From 56f8411942433d1f9aecb5249c20d88f1f63b27d Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Wed, 17 May 2017 09:28:31 -0400
Subject: [PATCH] Begin rewrite
---
.../data_combined_iedb_kim2014/README.md | 13 -
.../create-combined-class1-dataset.py | 167 -
.../create-iedb-class1-dataset.py | 171 -
.../GENERATE.sh | 22 +-
.../GENERATE.sh | 53 -
.../README.md | 29 -
.../models-summary/README.md | 12 -
.../models-summary/report.html | 18543 ----------------
.../models-summary/report.ipynb | 1093 -
.../models.py | 24 -
.../GENERATE.sh | 53 -
.../README.md | 21 -
.../imputer.json | 8 -
.../models.py | 15 -
.../GENERATE.sh | 54 -
.../README.md | 4 -
.../imputer.json | 8 -
.../models.py | 16 -
mhcflurry/__init__.py | 17 +-
mhcflurry/affinity_measurement_dataset.py | 843 -
mhcflurry/amino_acid.py | 90 +-
.../mhcflurry_trained_on_hits.py | 2 +-
.../class1_affinity_prediction/__init__.py | 7 +
.../class1_binding_predictor.py | 636 +
.../cv_and_train_command.py | 0
.../multi_allele_predictor_ensemble.py | 300 +
.../scoring.py | 0
.../train_allele_specific_models_command.py | 357 +
mhcflurry/class1_allele_specific/__init__.py | 21 -
...llele_specific_kmer_ic50_predictor_base.py | 172 -
.../class1_binding_predictor.py | 334 -
...ss1_single_model_multi_allele_predictor.py | 150 -
.../cross_validation.py | 203 -
mhcflurry/class1_allele_specific/train.py | 355 -
.../__init__.py | 12 -
.../class1_ensemble_multi_allele_predictor.py | 791 -
.../train_command.py | 232 -
mhcflurry/common.py | 68 +-
mhcflurry/dataset_helpers.py | 278 -
mhcflurry/encodable_sequences.py | 263 +
mhcflurry/feedforward.py | 140 -
mhcflurry/ic50_predictor_base.py | 96 -
mhcflurry/imputation_helpers.py | 149 -
mhcflurry/keras_layers/drop_mask.py | 16 -
.../masked_global_average_pooling.py | 31 -
.../keras_layers/masked_global_max_pooling.py | 24 -
mhcflurry/keras_layers/masked_slice.py | 37 -
mhcflurry/measurement_collection.py | 217 -
mhcflurry/parallelism.py | 120 -
mhcflurry/peptide_encoding.py | 407 -
mhcflurry/predict_command.py | 4 +-
mhcflurry/prediction.py | 60 -
mhcflurry/regression_target.py | 49 +-
mhcflurry/training_helpers.py | 155 -
requirements.txt | 5 +-
setup.py | 15 +-
...s1_allele_specific_cv_and_train_command.py | 8 +-
test/test_cross_validation.py | 4 +-
test/test_ensemble.py | 2 +-
test/test_hyperparameters.py | 2 +-
test/test_known_class1_epitopes.py | 4 +-
test/test_serialization.py | 2 +-
62 files changed, 1689 insertions(+), 25295 deletions(-)
delete mode 100644 downloads-generation/data_combined_iedb_kim2014/README.md
delete mode 100755 downloads-generation/data_combined_iedb_kim2014/create-combined-class1-dataset.py
delete mode 100755 downloads-generation/data_combined_iedb_kim2014/create-iedb-class1-dataset.py
rename downloads-generation/{data_combined_iedb_kim2014 => data_iedb}/GENERATE.sh (50%)
delete mode 100755 downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh
delete mode 100644 downloads-generation/models_class1_allele_specific_ensemble/README.md
delete mode 100644 downloads-generation/models_class1_allele_specific_ensemble/models-summary/README.md
delete mode 100644 downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.html
delete mode 100644 downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.ipynb
delete mode 100644 downloads-generation/models_class1_allele_specific_ensemble/models.py
delete mode 100755 downloads-generation/models_class1_allele_specific_single/GENERATE.sh
delete mode 100644 downloads-generation/models_class1_allele_specific_single/README.md
delete mode 100644 downloads-generation/models_class1_allele_specific_single/imputer.json
delete mode 100644 downloads-generation/models_class1_allele_specific_single/models.py
delete mode 100755 downloads-generation/models_class1_allele_specific_single_kim2014_only/GENERATE.sh
delete mode 100644 downloads-generation/models_class1_allele_specific_single_kim2014_only/README.md
delete mode 100644 downloads-generation/models_class1_allele_specific_single_kim2014_only/imputer.json
delete mode 100644 downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py
delete mode 100644 mhcflurry/affinity_measurement_dataset.py
create mode 100644 mhcflurry/class1_affinity_prediction/__init__.py
create mode 100644 mhcflurry/class1_affinity_prediction/class1_binding_predictor.py
rename mhcflurry/{class1_allele_specific => class1_affinity_prediction}/cv_and_train_command.py (100%)
create mode 100644 mhcflurry/class1_affinity_prediction/multi_allele_predictor_ensemble.py
rename mhcflurry/{class1_allele_specific => class1_affinity_prediction}/scoring.py (100%)
create mode 100644 mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
delete mode 100644 mhcflurry/class1_allele_specific/__init__.py
delete mode 100644 mhcflurry/class1_allele_specific/class1_allele_specific_kmer_ic50_predictor_base.py
delete mode 100644 mhcflurry/class1_allele_specific/class1_binding_predictor.py
delete mode 100644 mhcflurry/class1_allele_specific/class1_single_model_multi_allele_predictor.py
delete mode 100644 mhcflurry/class1_allele_specific/cross_validation.py
delete mode 100644 mhcflurry/class1_allele_specific/train.py
delete mode 100644 mhcflurry/class1_allele_specific_ensemble/__init__.py
delete mode 100644 mhcflurry/class1_allele_specific_ensemble/class1_ensemble_multi_allele_predictor.py
delete mode 100644 mhcflurry/class1_allele_specific_ensemble/train_command.py
delete mode 100644 mhcflurry/dataset_helpers.py
create mode 100644 mhcflurry/encodable_sequences.py
delete mode 100644 mhcflurry/feedforward.py
delete mode 100644 mhcflurry/ic50_predictor_base.py
delete mode 100644 mhcflurry/imputation_helpers.py
delete mode 100644 mhcflurry/keras_layers/drop_mask.py
delete mode 100644 mhcflurry/keras_layers/masked_global_average_pooling.py
delete mode 100644 mhcflurry/keras_layers/masked_global_max_pooling.py
delete mode 100644 mhcflurry/keras_layers/masked_slice.py
delete mode 100644 mhcflurry/measurement_collection.py
delete mode 100644 mhcflurry/parallelism.py
delete mode 100644 mhcflurry/peptide_encoding.py
delete mode 100644 mhcflurry/prediction.py
delete mode 100644 mhcflurry/training_helpers.py
diff --git a/downloads-generation/data_combined_iedb_kim2014/README.md b/downloads-generation/data_combined_iedb_kim2014/README.md
deleted file mode 100644
index 55672b6e..00000000
--- a/downloads-generation/data_combined_iedb_kim2014/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# The combined training set
-
-This download contains the data used to train the production class1 MHCflurry models. This data is derived from a recent [IEDB](http://www.iedb.org/home_v3.php) export as well as the data from [Kim 2014](http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241).
-
-The latest IEDB data is downloaded as part of generating this dataset. The Kim 2014 data is in its own MHCflurry download [here](../data_kim2014).
-
-Since affinity is measured using a variety of assays, some of which are incompatible, the `create-combined-class1-dataset.py` script filters the available Class I binding assays in IEDB by only retaining those with high correlation to overlapping measurements in BD2013.
-
-To generate this download run:
-
-```
-./GENERATE.sh
-```
\ No newline at end of file
diff --git a/downloads-generation/data_combined_iedb_kim2014/create-combined-class1-dataset.py b/downloads-generation/data_combined_iedb_kim2014/create-combined-class1-dataset.py
deleted file mode 100755
index 07b7f1d9..00000000
--- a/downloads-generation/data_combined_iedb_kim2014/create-combined-class1-dataset.py
+++ /dev/null
@@ -1,167 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Combine 2013 Kim/Peters NetMHCpan dataset[*] with more recent IEDB entries
-
-* = "AffinityMeasurementDataset size and composition impact the reliability..."
-"""
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
- unicode_literals
-)
-import pickle
-from collections import Counter
-import argparse
-
-import pandas as pd
-
-parser = argparse.ArgumentParser(usage=__doc__)
-
-parser.add_argument(
- "--ic50-fraction-tolerance",
- default=0.01,
- type=float,
- help=(
- "How much can the IEDB and NetMHCpan IC50 differ and still be"
- " considered compatible (as a fraction of the NetMHCpan value). "
- "Default: %(default)s"))
-
-parser.add_argument(
- "--min-assay-overlap-size",
- type=int,
- default=1,
- help="Minimum number of entries overlapping between IEDB assay and "
- "NetMHCpan data. Default: %(default)s")
-
-
-parser.add_argument(
- "--min-assay-fraction-same",
- type=float,
- help="Minimum fraction of peptides whose IC50 values agree with the "
- "NetMHCpan data. Default: %(default)s",
- default=0.9)
-
-parser.add_argument(
- "--iedb-pickle-path",
- required=True,
- help="Path to .pickle file containing dictionary of IEDB assay datasets.")
-
-parser.add_argument(
- "--netmhcpan-csv-path",
- required=True,
- help="Path to CSV with NetMHCpan dataset from 2013 Peters paper.")
-
-parser.add_argument(
- "--output-csv-filename",
- required=True,
- help="Name of combined CSV file.")
-
-parser.add_argument(
- "--extra-dataset-csv-path",
- default=[],
- action="append",
- help="Additional CSV data source with columns (species, mhc, peptide, meas)")
-
-if __name__ == "__main__":
- args = parser.parse_args()
-
- print("Reading %s..." % args.iedb_pickle_path)
- with open(args.iedb_pickle_path, "rb") as f:
- iedb_datasets = pickle.load(f)
-
- print("Reading %s..." % args.netmhcpan_csv_path)
- nielsen_data = pd.read_csv(args.netmhcpan_csv_path, sep="\t")
- print("Size of 2013 NetMHCpan dataset: %d" % len(nielsen_data))
-
- new_allele_counts = Counter()
- combined_columns = {
- "species": list(nielsen_data["species"]),
- "mhc": list(nielsen_data["mhc"]),
- "peptide": list(nielsen_data["sequence"]),
- "peptide_length": list(nielsen_data["peptide_length"]),
- "meas": list(nielsen_data["meas"]),
- }
-
- all_datasets = {
- path: pd.read_csv(path) for path in args.extra_dataset_csv_path
- }
- all_datasets.update(iedb_datasets)
- for assay, assay_dataset in sorted(all_datasets.items(), key=lambda x: len(x[1])):
- joined = nielsen_data.merge(
- assay_dataset,
- left_on=["mhc", "sequence"],
- right_on=["mhc", "peptide"],
- how="outer")
-
- if len(joined) == 0:
- continue
-
- # drop NaN binding values and entries without values in both datasets
- left_missing = joined["meas"].isnull()
- right_missing = joined["value"].isnull()
- overlap_filter_mask = ~(left_missing | right_missing)
- filtered = joined[overlap_filter_mask]
- n_overlap = len(filtered)
-
- if n_overlap < args.min_assay_overlap_size:
- continue
- # let's count what fraction of this IEDB assay is within 1% of the values in the
- # Nielsen dataset
- tolerance = filtered["meas"] * args.ic50_fraction_tolerance
- abs_diff = (filtered["value"] - filtered["meas"]).abs()
- similar_values = abs_diff <= tolerance
- fraction_similar = similar_values.mean()
- print("Assay=%s, count=%d" % (assay, len(assay_dataset)))
- print(" # entries w/ values in both data sets: %d" % n_overlap)
- print(" fraction similar binding values=%0.4f" % fraction_similar)
- new_peptides = joined[left_missing & ~right_missing]
- if fraction_similar > args.min_assay_fraction_same:
- print("---")
- print("\t using assay: %s" % (assay,))
- print("---")
- combined_columns["mhc"].extend(new_peptides["mhc"])
- combined_columns["peptide"].extend(new_peptides["peptide"])
- combined_columns["peptide_length"].extend(new_peptides["peptide"].str.len())
- combined_columns["meas"].extend(new_peptides["value"])
- # TODO: make this work for non-human data
- combined_columns["species"].extend(["human"] * len(new_peptides))
- for allele in new_peptides["mhc"]:
- new_allele_counts[allele] += 1
-
- combined_df = pd.DataFrame(
- combined_columns,
- columns=["species", "mhc", "peptide", "peptide_length", "meas"])
-
- # filter out post-translation modifications and peptides with unknown
- # residues
- modified_peptide_mask = combined_df.peptide.str.contains("\+")
- n_modified = modified_peptide_mask.sum()
- if n_modified > 0:
- print("Dropping %d modified peptides" % n_modified)
- combined_df = combined_df[~modified_peptide_mask]
-
- print("New entry allele distribution")
- for (allele, count) in new_allele_counts.most_common():
- print("%s: %d" % (allele, count))
- print("Combined DataFrame size: %d (+%d)" % (
- len(combined_df),
- len(combined_df) - len(nielsen_data)))
- print("Writing %s..." % args.output_csv_filename)
- combined_df.to_csv(args.output_csv_filename, index=False)
diff --git a/downloads-generation/data_combined_iedb_kim2014/create-iedb-class1-dataset.py b/downloads-generation/data_combined_iedb_kim2014/create-iedb-class1-dataset.py
deleted file mode 100755
index 3c770a40..00000000
--- a/downloads-generation/data_combined_iedb_kim2014/create-iedb-class1-dataset.py
+++ /dev/null
@@ -1,171 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Turn a raw CSV snapshot of the IEDB contents into a usable
-class I binding prediction dataset by grouping all unique pMHCs
-"""
-from collections import defaultdict
-import pickle
-import argparse
-
-import numpy as np
-import pandas as pd
-
-parser = argparse.ArgumentParser(usage=__doc__)
-
-parser.add_argument(
- "--input-csv",
- required=True,
- help="CSV file with IEDB's MHC binding data.")
-
-parser.add_argument(
- "--output-pickle-filename",
- required=True,
- help="Path to .pickle file containing dictionary of IEDB assay datasets.")
-
-parser.add_argument(
- "--alleles",
- metavar="ALLELE",
- nargs="+",
- default=[],
- help="Restrict dataset to specified alleles")
-
-
-def filter_class1_alleles(df):
- mhc_class = df["MHC"]["MHC allele class"]
- print("MHC class counts: \n%s" % (mhc_class.value_counts(),))
- class1_mask = mhc_class == "I"
- return df[class1_mask]
-
-
-def filter_allele_names(df):
- alleles = df["MHC"]["Allele Name"]
- invalid_allele_mask = alleles.str.contains(" ") | alleles.str.contains("/")
- invalid_alleles = alleles[invalid_allele_mask]
- print("-- Invalid allele names: %s" % (list(sorted(set(invalid_alleles)))))
- print("Dropping %d with complex alleles (e.g. descriptions of mutations)" %
- len(invalid_alleles))
- return df[~invalid_allele_mask]
-
-
-def filter_affinity_values(df):
- affinities = df["Assay"]["Quantitative measurement"]
- finite_affinity_mask = ~affinities.isnull() & np.isfinite(affinities)
- invalid_affinity_mask = ~finite_affinity_mask
-
- print("Dropping %d rows without finite affinity measurements" % (
- invalid_affinity_mask.sum(),))
- return df[finite_affinity_mask]
-
-
-def filter_mhc_dataframe(df):
- filter_functions = [
- filter_class1_alleles,
- filter_allele_names,
- filter_affinity_values,
- ]
-
- for fn in filter_functions:
- df = fn(df)
-
- return df
-
-
-def groupby_assay(df):
- assay_group = df["Assay"]["Assay Group"]
- assay_method = df["Assay"]["Method/Technique"]
- groups = df.groupby([assay_group, assay_method])
-
- # speed up repeated calls to np.log by caching log affinities as a column
- # in the dataframe
- df["_log_affinity"] = np.log(df["Assay"]["Quantitative measurement"])
-
- # speed up computing percent positive with the helper column
- qualitative = df["Assay"]["Qualitative Measure"]
- df["_qualitative_positive"] = qualitative.str.startswith("Positive")
- print("---")
- print("Assays")
- assay_dataframes = {}
- # create a dataframe for every distinct kind of assay which is used
- # by IEDB submitters to measure peptide-MHC affinity or stability
- for (assay_group, assay_method), group_data in sorted(
- groups,
- key=lambda x: len(x[1]),
- reverse=True):
- print("- %s (%s): %d" % (assay_group, assay_method, len(group_data)))
- group_alleles = group_data["MHC"]["Allele Name"]
- group_peptides = group_data["Epitope"]["Description"]
- distinct_pmhc = group_data.groupby([group_alleles, group_peptides])
- columns = defaultdict(list)
- for (allele, peptide), pmhc_group in distinct_pmhc:
- columns["mhc"].append(allele)
- columns["peptide"].append(peptide)
- positive = pmhc_group["_qualitative_positive"]
- count = len(pmhc_group)
- if count == 1:
- ic50 = pmhc_group["Assay"]["Quantitative measurement"].mean()
- else:
- ic50 = np.exp(np.mean(pmhc_group["_log_affinity"]))
- # averaging the log affinities preserves orders of magnitude better
- columns["value"].append(ic50)
- columns["percent_positive"].append(positive.mean())
- columns["count"].append(count)
- assay_dataframes[(assay_group, assay_method)] = pd.DataFrame(
- columns,
- columns=[
- "mhc",
- "peptide",
- "value",
- "percent_positive",
- "count"])
- print("# distinct pMHC entries: %d" % len(columns["mhc"]))
- return assay_dataframes
-
-if __name__ == "__main__":
- args = parser.parse_args()
- df = pd.read_csv(
- args.input_csv,
- error_bad_lines=False,
- encoding="latin-1",
- header=[0, 1])
-
- df = filter_mhc_dataframe(df)
-
- alleles = df["MHC"]["Allele Name"]
-
- n = len(alleles)
-
- print("# Class I rows: %d" % n)
- print("# Class I alleles: %d" % len(set(alleles)))
- print("Unique alleles: %s" % list(sorted(set(alleles))))
-
- if args.alleles:
- print("User-supplied allele whitelist: %s" % (args.alleles,))
- mask = np.zeros(n, dtype=bool)
- for pattern in args.alleles:
- pattern_mask = alleles.str.startswith(pattern)
- print("# %s: %d" % (pattern, pattern_mask.sum()))
- mask |= pattern_mask
- df = df[mask]
- print("# entries matching alleles %s: %d" % (
- args.alleles,
- len(df)))
-
- assay_dataframes = groupby_assay(df)
-
- with open(args.output_pickle_filename, "wb") as f:
- pickle.dump(assay_dataframes, f, pickle.HIGHEST_PROTOCOL)
diff --git a/downloads-generation/data_combined_iedb_kim2014/GENERATE.sh b/downloads-generation/data_iedb/GENERATE.sh
similarity index 50%
rename from downloads-generation/data_combined_iedb_kim2014/GENERATE.sh
rename to downloads-generation/data_iedb/GENERATE.sh
index 562a320d..5e16b6ce 100755
--- a/downloads-generation/data_combined_iedb_kim2014/GENERATE.sh
+++ b/downloads-generation/data_iedb/GENERATE.sh
@@ -3,10 +3,9 @@
set -e
set -x
-DOWNLOAD_NAME=data_combined_iedb_kim2014
+DOWNLOAD_NAME=data_iedb
SCRATCH_DIR=/tmp/mhcflurry-downloads-generation
SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
-SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
mkdir -p "$SCRATCH_DIR"
rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
@@ -18,28 +17,13 @@ exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
# Log some environment info
date
-pip freeze
-git rev-parse HEAD
-git status
-cd "$SCRATCH_DIR/$DOWNLOAD_NAME"
-
-mkdir .tmp # By starting with a dot, we won't include it in the tar archive
-cd .tmp
+cd $SCRATCH_DIR/$DOWNLOAD_NAME
wget --quiet http://www.iedb.org/doc/mhc_ligand_full.zip
unzip mhc_ligand_full.zip
+rm mhc_ligand_full.zip
-$SCRIPT_DIR/create-iedb-class1-dataset.py \
- --input-csv mhc_ligand_full.csv \
- --output-pickle-filename iedb_human_class1_assay_datasets.pickle
-
-$SCRIPT_DIR/create-combined-class1-dataset.py \
- --iedb-pickle-path iedb_human_class1_assay_datasets.pickle \
- --netmhcpan-csv-path "$(mhcflurry-downloads path data_kim2014)/bdata.20130222.mhci.public.1.txt" \
- --output-csv-filename ../combined_human_class1_dataset.csv
-
-cd ..
cp $SCRIPT_ABSOLUTE_PATH .
tar -cjf "../${DOWNLOAD_NAME}.tar.bz2" *
diff --git a/downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh b/downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh
deleted file mode 100755
index 0e45c61f..00000000
--- a/downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/bin/bash
-
-if [[ $# -eq 0 ]] ; then
- echo 'WARNING: This script is intended to be called with additional arguments to pass to mhcflurry-class1-allele-specific-cv-and-train'
- echo 'See README.md'
-fi
-
-set -e
-set -x
-
-DOWNLOAD_NAME=models_class1_allele_specific_ensemble
-SCRATCH_DIR=/tmp/mhcflurry-downloads-generation
-SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
-SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
-export PYTHONUNBUFFERED=1
-
-mkdir -p "$SCRATCH_DIR"
-rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
-mkdir "$SCRATCH_DIR/$DOWNLOAD_NAME"
-
-# Send stdout and stderr to a logfile included with the archive.
-exec > >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt")
-exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
-
-# Log some environment info
-date
-pip freeze
-git rev-parse HEAD
-git status
-
-cd $SCRATCH_DIR/$DOWNLOAD_NAME
-
-mkdir models
-
-cp $SCRIPT_DIR/models.py .
-python models.py > models.json
-
-time mhcflurry-class1-allele-specific-ensemble-train \
- --ensemble-size 16 \
- --model-architectures models.json \
- --train-data "$(mhcflurry-downloads path data_combined_iedb_kim2014)/combined_human_class1_dataset.csv" \
- --min-samples-per-allele 20 \
- --out-manifest selected_models.csv \
- --out-model-selection-manifest all_models.csv \
- --out-models models \
- --verbose \
- "$@"
-
-bzip2 all_models.csv
-cp $SCRIPT_ABSOLUTE_PATH .
-tar -cjf "../${DOWNLOAD_NAME}.tar.bz2" *
-
-echo "Created archive: $SCRATCH_DIR/$DOWNLOAD_NAME.tar.bz2"
diff --git a/downloads-generation/models_class1_allele_specific_ensemble/README.md b/downloads-generation/models_class1_allele_specific_ensemble/README.md
deleted file mode 100644
index 604852a6..00000000
--- a/downloads-generation/models_class1_allele_specific_ensemble/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Class I allele-specific models (ensemble)
-
-This download contains trained MHC Class I allele-specific MHCflurry models. For each allele, an ensemble of predictors is trained on random halves of the training data. Model architectures are selected based on performance on the other half of the dataset, so in general each ensemble contains predictors of different architectures. At prediction time the geometric mean IC50 is taken over the trained models. The training data used is in the [data_combined_iedb_kim2014](../data_combined_iedb_kim2014) MHCflurry download.
-
-The training script supports multi-node parallel execution using the [kubeface](https://github.com/hammerlab/kubeface) library.
-
-To use kubeface, you should make a google storage bucket and pass it below with the --storage-prefix argument.
-
-To generate this download we run:
-
-```
-./GENERATE.sh \
- --parallel-backend kubeface \
- --target-tasks 200 \
- --kubeface-backend kubernetes \
- --kubeface-storage gs://kubeface-tim \
- --kubeface-worker-image hammerlab/mhcflurry-misc:latest \
- --kubeface-kubernetes-task-resources-memory-mb 10000 \
- --kubeface-worker-path-prefix venv-py3/bin \
- --kubeface-max-simultaneous-tasks 200 \
- --kubeface-speculation-max-reruns 3 \
-```
-
-To debug locally:
-```
-./GENERATE.sh \
- --parallel-backend local-threads \
- --target-tasks 1
-```
diff --git a/downloads-generation/models_class1_allele_specific_ensemble/models-summary/README.md b/downloads-generation/models_class1_allele_specific_ensemble/models-summary/README.md
deleted file mode 100644
index 3cf13db5..00000000
--- a/downloads-generation/models_class1_allele_specific_ensemble/models-summary/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Class1 allele-specific ensemble models
-
-To generate the report, run:
-
-```
-time jupyter-nbconvert report.ipynb \
- --execute \
- --ExecutePreprocessor.kernel_name=python \
- --ExecutePreprocessor.timeout=60 \
- --to html \
- --stdout > report.html
-```
diff --git a/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.html b/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.html
deleted file mode 100644
index a98510c0..00000000
--- a/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.html
+++ /dev/null
@@ -1,18543 +0,0 @@
-<!DOCTYPE html>
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-.fa-sheqel:before,
-.fa-ils:before {
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-.fa-meanpath:before {
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-*
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-*
-*/
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-pre {
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-}
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-/* Flexible box model classes */
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-/* This file is a compatability layer. It allows the usage of flexible box
-model layouts accross multiple browsers, including older browsers. The newest,
-universal implementation of the flexible box model is used when available (see
-`Modern browsers` comments below). Browsers that are known to implement this
-new spec completely include:
-
- Firefox 28.0+
- Chrome 29.0+
- Internet Explorer 11+
- Opera 17.0+
-
-Browsers not listed, including Safari, are supported via the styling under the
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-*/
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-div.error {
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-div.traceback-wrapper {
- text-align: left;
- max-width: 800px;
- margin: auto;
-}
-/**
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- *
- * Author: Jupyter Development Team
- */
-body {
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- /* This makes sure that the body covers the entire window and needs to
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- right: 0px;
- top: 0px;
- bottom: 0px;
- overflow: visible;
-}
-body > #header {
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- background-color: #fff;
- /* Display over codemirror */
- position: relative;
- z-index: 100;
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-body > #header #header-container {
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- padding-top: 5px;
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-body > #header .header-bar {
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-@media print {
- body > #header {
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-@media (max-width: 991px) {
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-#noscript {
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- padding-bottom: 16px;
- text-align: center;
- font-size: 22px;
- color: red;
- font-weight: bold;
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-#ipython_notebook img {
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-#site {
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- display: none;
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- -moz-box-sizing: border-box;
- -webkit-box-sizing: border-box;
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-/* Smaller buttons */
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- font-size: 77%;
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-span#login_widget > .button,
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- background-color: #fff;
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-}
-span#login_widget > .button:focus,
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- -moz-box-align: stretch;
- display: box;
- box-orient: vertical;
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- .modal .modal-dialog {
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-}
-@media (min-width: 768px) {
- select.form-control {
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-/*!
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-* IPython auth
-*
-*/
-.center-nav {
- display: inline-block;
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-/*!
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-* IPython tree view
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-.alternate_upload .btn-upload {
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-/**
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- margin-bottom: 1em;
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-#running .panel-group .panel .panel-heading {
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-#running .panel-group .panel .panel-heading a:focus,
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- display: -webkit-box;
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- display: -webkit-box;
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- as that of bootstrap */
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- display: -webkit-box;
- -webkit-box-orient: vertical;
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- flex-direction: column;
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-@media (max-width: 540px) {
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- flex-direction: column;
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-/* This class is for the output subarea inside the output_area and after
- the prompt div. */
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- padding: 0.4em;
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- -webkit-box-flex: 1;
- -moz-box-flex: 1;
- box-flex: 1;
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-}
-/* The rest of the output_* classes are for special styling of the different
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-/* all text output has this class: */
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-/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
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-}
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-/* raw_input styles */
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-<style type="text/css">
-/* Overrides of notebook CSS for static HTML export */
-body {
- overflow: visible;
- padding: 8px;
-}
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- overflow: visible;
- border-top: none;
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- <div class="container" id="notebook-container">
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [1]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
-<span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span>
-
-<span class="kn">import</span> <span class="nn">mhcflurry</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">seaborn</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">pandas</span>
-<span class="kn">from</span> <span class="nn">os</span> <span class="k">import</span> <span class="n">environ</span>
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</span>
-<span class="kn">from</span> <span class="nn">mhcflurry.downloads</span> <span class="k">import</span> <span class="n">get_path</span>
-
-<span class="o">%</span> <span class="n">matplotlib</span> <span class="n">inline</span>
-
-<span class="kn">import</span> <span class="nn">IPython.core.display</span> <span class="k">as</span> <span class="nn">display</span>
-</pre></div>
-
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-<div class="output_subarea output_stream output_stderr output_text">
-<pre>Using Theano backend.
