From 527a608d34e6361b100271a3390cf867a0a94812 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Fri, 6 Mar 2020 19:28:10 -0500
Subject: [PATCH] Working on docs
---
mhcflurry/class1_presentation_predictor.py | 92 +++++++++++++++++++---
test/test_class1_presentation_predictor.py | 4 +-
2 files changed, 83 insertions(+), 13 deletions(-)
diff --git a/mhcflurry/class1_presentation_predictor.py b/mhcflurry/class1_presentation_predictor.py
index f2d8094e..ef3f5e48 100644
--- a/mhcflurry/class1_presentation_predictor.py
+++ b/mhcflurry/class1_presentation_predictor.py
@@ -1,14 +1,12 @@
from __future__ import print_function
-from os.path import join, exists, abspath
-from os import mkdir, environ
+from os.path import join, exists
+from os import mkdir
from socket import gethostname
from getpass import getuser
import time
import collections
-import json
-import hashlib
import logging
from six import string_types
@@ -17,7 +15,6 @@ import pandas
import sklearn
import sklearn.linear_model
-import mhcnames
try:
import tqdm
@@ -30,9 +27,7 @@ from .class1_processing_predictor import Class1ProcessingPredictor
from .class1_neural_network import DEFAULT_PREDICT_BATCH_SIZE
from .encodable_sequences import EncodableSequences
from .regression_target import from_ic50, to_ic50
-from .multiple_allele_encoding import MultipleAlleleEncoding
from .downloads import get_default_class1_presentation_models_dir
-from .common import load_weights
MAX_ALLELES_PER_SAMPLE = 6
@@ -41,6 +36,12 @@ PREDICT_CHUNK_SIZE = 100000 # currently used only for cleavage prediction
class Class1PresentationPredictor(object):
+ """
+ A logistic regression model over predicted binding affinity (BA) and antigen
+ processing (AP) score.
+
+ See load() and predict() methods for basic usage.
+ """
model_inputs = ["affinity_score", "processing_score"]
def __init__(
@@ -61,10 +62,16 @@ class Class1PresentationPredictor(object):
@property
def supported_alleles(self):
+ """
+ List of alleles supported by the underlying Class1AffinityPredictor
+ """
return self.affinity_predictor.supported_alleles
@property
def supported_peptide_lengths(self):
+ """
+ (min, max) of supported peptide lengths, inclusive.
+ """
return self.affinity_predictor.supported_peptide_lengths
def predict_affinity(
@@ -75,6 +82,21 @@ class Class1PresentationPredictor(object):
include_affinity_percentile=False,
verbose=1,
throw=True):
+ """
+
+ Parameters
+ ----------
+ peptides
+ experiment_names
+ alleles
+ include_affinity_percentile
+ verbose
+ throw
+
+ Returns
+ -------
+
+ """
df = pandas.DataFrame({
"peptide": numpy.array(peptides, copy=False),
"experiment_name": numpy.array(experiment_names, copy=False),
@@ -242,14 +264,62 @@ class Class1PresentationPredictor(object):
self,
sequences,
alleles,
- result="best", # or "all" or "filtered"
+ result="best",
comparison_quantity="presentation_score",
- comparison_value=None,
+ filter_value=None,
peptide_lengths=[8, 9, 10, 11],
use_flanks=True,
include_affinity_percentile=False,
verbose=1,
throw=True):
+ """
+ Predict across protein sequences.
+
+ Parameters
+ ----------
+ sequences : str, list of string, or string -> string dict
+ Protein sequences. If a dict is given, the keys are arbitrary (
+ e.g. protein names), and the values are the amino acid sequences.
+ alleles : str, list of string, list of list of string, or string -> string dict
+ MHC I alleles. Can be: (1) a string (a single allele), (2) a list of
+ strings (a single genotype), (3) a list of list of strings
+ (multiple genotypes, where the total number of genotypes must equal
+ the number of sequences), or (4) a dict (in which case the keys must
+ match the sequences dict keys).
+ result : string
+ One of:
+ - "best": return the strongest peptide for each sequence
+ - "all": return predictions for all peptides
+ - "filtered": return predictions stronger where comparison_quantity
+ is stronger than filter_value.
+ comparison_quantity : string
+ One of "presentation_score", "processing_score", or "affinity".
+ Quantity to use to rank (if result is "best") or filter (if result
+ is "filtered") results.
+ filter_value : float
+ Threshold value to use, only relevant when result is "filtered".
+ If comparison_quantity is "affinity", then all results less than
+ (i.e. tighter than) the specified nM affinity are retained. If it's
+ "presentation_score" or "processing_score" then results greater than
+ the indicated filter_value are retained.
+ peptide_lengths : list of int
+ Peptide lengths to predict for.
+ use_flanks : bool
+ Whether to include flanking sequences when running the AP predictor
+ (for better cleavage prediction).
+ include_affinity_percentile : bool
+ Whether to include affinity percentile ranks in output.
+ verbose : int
+ Set to 0 for quiet mode.
+ throw : boolean
+ Whether to throw exceptions (vs. log warnings) on invalid inputs.
+
+ Returns
+ -------
+ pandas.DataFrame with columns:
+ peptide, n_flank, c_flank, sequence_name, affinity, best_allele,
+ processing_score, presentation_score
+ """
processing_predictor = self.processing_predictor_with_flanks
if not use_flanks or processing_predictor is None:
@@ -339,11 +409,11 @@ class Class1PresentationPredictor(object):
elif result == "filtered":
if comparison_is_score:
result_df = result_df.loc[
- result_df[comparison_quantity] >= comparison_value
+ result_df[comparison_quantity] >= filter_value
]
else:
result_df = result_df.loc[
- result_df[comparison_quantity] <= comparison_value
+ result_df[comparison_quantity] <= filter_value
]
elif result == "all":
pass
diff --git a/test/test_class1_presentation_predictor.py b/test/test_class1_presentation_predictor.py
index f811ab6b..242f6087 100644
--- a/test/test_class1_presentation_predictor.py
+++ b/test/test_class1_presentation_predictor.py
@@ -158,7 +158,7 @@ def test_downloaded_predictor():
scan_results2 = PRESENTATION_PREDICTOR.predict_sequences(
result="filtered",
- comparison_value=500,
+ filter_value=500,
comparison_quantity="affinity",
sequences={
"seq1": "MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLE",
@@ -180,7 +180,7 @@ def test_downloaded_predictor():
scan_results3 = PRESENTATION_PREDICTOR.predict_sequences(
result="filtered",
- comparison_value=0.9,
+ filter_value=0.9,
comparison_quantity="presentation_score",
sequences={
"seq1": "MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLE",
--
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