_output_dim=8,
pseudosequence_use_embedding=False,
@@ -256,20 +260,26 @@ class Class1NeuralNetwork(object):
numpy.array
"""
encoder = EncodableSequences.create(peptides)
- if self.hyperparameters['use_embedding']:
+ if (self.hyperparameters['use_embedding'] or
+ self.hyperparameters['peptide_amino_acid_encoding'] == "embedding"):
encoded = encoder.variable_length_to_fixed_length_categorical(
max_length=self.hyperparameters['kmer_size'],
**self.input_encoding_hyperparameter_defaults.subselect(
self.hyperparameters))
- else:
- encoded = encoder.variable_length_to_fixed_length_one_hot(
+ elif (
+ self.hyperparameters['peptide_amino_acid_encoding'] in
+ available_vector_encodings()):
+ encoded = encoder.variable_length_to_fixed_length_vector_encoding(
+ self.hyperparameters['peptide_amino_acid_encoding'],
max_length=self.hyperparameters['kmer_size'],
**self.input_encoding_hyperparameter_defaults.subselect(
self.hyperparameters))
+ else:
+ raise ValueError("Unsupported peptide_amino_acid_encoding: %s" %
+ self.hyperparameters['peptide_amino_acid_encoding'])
assert len(encoded) == len(peptides)
return encoded
-
@property
def supported_peptide_lengths(self):
"""
@@ -302,7 +312,8 @@ class Class1NeuralNetwork(object):
if self.hyperparameters['pseudosequence_use_embedding']:
encoded = encoder.fixed_length_categorical()
else:
- encoded = encoder.fixed_length_one_hot()
+ raise NotImplementedError
+ # encoded = encoder.fixed_length_one_hot()
assert len(encoded) == len(pseudosequences)
return encoded
@@ -492,7 +503,8 @@ class Class1NeuralNetwork(object):
pseudosequences_input = self.pseudosequence_to_network_input(
allele_pseudosequences)
x_dict['pseudosequence'] = pseudosequences_input
- (predictions,) = numpy.array(self.network(borrow=True).predict(x_dict)).T
+ (predictions,) = numpy.array(
+ self.network(borrow=True).predict(x_dict), dtype="float64").T
return to_ic50(predictions)
def compile(self):
@@ -507,6 +519,7 @@ class Class1NeuralNetwork(object):
def make_network(
pseudosequence_length,
kmer_size,
+ peptide_amino_acid_encoding,
use_embedding,
embedding_input_dim,
embedding_output_dim,
@@ -524,7 +537,7 @@ class Class1NeuralNetwork(object):
"""
Helper function to make a keras network for class1 affinity prediction.
"""
- if use_embedding:
+ if use_embedding or peptide_amino_acid_encoding == "embedding":
peptide_input = Input(
shape=(kmer_size,), dtype='int32', name='peptide')
current_layer = Embedding(
@@ -535,7 +548,11 @@ class Class1NeuralNetwork(object):
name="peptide_embedding")(peptide_input)
else:
peptide_input = Input(
- shape=(kmer_size, 21), dtype='float32', name='peptide')
+ shape=(
+ kmer_size,
+ vector_encoding_length(peptide_amino_acid_encoding)),
+ dtype='float32',
+ name='peptide')
current_layer = peptide_input
inputs = [peptide_input]
@@ -609,4 +626,7 @@ class Class1NeuralNetwork(object):
inputs=inputs,
outputs=[output],
name="predictor")
+
+ print("*** ARCHITECTURE ***")
+ model.summary()
return model
diff --git a/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
index 6f0aa97529613321e2f3986a9d38201f3bd27983..1128ca99a3dfaef999d2b131ea3f6865faf53127 100644
--- a/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
+++ b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
@@ -5,7 +5,7 @@ Train Class1 single allele models.