-/Users/tim/miniconda3/envs/py3k/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
- "This module will be removed in 0.20.", DeprecationWarning)
-</pre>
-</div>
-</div>
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-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">IPython.core.display</span> <span class="k">as</span> <span class="nn">di</span>
-
-<span class="c1"># This line will hide code by default when the notebook is exported as HTML</span>
-<span class="n">di</span><span class="o">.</span><span class="n">display_html</span><span class="p">(</span><span class="s1">'<script>jQuery(function() {if (jQuery("body.notebook_app").length == 0) { jQuery(".input_area").toggle(); jQuery(".prompt").toggle(); jQuery("div.output_stderr").toggle();}});</script>'</span><span class="p">,</span> <span class="n">raw</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
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-<h1 id="MHCflurry-models">MHCflurry models<a class="anchor-link" href="#MHCflurry-models">¶</a></h1><h2 id="Class-1-allele-specific-ensemble-models">Class 1 allele specific ensemble models<a class="anchor-link" href="#Class-1-allele-specific-ensemble-models">¶</a></h2><p>This report describes the models published with MHCflurry for Class I affinity prediction. These models were trained on the "data_combined_iedb_kim2014" affinity measurement dataset (mostly from IEDB) distributed with MHCflurry.</p>
-<p>Each allele's predictor is an ensemble of 16 models. The models were trained on a random 1/2 of the data for the allele and tested on the other half. The best performing model in terms of sum of AUC (at 500nM), F1, and Kendall Tau for each 50/50 split of the data was selected for inclusion in the ensemble.</p>
-
-</div>
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-<div class="input">
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-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">all_models_df</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">get_path</span><span class="p">(</span><span class="s2">"models_class1_allele_specific_ensemble"</span><span class="p">,</span> <span class="s2">"all_models.csv.bz2"</span><span class="p">))</span>
-<span class="n">all_models_df</span><span class="p">[</span><span class="s2">"hyperparameters_layer_sizes"</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="p">[</span><span class="s2">"hyperparameters_layer_sizes"</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="nb">eval</span><span class="p">)</span>
-
-<span class="n">full_training_data</span> <span class="o">=</span> <span class="n">mhcflurry</span><span class="o">.</span><span class="n">affinity_measurement_dataset</span><span class="o">.</span><span class="n">AffinityMeasurementDataset</span><span class="o">.</span><span class="n">from_csv</span><span class="p">(</span>
- <span class="n">get_path</span><span class="p">(</span><span class="s2">"data_combined_iedb_kim2014"</span><span class="p">,</span> <span class="s2">"combined_human_class1_dataset.csv"</span><span class="p">))</span>
-
-<span class="n">training_sizes</span> <span class="o">=</span> <span class="n">full_training_data</span><span class="o">.</span><span class="n">to_dataframe</span><span class="p">()</span><span class="o">.</span><span class="n">allele</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
-
-<span class="n">all_models_df</span><span class="p">[</span><span class="s2">"train_size"</span><span class="p">]</span> <span class="o">=</span> <span class="n">training_sizes</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">all_models_df</span><span class="o">.</span><span class="n">allele</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="p">(</span><span class="n">ensemble_size</span><span class="p">,)</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">ensemble_size</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">index</span>
-<span class="n">ensemble_size</span>
-
-<span class="n">selected_models_df</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">all_models_df</span><span class="o">.</span><span class="n">weight</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span>
-<span class="n">selected_models_df</span><span class="o">.</span><span class="n">shape</span>
-
-<span class="n">alleles</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">training_sizes</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span><span class="o">.</span><span class="n">index</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">selected_models_df</span><span class="o">.</span><span class="n">allele</span><span class="o">.</span><span class="n">values</span><span class="p">]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [4]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">training_sizes_for_included_alleles</span> <span class="o">=</span> <span class="n">training_sizes</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span>
- <span class="n">training_sizes</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">all_models_df</span><span class="o">.</span><span class="n">allele</span><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="n">lines</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">def</span> <span class="nf">row</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
- <span class="n">lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'<tr><td><b></span><span class="si">%s</span><span class="s1"></b></td><td></span><span class="si">%s</span><span class="s1"></td></tr>'</span> <span class="o">%</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">value</span><span class="p">))</span>
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-<span class="n">lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'<h1>Models summary</h1>'</span><span class="p">)</span>
-<span class="n">lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'<table>'</span><span class="p">)</span>
-
-<span class="n">row</span><span class="p">(</span><span class="s2">"Num Alleles"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{:,d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">all_models_df</span><span class="o">.</span><span class="n">allele</span><span class="o">.</span><span class="n">nunique</span><span class="p">()))</span>
-<span class="n">row</span><span class="p">(</span><span class="s2">"Ensemble size"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{:,d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ensemble_size</span><span class="p">))</span>
-<span class="n">row</span><span class="p">(</span><span class="s2">"Num architectures"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{:,d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">all_models_df</span><span class="o">.</span><span class="n">hyperparameters_architecture_num</span><span class="o">.</span><span class="n">nunique</span><span class="p">()))</span>
-<span class="n">row</span><span class="p">(</span><span class="s2">"Num selected models"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{:,d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">all_models_df</span><span class="o">.</span><span class="n">weight</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
-<span class="n">row</span><span class="p">(</span><span class="s2">"Total models tested"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{:,d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">all_models_df</span><span class="p">)))</span>
-<span class="n">row</span><span class="p">(</span><span class="s2">"Total training measurements"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{:,d}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">training_sizes_for_included_alleles</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
-<span class="n">row</span><span class="p">(</span><span class="s2">"Training measurement per allele"</span><span class="p">,</span>
- <span class="s2">"min=</span><span class="si">{:,g}</span><span class="s2">; max=</span><span class="si">{:,g}</span><span class="s2">; median=</span><span class="si">{:,g}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">training_sizes_for_included_alleles</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
- <span class="n">training_sizes_for_included_alleles</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
- <span class="n">training_sizes_for_included_alleles</span><span class="o">.</span><span class="n">median</span><span class="p">()))</span>
-
-
-<span class="n">lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'</table>'</span><span class="p">)</span>
-<span class="n">lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"<p><b>Alleles included: </b></span><span class="si">%s</span><span class="s2"></p>"</span> <span class="o">%</span> <span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
- <span class="n">training_sizes_for_included_alleles</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
-
-
-<span class="n">di</span><span class="o">.</span><span class="n">display_html</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">lines</span><span class="p">),</span> <span class="n">raw</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
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-<h1>Models summary</h1>
-<table>
-<tr><td><b>Num Alleles</b></td><td>132</td></tr>
-<tr><td><b>Ensemble size</b></td><td>16</td></tr>
-<tr><td><b>Num architectures</b></td><td>162</td></tr>
-<tr><td><b>Num selected models</b></td><td>2,112</td></tr>
-<tr><td><b>Total models tested</b></td><td>342,144</td></tr>
-<tr><td><b>Total training measurements</b></td><td>192,177</td></tr>
-<tr><td><b>Training measurement per allele</b></td><td>min=26; max=12,357; median=721.5</td></tr>
-</table>
-<p><b>Alleles included: </b>HLA-A0201 HLA-A0301 HLA-A0203 HLA-A1101 H-2-KB HLA-A3101 HLA-A0206 HLA-A6802 H-2-DB HLA-A0101 HLA-B0702 HLA-A2601 HLA-B1501 HLA-A0202 HLA-A6801 HLA-A3301 HLA-B2705 HLA-B0801 HLA-A2402 HLA-B4001 HLA-B3501 HLA-B5801 HLA-B5101 HLA-B5701 HLA-A3001 HLA-B1801 HLA-A2902 Mamu-A01 HLA-A6901 HLA-A2301 HLA-B4402 Mamu-A100101 HLA-A3002 HLA-B4601 Mamu-B17 HLA-B3901 HLA-B5301 HLA-B1517 HLA-B4403 Mamu-A02 Mamu-B01704 Mamu-A11 HLA-A0219 HLA-A2403 HLA-B5401 HLA-A0212 HLA-A8001 Mamu-B03 HLA-A3201 Mamu-B08 H-2-KD HLA-A0211 HLA-B4501 HLA-B4002 HLA-B0802 HLA-A2501 HLA-A0216 Patr-B0101 Mamu-A101101 Mamu-B52 HLA-B4801 Mamu-B01 HLA-B2703 HLA-B1509 Patr-A0901 H-2-KK HLA-B1503 Mamu-A2201 Mamu-A07 Patr-A0701 HLA-A2602 H-2-DD Mamu-A100201 HLA-A2603 Patr-A0101 HLA-C0401 HLA-B3801 H-2-LD HLA-B0803 Mamu-B3901 Mamu-B8301 Patr-B2401 HLA-C0602 Patr-A0301 HLA-B1542 HLA-B4506 HLA-A0217 HLA-B8301 Patr-A0401 Patr-B1301 HLA-B3503 HLA-C1402 HLA-EQCA100101 HLA-B4201 Mamu-A2601 HLA-B1402 HLA-C1502 HLA-C1203 HLA-C0501 HLA-B1502 HLA-C0303 Mamu-B1001 Mamu-B8701 Mamu-A20102 HLA-C0702 HLA-RT1A HLA-A0250 HLA-B7301 HLA-A0205 Mamu-A70103 Mamu-B6601 HLA-B2720 HLA-A3207 HLA-C0802 HLA-A6823 HLA-B7 HLA-A6601 HLA-A0207 HLA-A2 HLA-A11 HLA-A3215 HLA-B3701 HLA-E0103 HLA-BOLA601301 HLA-B4013 HLA-BOLAHD6 HLA-B5802 HLA-B1401 HLA-B5703 HLA-A0319 HLA-B8101 HLA-A0302</p>
-</div>
-
-</div>
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-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [5]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">architecture_num_to_row</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"hyperparameters_architecture_num"</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="n">hyperparameters</span> <span class="o">=</span> <span class="p">[</span>
- <span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">architecture_num_to_row</span><span class="o">.</span><span class="n">columns</span>
- <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"hyperparameters_"</span><span class="p">)</span> <span class="ow">and</span> <span class="n">pandas</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span>
- <span class="nb">str</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">architecture_num_to_row</span><span class="p">[</span><span class="n">x</span><span class="p">]</span>
- <span class="p">])</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span>
-<span class="p">]</span>
-<span class="n">architecture_num_to_hyperparameters</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">architecture_num_to_row</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
- <span class="n">architecture_num_to_hyperparameters</span><span class="p">[</span><span class="n">row</span><span class="o">.</span><span class="n">hyperparameters_architecture_num</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
- <span class="n">row</span><span class="p">[</span><span class="n">hyperparameters</span><span class="p">]</span><span class="o">.</span><span class="n">to_dict</span><span class="p">())</span>
-</pre></div>
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-<h1 id="Best-models">Best models<a class="anchor-link" href="#Best-models">¶</a></h1><p>This table gives the models most often selected for alleles with less than or equal to the given number of training samples.</p>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_df</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">cutoffs</span> <span class="o">=</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">inf</span><span class="p">]</span>
-<span class="k">for</span> <span class="n">cutoff</span> <span class="ow">in</span> <span class="n">cutoffs</span><span class="p">:</span>
- <span class="n">selected_rates</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span>
- <span class="n">all_models_df</span><span class="o">.</span><span class="n">train_size</span> <span class="o"><=</span> <span class="n">cutoff</span>
- <span class="p">]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"hyperparameters_architecture_num"</span><span class="p">)</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">best_architecture</span> <span class="o">=</span> <span class="n">selected_rates</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="n">d</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
- <span class="p">(</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"hyperparameters_"</span><span class="p">,</span> <span class="s2">""</span><span class="p">),</span> <span class="n">value</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="ow">in</span>
- <span class="n">architecture_num_to_hyperparameters</span><span class="p">[</span><span class="n">best_architecture</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
- <span class="n">d</span><span class="p">[</span><span class="s2">"architecture selection rate (%)"</span><span class="p">]</span> <span class="o">=</span> <span class="n">selected_rates</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">best_architecture</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span>
- <span class="n">result_df</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-<span class="n">result_df</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">result_df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">cutoffs</span><span class="p">)</span>
-<span class="n">result_df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Training size cutoff"</span>
-<span class="n">result_df</span><span class="o">.</span><span class="n">T</span>
-</pre></div>
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-<table border="1" class="dataframe">
- <thead>
- <tr style="text-align: right;">
- <th>Training size cutoff</th>
- <th>100.0</th>
- <th>500.0</th>
- <th>1000.0</th>
- <th>inf</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <th>architecture selection rate (%)</th>
- <td>3.27381</td>
- <td>2.04545</td>
- <td>2.27273</td>
- <td>3.64583</td>
- </tr>
- <tr>
- <th>architecture_num</th>
- <td>112</td>
- <td>81</td>
- <td>112</td>
- <td>27</td>
- </tr>
- <tr>
- <th>dropout_probability</th>
- <td>0.1</td>
- <td>0</td>
- <td>0.1</td>
- <td>0.1</td>
- </tr>
- <tr>
- <th>embedding_output_dim</th>
- <td>8</td>
- <td>8</td>
- <td>8</td>
- <td>8</td>
- </tr>
- <tr>
- <th>fraction_negative</th>
- <td>0.1</td>
- <td>0</td>
- <td>0.1</td>
- <td>0</td>
- </tr>
- <tr>
- <th>impute</th>
- <td>True</td>
- <td>True</td>
- <td>True</td>
- <td>False</td>
- </tr>
- <tr>
- <th>layer_sizes</th>
- <td>[64]</td>
- <td>[12]</td>
- <td>[64]</td>
- <td>[12]</td>
- </tr>
- </tbody>
-</table>
-</div>
-</div>
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-</div>
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [7]:</div>
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- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">all_scores</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">scores_auc</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
-<span class="n">selected_scores</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">all_models_df</span><span class="o">.</span><span class="n">weight</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scores_auc</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
-
-<span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">seaborn</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">all_scores</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"All. Mean=</span><span class="si">%0.2f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">all_scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-<span class="n">seaborn</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">selected_scores</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Selected. Mean=</span><span class="si">%0.2f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">selected_scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-<span class="c1">#seaborn.set_context('talk')</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">"x-large"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">xmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"AUC"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">"x-large"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"AUCs across models and alleles"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s2">"xx-large"</span><span class="p">)</span>
-</pre></div>
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-<pre><matplotlib.text.Text at 0x1177537b8></pre>
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->
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-</div>
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [8]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scores</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"allele"</span><span class="p">)</span><span class="o">.</span><span class="n">scores_auc</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">"scores_auc"</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">scores</span><span class="p">[</span><span class="s2">"size"</span><span class="p">]</span> <span class="o">=</span> <span class="n">training_sizes</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">scores</span><span class="o">.</span><span class="n">allele</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-
-<span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">30</span><span class="p">))</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Mean AUC over all models"</span><span class="p">)</span>
-<span class="n">seaborn</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"allele"</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"scores_auc"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">scores</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">"h"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"black"</span><span class="p">)</span>
-
-<span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Mean AUC over all models"</span><span class="p">)</span>
-<span class="n">seaborn</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"size"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"scores_auc"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">scores</span><span class="p">,</span> <span class="n">logx</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
-</pre></div>
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-<div class="output_area"><div class="prompt output_prompt">Out[8]:</div>
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-<div class="output_text output_subarea output_execute_result">
-<pre>(0.5, 1.0)</pre>
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->
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [9]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scores</span> <span class="o">=</span> <span class="n">all_models_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"hyperparameters_dropout_probability"</span><span class="p">)</span><span class="o">.</span><span class="n">scores_auc</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">"scores_auc"</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Dropout"</span><span class="p">)</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">seaborn</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"hyperparameters_dropout_probability"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"scores_auc"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">all_models_df</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
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-<div class="output_area"><div class="prompt output_prompt">Out[9]:</div>
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-<pre>(0.5, 1)</pre>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">count_impute</span> <span class="o">=</span> <span class="n">selected_models_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"allele"</span><span class="p">)</span><span class="o">.</span><span class="n">hyperparameters_impute</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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-<span class="n">seaborn</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">training_sizes</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">alleles</span><span class="p">],</span> <span class="n">count_impute</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">alleles</span><span class="p">])</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">xmax</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Number of models (out of 16 total in each allele's ensemble)</span><span class="se">\n</span><span class="s2">that use imputation"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Training points for allele"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Num models"</span><span class="p">)</span>
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->
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-<div class="input">
-<div class="prompt input_prompt">In [11]:</div>
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- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">impute_cums_df</span> <span class="o">=</span> <span class="n">selected_models_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"allele"</span><span class="p">)</span><span class="o">.</span><span class="n">hyperparameters_impute</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span>
-<span class="n">impute_cums_df</span><span class="p">[</span><span class="s2">"size"</span><span class="p">]</span> <span class="o">=</span> <span class="n">training_sizes</span>
-<span class="n">impute_cums_df</span> <span class="o">=</span> <span class="n">impute_cums_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">"size"</span><span class="p">)</span>
-<span class="n">impute_cums_df</span><span class="p">[</span><span class="s2">"cum_mean"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
- <span class="n">impute_cums_df</span><span class="o">.</span><span class="n">hyperparameters_impute</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span> <span class="o">/</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">impute_cums_df</span><span class="p">))</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-<span class="n">impute_cums_df</span>
-<span class="n">seaborn</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="s2">"size"</span><span class="p">,</span> <span class="s2">"cum_mean"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">impute_cums_df</span><span class="p">,</span> <span class="n">fit_reg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">logx</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xscale</span><span class="p">(</span><span class="s2">"log"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"IEDB Measurements"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Fraction of models using imputation"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span>
- <span class="s2">"Fraction of best models for alleles with <= x measurements</span><span class="se">\n</span><span class="s2">"</span>
- <span class="s2">"that use imputation"</span><span class="p">)</span>
-</pre></div>
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-<pre><matplotlib.text.Text at 0x11977ab00></pre>
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->
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [12]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seaborn</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">train_size</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">hyperparameters_dropout_probability</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">x_jitter</span><span class="o">=.</span><span class="mi">015</span><span class="p">,</span>
- <span class="n">y_jitter</span><span class="o">=.</span><span class="mi">015</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Dropout rate of selected models"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Training points for allele"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Dropout rate"</span><span class="p">)</span>
-</pre></div>
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-<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>
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-<pre><matplotlib.text.Text at 0x11f7f0c50></pre>
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [13]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seaborn</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">train_size</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">hyperparameters_embedding_output_dim</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">x_jitter</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
- <span class="n">y_jitter</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Embedding output dimensions of selected models"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Training points for allele"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Embedding output dimensions"</span><span class="p">)</span>
-</pre></div>
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-<div class="output_wrapper">
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-<div class="output_area"><div class="prompt output_prompt">Out[13]:</div>
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-<pre><matplotlib.text.Text at 0x11f7e65c0></pre>
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->
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [14]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seaborn</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">train_size</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">hyperparameters_layer_sizes</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">x_jitter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
- <span class="n">y_jitter</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Hidden layer size of selected models"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Training points for allele"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Hidden layer size"</span><span class="p">)</span>
-</pre></div>
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->
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [15]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seaborn</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">hyperparameters_embedding_output_dim</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">selected_models_df</span><span class="o">.</span><span class="n">hyperparameters_layer_sizes</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
- <span class="n">x_jitter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
- <span class="n">y_jitter</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Hidden layer size vs. embedding output dims of selected models"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Embedding output dimensions"</span><span class="p">)</span>
-<span class="n">pyplot</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Hidden layer size"</span><span class="p">)</span>
-</pre></div>
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-<div class="prompt input_prompt">In [16]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">log_path</span> <span class="o">=</span> <span class="n">get_path</span><span class="p">(</span><span class="s2">"models_class1_allele_specific_ensemble"</span><span class="p">,</span> <span class="s2">"GENERATE.sh"</span><span class="p">)</span>
-<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">log_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
- <span class="n">di</span><span class="o">.</span><span class="n">display_html</span><span class="p">(</span><span class="s2">"<h1>Model selection invocation</h1><pre></span><span class="si">%s</span><span class="s2"></pre>"</span> <span class="o">%</span> <span class="n">fd</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="n">raw</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-
-<div class="output_html rendered_html output_subarea ">
-<h1>Model selection invocation</h1><pre>#!/bin/bash
-
-if [[ $# -eq 0 ]] ; then
- echo 'WARNING: This script is intended to be called with additional arguments to pass to mhcflurry-class1-allele-specific-cv-and-train'
- echo 'See README.md'
-fi
-
-set -e
-set -x
-
-DOWNLOAD_NAME=models_class1_allele_specific_ensemble
-SCRATCH_DIR=/tmp/mhcflurry-downloads-generation
-SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
-SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
-export PYTHONUNBUFFERED=1
-
-mkdir -p "$SCRATCH_DIR"
-rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
-mkdir "$SCRATCH_DIR/$DOWNLOAD_NAME"
-
-# Send stdout and stderr to a logfile included with the archive.
-exec > >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt")
-exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
-
-# Log some environment info
-date
-pip freeze
-git rev-parse HEAD
-git status
-
-cd $SCRATCH_DIR/$DOWNLOAD_NAME
-
-mkdir models
-
-cp $SCRIPT_DIR/models.py .
-python models.py > models.json
-
-time mhcflurry-class1-allele-specific-ensemble-train \
- --ensemble-size 16 \
- --model-architectures models.json \
- --train-data "$(mhcflurry-downloads path data_combined_iedb_kim2014)/combined_human_class1_dataset.csv" \
- --min-samples-per-allele 20 \
- --out-manifest selected_models.csv \
- --out-model-selection-manifest all_models.csv \
- --out-models models \
- --verbose \
- "$@"
-
-bzip2 all_models.csv
-cp $SCRIPT_ABSOLUTE_PATH .
-tar -cjf "../${DOWNLOAD_NAME}.tar.bz2" *
-
-echo "Created archive: $SCRATCH_DIR/$DOWNLOAD_NAME.tar.bz2"
-</pre>
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In [17]:</div>
-<div class="inner_cell">
- <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">log_path</span> <span class="o">=</span> <span class="n">get_path</span><span class="p">(</span><span class="s2">"models_class1_allele_specific_ensemble"</span><span class="p">,</span> <span class="s2">"LOG.txt"</span><span class="p">)</span>
-<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">log_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
- <span class="n">lines</span> <span class="o">=</span> <span class="n">fd</span><span class="o">.</span><span class="n">readlines</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
- <span class="n">di</span><span class="o">.</span><span class="n">display_html</span><span class="p">(</span><span class="s2">"<h1>Model selection log (beginning)</h1><pre></span><span class="si">%s</span><span class="s2"></pre>"</span> <span class="o">%</span> <span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">lines</span><span class="p">),</span> <span class="n">raw</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-
-<div class="output_html rendered_html output_subarea ">
-<h1>Model selection log (beginning)</h1><pre>+ date
-Thu Mar 16 13:18:34 UTC 2017
-+ pip freeze
-alabaster==0.7.9
-anaconda-clean==1.0
-anaconda-client==1.5.1
-anaconda-navigator==1.3.1
-appdirs==1.4.0
-argcomplete==1.0.0
-astroid==1.4.7
-astropy==1.2.1
-Babel==2.3.4
-backports.shutil-get-terminal-size==1.0.0
-beautifulsoup4==4.5.1
-biopython==1.68
-bitarray==0.8.1
-blaze==0.10.1
-bokeh==0.12.2
-boto==2.42.0
-bottle==0.12.13
-Bottleneck==1.1.0
-cffi==1.7.0
-chest==0.2.3
-click==6.6
-climate==0.4.6
-cloudpickle==0.2.1
-clyent==1.2.2
-colorama==0.3.7
-conda==4.2.9
-conda-build==2.0.2
-configobj==5.0.6
-contextlib2==0.5.3
-cryptography==1.5
-CVXcanon==0.1.1
-cvxpy==0.4.8
-cycler==0.10.0
-Cython==0.24.1
-cytoolz==0.8.0
-dask==0.11.0
-datacache==0.4.20
-datashape==0.5.2
-decorator==4.0.10
-dill==0.2.5
-docutils==0.12
-downhill==0.4.0
-dynd==0.7.3.dev1
-ecos==2.0.4
-et-xmlfile==1.0.1
-fancyimpute==0.1.0
-fastcache==1.0.2
-filelock==2.0.6
-Flask==0.11.1
-Flask-Cors==2.1.2
-gevent==1.1.2
-google-api-python-client==1.5.5
-greenlet==0.4.10
-gtfparse==0.0.6
-h5py==2.6.0
-HeapDict==1.0.0
-httplib2==0.9.2
-humanize==0.5.1
-idna==2.1
-imagesize==0.7.1
-ipdb==0.10.2
-ipykernel==4.5.0
-ipython==5.1.0
-ipython-genutils==0.1.0
-ipywidgets==5.2.2
-itsdangerous==0.24
-jdcal==1.2
-jedi==0.9.0
-Jinja2==2.8
-joblib==0.10.3
-jsonschema==2.5.1
-jupyter==1.0.0
-jupyter-client==4.4.0
-jupyter-console==5.0.0
-jupyter-core==4.2.0
-Keras==1.2.0
-knnimpute==0.0.1
--e git+git@github.com:hammerlab/kubeface.git@91fa80a571b9f870c4ec945b834a97fdf863fbc7#egg=kubeface
-lazy-object-proxy==1.2.1
-llvmlite==0.13.0
-locket==0.2.0
-lxml==3.6.4
-MarkupSafe==0.23
-matplotlib==1.5.3
-memoized-property==1.0.3
--e git+git@github.com:hammerlab/mhcflurry.git@2925ce8d6c08e8ac0170504b06f1be384a0fc169#egg=mhcflurry
-mhcnames==0.1.0
-mhctools==0.4.1
-mistune==0.7.3
-mock==2.0.0
-mpmath==0.19
-multipledispatch==0.4.8
-multiprocess==0.70.4
-nb-anacondacloud==1.2.0
-nb-conda==2.0.0
-nb-conda-kernels==2.0.0
-nbconvert==4.2.0
-nbformat==4.1.0
-nbpresent==3.0.2
--e git+git@github.com:hammerlab/neon.git@f343737d19e1b9509137bf63b9d291d2d8c8bcaf#egg=neon
-networkx==1.11
-nltk==3.2.1
-nose==1.3.7
-notebook==4.2.3
-numba==0.28.1
-numexpr==2.6.1
-numpy==1.11.1
-oauth2client==4.0.0
-odo==0.5.0
-openpyxl==2.3.2
-pandas==0.18.1
-parse==1.6.6
-partd==0.3.6
-path.py==0.0.0
-pathlib2==2.1.0
-patsy==0.4.1
-pbr==1.10.0
-pep8==1.7.0
-pepdata==0.7.0
-pexpect==4.0.1
-pickleshare==0.7.4
-Pillow==3.3.1
-pkginfo==1.3.2
-plac==0.9.6
-ply==3.9
-progressbar33==2.4
-prompt-toolkit==1.0.3
-psutil==4.3.1
-ptyprocess==0.5.1
-py==1.4.31
-pyasn1==0.1.9
-pyasn1-modules==0.0.8
-pycosat==0.6.1
-pycparser==2.14
-pycrypto==2.6.1
-pycurl==7.43.0
-pyensembl==1.0.3
-pyflakes==1.3.0
-Pygments==2.1.3
-pylint==1.5.4
-pyopen==0.0.6
-pyOpenSSL==16.0.0
-pyparsing==2.1.4
-pytest==2.9.2
-python-dateutil==2.5.3
-pytz==2016.6.1
-PyVCF==0.6.8
-PyYAML==3.12
-pyzmq==15.4.0
-QtAwesome==0.3.3
-qtconsole==4.2.1
-QtPy==1.1.2
-redis==2.10.5
-requests==2.11.1
-rope-py3k==0.9.4.post1
-rsa==3.4.2
-ruamel-yaml===-VERSION
-scikit-image==0.12.3
-scikit-learn==0.18.1
-scipy==0.18.1
-scs==1.2.6
-seaborn==0.7.1
-sercol==0.0.2
-serializable==0.1.1
-simplegeneric==0.8.1
-simplejson==3.10.0
-singledispatch==3.4.0.3
-six==1.10.0
-sklearn==0.0
-snowballstemmer==1.2.1
-sockjs-tornado==1.0.3
-Sphinx==1.4.6
-spyder==3.0.0
-SQLAlchemy==1.0.13
-statsmodels==0.6.1
-sympy==1.0
-tables==3.2.3.1
-terminado==0.6
-Theano==0.8.2
-tinytimer==0.0.0
-toolz==0.8.0
-tornado==4.4.1
-traitlets==4.3.0
-typechecks==0.0.2
-unicodecsv==0.14.1
-uritemplate==3.0.0
-varcode==0.5.11
-wcwidth==0.1.7
-Werkzeug==0.11.11
-widgetsnbextension==1.2.6
-wrapt==1.10.6
-xlrd==1.0.0
-XlsxWriter==0.9.3
-xlwt==1.1.2
-You are using pip version 8.1.2, however version 9.0.1 is available.
-You should consider upgrading via the 'pip install --upgrade pip' command.
-+ git rev-parse HEAD
-2925ce8d6c08e8ac0170504b06f1be384a0fc169
-+ git status
-On branch add-class1-ensemble
-Your branch is up-to-date with 'origin/add-class1-ensemble'.
-nothing to commit, working directory clean
-+ cd /tmp/mhcflurry-downloads-generation/models_class1_allele_specific_ensemble
-+ mkdir models
-+ cp /home/tim/sinai/git/mhcflurry/downloads-generation/models_class1_allele_specific_ensemble/models.py .
-+ python models.py
-Using Theano backend.
-/home/tim/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
- "This module will be removed in 0.20.", DeprecationWarning)
-Models: 162
-++ mhcflurry-downloads path data_combined_iedb_kim2014
-Using Theano backend.
-/home/tim/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
- "This module will be removed in 0.20.", DeprecationWarning)
-+ mhcflurry-class1-allele-specific-ensemble-train --ensemble-size 16 --model-architectures models.json --train-data /home/tim/.local/share/mhcflurry/4/0.0.8/data_combined_iedb_kim2014//combined_human_class1_dataset.csv --min-samples-per-allele 20 --out-manifest selected_models.csv --out-model-selection-manifest all_models.csv --out-models models --verbose --parallel-backend kubeface --target-tasks 10000 --kubeface-backend kubernetes --kubeface-storage gs://kubeface-tim --kubeface-worker-image hammerlab/mhcflurry-misc:latest --kubeface-kubernetes-task-resources-memory-mb 6000 --kubeface-worker-path-prefix venv-py3/bin --kubeface-max-simultaneous-tasks 200 --kubeface-speculation-max-reruns 3 --kubeface-cache-key-prefix tim-note-tim-2017-03-12-16-37-22-27499bde
-Using Theano backend.
