From 99e74eeb107664e7c784c72399f9fb305da35614 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Wed, 15 Nov 2017 18:47:30 -0500
Subject: [PATCH] Support percentile ranks
* First cut of supporting quantiles ("percent ranks") per allele. Closes #87.
* Some refactoring of amino_acid.py
This builds on #114
---
mhcflurry/amino_acid.py | 107 ++++++++++++
.../class1_affinity_predictor.py | 153 ++++++++++++++++--
.../class1_neural_network.py | 6 +-
.../train_allele_specific_models_command.py | 17 ++
mhcflurry/encodable_sequences.py | 110 +------------
mhcflurry/percent_rank_transform.py | 77 +++++++++
...odable_sequences.py => test_amino_acid.py} | 6 +-
test/test_percent_rank_transform.py | 24 +++
...st_train_allele_specific_models_command.py | 47 +++---
9 files changed, 403 insertions(+), 144 deletions(-)
create mode 100644 mhcflurry/percent_rank_transform.py
rename test/{test_encodable_sequences.py => test_amino_acid.py} (84%)
create mode 100644 test/test_percent_rank_transform.py
diff --git a/mhcflurry/amino_acid.py b/mhcflurry/amino_acid.py
index e5e59cf1..eba9de5f 100644
--- a/mhcflurry/amino_acid.py
+++ b/mhcflurry/amino_acid.py
@@ -20,6 +20,11 @@ from __future__ import (
import collections
from copy import copy
+import pandas
+import numpy
+from six import StringIO
+
+
COMMON_AMINO_ACIDS = collections.OrderedDict(sorted({
"A": "Alanine",
"R": "Arginine",
@@ -47,3 +52,105 @@ COMMON_AMINO_ACIDS_WITH_UNKNOWN["X"] = "Unknown"
AMINO_ACID_INDEX = dict(
(letter, i) for (i, letter) in enumerate(COMMON_AMINO_ACIDS_WITH_UNKNOWN))
+
+AMINO_ACIDS = list(COMMON_AMINO_ACIDS_WITH_UNKNOWN.keys())
+
+BLOSUM62_MATRIX = pandas.read_table(StringIO("""
+ A R N D C Q E G H I L K M F P S T W Y V X
+A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 0
+R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 0
+N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 0
+D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 0
+C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 0
+Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0
+E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 0
+G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 0
+H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0
+I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 0
+L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 0
+K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0
+M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 0
+F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 0
+P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 0
+S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0
+T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 0
+W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 0
+Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 0
+V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 0
+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 = {
+ "BLOSUM62": BLOSUM62_MATRIX,
+ "one-hot": pandas.DataFrame([
+ [1 if i == j else 0 for i in range(len(AMINO_ACIDS))]
+ for j in range(len(AMINO_ACIDS))
+ ], index=AMINO_ACIDS, columns=AMINO_ACIDS)
+}
+
+
+def available_vector_encodings():
+ """
+ Return list of supported amino acid vector encodings.
+
+ Returns
+ -------
+ list of string
+
+ """
+ return list(ENCODING_DFS)
+
+
+def vector_encoding_length(name):
+ """
+ Return the length of the given vector encoding.
+
+ Parameters
+ ----------
+ name : string
+
+ Returns
+ -------
+ int
+ """
+ return ENCODING_DFS[name].shape[1]
+
+
+def index_encoding(sequences, letter_to_index_dict):
+ """
+ Given a sequence of n strings all of length k, return a k * n array where
+ the (i, j)th element is letter_to_index_dict[sequence[i][j]].
+
+ Parameters
+ ----------
+ sequences : list of length n of strings of length k
+ letter_to_index_dict : dict : string -> int
+
+ Returns
+ -------
+ numpy.array of integers with shape (k, n)
+ """
+ df = pandas.DataFrame(iter(s) for s in sequences)
+ result = df.replace(letter_to_index_dict)
+ return result.values
+
+
+def fixed_vectors_encoding(sequences, letter_to_vector_function):
+ """
+ Given a sequence of n strings all of length k, return a n * k * m array where
+ the (i, j)th element is letter_to_vector_function(sequence[i][j]).