import os
import sys
import argparse
-import json
+import yaml
import pandas
@@ -31,7 +31,7 @@ parser.add_argument(
"--hyperparameters",
metavar="FILE.json",
required=True,
- help="JSON of hyperparameters")
+ help="JSON or YAML of hyperparameters")
parser.add_argument(
"--allele",
default=None,
@@ -61,7 +61,7 @@ def run(argv=sys.argv[1:]):
configure_logging(verbose=args.verbosity > 1)
- hyperparameters_lst = json.load(open(args.hyperparameters))
+ hyperparameters_lst = yaml.load(open(args.hyperparameters))
assert isinstance(hyperparameters_lst, list)
print("Loaded hyperparameters list: %s" % str(hyperparameters_lst))
diff --git a/mhcflurry/encodable_sequences.py b/mhcflurry/encodable_sequences.py
index d5e9c7a0b0e1cd4c18054d546f2fa04c456a5645..4abc94a1032860594bc091ba9043cc94320ffd30 100644
--- a/mhcflurry/encodable_sequences.py
+++ b/mhcflurry/encodable_sequences.py
@@ -22,11 +22,75 @@ import math
import pandas
import numpy
+from six import StringIO
import typechecks
from . import amino_acid
+AMINO_ACIDS = list(amino_acid.COMMON_AMINO_ACIDS_WITH_UNKNOWN.keys())
+
+BLOSUM62_MATRIX = pandas.read_table(StringIO("""
+ A R N D C Q E G H I L K M F P S T W Y V X
+A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 0
+R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 0
+N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 0
+D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 0
+C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 0
+Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0
+E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 0
+G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 0
+H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0
+I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 0
+L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 0
+K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0
+M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 0
+F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 0
+P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 0
+S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0
+T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 0
+W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 0
+Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 0
+V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 0
+X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+"""), sep='\s+').loc[AMINO_ACIDS, AMINO_ACIDS]
+assert (BLOSUM62_MATRIX == BLOSUM62_MATRIX.T).all().all()
+
+ENCODING_DFS = {
+ "BLOSUM62": BLOSUM62_MATRIX,
+ "one-hot": pandas.DataFrame([
+ [1 if i == j else 0 for i in range(len(AMINO_ACIDS))]
+ for j in range(len(AMINO_ACIDS))
+ ], index=AMINO_ACIDS, columns=AMINO_ACIDS)
+}
+
+
+def available_vector_encodings():
+ """
+ Return list of supported amino acid vector encodings.
+
+ Returns
+ -------
+ list of string
+
+ """
+ return list(ENCODING_DFS)
+
+
+def vector_encoding_length(name):
+ """
+ Return the length of the given vector encoding.
+
+ Parameters
+ ----------
+ name : string
+
+ Returns
+ -------
+ int
+ """
+ return ENCODING_DFS[name].shape[1]
+
def index_encoding(sequences, letter_to_index_dict):
"""
@@ -47,38 +111,23 @@ def index_encoding(sequences, letter_to_index_dict):
return result.values
-def one_hot_encoding(index_encoded, alphabet_size):
+def fixed_vectors_encoding(sequences, letter_to_vector_function):
"""
- Given an n * k array of integers in the range [0, alphabet_size), return
- an n * k * alphabet_size array where element (i, k, j) is 1 if element
- (i, k) == j in the input array and zero otherwise.
-
+ Given a sequence of n strings all of length k, return a n * k * m array where
+ the (i, j)th element is letter_to_vector_function(sequence[i][j]).
+
Parameters
----------
- index_encoded : numpy.array of integers with shape (n, k)
- alphabet_size : int
+ sequences : list of length n of strings of length k
+ letter_to_vector_function : function : string -> vector of length m
Returns
-------
- numpy.array of integers of shape (n, k, alphabet_size)
-
+ numpy.array of integers with shape (n, k, m)
"""
- alphabet_size = int(alphabet_size)
- (num_sequences, sequence_length) = index_encoded.shape
- result = numpy.zeros(
- (num_sequences, sequence_length, alphabet_size),
- dtype='int32')
-
- # Transform the index encoded array into an array of indices into the
- # flattened result, which we will set to 1.