-/home/tim/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
- "This module will be removed in 0.20.", DeprecationWarning)
-To show stack trace, run:
-kill -s USR1 992
-INFO:root:Running with arguments: Namespace(alleles=None, dask_scheduler=None, ensemble_size=16, kubeface_backend='kubernetes', kubeface_cache_key_prefix='tim-note-tim-2017-03-12-16-37-22-27499bde', kubeface_kubernetes_cluster=None, kubeface_kubernetes_image_pull_policy='Always', kubeface_kubernetes_retries=12, kubeface_kubernetes_task_resources_cpu=1, kubeface_kubernetes_task_resources_memory_mb=6000.0, kubeface_local_process_docker_command='docker', kubeface_max_simultaneous_tasks=200, kubeface_never_cleanup=False, kubeface_poll_seconds=30.0, kubeface_speculation_max_reruns=3, kubeface_speculation_percent=20, kubeface_speculation_runtime_percentile=99, kubeface_storage='gs://kubeface-tim', kubeface_wait_to_raise_task_exception=False, kubeface_worker_image='hammerlab/mhcflurry-misc:latest', kubeface_worker_kubeface_install_command='{pip} install https://github.com/hammerlab/kubeface/archive/master.zip', kubeface_worker_kubeface_install_policy='if-not-present', kubeface_worker_path_prefix='venv-py3/bin', kubeface_worker_pip='pip', kubeface_worker_pip_packages=[], max_models=None, min_samples_per_allele=20, model_architectures=<_io.TextIOWrapper name='models.json' mode='r' encoding='UTF-8'>, num_local_processes=None, num_local_threads=1, out_manifest='selected_models.csv', out_model_selection_manifest='all_models.csv', out_models_dir='models', parallel_backend='kubeface', quiet=False, target_tasks=10000, train_data='/home/tim/.local/share/mhcflurry/4/0.0.8/data_combined_iedb_kim2014//combined_human_class1_dataset.csv', verbose=True)
-Using parallel backend: <Kubeface backend, client=<kubeface.client.Client object at 0x7fe5429fdac8>>
-INFO:root:Read 162 model architectures
-INFO:root:Loaded training data: Dataset(n=192550, alleles=['ELA-A1', 'Gogo-B0101', 'H-2-DB', 'H-2-DD', 'H-2-KB', 'H-2-KBM8', 'H-2-KD', 'H-2-KK', 'H-2-LD', 'H-2-LQ', 'HLA-A0101', 'HLA-A0201', 'HLA-A0202', 'HLA-A0203', 'HLA-A0204', 'HLA-A0205', 'HLA-A0206', 'HLA-A0207', 'HLA-A0210', 'HLA-A0211', 'HLA-A0212', 'HLA-A0216', 'HLA-A0217', 'HLA-A0219', 'HLA-A0250', 'HLA-A0301', 'HLA-A0302', 'HLA-A0319', 'HLA-A1', 'HLA-A11', 'HLA-A1101', 'HLA-A1102', 'HLA-A2', 'HLA-A2301', 'HLA-A24', 'HLA-A2402', 'HLA-A2403', 'HLA-A2501', 'HLA-A26', 'HLA-A2601', 'HLA-A2602', 'HLA-A2603', 'HLA-A2902', 'HLA-A3', 'HLA-A3/11', 'HLA-A3001', 'HLA-A3002', 'HLA-A3101', 'HLA-A3201', 'HLA-A3207', 'HLA-A3215', 'HLA-A3301', 'HLA-A6601', 'HLA-A6801', 'HLA-A6802', 'HLA-A6823', 'HLA-A6901', 'HLA-A7401', 'HLA-A8001', 'HLA-B0702', 'HLA-B0801', 'HLA-B0802', 'HLA-B0803', 'HLA-B1401', 'HLA-B1402', 'HLA-B1501', 'HLA-B1502', 'HLA-B1503', 'HLA-B1509', 'HLA-B1517', 'HLA-B1542', 'HLA-B1801', 'HLA-B27', 'HLA-B2701', 'HLA-B2702', 'HLA-B2703', 'HLA-B2704', 'HLA-B2705', 'HLA-B2706', 'HLA-B2710', 'HLA-B2720', 'HLA-B3501', 'HLA-B3503', 'HLA-B3508', 'HLA-B3701', 'HLA-B3801', 'HLA-B39', 'HLA-B3901', 'HLA-B40', 'HLA-B4001', 'HLA-B4002', 'HLA-B4013', 'HLA-B4201', 'HLA-B4202', 'HLA-B44', 'HLA-B4402', 'HLA-B4403', 'HLA-B4501', 'HLA-B4506', 'HLA-B4601', 'HLA-B4801', 'HLA-B51', 'HLA-B5101', 'HLA-B5201', 'HLA-B5301', 'HLA-B5401', 'HLA-B5701', 'HLA-B5702', 'HLA-B5703', 'HLA-B58', 'HLA-B5801', 'HLA-B5802', 'HLA-B60', 'HLA-B62', 'HLA-B7', 'HLA-B7301', 'HLA-B8', 'HLA-B8101', 'HLA-B8301', 'HLA-BOLA102101', 'HLA-BOLA200801', 'HLA-BOLA201201', 'HLA-BOLA402401', 'HLA-BOLA601301', 'HLA-BOLA601302', 'HLA-BOLAHD6', 'HLA-C0303', 'HLA-C0401', 'HLA-C0501', 'HLA-C0602', 'HLA-C0702', 'HLA-C0802', 'HLA-C1', 'HLA-C1203', 'HLA-C1402', 'HLA-C1502', 'HLA-C4', 'HLA-E0101', 'HLA-E0103', 'HLA-EQCA100101', 'HLA-RT1A', 'HLA-RT1BL', 'HLA-SLA10401', 'Mamu-A01', 'Mamu-A02', 'Mamu-A07', 'Mamu-A100101', 'Mamu-A100201', 'Mamu-A101101', 'Mamu-A11', 'Mamu-A20102', 'Mamu-A2201', 'Mamu-A2601', 'Mamu-A70103', 'Mamu-B01', 'Mamu-B01704', 'Mamu-B03', 'Mamu-B04', 'Mamu-B06502', 'Mamu-B08', 'Mamu-B1001', 'Mamu-B17', 'Mamu-B3901', 'Mamu-B52', 'Mamu-B6601', 'Mamu-B8301', 'Mamu-B8701', 'Patr-A0101', 'Patr-A0301', 'Patr-A0401', 'Patr-A0602', 'Patr-A0701', 'Patr-A0901', 'Patr-B0101', 'Patr-B0901', 'Patr-B1301', 'Patr-B1701', 'Patr-B2401'])
-INFO:root:Filtered training dataset to alleles with >= 20 observations: Dataset(n=192177, alleles=['H-2-DB', 'H-2-DD', 'H-2-KB', 'H-2-KD', 'H-2-KK', 'H-2-LD', 'HLA-A0101', 'HLA-A0201', 'HLA-A0202', 'HLA-A0203', 'HLA-A0205', 'HLA-A0206', 'HLA-A0207', 'HLA-A0211', 'HLA-A0212', 'HLA-A0216', 'HLA-A0217', 'HLA-A0219', 'HLA-A0250', 'HLA-A0301', 'HLA-A0302', 'HLA-A0319', 'HLA-A11', 'HLA-A1101', 'HLA-A2', 'HLA-A2301', 'HLA-A2402', 'HLA-A2403', 'HLA-A2501', 'HLA-A2601', 'HLA-A2602', 'HLA-A2603', 'HLA-A2902', 'HLA-A3001', 'HLA-A3002', 'HLA-A3101', 'HLA-A3201', 'HLA-A3207', 'HLA-A3215', 'HLA-A3301', 'HLA-A6601', 'HLA-A6801', 'HLA-A6802', 'HLA-A6823', 'HLA-A6901', 'HLA-A8001', 'HLA-B0702', 'HLA-B0801', 'HLA-B0802', 'HLA-B0803', 'HLA-B1401', 'HLA-B1402', 'HLA-B1501', 'HLA-B1502', 'HLA-B1503', 'HLA-B1509', 'HLA-B1517', 'HLA-B1542', 'HLA-B1801', 'HLA-B2703', 'HLA-B2705', 'HLA-B2720', 'HLA-B3501', 'HLA-B3503', 'HLA-B3701', 'HLA-B3801', 'HLA-B3901', 'HLA-B4001', 'HLA-B4002', 'HLA-B4013', 'HLA-B4201', 'HLA-B4402', 'HLA-B4403', 'HLA-B4501', 'HLA-B4506', 'HLA-B4601', 'HLA-B4801', 'HLA-B5101', 'HLA-B5301', 'HLA-B5401', 'HLA-B5701', 'HLA-B5703', 'HLA-B5801', 'HLA-B5802', 'HLA-B7', 'HLA-B7301', 'HLA-B8101', 'HLA-B8301', 'HLA-BOLA601301', 'HLA-BOLAHD6', 'HLA-C0303', 'HLA-C0401', 'HLA-C0501', 'HLA-C0602', 'HLA-C0702', 'HLA-C0802', 'HLA-C1203', 'HLA-C1402', 'HLA-C1502', 'HLA-E0103', 'HLA-EQCA100101', 'HLA-RT1A', 'Mamu-A01', 'Mamu-A02', 'Mamu-A07', 'Mamu-A100101', 'Mamu-A100201', 'Mamu-A101101', 'Mamu-A11', 'Mamu-A20102', 'Mamu-A2201', 'Mamu-A2601', 'Mamu-A70103', 'Mamu-B01', 'Mamu-B01704', 'Mamu-B03', 'Mamu-B08', 'Mamu-B1001', 'Mamu-B17', 'Mamu-B3901', 'Mamu-B52', 'Mamu-B6601', 'Mamu-B8301', 'Mamu-B8701', 'Patr-A0101', 'Patr-A0301', 'Patr-A0401', 'Patr-A0701', 'Patr-A0901', 'Patr-B0101', 'Patr-B1301', 'Patr-B2401'])
-INFO:root:Imputing: 16 tasks, imputation args: {'impute_min_observations_per_peptide': 3, 'imputer_args': {'n_imputations': 50, 'n_burn_in': 5, 'n_nearest_columns': 25}, 'impute_method': 'mice', 'impute_min_observations_per_allele': 3}
-Job status available at:
- https://storage.cloud.google.com/kubeface-tim/active::json::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.json [ gs://kubeface-tim/active::json::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.json ]
- https://storage.cloud.google.com/kubeface-tim/active::html::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.html [ gs://kubeface-tim/active::html::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.html ]
-WARNING:googleapiclient.discovery_cache:file_cache is unavailable when using oauth2client >= 4.0.0
-Traceback (most recent call last):
- File "/home/tim/anaconda3/lib/python3.5/site-packages/googleapiclient/discovery_cache/__init__.py", line 36, in autodetect
- from google.appengine.api import memcache
-ImportError: No module named 'google'
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
- File "/home/tim/anaconda3/lib/python3.5/site-packages/googleapiclient/discovery_cache/file_cache.py", line 33, in <module>
- from oauth2client.contrib.locked_file import LockedFile
-ImportError: No module named 'oauth2client.contrib.locked_file'
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
- File "/home/tim/anaconda3/lib/python3.5/site-packages/googleapiclient/discovery_cache/file_cache.py", line 37, in <module>
- from oauth2client.locked_file import LockedFile
-ImportError: No module named 'oauth2client.locked_file'
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
- File "/home/tim/anaconda3/lib/python3.5/site-packages/googleapiclient/discovery_cache/__init__.py", line 41, in autodetect
- from . import file_cache
- File "/home/tim/anaconda3/lib/python3.5/site-packages/googleapiclient/discovery_cache/file_cache.py", line 41, in <module>
- 'file_cache is unavailable when using oauth2client >= 4.0.0')
-ImportError: file_cache is unavailable when using oauth2client >= 4.0.0
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/discovery/v1/apis/storage/v1/rest
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000&alt=json&maxResults=100000
-INFO:oauth2client.transport:Attempting refresh to obtain initial access_token
-INFO:root:Submitting 200 tasks
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000000+0+1489457655+1489457740++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000001+1+1489457864+1489457929++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000002+0+1489457663+1489457721++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000003+0+1489457666+1489457726++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000004+0+1489457670+1489457730++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000005+0+1489457673+1489457758++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000006+0+1489457677+1489457770++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000007+0+1489457680+1489457744++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000008+1+1489457864+1489457929++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000009+0+1489457688+1489457749++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000010+0+1489457692+1489457802++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000011+0+1489457696+1489457783++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000012+0+1489457700+1489457809++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000013+0+1489457704+1489457802++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000014+0+1489457708+1489457792++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000015+1+1489457864+1489457923++value
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000&alt=json&maxResults=100000
-INFO:googleapiclient.discovery:URL being requested: POST https://www.googleapis.com/upload/storage/v1/b/kubeface-tim/o?uploadType=multipart&alt=json
-INFO:googleapiclient.discovery:URL being requested: POST https://www.googleapis.com/upload/storage/v1/b/kubeface-tim/o?uploadType=multipart&alt=json
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000&alt=json&maxResults=100000
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000000%2B0%2B1489457655%2B1489457740%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:21 2017
- * run time : 0:01:18.176892
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000000-700a7d3f
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000000
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000000+0+1489457655+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000001%2B1%2B1489457864%2B1489457929%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:17:49 2017
- * run time : 0:00:58.846496
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000001-ffcc73bc
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000001
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000001+1+1489457864+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000002%2B0%2B1489457663%2B1489457721%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:27 2017
- * run time : 0:00:52.098150
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000002-8d3af68e
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000002
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000002+0+1489457663+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000003%2B0%2B1489457666%2B1489457726%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:31 2017
- * run time : 0:00:53.772626
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000003-d1067c7f
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000003
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000003+0+1489457666+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000004%2B0%2B1489457670%2B1489457730%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:36 2017
- * run time : 0:00:53.499716
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000004-dc19071a
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000004
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000004+0+1489457670+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000005%2B0%2B1489457673%2B1489457758%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:38 2017
- * run time : 0:01:18.616956
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000005-839b9d82
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000005
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000005+0+1489457673+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000006%2B0%2B1489457677%2B1489457770%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:42 2017
- * run time : 0:01:26.611738
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000006-20b45486
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000006
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000006+0+1489457677+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000007%2B0%2B1489457680%2B1489457744%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:46 2017
- * run time : 0:00:56.961026
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000007-5eefd3f5
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000007
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000007+0+1489457680+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000008%2B1%2B1489457864%2B1489457929%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:17:49 2017
- * run time : 0:00:58.927556
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000008-524729ed
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000008
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000008+1+1489457864+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000009%2B0%2B1489457688%2B1489457749%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:53 2017
- * run time : 0:00:54.847812
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000009-502c6a6d
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000009
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000009+0+1489457688+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000010%2B0%2B1489457692%2B1489457802%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:14:57 2017
- * run time : 0:01:43.951529
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000010-1b765a46
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000010
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000010+0+1489457692+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000011%2B0%2B1489457696%2B1489457783%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:15:03 2017
- * run time : 0:01:19.163020
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000011-47f45acf
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000011
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000011+0+1489457696+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000012%2B0%2B1489457700%2B1489457809%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:15:07 2017
- * run time : 0:01:41.604208
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000012-2408acf0
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000012
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000012+0+1489457700+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000013%2B0%2B1489457704%2B1489457802%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:15:10 2017
- * run time : 0:01:30.512149
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000013-74fc3d7a
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000013
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000013+0+1489457704+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000014%2B0%2B1489457708%2B1489457792%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:15:15 2017
- * run time : 0:01:16.764111
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000014-be568f1b
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000014
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000014+0+1489457708+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o/result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3A000015%2B1%2B1489457864%2B1489457923%2B%2Bvalue?alt=media
-DEBUG:root:Download 100%.
-DEBUG:root:Result (success):
- * result type : value
- * start time : Tue Mar 14 02:17:49 2017
- * run time : 0:00:52.669978
- * hostname : tim-note-tim-2017-03-12-16-37-22-27499bde-000--000015-2dc3bbf2
- * platform : Linux-4.4.21+-x86_64-with-Ubuntu-14.04-trusty
- * python version : 3.4.3 (default, Nov 17 2016, 01:08:31)
- * [GCC 4.8.4]
- * invocation arguments : venv-py3/bin/_kubeface-run-task
- * gs://kubeface-tim/input::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000015
- * gs://kubeface-tim/result::tim-note-tim-2017-03-12-16-37-22-27499bde-000::000015+1+1489457864+{result_time}++{result_type}
- * --verbose
- * input size : 10.0 MiB
- * result size : 956.0 B
- * return value type : <class 'list'>
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=active%3A%3Ahtml%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.html&alt=json&maxResults=100000
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=active%3A%3Ajson%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.json&alt=json&maxResults=100000
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=done%3A%3Ahtml%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.html&alt=json&maxResults=100000
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=done%3A%3Ajson%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.json&alt=json&maxResults=100000
-INFO:root:Marking job 'tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332' done: renaming active::html::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.html -> done::html::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.html
-INFO:googleapiclient.discovery:URL being requested: POST https://www.googleapis.com/storage/v1/b/kubeface-tim/o/active%3A%3Ahtml%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.html/rewriteTo/b/kubeface-tim/o/done%3A%3Ahtml%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.html?alt=json
-INFO:googleapiclient.discovery:URL being requested: DELETE https://www.googleapis.com/storage/v1/b/kubeface-tim/o/active%3A%3Ahtml%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.html?
-INFO:root:Marking job 'tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332' done: renaming active::json::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.json -> done::json::tim-note-tim-2017-03-12-16-37-22-27499bde-000::node-master::dc8df332.json
-INFO:googleapiclient.discovery:URL being requested: POST https://www.googleapis.com/storage/v1/b/kubeface-tim/o/active%3A%3Ajson%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.json/rewriteTo/b/kubeface-tim/o/done%3A%3Ajson%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.json?alt=json
-INFO:googleapiclient.discovery:URL being requested: DELETE https://www.googleapis.com/storage/v1/b/kubeface-tim/o/active%3A%3Ajson%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-000%3A%3Anode-master%3A%3Adc8df332.json?
-INFO:root:Imputation completed.
-INFO:root:Training and scoring models: 9504 tasks (target was 10000), total work: 132 alleles * 16 ensemble size * 162 models = 342144, allele/models per task: (min=36 mean=36.000000 max=36)
-Job status available at:
- https://storage.cloud.google.com/kubeface-tim/active::json::tim-note-tim-2017-03-12-16-37-22-27499bde-001::node-master::bd30c322.json [ gs://kubeface-tim/active::json::tim-note-tim-2017-03-12-16-37-22-27499bde-001::node-master::bd30c322.json ]
- https://storage.cloud.google.com/kubeface-tim/active::html::tim-note-tim-2017-03-12-16-37-22-27499bde-001::node-master::bd30c322.html [ gs://kubeface-tim/active::html::tim-note-tim-2017-03-12-16-37-22-27499bde-001::node-master::bd30c322.html ]
-INFO:googleapiclient.discovery:URL being requested: GET https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDAwOTk5KzArMTQ4OTQ2OTY4MCsxNDg5NDczOTg4Kyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDAxOTk5KzArMTQ4OTQ4NDQxOCsxNDg5NDg2NjIwKyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDAyOTk5KzArMTQ4OTQ5OTU0MCsxNDg5NTAwMjU4Kyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDAzOTk5KzArMTQ4OTUxNDUxNSsxNDg5NTE1NzczKyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDA0OTk5KzArMTQ4OTUzNzY1MSsxNDg5NTM4NDQxKyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDA1OTkzKzArMTQ4OTU5ODE1MisxNDg5NTk5NjAzKyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDA2OTkzKzArMTQ4OTYxMjIxNisxNDg5NjE1MjA0Kyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDA3OTkzKzArMTQ4OTYyNjI5MisxNDg5NjI3MjU2Kyt2YWx1ZQ%3D%3D
-INFO:googleapiclient.discovery:URL being requested: list_next https://www.googleapis.com/storage/v1/b/kubeface-tim/o?fields=nextPageToken%2Citems%28name%29&prefix=result%3A%3Atim-note-tim-2017-03-12-16-37-22-27499bde-001&alt=json&maxResults=100000&pageToken=ClxyZXN1bHQ6OnRpbS1ub3RlLXRpbS0yMDE3LTAzLTEyLTE2LTM3LTIyLTI3NDk5YmRlLTAwMTo6MDA4OTkzKzArMTQ4OTY0MDc5MSsxNDg5NjQxODkwKyt2YWx1ZQ%3D%3D
-INFO:root:Submitting 200 tasks
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000000+0+1489458019+1489458400++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000001+0+1489458019+1489458434++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000002+0+1489458021+1489458422++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000003+0+1489458021+1489458476++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000004+0+1489458021+1489458540++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000005+0+1489458022+1489458562++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000006+0+1489458022+1489458629++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000007+0+1489458023+1489458656++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000008+0+1489458023+1489458869++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000009+0+1489458024+1489458596++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000010+0+1489595903+1489596458++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000011+0+1489458025+1489458441++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000012+0+1489458025+1489458467++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000013+0+1489458025+1489458503++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000014+0+1489458026+1489458515++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000015+0+1489458026+1489458633++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000016+0+1489458027+1489458556++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000017+0+1489458027+1489458634++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000018+0+1489458028+1489458658++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000019+0+1489458028+1489458958++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000020+0+1489458029+1489458591++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000021+0+1489458029+1489458813++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000022+0+1489595903+1489596632++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000023+0+1489458030+1489458505++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000024+0+1489458031+1489458523++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000025+0+1489458031+1489458547++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000026+0+1489595904+1489596627++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000027+0+1489458032+1489458651++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000028+0+1489458033+1489458723++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000029+0+1489458033+1489458772++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000030+0+1489458034+1489459085++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000031+0+1489458034+1489458677++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000032+0+1489458035+1489458961++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000033+0+1489458035+1489458625++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000034+0+1489458036+1489458532++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000035+0+1489458036+1489458589++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000036+0+1489458037+1489458619++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000037+0+1489595904+1489596492++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000038+0+1489458038+1489458744++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000039+0+1489458038+1489458958++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000040+0+1489595905+1489596441++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000041+0+1489458039+1489459572++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000042+0+1489458040+1489458866++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000043+0+1489458041+1489459228++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000044+0+1489595906+1489596703++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000045+0+1489458042+1489458602++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000046+0+1489595906+1489596653++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000047+0+1489458042+1489458736++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000048+0+1489458043+1489458855++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000049+0+1489458043+1489458868++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000050+0+1489458044+1489459058++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000051+0+1489458045+1489459204++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000052+0+1489458045+1489459723++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000053+0+1489458046+1489458939++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000054+0+1489458046+1489459355++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000055+0+1489458047+1489458660++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000056+0+1489458047+1489458625++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000057+0+1489458048+1489458664++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000058+0+1489458048+1489458779++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000059+0+1489458048+1489458991++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000060+0+1489595907+1489597567++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000061+0+1489458049+1489459214++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000062+0+1489595907+1489597108++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000063+0+1489458050+1489459831++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000064+0+1489595908+1489597561++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000065+0+1489458052+1489459628++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000066+0+1489458052+1489458700++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000067+0+1489458053+1489458746++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000068+0+1489458053+1489458764++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000069+1+1489649935+1489650676++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000070+0+1489458054+1489459238++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000071+0+1489458055+1489459195++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000072+0+1489458055+1489459311++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000073+0+1489458056+1489459636++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000074+0+1489458056+1489460483++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000075+0+1489458057+1489459463++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000076+0+1489458057+1489460044++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000077+0+1489458058+1489458802++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000078+0+1489458058+1489458831++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000079+0+1489458059+1489458924++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000080+0+1489595909+1489597328++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000081+0+1489458060+1489459267++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000082+1+1489649541+1489651104++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000083+0+1489458061+1489459426++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000084+0+1489458062+1489459630++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000085+0+1489458062+1489460680++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000086+0+1489458063+1489459519++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000087+0+1489458063+1489460252++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000088+0+1489458064+1489458834++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000089+0+1489458064+1489458960++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000090+0+1489595910+1489597719++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000091+0+1489458065+1489459041++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000092+0+1489458066+1489459377++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000093+0+1489458066+1489459268++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000094+1+1489649112+1489650749++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000095+0+1489458067+1489459895++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000096+0+1489458067+1489460869++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000097+0+1489458068+1489459671++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000098+0+1489458068+1489460449++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000099+0+1489458069+1489458549++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000100+0+1489595911+1489596353++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000101+0+1489595911+1489596327++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000102+0+1489458070+1489458642++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000103+0+1489458071+1489458742++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000104+0+1489458071+1489460003++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000105+0+1489458072+1489460432++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000106+0+1489458072+1489460360++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000107+0+1489458073+1489461423++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000108+0+1489458073+1489459744++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000109+0+1489458074+1489462692++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000110+0+1489458074+1489458948++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000111+0+1489458074+1489462203++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000112+0+1489458075+1489462657++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000113+0+1489458075+1489459352++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000114+0+1489458076+1489459170++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000115+0+1489458076+1489460944++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000116+0+1489458077+1489459340++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000117+0+1489458077+1489460231++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000118+0+1489458078+1489460638++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000119+0+1489458078+1489460697++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000120+0+1489458079+1489459698++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000121+0+1489458081+1489464019++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000122+0+1489458082+1489459067++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000123+0+1489458083+1489459098++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000124+0+1489458083+1489459346++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000125+0+1489458084+1489459511++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000126+0+1489458084+1489460475++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000127+0+1489458085+1489459904++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000128+0+1489458085+1489459917++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000129+0+1489458086+1489465379++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000130+0+1489458086+1489459792++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000131+0+1489458087+1489461125++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000132+0+1489458087+1489459758++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000133+0+1489458088+1489459424++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000134+0+1489458088+1489461160++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000135+0+1489458089+1489459631++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000136+0+1489458089+1489462651++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000137+0+1489458089+1489463343++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000138+0+1489458090+1489459677++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000139+0+1489458090+1489460631++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000140+0+1489458091+1489464476++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000141+0+1489458091+1489459731++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000142+0+1489458092+1489460031++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000143+0+1489458092+1489459741++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000144+0+1489458093+1489464063++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000145+0+1489458093+1489461520++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000146+0+1489458093+1489462194++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000147+0+1489458094+1489460258++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000148+0+1489458094+1489459444++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000149+0+1489458095+1489460788++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000150+0+1489458095+1489461183++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000151+0+1489458096+1489461212++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000152+0+1489458096+1489461615++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000153+0+1489458097+1489461864++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000154+0+1489458097+1489464987++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000155+0+1489458098+1489460949++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000156+0+1489458098+1489461545++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000157+0+1489458098+1489461130++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000158+0+1489458099+1489461456++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000159+0+1489458099+1489459654++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000160+0+1489458100+1489459550++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000161+0+1489458100+1489460440++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000162+0+1489458101+1489460927++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000163+0+1489458101+1489462167++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000164+0+1489458102+1489460262++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000165+0+1489458102+1489460918++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000166+0+1489458103+1489459525++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000167+0+1489458112+1489459825++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000168+0+1489458113+1489459516++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000169+0+1489458113+1489459856++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000170+0+1489458114+1489459656++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000171+0+1489458115+1489460286++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000172+0+1489458115+1489460216++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000173+0+1489458116+1489461338++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000174+0+1489458116+1489460060++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000175+0+1489458117+1489460829++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000176+0+1489458117+1489460440++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000177+0+1489458118+1489462512++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000178+0+1489458118+1489459587++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000179+0+1489458119+1489461929++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000180+0+1489458119+1489460111++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000181+0+1489458120+1489459974++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000182+0+1489458120+1489460400++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000183+0+1489458121+1489461809++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000184+0+1489458121+1489461880++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000185+0+1489458122+1489460176++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000186+0+1489458122+1489463550++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000187+0+1489458123+1489460379++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000188+0+1489458123+1489459415++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000189+0+1489458123+1489461261++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000190+0+1489458124+1489460594++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000191+0+1489458124+1489460791++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000192+0+1489458125+1489462264++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000193+0+1489458126+1489461229++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000194+0+1489458126+1489460700++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000195+0+1489458126+1489461467++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000196+0+1489458127+1489461427++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000197+0+1489458128+1489460984++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000198+0+1489458128+1489460369++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000199+0+1489458129+1489460011++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000200+0+1489458431+1489458925++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000201+0+1489458432+1489459877++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000202+0+1489458462+1489459267++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000203+0+1489458463+1489460501++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000204+0+1489458493+1489460057++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000205+0+1489458494+1489460351++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000206+0+1489458525+1489462540++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000207+0+1489458525+1489459254++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000208+0+1489458526+1489459525++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000209+0+1489458556+1489459467++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000210+0+1489458557+1489462883++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000211+0+1489458557+1489459122++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000212+0+1489458557+1489460878++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000213+0+1489458558+1489463175++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000214+0+1489458588+1489461622++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000215+0+1489458589+1489461340++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000216+0+1489458620+1489459603++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000217+0+1489458620+1489460853++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000218+0+1489458621+1489460535++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000219+0+1489458621+1489460341++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000220+0+1489458652+1489459237++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000221+0+1489458652+1489460268++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000222+0+1489458653+1489459870++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000223+0+1489458653+1489461243++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000224+0+1489458653+1489459707++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000225+0+1489458654+1489460605++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000226+0+1489458654+1489459590++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000227+0+1489458685+1489459604++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000228+0+1489458685+1489462983++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000229+0+1489458686+1489459701++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000230+0+1489458686+1489460851++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000231+0+1489458687+1489459413++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000232+0+1489458687+1489460586++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000233+0+1489458718+1489459448++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000234+0+1489458749+1489459606++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000235+0+1489458749+1489460541++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000236+0+1489458780+1489459678++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000237+0+1489458781+1489459860++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000238+0+1489458781+1489460537++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000239+0+1489458781+1489461773++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000240+0+1489458782+1489460351++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000241+0+1489458813+1489461391++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000242+0+1489458813+1489460298++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000243+0+1489458844+1489459914++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000244+0+1489458844+1489459683++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000245+0+1489458845+1489460611++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000246+0+1489458875+1489462491++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000247+0+1489458906+1489460183++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000248+0+1489458906+1489460638++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000249+0+1489458907+1489465041++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000250+0+1489458937+1489460986++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000251+0+1489458938+1489460314++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000252+0+1489458969+1489460935++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000253+0+1489458969+1489459822++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000254+0+1489458970+1489461192++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000255+0+1489458970+1489460899++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000256+0+1489458971+1489460860++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000257+0+1489459001+1489460429++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000258+0+1489459032+1489462464++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000259+0+1489459062+1489461346++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000260+0+1489459093+1489460789++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000261+0+1489459093+1489461614++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000262+0+1489459094+1489460419++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000263+0+1489459124+1489464016++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000264+0+1489459155+1489460363++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000265+0+1489459186+1489462876++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000266+0+1489459216+1489461356++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000267+0+1489459247+1489460143++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000268+0+1489459247+1489463170++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000269+0+1489459248+1489460951++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000270+0+1489459248+1489461126++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000271+0+1489459279+1489463130++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000272+0+1489459280+1489461692++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000273+0+1489459280+1489460944++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000274+0+1489459310+1489461449++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000275+0+1489459311+1489461706++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000276+0+1489459342+1489460699++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000277+0+1489459372+1489461760++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000278+0+1489459373+1489460833++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000279+0+1489459373+1489461744++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000280+0+1489459374+1489460955++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000281+0+1489459404+1489466817++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000282+0+1489459435+1489464774++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000283+0+1489459435+1489462883++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000284+0+1489459436+1489461553++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000285+0+1489459466+1489463958++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000286+0+1489459467+1489461781++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000287+0+1489459467+1489461059++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000288+0+1489459498+1489460715++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000289+0+1489459498+1489461807++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000290+0+1489459530+1489461549++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000291+0+1489459530+1489467193++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000292+0+1489459561+1489461609++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000293+0+1489459561+1489461793++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000294+0+1489459562+1489470429++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000295+0+1489459562+1489466141++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000296+0+1489459593+1489463045++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000297+0+1489459623+1489462060++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000298+0+1489459624+1489460237++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000299+0+1489459624+1489461582++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000300+0+1489459625+1489461420++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000301+0+1489459625+1489460967++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000302+0+1489459656+1489461143++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000303+0+1489459656+1489461899++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000304+0+1489459657+1489461990++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000305+0+1489459687+1489461698++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000306+0+1489459688+1489461239++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000307+0+1489459689+1489460797++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000308+0+1489459689+1489464727++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000