+
+ Parameters
+ ----------
+ sequences : list of length n of strings of length k
+ letter_to_vector_function : function : string -> vector of length m
+
+ Returns
+ -------
+ numpy.array of integers with shape (n, k, m)
+ """
+ arr = numpy.array([list(s) for s in sequences])
+ result = numpy.vectorize(
+ letter_to_vector_function, signature='()->(n)')(arr)
+ return result
\ No newline at end of file
diff --git a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
index bfba10cb..21a92e72 100644
--- a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
@@ -5,14 +5,19 @@ import json
from os.path import join, exists
from six import string_types
import logging
+import warnings
import numpy
import pandas
+from numpy.testing import assert_equal
import mhcnames
from ..encodable_sequences import EncodableSequences
from ..downloads import get_path
+from ..common import random_peptides
+from ..percent_rank_transform import PercentRankTransform
+from ..regression_target import to_ic50
from .class1_neural_network import Class1NeuralNetwork
@@ -32,7 +37,8 @@ class Class1AffinityPredictor(object):
allele_to_allele_specific_models=None,
class1_pan_allele_models=None,
allele_to_pseudosequence=None,
- manifest_df=None):
+ manifest_df=None,
+ allele_to_percent_rank_transform=None):
"""
Parameters
----------
@@ -50,6 +56,9 @@ class Class1AffinityPredictor(object):
Only required if you want to update an existing serialization of a
Class1AffinityPredictor. Otherwise this dataframe will be generated
automatically based on the supplied models.
+
+ allele_to_percent_rank_transform : dict of string -> PercentRankTransform, optional
+ PercentRankTransform instances to use for each allele
"""
if allele_to_allele_specific_models is None:
@@ -86,6 +95,11 @@ class Class1AffinityPredictor(object):
columns=["model_name", "allele", "config_json", "model"])
self.manifest_df = manifest_df
+ if allele_to_percent_rank_transform is None:
+ allele_to_percent_rank_transform = {}
+ self.allele_to_percent_rank_transform = allele_to_percent_rank_transform
+
+
@property
def supported_alleles(self):
"""
@@ -165,6 +179,21 @@ class Class1AffinityPredictor(object):
write_manifest_df.to_csv(manifest_path, index=False)
logging.info("Wrote: %s" % manifest_path)
+ if self.allele_to_percent_rank_transform:
+ percent_ranks_df = None
+ for (allele, transform) in self.allele_to_percent_rank_transform.items():
+ series = transform.to_series()
+ if percent_ranks_df is None:
+ percent_ranks_df = pandas.DataFrame(index=series.index)
+ assert_equal(series.index.values, percent_ranks_df.index.values)
+ percent_ranks_df[allele] = series
+ percent_ranks_path = join(models_dir, "percent_ranks.csv")
+ percent_ranks_df.to_csv(
+ percent_ranks_path,
+ index=True,
+ index_label="bin")
+ logging.info("Wrote: %s" % percent_ranks_path)
+
@staticmethod
def load(models_dir=None, max_models=None):
"""
@@ -211,11 +240,20 @@ class Class1AffinityPredictor(object):
join(models_dir, "pseudosequences.csv"),
index_col="allele").to_dict()
+ allele_to_percent_rank_transform = {}
+ percent_ranks_path = join(models_dir, "percent_ranks.csv")
+ if exists(percent_ranks_path):
+ percent_ranks_df = pandas.read_csv(percent_ranks_path, index_col=0)
+ for allele in percent_ranks_df.columns:
+ allele_to_percent_rank_transform[allele] = (
+ PercentRankTransform.from_series(percent_ranks_df[allele]))
+
logging.info(
- "Loaded %d class1 pan allele predictors, %d pseudosequences, and "
- "%d allele specific models: %s" % (
+ "Loaded %d class1 pan allele predictors, %d pseudosequences, "
+ "%d percent rank distributions, and %d allele specific models: %s" % (
len(class1_pan_allele_models),
len(pseudosequences) if pseudosequences else 0,
+ len(allele_to_percent_rank_transform),
sum(len(v) for v in allele_to_allele_specific_models.values()),
", ".join(
"%s (%d)" % (allele, len(v))
@@ -226,7 +264,9 @@ class Class1AffinityPredictor(object):
allele_to_allele_specific_models=allele_to_allele_specific_models,
class1_pan_allele_models=class1_pan_allele_models,
allele_to_pseudosequence=pseudosequences,
- manifest_df=manifest_df)
+ manifest_df=manifest_df,
+ allele_to_percent_rank_transform=allele_to_percent_rank_transform,
+ )
return result
@staticmethod
@@ -441,6 +481,88 @@ class Class1AffinityPredictor(object):
verbose=verbose)
yield model
+ def calibrate_percentile_ranks(
+ self,
+ peptides=None,
+ num_peptides_per_length=int(1e6),
+ alleles=None,
+ bins=None):
+ """
+ Compute the cumulative distribution of ic50 values for a set of alleles
+ over a large universe of random peptides, to enable computing quantiles in
+ this distribution later.
