From 79060d38d6b61aff01dfc7bf20b4a3795d8d4645 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 15 Feb 2018 15:33:54 -0500
Subject: [PATCH] test fixes
---
mhcflurry/class1_neural_network.py | 24 ++++++++-----------
test/test_class1_neural_network.py | 3 +++
...st_train_allele_specific_models_command.py | 2 +-
3 files changed, 14 insertions(+), 15 deletions(-)
diff --git a/mhcflurry/class1_neural_network.py b/mhcflurry/class1_neural_network.py
index 6ff042aa..3aa75abc 100644
--- a/mhcflurry/class1_neural_network.py
+++ b/mhcflurry/class1_neural_network.py
@@ -163,10 +163,7 @@ class Class1NeuralNetwork(object):
self.network_weights = None
self.network_weights_loader = None
- self.loss_history = None
- self.fit_seconds = None
- self.fit_num_points = []
-
+ self.fit_info = []
self.prediction_cache = weakref.WeakKeyDictionary()
KERAS_MODELS_CACHE = {}
@@ -310,7 +307,6 @@ class Class1NeuralNetwork(object):
"""
config = dict(config)
instance = cls(**config.pop('hyperparameters'))
- assert all(hasattr(instance, key) for key in config), config.keys()
instance.__dict__.update(config)
instance.network_weights = weights
instance.network_weights_loader = weights_loader
@@ -471,9 +467,6 @@ class Class1NeuralNetwork(object):
How often (in seconds) to print progress update. Set to None to
disable.
"""
-
- self.fit_num_points.append(len(peptides))
-
encodable_peptides = EncodableSequences.create(peptides)
peptide_encoding = self.peptides_to_network_input(encodable_peptides)
@@ -629,7 +622,7 @@ class Class1NeuralNetwork(object):
min_val_loss_iteration = None
min_val_loss = None
- self.loss_history = collections.defaultdict(list)
+ fit_info = collections.defaultdict(list)
start = time.time()
last_progress_print = None
x_dict_with_random_negatives = {}
@@ -692,7 +685,7 @@ class Class1NeuralNetwork(object):
sample_weight=sample_weights_with_random_negatives)
for (key, value) in fit_history.history.items():
- self.loss_history[key].extend(value)
+ fit_info[key].extend(value)
# Print progress no more often than once every few seconds.
if progress_print_interval is not None and (
@@ -704,13 +697,13 @@ class Class1NeuralNetwork(object):
"Min val loss (%s) at epoch %s" % (
i,
self.hyperparameters['max_epochs'],
- self.loss_history['loss'][-1],
+ fit_info['loss'][-1],
str(min_val_loss),
min_val_loss_iteration)).strip())
last_progress_print = time.time()
if self.hyperparameters['validation_split']:
- val_loss = self.loss_history['val_loss'][-1]
+ val_loss = fit_info['val_loss'][-1]
val_losses.append(val_loss)
if min_val_loss is None or val_loss <= min_val_loss:
@@ -728,11 +721,14 @@ class Class1NeuralNetwork(object):
"Min val loss (%s) at epoch %s" % (
i,
self.hyperparameters['max_epochs'],
- self.loss_history['loss'][-1],
+ fit_info['loss'][-1],
str(min_val_loss),
min_val_loss_iteration)).strip())
break
- self.fit_seconds = time.time() - start
+
+ fit_info["time"] = time.time() - start
+ fit_info["num_points"] = len(peptides)
+ self.fit_info.append(dict(fit_info))
def predict(self, peptides, allele_encoding=None, batch_size=4096):
"""
diff --git a/test/test_class1_neural_network.py b/test/test_class1_neural_network.py
index b082f28c..8ba330cf 100644
--- a/test/test_class1_neural_network.py
+++ b/test/test_class1_neural_network.py
@@ -89,9 +89,12 @@ def test_inequalities():
# Memorize the dataset.
hyperparameters = dict(
loss="custom:mse_with_inequalities",
+ peptide_amino_acid_encoding="one-hot",
activation="tanh",
layer_sizes=[16],
max_epochs=50,
+ minibatch_size=32,
+ random_negative_rate=0.0,
early_stopping=False,
validation_split=0.0,
locally_connected_layers=[
diff --git a/test/test_train_allele_specific_models_command.py b/test/test_train_allele_specific_models_command.py
index fab19ce7..a1e597ca 100644
--- a/test/test_train_allele_specific_models_command.py
+++ b/test/test_train_allele_specific_models_command.py
@@ -12,7 +12,7 @@ from mhcflurry.downloads import get_path
HYPERPARAMETERS = [
{
"n_models": 2,
- "max_epochs": 2,
+ "max_epochs": 20,
"patience": 10,
"early_stopping": True,
"validation_split": 0.2,
--
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