309+0+1489459720+1489461921++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000310+0+1489459720+1489461844++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000311+0+1489459721+1489461255++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000312+0+1489459721+1489461862++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000313+0+1489459721+1489461143++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000314+0+1489459722+1489460608++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000315+0+1489459752+1489460863++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000316+0+1489459753+1489461305++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000317+0+1489459753+1489461080++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000318+0+1489459754+1489460993++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000319+0+1489459784+1489462420++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000320+0+1489459815+1489462019++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000321+0+1489459846+1489461131++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000322+0+1489459846+1489461432++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000323+0+1489459877+1489461071++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000324+0+1489459877+1489466127++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000325+0+1489459877+1489463792++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000326+0+1489459878+1489461192++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000327+0+1489459908+1489465753++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000328+0+1489459939+1489463234++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000329+0+1489459940+1489461722++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000330+0+1489459940+1489463715++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000331+0+1489459941+1489466134++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000332+0+1489460002+1489462115++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000333+0+1489460032+1489463008++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000334+0+1489460033+1489462221++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000335+0+1489460063+1489461636++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000336+0+1489460094+1489462406++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000337+0+1489460094+1489463155++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000338+0+1489460095+1489463464++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000339+0+1489460156+1489462669++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000340+0+1489460186+1489464537++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000341+0+1489460217+1489461693++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000342+0+1489460217+1489461078++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000343+0+1489460248+1489461718++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000344+0+1489460248+1489463691++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000345+0+1489460249+1489464702++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000346+0+1489460279+1489466194++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000347+0+1489460280+1489462252++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000348+0+1489460280+1489462679++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000349+0+1489460281+1489463338++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000350+0+1489460312+1489463423++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000351+0+1489460312+1489466139++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000352+0+1489460343+1489461567++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000353+0+1489460373+1489463040++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000354+0+1489460374+1489462086++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000355+0+1489460374+1489461508++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000356+0+1489460375+1489462151++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000357+0+1489460375+1489461795++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000358+0+1489460376+1489461811++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000359+0+1489460406+1489462189++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000360+0+1489460437+1489462732++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000361+0+1489460438+1489462751++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000362+0+1489460468+1489462740++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000363+0+1489460469+1489462119++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000364+0+1489460469+1489461845++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000365+0+1489460470+1489461930++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000366+0+1489460500+1489462283++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000367+0+1489460501+1489462114++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000368+0+1489460532+1489463354++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000369+0+1489460532+1489464241++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000370+0+1489460563+1489463763++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000371+0+1489460563+1489464682++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000372+0+1489460564+1489463069++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000373+0+1489460595+1489463420++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000374+0+1489460626+1489462476++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000375+0+1489460627+1489461669++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000376+0+1489460627+1489461739++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000377+0+1489460628+1489462690++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000378+0+1489460658+1489462958++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000379+0+1489460659+1489463224++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000380+0+1489460659+1489463576++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000381+0+1489460720+1489463354++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000382+0+1489460721+1489464660++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000383+0+1489460721+1489463038++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000384+0+1489460752+1489464495++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000385+0+1489460752+1489462627++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000386+0+1489460813+1489467029++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000387+0+1489460814+1489461976++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000388+0+1489460814+1489466797++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000389+0+1489460815+1489462657++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000390+0+1489460845+1489462745++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000391+0+1489460876+1489463007++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000392+0+1489460876+1489464013++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000393+0+1489460877+1489465761++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000394+0+1489460877+1489464147++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000395+0+1489460877+1489466287++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000396+0+1489460908+1489462448++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000397+0+1489460909+1489462779++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000398+0+1489460909+1489464407++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000399+0+1489460940+1489464875++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000400+0+1489460971+1489462720++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000401+0+1489460971+1489463610++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000402+0+1489460972+1489463361++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000403+0+1489460972+1489462261++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000404+0+1489460972+1489462362++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000405+0+1489460973+1489463671++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000406+0+1489461003+1489462305++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000407+0+1489461004+1489463390++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000408+0+1489461005+1489465235++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000409+0+1489461005+1489461994++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000410+0+1489461036+1489463564++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000411+0+1489461096+1489462659++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000412+0+1489461097+1489462567++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000413+0+1489461098+1489464166++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000414+0+1489461098+1489463177++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000415+0+1489461159+1489464328++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000416+0+1489461160+1489465001++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000417+0+1489461160+1489462994++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000418+0+1489461161+1489461903++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000419+0+1489461161+1489463730++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000420+0+1489461161+1489463854++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000421+0+1489461192+1489462422++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000422+0+1489461223+1489465175++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000423+0+1489461223+1489462167++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000424+0+1489461223+1489463329++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000425+0+1489461254+1489462726++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000426+0+1489461255+1489464227++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000427+0+1489461255+1489463434++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000428+0+1489461256+1489465876++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000429+0+1489461286+1489462817++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000430+0+1489461287+1489462344++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000431+0+1489461317+1489462364++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000432+0+1489461348+1489462905++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000433+0+1489461379+1489464705++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000434+0+1489461379+1489463804++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000435+0+1489461379+1489463515++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000436+0+1489461410+1489463225++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000437+0+1489461441+1489464736++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000438+0+1489461441+1489464152++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000439+0+1489461442+1489465996++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000440+0+1489461473+1489464533++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000441+0+1489461473+1489462714++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000442+0+1489461504+1489462752++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000443+0+1489461504+1489462591++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000444+0+1489461535+1489464057++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000445+0+1489461535+1489463552++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000446+0+1489461566+1489464037++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000447+0+1489461596+1489463830++value
-INFO:root:Using existing result: result::tim-note-tim-2017-03-12-16-37-22-27499bde-001::000448+0+1489461597+1489465607++value
-</pre>
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diff --git a/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.ipynb b/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.ipynb
deleted file mode 100644
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--- a/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.ipynb
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-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 61,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.simplefilter(\"ignore\")\n",
- "\n",
- "import mhcflurry\n",
- "import numpy\n",
- "import seaborn\n",
- "import logging\n",
- "import pandas\n",
- "from os import environ\n",
- "from matplotlib import pyplot\n",
- "from mhcflurry.downloads import get_path\n",
- "\n",
- "% matplotlib inline\n",
- "\n",
- "import IPython.core.display as display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import IPython.core.display as di\n",
- "\n",
- "# This line will hide code by default when the notebook is exported as HTML\n",
- "di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle(); jQuery(\"div.output_stderr\").toggle();}});</script>', raw=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# MHCflurry models\n",
- "\n",
- "## Class 1 allele specific ensemble models\n",
- "\n",
- "This report describes the models published with MHCflurry for Class I affinity prediction. These models were trained on the \"data_combined_iedb_kim2014\" affinity measurement dataset (mostly from IEDB) distributed with MHCflurry.\n",
- "\n",
- "Each allele's predictor is an ensemble of 16 models. The models were trained on a random 1/2 of the data for the allele and tested on the other half. The best performing model in terms of sum of AUC (at 500nM), F1, and Kendall Tau for each 50/50 split of the data was selected for inclusion in the ensemble."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "all_models_df = pandas.read_csv(get_path(\"models_class1_allele_specific_ensemble\", \"all_models.csv.bz2\"))\n",
- "all_models_df[\"hyperparameters_layer_sizes\"] = all_models_df[\"hyperparameters_layer_sizes\"].map(eval)\n",
- "\n",
- "full_training_data = mhcflurry.affinity_measurement_dataset.AffinityMeasurementDataset.from_csv(\n",
- " get_path(\"data_combined_iedb_kim2014\", \"combined_human_class1_dataset.csv\"))\n",
- "\n",
- "training_sizes = full_training_data.to_dataframe().allele.value_counts()\n",
- "\n",
- "all_models_df[\"train_size\"] = training_sizes.ix[all_models_df.allele].values\n",
- "\n",
- "(ensemble_size,) = all_models_df.ensemble_size.value_counts().index\n",
- "ensemble_size\n",
- "\n",
- "selected_models_df = all_models_df.ix[all_models_df.weight > 0]\n",
- "selected_models_df.shape\n",
- "\n",
- "alleles = [x for x in training_sizes.sort_values().index if x in selected_models_df.allele.values]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<h1>Models summary</h1>\n",
- "<table>\n",
- "<tr><td><b>Num Alleles</b></td><td>132</td></tr>\n",
- "<tr><td><b>Ensemble size</b></td><td>16</td></tr>\n",
- "<tr><td><b>Num architectures</b></td><td>162</td></tr>\n",
- "<tr><td><b>Num selected models</b></td><td>2,112</td></tr>\n",
- "<tr><td><b>Total models tested</b></td><td>342,144</td></tr>\n",
- "<tr><td><b>Total training measurements</b></td><td>192,177</td></tr>\n",
- "<tr><td><b>Training measurement per allele</b></td><td>min=26; max=12,357; median=721.5</td></tr>\n",
- "</table>\n",
- "<p><b>Alleles included: </b>HLA-A0201 HLA-A0301 HLA-A0203 HLA-A1101 H-2-KB HLA-A3101 HLA-A0206 HLA-A6802 H-2-DB HLA-A0101 HLA-B0702 HLA-A2601 HLA-B1501 HLA-A0202 HLA-A6801 HLA-A3301 HLA-B2705 HLA-B0801 HLA-A2402 HLA-B4001 HLA-B3501 HLA-B5801 HLA-B5101 HLA-B5701 HLA-A3001 HLA-B1801 HLA-A2902 Mamu-A01 HLA-A6901 HLA-A2301 HLA-B4402 Mamu-A100101 HLA-A3002 HLA-B4601 Mamu-B17 HLA-B3901 HLA-B5301 HLA-B1517 HLA-B4403 Mamu-A02 Mamu-B01704 Mamu-A11 HLA-A0219 HLA-A2403 HLA-B5401 HLA-A0212 HLA-A8001 Mamu-B03 HLA-A3201 Mamu-B08 H-2-KD HLA-A0211 HLA-B4501 HLA-B4002 HLA-B0802 HLA-A2501 HLA-A0216 Patr-B0101 Mamu-A101101 Mamu-B52 HLA-B4801 Mamu-B01 HLA-B2703 HLA-B1509 Patr-A0901 H-2-KK HLA-B1503 Mamu-A2201 Mamu-A07 Patr-A0701 HLA-A2602 H-2-DD Mamu-A100201 HLA-A2603 HLA-C0401 Patr-A0101 HLA-B3801 H-2-LD HLA-B0803 Mamu-B3901 Mamu-B8301 Patr-B2401 HLA-C0602 Patr-A0301 HLA-B1542 HLA-B4506 HLA-A0217 HLA-B8301 Patr-A0401 Patr-B1301 HLA-B3503 HLA-C1402 HLA-EQCA100101 HLA-B4201 Mamu-A2601 HLA-B1402 HLA-C1502 HLA-C0501 HLA-C1203 HLA-B1502 HLA-C0303 Mamu-B1001 Mamu-B8701 Mamu-A20102 HLA-C0702 HLA-RT1A HLA-A0250 HLA-B7301 HLA-A0205 Mamu-A70103 Mamu-B6601 HLA-B2720 HLA-C0802 HLA-A3207 HLA-B7 HLA-A6823 HLA-A6601 HLA-A0207 HLA-A2 HLA-A11 HLA-A3215 HLA-B3701 HLA-E0103 HLA-B4013 HLA-BOLA601301 HLA-BOLAHD6 HLA-B5802 HLA-B1401 HLA-B5703 HLA-A0319 HLA-A0302 HLA-B8101</p>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "training_sizes_for_included_alleles = training_sizes.ix[\n",
- " training_sizes.index.isin(all_models_df.allele)\n",
- "]\n",
- "\n",
- "lines = []\n",
- "def row(label, value):\n",
- " lines.append('<tr><td><b>%s</b></td><td>%s</td></tr>' % (label, value))\n",
- "\n",
- "lines.append('<h1>Models summary</h1>')\n",
- "lines.append('<table>')\n",
- "\n",
- "row(\"Num Alleles\", \"{:,d}\".format(all_models_df.allele.nunique()))\n",
- "row(\"Ensemble size\", \"{:,d}\".format(ensemble_size))\n",
- "row(\"Num architectures\", \"{:,d}\".format(all_models_df.hyperparameters_architecture_num.nunique()))\n",
- "row(\"Num selected models\", \"{:,d}\".format((all_models_df.weight > 0).sum()))\n",
- "row(\"Total models tested\", \"{:,d}\".format(len(all_models_df)))\n",
- "row(\"Total training measurements\", \"{:,d}\".format(training_sizes_for_included_alleles.sum()))\n",
- "row(\"Training measurement per allele\",\n",
- " \"min={:,g}; max={:,g}; median={:,g}\".format(\n",
- " training_sizes_for_included_alleles.min(),\n",
- " training_sizes_for_included_alleles.max(),\n",
- " training_sizes_for_included_alleles.median()))\n",
- "\n",
- "\n",
- "lines.append('</table>')\n",
- "lines.append(\"<p><b>Alleles included: </b>%s</p>\" % \" \".join(\n",
- " training_sizes_for_included_alleles.index))\n",
- "\n",
- "\n",
- "di.display_html(\"\\n\".join(lines), raw=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "architecture_num_to_row = all_models_df.groupby(\"hyperparameters_architecture_num\").apply(lambda df: df.iloc[0])\n",
- "\n",
- "hyperparameters = [\n",
- " x for x in architecture_num_to_row.columns\n",
- " if x.startswith(\"hyperparameters_\") and pandas.Series([\n",
- " str(item) for item in architecture_num_to_row[x]\n",
- " ]).nunique() > 1\n",
- "]\n",
- "architecture_num_to_hyperparameters = {}\n",
- "for _, row in architecture_num_to_row.iterrows():\n",
- " architecture_num_to_hyperparameters[row.hyperparameters_architecture_num] = (\n",
- " row[hyperparameters].to_dict())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Best models\n",
- "\n",
- "This table gives the models most often selected for alleles with less than or equal to the given number of training samples."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 100,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th>Training size cutoff</th>\n",
- " <th>100.0</th>\n",
- " <th>500.0</th>\n",
- " <th>1000.0</th>\n",
- " <th>inf</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>architecture_num</th>\n",
- " <td>112</td>\n",
- " <td>81</td>\n",
- " <td>112</td>\n",
- " <td>27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>best architecture selected rate (%)</th>\n",
- " <td>3.27381</td>\n",
- " <td>2.04545</td>\n",
- " <td>2.27273</td>\n",
- " <td>3.64583</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>dropout_probability</th>\n",
- " <td>0.1</td>\n",
- " <td>0</td>\n",
- " <td>0.1</td>\n",
- " <td>0.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>embedding_output_dim</th>\n",
- " <td>8</td>\n",
- " <td>8</td>\n",
- " <td>8</td>\n",
- " <td>8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>fraction_negative</th>\n",
- " <td>0.1</td>\n",
- " <td>0</td>\n",
- " <td>0.1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>impute</th>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>layer_sizes</th>\n",
- " <td>[64]</td>\n",
- " <td>[12]</td>\n",
- " <td>[64]</td>\n",
- " <td>[12]</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- "Training size cutoff 100.000000 500.000000 1000.000000 \\\n",
- "architecture_num 112 81 112 \n",
- "best architecture selected rate (%) 3.27381 2.04545 2.27273 \n",
- "dropout_probability 0.1 0 0.1 \n",
- "embedding_output_dim 8 8 8 \n",
- "fraction_negative 0.1 0 0.1 \n",
- "impute True True True \n",
- "layer_sizes [64] [12] [64] \n",
- "\n",
- "Training size cutoff inf \n",
- "architecture_num 27 \n",
- "best architecture selected rate (%) 3.64583 \n",
- "dropout_probability 0.1 \n",
- "embedding_output_dim 8 \n",
- "fraction_negative 0 \n",
- "impute False \n",
- "layer_sizes [12] "
- ]
- },
- "execution_count": 100,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "result_df = []\n",
- "cutoffs = [100, 500, 1000, numpy.inf]\n",
- "for cutoff in cutoffs:\n",
- " selected_rates = all_models_df.ix[\n",
- " all_models_df.train_size <= cutoff\n",
- " ].groupby(\"hyperparameters_architecture_num\").weight.mean().sort_values(ascending=False)\n",
- " best_architecture = selected_rates.index[0]\n",
- " d = dict(\n",
- " (key.replace(\"hyperparameters_\", \"\"), value) for (key, value) in \n",
- " architecture_num_to_hyperparameters[best_architecture].items())\n",
- " d[\"architecture selection rate (%)\"] = selected_rates.ix[best_architecture] * 100\n",
- " result_df.append(d)\n",
- "result_df = pandas.DataFrame(result_df, index=cutoffs)\n",
- "result_df.index.name = \"Training size cutoff\"\n",
- "result_df.T"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Full hyperparameters of best model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 112,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'activation': 'tanh',\n",
- " 'architecture_num': 27,\n",
- " 'batch_normalization': True,\n",
- " 'batch_size': 128,\n",
- " 'dropout_probability': 0.1,\n",
- " 'embedding_output_dim': 8,\n",
- " 'fraction_negative': 0,\n",
- " 'impute': False,\n",
- " 'impute_method': 'mice',\n",
- " 'impute_min_observations_per_allele': 3,\n",
- " 'impute_min_observations_per_peptide': 3,\n",
- " 'imputer_args': {'n_burn_in': 5,\n",
- " 'n_imputations': 50,\n",
- " 'n_nearest_columns': 25},\n",
- " 'include_ms': True,\n",
- " 'init': 'glorot_uniform',\n",
- " 'kmer_size': 9,\n",
- " 'layer_sizes': [12],\n",
- " 'loss': 'mse',\n",
- " 'max_ic50': 50000.0,\n",
- " 'ms_decoy_affinity': 20000.0,\n",
- " 'ms_hit_affinity': 1.0,\n",
- " 'n_training_epochs': 250,\n",
- " 'optimizer': 'rmsprop',\n",
- " 'output_activation': 'sigmoid',\n",
- " 'pretrain_decay': 'numpy.exp(-epoch)'}"
- ]
- },
- "execution_count": 112,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "best_hyperparameters = eval(\n",
- " architecture_num_to_row.ix[result_df.ix[numpy.inf].architecture_num].hyperparameters)\n",
- "best_hyperparameters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Accuracy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x11b5ecd68>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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00//A4XBQU1OD2+1uU31CCCG6N0mqfGDatBlMmzaDyspKNm1az6effsw77/yP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\nBFVCCCGEEAn4/wEZrk1ApZR2QgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x125007be0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "all_scores = all_models_df.scores_auc.dropna()\n",
- "selected_scores = all_models_df.ix[all_models_df.weight > 0].scores_auc.dropna()\n",
- "\n",
- "pyplot.figure(figsize=(10, 5))\n",
- "seaborn.distplot(all_scores, label=\"All. Mean=%0.2f\" % all_scores.mean())\n",
- "seaborn.distplot(selected_scores, label=\"Selected. Mean=%0.2f\" % selected_scores.mean())\n",
- "#seaborn.set_context('talk')\n",
- "pyplot.legend(loc='upper left', fontsize=\"x-large\")\n",
- "pyplot.xlim(xmin=0.5, xmax=1)\n",
- "pyplot.xlabel(\"AUC\", fontsize=\"x-large\")\n",
- "pyplot.title(\"AUCs across models and alleles\", fontsize=\"xx-large\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0.5, 1.0)"
- ]
- },
- "execution_count": 63,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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jMdldiIiIiB7Sa7lDZlr7RKvVGt248aHJ7kbsgNmzZ5Cx610Zv96VsettGb+d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ARyT918butVqt4t0DzMzMrKy2GeoBbAW9P/vp6acdgIg4EHicVPp1bD99PkQqFHCYpMOADwPf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\n4MvAu8b5e5wMvFDSKZT88jPGqT8POL/2+TfA01veG673HRFdKDOtERETa1P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- "text/plain": [
- "<matplotlib.figure.Figure at 0x11b990860>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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44UAP6QX7fO7uT/PKpey84/WIOBCPRYnHIiRifiXNcSIAPPr0Bb7ynQsA2LMT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\nm5mZmVlBuHAzMzMzKwgXbmZmZmYF8R9ZJvKPJa3c4gAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x12da37128>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "scores = all_models_df.groupby(\"allele\").scores_auc.mean().to_frame().reset_index().sort_values(\"scores_auc\", ascending=False)\n",
- "scores[\"size\"] = training_sizes.ix[scores.allele].values\n",
- "\n",
- "pyplot.figure(figsize=(10, 30))\n",
- "pyplot.title(\"Mean AUC over all models\")\n",
- "seaborn.barplot(y=\"allele\", x=\"scores_auc\", data=scores, orient=\"h\", color=\"black\")\n",
- "\n",
- "pyplot.figure(figsize=(10, 5))\n",
- "pyplot.title(\"Mean AUC over all models\")\n",
- "seaborn.regplot(x=\"size\", y=\"scores_auc\", data=scores, logx=True)\n",
- "pyplot.xlim(xmin=0)\n",
- "pyplot.ylim(ymin=0.5, ymax=1.0)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0.5, 1)"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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H/0C7Af9cD8TeuT7OwBACfxtMoBozdGBETImISVQ9JK+v21ZExASq3qhtI+K5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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x117545898>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "scores = all_models_df.groupby(\"hyperparameters_dropout_probability\").scores_auc.mean().to_frame().reset_index().sort_values(\"scores_auc\", ascending=False)\n",
- "\n",
- "pyplot.figure(figsize=(10, 5))\n",
- "pyplot.title(\"Dropout\")\n",
- "ax = seaborn.barplot(x=\"hyperparameters_dropout_probability\", y=\"scores_auc\", data=all_models_df)\n",
- "pyplot.ylim(ymin=0.5, ymax=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x120f4c0b8>"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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3bnEv0G1mOWBLYKTRwj/9/X0sH1zPdosXsPce23D3Q6u5+5HV5HKw9x7bcsCe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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x11f7077f0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "count_impute = selected_models_df.groupby(\"allele\").hyperparameters_impute.sum()\n",
- "\n",
- "seaborn.regplot(training_sizes.ix[alleles], count_impute.ix[alleles])\n",
- "pyplot.xlim(xmin=0, xmax=10000)\n",
- "pyplot.ylim(ymin=0, ymax=16)\n",
- "pyplot.title(\"Number of models (out of 16 total in each allele's ensemble)\\nthat use imputation\")\n",
- "pyplot.xlabel(\"Training points for allele\")\n",
- "pyplot.ylabel(\"Num models\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x120fb1518>"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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LXv80oWazMj51ibs/F/tUXovPvejuF5XwnkREpMKSNFv9DLja3f8IYGb/BfyC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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x120dcf320>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "impute_cums_df = selected_models_df.groupby(\"allele\").hyperparameters_impute.mean().to_frame()\n",
- "impute_cums_df[\"size\"] = training_sizes\n",
- "impute_cums_df = impute_cums_df.sort_values(\"size\")\n",
- "impute_cums_df[\"cum_mean\"] = (\n",
- " impute_cums_df.hyperparameters_impute.cumsum() / (numpy.arange(len(impute_cums_df)) + 1))\n",
- "impute_cums_df\n",
- "seaborn.regplot(\"size\", \"cum_mean\", data=impute_cums_df, fit_reg=False, logx=True)\n",
- "pyplot.xscale(\"log\")\n",
- "pyplot.xlabel(\"IEDB Measurements\")\n",
- "pyplot.ylabel(\"Fraction of models using imputation\")\n",
- "pyplot.title(\n",
- " \"Fraction of best models for alleles with <= x measurements\\n\"\n",
- " \"that use imputation\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x11e610da0>"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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4Dq/3ij3LxXPnhrz41OwsMGCqRciKuenmo6lAAbhweyUsVRdNs1xcMpZN3JOI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\nhAAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x120393550>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "seaborn.regplot(\n",
- " selected_models_df.train_size.values,\n",
- " selected_models_df.hyperparameters_dropout_probability.values,\n",
- " x_jitter=.015,\n",
- " y_jitter=.015)\n",
- "pyplot.xlim(xmin=0)\n",
- "pyplot.ylim(ymin=0)\n",
- "pyplot.title(\"Dropout rate of selected models\")\n",
- "pyplot.xlabel(\"Training points for allele\")\n",
- "pyplot.ylabel(\"Dropout rate\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x120db5f98>"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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4c0dPxTvzZ5+6AaBw8Xddl9v+fBCAyUSG6XQW27ZoqIsWLnJhmkKuetFJALPm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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x11e6162b0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "seaborn.regplot(\n",
- " selected_models_df.train_size.values,\n",
- " selected_models_df.hyperparameters_embedding_output_dim.values,\n",
- " x_jitter=1,\n",
- " y_jitter=1)\n",
- "pyplot.xlim(xmin=0)\n",
- "pyplot.ylim(ymin=0)\n",
- "pyplot.title(\"Embedding output dimensions of selected models\")\n",
- "pyplot.xlabel(\"Training points for allele\")\n",
- "pyplot.ylabel(\"Embedding output dimensions\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x1203ab588>"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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SPzfsquPgvgaeae2lc2CccCRBkd9DY10pN+9rwGUYdv2myYOPnqSzf5zGulLe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\nsgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x1208414a8>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "seaborn.regplot(\n",
- " selected_models_df.train_size.values,\n",
- " selected_models_df.hyperparameters_layer_sizes.map(lambda x: x[0]).values,\n",
- " x_jitter=5,\n",
- " y_jitter=5)\n",
- "pyplot.xlim(xmin=0)\n",
- "pyplot.ylim(ymin=0)\n",
- "pyplot.title(\"Hidden layer size of selected models\")\n",
- "pyplot.xlabel(\"Training points for allele\")\n",
- "pyplot.ylabel(\"Hidden layer size\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x11e6cbe10>"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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XcMgVbRzHIxY12dCu9v64bsfGslIAWNMS59CJEVLZIrbj4npgGOB58JUfS6IRk2zBwTIN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\nAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x11fbc9c18>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "seaborn.regplot(\n",
- " selected_models_df.hyperparameters_embedding_output_dim.values,\n",
- " selected_models_df.hyperparameters_layer_sizes.map(lambda x: x[0]).values,\n",
- " x_jitter=5,\n",
- " y_jitter=5)\n",
- "pyplot.xlim(xmin=0)\n",
- "pyplot.ylim(ymin=0)\n",
- "pyplot.title(\"Hidden layer size vs. embedding output dims of selected models\")\n",
- "pyplot.xlabel(\"Embedding output dimensions\")\n",
- "pyplot.ylabel(\"Hidden layer size\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 101,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<h1>Model selection invocation</h1><pre>#!/bin/bash\n",
- "\n",
- "if [[ $# -eq 0 ]] ; then\n",
- " echo 'WARNING: This script is intended to be called with additional arguments to pass to mhcflurry-class1-allele-specific-cv-and-train'\n",
- " echo 'See README.md'\n",
- "fi\n",
- "\n",
- "set -e\n",
- "set -x\n",
- "\n",
- "DOWNLOAD_NAME=models_class1_allele_specific_ensemble\n",
- "SCRATCH_DIR=/tmp/mhcflurry-downloads-generation\n",
- "SCRIPT_ABSOLUTE_PATH=\"$(cd \"$(dirname \"${BASH_SOURCE[0]}\")\" && pwd)/$(basename \"${BASH_SOURCE[0]}\")\"\n",
- "SCRIPT_DIR=$(dirname \"$SCRIPT_ABSOLUTE_PATH\")\n",
- "export PYTHONUNBUFFERED=1\n",
- "\n",
- "mkdir -p \"$SCRATCH_DIR\"\n",
- "rm -rf \"$SCRATCH_DIR/$DOWNLOAD_NAME\"\n",
- "mkdir \"$SCRATCH_DIR/$DOWNLOAD_NAME\"\n",
- "\n",
- "# Send stdout and stderr to a logfile included with the archive.\n",
- "exec > >(tee -ia \"$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt\")\n",
- "exec 2> >(tee -ia \"$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt\" >&2)\n",
- "\n",
- "# Log some environment info\n",
- "date\n",
- "pip freeze\n",
- "git rev-parse HEAD\n",
- "git status\n",
- "\n",
- "cd $SCRATCH_DIR/$DOWNLOAD_NAME\n",
- "\n",
- "mkdir models\n",
- "\n",
- "cp $SCRIPT_DIR/models.py .\n",
- "python models.py > models.json\n",
- "\n",
- "time mhcflurry-class1-allele-specific-ensemble-train \\\n",
- " --ensemble-size 16 \\\n",
- " --model-architectures models.json \\\n",
- " --train-data \"$(mhcflurry-downloads path data_combined_iedb_kim2014)/combined_human_class1_dataset.csv\" \\\n",
- " --min-samples-per-allele 20 \\\n",
- " --out-manifest selected_models.csv \\\n",
- " --out-model-selection-manifest all_models.csv \\\n",
- " --out-models models \\\n",
- " --verbose \\\n",
- " \"$@\"\n",
- "\n",
- "bzip2 all_models.csv\n",
- "cp $SCRIPT_ABSOLUTE_PATH .\n",
- "tar -cjf \"../${DOWNLOAD_NAME}.tar.bz2\" *\n",
- "\n",
- "echo \"Created archive: $SCRATCH_DIR/$DOWNLOAD_NAME.tar.bz2\"\n",
- "</pre>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "log_path = get_path(\"models_class1_allele_specific_ensemble\", \"GENERATE.sh\")\n",
- "with open(log_path) as fd:\n",
- " di.display_html(\"<h1>Model selection invocation</h1><pre>%s</pre>\" % fd.read(), raw=True)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 102,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<h1>Model selection log (beginning)</h1><pre>+ date\n",
- "Thu Mar 16 13:18:34 UTC 2017\n",
- "+ pip freeze\n",
- "alabaster==0.7.9\n",
- "anaconda-clean==1.0\n",
- "anaconda-client==1.5.1\n",
- "anaconda-navigator==1.3.1\n",
- "appdirs==1.4.0\n",
- "argcomplete==1.0.0\n",
- "astroid==1.4.7\n",
- "astropy==1.2.1\n",
- "Babel==2.3.4\n",
- "backports.shutil-get-terminal-size==1.0.0\n",
- "beautifulsoup4==4.5.1\n",
- "biopython==1.68\n",
- "bitarray==0.8.1\n",
- "blaze==0.10.1\n",
- "bokeh==0.12.2\n",
- "boto==2.42.0\n",
- "bottle==0.12.13\n",
- "Bottleneck==1.1.0\n",
- "cffi==1.7.0\n",
- "chest==0.2.3\n",
- "click==6.6\n",
- "climate==0.4.6\n",
- "cloudpickle==0.2.1\n",
- "clyent==1.2.2\n",
- "colorama==0.3.7\n",
- "conda==4.2.9\n",
- "conda-build==2.0.2\n",
- "configobj==5.0.6\n",
- "contextlib2==0.5.3\n",
- "cryptography==1.5\n",
- "CVXcanon==0.1.1\n",
- "cvxpy==0.4.8\n",
- "cycler==0.10.0\n",
- "Cython==0.24.1\n",
- "cytoolz==0.8.0\n",
- "dask==0.11.0\n",
- "datacache==0.4.20\n",
- "datashape==0.5.2\n",
- "decorator==4.0.10\n",
- "dill==0.2.5\n",
- "docutils==0.12\n",
- "downhill==0.4.0\n",
- "dynd==0.7.3.dev1\n",
- "ecos==2.0.4\n",
- "et-xmlfile==1.0.1\n",
- "fancyimpute==0.1.0\n",
- "fastcache==1.0.2\n",
- "filelock==2.0.6\n",
- "Flask==0.11.1\n",
- "Flask-Cors==2.1.2\n",
- "gevent==1.1.2\n",
- "google-api-python-client==1.5.5\n",
- "greenlet==0.4.10\n",
- "gtfparse==0.0.6\n",
- "h5py==2.6.0\n",
- "HeapDict==1.0.0\n",
- "httplib2==0.9.2\n",
- "humanize==0.5.1\n",
- "idna==2.1\n",
- "imagesize==0.7.1\n",
- "ipdb==0.10.2\n",
- "ipykernel==4.5.0\n",
- "ipython==5.1.0\n",
- "ipython-genutils==0.1.0\n",
- "ipywidgets==5.2.2\n",
- "itsdangerous==0.24\n",
- "jdcal==1.2\n",
- "jedi==0.9.0\n",
- "Jinja2==2.8\n",
- "joblib==0.10.3\n",
- "jsonschema==2.5.1\n",
- "jupyter==1.0.0\n",
- "jupyter-client==4.4.0\n",
- "jupyter-console==5.0.0\n",
- "jupyter-core==4.2.0\n",
- "Keras==1.2.0\n",
- "knnimpute==0.0.1\n",
- "-e git+git@github.com:hammerlab/kubeface.git@91fa80a571b9f870c4ec945b834a97fdf863fbc7#egg=kubeface\n",
- "lazy-object-proxy==1.2.1\n",
- "llvmlite==0.13.0\n",
- "locket==0.2.0\n",
- "lxml==3.6.4\n",
- "MarkupSafe==0.23\n",
- "matplotlib==1.5.3\n",
- "memoized-property==1.0.3\n",
- "-e git+git@github.com:hammerlab/mhcflurry.git@2925ce8d6c08e8ac0170504b06f1be384a0fc169#egg=mhcflurry\n",
- "mhcnames==0.1.0\n",
- "mhctools==0.4.1\n",
- "mistune==0.7.3\n",
- "mock==2.0.0\n",
- "mpmath==0.19\n",
- "multipledispatch==0.4.8\n",
- "multiprocess==0.70.4\n",
- "nb-anacondacloud==1.2.0\n",
- "nb-conda==2.0.0\n",
- "nb-conda-kernels==2.0.0\n",
- "nbconvert==4.2.0\n",
- "nbformat==4.1.0\n",
- "nbpresent==3.0.2\n",
- "-e git+git@github.com:hammerlab/neon.git@f343737d19e1b9509137bf63b9d291d2d8c8bcaf#egg=neon\n",
- "networkx==1.11\n",
- "nltk==3.2.1\n",
- "nose==1.3.7\n",
- "notebook==4.2.3\n",
- "numba==0.28.1\n",
- "numexpr==2.6.1\n",
- "numpy==1.11.1\n",
- "oauth2client==4.0.0\n",
- "odo==0.5.0\n",
- "openpyxl==2.3.2\n",
- "pandas==0.18.1\n",
- "parse==1.6.6\n",
- "partd==0.3.6\n",
- "path.py==0.0.0\n",
- "pathlib2==2.1.0\n",
- "patsy==0.4.1\n",
- "pbr==1.10.0\n",
- "pep8==1.7.0\n",
- "pepdata==0.7.0\n",
- "pexpect==4.0.1\n",
- "pickleshare==0.7.4\n",
- "Pillow==3.3.1\n",
- "pkginfo==1.3.2\n",
- "plac==0.9.6\n",
- "ply==3.9\n",
- "progressbar33==2.4\n",
- "prompt-toolkit==1.0.3\n",
- "psutil==4.3.1\n",
- "ptyprocess==0.5.1\n",
- "py==1.4.31\n",
- "pyasn1==0.1.9\n",
- "pyasn1-modules==0.0.8\n",
- "pycosat==0.6.1\n",
- "pycparser==2.14\n",
- "pycrypto==2.6.1\n",
- "pycurl==7.43.0\n",
- "pyensembl==1.0.3\n",
- "pyflakes==1.3.0\n",
- "Pygments==2.1.3\n",
- "pylint==1.5.4\n",
- "pyopen==0.0.6\n",
- "pyOpenSSL==16.0.0\n",
- "pyparsing==2.1.4\n",
- "pytest==2.9.2\n",
- "python-dateutil==2.5.3\n",
- "pytz==2016.6.1\n",
- "PyVCF==0.6.8\n",
- "PyYAML==3.12\n",
- "pyzmq==15.4.0\n",
- "QtAwesome==0.3.3\n",
- "qtconsole==4.2.1\n",
- "QtPy==1.1.2\n",
- "redis==2.10.5\n",
- "requests==2.11.1\n",
- "rope-py3k==0.9.4.post1\n",
- "rsa==3.4.2\n",
- "ruamel-yaml===-VERSION\n",
- "scikit-image==0.12.3\n",
- "scikit-learn==0.18.1\n",
- "scipy==0.18.1\n",
- "scs==1.2.6\n",
- "seaborn==0.7.1\n",
- "sercol==0.0.2\n",
- "serializable==0.1.1\n",
- "simplegeneric==0.8.1\n",
- "simplejson==3.10.0\n",
- "singledispatch==3.4.0.3\n",
- "six==1.10.0\n",
- "sklearn==0.0\n",
- "snowballstemmer==1.2.1\n",
- "sockjs-tornado==1.0.3\n",
- "Sphinx==1.4.6\n",
- "spyder==3.0.0\n",
- "SQLAlchemy==1.0.13\n",
- "statsmodels==0.6.1\n",
- "sympy==1.0\n",
- "tables==3.2.3.1\n",
- "terminado==0.6\n",
- "Theano==0.8.2\n",
- "tinytimer==0.0.0\n",
- "toolz==0.8.0\n",
- "tornado==4.4.1\n",
- "traitlets==4.3.0\n",
- "typechecks==0.0.2\n",
- "unicodecsv==0.14.1\n",
- "uritemplate==3.0.0\n",
- "varcode==0.5.11\n",
- "wcwidth==0.1.7\n",
- "Werkzeug==0.11.11\n",
- "widgetsnbextension==1.2.6\n",
- "wrapt==1.10.6\n",
- "xlrd==1.0.0\n",
- "XlsxWriter==0.9.3\n",
- "xlwt==1.1.2\n",
- "You are using pip version 8.1.2, however version 9.0.1 is available.\n",
- "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
- "+ git rev-parse HEAD\n",
- "2925ce8d6c08e8ac0170504b06f1be384a0fc169\n",
- "+ git status\n",
- "On branch add-class1-ensemble\n",
- "Your branch is up-to-date with 'origin/add-class1-ensemble'.\n",
- "nothing to commit, working directory clean\n",
- "+ cd /tmp/mhcflurry-downloads-generation/models_class1_allele_specific_ensemble\n",
- "+ mkdir models\n",
- "+ cp /home/tim/sinai/git/mhcflurry/downloads-generation/models_class1_allele_specific_ensemble/models.py .\n",
- "+ python models.py\n",
- "Using Theano backend.\n",
- "/home/tim/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
- " \"This module will be removed in 0.20.\", DeprecationWarning)\n",
- "Models: 162\n",
- "++ mhcflurry-downloads path data_combined_iedb_kim2014\n",
- "Using Theano backend.\n",
- "/home/tim/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
- " \"This module will be removed in 0.20.\", DeprecationWarning)\n",
- "+ mhcflurry-class1-allele-specific-ensemble-train --ensemble-size 16 --model-architectures models.json --train-data /home/tim/.local/share/mhcflurry/4/0.0.8/data_combined_iedb_kim2014//combined_human_class1_dataset.csv --min-samples-per-allele 20 --out-manifest selected_models.csv --out-model-selection-manifest all_models.csv --out-models models --verbose --parallel-backend kubeface --target-tasks 10000 --kubeface-backend kubernetes --kubeface-storage gs://kubeface-tim --kubeface-worker-image hammerlab/mhcflurry-misc:latest --kubeface-kubernetes-task-resources-memory-mb 6000 --kubeface-worker-path-prefix venv-py3/bin --kubeface-max-simultaneous-tasks 200 --kubeface-speculation-max-reruns 3 --kubeface-cache-key-prefix tim-note-tim-2017-03-12-16-37-22-27499bde\n",
- "Using Theano backend.\n",
- "/home/tim/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
- " \"This module will be removed in 0.20.\", DeprecationWarning)\n",
- "To show stack trace, run:\n",
- "kill -s USR1 992\n",
- "INFO:root:Running with arguments: Namespace(alleles=None, dask_scheduler=None, ensemble_size=16, kubeface_backend='kubernetes', kubeface_cache_key_prefix='tim-note-tim-2017-03-12-16-37-22-27499bde', kubeface_kubernetes_cluster=None, kubeface_kubernetes_image_pull_policy='Always', kubeface_kubernetes_retries=12, kubeface_kubernetes_task_resources_cpu=1, kubeface_kubernetes_task_resources_memory_mb=6000.0, kubeface_local_process_docker_command='docker', kubeface_max_simultaneous_tasks=200, kubeface_never_cleanup=False, kubeface_poll_seconds=30.0, kubeface_speculation_max_reruns=3, kubeface_speculation_percent=20, kubeface_speculation_runtime_percentile=99, kubeface_storage='gs://kubeface-tim', kubeface_wait_to_raise_task_exception=False, kubeface_worker_image='hammerlab/mhcflurry-misc:latest', kubeface_worker_kubeface_install_command='{pip} install https://github.com/hammerlab/kubeface/archive/master.zip', kubeface_worker_kubeface_install_policy='if-not-present', kubeface_worker_path_prefix='venv-py3/bin', kubeface_worker_pip='pip', kubeface_worker_pip_packages=[], max_models=None, min_samples_per_allele=20, model_architectures=<_io.TextIOWrapper name='models.json' mode='r' encoding='UTF-8'>, num_local_processes=None, num_local_threads=1, out_manifest='selected_models.csv', out_model_selection_manifest='all_models.csv', out_models_dir='models', parallel_backend='kubeface', quiet=False, target_tasks=10000, train_data='/home/tim/.local/share/mhcflurry/4/0.0.8/data_combined_iedb_kim2014//combined_human_class1_dataset.csv', verbose=True)\n",
- "Using parallel backend: <Kubeface backend, client=<kubeface.client.Client object at 0x7fe5429fdac8>>\n",
- "INFO:root:Read 162 model architectures\n",
- "INFO:root:Loaded training data: Dataset(n=192550, alleles=['ELA-A1', 'Gogo-B0101', 'H-2-DB', 'H-2-DD', 'H-2-KB', 'H-2-KBM8', 'H-2-KD', 'H-2-KK', 'H-2-LD', 'H-2-LQ', 'HLA-A0101', 'HLA-A0201', 'HLA-A0202', 'HLA-A0203', 'HLA-A0204', 'HLA-A0205', 'HLA-A0206', 'HLA-A0207', 'HLA-A0210', 'HLA-A0211', 'HLA-A0212', 'HLA-A0216', 'HLA-A0217', 'HLA-A0219', 'HLA-A0250', 'HLA-A0301', 'HLA-A0302', 'HLA-A0319', 'HLA-A1', 'HLA-A11', 'HLA-A1101', 'HLA-A1102', 'HLA-A2', 'HLA-A2301', 'HLA-A24', 'HLA-A2402', 'HLA-A2403', 'HLA-A2501', 'HLA-A26', 'HLA-A2601', 'HLA-A2602', 'HLA-A2603', 'HLA-A2902', 'HLA-A3', 'HLA-A3/11', 'HLA-A3001', 'HLA-A3002', 'HLA-A3101', 'HLA-A3201', 'HLA-A3207', 'HLA-A3215', 'HLA-A3301', 'HLA-A6601', 'HLA-A6801', 'HLA-A6802', 'HLA-A6823', 'HLA-A6901', 'HLA-A7401', 'HLA-A8001', 'HLA-B0702', 'HLA-B0801', 'HLA-B0802', 'HLA-B0803', 'HLA-B1401', 'HLA-B1402', 'HLA-B1501', 'HLA-B1502', 'HLA-B1503', 'HLA-B1509', 'HLA-B1517', 'HLA-B1542', 'HLA-B1801', 'HLA-B27', 'HLA-B2701', 'HLA-B2702', 'HLA-B2703', 'HLA-B2704', 'HLA-B2705', 'HLA-B2706', 'HLA-B2710', 'HLA-B2720', 'HLA-B3501', 'HLA-B3503', 'HLA-B3508', 'HLA-B3701', 'HLA-B3801', 'HLA-B39', 'HLA-B3901', 'HLA-B40', 'HLA-B4001', 'HLA-B4002', 'HLA-B4013', 'HLA-B4201', 'HLA-B4202', 'HLA-B44', 'HLA-B4402', 'HLA-B4403', 'HLA-B4501', 'HLA-B4506', 'HLA-B4601', 'HLA-B4801', 'HLA-B51', 'HLA-B5101', 'HLA-B5201', 'HLA-B5301', 'HLA-B5401', 'HLA-B5701', 'HLA-B5702', 'HLA-B5703', 'HLA-B58', 'HLA-B5801', 'HLA-B5802', 'HLA-B60', 'HLA-B62', 'HLA-B7', 'HLA-B7301', 'HLA-B8', 'HLA-B8101', 'HLA-B8301', 'HLA-BOLA102101', 'HLA-BOLA200801', 'HLA-BOLA201201', 'HLA-BOLA402401', 'HLA-BOLA601301', 'HLA-BOLA601302', 'HLA-BOLAHD6', 'HLA-C0303', 'HLA-C0401', 'HLA-C0501', 'HLA-C0602', 'HLA-C0702', 'HLA-C0802', 'HLA-C1', 'HLA-C1203', 'HLA-C1402', 'HLA-C1502', 'HLA-C4', 'HLA-E0101', 'HLA-E0103', 'HLA-EQCA100101', 'HLA-RT1A', 'HLA-RT1BL', 'HLA-SLA10401', 'Mamu-A01', 'Mamu-A02', 'Mamu-A07', 'Mamu-A100101', 'Mamu-A100201', 'Mamu-A101101', 'Mamu-A11', 'Mamu-A20102', 'Mamu-A2201', 'Mamu-A2601', 'Mamu-A70103', 'Mamu-B01', 'Mamu-B01704', 'Mamu-B03', 'Mamu-B04', 'Mamu-B06502', 'Mamu-B08', 'Mamu-B1001', 'Mamu-B17', 'Mamu-B3901', 'Mamu-B52', 'Mamu-B6601', 'Mamu-B8301', 'Mamu-B8701', 'Patr-A0101', 'Patr-A0301', 'Patr-A0401', 'Patr-A0602', 'Patr-A0701', 'Patr-A0901', 'Patr-B0101', 'Patr-B0901', 'Patr-B1301', 'Patr-B1701', 'Patr-B2401'])\n",
- "</pre>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "log_path = get_path(\"models_class1_allele_specific_ensemble\", \"LOG.txt\")\n",
- "with open(log_path) as fd:\n",
- " lines = fd.readlines(100000)\n",
- " di.display_html(\"<h1>Model selection log (beginning)</h1><pre>%s</pre>\" % \"\".join(lines), raw=True)\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python [py3k]",
- "language": "python",
- "name": "Python [py3k]"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/downloads-generation/models_class1_allele_specific_ensemble/models.py b/downloads-generation/models_class1_allele_specific_ensemble/models.py
deleted file mode 100644
index 08be5252..00000000
--- a/downloads-generation/models_class1_allele_specific_ensemble/models.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import sys
-from mhcflurry.class1_allele_specific_ensemble import HYPERPARAMETER_DEFAULTS
-import json
-
-models = HYPERPARAMETER_DEFAULTS.models_grid(
- impute=[False, True],
- activation=["tanh"],
- layer_sizes=[[12], [64], [128]],
- embedding_output_dim=[8, 32, 64],
- dropout_probability=[0, .1, .25],
- fraction_negative=[0, .1, .2],
- n_training_epochs=[250],
-
- # Imputation arguments
- impute_method=["mice"],
- imputer_args=[
- # Arguments specific to imputation method (mice)
- {"n_burn_in": 5, "n_imputations": 50, "n_nearest_columns": 25}
- ],
- impute_min_observations_per_peptide=[3],
- impute_min_observations_per_allele=[3])
-
-sys.stderr.write("Models: %d\n" % len(models))
-print(json.dumps(models, indent=4))
diff --git a/downloads-generation/models_class1_allele_specific_single/GENERATE.sh b/downloads-generation/models_class1_allele_specific_single/GENERATE.sh
deleted file mode 100755
index 93463556..00000000
--- a/downloads-generation/models_class1_allele_specific_single/GENERATE.sh
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/bin/bash
-
-if [[ $# -eq 0 ]] ; then
- echo 'WARNING: This script is intended to be called with additional arguments to pass to mhcflurry-class1-allele-specific-cv-and-train'
- echo 'At minimum you probably want to pass --dask-scheduler <IP:PORT> as training many models on one node is extremely '
- echo 'slow.'
