From 2b6fbe8478953fde0def37969cae9515605a8f52 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Mon, 17 Jun 2019 12:01:28 -0400
Subject: [PATCH] fixes
---
mhcflurry/class1_neural_network.py | 2 ++
mhcflurry/custom_loss.py | 17 +++++++++----
test/test_multi_output.py | 38 +++++++++++++++++++++++-------
3 files changed, 45 insertions(+), 12 deletions(-)
diff --git a/mhcflurry/class1_neural_network.py b/mhcflurry/class1_neural_network.py
index fa4f4e41..40743c1b 100644
--- a/mhcflurry/class1_neural_network.py
+++ b/mhcflurry/class1_neural_network.py
@@ -536,6 +536,8 @@ class Class1NeuralNetwork(object):
x_dict_without_random_negatives[key][shuffle_permutation])
if sample_weights is not None:
sample_weights = sample_weights[shuffle_permutation]
+ if output_indices is not None:
+ output_indices = output_indices[shuffle_permutation]
if self.hyperparameters['loss'].startswith("custom:"):
# Using a custom loss
diff --git a/mhcflurry/custom_loss.py b/mhcflurry/custom_loss.py
index 7cad898b..8bd1b953 100644
--- a/mhcflurry/custom_loss.py
+++ b/mhcflurry/custom_loss.py
@@ -90,10 +90,12 @@ class MSEWithInequalities(object):
diff3 *= K.cast(y_true >= 4.0, "float32")
diff3 *= K.cast(diff3 > 0.0, "float32")
- return (
- K.sum(K.square(diff1), axis=-1) +
- K.sum(K.square(diff2), axis=-1) +
- K.sum(K.square(diff3), axis=-1)) / K.cast(K.shape(y_true)[0], "float32")
+ result = (
+ K.sum(K.square(diff1)) +
+ K.sum(K.square(diff2)) +
+ K.sum(K.square(diff3))) / K.cast(K.shape(y_true)[0], "float32")
+
+ return result
class MSEWithInequalitiesAndMultipleOutputs(object):
@@ -128,6 +130,11 @@ class MSEWithInequalitiesAndMultipleOutputs(object):
def loss(y_true, y_pred):
from keras import backend as K
+ #y_true = K.print_tensor(y_true, "y_true1")
+ #y_pred = K.print_tensor(y_pred, "y_pred1")
+
+ y_true = K.flatten(y_true)
+
output_indices = y_true // 10
updated_y_true = y_true - (10 * output_indices)
@@ -136,6 +143,8 @@ class MSEWithInequalitiesAndMultipleOutputs(object):
ordinals = K.arange(K.shape(y_true)[0])
flattened_indices = (
ordinals * y_pred.shape[1] + K.cast(output_indices, "int32"))
+ import tensorflow
+ #flattened_indices = tensorflow.Print(flattened_indices, [flattened_indices], "flattened_indices", summarize=1000)
updated_y_pred = K.gather(K.flatten(y_pred), flattened_indices)
# Alternative implementation using tensorflow, which could be used if
diff --git a/test/test_multi_output.py b/test/test_multi_output.py
index 436c4c9d..31e75b0d 100644
--- a/test/test_multi_output.py
+++ b/test/test_multi_output.py
@@ -20,8 +20,8 @@ def test_multi_output():
loss="custom:mse_with_inequalities_and_multiple_outputs",
activation="tanh",
layer_sizes=[16],
- max_epochs=50,
- minibatch_size=32,
+ max_epochs=500,
+ minibatch_size=250,
random_negative_rate=0.0,
random_negative_constant=0.0,
early_stopping=False,
@@ -30,12 +30,14 @@ def test_multi_output():
],
dense_layer_l1_regularization=0.0,
dropout_probability=0.0,
- num_outputs=2)
+ optimizer="adam",
+ num_outputs=3)
df = pandas.DataFrame()
df["peptide"] = random_peptides(10000, length=9)
- df["output1"] = df.peptide.map(lambda s: s[4] == 'K').astype(int) * 10000 + 0.01
- df["output2"] = df.peptide.map(lambda s: s[3] == 'Q').astype(int) * 10000 + 0.01
+ df["output1"] = df.peptide.map(lambda s: s[4] == 'K').astype(int) * 49000 + 1
+ df["output2"] = df.peptide.map(lambda s: s[3] == 'Q').astype(int) * 49000 + 1
+ df["output3"] = df.peptide.map(lambda s: s[4] == 'K' or s[3] == 'Q').astype(int) * 49000 + 1
print("output1 mean", df.output1.mean())
print("output2 mean", df.output2.mean())
@@ -45,6 +47,7 @@ def test_multi_output():
stacked["output_index"] = stacked.output_name.map({
"output1": 0,
"output2": 1,
+ "output3": 2,
})
assert not stacked.output_index.isnull().any(), stacked
@@ -53,17 +56,36 @@ def test_multi_output():
}
predictor = Class1NeuralNetwork(**hyperparameters)
+ stacked_train = stacked
predictor.fit(
- stacked.peptide.values,
- stacked.value.values,
- output_indices=stacked.output_index.values,
+ stacked_train.peptide.values,
+ stacked_train.value.values,
+ output_indices=stacked_train.output_index.values,
**fit_kwargs)
+
result = predictor.predict(df.peptide.values, output_index=None)
print(df.shape, result.shape)
print(result)
df["prediction1"] = result[:,0]
df["prediction2"] = result[:,1]
+ df["prediction3"] = result[:,2]
+
+ df_by_peptide = df.set_index("peptide")
+
+ correlation = pandas.DataFrame(
+ numpy.corrcoef(df_by_peptide.T),
+ columns=df_by_peptide.columns,
+ index=df_by_peptide.columns)
+ print(correlation)
+
+ sub_correlation = correlation.loc[
+ ["output1", "output2", "output3"],
+ ["prediction1", "prediction2", "prediction3"],
+ ]
+ assert sub_correlation.iloc[0, 0] > 0.99, correlation
+ assert sub_correlation.iloc[1, 1] > 0.99, correlation
+ assert sub_correlation.iloc[2, 2] > 0.99, correlation
import ipdb ; ipdb.set_trace()
--
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