+
+ Parameters
+ ----------
+ peptides : sequence of string, optional
+ Peptides to use
+ num_peptides_per_length : int, optional
+ If peptides argument is not specified, then num_peptides_per_length
+ peptides are randomly sampled from a uniform distribution for each
+ supported length
+ alleles : sequence of string, optional
+ Alleles to perform calibration for. If not specified all supported
+ alleles will be calibrated.
+ """
+ if bins is None:
+ bins = to_ic50(numpy.linspace(1, 0, 1000))
+
+ if alleles is None:
+ alleles = self.supported_alleles
+
+ if peptides is None:
+ peptides = []
+ lengths = range(
+ self.supported_peptide_lengths[0],
+ self.supported_peptide_lengths[1] + 1)
+ for length in lengths:
+ peptides.extend(
+ random_peptides(num_peptides_per_length, length))
+
+ for allele in alleles:
+ predictions = self.predict(peptides, allele=allele)
+ transform = PercentRankTransform()
+ transform.fit(predictions, bins=bins)
+ self.allele_to_percent_rank_transform[allele] = transform
+
+ def percentile_ranks(self, affinities, allele=None, alleles=None):
+ """
+ Return percentile ranks for the given ic50 affinities and alleles.
+
+ The 'allele' and 'alleles' argument are as in the predict() method.
+ Specify one of these.
+
+ Parameters
+ ----------
+ affinities : sequence of float
+ nM affinities
+ allele : string
+ alleles : sequence of string
+
+ Returns
+ -------
+ numpy.array of float
+ """
+ if allele is not None:
+ try:
+ transform = self.allele_to_percent_rank_transform[allele]
+ return transform.transform(affinities)
+ except KeyError:
+ raise ValueError(
+ "Allele %s has no percentile rank information" % allele)
+
+ if alleles is None:
+ raise ValueError("Specify allele or alleles")
+
+ df = pandas.DataFrame({"affinity": affinities})
+ df["allele"] = alleles
+ df["result"] = numpy.nan
+ for (allele, sub_df) in df.groupby("allele"):
+ df.loc[sub_df.index, "result"] = self.percentile_ranks\
+ (sub_df.affinity, allele=allele)
+ assert not df.result.isnull().any()
+ return df.result.values
+
def predict(self, peptides, alleles=None, allele=None, throw=True):
"""
Predict nM binding affinities.
@@ -472,6 +594,7 @@ class Class1AffinityPredictor(object):
alleles=alleles,
allele=allele,
throw=throw,
+ include_percentile_ranks=False,
)
return df.prediction.values
@@ -481,7 +604,8 @@ class Class1AffinityPredictor(object):
alleles=None,
allele=None,
throw=True,
- include_individual_model_predictions=False):
+ include_individual_model_predictions=False,
+ include_percentile_ranks=True):
"""
Predict nM binding affinities. Gives more detailed output than `predict`
method, including 5-95% prediction intervals.
@@ -499,13 +623,17 @@ class Class1AffinityPredictor(object):
peptides : EncodableSequences or list of string
alleles : list of string
allele : string
- include_individual_model_predictions : boolean
- If True, the predictions of each individual model are incldued as
- columns in the result dataframe.
throw : boolean
If True, a ValueError will be raised in the case of unsupported
alleles or peptide lengths. If False, a warning will be logged and
the predictions for the unsupported alleles or peptides will be NaN.
+ include_individual_model_predictions : boolean
+ If True, the predictions of each individual model are included as
+ columns in the result dataframe.
+ include_percentile_ranks : boolean, default True
+ If True, a "prediction_percentile" column will be included giving the
+ percentile ranks. If no percentile rank information is available,
+ this will be ignored with a warning.
Returns
-------
@@ -619,8 +747,15 @@ class Class1AffinityPredictor(object):
columns = [
c for c in df.columns if c not in df_predictions.columns
]
- return df[columns]
+ result = df[columns].copy()
+ if include_percentile_ranks:
+ if self.allele_to_percent_rank_transform:
+ result["prediction_percentile"] = self.percentile_ranks(
+ df.prediction, alleles=df.allele.values)
+ else:
+ warnings.warn("No percentile rank information available.")