-fi
-
-set -e
-set -x
-
-DOWNLOAD_NAME=models_class1_allele_specific_single
-SCRATCH_DIR=/tmp/mhcflurry-downloads-generation
-SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
-SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
-export PYTHONUNBUFFERED=1
-
-mkdir -p "$SCRATCH_DIR"
-rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
-mkdir "$SCRATCH_DIR/$DOWNLOAD_NAME"
-
-# Send stdout and stderr to a logfile included with the archive.
-exec > >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt")
-exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
-
-# Log some environment info
-date
-# pip freeze
-# git rev-parse HEAD
-# git status
-
-cd $SCRATCH_DIR/$DOWNLOAD_NAME
-
-mkdir models
-
-cp $SCRIPT_DIR/models.py $SCRIPT_DIR/imputer.json .
-python models.py > models.json
-
-time mhcflurry-class1-allele-specific-cv-and-train \
- --model-architectures models.json \
- --imputer-description imputer.json \
- --train-data "$(mhcflurry-downloads path data_combined_iedb_kim2014)/combined_human_class1_dataset.csv" \
- --min-samples-per-allele 200 \
- --out-cv-results cv.csv \
- --out-production-results production.csv \
- --out-models models \
- --verbose \
- "$@"
-
-cp $SCRIPT_ABSOLUTE_PATH .
-tar -cjf "../${DOWNLOAD_NAME}.tar.bz2" *
-
-echo "Created archive: $SCRATCH_DIR/$DOWNLOAD_NAME.tar.bz2"
diff --git a/downloads-generation/models_class1_allele_specific_single/README.md b/downloads-generation/models_class1_allele_specific_single/README.md
deleted file mode 100644
index 0003e3cd..00000000
--- a/downloads-generation/models_class1_allele_specific_single/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Class I allele-specific models (single)
-
-This download contains trained MHC Class I allele-specific MHCflurry models. The training data used is in the [data_combined_iedb_kim2014](../data_combined_iedb_kim2014) MHCflurry download. We first select network hyperparameters for each allele individually using cross validation over the models enumerated in [models.py](models.py). The best hyperparameter settings are selected via average of AUC (at 500nm), F1, and Kendall's Tau over the training folds. We then train the production models over the full training set using the selected hyperparameters.
-
-The training script supports multi-node parallel execution using the [kubeface](https://github.com/hammerlab/kubeface) librarie.
-
-To use kubeface, you should make a google storage bucket and pass it below with the --storage-prefix argument.
-
-To generate this download we run:
-
-```
-./GENERATE.sh \
- --cv-folds-per-task 10 \
- --backend kubernetes \
- --storage-prefix gs://kubeface \
- --worker-image hammerlab/mhcflurry:latest \
- --kubernetes-task-resources-memory-mb 10000 \
- --worker-path-prefix venv-py3/bin \
- --max-simultaneous-tasks 200 \
-
-```
diff --git a/downloads-generation/models_class1_allele_specific_single/imputer.json b/downloads-generation/models_class1_allele_specific_single/imputer.json
deleted file mode 100644
index f7614316..00000000
--- a/downloads-generation/models_class1_allele_specific_single/imputer.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
- "imputation_method_name": "mice",
- "n_burn_in": 5,
- "n_imputations": 50,
- "n_nearest_columns": 25,
- "min_observations_per_peptide": 5,
- "min_observations_per_allele": 100
-}
diff --git a/downloads-generation/models_class1_allele_specific_single/models.py b/downloads-generation/models_class1_allele_specific_single/models.py
deleted file mode 100644
index 30f8e3d5..00000000
--- a/downloads-generation/models_class1_allele_specific_single/models.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import sys
-from mhcflurry.class1_allele_specific.train import HYPERPARAMETER_DEFAULTS
-import json
-
-models = HYPERPARAMETER_DEFAULTS.models_grid(
- impute=[False, True],
- activation=["tanh"],
- layer_sizes=[[12], [64], [128]],
- embedding_output_dim=[8, 32, 64],
- dropout_probability=[0, .1, .25],
- fraction_negative=[0, .1, .2],
- n_training_epochs=[250])
-
-sys.stderr.write("Models: %d\n" % len(models))
-print(json.dumps(models, indent=4))
diff --git a/downloads-generation/models_class1_allele_specific_single_kim2014_only/GENERATE.sh b/downloads-generation/models_class1_allele_specific_single_kim2014_only/GENERATE.sh
deleted file mode 100755
index 943cc6fc..00000000
--- a/downloads-generation/models_class1_allele_specific_single_kim2014_only/GENERATE.sh
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/bash
-
-if [[ $# -eq 0 ]] ; then
- echo 'WARNING: This script is intended to be called with additional arguments to pass to mhcflurry-class1-allele-specific-cv-and-train'
- echo 'At minimum you probably want to pass --dask-scheduler <IP:PORT> as training many models on one node is extremely '
- echo 'slow.'
-fi
-
-set -e
-set -x
-
-DOWNLOAD_NAME=models_class1_allele_specific_single_kim2014_only
-SCRATCH_DIR=/tmp/mhcflurry-downloads-generation
-SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
-SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
-export PYTHONUNBUFFERED=1
-
-mkdir -p "$SCRATCH_DIR"
-rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
-mkdir "$SCRATCH_DIR/$DOWNLOAD_NAME"
-
-# Send stdout and stderr to a logfile included with the archive.
-exec > >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt")
-exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
-
-# Log some environment info
-date
-pip freeze
-git rev-parse HEAD
-git status
-
-cd $SCRATCH_DIR/$DOWNLOAD_NAME
-
-mkdir models
-
-cp $SCRIPT_DIR/models.py $SCRIPT_DIR/imputer.json .
-python models.py > models.json
-
-time mhcflurry-class1-allele-specific-cv-and-train \
- --model-architectures models.json \
- --imputer-description imputer.json \
- --train-data "$(mhcflurry-downloads path data_kim2014)/bdata.2009.mhci.public.1.txt" \
- --test-data "$(mhcflurry-downloads path data_kim2014)/bdata.2013.mhci.public.blind.1.txt" \
- --min-samples-per-allele 50 \
- --out-cv-results cv.csv \
- --out-production-results production.csv \
- --out-models models \
- --verbose \
- "$@"
-
-cp $SCRIPT_ABSOLUTE_PATH .
-tar -cjf "../${DOWNLOAD_NAME}.tar.bz2" *
-
-echo "Created archive: $SCRATCH_DIR/$DOWNLOAD_NAME.tar.bz2"
diff --git a/downloads-generation/models_class1_allele_specific_single_kim2014_only/README.md b/downloads-generation/models_class1_allele_specific_single_kim2014_only/README.md
deleted file mode 100644
index 6cecfebb..00000000
--- a/downloads-generation/models_class1_allele_specific_single_kim2014_only/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-# Class I allele specific models (single) trained and tested in Kim 2014 dataset
-
-This is a reimplementation of the analysis in [Predicting Peptide-MHC Binding Affinities With Imputed Training Data](http://biorxiv.org/content/early/2016/05/22/054775).
-
diff --git a/downloads-generation/models_class1_allele_specific_single_kim2014_only/imputer.json b/downloads-generation/models_class1_allele_specific_single_kim2014_only/imputer.json
deleted file mode 100644
index c17f86cc..00000000
--- a/downloads-generation/models_class1_allele_specific_single_kim2014_only/imputer.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
- "imputation_method_name": "mice",
- "n_burn_in": 5,
- "n_imputations": 50,
- "n_nearest_columns": 25,
- "min_observations_per_peptide": 2,
- "min_observations_per_allele": 2
-}
diff --git a/downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py b/downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py
deleted file mode 100644
index 6375cd45..00000000
--- a/downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import sys
-from mhcflurry.class1_allele_specific.train import HYPERPARAMETER_DEFAULTS
-import json
-
-models = HYPERPARAMETER_DEFAULTS.models_grid(
- #impute=[False, True],
- impute=[False],
- activation=["tanh"],
- layer_sizes=[[12], [64], [128]],
- embedding_output_dim=[8, 32, 64],
- dropout_probability=[0, .1, .25],
- # fraction_negative=[0, .1, .2],
- n_training_epochs=[250])
-
-sys.stderr.write("Models: %d\n" % len(models))
-print(json.dumps(models, indent=4))
diff --git a/mhcflurry/__init__.py b/mhcflurry/__init__.py
index bb50dabc..4420dcbb 100644
--- a/mhcflurry/__init__.py
+++ b/mhcflurry/__init__.py
@@ -12,24 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-from .class1_allele_specific.class1_binding_predictor import (
+from .class1_affinity_prediction.class1_binding_predictor import (
Class1BindingPredictor)
-from .prediction import predict
-from .affinity_measurement_dataset import AffinityMeasurementDataset
-from .class1_allele_specific_ensemble import Class1EnsembleMultiAllelePredictor
-from .class1_allele_specific import Class1SingleModelMultiAllelePredictor
-from .measurement_collection import MeasurementCollection
-from . import parallelism
+from .class1_affinity_prediction.multi_allele_predictor_ensemble import (
+ MultiAllelePredictorEnsemble)
__version__ = "0.2.0"
__all__ = [
"Class1BindingPredictor",
- "predict",
- "parallelism",
- "AffinityMeasurementDataset",
- "Class1EnsembleMultiAllelePredictor",
- "Class1SingleModelMultiAllelePredictor",
- "MeasurementCollection",
+ "MultiAllelePredictorEnsemble",
"__version__",
]
diff --git a/mhcflurry/affinity_measurement_dataset.py b/mhcflurry/affinity_measurement_dataset.py
deleted file mode 100644
index 72444219..00000000
--- a/mhcflurry/affinity_measurement_dataset.py
+++ /dev/null
@@ -1,843 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function, division, absolute_import
-from collections import defaultdict, OrderedDict
-import logging
-
-
-from six import string_types
-import pandas as pd
-import numpy as np
-from typechecks import require_iterable_of
-from sklearn.cross_validation import StratifiedKFold
-
-from .common import geometric_mean, groupby_indices, shuffle_split_list
-from .dataset_helpers import (
- prepare_pMHC_affinity_arrays,
- load_dataframe
-)
-from .peptide_encoding import fixed_length_index_encoding
-from .imputation_helpers import (
- check_dense_pMHC_array,
- prune_dense_matrix_and_labels,
- dense_pMHC_matrix_to_nested_dict,
- imputer_from_name
-)
-
-
-class AffinityMeasurementDataset(object):
- """
- Peptide-MHC binding dataset with helper methods for constructing
- different representations (arrays, DataFrames, dictionaries, &c).
-
- This class is specific for affinity measurements (IC50s), whereas the
- MeasurementCollection class supports both affinitites and other
- measurement types like mass spec hits.
-
- Design considerations:
- - want to allow multiple measurements for each pMHC pair (which can
- be dynamically combined)
- - optional sample weights associated with each pMHC measurement
- """
- def __init__(self, df):
- """
- Constructs a AffinityMeasurementDataset from a pandas DataFrame with the following
- columns:
- - allele
- - peptide
- - affinity
-
- Also, there is an optional column:
- - sample_weight
-
- If `sample_weight` is missing then it is filled with a default value
- of 1.0
-
- Parameters
- ----------
- df : pandas.DataFrame
- """
- columns = set(df.columns)
-
- for expected_column_name in {"allele", "peptide", "affinity"}:
- if expected_column_name not in columns:
- raise ValueError(
- "Missing column '%s' from DataFrame" %
- expected_column_name)
- # make allele and peptide columns the index, and copy it
- # so we can add a column without any observable side-effect in
- # the calling code
- df = df.set_index(["allele", "peptide"], drop=False)
-
- if "sample_weight" not in columns:
- df["sample_weight"] = np.ones(len(df), dtype=float)
-
- self._df = df
- self._alleles = np.asarray(df["allele"])
- self._peptides = np.asarray(df["peptide"])
- self._affinities = np.asarray(df["affinity"])
- self._sample_weights = np.asarray(df["sample_weight"])
-
- def to_dataframe(self):
- """
- Returns DataFrame representation of data contained in AffinityMeasurementDataset
- """
- return self._df
-
- @property
- def peptides(self):
- """
- Array of peptides from pMHC measurements.
- """
- return self._df["peptide"].values
-
- @property
- def alleles(self):
- """
- Array of MHC allele names from pMHC measurements.
- """
- return self.to_dataframe()["allele"].values
-
- @property
- def affinities(self):
- """
- Array of affinities from pMHC measurements.
- """
- return self.to_dataframe()["affinity"].values
-
- @property
- def sample_weights(self):
- """
- Array of sample weights for each pMHC measurement.
- """
- return self.to_dataframe()["sample_weight"].values
-
- def __len__(self):
- return len(self.to_dataframe())
-
- def __str__(self):
- return "AffinityMeasurementDataset(n=%d, alleles=%s)" % (
- len(self), list(sorted(self.unique_alleles())))
-
- def __repr__(self):
- return str(self)
-
- def __eq__(self, other):
- """
- Two datasets are equal if they contain the same number of samples
- with the same properties and values.
- """
- if type(other) is not AffinityMeasurementDataset:
- return False
- elif len(self) != len(other):
- return False
-
- columns = self.columns
- if len(columns) != len(other.columns):
- return False
- elif set(columns) != set(other.columns):
- return False
-
- # test for equality of the rows of the two DataFrames regardless
- # of order
- my_dict = self.allele_and_peptide_pair_to_row_dictionary()
- other_dict = other.allele_and_peptide_pair_to_row_dictionary()
-
- if set(my_dict.keys()) != set(other_dict.keys()):
- return False
-
- for key, my_row in my_dict.items():
- for column in columns:
- if my_row[column] != other_dict[key][column]:
- return False
- return True
-
- def iterrows(self):
- """
- Iterate over tuples containing: (allele, peptide), other_fields
- for each pMHC measurement.
- """
- return self.to_dataframe().iterrows()
-
- def allele_and_peptide_pair_to_row_dictionary(self):
- """
- Returns a dictionary mapping (allele, peptide) pairs to rows.
- """
- return {key: row for (key, row) in self.iterrows()}
-
- @property
- def columns(self):
- return self.to_dataframe().columns
-
- def unique_alleles(self):
- """
- Returns the set of allele names contained in this AffinityMeasurementDataset.
- """
- return set(self.alleles)
-
- def unique_peptides(self):
- """
- Returns the set of peptide sequences contained in this AffinityMeasurementDataset.
- """
- return set(self.peptides)
-
- def unique_allele_peptide_pairs(self):
- """
- Returns set of every unique pMHC pairing in the dataset.
- """
- return set(zip(self.alleles, self.peptides))
-
- def groupby_allele(self):
- """
- Yields a sequence of tuples of allele names with Datasets containing
- entries just for that allele.
- """
- for (allele_name, group_df) in self.to_dataframe().groupby("allele"):
- yield (allele_name, AffinityMeasurementDataset(group_df))
-
- def groupby_allele_dictionary(self):
- """
- Returns dictionary mapping each allele name to a AffinityMeasurementDataset containing
- only entries from that allele.
- """
- return dict(self.groupby_allele())
-
- def allele_counts_dictionary(self):
- """
- Returns a dictionary mapping each allele name to the number of entries
- associated with it.
- """
- return {
- allele_name: len(allele_dataset)
- for allele_name, allele_dataset
- in self.groupby_allele()
- }
-
- def filter_alleles_by_count(self, min_peptides_per_allele=0):
- return self.concat([
- allele_dataset
- for (_, allele_dataset)
- in self.groupby_allele()
- if len(allele_dataset) >= min_peptides_per_allele])
-
- def to_nested_dictionary(self, combine_fn=geometric_mean):
- """
- Returns a dictionary mapping from allele name to a dictionary which
- maps from peptide to measured value. Caution, this eliminates sample
- weights!
-
- Parameters
- ----------
- combine_fn : function
- How to combine multiple measurements for the same pMHC complex.
- Takes affinities and optional `weights` argument.
- """
- allele_to_peptide_to_affinities_dict = defaultdict(dict)
- allele_to_peptide_to_weights_dict = defaultdict(dict)
- key_pairs = set([])
- for allele, peptide, affinity, weight in zip(
- self.alleles, self.peptides, self.affinities, self.sample_weights):
- # dictionary mapping each peptide to a list of affinities
- if peptide not in allele_to_peptide_to_affinities_dict[allele]:
- allele_to_peptide_to_affinities_dict[allele][peptide] = [affinity]
- allele_to_peptide_to_weights_dict[allele][peptide] = [weight]
- else:
- allele_to_peptide_to_affinities_dict[allele][peptide].append(affinity)
- allele_to_peptide_to_weights_dict[allele][peptide].append(weight)
- key_pairs.add((allele, peptide))
- return {
- allele: {
- peptide: combine_fn(
- allele_to_peptide_to_affinities_dict[allele][peptide],
- allele_to_peptide_to_weights_dict[allele][peptide])
- for peptide in allele_to_peptide_to_affinities_dict[allele].keys()
- }
- for allele in allele_to_peptide_to_affinities_dict.keys()
- }
-
- @classmethod
- def from_sequences(
- cls,
- alleles,
- peptides,
- affinities,
- sample_weights=None,
- extra_columns={}):
- """
- Parameters
- ----------
- alleles : numpy.ndarray, pandas.Series, or list
- Name of allele for that pMHC measurement
-
- peptides : numpy.ndarray, pandas.Series, or list
- Sequence of peptide in that pMHC measurement.
-
- affinities : numpy.ndarray, pandas.Series, or list
- Affinity value (typically IC50 concentration) for that pMHC
-
- sample_weights : numpy.ndarray of float, optional
-
- extra_columns : dict
- Dictionary of any extra properties associated with a
- pMHC measurement
- """
- alleles, peptides, affinities, sample_weights = \
- prepare_pMHC_affinity_arrays(
- alleles=alleles,
- peptides=peptides,
- affinities=affinities,
- sample_weights=sample_weights)
- df = pd.DataFrame()
- df["allele"] = alleles
- df["peptide"] = peptides
- df["affinity"] = affinities
- df["sample_weight"] = sample_weights
- for column_name, column in extra_columns.items():
- if len(column) != len(alleles):
- raise ValueError(
- "Wrong length for column '%s', expected %d but got %d" % (
- column_name,
- len(alleles),
- len(column)))
- df[column_name] = np.asarray(column)
- return cls(df)
-
- @classmethod
- def from_single_allele_dataframe(cls, allele_name, single_allele_df):
- """
- Construct a AffinityMeasurementDataset from a single MHC allele's DataFrame
- """
- df = single_allele_df.copy()
- df["allele"] = allele_name
- return cls(df)
-
- @classmethod
- def from_nested_dictionary(
- cls,
- allele_to_peptide_to_affinity_dict):
- """
- Given nested dictionaries mapping allele -> peptide -> affinity,
- construct a AffinityMeasurementDataset with uniform sample weights.
- """
- alleles = []
- peptides = []
- affinities = []
- for allele, allele_dict in allele_to_peptide_to_affinity_dict.items():
- for peptide, affinity in allele_dict.items():
- alleles.append(allele)
- peptides.append(peptide)
- affinities.append(affinity)
- return cls.from_sequences(
- alleles=alleles,
- peptides=peptides,
- affinities=affinities)
-
- @classmethod
- def create_empty(cls):
- """
- Returns an empty AffinityMeasurementDataset containing no pMHC entries.
- """
- return cls.from_nested_dictionary({})
-
- @classmethod
- def from_single_allele_dictionary(
- cls,
- allele_name,
- peptide_to_affinity_dict):
- """
- Given a peptide->affinity dictionary for a single allele,
- create a AffinityMeasurementDataset.
- """
- return cls.from_nested_dictionary({allele_name: peptide_to_affinity_dict})
-
- @classmethod
- def from_csv(
- cls,
- filename,
- sep=None,
- allele_column_name=None,
- peptide_column_name=None,
- affinity_column_name=None):
- df, allele_column_name, peptide_column_name, affinity_column_name = \
- load_dataframe(
- filename=filename,
- sep=sep,
- allele_column_name=allele_column_name,
- peptide_column_name=peptide_column_name,
- affinity_column_name=affinity_column_name)
- df = df.rename(columns={
- allele_column_name: "allele",
- peptide_column_name: "peptide",
- affinity_column_name: "affinity"})
- return cls(df)
-
- def get_allele(self, allele_name):
- """
- Get AffinityMeasurementDataset for a single allele
- """
- if allele_name not in self.unique_alleles():
- raise KeyError("Allele '%s' not found, available alleles: %s" % (
- allele_name, list(sorted(self.unique_alleles()))))
- df = self.to_dataframe()
- df_allele = df[df.allele == allele_name]
- return self.__class__(df_allele)
-
- def get_alleles(self, allele_names):
- """
- Restrict AffinityMeasurementDataset to several allele names.
- """
- datasets = []
- for allele_name in allele_names:
- datasets.append(self.get_allele(allele_name))
- return self.concat(datasets)
-
- @classmethod
- def concat(cls, datasets):
- """
- Concatenate several datasets into a single object.
- """
- dataframes = [dataset.to_dataframe() for dataset in datasets]
- return cls(pd.concat(dataframes))
-
- def replace_allele(self, allele_name, new_dataset):
- """
- Replace data for given allele with new entries.
- """
- if allele_name not in self.unique_alleles():
- raise ValueError("Allele '%s' not found" % (allele_name,))
- df = self.to_dataframe()
- df_without = df[df.allele != allele_name]
- new_df = new_dataset.to_dataframe()
- combined_df = pd.concat([df_without, new_df])
- return self.__class__(combined_df)
-
- def flatmap_peptides(self, peptide_fn):
- """
- Create zero or more peptides from each pMHC entry. The affinity of all
- new peptides is identical to the original, but sample weights are
- divided across the number of new peptides.
-
- Parameters
- ----------
- peptide_fn : function
- Maps each peptide to a list of peptides.
- """
- columns = self.to_dataframe().columns
- new_data_dict = OrderedDict(
- (column_name, [])
- for column_name in columns
- )
- if "original_peptide" not in new_data_dict:
- create_original_peptide_column = True
- new_data_dict["original_peptide"] = []
-
- for (allele, peptide), row in self.iterrows():
- new_peptides = peptide_fn(peptide)
- n = len(new_peptides)
- weight = row["sample_weight"]
- # we're either going to create a fresh original peptide column
- # or extend the existing original peptide tuple that tracks
- # the provenance of entries in the new AffinityMeasurementDataset
- original_peptide = row.get("original_peptide")
- if original_peptide is None:
- original_peptide = ()
- elif isinstance(original_peptide, string_types):
- original_peptide = (original_peptide,)
- else:
- original_peptide = tuple(original_peptide)
-
- for new_peptide in new_peptides:
- for column_name in columns:
- if column_name == "peptide":
- new_data_dict["peptide"].append(new_peptide)
- elif column_name == "sample_weight":
- new_data_dict["sample_weight"].append(weight / n)
- elif column_name == "original_peptide":
- new_data_dict["original_peptide"] = original_peptide + (peptide,)
- else:
- new_data_dict[column_name].append(row[column_name])
- if create_original_peptide_column:
- new_data_dict["original_peptide"].append((peptide,))
- df = pd.DataFrame(new_data_dict)
- return self.__class__(df)
-
- def kmer_index_encoding(
- self,
- kmer_size=9,
- allow_unknown_amino_acids=True):
- """
- Encode peptides in this dataset using a fixed-length vector
- representation.
-
- Parameters
- ----------
- kmer_size : int
- Length of encoding for each peptide
-
- allow_unknown_amino_acids : bool
- If True, then extend shorter amino acids using "X" character,
- otherwise fill in all possible combinations of real amino acids.
-
- Returns:
- - 2d array of encoded kmers
- - 1d array of affinity value corresponding to the source
- peptide for each kmer
- - sample_weights (1 / kmer count per peptide)
- - indices of original peptides from which kmers were extracted
- """
- if len(self.peptides) == 0:
- return (
- np.empty((0, kmer_size), dtype=int),
- np.empty((0,), dtype=float),
- np.empty((0,), dtype=float),
- np.empty((0,), dtype=int)
- )
-
- X_index, _, original_peptide_indices, counts = \
- fixed_length_index_encoding(
- peptides=self.peptides,
- desired_length=kmer_size,
- start_offset_shorten=0,
- end_offset_shorten=0,
- start_offset_extend=0,
- end_offset_extend=0,
- allow_unknown_amino_acids=allow_unknown_amino_acids)
- original_peptide_indices = np.asarray(original_peptide_indices)
-
- counts = np.asarray(counts)
- kmer_affinities = self.affinities[original_peptide_indices]
- kmer_sample_weights = self.sample_weights[original_peptide_indices]
-
- assert len(original_peptide_indices) == len(kmer_affinities)
- assert len(counts) == len(kmer_affinities)
- assert len(kmer_sample_weights) == len(kmer_affinities)
-
- # combine the original sample weights of varying length peptides
- # with a 1/n_kmers factor for the number of kmers pulled out of each
- # original peptide
- combined_sample_weights = kmer_sample_weights * (1.0 / counts)
- return X_index, kmer_affinities, combined_sample_weights, original_peptide_indices
-
- def to_dense_pMHC_affinity_matrix(
- self,
- min_observations_per_peptide=1,
- min_observations_per_allele=1):
- """
- Returns a tuple with a dense matrix of affinities, a dense matrix of
- sample weights, a list of peptide labels for each row and a list of
- allele labels for each column.
