From 8aeda84785ba911f0f10b1e2a1737b39e3e48cdb Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 5 Dec 2019 15:44:22 -0500
Subject: [PATCH] tests
---
test/test_class1_presentation_predictor.py | 23 +++++++++++++++-------
1 file changed, 16 insertions(+), 7 deletions(-)
diff --git a/test/test_class1_presentation_predictor.py b/test/test_class1_presentation_predictor.py
index 69a8da80..b54bee2a 100644
--- a/test/test_class1_presentation_predictor.py
+++ b/test/test_class1_presentation_predictor.py
@@ -91,7 +91,7 @@ def teardown():
cleanup()
-def Xtest_basic():
+def test_basic():
affinity_predictor = PAN_ALLELE_PREDICTOR_NO_MASS_SPEC
models = []
for affinity_network in affinity_predictor.class1_pan_allele_models:
@@ -99,11 +99,10 @@ def Xtest_basic():
optimizer="adam",
random_negative_rate=0.0,
random_negative_constant=0,
- max_epochs=5,
+ max_epochs=100,
learning_rate=0.001,
batch_generator_batch_size=256)
presentation_network.load_from_class1_neural_network(affinity_network)
- print(presentation_network.network.get_config())
models.append(presentation_network)
predictor = Class1PresentationPredictor(
@@ -164,12 +163,14 @@ def Xtest_basic():
train_df = pandas.DataFrame({
"peptide": numpy.concatenate([
random_peptides(256, length=length)
- for length in range(8,16)
+ for length in [9]
]),
})
train_df["allele"] = "HLA-A*02:20"
train_df["experiment"] = "experiment1"
train_df["label"] = train_df.peptide.str.match("^[KSEQ]").astype("float32")
+ train_df["pre_train_affinity_prediction"] = affinity_predictor.predict(
+ train_df.peptide.values, alleles=train_df.allele.values)
allele_encoding = MultipleAlleleEncoding(
experiment_names=train_df.experiment.values,
experiment_to_allele_list={
@@ -189,8 +190,18 @@ def Xtest_basic():
train_df["updated_score"] = new_predictor.predict(
train_df.peptide.values,
alleles=["HLA-A*02:20"])
+ train_df["score_diff"] = train_df.updated_score - train_df.original_score
+ mean_change = train_df.groupby("label").score_diff.mean()
+ print("Mean change:")
+ print(mean_change)
+ assert_greater(mean_change[1.0], mean_change[0.0])
print(train_df)
- #import ipdb ; ipdb.set_trace()
+ train_df["post_train_affinity_prediction"] = affinity_predictor.predict(
+ train_df.peptide.values,
+ alleles=train_df.allele.values)
+ assert_array_equal(
+ train_df.pre_train_affinity_prediction.values,
+ train_df.post_train_affinity_prediction.values)
def scramble_peptide(peptide):
@@ -279,8 +290,6 @@ def test_loss():
neg = y_pred[(y_true == 0.0).astype(bool)]
term = neg.reshape((-1, 1)) - pos + delta
- print("Term:")
- print(term)
expected2 = (
numpy.maximum(0, term)**2).sum() / (
len(pos) * neg.shape[0] * neg.shape[1])
--
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