From c4593f1639e7020b713c41c1402694262519d76d Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Mon, 17 Jun 2019 15:51:25 -0400
Subject: [PATCH] fix
---
mhcflurry/custom_loss.py | 4 +-
test/test_class1_neural_network.py | 4 +-
test/test_custom_loss.py | 4 +-
test/test_multi_output.py | 61 ------------------------------
4 files changed, 6 insertions(+), 67 deletions(-)
diff --git a/mhcflurry/custom_loss.py b/mhcflurry/custom_loss.py
index 35440ca0..4612bf60 100644
--- a/mhcflurry/custom_loss.py
+++ b/mhcflurry/custom_loss.py
@@ -72,8 +72,6 @@ class MSEWithInequalities(object):
# without tensorflow debug output, etc.
from keras import backend as K
- y_pred = K.flatten(y_pred)
-
# Handle (=) inequalities
diff1 = y_pred - y_true
diff1 *= K.cast(y_true >= 0.0, "float32")
@@ -93,7 +91,7 @@ class MSEWithInequalities(object):
result = (
K.sum(K.square(diff1)) +
K.sum(K.square(diff2)) +
- K.sum(K.square(diff3))) / K.cast(K.shape(y_true)[0], "float32")
+ K.sum(K.square(diff3))) / K.cast(K.shape(y_pred)[0], "float32")
return result
diff --git a/test/test_class1_neural_network.py b/test/test_class1_neural_network.py
index 8ba330cf..f232d0d8 100644
--- a/test/test_class1_neural_network.py
+++ b/test/test_class1_neural_network.py
@@ -88,7 +88,6 @@ def test_class1_neural_network_a0205_training_accuracy():
def test_inequalities():
# Memorize the dataset.
hyperparameters = dict(
- loss="custom:mse_with_inequalities",
peptide_amino_acid_encoding="one-hot",
activation="tanh",
layer_sizes=[16],
@@ -105,7 +104,8 @@ def test_inequalities():
}
],
dense_layer_l1_regularization=0.0,
- dropout_probability=0.0)
+ dropout_probability=0.0,
+ loss="custom:mse_with_inequalities_and_multiple_outputs")
df = pandas.DataFrame()
df["peptide"] = random_peptides(1000, length=9)
diff --git a/test/test_custom_loss.py b/test/test_custom_loss.py
index 005418ad..55bfa7b5 100644
--- a/test/test_custom_loss.py
+++ b/test/test_custom_loss.py
@@ -17,8 +17,10 @@ def evaluate_loss(loss, y_true, y_pred):
y_pred = numpy.array(y_pred)
if y_pred.ndim == 1:
y_pred = y_pred.reshape((len(y_pred), 1))
+ if y_true.ndim == 1:
+ y_true = y_true.reshape((len(y_true), 1))
- assert y_true.ndim == 1
+ assert y_true.ndim == 2
assert y_pred.ndim == 2
if K.backend() == "tensorflow":
diff --git a/test/test_multi_output.py b/test/test_multi_output.py
index 97431627..2e126154 100644
--- a/test/test_multi_output.py
+++ b/test/test_multi_output.py
@@ -10,12 +10,10 @@ import logging
logging.getLogger('tensorflow').disabled = True
from mhcflurry.class1_neural_network import Class1NeuralNetwork
-from mhcflurry.downloads import get_path
from mhcflurry.common import random_peptides
def test_multi_output():
- # Memorize the dataset.
hyperparameters = dict(
loss="custom:mse_with_inequalities_and_multiple_outputs",
activation="tanh",
@@ -87,62 +85,3 @@ def test_multi_output():
assert sub_correlation.iloc[1, 1] > 0.99, correlation
assert sub_correlation.iloc[2, 2] > 0.99, correlation
- import ipdb ; ipdb.set_trace()
-
-
-
- # Prediction2 has a (<) inequality on binders and an (=) on non-binders
- predictor = Class1NeuralNetwork(**hyperparameters)
- predictor.fit(
- df.peptide.values,
- df.value.values,
- inequalities=df.inequality2.values,
- **fit_kwargs)
- df["prediction2"] = predictor.predict(df.peptide.values)
-
- # Prediction3 has a (=) inequality on binders and an (>) on non-binders
- predictor = Class1NeuralNetwork(**hyperparameters)
- predictor.fit(
- df.peptide.values,
- df.value.values,
- inequalities=df.inequality3.values,
- **fit_kwargs)
- df["prediction3"] = predictor.predict(df.peptide.values)
-
- df_binders = df.loc[df.binder]
- df_nonbinders = df.loc[~df.binder]
-
- print("***** Binders: *****")
- print(df_binders.head(5))
-
- print("***** Non-binders: *****")
- print(df_nonbinders.head(5))
-
- # Binders should always be given tighter predicted affinity than non-binders
- assert_less(df_binders.prediction1.mean(), df_nonbinders.prediction1.mean())
- assert_less(df_binders.prediction2.mean(), df_nonbinders.prediction2.mean())
- assert_less(df_binders.prediction3.mean(), df_nonbinders.prediction3.mean())
-
- # prediction2 binders should be tighter on average than prediction1
- # binders, since prediction2 has a (<) inequality for binders.
- # Non-binders should be about the same between prediction2 and prediction1
- assert_less(df_binders.prediction2.mean(), df_binders.prediction1.mean())
- assert_almost_equal(
- df_nonbinders.prediction2.mean(),
- df_nonbinders.prediction1.mean(),
- delta=3000)
-
- # prediction3 non-binders should be weaker on average than prediction2 (or 1)
- # non-binders, since prediction3 has a (>) inequality for these peptides.
- # Binders should be about the same.
- assert_greater(
- df_nonbinders.prediction3.mean(),
- df_nonbinders.prediction2.mean())
- assert_greater(
- df_nonbinders.prediction3.mean(),
- df_nonbinders.prediction1.mean())
- assert_almost_equal(
- df_binders.prediction3.mean(),
- df_binders.prediction1.mean(),
- delta=3000)
-
--
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