-
- Parameters
- ----------
- min_observations_per_peptide : int
- Drop peptide rows with fewer than this number of observed values.
-
- min_observations_per_allele : int
- Drop allele columns with fewer than this number of observed values.
- """
- allele_to_peptide_to_affinity_dict = self.to_nested_dictionary()
- peptides_list = list(sorted(self.unique_peptides()))
- peptide_order = {p: i for (i, p) in enumerate(peptides_list)}
- n_peptides = len(peptides_list)
- alleles_list = list(sorted(self.unique_alleles()))
- allele_order = {a: i for (i, a) in enumerate(alleles_list)}
- n_alleles = len(alleles_list)
- shape = (n_peptides, n_alleles)
- X = np.ones(shape, dtype=float) * np.nan
- for (allele, allele_dict) in allele_to_peptide_to_affinity_dict.items():
- column_index = allele_order[allele]
- for (peptide, affinity) in allele_dict.items():
- row_index = peptide_order[peptide]
- X[row_index, column_index] = affinity
-
- check_dense_pMHC_array(X, peptides_list, alleles_list)
-
- # drop alleles and peptides with small amounts of data
- return prune_dense_matrix_and_labels(
- X, peptides_list, alleles_list,
- min_observations_per_peptide=min_observations_per_peptide,
- min_observations_per_allele=min_observations_per_allele)
-
- def slice(self, indices):
- """
- Create a new AffinityMeasurementDataset by slicing through all columns of this dataset
- with the given indices.
- """
- indices = np.asarray(indices)
- max_index = indices.max()
- n_total = len(self)
- if max_index >= len(self):
- raise ValueError("Invalid index %d for AffinityMeasurementDataset of size %d" % (
- max_index, n_total))
-
- df = self.to_dataframe()
- df_subset = pd.DataFrame()
- for column_name in df.columns:
- df_subset[column_name] = np.asarray(df[column_name].values)[indices]
- return self.__class__(df_subset)
-
- def random_split(self, n=None, stratify_fn=None):
- """
- Randomly split the AffinityMeasurementDataset into smaller AffinityMeasurementDataset objects.
-
- Parameters
- ----------
- n : int, optional
- Size of the left split, half of the dataset if omitted.
-
- stratify_fn : function, optional
- Function that takes a row and returns bool, stratifying sampling
- into two groups.
-
- Returns a pair of AffinityMeasurementDataset objects.
- """
- n_total = len(self)
- if n is None:
- n = n_total // 2
- elif n >= n_total:
- raise ValueError(
- "Training subset can't have more than %d samples (given n=%d)" % (
- n_total - 1,
- n))
-
- index_groups = groupby_indices(
- iterable=(pair[1] for pair in self.iterrows()),
- key_fn=stratify_fn if stratify_fn else lambda _: 0)
-
- fraction = float(n) / n_total
-
- left_indices = []
- right_indices = []
-
- for _, group_indices in index_groups.items():
- left, right = shuffle_split_list(group_indices, fraction)
- left_indices.extend(left)
- right_indices.extend(right)
-
- left = self.slice(left_indices)
- right = self.slice(right_indices)
- return left, right
-
- def cross_validation_iterator(
- self,
- test_allele=None,
- n_folds=3,
- shuffle=True,
- stratify_fn=None):
- """
- Yields a sequence of training/test splits of this dataset.
-
- If test_allele is None then split across all pMHC entries, otherwise
- only split the measurements of the specified allele (other alleles
- will then always be included in the training datasets).
- """
-
- n_total = len(self)
- if test_allele is None:
- test_samples = self
- test_sample_indices = np.arange(n_total)
- elif test_allele not in self.unique_alleles():
- raise ValueError("Allele '%s' not in AffinityMeasurementDataset" % test_allele)
-
- else:
- test_sample_indices = np.where(self.alleles == test_allele)[0]
- test_samples = self.slice(test_sample_indices)
-
- n_test_samples = len(test_sample_indices)
-
- # for uniformity we're using StratifiedKFold even for regular CV
- # but with a single label/category
- if stratify_fn is None:
- stratify_labels = [0] * n_test_samples
- else:
- stratify_labels = [
- stratify_fn(row) for (_, row) in test_samples.iterrows()
- ]
-
- assert len(stratify_labels) == n_test_samples
-
- for _, test_indices_in_single_allele in StratifiedKFold(
- y=stratify_labels,
- n_folds=n_folds,
- shuffle=shuffle):
- test_data = test_samples.slice(test_indices_in_single_allele)
- test_indices_across_alleles = test_sample_indices[test_indices_in_single_allele]
- train_mask = np.ones(n_total, dtype=bool)
- train_mask[test_indices_across_alleles] = False
- train_data = self.slice(train_mask)
- yield train_data, test_data
-
- def split_allele_randomly_and_impute_training_set(
- self,
- allele,
- n_training_samples=None,
- stratify_fn=None,
- **kwargs):
- """
- Split an allele into training and test sets, and then impute values
- for peptides missing from the training set using data from other alleles
- in this AffinityMeasurementDataset.
-
- (apologies for the wordy name, this turns out to be a common operation)
-
- Parameters
- ----------
- allele : str
- Name of allele
-
- n_training_samples : int, optional
- Size of the training set to return for this allele.
-
- stratify_fn : function
- Function mapping from rows of the AffinityMeasurementDataset to booleans for stratifying
- by two groups.
-
- **kwargs : dict
- Extra keyword arguments passed to AffinityMeasurementDataset.impute_missing_values
-
- Returns three AffinityMeasurementDataset objects:
- - training set with original pMHC affinities for given allele
- - larger imputed training set for given allele
- - test set
- """
- dataset_allele = self.get_allele(allele)
- dataset_allele_train, dataset_allele_test = dataset_allele.random_split(
- n=n_training_samples, stratify_fn=stratify_fn)
- full_dataset_without_test_samples = self.difference(dataset_allele_test)
- imputed_dataset = full_dataset_without_test_samples.impute_missing_values(**kwargs)
- imputed_dataset_allele = imputed_dataset.get_allele(allele)
- return dataset_allele_train, imputed_dataset_allele, dataset_allele_test
-
- def drop_allele_peptide_lists(self, alleles, peptides):
- """
- Drop all allele-peptide pairs in the given lists.
-
- Parameters
- ----------
- alleles : list of str
-
- peptides : list of str
-
- The two arguments are assumed to be the same length.
-
- Returns AffinityMeasurementDataset of equal or smaller size.
- """
- if len(alleles) != len(peptides):
- raise ValueError(
- "Expected alleles to be same length (%d) as peptides (%d)" % (
- len(alleles), len(peptides)))
- return self.drop_allele_peptide_pairs(list(zip(alleles, peptides)))
-
- def drop_allele_peptide_pairs(self, allele_peptide_pairs):
- """
- Drop all allele-peptide tuple pairs in the given list.
-
- Parameters
- ----------
- allele_peptide_pairs : list of (str, str) tuples
- The two arguments are assumed to be the same length.
-
- Returns AffinityMeasurementDataset of equal or smaller size.
- """
- require_iterable_of(allele_peptide_pairs, tuple)
- keys_to_remove_set = set(allele_peptide_pairs)
- remove_mask = np.array([
- (k in keys_to_remove_set)
- for k in zip(self.alleles, self.peptides)
- ])
- keep_mask = remove_mask == False
- return self.slice(keep_mask)
-
- def difference(self, other_dataset):
- """
- Remove all pMHC pairs in the other dataset from this one.
-
- Parameters
- ----------
- other_dataset : AffinityMeasurementDataset
-
- Returns a new Dataset object of equal or lesser size.
- """
- return self.drop_allele_peptide_lists(
- alleles=other_dataset.alleles,
- peptides=other_dataset.peptides)
-
- def intersection(self, other_dataset):
- not_in_other = self.difference(other_dataset)
- return self.difference(not_in_other)
-
- def impute_missing_values(
- self,
- imputation_method,
- log_transform=True,
- min_observations_per_peptide=1,
- min_observations_per_allele=1):
- """
- Synthesize new measurements for missing pMHC pairs using the given
- imputation_method.
-
- Parameters
- ----------
- imputation_method : object
- Expected to have a method called `complete` which takes a 2d array
- of floats and replaces some or all NaN values with synthetic
- affinities.
-
- log_transform : bool
- Transform affinities with to log10 values before imputation
- (and then transform back afterward).
-
- min_observations_per_peptide : int
- Drop peptide rows with fewer than this number of observed values.
-
- min_observations_per_allele : int
- Drop allele columns with fewer than this number of observed values.
-
- Returns AffinityMeasurementDataset with original pMHC affinities and additional
- synthetic samples.
- """
- if isinstance(imputation_method, string_types):
- imputation_method = imputer_from_name(imputation_method)
-
- X_incomplete, peptide_list, allele_list = self.to_dense_pMHC_affinity_matrix(
- min_observations_per_peptide=min_observations_per_peptide,
- min_observations_per_allele=min_observations_per_allele)
-
- if imputation_method is None:
- logging.warn("No imputation method given")
- # without an imputation method we should leave all the values
- # incomplete and return an empty dataset
- X_complete = np.ones_like(X_incomplete) * np.nan
- else:
- if log_transform:
- X_incomplete = np.log(X_incomplete)
-
- if np.isnan(X_incomplete).sum() == 0:
- # if all entries in the matrix are already filled in then don't
- # try using an imputation algorithm since it might raise an
- # exception.
- logging.warn("No missing values, using original data instead of imputation")
- X_complete = X_incomplete
- else:
- X_complete = imputation_method.complete(X_incomplete)
-
- if log_transform:
- X_complete = np.exp(X_complete)
-
- allele_to_peptide_to_affinity_dict = dense_pMHC_matrix_to_nested_dict(
- X=X_complete,
- peptide_list=peptide_list,
- allele_list=allele_list)
- return self.from_nested_dictionary(allele_to_peptide_to_affinity_dict)
diff --git a/mhcflurry/amino_acid.py b/mhcflurry/amino_acid.py
index 3762a42e..e5e59cf1 100644
--- a/mhcflurry/amino_acid.py
+++ b/mhcflurry/amino_acid.py
@@ -17,84 +17,10 @@ from __future__ import (
division,
absolute_import,
)
-import numpy as np
+import collections
+from copy import copy
-
-class Alphabet(object):
- """
- Used to track the order of amino acids used for peptide encodings
- """
-
- def __init__(self, **kwargs):
- self.letters_to_names = {}
- for (k, v) in kwargs.items():
- self.add(k, v)
-
- def add(self, letter, name):
- assert letter not in self.letters_to_names
- assert len(letter) == 1
- self.letters_to_names[letter] = name
-
- def letters(self):
- return list(sorted(self.letters_to_names.keys()))
-
- def names(self):
- return [self.letters_to_names[k] for k in self.letters()]
-
- def index_dict(self):
- return {c: i for (i, c) in enumerate(self.letters())}
-
- def copy(self):
- return Alphabet(**self.letters_to_names)
-
- def __getitem__(self, k):
- return self.letters_to_names[k]
-
- def __setitem__(self, k, v):
- self.add(k, v)
-
- def __len__(self):
- return len(self.letters_to_names)
-
- def index_encoding_list(self, peptides):
- index_dict = self.index_dict()
- return [
- [index_dict[amino_acid] for amino_acid in peptide]
- for peptide in peptides
- ]
-
- def index_encoding(self, peptides, peptide_length):
- """
- Encode a set of equal length peptides as a matrix of their
- amino acid indices.
- """
- X = np.zeros((len(peptides), peptide_length), dtype=int)
- index_dict = self.index_dict()
- for i, peptide in enumerate(peptides):
- for j, amino_acid in enumerate(peptide):
- X[i, j] = index_dict[amino_acid]
- return X
-
- def hotshot_encoding(
- self,
- peptides,
- peptide_length):
- """
- Encode a set of equal length peptides as a binary matrix,
- where each letter is transformed into a length 20 vector with a single
- element that is 1 (and the others are 0).
- """
- shape = (len(peptides), peptide_length, 20)
- index_dict = self.index_dict()
- X = np.zeros(shape, dtype=bool)
- for i, peptide in enumerate(peptides):
- for j, amino_acid in enumerate(peptide):
- k = index_dict[amino_acid]
- X[i, j, k] = 1
- return X
-
-
-common_amino_acids = Alphabet(**{
+COMMON_AMINO_ACIDS = collections.OrderedDict(sorted({
"A": "Alanine",
"R": "Arginine",
"N": "Asparagine",
@@ -115,9 +41,9 @@ common_amino_acids = Alphabet(**{
"W": "Tryptophan",
"Y": "Tyrosine",
"V": "Valine",
-})
-common_amino_acid_letters = common_amino_acids.letters()
+}.items()))
+COMMON_AMINO_ACIDS_WITH_UNKNOWN = copy(COMMON_AMINO_ACIDS)
+COMMON_AMINO_ACIDS_WITH_UNKNOWN["X"] = "Unknown"
-amino_acids_with_unknown = common_amino_acids.copy()
-amino_acids_with_unknown.add("X", "Unknown")
-amino_acids_with_unknown_letters = amino_acids_with_unknown.letters()
+AMINO_ACID_INDEX = dict(
+ (letter, i) for (i, letter) in enumerate(COMMON_AMINO_ACIDS_WITH_UNKNOWN))
diff --git a/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_trained_on_hits.py b/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_trained_on_hits.py
index 47be4dca..58d0101e 100644
--- a/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_trained_on_hits.py
+++ b/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_trained_on_hits.py
@@ -4,7 +4,7 @@ import pandas
from numpy import log, exp, nanmean
from ...affinity_measurement_dataset import AffinityMeasurementDataset
-from ...class1_allele_specific import Class1BindingPredictor
+from ...class1_affinity_prediction import Class1BindingPredictor
from ...common import normalize_allele_name
from .mhc_binding_component_model_base import MHCBindingComponentModelBase
diff --git a/mhcflurry/class1_affinity_prediction/__init__.py b/mhcflurry/class1_affinity_prediction/__init__.py
new file mode 100644
index 00000000..7deec3d1
--- /dev/null
+++ b/mhcflurry/class1_affinity_prediction/__init__.py
@@ -0,0 +1,7 @@
+from __future__ import absolute_import
+
+from .class1_binding_predictor import Class1BindingPredictor
+
+__all__ = [
+ 'Class1BindingPredictor',
+]
diff --git a/mhcflurry/class1_affinity_prediction/class1_binding_predictor.py b/mhcflurry/class1_affinity_prediction/class1_binding_predictor.py
new file mode 100644
index 00000000..4424aee9
--- /dev/null
+++ b/mhcflurry/class1_affinity_prediction/class1_binding_predictor.py
@@ -0,0 +1,636 @@
+import time
+import os
+import tempfile
+import logging
+
+import numpy
+import pandas
+
+import keras.models
+import keras.layers.pooling
+import keras.regularizers
+from keras.layers import Input
+import keras.layers.merge
+from keras.layers.core import Dense, Flatten, Dropout, Reshape
+from keras.layers.embeddings import Embedding
+from keras.layers.normalization import BatchNormalization
+from keras.callbacks import EarlyStopping
+import keras.backend as K
+import theano.tensor
+
+from mhcflurry.hyperparameters import HyperparameterDefaults
+
+from ..encodable_sequences import EncodableSequences
+from ..regression_target import to_ic50, from_ic50
+from ..common import random_peptides, amino_acid_distribution
+
+
+
+class Class1BindingPredictor(object):
+ network_hyperparameter_defaults = HyperparameterDefaults(
+ kmer_size=15,
+ use_embedding=True,
+ embedding_input_dim=21,
+ embedding_output_dim=8,
+ pseudosequence_use_embedding=True,
+ pseudosequence_generate_weights=False,
+ extra_data_length=None,
+ extra_data_layer_sizes=(),
+ multiple_output_strategy=None,
+ multiple_output_activity_regularizer=1.0,
+ layer_sizes=[100, 32],
+ dense_layer_l1_regularization=0.0,
+ dense_layer_l2_regularization=0.0,
+ activation="tanh",
+ init="glorot_uniform",
+ output_activation="sigmoid",
+ dropout_probability=0.0,
+ batch_normalization=True,
+ embedding_init_method="glorot_uniform",
+ locally_connected=None,
+ concatenate_locally_connected_with_raw_embedding=False,
+ optimizer="rmsprop",
+ )
+
+ input_encoding_hyperparameter_defaults = HyperparameterDefaults(
+ left_edge=4,
+ right_edge=4)
+
+ fit_hyperparameter_defaults = HyperparameterDefaults(
+ max_epochs=250,
+ validation_split=None,
+ early_stopping=False,
+ take_best_epoch=False,
+ random_negative_rate=0.0,
+ random_negative_constant=0,
+ random_negative_affinity_min=50000.0,
+ random_negative_affinity_max=50000.0,
+ random_negative_match_distribution=True,
+ random_negative_distribution_smoothing=0.0)
+
+ early_stopping_hyperparameter_defaults = HyperparameterDefaults(
+ monitor='val_loss',
+ min_delta=0,
+ patience=0,
+ verbose=1,
+ mode='auto')
+
+ hyperparameter_defaults = network_hyperparameter_defaults.extend(
+ input_encoding_hyperparameter_defaults).extend(
+ fit_hyperparameter_defaults).extend(
+ early_stopping_hyperparameter_defaults)
+
+ def __init__(self, **hyperparameters):
+ self.hyperparameters = self.hyperparameter_defaults.with_defaults(
+ hyperparameters)
+ self.network = None
+ self.fit_history = None
+ self.fit_seconds = None
+ self.output_names = None
+
+ def __getstate__(self):
+ result = dict(self.__dict__)
+ del result['network']
+ result['fit_history'] = None
+ result['network_json'] = self.network.to_json()
+ result['network_weights'] = self.get_weights()
+ return result
+
+ def __setstate__(self, state):
+ network_json = state.pop('network_json')
+ network_weights = state.pop('network_weights')
+ self.__dict__.update(state)
+ self.network = keras.models.model_from_json(network_json)
+ self.set_weights(network_weights)
+
+ def get_weights(self):
+ """
+ Returns weights, which can be passed to set_weights later.
+ """
+ return [x.copy() for x in self.network.get_weights()]
+
+ def set_weights(self, weights):
+ """
+ Reset the model weights.
+ """
+ self.network.set_weights(weights)
+
+ def peptides_to_network_input(self, peptides):
+ encoder = EncodableSequences.create(peptides)
+ if self.hyperparameters['use_embedding']:
+ encoded = encoder.fixed_length_categorical_encoding(
+ max_length=self.hyperparameters['kmer_size'],
+ **self.input_encoding_hyperparameter_defaults.subselect(
+ self.hyperparameters))
+ else:
+ encoded = encoder.fixed_length_one_hot_encoding(
+ max_length=self.hyperparameters['kmer_size'],
+ **self.input_encoding_hyperparameter_defaults.subselect(
+ self.hyperparameters))
+ assert len(encoded) == len(peptides)
+ return encoded
+
+ def pseudosequence_to_network_input(self, pseudosequences):
+ encoder = EncodableSequences.create(pseudosequences)
+ if self.hyperparameters['pseudosequence_use_embedding']:
+ encoded = encoder.categorical_encoding()
+ else:
+ encoded = encoder.one_hot_encoding()
+ assert len(encoded) == len(pseudosequences)
+ return encoded
+
+ def fit(
+ self,
+ peptides,
+ affinities,
+ output_assignments,
+ allele_pseudosequences=None,
+ sample_weights=None,
+ verbose=1):
+ self.output_names = sorted(set(output_assignments))
+
+ encodable_peptides = EncodableSequences.create(peptides)
+ peptide_encoding = self.peptides_to_network_input(encodable_peptides)
+ peptide_to_encoding = dict(
+ zip(encodable_peptides.sequences, peptide_encoding))
+
+ length_counts = (
+ pandas.Series(encodable_peptides.sequences)
+ .str.len().value_counts().to_dict())
+
+ num_random_negative = {}
+ for length in range(8, 16):
+ num_random_negative[length] = int(
+ length_counts.get(length, 0) *
+ self.hyperparameters['random_negative_rate'] +
+ self.hyperparameters['random_negative_constant'])
+ num_random_negative = pandas.Series(num_random_negative)
+ print("Random negative counts per length: %s" % (
+ str(num_random_negative)))
+
+ aa_distribution = None
+ if self.hyperparameters['random_negative_match_distribution']:
+ aa_distribution = amino_acid_distribution(
+ encodable_peptides.sequences,
+ smoothing=self.hyperparameters[
+ 'random_negative_distribution_smoothing'])
+ print("Using amino acid distribution for random negative: %s" % (
+ str(aa_distribution)))
+
+ y_values = from_ic50(affinities)
+ assert numpy.isnan(y_values).sum() == 0, (
+ numpy.isnan(y_values).sum())
+
+ if self.hyperparameters['multiple_output_strategy'] is not None:
+ network_output_names = self.output_names
+ y_df = pandas.DataFrame({
+ 'y': y_values,
+ 'output_assignment': output_assignments,
+ }).pivot(values="y", columns="output_assignment")
+ y_df["peptide"] = encodable_peptides.sequences
+ y_df.groupby("peptide").mean()
+ network_output_names = self.output_names
+
+ y_dict = dict((c, y_df[c].values) for c in y_df.columns)
+ x = numpy.stack(
+ y_df.peptide.map(peptide_to_encoding).values)
+ else:
+ network_output_names = ["output"]
+ y_dict = {'output': y_values}
+ x = peptide_encoding
+
+ try:
+ callbacks = []
+ if self.hyperparameters['take_best_epoch']:
+ weights_file_fd = tempfile.NamedTemporaryFile(
+ prefix="mhcflurry-model-checkpoint-",
+ suffix=".hdf5",
+ delete=False)
+ weights_file = weights_file_fd.name
+ print("Checkpointing to: %s" % weights_file)
+ weights_file_fd.close()
+
+ checkpointer = keras.callbacks.ModelCheckpoint(
+ weights_file,
+ monitor="val_loss",
+ save_best_only=True,
+ save_weights_only=False)
+ callbacks.append(checkpointer)
+ else:
+ weights_file = None
+
+ if self.hyperparameters['early_stopping']:
+ assert self.hyperparameters['validation_split'] > 0
+ callback = EarlyStopping(
+ **self.early_stopping_hyperparameter_defaults.subselect(
+ self.hyperparameters))
+ callbacks.append(callback)
+
+ x_dict = {
+ 'peptide': x,
+ }
+ pseudosequence_length = None
+ if allele_pseudosequences is not None:
+ pseudosequences_input = self.pseudosequence_to_network_input(
+ allele_pseudosequences)
+ pseudosequence_length = len(pseudosequences_input[0])
+ x_dict['pseudosequence'] = pseudosequences_input
+
+ if self.network is None:
+ self.network = self.make_network(
+ output_names=network_output_names,
+ pseudosequence_length=pseudosequence_length,
+ **self.network_hyperparameter_defaults.subselect(
+ self.hyperparameters))
+
+ start = time.time()
+ if num_random_negative.sum() == 0:
+ self.fit_history = self.network.fit(
+ x_dict,
+ y_dict,
+ shuffle=True,
+ verbose=verbose,
+ epochs=self.hyperparameters['max_epochs'],
+ validation_split=self.hyperparameters['validation_split'],
+ sample_weight=sample_weights,
+ callbacks=callbacks)
+ else:
+ assert len(y_dict) == 1
+ y_dict['output'] = numpy.concatenate([
+ from_ic50(
+ numpy.random.uniform(
+ self.hyperparameters[
+ 'random_negative_affinity_min'],
+ self.hyperparameters[
+ 'random_negative_affinity_max'],
+ int(num_random_negative.sum()))),
+ y_dict['output'],
+ ])
+ if sample_weights is not None:
+ sample_weights = numpy.concatenate([
+ numpy.ones(int(num_random_negative.sum())),
+ sample_weights])
+ val_losses = []
+ min_val_loss_iteration = None
+ min_val_loss = None
+
+ for i in range(self.hyperparameters['max_epochs']):
+ # TODO: handle pseudosequence here
+ assert len(x_dict) == 1
+ random_negative_peptides_list = []
+ for (length, count) in num_random_negative.items():
+ random_negative_peptides_list.extend(
+ random_peptides(
+ count,
+ length=length,
+ distribution=aa_distribution))
+ #peptide_lengths.extend([length] * count)
+ #peptide_lengths.extend([
+ # len(s) for s in encodable_peptides.sequences
+ #])
+ random_negative_peptides_encodable = (
+ EncodableSequences.create(
+ random_negative_peptides_list))
+ random_negative_peptides_encoding = (
+ self.peptides_to_network_input(
+ random_negative_peptides_encodable))
+ x_dict["peptide"] = numpy.concatenate([
+ random_negative_peptides_encoding, x
+ ])
+ print("Epoch %3d / %3d. Min val loss at epoch %s" % (
+ i,
+ self.hyperparameters['max_epochs'],
+ min_val_loss_iteration))
+ self.fit_history = self.network.fit(
+ x_dict,
+ y_dict,
+ shuffle=True,
+ verbose=verbose,
+ epochs=1,
+ validation_split=self.hyperparameters[
+ 'validation_split'],
+ sample_weight=sample_weights,
+ callbacks=callbacks)
+
+ if self.hyperparameters['validation_split']:
+ val_loss = self.fit_history.history['val_loss'][-1]
+ val_losses.append(val_loss)
+
+ if min_val_loss is None or val_loss <= min_val_loss:
+ min_val_loss = val_loss
+ min_val_loss_iteration = i
+
+ if self.hyperparameters['early_stopping']:
+ threshold = (
+ min_val_loss_iteration +
+ self.hyperparameters['patience'])
+ if i > threshold:
+ print("Early stopping")
+ break
+ if weights_file is not None:
+ self.network.load_weights(weights_file)
+ self.fit_seconds = time.time() - start
+
+ finally:
+ if weights_file is not None:
+ os.unlink(weights_file)
+
+ def predict(self, peptides, allele_pseudosequences=None):
+ x_dict = {
+ 'peptide': self.peptides_to_network_input(peptides)
+ }
+ if allele_pseudosequences is not None:
+ pseudosequences_input = self.pseudosequence_to_network_input(
+ allele_pseudosequences)
+ x_dict['pseudosequence'] = pseudosequences_input
+ predictions_raw = numpy.array(self.network.predict(x_dict))
+ if predictions_raw.ndim == 3:
+ predictions_raw = numpy.squeeze(predictions_raw, axis=2).T
+
+ assert predictions_raw.shape == (
+ len(peptides),
+ len(self.network.output_layers)), predictions_raw.shape
+
+ result = dict(
+ (k.name, to_ic50(v))
+ for (k, v)
+ in zip(self.network.output_layers, predictions_raw.T))
+
+ if set(result) != set(self.output_names):
+ # Simulate multiple outputs
+ assert set(result) == set(["output"]), set(result)
+ result = dict((k, result["output"]) for k in self.output_names)
+ return result
+
+ @staticmethod
+ def make_network(
+ output_names,
+ pseudosequence_length,
+ kmer_size,
+ use_embedding,
+ embedding_input_dim,
+ embedding_output_dim,
+ pseudosequence_use_embedding,
+ pseudosequence_generate_weights,
+ extra_data_length,
+ extra_data_layer_sizes,
+ multiple_output_strategy,
+ multiple_output_activity_regularizer,
+ layer_sizes,
+ dense_layer_l1_regularization,
+ dense_layer_l2_regularization,
+ activation,
+ init,
+ output_activation,
+ dropout_probability,
+ batch_normalization,
+ embedding_init_method,
+ locally_connected,
+ concatenate_locally_connected_with_raw_embedding,
+ optimizer):
+
+ if multiple_output_strategy is None:
+ assert len(output_names) == 1
+ else:
+ assert multiple_output_strategy in ("simple", "bottleneck")
+
+ if use_embedding:
+ peptide_input = Input(
+ shape=(kmer_size,), dtype='int32', name='peptide')
+ raw_embedding_layer = Embedding(
+ input_dim=embedding_input_dim,
+ output_dim=embedding_output_dim,
+ input_length=kmer_size,
+ embeddings_initializer=embedding_init_method)(peptide_input)
+ else:
+ peptide_input = Input(
+ shape=(kmer_size, 21), dtype='float32', name='peptide')
+ raw_embedding_layer = peptide_input
+
+ inputs = [peptide_input]
+
+ embedding_layer = raw_embedding_layer
+
+ if locally_connected is not None:
+ for locally_connected_params in locally_connected:
+ embedding_layer = keras.layers.LocallyConnected1D(
+ **locally_connected_params)(embedding_layer)
+ if concatenate_locally_connected_with_raw_embedding:
+ embedding_layer = keras.layers.concatenate([
+ Flatten()(raw_embedding_layer),
+ Flatten()(embedding_layer),
+ ])
+ else:
+ embedding_layer = Flatten()(embedding_layer)
+ else:
+ embedding_layer = Flatten()(embedding_layer)
+ if batch_normalization:
+ embedding_layer = BatchNormalization()(embedding_layer)
+ if dropout_probability:
+ embedding_layer = Dropout(dropout_probability)(embedding_layer)
+
+ if extra_data_length:
+ extra_info_input = Input(
+ shape=(extra_data_length,), dtype='float32', name='extra')
+ inputs.append(extra_info_input)
+
+ for layer_size in extra_data_layer_sizes:
+ extra_info_input = Dense(layer_size, activation=activation)(
+ extra_info_input)
+ if batch_normalization:
+ extra_info_input = BatchNormalization()(
+ extra_info_input)
+ if dropout_probability > 0:
+ extra_info_input = Dropout(dropout_probability)(
+ extra_info_input)
+ x = keras.layers.concatenate([embedding_layer, extra_info_input])
+ else:
+ x = embedding_layer
+
+ if pseudosequence_length:
+ if pseudosequence_use_embedding:
+ pseudosequence_input = Input(
+ shape=(pseudosequence_length,),
+ dtype='int32',
+ name='pseudosequence')
+ pseudo_embedding_layer = Embedding(
+ input_dim=embedding_input_dim,
+ output_dim=embedding_output_dim,
+ input_length=pseudosequence_length,
+ embeddings_initializer=embedding_init_method)(
+ pseudosequence_input)
+ else:
+ pseudosequence_input = Input(
+ shape=(pseudosequence_length, 21),
+ dtype='float32', name='peptide')
+ pseudo_embedding_layer = pseudosequence_input
+ inputs.append(pseudosequence_input)
+ pseudo_embedding_layer = Flatten()(pseudo_embedding_layer)
+
+ if pseudosequence_generate_weights:
+ pseudo_dense = Dense(
+ 32, activation="tanh")(pseudo_embedding_layer)
+
+ num_filters = 7
+ kernel_size = 3
+ output_length = 11
+
+ pseudo_lc1_kernel_shape = (output_length * kernel_size * embedding_output_dim, num_filters)
+ pseudo_lc2_kernel_shape = (output_length * kernel_size * num_filters, num_filters)
+
+
+ pseudo_lc1_kernel = Dense(numpy.prod(pseudo_lc1_kernel_shape), activation="tanh")(pseudo_dense)
+ pseudo_lc1_bias = Dense(num_filters * 11, activation="tanh")(pseudo_dense)
+ pseudo_lc2_kernel = Dense(numpy.prod(pseudo_lc2_kernel_shape), activation="tanh")(pseudo_dense)
+ pseudo_lc2_bias = Dense(num_filters * 11, activation="tanh")(pseudo_dense)