- flattened_indices = (
- index_encoded +
- (
- sequence_length * alphabet_size * numpy.arange(num_sequences)
- ).reshape((-1, 1)) +
- numpy.tile(numpy.arange(sequence_length),
- (num_sequences, 1)) * alphabet_size)
- result.put(flattened_indices, 1)
+ arr = numpy.array([list(s) for s in sequences])
+ result = numpy.vectorize(
+ letter_to_vector_function, signature='()->(n)')(arr)
return result
@@ -114,40 +163,6 @@ class EncodableSequences(object):
def __len__(self):
return len(self.sequences)
- def fixed_length_categorical(self):
- """
- Returns a categorical encoding (i.e. integers 0 <= x < 21) of the
- sequences, which must already be all the same length.
-
- Returns
- -------
- numpy.array of integers
- """
- cache_key = ("categorical",)
- if cache_key not in self.encoding_cache:
- assert self.fixed_sequence_length
- self.encoding_cache[cache_key] = index_encoding(
- self.sequences, amino_acid.AMINO_ACID_INDEX)
- return self.encoding_cache[cache_key]
-
- def fixed_length_one_hot(self):
- """
- Returns a binary one-hot encoding of the sequences, which must already
- be all the same length.
-
- Returns
- -------
- numpy.array of integers
- """
- cache_key = ("one_hot",)
- if cache_key not in self.encoding_cache:
- assert self.fixed_sequence_length
- encoded = self.categorical_encoding()
- result = one_hot_encoding(
- encoded, alphabet_size=len(amino_acid.AMINO_ACID_INDEX))
- self.encoding_cache[cache_key] = result
- return self.encoding_cache[cache_key]
-
def variable_length_to_fixed_length_categorical(
self, left_edge=4, right_edge=4, max_length=15):
"""
@@ -187,8 +202,8 @@ class EncodableSequences(object):
fixed_length_sequences, amino_acid.AMINO_ACID_INDEX)
return self.encoding_cache[cache_key]
- def variable_length_to_fixed_length_one_hot(
- self, left_edge=4, right_edge=4, max_length=15):
+ def variable_length_to_fixed_length_vector_encoding(
+ self, vector_encoding_name, left_edge=4, right_edge=4, max_length=15):
"""
Encode variable-length sequences using a fixed-length encoding designed
for preserving the anchor positions of class I peptides.
@@ -198,35 +213,43 @@ class EncodableSequences(object):
Parameters
----------
+ vector_encoding_name : string
+ How to represent amino acids.
+ One of "BLOSUM62", "one-hot", etc. Full list of supported vector
+ encodings is given by available_vector_encodings().
left_edge : int, size of fixed-position left side
right_edge : int, size of the fixed-position right side
max_length : sequence length of the resulting encoding
Returns
-------
- binary numpy.array with shape (num sequences, max_length, 21)
- """
+ numpy.array with shape (num sequences, max_length, m) where m is
+ vector_encoding_length(vector_encoding_name)
+ """
cache_key = (
- "fixed_length_one_hot",
+ "fixed_length_vector_encoding",
+ vector_encoding_name,
left_edge,
right_edge,
max_length)
-
if cache_key not in self.encoding_cache:
- encoded = self.variable_length_to_fixed_length_categorical(
- left_edge=left_edge,
- right_edge=right_edge,
- max_length=max_length)
- result = one_hot_encoding(
- encoded, alphabet_size=len(amino_acid.AMINO_ACID_INDEX))
- assert result.shape == (
- len(self.sequences),
- encoded.shape[1],
- len(amino_acid.AMINO_ACID_INDEX))
+ fixed_length_sequences = [
