From d1e6fbb5edc830b3910c26b997b9860b835fdd8d Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Mon, 24 Jun 2019 12:35:47 -0400
Subject: [PATCH] fix
---
mhcflurry/class1_neural_network.py | 2 ++
mhcflurry/custom_loss.py | 12 ++++++------
2 files changed, 8 insertions(+), 6 deletions(-)
diff --git a/mhcflurry/class1_neural_network.py b/mhcflurry/class1_neural_network.py
index c1f8e106..80e58b88 100644
--- a/mhcflurry/class1_neural_network.py
+++ b/mhcflurry/class1_neural_network.py
@@ -719,6 +719,8 @@ class Class1NeuralNetwork(object):
]),
}
adjusted_inequalities_with_random_negatives = None
+ assert numpy.isnan(y_dict_with_random_negatives['output']).sum() == 0, (
+ y_dict_with_random_negatives)
if sample_weights is not None:
sample_weights_with_random_negatives = numpy.concatenate([
numpy.ones(int(num_random_negative.sum())),
diff --git a/mhcflurry/custom_loss.py b/mhcflurry/custom_loss.py
index eabdd130..fcb970bd 100644
--- a/mhcflurry/custom_loss.py
+++ b/mhcflurry/custom_loss.py
@@ -82,11 +82,11 @@ class MSEWithInequalities(Loss):
def encode_y(y, inequalities=None):
y = array(y, dtype="float32")
if isnan(y).any():
- raise ValueError("y contains NaN: %s" % str(y))
+ raise ValueError("y contains NaN", y)
if (y > 1.0).any():
- raise ValueError("y contains values > 1.0")
+ raise ValueError("y contains values > 1.0", y)
if (y < 0.0).any():
- raise ValueError("y contains values < 0.0")
+ raise ValueError("y contains values < 0.0", y)
if inequalities is None:
encoded = y
@@ -141,11 +141,11 @@ class MSEWithInequalitiesAndMultipleOutputs(Loss):
def encode_y(y, inequalities=None, output_indices=None):
y = array(y, dtype="float32")
if isnan(y).any():
- raise ValueError("y contains NaN: %s" % str(y))
+ raise ValueError("y contains NaN", y)
if (y > 1.0).any():
- raise ValueError("y contains values > 1.0")
+ raise ValueError("y contains values > 1.0", y)
if (y < 0.0).any():
- raise ValueError("y contains values < 0.0")
+ raise ValueError("y contains values < 0.0", y)
encoded = MSEWithInequalities.encode_y(
y, inequalities=inequalities)
--
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