+ pseudo_hidden_kernel = Dense(num_filters * 11 * 32, activation="tanh")(pseudo_dense)
+ pseudo_hidden_bias = Dense(32, activation="tanh")(pseudo_dense)
+
+ pseudo_lc1_kernel = Reshape(pseudo_lc1_kernel_shape)(pseudo_lc1_kernel)
+ #pseudo_lc1_bias = Reshape((11, num_filters))(pseudo_lc1_bias)
+ pseudo_lc2_kernel = Reshape(pseudo_lc2_kernel_shape)(pseudo_lc2_kernel)
+ #pseudo_lc2_bias = Reshape((11, num_filters))(pseudo_lc2_bias)
+ pseudo_hidden_kernel = Reshape((num_filters * 11, 32))(pseudo_hidden_kernel)
+
+ def make_peptide_tiles(input_tensor):
+ components = []
+ for start in range(11):
+ components.append(K.flatten(
+ input_tensor[:, start : start + kernel_size]))
+ return K.concatenate(components, axis=0)
+
+ print("Raw embedding layer", raw_embedding_layer)
+ peptide_tiles = keras.layers.Lambda(
+ make_peptide_tiles,
+ output_shape=(11 * kernel_size * embedding_output_dim,))(
+ raw_embedding_layer)
+
+ def merger(inputs):
+ # TODO: A*b + c
+ print("inside merger", inputs)
+ kernel = inputs[0]
+ bias = inputs[1]
+ data = inputs[2]
+ print("kernel", kernel._keras_shape)
+ print("data", data._keras_shape)
+ _, kernel_size, filters = kernel._keras_shape
+ print(kernel_size, filters)
+
+ #dots = []
+ #for f in range(filters):
+ # dots.append(K.dot(data, kernel[:,:,f]))))
+
+ #result = K.reshape(K.concatenate(dots), (-1, )
+ #print("after dot", result._keras_shape)
+ #assert result._keras_shape[1:] == (), result._keras_shape
+ #result += K.reshape(bias, (1, output_length, filters))
+ return result
+
+ lc1_output = keras.layers.merge(
+ [pseudo_lc1_kernel, pseudo_lc1_bias, peptide_tiles],
+ mode=merger,
+ output_shape=(11 * num_filters,)
+ )
+ print("merged", lc1_output._keras_shape)
+ lc1_output = keras.layers.Activation("relu")(lc1_output)
+
+ #lc1_output_tiles = keras.layers.Lambda(
+ # make_peptide_tiles,
+ # output_shape=(11 * kernel_size * num_filters,))(lc1_output)
+
+ #lc2_output = keras.layers.merge(
+ # [pseudo_lc1_kernel, pseudo_lc1_bias, lc1_output_tiles],
+ # mode=merger,
+ # output_shape=(11, num_filters)
+ #)
+ #lc2_output = keras.layers.Activation("relu")(lc2_output)
+ #lc2_output = Flatten()(lc2_output)
+ lc2_output = lc1_output
+
+ #def dense_merger(inputs):
+ # print("inside dense merger", inputs)
+ # print([i._keras_shape for i in inputs])
+ # return inputs[0]
+
+ x = keras.layers.merge(
+ [pseudo_hidden_kernel, pseudo_hidden_bias, lc2_output],
+ mode=merger,
+ output_shape=(32, 1))
+ x = keras.layers.Activation("relu")(x)
+ print("x shape", type(x), x._keras_shape)
+ else:
+ x = keras.layers.concatenate([
+ x, pseudo_embedding_layer
+ ])
+
+ for layer_size in layer_sizes:
+ kernel_regularizer = None
+ l1 = dense_layer_l1_regularization
+ l2 = dense_layer_l2_regularization
+ if l1 > 0 or l2 > 0:
+ kernel_regularizer = keras.regularizers.l1_l2(l1, l2)
+
+ x = Dense(
+ layer_size,
+ activation=activation,
+ kernel_regularizer=kernel_regularizer)(x)
+ if batch_normalization:
+ x = BatchNormalization()(x)
+ if dropout_probability > 0:
+ x = Dropout(dropout_probability)(x)
+
+ outputs = []
+ if multiple_output_strategy == "bottleneck":
+ print("x shape2", type(x), x._keras_shape)
+ bottleneck = Dense(
+ 1,
+ kernel_initializer=init,
+ activation="linear")(x)
+
+ peptide_average = keras.layers.pooling.AveragePooling1D(
+ pool_size=1)(raw_embedding_layer)
+ peptide_average = Flatten()(peptide_average)
+
+ peptide_and_bottleneck = keras.layers.concatenate([
+ bottleneck, peptide_average
+ ])
+
+ for output_name in output_names:
+ nudge = Dense(
+ 8,
+ kernel_initializer=init,
+ activation=activation,
+ )(peptide_and_bottleneck)
+
+ nudge = Dense(
+ 1,
+ kernel_initializer="zeros",
+ activity_regularizer=keras.regularizers.l2(
+ multiple_output_activity_regularizer))(nudge)
+
+ output = keras.layers.add(
+ [bottleneck, nudge])
+ output = keras.layers.Activation(
+ output_activation, name=output_name)(output)
+ outputs.append(output)
+ else:
+ for output_name in output_names:
+ output = Dense(
+ 1,
+ kernel_initializer=init,
+ activation=output_activation,
+ name=output_name)(x)
+ outputs.append(output)
+ model = keras.models.Model(inputs=inputs, outputs=outputs)
+ model.compile(
+ loss="mse" if len(output_names) == 1 else mse_loss_supporting_nans,
+ optimizer=optimizer)
+ return model
+
+
+def mse_loss_supporting_nans(y_true, y_pred):
+ squared = K.square(y_pred - y_true)
+ loss = K.sum(
+ K.switch(theano.tensor.isnan(y_true), 0.0, squared),
+ axis=-1)
+ return loss
diff --git a/mhcflurry/class1_allele_specific/cv_and_train_command.py b/mhcflurry/class1_affinity_prediction/cv_and_train_command.py
similarity index 100%
rename from mhcflurry/class1_allele_specific/cv_and_train_command.py
rename to mhcflurry/class1_affinity_prediction/cv_and_train_command.py
diff --git a/mhcflurry/class1_affinity_prediction/multi_allele_predictor_ensemble.py b/mhcflurry/class1_affinity_prediction/multi_allele_predictor_ensemble.py
new file mode 100644
index 00000000..4e8b965c
--- /dev/null
+++ b/mhcflurry/class1_affinity_prediction/multi_allele_predictor_ensemble.py
@@ -0,0 +1,300 @@
+import collections
+import pickle
+import time
+import hashlib
+from os.path import join, exists
+
+import numpy
+import pandas
+
+import mhcnames
+
+from ..encodable_sequences import EncodableSequences
+
+from .class1_binding_predictor import Class1BindingPredictor
+
+
+class MultiAllelePredictorEnsemble(object):
+ def __init__(
+ self,
+ allele_to_allele_specific_models={},
+ class1_pan_allele_models=[],
+ allele_to_pseudosequence=None,
+ manifest_df=None):
+
+ if class1_pan_allele_models:
+ assert allele_to_pseudosequence, "Pseudosequences required"
+
+ self.allele_to_allele_specific_models = (
+ allele_to_allele_specific_models)
+ self.class1_pan_allele_models = class1_pan_allele_models
+ self.allele_to_pseudosequence = allele_to_pseudosequence
+
+ if manifest_df is None:
+ manifest_df = pandas.DataFrame()
+ manifest_df["name"] = []
+ manifest_df["allele"] = []
+ manifest_df["hyperparameters"] = []
+ manifest_df["history"] = []
+ manifest_df["num_measurements"] = []
+ manifest_df["random_negative_rate"] = []
+ manifest_df["sources"] = []
+ manifest_df["fit_seconds"] = []
+ manifest_df["model"] = []
+ self.manifest_df = manifest_df
+
+ def save(self, models_dir, model_names_to_write=None):
+ num_models = len(self.class1_pan_allele_models) + sum(
+ len(v) for v in self.allele_to_allele_specific_models.values())
+ assert len(self.manifest_df) == num_models, (
+ "Manifest seems out of sync with models: %d vs %d entries" % (
+ len(self.manifest_df), num_models))
+
+ if model_names_to_write is None:
+ # Write all models
+ models_names_to_write = self.manifest_df.model_name.values
+
+ sub_manifest_df = self.manifest_df.ix[
+ self.manifest_df.model_name.isin(models_names_to_write)
+ ]
+
+ for (_, row) in sub_manifest_df.iterrows():
+ model_path = join(models_dir, "%s.pickle" % row.name)
+ with open(join(model_path), 'wb') as fd:
+ pickle.dump(row.model, fd, protocol=2)
+ print("Wrote: %s" % model_path)
+
+ write_manifest_df = self.manifest_df[[
+ c for c in self.manifest_df.columns if c != "model"
+ ]]
+ manifest_path = join(models_dir, "manifest.csv")
+ write_manifest_df.to_csv(manifest_path, index=False)
+ print("Wrote: %s" % manifest_path)
+
+ @staticmethod
+ def model_name(allele, num):
+ random_string = hashlib.sha1(
+ str(time.time()).encode()).hexdigest()[:16]
+ return "%s-%d-%s" % (allele, num, random_string)
+
+ @staticmethod
+ def load(models_dir, max_models=None):
+ manifest_path = join(models_dir, "manifest.csv")
+ manifest_df = pandas.read_csv(manifest_path, nrows=max_models)
+ manifest_df["hyperparameters"] = manifest_df.hyperparameters.map(eval)
+ manifest_df["history"] = manifest_df.history.map(eval)
+
+ allele_to_allele_specific_models = collections.defaultdict(list)
+ class1_pan_allele_models = []
+ all_models = []
+ for (_, row) in manifest_df.iterrows():
+ model_path = join(models_dir, "%s.pickle" % row["name"])
+ print("Loading model: %s" % model_path)
+ with open(model_path, 'rb') as fd:
+ model = pickle.load(fd)
+ if row.allele == "pan-class1":
+ class1_pan_allele_models.append(model)
+ else:
+ allele_to_allele_specific_models[row.allele].append(model)
+ all_models.append(model)
+
+ manifest_df["model"] = all_models
+
+ pseudosequences = None
+ if exists(join(models_dir, "pseudosequences.csv")):
+ pseudosequences = pandas.read_csv(
+ join(models_dir, "pseudosequences.csv"),
+ index_col="allele").to_dict()
+
+ print(
+ "Loaded %d class1 pan allele predictors, %d pseudosequences, and "
+ "%d allele specific models: %s" % (
+ len(class1_pan_allele_models),
+ len(pseudosequences) if pseudosequences else 0,
+ sum(len(v) for v in allele_to_allele_specific_models.values()),
+ ", ".join(
+ "%s (%d)" % (allele, len(v))
+ for (allele, v)
+ in sorted(allele_to_allele_specific_models.items()))))
+
+ result = MultiAllelePredictorEnsemble(
+ 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)
+ return result
+
+ def fit_allele_specific_predictors(
+ self,
+ n_models,
+ architecture_hyperparameters,
+ allele,
+ peptides,
+ affinities,
+ output_assignments=None,
+ models_dir_for_save=None,
+ verbose=1):
+
+ allele = mhcnames.normalize_allele_name(allele)
+ models = self._fit_predictors(
+ n_models=n_models,
+ architecture_hyperparameters=architecture_hyperparameters,
+ peptide=peptides,
+ affinities=affinities,
+ output_assignments=output_assignments,
+ allele_pseudosequences=None,
+ verbose=verbose)
+
+ models_list = []
+ for (i, model) in enumerate(models):
+ name = self.model_name(allele, i)
+ models_list.append(model) # models is a generator
+ row = pandas.Series({
+ "allele": allele,
+ "hyperparameters": architecture_hyperparameters,
+ "history": model.fit_history.history,
+ "name": name,
+ "num_measurements": len(peptides),
+ "fit_seconds": model.fit_seconds,
+ "model": model,
+ }).to_frame().T
+ self.manifest_df = pandas.concat(
+ [self.manifest_df, row], ignore_index=True)
+ if models_dir_for_save:
+ self.save(models_dir_for_save, model_names_to_write=[name])
+
+ if allele not in self.allele_to_allele_specific_models:
+ self.allele_to_allele_specific_models[allele] = []
+ self.allele_to_allele_specific_models[allele].extend(models_list)
+ return models
+
+ def fit_class1_pan_allele_models(
+ self,
+ n_models,
+ architecture_hyperparameters,
+ alleles,
+ peptides,
+ affinities,
+ output_assignments=None,
+ models_dir_for_save=None,
+ verbose=1):
+
+ alleles = pandas.Series(alleles).map(mhcnames.normalize_allele_name)
+ allele_pseudosequences = alleles.map(self.allele_to_pseudosequence)
+
+ models = self._fit_predictors(
+ n_models=n_models,
+ architecture_hyperparameters=architecture_hyperparameters,
+ peptides=peptides,
+ affinities=affinities,
+ output_assignments=output_assignments,
+ allele_pseudosequences=allele_pseudosequences)
+
+ models_list = []
+ for (i, model) in enumerate(models):
+ name = self.model_name("pan-class1", i)
+ models_list.append(model) # models is a generator
+ row = pandas.Series({
+ "allele": "pan-class1",
+ "hyperparameters": architecture_hyperparameters,
+ "history": model.fit_history.history,
+ "name": name,
+ "num_measurements": len(peptides),
+ "fit_seconds": model.fit_seconds,
+ "model": model,
+ }).to_frame().T
+ self.manifest_df = pandas.concat(
+ [self.manifest_df, row], ignore_index=True)
+ if models_dir_for_save:
+ self.save(models_dir_for_save, model_names_to_write=[name])
+
+ self.class1_pan_allele_models.extend(models_list)
+ return models
+
+ def _fit_predictors(
+ self,
+ n_models,
+ architecture_hyperparameters,
+ peptides,
+ affinities,
+ output_assignments,
+ allele_pseudosequences,
+ verbose=1):
+
+ encodable_peptides = EncodableSequences.create(peptides)
+ if output_assignments is None:
+ output_assignments = ["output"] * len(encodable_peptides.sequences)
+ for i in range(n_models):
+ print("Training model %d / %d" % (i + 1, n_models))
+ model = Class1BindingPredictor(**architecture_hyperparameters)
+ model.fit(
+ encodable_peptides,
+ affinities,
+ output_assignments=output_assignments,
+ allele_pseudosequences=allele_pseudosequences,
+ verbose=verbose)
+ yield model
+
+ def predict(
+ self,
+ peptides,
+ alleles,
+ include_mean=True,
+ include_peptides_and_alleles=True):
+ input_df = pandas.DataFrame({
+ 'peptide': peptides,
+ 'allele': alleles,
+ })
+ input_df["allele"] = input_df.allele.map(
+ mhcnames.normalize_allele_name)
+
+ result_dataframes = []
+
+ if self.class1_pan_allele_models:
+ allele_pseudosequences = input_df.allele.map(
+ self.allele_to_pseudosequence)
+ encodable_peptides = EncodableSequences.create(
+ input_df.peptide.values)
+ for model in self.class1_pan_allele_models:
+ result_df = pandas.DataFrame(
+ model.predict(
+ encodable_peptides,
+ allele_pseudosequences=allele_pseudosequences))
+ result_dataframes.append(result_df)
+
+ if self.allele_to_allele_specific_models:
+ for allele in input_df.allele.unique():
+ mask = (input_df.allele == allele).values
+ allele_peptides = EncodableSequences.create(
+ input_df.ix[mask].peptide.values)
+ models = self.allele_to_allele_specific_models.get(allele, [])
+ for model in models:
+ result_df = pandas.DataFrame(
+ model.predict(allele_peptides),
+ index=input_df.index[mask].values)
+ result_dataframes.append(result_df)
+
+ model_predictions = pandas.Panel(
+ dict(enumerate(result_dataframes)),
+ major_axis=input_df.index)
+
+ # Geometric mean
+ log_means = numpy.log(model_predictions).mean(0)
+ first_columns = []
+ if include_mean:
+ log_means["mean"] = log_means.mean(1)
+ first_columns.append("mean")
+
+ result = numpy.exp(log_means)
+
+ if include_peptides_and_alleles:
+ result["peptide"] = input_df.peptide.values
+ result["allele"] = input_df.allele.values
+ first_columns.append("allele")
+ first_columns.append("peptide")
+
+ assert len(result) == len(peptides), result.shape
+ return result[
+ list(reversed(first_columns)) +
+ [c for c in result.columns if c not in first_columns]
+ ]
diff --git a/mhcflurry/class1_allele_specific/scoring.py b/mhcflurry/class1_affinity_prediction/scoring.py
similarity index 100%
rename from mhcflurry/class1_allele_specific/scoring.py
rename to mhcflurry/class1_affinity_prediction/scoring.py
diff --git a/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
new file mode 100644
index 00000000..359cdaaa
--- /dev/null
+++ b/mhcflurry/class1_affinity_prediction/train_allele_specific_models_command.py
@@ -0,0 +1,357 @@
+"""
+Train single allele models
+
+"""
+import sys
+import argparse
+import json
+import os
+import pickle
+
+import pandas
+
+import mhcnames
+
+
+from .class1_binding_predictor import Class1BindingPredictor
+from ..common import random_peptides
+
+
+def normalize_allele_name(s):
+ try:
+ return mhcnames.normalize_allele_name(s)
+ except Exception:
+ return "UNKNOWN"
+
+
+parser = argparse.ArgumentParser(usage=__doc__)
+
+parser.add_argument(
+ "--data-csv",
+ help="Path to data csv")
+parser.add_argument(
+ "--iedb-data-csv",
+ help="Path to IEDB mhc_ligand_full.csv")
+
+parser.add_argument(
+ "--out-models-dir",
+ help="Directory to write models and manifest")
+parser.add_argument(
+ "--hyperparameters",
+ required=True,
+ help="JSON of hyperparameters")
+parser.add_argument(
+ "--allele",
+ default=None,
+ nargs="+",
+ help="Alleles")
+parser.add_argument(
+ "--min-measurements-per-category",
+ type=int,
+ default=500,
+ help="Alleles")
+parser.add_argument(
+ "--min-measurements-per-allele",
+ type=int,
+ default=50,
+ help="Alleles")
+parser.add_argument(
+ "--random-negative-rate",
+ type=float,
+ default=0.0)
+parser.add_argument(
+ "--random-negative-fixed",
+ type=int,
+ default=0)
+parser.add_argument(
+ "--pretrain",
+ action="store_true",
+ default=False)
+parser.add_argument(
+ "--only-quantitative",
+ action="store_true",
+ default=False)
+parser.add_argument(
+ "--verbose",
+ type=int,
+ default=1,
+ help="Alleles")
+
+
+def add_random_negative_peptides(
+ df,
+ rate=1,
+ fixed=0,
+ affinity=50000.0,
+ weight=1.0,
+ lengths=range(8, 16)):
+ new_dfs = [df]
+ measurement_sources = df.measurement_source.unique()
+ (allele,) = df.allele.unique()
+ length_counts = df.peptide.str.len().value_counts().to_dict()
+ for length in lengths:
+ count = length_counts.get(length, 0)
+ desired = int((count * rate + fixed))
+ print("Adding %d * %d + %d = %d random negative %d-mers" % (
+ count, rate, fixed, desired, length))
+ peptides = random_peptides(desired, length=length)
+
+ for measurement_source in measurement_sources:
+ new_df = pandas.DataFrame({
+ "allele": allele,
+ "peptide": peptides,
+ "measurement_type": "affinity",
+ "measurement_source": measurement_source,
+ "measurement_value": affinity,
+ "weight": weight,
+ })
+ new_dfs.append(new_df)
+ result = pandas.concat(new_dfs, ignore_index=True)
+ print("Final result shape: %s" % str(result.shape))
+ return result
+
+
+def load_data_csv(filename, alleles):
+ df = pandas.read_csv(filename)
+ if alleles:
+ df = df.ix[df.allele.isin(alleles)]
+ return df
+
+
+upper_thresholds = {
+ "Negative": 50000.0,
+ "Positive": 100.0,
+ "Positive-High": 50.0,
+ "Positive-Intermediate": 500.0,
+ "Positive-Low": 5000.0,
+}
+
+
+def load_iedb_data_csv(
+ iedb_csv,
+ alleles=None,
+ min_measurements_per_category=100,
+ min_measurements_per_allele=50,
+ include_qualitative=True):
+ iedb_df = pandas.read_csv(iedb_csv, skiprows=1)
+ print("Loaded iedb data: %s" % str(iedb_df.shape))
+ iedb_df["allele"] = iedb_df["Allele Name"].map(normalize_allele_name)
+ print("Dropping un-parseable alleles: %s" % ", ".join(
+ iedb_df.ix[iedb_df.allele == "UNKNOWN"]["Allele Name"].unique()))
+ iedb_df = iedb_df.ix[iedb_df.allele != "UNKNOWN"]
+
+ if not alleles:
+ print("Taking all alleles with %d measurements" % (
+ min_measurements_per_allele))
+ allele_counts = iedb_df.allele.value_counts()
+ alleles = list(allele_counts.ix[
+ allele_counts > min_measurements_per_allele
+ ].index)
+ print("Selected alleles: %s" % ' '.join(alleles))
+
+ iedb_df = iedb_df.ix[
+ iedb_df.allele.isin(alleles)
+ ]
+ print("IEDB measurements per allele:\n%s" % iedb_df.allele.value_counts())
+
+ quantitative = iedb_df.ix[iedb_df["Units"] == "nM"]
+ print("Quantitative measurements: %d" % len(quantitative))
+
+ qualitative = iedb_df.ix[iedb_df["Units"] != "nM"].copy()
+ print("Qualitative measurements: %d" % len(qualitative))
+ non_mass_spec_qualitative = qualitative.ix[
+ (~qualitative["Method/Technique"].str.contains("mass spec"))
+ ].copy()
+ non_mass_spec_qualitative["Quantitative measurement"] = (
+ non_mass_spec_qualitative["Qualitative Measure"].map(upper_thresholds))
+ print("Qualitative measurements after dropping MS: %d" % (
+ len(non_mass_spec_qualitative)))
+
+ iedb_df = pandas.concat(
+ (
+ ([quantitative]) +
+ ([non_mass_spec_qualitative] if include_qualitative else [])),
+ ignore_index=True)
+
+ print("IEDB measurements per allele:\n%s" % iedb_df.allele.value_counts())
+
+ print("Subselecting to valid peptides. Starting with: %d" % len(iedb_df))
+ iedb_df["Description"] = iedb_df.Description.str.strip()
+ iedb_df = iedb_df.ix[
+ iedb_df.Description.str.match("^[ACDEFGHIKLMNPQRSTVWY]+$")
+ ]
+ print("Now: %d" % len(iedb_df))
+
+ print("Subselecting to 8-to-15-mers")
+ iedb_df = iedb_df.ix[
+ (iedb_df["Description"].str.len() >= 8) &
+ (iedb_df["Description"].str.len() <= 15)
+ ].copy()
+ print("IEDB measurements per allele:\n%s" % iedb_df.allele.value_counts())
+
+ print("Annotating last author and category")
+ iedb_df["last_author"] = iedb_df.Authors.map(
+ lambda x: (
+ x.split(";")[-1]
+ .split(",")[-1]
+ .split(" ")[-1]
+ .strip()
+ .replace("*", "")))
+ iedb_df["category"] = (
+ iedb_df["last_author"] + " - " + iedb_df["Method/Technique"])
+
+ to_concat = []
+
+ for allele in alleles:
+ sub_df = iedb_df.ix[iedb_df.allele == allele]
+ top_categories = sub_df.category.value_counts().ix[
+ sub_df.category.value_counts() >
+ min_measurements_per_category
+ ]
+
+ top_categories = top_categories.index
+
+ train_data = pandas.DataFrame()
+ train_data["peptide"] = sub_df.Description
+ train_data["measurement_value"] = sub_df[
+ "Quantitative measurement"
+ ]
+ train_data["original_measurement_source"] = (
+ sub_df.category.values)
+
+ train_data["allele"] = sub_df["allele"]
+ train_data["measurement_type"] = "affinity"
+ train_data["measurement_source"] = [
+ s if s in (top_categories) else "other"
+ for s in train_data.original_measurement_source
+ ]
+ train_data["weight"] = 1.0
+ train_data = train_data.drop_duplicates().reset_index(
+ drop=True)
+ to_concat.append(train_data)
+
+ return pandas.concat(to_concat, ignore_index=True)
+
+
+def run():
+ args = parser.parse_args(sys.argv[1:])
+
+ hyperparameters_lst = json.load(open(args.hyperparameters))
+ if not isinstance(hyperparameters_lst, list):
+ hyperparameters_lst = [hyperparameters_lst]
+ print("Loaded hyperparameters list: %s" % str(hyperparameters_lst))
+
+ dfs = []
+ if args.iedb_data_csv:
+ iedb_df = load_iedb_data_csv(
+ args.iedb_data_csv,
+ alleles=args.allele,
+ min_measurements_per_category=args.min_measurements_per_category,
+ include_qualitative=not args.only_quantitative)
+ dfs.append(iedb_df)
+ if args.data_csv:
+ extra_data_csv = load_data_csv(
+ args.data_csv, alleles=args.allele)
+ print("Loaded extra data csv: %s %s" % (
+ args.data_csv, str(extra_data_csv.shape)))
+ dfs.append(extra_data_csv)
+
+ df = pandas.concat(dfs, ignore_index=True)
+ print("Combined df: %s" % (str(df.shape)))
+ allele_counts = df.allele.value_counts()
+ alleles = list(allele_counts.ix[
+ allele_counts > args.min_measurements_per_allele
+ ].index)
+ print("Selected alleles: %s" % ' '.join(alleles))
+
+ df = df.ix[df.allele.isin(alleles)]
+
+ print("Combined allele-selected df: %s" % (str(df.shape)))
+
+ manifest = pandas.DataFrame()
+ manifest["name"] = []
+ manifest["hyperparameters_index"] = []
+ manifest["model_group"] = []
+ manifest["allele"] = []
+ manifest["hyperparameters"] = []
+ manifest["history"] = []
+ manifest["num_measurements"] = []
+ manifest["random_negative_rate"] = []
+ manifest["random_negative_fixed"] = []
+ manifest["sources"] = []
+ manifest["fit_seconds"] = []
+
+ manifest_path = os.path.join(args.out_models_dir, "manifest.csv")
+
+ for (h, hyperparameters) in enumerate(hyperparameters_lst):
+ n_models = hyperparameters.pop("n_models")
+ for model_group in range(n_models):
+ for (i, allele) in enumerate(alleles):
+ print(
+ "[%2d / %2d hyperparameters] "
+ "[%2d / %2d replicates] "
+ "[%4d / %4d alleles]: %s" % (
+ h + 1,
+ len(hyperparameters_lst),
+ model_group + 1,
+ n_models,
+ i + 1,
+ len(alleles), allele))
+
+ train_data = df.ix[df.allele == allele]
+
+ train_data_expanded = add_random_negative_peptides(
+ train_data,
+ rate=args.random_negative_rate,
+ fixed=args.random_negative_fixed)
+
+ train_data_expanded = train_data_expanded.dropna().sample(
+ frac=1.0)
+
+ print("Measurement sources:\n%s" % (
+ train_data_expanded.measurement_source.value_counts()))
+
+ model = Class1BindingPredictor(
+ verbose=args.verbose,
+ **hyperparameters)
+
+ model.fit(
+ train_data_expanded.peptide.values,
+ train_data_expanded.measurement_value.values,
+ output_assignments=(
+ train_data_expanded.measurement_source.values))
+ print("Done fitting in %0.2f sec" % model.fit_seconds)
+
+ name = "%s-%d-%d" % (
+ allele.replace("*", "_"),
+ h,
+ model_group)
+
+ row = pandas.Series({
+ "hyperparameters_index": h,
+ "model_group": model_group,
+ "allele": allele,
+ "hyperparameters": hyperparameters,
+ "history": model.fit_history.history,
+ "name": name,
+ "num_measurements": len(train_data),
+ "random_negative_rate": args.random_negative_rate,
+ "random_negative_fixed": args.random_negative_fixed,
+ "sources": train_data_expanded.measurement_source.unique(),
+ "fit_seconds": model.fit_seconds,
+ }).to_frame().T
+ manifest = pandas.concat([manifest, row], ignore_index=True)
+ print(manifest)
+
+ manifest.to_csv(manifest_path, index=False)
+ print("Wrote: %s" % manifest_path)
+
+ model_path = os.path.join(
+ args.out_models_dir, "%s.pickle" % name)
+ with open(model_path, 'wb') as fd:
+ pickle.dump(model, fd, protocol=2)
+ print("Wrote: %s" % model_path)
+
+
+if __name__ == '__main__':
+ run()
diff --git a/mhcflurry/class1_allele_specific/__init__.py b/mhcflurry/class1_allele_specific/__init__.py
deleted file mode 100644
index 228b33d9..00000000
--- a/mhcflurry/class1_allele_specific/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from __future__ import absolute_import
-
-from .class1_binding_predictor import Class1BindingPredictor
-from .train import train_across_models_and_folds, AlleleSpecificTrainTestFold
-from .cross_validation import cross_validation_folds
-from .class1_single_model_multi_allele_predictor import (
- from_allele_name,
- supported_alleles,
- get_downloaded_predictor,
- Class1SingleModelMultiAllelePredictor)
-
-__all__ = [
- 'Class1BindingPredictor',
- 'AlleleSpecificTrainTestFold',
- 'cross_validation_folds',
- 'train_across_models_and_folds',
- 'from_allele_name',
- 'supported_alleles',
- 'get_downloaded_predictor',
- 'Class1SingleModelMultiAllelePredictor',
-]
diff --git a/mhcflurry/class1_allele_specific/class1_allele_specific_kmer_ic50_predictor_base.py b/mhcflurry/class1_allele_specific/class1_allele_specific_kmer_ic50_predictor_base.py
deleted file mode 100644
index b309df00..00000000
--- a/mhcflurry/class1_allele_specific/class1_allele_specific_kmer_ic50_predictor_base.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-
-from six import string_types
-
-from ..peptide_encoding import encode_peptides
-from ..amino_acid import (
- amino_acids_with_unknown,
- common_amino_acids
-)
-from ..ic50_predictor_base import IC50PredictorBase
-from ..hyperparameters import HyperparameterDefaults
-
-
-class Class1AlleleSpecificKmerIC50PredictorBase(IC50PredictorBase):
- """
- Base class for all mhcflurry predictors which used fixed-length
- k-mer representation of peptides and don't require scanning over
- a longer sequence to find a binding core (like you might for Class II).
- """
- hyperparameter_defaults = (HyperparameterDefaults(
- kmer_size=9)
- .extend(IC50PredictorBase.hyperparameter_defaults))
-
- def __init__(
- self,
- name,
- allow_unknown_amino_acids,
- verbose,
- **hyperparameters):
- effective_hyperparameters = (
- self.hyperparameter_defaults.with_defaults(hyperparameters))
- IC50PredictorBase.__init__(
- self,
- name=name,
- verbose=verbose,
- **IC50PredictorBase.hyperparameter_defaults.subselect(
- effective_hyperparameters))
- self.allow_unknown_amino_acids = allow_unknown_amino_acids
- self.kmer_size = effective_hyperparameters["kmer_size"]
-
- def __repr__(self):
- return (
- "%s(name=%s, max_ic50=%f, allow_unknown_amino_acids=%s, "
- "kmer_size=%d)" % (
- self.__class__.__name__,
- self.name,
- self.max_ic50,
- self.allow_unknown_amino_acids,
- self.kmer_size))
-
- def __str__(self):
- return repr(self)
-
- @property
- def amino_acids(self):
- """
- Amino acid alphabet used for encoding peptides, may include
- "X" if allow_unknown_amino_acids is True.
- """
- if self.allow_unknown_amino_acids:
- return amino_acids_with_unknown
- else:
- return common_amino_acids
-
- @property
- def max_amino_acid_encoding_value(self):
- return len(self.amino_acids)
-
- def encode_peptides(self, peptides):
- return encode_peptides(
- peptides, kmer_size=self.kmer_size, allow_unknown_amino_acids=self.allow_unknown_amino_acids)
-
- def predict_scores(self, peptides):
- """
- Given a list of peptides of any length, returns an array of predicted
- normalized affinity values. Unlike IC50, a higher value here
- means a stronger affinity. Peptides of lengths other than 9 are
- transformed into a set of k-mers either by deleting or inserting
- amino acid characters. The prediction for a single peptides will be
- the average of expanded k-mers.
- """
- if isinstance(peptides, string_types):
- raise TypeError("Input must be a list of peptides, not %s : %s" % (
- peptides, type(peptides)))
-
- encoded_peptides = self.encode_peptides(peptides)
- return encoded_peptides.combine_predictions(
- self.predict_scores_for_kmer_encoded_array(encoded_peptides.encoded_matrix))
-
- def fit_dataset(
- self,
- dataset,
- pretraining_dataset=None,
- sample_censored_affinities=False,
- **kwargs):
- """
- Fit the model parameters on the given training data.
-
- Parameters
- ----------
- dataset : AffinityMeasurementDataset
-
- pretraining_dataset : AffinityMeasurementDataset
-
- sample_censored_affinities : bool
- If a column named 'inequality' is in the AffinityMeasurementDataset then every
- peptide with a value of '>' on each training epoch, gets a
- randomly sampled IC50 between its observed value and the
- max_ic50 of the predictor. Default is False.
-
- **kwargs : dict
- Extra arguments are passed on to the fit_encoded_kmer_arrays()
- method.
