From 18d451d26a1fb45b46a8388fddef690b45c8fc77 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 5 Sep 2019 17:31:35 -0400
Subject: [PATCH] docs
---
.travis.yml | 2 +-
mhcflurry/train_pan_allele_models_command.py | 44 ++++++++++++++++++++
2 files changed, 45 insertions(+), 1 deletion(-)
diff --git a/.travis.yml b/.travis.yml
index 6676b2e9..a75ba01c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -50,7 +50,7 @@ env:
- KERAS_BACKEND=tensorflow
script:
# download data and models, then run tests
- - mhcflurry-downloads fetch
+ - mhcflurry-downloads fetch data_curated models_class1 models_class1_pan
- mhcflurry-downloads info # just to test this command works
- nosetests test -sv
- ./lint.sh
diff --git a/mhcflurry/train_pan_allele_models_command.py b/mhcflurry/train_pan_allele_models_command.py
index cf6a8624..d925f991 100644
--- a/mhcflurry/train_pan_allele_models_command.py
+++ b/mhcflurry/train_pan_allele_models_command.py
@@ -133,6 +133,35 @@ add_cluster_parallelism_args(parser)
def assign_folds(df, num_folds, held_out_fraction, held_out_max):
+ """
+ Split training data into multple test/train pairs, which we refer to as
+ folds. Note that a given data point may be assigned to multiple test or
+ train sets; these folds are NOT a non-overlapping partition as used in cross
+ validation.
+
+ A fold is defined by a boolean value for each data point, indicating whether
+ it is included in the training data for that fold. If it's not in the
+ training data, then it's in the test data.
+
+ Folds are balanced in terms of allele content.
+
+ Parameters
+ ----------
+ df : pandas.DataFrame
+ training data
+ num_folds : int
+ held_out_fraction : float
+ Fraction of data to hold out as test data in each fold
+ held_out_max
+ For a given allele, do not hold out more than held_out_max number of
+ data points in any fold.
+
+ Returns
+ -------
+ pandas.DataFrame
+ index is same as df.index, columns are "fold_0", ... "fold_N" giving
+ whether the data point is in the training data for the fold
+ """
result_df = pandas.DataFrame(index=df.index)
for fold in range(num_folds):
@@ -183,6 +212,21 @@ def pretrain_data_iterator(
filename,
master_allele_encoding,
peptides_per_chunk=1024):
+ """
+ Step through a CSV file giving predictions for a large number of peptides
+ (rows) and alleles (columns).
+
+ Parameters
+ ----------
+ filename : string
+ master_allele_encoding : AlleleEncoding
+ peptides_per_chunk : int
+
+ Returns
+ -------
+ Generator of (AlleleEncoding, EncodableSequences, float affinities) tuples
+
+ """
empty = pandas.read_csv(filename, index_col=0, nrows=0)
usable_alleles = [
c for c in empty.columns
--
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