+ return result
@staticmethod
def save_weights(weights_list, filename):
diff --git a/mhcflurry/class1_affinity_prediction/class1_neural_network.py b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
index 13a7f55c..d685669c 100644
--- a/mhcflurry/class1_affinity_prediction/class1_neural_network.py
+++ b/mhcflurry/class1_affinity_prediction/class1_neural_network.py
@@ -16,10 +16,8 @@ from keras.layers.normalization import BatchNormalization
from mhcflurry.hyperparameters import HyperparameterDefaults
-from ..encodable_sequences import (
- EncodableSequences,
- available_vector_encodings,
- vector_encoding_length)
+from ..encodable_sequences import EncodableSequences
+from ..amino_acid import available_vector_encodings, vector_encoding_length
from ..regression_target import to_ic50, from_ic50
from ..common import random_peptides, amino_acid_distribution
diff --git a/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
index 1128ca99..aa12add4 100644
--- a/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
+++ b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
@@ -6,6 +6,7 @@ import os
import sys
import argparse
import yaml
+import time
import pandas
@@ -49,6 +50,13 @@ parser.add_argument(
action="store_true",
default=False,
help="Use only quantitative training data")
+parser.add_argument(
+ "--percent-rank-calibration-num-peptides-per-length",
+ type=int,
+ default=int(1e5),
+ help="Number of peptides per length to use to calibrate percent ranks. "
+ "Set to 0 to disable percent rank calibration. The resulting models will "
+ "not support percent ranks")
parser.add_argument(
"--verbosity",
type=int,
@@ -126,6 +134,15 @@ def run(argv=sys.argv[1:]):
affinities=train_data.measurement_value.values,
models_dir_for_save=args.out_models_dir)
+ if args.percent_rank_calibration_num_peptides_per_length > 0:
+ start = time.time()
+ print("Performing percent rank calibration.")
+ predictor.calibrate_percentile_ranks(
+ num_peptides_per_length=args.percent_rank_calibration_num_peptides_per_length)
+ print("Finished calibrating percent ranks in %0.2f sec." % (
+ time.time() - start))
+ predictor.save(args.out_models_dir, model_names_to_write=[])
+
if __name__ == '__main__':
run()
diff --git a/mhcflurry/encodable_sequences.py b/mhcflurry/encodable_sequences.py
index 4abc94a1..67553d0f 100644
--- a/mhcflurry/encodable_sequences.py
+++ b/mhcflurry/encodable_sequences.py
@@ -20,116 +20,12 @@ from __future__ import (
import math
-import pandas
import numpy
-from six import StringIO
import typechecks
from . import amino_acid
-AMINO_ACIDS = list(amino_acid.COMMON_AMINO_ACIDS_WITH_UNKNOWN.keys())
-
-BLOSUM62_MATRIX = pandas.read_table(StringIO("""
- A R N D C Q E G H I L K M F P S T W Y V X
-A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 0
-R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 0
-N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 0
-D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 0
-C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 0
-Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0
-E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 0
-G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 0
-H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0
-I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 0
-L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 0
-K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0
-M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 0
-F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 0
-P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 0
-S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0
-T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 0
-W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 0
-Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 0
-V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 0
-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 = {
- "BLOSUM62": BLOSUM62_MATRIX,
- "one-hot": pandas.DataFrame([
- [1 if i == j else 0 for i in range(len(AMINO_ACIDS))]
- for j in range(len(AMINO_ACIDS))
- ], index=AMINO_ACIDS, columns=AMINO_ACIDS)
-}
-
-
-def available_vector_encodings():
- """
- Return list of supported amino acid vector encodings.
-
- Returns
- -------
- list of string
-
- """
- return list(ENCODING_DFS)
-
-
-def vector_encoding_length(name):
- """
- Return the length of the given vector encoding.
-
- Parameters
- ----------
- name : string
-
- Returns
- -------
- int
- """
- return ENCODING_DFS[name].shape[1]
-
-
-def index_encoding(sequences, letter_to_index_dict):
- """
- Given a sequence of n strings all of length k, return a k * n array where
- the (i, j)th element is letter_to_index_dict[sequence[i][j]].
-
- Parameters
- ----------
- sequences : list of length n of strings of length k
- letter_to_index_dict : dict : string -> int
-
- Returns
- -------
- numpy.array of integers with shape (k, n)
- """
- df = pandas.DataFrame(iter(s) for s in sequences)
- result = df.replace(letter_to_index_dict)
- return result.values
-
-
-def fixed_vectors_encoding(sequences, letter_to_vector_function):
- """
- Given a sequence of n strings all of length k, return a n * k * m array where
- the (i, j)th element is letter_to_vector_function(sequence[i][j]).