+ self.sequence_to_fixed_length_string(
+ sequence,
+ left_edge=left_edge,
+ right_edge=right_edge,
+ max_length=max_length)
+ for sequence in self.sequences
+ ]
+ result = fixed_vectors_encoding(
+ fixed_length_sequences,
+ ENCODING_DFS[vector_encoding_name].loc.__getitem__)
+ assert result.shape[0] == len(self.sequences)
self.encoding_cache[cache_key] = result
return self.encoding_cache[cache_key]
+
@classmethod
def sequence_to_fixed_length_string(
klass, sequence, left_edge=4, right_edge=4, max_length=15):
diff --git a/requirements.txt b/requirements.txt
index 51a6f3a0b6275aaed36f757a1695f2150e59acd8..8d31007f08081ee473243222f7edf4eef1b2a4b4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,10 @@
six
-numpy>= 1.11
+numpy>=1.11
pandas>=0.13.1
-Keras==2.0.8
+Keras==2.0.9
+tensorflow>=1.1.0
appdirs
-tensorflow
scikit-learn
typechecks
mhcnames
+pyyaml
diff --git a/setup.py b/setup.py
index ae7b0a20d219218b0e7980dff534a08887f6a55c..6f45d03764462b9b7248bd59b61e402893aace5b 100644
--- a/setup.py
+++ b/setup.py
@@ -30,14 +30,14 @@ try:
with open(readme_filename, 'r') as f:
readme = f.read()
except:
- logging.warn("Failed to load %s" % readme_filename)
+ logging.warning("Failed to load %s" % readme_filename)
readme = ""
try:
import pypandoc
readme = pypandoc.convert(readme, to='rst', format='md')
except:
- logging.warn("Conversion of long_description from MD to RST failed")
+ logging.warning("Conversion of long_description from MD to RST failed")
pass
with open('mhcflurry/__init__.py', 'r') as f:
@@ -51,12 +51,13 @@ if __name__ == '__main__':
'six',
'numpy>=1.11',
'pandas>=0.13.1',
- 'Keras==2.0.8',
+ 'Keras==2.0.9',
'appdirs',
- 'tensorflow',
+ 'tensorflow>=1.1.0',
'scikit-learn',
'typechecks',
'mhcnames',
+ 'pyyaml',
]
if PY2:
# concurrent.futures is a standard library in Py3 but Py2
diff --git a/test/test_encodable_sequences.py b/test/test_encodable_sequences.py
index 8252f00ac72fcdc7d58aed6caa4357a08f696379..cc47290a08eb05e3aa8f282f7cefbe57af669b5a 100644
--- a/test/test_encodable_sequences.py
+++ b/test/test_encodable_sequences.py
@@ -1,6 +1,7 @@
from mhcflurry import encodable_sequences
from nose.tools import eq_
from numpy.testing import assert_equal
+import numpy
letter_to_index_dict = {
'A': 0,
@@ -18,7 +19,13 @@ def test_index_and_one_hot_encoding():
[0, 0, 0, 0],
[0, 1, 2, 0],
])
- one_hot = encodable_sequences.one_hot_encoding(index_encoding, 3)
+ one_hot = encodable_sequences.fixed_vectors_encoding(
+ index_encoding,
+ {
+ 0: numpy.array([1, 0, 0]),
+ 1: numpy.array([0, 1, 0]),
+ 2: numpy.array([0, 0, 1]),
+ }.get)
eq_(one_hot.shape, (2, 4, 3))
assert_equal(
one_hot[0],
diff --git a/test/test_train_allele_specific_models_command.py b/test/test_train_allele_specific_models_command.py
index 41fa9e3f88d3acdb365fbb2eb957c87573ddead8..5c3fbcfc961d1e2690c6dc46ca79262b11fe3451 100644
--- a/test/test_train_allele_specific_models_command.py
+++ b/test/test_train_allele_specific_models_command.py
@@ -53,7 +53,7 @@ def test_run():
try:
models_dir = tempfile.mkdtemp(prefix="mhcflurry-test-models")
hyperparameters_filename = os.path.join(
- models_dir, "hyperparameters.json")
+ models_dir, "hyperparameters.yaml")
with open(hyperparameters_filename, "w") as fd:
json.dump(HYPERPARAMETERS, fd)