From 0f9394e2571bec4da61efd16f6b8bd71583cc8b6 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Mon, 4 Dec 2017 13:39:32 -0500
Subject: [PATCH] Address code review comments
---
README.md | 1 +
.../cross_validation_class1/GENERATE.sh | 7 ++++++-
.../cross_validation_class1/score.py | 2 ++
.../cross_validation_class1/split_folds.py | 12 ++++++++++--
downloads-generation/data_curated/GENERATE.sh | 6 +++++-
downloads-generation/data_iedb/GENERATE.sh | 4 +++-
downloads-generation/data_kim2014/GENERATE.sh | 5 ++++-
downloads-generation/models_class1/GENERATE.sh | 6 +++++-
.../models_class1_experiments1/GENERATE.sh | 6 +++++-
mhcflurry/amino_acid.py | 6 +++---
.../class1_affinity_predictor.py | 13 +++++++++++++
mhcflurry/encodable_sequences.py | 2 +-
12 files changed, 58 insertions(+), 12 deletions(-)
diff --git a/README.md b/README.md
index a4373e27..e47650dd 100644
--- a/README.md
+++ b/README.md
@@ -138,6 +138,7 @@ mhctools \
--mhc-predictor mhcflurry \
--input-fasta-file INPUT.fasta \
--mhc-alleles A02:01,A03:01 \
+ --mhc-peptide-lengths 8,9,10,11 \
--extract-subsequences \
--out RESULT.csv
```
diff --git a/downloads-generation/cross_validation_class1/GENERATE.sh b/downloads-generation/cross_validation_class1/GENERATE.sh
index 658460f6..c35e0862 100755
--- a/downloads-generation/cross_validation_class1/GENERATE.sh
+++ b/downloads-generation/cross_validation_class1/GENERATE.sh
@@ -1,5 +1,10 @@
#!/bin/bash
-
+#
+# Cross validation using the standard class I models.
+# Splits training data into 5 folds (stratifying on allele), trains and tests a
+# predictor on each (train, test) fold, and writes a summary CSV giving
+# performance for each allele on each fold.
+#
set -e
set -x
diff --git a/downloads-generation/cross_validation_class1/score.py b/downloads-generation/cross_validation_class1/score.py
index dcf366de..7af791c4 100644
--- a/downloads-generation/cross_validation_class1/score.py
+++ b/downloads-generation/cross_validation_class1/score.py
@@ -75,6 +75,8 @@ def run(argv):
scores['kind'] = "single" if "single" in prediction_col else "ensemble"
scores['train_size'] = allele_df[prediction_col].isnull().sum()
scores['test_size'] = len(sub_df)
+
+ # make_scores returns a dict with entries "auc", "f1", "tau"
scores.update(
make_scores(
sub_df.measurement_value, sub_df[prediction_col]))
diff --git a/downloads-generation/cross_validation_class1/split_folds.py b/downloads-generation/cross_validation_class1/split_folds.py
index 3f95337f..dd49085f 100644
--- a/downloads-generation/cross_validation_class1/split_folds.py
+++ b/downloads-generation/cross_validation_class1/split_folds.py
@@ -6,6 +6,7 @@ import sys
from os.path import abspath
import pandas
+import numpy
from sklearn.model_selection import StratifiedKFold
parser = argparse.ArgumentParser(usage = __doc__)
@@ -68,18 +69,25 @@ def run(argv):
else:
alleles = list(
allele_counts.ix[
- allele_counts > args.min_measurements_per_allele
+ allele_counts > args.min_measurements_per_allele
].index)
- df = df.ix[df.allele.isin(alleles)]
+ df = df.loc[df.allele.isin(alleles)].copy()
print("Potentially subselected by allele to: %s" % str(df.shape))
print("Data has %d alleles: %s" % (
df.allele.nunique(), " ".join(df.allele.unique())))
+ print(df.head())
+
+ # Take log before taking median (in case of even number of samples).
+ df["measurement_value"] = numpy.log1p(df.measurement_value)
df = df.groupby(["allele", "peptide"]).measurement_value.median().reset_index()
+ df["measurement_value"] = numpy.expm1(df.measurement_value)
print("Took median for each duplicate peptide/allele pair: %s" % str(df.shape))
+ print(df.head())
+
if args.subsample:
df = df.head(args.subsample)
print("Subsampled to: %s" % str(df.shape))
diff --git a/downloads-generation/data_curated/GENERATE.sh b/downloads-generation/data_curated/GENERATE.sh
index 89982e6a..f0a3d54a 100755
--- a/downloads-generation/data_curated/GENERATE.sh
+++ b/downloads-generation/data_curated/GENERATE.sh
@@ -1,5 +1,9 @@
#!/bin/bash
-
+#
+# Create "curated" training data, which combines an IEDB download with additional
+# published data, removes unusable entries, normalizes allele name, and performs
+# other filtering and standardization.