-
- Parameters
- ----------
- sequences : list of length n of strings of length k
- letter_to_vector_function : function : string -> vector of length m
-
- Returns
- -------
- numpy.array of integers with shape (n, k, m)
- """
- arr = numpy.array([list(s) for s in sequences])
- result = numpy.vectorize(
- letter_to_vector_function, signature='()->(n)')(arr)
- return result
-
class EncodableSequences(object):
"""
@@ -198,7 +94,7 @@ class EncodableSequences(object):
max_length=max_length)
for sequence in self.sequences
]
- self.encoding_cache[cache_key] = index_encoding(
+ self.encoding_cache[cache_key] = amino_acid.index_encoding(
fixed_length_sequences, amino_acid.AMINO_ACID_INDEX)
return self.encoding_cache[cache_key]
@@ -242,9 +138,9 @@ class EncodableSequences(object):
max_length=max_length)
for sequence in self.sequences
]
- result = fixed_vectors_encoding(
+ result = amino_acid.fixed_vectors_encoding(
fixed_length_sequences,
- ENCODING_DFS[vector_encoding_name].loc.__getitem__)
+ amino_acid.ENCODING_DFS[vector_encoding_name].loc.__getitem__)
assert result.shape[0] == len(self.sequences)
self.encoding_cache[cache_key] = result
return self.encoding_cache[cache_key]
diff --git a/mhcflurry/percent_rank_transform.py b/mhcflurry/percent_rank_transform.py
new file mode 100644
index 00000000..80c32409
--- /dev/null
+++ b/mhcflurry/percent_rank_transform.py
@@ -0,0 +1,77 @@
+import numpy
+import pandas
+
+class PercentRankTransform(object):
+ """
+ Transform arbitrary values into percent ranks.
+ """
+
+ def __init__(self):
+ self.cdf = None
+ self.bin_edges = None
+
+ def fit(self, values, bins):
+ """
+ Fit the transform using the given values, which are used to
+ establish percentiles.
+ """
+ assert self.cdf is None
+ assert self.bin_edges is None
+ assert len(values) > 0
+ (hist, self.bin_edges) = numpy.histogram(values, bins=bins)
+ self.cdf = numpy.ones(len(hist) + 3) * numpy.nan
+ self.cdf[0] = 0.0
+ self.cdf[1] = 0.0
+ self.cdf[-1] = 100.0
+ numpy.cumsum(hist * 100.0 / numpy.sum(hist), out=self.cdf[2:-1])
+ assert not numpy.isnan(self.cdf).any()
+
+ def transform(self, values):
+ """
+ Return percent ranks (range [0, 100]) for the given values.
+ """
+ assert self.cdf is not None
+ assert self.bin_edges is not None
+ indices = numpy.searchsorted(self.bin_edges, values)
+ result = self.cdf[indices]
+ assert len(result) == len(values)
+ return result
+
+ def to_series(self):
+ """
+ Serialize the fit to a pandas.Series.
+
+ The index on the series gives the bin edges and the valeus give the CDF.
+
+ Returns
+ -------
+ pandas.Series
+
+ """
+ return pandas.Series(
+ self.cdf, index=[numpy.nan] + list(self.bin_edges) + [numpy.nan])
+
+ @staticmethod
+ def from_series(series):
+ """
+ Deseralize a PercentRankTransform the given pandas.Series, as returned
+ by `to_series()`.
+
+ Parameters
+ ----------
+ series : pandas.Series
+
+ Returns
+ -------
+ PercentRankTransform
+
+ """
+ result = PercentRankTransform()
+ result.cdf = series.values
+ result.bin_edges = series.index.values[1:-1]
+ return result
+
+
+
+
+
diff --git a/test/test_encodable_sequences.py b/test/test_amino_acid.py
similarity index 84%
rename from test/test_encodable_sequences.py
rename to test/test_amino_acid.py
index cc47290a..26c50b7b 100644
--- a/test/test_encodable_sequences.py
+++ b/test/test_amino_acid.py
@@ -1,4 +1,4 @@
-from mhcflurry import encodable_sequences
+from mhcflurry import amino_acid
from nose.tools import eq_
from numpy.testing import assert_equal
import numpy
@@ -11,7 +11,7 @@ letter_to_index_dict = {
def test_index_and_one_hot_encoding():
- index_encoding = encodable_sequences.index_encoding(
+ index_encoding = amino_acid.index_encoding(
["AAAA", "ABCA"], letter_to_index_dict)
assert_equal(
index_encoding,
@@ -19,7 +19,7 @@ def test_index_and_one_hot_encoding():
[0, 0, 0, 0],
[0, 1, 2, 0],
])
- one_hot = encodable_sequences.fixed_vectors_encoding(
+ one_hot = amino_acid.fixed_vectors_encoding(
index_encoding,
{
0: numpy.array([1, 0, 0]),
diff --git a/test/test_percent_rank_transform.py b/test/test_percent_rank_transform.py
new file mode 100644
index 00000000..e30aa7bb
--- /dev/null
+++ b/test/test_percent_rank_transform.py
@@ -0,0 +1,24 @@
+import numpy
+
+from mhcflurry.percent_rank_transform import PercentRankTransform
+
+from numpy.testing import assert_allclose, assert_equal
+
+
+def test_percent_rank_transform():
+ model = PercentRankTransform()
+ model.fit(numpy.arange(1000), bins=100)
+ assert_allclose(
+ model.transform([-2, 0, 50, 100, 2000]),
+ [0.0, 0.0, 5.0, 10.0, 100.0],
+ err_msg=str(model.__dict__))
+
+ model2 = PercentRankTransform.from_series(model.to_series())
+ assert_allclose(
+ model2.transform([-2, 0, 50, 100, 2000]),
+ [0.0, 0.0, 5.0, 10.0, 100.0],
+ err_msg=str(model.__dict__))
+
+ assert_equal(model.cdf, model2.cdf)
+ assert_equal(model.bin_edges, model2.bin_edges)
+
diff --git a/test/test_train_allele_specific_models_command.py b/test/test_train_allele_specific_models_command.py
index 5c3fbcfc..f08963a1 100644
--- a/test/test_train_allele_specific_models_command.py
+++ b/test/test_train_allele_specific_models_command.py
@@ -21,6 +21,7 @@ HYPERPARAMETERS = [
"random_negative_rate": 0.0,
"random_negative_constant": 25,
+ "peptide_amino_acid_encoding": "BLOSUM62",
"use_embedding": False,
"kmer_size": 15,
"batch_normalization": False,
@@ -50,29 +51,33 @@ HYPERPARAMETERS = [
def test_run():
- try:
- models_dir = tempfile.mkdtemp(prefix="mhcflurry-test-models")
- hyperparameters_filename = os.path.join(
- models_dir, "hyperparameters.yaml")
- with open(hyperparameters_filename, "w") as fd:
- json.dump(HYPERPARAMETERS, fd)
+ models_dir = tempfile.mkdtemp(prefix="mhcflurry-test-models")
+ hyperparameters_filename = os.path.join(
+ models_dir, "hyperparameters.yaml")
+ with open(hyperparameters_filename, "w") as fd:
+ json.dump(HYPERPARAMETERS, fd)
- args = [
- "--data", get_path("data_curated", "curated_training_data.csv.bz2"),
- "--hyperparameters", hyperparameters_filename,
- "--min-measurements-per-allele", "9000",
- "--out-models-dir", models_dir,
- ]
- print("Running with args: %s" % args)
- train_allele_specific_models_command.run(args)
+ args = [
+ "--data", get_path("data_curated", "curated_training_data.csv.bz2"),
+ "--hyperparameters", hyperparameters_filename,
+ "--min-measurements-per-allele", "9000",
+ "--out-models-dir", models_dir,
+ "--percent-rank-calibration-num-peptides-per-length", "1000",
+ ]
+ print("Running with args: %s" % args)
+ train_allele_specific_models_command.run(args)
- result = Class1AffinityPredictor.load(models_dir)
- predictions = result.predict(
+ result = Class1AffinityPredictor.load(models_dir)
+ predictions = result.predict(
+ peptides=["SLYNTVATL"],
+ alleles=["HLA-A*02:01"])
+ assert_equal(predictions.shape, (1,))
+ assert_array_less(predictions, 500)
+ df = result.predict_to_dataframe(
peptides=["SLYNTVATL"],
alleles=["HLA-A*02:01"])
- assert_equal(predictions.shape, (1,))
- assert_array_less(predictions, 500)
+ print(df)
+ assert "prediction_percentile" in df.columns
- finally:
- print("Deleting: %s" % models_dir)
- shutil.rmtree(models_dir)
+ print("Deleting: %s" % models_dir)
+ shutil.rmtree(models_dir)
--
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