+#
set -e
set -x
diff --git a/downloads-generation/data_iedb/GENERATE.sh b/downloads-generation/data_iedb/GENERATE.sh
index 55156647..a6067a36 100755
--- a/downloads-generation/data_iedb/GENERATE.sh
+++ b/downloads-generation/data_iedb/GENERATE.sh
@@ -1,5 +1,7 @@
#!/bin/bash
-
+#
+# Download latest MHC I ligand data from IEDB.
+#
set -e
set -x
diff --git a/downloads-generation/data_kim2014/GENERATE.sh b/downloads-generation/data_kim2014/GENERATE.sh
index baf8a3a4..dbda0fe8 100755
--- a/downloads-generation/data_kim2014/GENERATE.sh
+++ b/downloads-generation/data_kim2014/GENERATE.sh
@@ -1,5 +1,8 @@
#!/bin/bash
-
+#
+# Download some published MHC I ligand data from a location on Dropbox.
+#
+#
set -e
set -x
diff --git a/downloads-generation/models_class1/GENERATE.sh b/downloads-generation/models_class1/GENERATE.sh
index 2e509d23..b72334b5 100755
--- a/downloads-generation/models_class1/GENERATE.sh
+++ b/downloads-generation/models_class1/GENERATE.sh
@@ -1,5 +1,9 @@
#!/bin/bash
-
+#
+# Train standard MHCflurry Class I models.
+# Calls mhcflurry-class1-train-allele-specific-models on curated training data
+# using the hyperparameters in "hyperparameters.yaml".
+#
set -e
set -x
diff --git a/downloads-generation/models_class1_experiments1/GENERATE.sh b/downloads-generation/models_class1_experiments1/GENERATE.sh
index 6edd4348..89921d92 100755
--- a/downloads-generation/models_class1_experiments1/GENERATE.sh
+++ b/downloads-generation/models_class1_experiments1/GENERATE.sh
@@ -1,5 +1,9 @@
#!/bin/bash
-
+#
+# Train "experimental" models using various hyperparameter combinations.
+# This trains models only for a small number of alleles for which we have good
+# mass-spec validation data.
+#
set -e
set -x
diff --git a/mhcflurry/amino_acid.py b/mhcflurry/amino_acid.py
index 78b3a854..ffa5cffc 100644
--- a/mhcflurry/amino_acid.py
+++ b/mhcflurry/amino_acid.py
@@ -80,7 +80,7 @@ X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
"""), sep='\s+').loc[AMINO_ACIDS, AMINO_ACIDS]
assert (BLOSUM62_MATRIX == BLOSUM62_MATRIX.T).all().all()
-ENCODING_DFS = {
+ENCODING_DATA_FRAMES = {
"BLOSUM62": BLOSUM62_MATRIX,
"one-hot": pandas.DataFrame([
[1 if i == j else 0 for i in range(len(AMINO_ACIDS))]
@@ -98,7 +98,7 @@ def available_vector_encodings():
list of string
"""
- return list(ENCODING_DFS)
+ return list(ENCODING_DATA_FRAMES)
def vector_encoding_length(name):
@@ -113,7 +113,7 @@ def vector_encoding_length(name):
-------
int
"""
- return ENCODING_DFS[name].shape[1]
+ return ENCODING_DATA_FRAMES[name].shape[1]
def index_encoding(sequences, letter_to_index_dict):
diff --git a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
index ada2e9e5..af94cfa0 100644
--- a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
@@ -760,6 +760,19 @@ class Class1AffinityPredictor(object):
'peptide': peptides.sequences,
'allele': alleles,
})
+ if len(df) == 0:
+ # No predictions.
+ logging.warning("Predicting for 0 peptides.")
+ empty_result = pandas.DataFrame(
+ columns=[
+ 'peptide',
+ 'allele',
+ 'prediction',
+ 'prediction_low',
+ 'prediction_high'
+ ])
+ return empty_result
+
df["normalized_allele"] = df.allele.map(
mhcnames.normalize_allele_name)
diff --git a/mhcflurry/encodable_sequences.py b/mhcflurry/encodable_sequences.py
index b98fb862..8477938b 100644
--- a/mhcflurry/encodable_sequences.py
+++ b/mhcflurry/encodable_sequences.py
@@ -135,7 +135,7 @@ class EncodableSequences(object):
max_length=max_length))
result = amino_acid.fixed_vectors_encoding(
fixed_length_sequences,
- amino_acid.ENCODING_DFS[vector_encoding_name])
+ amino_acid.ENCODING_DATA_FRAMES[vector_encoding_name])
assert result.shape[0] == len(self.sequences)
self.encoding_cache[cache_key] = result
return self.encoding_cache[cache_key]
--
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