- """
- if len(dataset.unique_alleles()) > 1:
- raise ValueError(
- "Allele-specific predictor can't be trained on multi-allele "
- "data: %s" % dataset)
-
- if pretraining_dataset and len(pretraining_dataset.unique_alleles()) > 1:
- raise ValueError(
- "Allele-specific predictor can't pretrain on data from multiple alleles: %s" %
- (pretraining_dataset,))
-
- X, ic50, sample_weights, original_peptide_indices = \
- dataset.kmer_index_encoding(
- kmer_size=self.kmer_size,
- allow_unknown_amino_acids=self.allow_unknown_amino_acids)
- if pretraining_dataset is None:
- X_pretrain = ic50_pretrain = sample_weights_pretrain = None
- else:
- X_pretrain, ic50_pretrain, sample_weights_pretrain, _ = \
- pretraining_dataset.kmer_index_encoding(
- kmer_size=self.kmer_size,
- allow_unknown_amino_acids=self.allow_unknown_amino_acids)
-
- if sample_censored_affinities and 'inequality' in dataset.columns:
- df = dataset.to_dataframe()
- inequalities = df["inequality"]
- censored_mask_for_variable_length_peptides = (inequalities == ">")
- censored_mask_for_kmers = censored_mask_for_variable_length_peptides[
- original_peptide_indices]
- else:
- censored_mask_for_kmers = None
-
- return self.fit_kmer_encoded_arrays(
- X=X,
- ic50=ic50,
- sample_weights=sample_weights,
- right_censoring_mask=censored_mask_for_kmers,
- X_pretrain=X_pretrain,
- ic50_pretrain=ic50_pretrain,
- sample_weights_pretrain=sample_weights_pretrain,
- **kwargs)
diff --git a/mhcflurry/class1_allele_specific/class1_binding_predictor.py b/mhcflurry/class1_allele_specific/class1_binding_predictor.py
deleted file mode 100644
index 31024b67..00000000
--- a/mhcflurry/class1_allele_specific/class1_binding_predictor.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Allele specific MHC Class I binding affinity predictor
-"""
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-
-import tempfile
-import os
-
-import numpy as np
-
-import keras.models
-
-from ..feedforward import make_embedding_network
-from .class1_allele_specific_kmer_ic50_predictor_base import (
- Class1AlleleSpecificKmerIC50PredictorBase,
-)
-from ..peptide_encoding import check_valid_index_encoding_array
-from ..regression_target import MAX_IC50, ic50_to_regression_target
-from ..training_helpers import (
- combine_training_arrays,
- extend_with_negative_random_samples,
-)
-from ..regression_target import regression_target_to_ic50
-from ..hyperparameters import HyperparameterDefaults
-
-
-class Class1BindingPredictor(Class1AlleleSpecificKmerIC50PredictorBase):
- """
- Allele-specific Class I MHC binding predictor which uses
- fixed-length (k-mer) index encoding for inputs and outputs
- a value between 0 and 1 (where 1 is the strongest binder).
- """
-
- network_hyperparameter_defaults = HyperparameterDefaults(
- embedding_output_dim=32,
- layer_sizes=[64],
- init="glorot_uniform",
- loss="mse",
- optimizer="rmsprop",
- output_activation="sigmoid",
- activation="tanh",
- dropout_probability=0.0)
-
- fit_hyperparameter_defaults = HyperparameterDefaults(
- n_training_epochs=250,
- batch_size=128,
- pretrain_decay="numpy.exp(-epoch)",
- fraction_negative=0.0,
- batch_normalization=True)
-
- hyperparameter_defaults = (
- Class1AlleleSpecificKmerIC50PredictorBase.hyperparameter_defaults
- .extend(network_hyperparameter_defaults)
- .extend(fit_hyperparameter_defaults))
-
- def __init__(
- self,
- model=None,
- name=None,
- max_ic50=MAX_IC50,
- allow_unknown_amino_acids=True,
- kmer_size=9,
- n_amino_acids=20,
- verbose=False,
- **hyperparameters):
- Class1AlleleSpecificKmerIC50PredictorBase.__init__(
- self,
- name=name,
- max_ic50=max_ic50,
- allow_unknown_amino_acids=allow_unknown_amino_acids,
- verbose=verbose,
- kmer_size=kmer_size)
-
- specified_network_hyperparameters = (
- self.network_hyperparameter_defaults.subselect(hyperparameters))
-
- effective_hyperparameters = (
- self.hyperparameter_defaults.with_defaults(hyperparameters))
-
- if model is None:
- model = make_embedding_network(
- peptide_length=kmer_size,
- n_amino_acids=n_amino_acids + int(allow_unknown_amino_acids),
- **self.network_hyperparameter_defaults.subselect(
- effective_hyperparameters))
- elif specified_network_hyperparameters:
- raise ValueError(
- "Do not specify network hyperparameters when passing a model. "
- "Network hyperparameters specified: %s"
- % " ".join(specified_network_hyperparameters))
-
- self.hyperparameters = effective_hyperparameters
- self.name = name
- self.model = model
-
- def __getstate__(self):
- result = dict(self.__dict__)
- del result['model']
- result['model_json'] = self.model.to_json()
- result['model_weights'] = self.get_weights()
- return result
-
- def __setstate__(self, state):
- model_bytes = model_json = model_weights = None
- try:
- model_bytes = state.pop('model_bytes')
- except KeyError:
- model_json = state.pop('model_json')
- model_weights = state.pop('model_weights')
- self.__dict__.update(state)
-
- if model_bytes is not None:
- # Old format
- fd = tempfile.NamedTemporaryFile(suffix='.hdf5', delete=False)
- try:
- fd.write(model_bytes)
-
- # HDF5 has issues when the file is open multiple times, so we close
- # it here before loading it into keras.
- fd.close()
- self.model = keras.models.load_model(fd.name)
- finally:
- os.unlink(fd.name)
- else:
- self.model = keras.models.model_from_json(model_json)
- self.set_weights(model_weights)
-
- def get_weights(self):
- """
- Returns weights, which can be passed to set_weights later.
- """
- return [x.copy() for x in self.model.get_weights()]
-
- def set_weights(self, weights):
- """
- Reset the model weights.
- """
- self.model.set_weights(weights)
-
- def fit_kmer_encoded_arrays(
- self,
- X,
- ic50,
- sample_weights=None,
- right_censoring_mask=None,
- X_pretrain=None,
- ic50_pretrain=None,
- sample_weights_pretrain=None,
- n_random_negative_samples=None,
- pretrain_decay=None,
- n_training_epochs=None,
- batch_size=None,
- verbose=False):
- """
- Train predictive model from index encoding of fixed length k-mer
- peptides.
-
- Parameters
- ----------
- X : array
- Training data with shape (n_samples, n_dims)
-
- ic50 : array
- Training IC50 values with shape (n_samples,)
-
- sample_weights : array
- Weight of each training sample with shape (n_samples,)
-
- right_censoring_mask : array, optional
- Boolean array which indicates whether each IC50 value is actually
- right censored (a lower bound on the true value). Censored values
- are transformed during training by sampling between the observed
- and maximum values on each iteration.
-
- X_pretrain : array
- Extra samples used for soft pretraining of the predictor,
- should have same number of dimensions as X.
- During training the weights of these samples will decay after
- each epoch.
-
- ic50_pretrain : array
- IC50 values for extra samples, shape
-
- pretrain_decay : int -> float function
- decay function for pretraining, mapping epoch number to decay
- factor
-
- sample_weights_pretrain : array
- Initial weights for the rows of X_pretrain. If not specified then
- initialized to ones.
-
- n_random_negative_samples : int
- Number of random samples to generate as negative examples.
-
- n_training_epochs : int
-
- verbose : bool
-
- batch_size : int
- """
-
- # Apply defaults from hyperparameters
- if n_random_negative_samples is None:
- n_random_negative_samples = (
- int(self.hyperparameters["fraction_negative"] * len(ic50)))
-
- if pretrain_decay is None:
- pretrain_decay = (
- lambda epoch:
- eval(
- self.hyperparameters["pretrain_decay"],
- {'epoch': epoch, 'numpy': np}))
-
- if n_training_epochs is None:
- n_training_epochs = self.hyperparameters["n_training_epochs"]
-
- if batch_size is None:
- batch_size = self.hyperparameters["batch_size"]
-
- X_combined, ic50_combined, combined_weights, n_pretrain = \
- combine_training_arrays(
- X, ic50, sample_weights,
- X_pretrain, ic50_pretrain, sample_weights_pretrain)
-
- Y_combined = ic50_to_regression_target(
- ic50_combined, max_ic50=self.max_ic50)
-
- # create a censored IC50 mask for all combined samples and then fill
- # in the training censoring mask if it's given
- right_censoring_mask_combined = np.zeros(len(Y_combined), dtype=bool)
- if right_censoring_mask is not None:
- right_censoring_mask = np.asarray(right_censoring_mask)
- if len(right_censoring_mask.shape) != 1:
- raise ValueError("Expected 1D censor mask, got shape %s" % (
- right_censoring_mask.shape,))
- if len(right_censoring_mask) != len(ic50):
- raise ValueError(
- "Wrong length for censoring mask, expected %d not %d" % (
- len(ic50),
- len(right_censoring_mask)))
- right_censoring_mask_combined[n_pretrain:] = right_censoring_mask
-
- n_censored = right_censoring_mask_combined.sum()
-
- total_pretrain_sample_weight = combined_weights[:n_pretrain].sum()
- total_train_sample_weight = combined_weights[n_pretrain:].sum()
- total_combined_sample_weight = (
- total_pretrain_sample_weight + total_train_sample_weight)
-
- for epoch in range(n_training_epochs):
- decay_factor = pretrain_decay(epoch)
-
- # if the contribution of synthetic samples is less than a
- # thousandth of the actual data, then stop using it
- pretrain_contribution = total_pretrain_sample_weight * decay_factor
- pretrain_fraction_contribution = (
- pretrain_contribution / total_combined_sample_weight)
-
- if n_censored > 0:
- # shrink the output values by a uniform amount to some value
- # between the lowest representable affinity and the observed
- # censored value
- Y_adjusted_for_censoring = Y_combined.copy()
- Y_adjusted_for_censoring[right_censoring_mask_combined] *= (
- np.random.rand(n_censored))
- else:
- Y_adjusted_for_censoring = Y_combined
-
- # only use synthetic data if it contributes at least 1/1000th of
- # sample weight
- if pretrain_fraction_contribution > 0.001:
- combined_weights[:n_pretrain] *= decay_factor
- X_curr_iter = X_combined
- Y_curr_iter = Y_adjusted_for_censoring
- weights_curr_iter = combined_weights
- else:
- X_curr_iter = X_combined[n_pretrain:]
- Y_curr_iter = Y_adjusted_for_censoring[n_pretrain:]
- weights_curr_iter = combined_weights[n_pretrain:]
-
- if n_random_negative_samples > 0:
- X_curr_iter, Y_curr_iter, weights_curr_iter = \
- extend_with_negative_random_samples(
- X_curr_iter,
- Y_curr_iter,
- weights_curr_iter,
- n_random_negative_samples,
- max_amino_acid_encoding_value=(
- self.max_amino_acid_encoding_value))
-
- self.model.fit(
- X_curr_iter,
- Y_curr_iter,
- sample_weight=weights_curr_iter,
- nb_epoch=1,
- verbose=0,
- batch_size=batch_size,
- shuffle=True)
-
- def predict_scores_for_kmer_encoded_array(self, X):
- """
- Given an encoded array of amino acid indices, returns a vector
- of affinity scores (values between 0 and 1).
- """
- X = check_valid_index_encoding_array(
- X,
- allow_unknown_amino_acids=self.allow_unknown_amino_acids)
- return self.model.predict(X, verbose=False).flatten()
-
- def predict_ic50_for_kmer_encoded_array(self, X):
- """
- Given an encoded array of amino acid indices,
- returns a vector of IC50 predictions.
- """
- scores = self.predict_scores_for_kmer_encoded_array(X)
- return regression_target_to_ic50(scores, max_ic50=self.max_ic50)
diff --git a/mhcflurry/class1_allele_specific/class1_single_model_multi_allele_predictor.py b/mhcflurry/class1_allele_specific/class1_single_model_multi_allele_predictor.py
deleted file mode 100644
index 1dca8766..00000000
--- a/mhcflurry/class1_allele_specific/class1_single_model_multi_allele_predictor.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-'''
-Load predictors
-'''
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-import pickle
-from os.path import join
-
-import pandas
-
-from ..downloads import get_path
-from ..common import normalize_allele_name, UnsupportedAllele
-
-CACHED_PREDICTOR = None
-
-
-def from_allele_name(allele_name):
- """
- Load a single-allele predictor.
-
- Parameters
- ----------
- allele_name : class I allele name
-
- Returns
- ----------
- Class1BindingPredictor
- """
- return get_downloaded_predictor().predictor_for_allele(allele_name)
-
-
-def supported_alleles():
- """
- Return a list of the names of the alleles for which there are trained
- predictors in the default laoder.
- """
- return get_downloaded_predictor().supported_alleles
-
-
-def get_downloaded_predictor():
- """
- Return a Class1AlleleSpecificPredictorLoader that uses downloaded models.
- """
- global CACHED_PREDICTOR
-
- # Some of the unit tests manipulate the downloads directory configuration
- # so get_path here may return different results in the same Python process.
- # For this reason we check the path and invalidate the loader if it's
- # different.
- path = get_path("models_class1_allele_specific_single")
- if CACHED_PREDICTOR is None or path != CACHED_PREDICTOR.path:
- CACHED_PREDICTOR = (
- Class1SingleModelMultiAllelePredictor
- .load_from_download_directory(path))
- return CACHED_PREDICTOR
-
-
-class Class1SingleModelMultiAllelePredictor(object):
- """
- Factory for Class1BindingPredictor instances that are stored on disk
- using this directory structure:
-
- production.csv - Manifest file giving information on all models
-
- models/ - directory of models with names given in the manifest file
- MODEL-BAR.pickle
- MODEL-FOO.pickle
- ...
- """
-
- @staticmethod
- def load_from_download_directory(directory):
- return Class1SingleModelMultiAllelePredictor(directory)
-
- def __init__(self, path):
- """
- Parameters
- ----------
- path : string
- Path to directory containing manifest and models
- """
- self.path = path
- self.path_to_models_csv = join(path, "production.csv")
- self.df = pandas.read_csv(self.path_to_models_csv)
- self.df.index = self.df["allele"]
- self.supported_alleles = list(sorted(self.df.allele))
- self.predictors_cache = {}
-
- def predictor_for_allele(self, allele):
- """
- Load a predictor for an allele.
-
- Parameters
- ----------
- allele : class I allele name
-
- Returns
- ----------
- Class1BindingPredictor
- """
- allele = normalize_allele_name(allele)
- if allele not in self.predictors_cache:
- try:
- predictor_name = self.df.ix[allele].predictor_name
- except KeyError:
- raise UnsupportedAllele(
- "No models for allele '%s'. Alleles with models: %s"
- " in models file: %s" % (
- allele,
- ' '.join(self.supported_alleles),
- self.path_to_models_csv))
-
- model_path = join(self.path, "models", predictor_name + ".pickle")
- with open(model_path, 'rb') as fd:
- self.predictors_cache[allele] = pickle.load(fd)
- return self.predictors_cache[allele]
-
- def predict(self, measurement_collection):
- if (measurement_collection.df.measurement_type != "affinity").any():
- raise ValueError("Only affinity measurements supported")
-
- result = pandas.Series(
- index=measurement_collection.df.index)
- for (allele, sub_df) in measurement_collection.df.groupby("allele"):
- result.loc[sub_df.index] = self.predict_for_allele(
- allele, sub_df.peptide.values)
- assert not result.isnull().any()
- return result
-
- def predict_for_allele(self, allele, peptides):
- predictor = self.predictor_for_allele(allele)
- result = predictor.predict(peptides)
- assert len(result) == len(peptides)
- return result
diff --git a/mhcflurry/class1_allele_specific/cross_validation.py b/mhcflurry/class1_allele_specific/cross_validation.py
deleted file mode 100644
index c819bc4e..00000000
--- a/mhcflurry/class1_allele_specific/cross_validation.py
+++ /dev/null
@@ -1,203 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-import collections
-import logging
-
-from pepdata.reduced_alphabet import make_alphabet_transformer, gbmr4
-
-from .train import impute_and_select_allele, AlleleSpecificTrainTestFold
-from ..parallelism import get_default_backend
-
-gbmr4_transformer = make_alphabet_transformer(gbmr4)
-
-
-def default_projector(peptide):
- """
- Given a peptide, return a list of projections for it. The projections are:
- - the gbmr4 reduced representation
- - for all positions in the peptide, the peptide with a "." replacing
- the residue at that position
-
- Peptides with overlapping projections are considered similar when doing
- cross validation.
-
- Parameters
- ----------
- peptide : string
-
- Returns
- ----------
- string list
- """
- def projections(peptide, edit_distance=1):
- if edit_distance == 0:
- return set([peptide])
- return set.union(*[
- projections(p, edit_distance - 1)
- for p in (
- peptide[0:i] + "." + peptide[(i + 1):]
- for i in range(len(peptide)))
- ])
- return sorted(projections(peptide)) + [gbmr4_transformer(peptide)]
-
-
-def similar_peptides(set1, set2, projector=default_projector):
- """
- Given two sets of peptides, return a list of the peptides whose reduced
- representations are found in both sets.
-
- Parameters
- ----------
- projector : (string -> string) or (string -> string list)
- Function giving projection(s) of a peptide
-
- Returns
- ----------
- string list of peptides which approximately overlap between the two input
- sets.
- """
- result = collections.defaultdict(lambda: ([], []))
- for (index, peptides) in enumerate([set1, set2]):
- for peptide in peptides:
- projections = projector(peptide)
- if not isinstance(projections, list):
- projections = [projections]
- for projection in projections:
- result[projection][index].append(peptide)
-
- common = set()
- for (peptides1, peptides2) in result.values():
- if peptides1 and peptides2:
- common.update(peptides1 + peptides2)
-
- return sorted(common)
-
-
-def cross_validation_folds(
- train_data,
- alleles=None,
- n_folds=3,
- drop_similar_peptides=False,
- imputer=None,
- impute_kwargs={
- 'min_observations_per_peptide': 2,
- 'min_observations_per_allele': 2,
- },
- parallel_backend=None):
- '''
- Split a AffinityMeasurementDataset into n_folds cross validation folds for each allele,
- optionally performing imputation.
-
- Parameters
- -----------
- train_data : mhcflurry.AffinityMeasurementDataset
-
- alleles : string list, optional
- Alleles to run cross validation on. Default: all alleles in
- train_data.
-
- n_folds : int, optional
- Number of cross validation folds for each allele.
-
- drop_similar_peptides : boolean, optional
- For each fold, remove peptides from the test data that are similar
- to peptides in the train data. Similarity is defined as in the
- similar_peptides function.
-
- imputer : fancyimpute.Solver, optional
- Imputer to use. If not specified, no imputation is done.
-
- impute_kwargs : dict, optional
- Additional kwargs to pass to mhcflurry.AffinityMeasurementDataset.impute_missing_values.
-
- parallel_backend : mhcflurry.parallelism.ParallelBackend, optional
- Futures implementation to use for running on multiple threads,
- processes, or nodes
-
- Returns
- -----------
- list of AlleleSpecificTrainTestFold of length num alleles * n_folds
-
- '''
- if parallel_backend is None:
- parallel_backend = get_default_backend()
-
- if alleles is None:
- alleles = train_data.unique_alleles()
-
- result_folds = []
- imputation_args = []
- for allele in alleles:
- logging.info("Allele: %s" % allele)
- cv_iter = train_data.cross_validation_iterator(
- allele, n_folds=n_folds, shuffle=True)
- for (all_allele_train_split, full_test_split) in cv_iter:
- peptides_to_remove = []
- if drop_similar_peptides:
- peptides_to_remove = similar_peptides(
- all_allele_train_split.get_allele(allele).peptides,
- full_test_split.get_allele(allele).peptides
- )
-
- if peptides_to_remove:
- # TODO: instead of dropping peptides, downweight the
- # peptides which get grouped together
- # For example, we could replace this code with
- # test_peptides, test_peptide_weights = ....
- test_split = full_test_split.drop_allele_peptide_lists(
- [allele] * len(peptides_to_remove),
- peptides_to_remove)
- logging.info(
- "After dropping similar peptides, test size %d->%d" % (
- len(full_test_split), len(test_split)))
- else:
- test_split = full_test_split
-
- if imputer is not None:
- base_args = dict(impute_kwargs)
- base_args.update(dict(
- dataset=all_allele_train_split,
- imputer=imputer,
- allele=allele))
- imputation_args.append(base_args)
-
- train_split = all_allele_train_split.get_allele(allele)
- fold = AlleleSpecificTrainTestFold(
- imputed_train=None, # updated later
- allele=allele,
- train=train_split,
- test=test_split)
- result_folds.append(fold)
-
- if imputation_args:
- assert len(imputation_args) == len(result_folds)
- imputation_results = parallel_backend.map(
- lambda kwargs: impute_and_select_allele(**kwargs),
- imputation_args)
-
- # Here _replace is a method on named tuples that returns a new named
- # tuple with the specified key set to the given value and all other
- # key/values the same as the original.
- return [
- result_fold._replace(imputed_train=imputation_result)
- for (result_fold, imputation_result) in zip(
- result_folds, imputation_results)
- ]
- return result_folds
diff --git a/mhcflurry/class1_allele_specific/train.py b/mhcflurry/class1_allele_specific/train.py
deleted file mode 100644
index cfc22344..00000000
--- a/mhcflurry/class1_allele_specific/train.py
+++ /dev/null
@@ -1,355 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-import collections
-import logging
-import time
-import socket
-import math
-
-import numpy
-import pandas
-
-from .scoring import make_scores
-from .class1_binding_predictor import Class1BindingPredictor
-from ..hyperparameters import HyperparameterDefaults
-from ..parallelism import get_default_backend
-
-
-TRAIN_HYPERPARAMETER_DEFAULTS = HyperparameterDefaults(impute=False)
-HYPERPARAMETER_DEFAULTS = (
- Class1BindingPredictor.hyperparameter_defaults
- .extend(TRAIN_HYPERPARAMETER_DEFAULTS))
-
-
-AlleleSpecificTrainTestFold = collections.namedtuple(
- "AlleleSpecificTrainTestFold",
- "allele train imputed_train test")
-
-
-def impute_and_select_allele(dataset, imputer, allele=None, **kwargs):
- '''
- Run imputation and optionally filter to the specified allele.
-
- Useful as a parallelized task where we want to filter to the desired
- data *before* sending the result back to the master process.
-
- Parameters
- -----------
- dataset : mhcflurry.AffinityMeasurementDataset
-
- imputer : object or string
- See AffinityMeasurementDataset.impute_missing_values
-
- allele : string [optional]
- Allele name to subselect to after imputation
-
- **kwargs : passed on to dataset.impute_missing_values
-
- Returns
- -----------
- list of dict
- '''
- result = dataset.impute_missing_values(imputer, **kwargs)
-
- if allele is not None:
- try:
- result = result.get_allele(allele)
- except KeyError:
- result = None
- return result
-
-
-def train_and_test_one_model(model_description, folds, **kwargs):
- '''
- Train one model on some number of folds.
-
- Parameters
- -----------
- model_description : dict of model hyperparameters
-
- folds : list of AlleleSpecificTrainTestFold
-
- **kwargs : passed on to train_and_test_one_model_one_fold
-
- Returns
- -----------
- list of dict giving the train and test results for each fold
- '''
- logging.info("Training 1 model on %d folds: %s" % (len(folds), folds))
-
- return [
- train_and_test_one_model_one_fold(
- model_description,
- fold.train,
- fold.test,
- fold.imputed_train,
- **kwargs)
- for fold in folds
- ]
-
-
-def train_and_test_one_model_one_fold(
- model_description,
- train_dataset,
- test_dataset=None,
- imputed_train_dataset=None,
- return_train_scores=True,
- return_predictor=False,
- return_train_predictions=False,
- return_test_predictions=False):
- '''
- Task for instantiating, training, and testing one model on one fold.
-
- Parameters
- -----------
- model_description : dict of model parameters
-
- train_dataset : mhcflurry.AffinityMeasurementDataset
- AffinityMeasurementDataset to train on. Must include only one allele.
-
- test_dataset : mhcflurry.AffinityMeasurementDataset, optional
- AffinityMeasurementDataset to test on. Must include only one allele. If not specified
- no testing is performed.
-
- imputed_train_dataset : mhcflurry.AffinityMeasurementDataset, optional
- Required only if model_description["impute"] == True
-
- return_train_scores : boolean
- Calculate and include in the result dict the auc/f1/tau scores on the
- training data.
-
- return_predictor : boolean
- Calculate and include in the result dict the trained predictor.
-
- return_train_predictions : boolean
- Calculate and include in the result dict the model predictions on the
- train data.
-
- return_test_predictions : boolean
- Calculate and include in the result dict the model predictions on the
- test data.
-
- Returns
- -----------
- dict
- '''
- assert len(train_dataset.unique_alleles()) == 1, "Multiple train alleles"
- allele = train_dataset.alleles[0]
- if test_dataset is not None:
- assert len(train_dataset.unique_alleles()) == 1, \
- "Multiple test alleles"
- assert train_dataset.alleles[0] == allele, \
- "Wrong test allele %s != %s" % (train_dataset.alleles[0], allele)
- if imputed_train_dataset is not None:
- assert len(imputed_train_dataset.unique_alleles()) == 1, \
- "Multiple imputed train alleles"
- assert imputed_train_dataset.alleles[0] == allele, \
- "Wrong imputed train allele %s != %s" % (
- imputed_train_dataset.alleles[0], allele)
-
- if model_description["impute"]:
- assert imputed_train_dataset is not None
-
- # Make a predictor
- model_params = dict(model_description)
- fraction_negative = model_params.pop("fraction_negative")
- impute = model_params.pop("impute")
- n_training_epochs = model_params.pop("n_training_epochs")
- pretrain_decay = model_params.pop("pretrain_decay")
- batch_size = model_params.pop("batch_size")
- max_ic50 = model_params.pop("max_ic50")
-
- logging.info(
- "%10s train_size=%d test_size=%d impute=%s model=%s" %
- (allele,
- len(train_dataset),
- len(test_dataset) if test_dataset is not None else 0,
- impute,
- model_description))
-
- predictor = Class1BindingPredictor(
- max_ic50=max_ic50,
- **model_params)
-
- # Train predictor
- fit_time = -time.time()
- predictor.fit_dataset(
- train_dataset,
- pretrain_decay=lambda epoch: eval(pretrain_decay, {
- 'epoch': epoch, 'numpy': numpy}),
- pretraining_dataset=imputed_train_dataset if impute else None,
- verbose=True,
- batch_size=batch_size,
- n_training_epochs=n_training_epochs,
- n_random_negative_samples=int(fraction_negative * len(train_dataset)))
- fit_time += time.time()
-
- result = {
- 'fit_time': fit_time,
- 'fit_host': socket.gethostname(),
- }
-
- if return_predictor:
- result['predictor'] = predictor
-
- if return_train_scores or return_train_predictions:
- train_predictions = predictor.predict(train_dataset.peptides)
- if return_train_scores:
- result['train_scores'] = make_scores(
- train_dataset.affinities,
- train_predictions,
- max_ic50=model_description["max_ic50"])
- if return_train_predictions:
- result['train_predictions'] = train_predictions
-
- if test_dataset is not None:
- test_predictions = predictor.predict(test_dataset.peptides)
- result['test_scores'] = make_scores(
- test_dataset.affinities,
- test_predictions,
- max_ic50=model_description["max_ic50"])
- if return_test_predictions:
- result['test_predictions'] = test_predictions
- logging.info("Training result: %s" % result)
- return result
-
-
-def train_across_models_and_folds(
- folds,
- model_descriptions,
- cartesian_product_of_folds_and_models=True,
- return_predictors=False,
- folds_per_task=1,
- parallel_backend=None):
- '''
- Train and optionally test any number of models across any number of folds.
-
- Parameters
- -----------
- folds : list of AlleleSpecificTrainTestFold
-
- model_descriptions : list of dict
- Models to test
-
- cartesian_product_of_folds_and_models : boolean, optional
- If true, then a predictor is treained for each fold and model
- description.
- If false, then len(folds) must equal len(model_descriptions), and
- the i'th model is trained on the i'th fold.
-
- return_predictors : boolean, optional
- Include the trained predictors in the result.
-
- parallel_backend : mhcflurry.parallelism.ParallelBackend, optional
- Futures implementation to use for running on multiple threads,
- processes, or nodes
-
- Returns
- -----------
- pandas.DataFrame
- '''
- if parallel_backend is None:
- parallel_backend = get_default_backend()
-
- if cartesian_product_of_folds_and_models:
- tasks_per_model = int(math.ceil(float(len(folds)) / folds_per_task))
- fold_index_groups = [[] for _ in range(tasks_per_model)]
- index_group = 0
- for index in range(len(folds)):
- fold_index_groups[index_group].append(index)
- index_group += 1
- if index_group == len(fold_index_groups):
- index_group = 0
-
- task_model_and_fold_indices = [
- (model_num, group)
- for group in fold_index_groups
- for model_num in range(len(model_descriptions))
- ]
- else:
- assert len(folds) == len(model_descriptions), \
- "folds and models have different lengths and " \
- "cartesian_product_of_folds_and_models is False"
-
- task_model_and_fold_indices = [
- (num, [num])
- for num in range(len(folds))
- ]
-
- logging.info("Training %d architectures on %d folds = %d tasks." % (
- len(model_descriptions), len(folds), len(task_model_and_fold_indices)))
-
- def train_and_test_one_model_task(model_and_fold_nums_pair):
- (model_num, fold_nums) = model_and_fold_nums_pair
- return train_and_test_one_model(
- model_descriptions[model_num],
- [folds[i] for i in fold_nums],
- return_predictor=return_predictors)
-
- task_results = parallel_backend.map(
- train_and_test_one_model_task,
- task_model_and_fold_indices)
-
- logging.info("Done.")
-
- results_dict = collections.OrderedDict()
-
- def column(key, value):
- if key not in results_dict:
- results_dict[key] = []
- results_dict[key].append(value)
-
- for ((model_num, fold_nums), task_results_for_folds) in zip(
- task_model_and_fold_indices, task_results):
- for (fold_num, task_result) in zip(fold_nums, task_results_for_folds):
- fold = folds[fold_num]
- model_description = model_descriptions[model_num]
-
- column("allele", fold.allele)
- column("fold_num", fold_num)
- column("model_num", model_num)
-
- column("train_size", len(fold.train))
-
- column(
- "test_size",
- len(fold.test) if fold.test is not None else None)
-
- column(
- "imputed_train_size",
- len(fold.imputed_train)
- if fold.imputed_train is not None else None)
-
- # Scores
- for score_kind in ['train', 'test']:
- field = "%s_scores" % score_kind
- for (score, value) in task_result.pop(field, {}).items():
- column("%s_%s" % (score_kind, score), value)
-
- # Misc. fields
- for (key, value) in task_result.items():
- column(key, value)
-
- # Model parameters
- for (model_param, value) in model_description.items():
- column("model_%s" % model_param, value)
-
- results_df = pandas.DataFrame(results_dict)
- return results_df
diff --git a/mhcflurry/class1_allele_specific_ensemble/__init__.py b/mhcflurry/class1_allele_specific_ensemble/__init__.py
deleted file mode 100644
index 4bc67405..00000000
--- a/mhcflurry/class1_allele_specific_ensemble/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .class1_ensemble_multi_allele_predictor import (
- Class1EnsembleMultiAllelePredictor,
- get_downloaded_predictor,
- supported_alleles,
- HYPERPARAMETER_DEFAULTS)
-
-__all__ = [
- "Class1EnsembleMultiAllelePredictor",
- "get_downloaded_predictor",
- "supported_alleles",
- "HYPERPARAMETER_DEFAULTS",
-]
diff --git a/mhcflurry/class1_allele_specific_ensemble/class1_ensemble_multi_allele_predictor.py b/mhcflurry/class1_allele_specific_ensemble/class1_ensemble_multi_allele_predictor.py
deleted file mode 100644
index c3ec0ae7..00000000
--- a/mhcflurry/class1_allele_specific_ensemble/class1_ensemble_multi_allele_predictor.py
+++ /dev/null
@@ -1,791 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This module defines a multi-allele Class I affinity predictor,
-Class1EnsembleMultiAllelePredictor, whose predictions are generated by an
-ensemble of Class1BindingPredictor instances.
-"""
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-
-import pickle
-import os
-import math
-import logging
-import collections
-import time
-from functools import partial
-
-import numpy
-import pandas
-
-from ..hyperparameters import HyperparameterDefaults
-from ..class1_allele_specific import Class1BindingPredictor, scoring
-from ..downloads import get_path
-from ..common import normalize_allele_name, UnsupportedAllele
-from .. import parallelism, common
-from ..peptide_encoding import encode_peptides
-
-
-MEASUREMENT_COLLECTION_HYPERPARAMETER_DEFAULTS = HyperparameterDefaults(
- include_ms=True,
- ms_hit_affinity=1.0,
- ms_decoy_affinity=20000.0)
-
-IMPUTE_HYPERPARAMETER_DEFAULTS = HyperparameterDefaults(
- impute_method='mice',
- impute_min_observations_per_peptide=5,
- impute_min_observations_per_allele=5,
- imputer_args={"n_burn_in": 5, "n_imputations": 25})
-
-HYPERPARAMETER_DEFAULTS = (
- HyperparameterDefaults(
- impute=True,
- architecture_num=None)
- .extend(MEASUREMENT_COLLECTION_HYPERPARAMETER_DEFAULTS)
- .extend(IMPUTE_HYPERPARAMETER_DEFAULTS)
- .extend(Class1BindingPredictor.hyperparameter_defaults))
-
-
-CACHED_PREDICTOR = None
-CACHED_PREDICTOR_PATH = None
-
-
-def supported_alleles():
- """
- Return a list of the names of the alleles for which there are trained
- predictors in the default laoder.
- """
- return get_downloaded_predictor().supported_alleles
-
-
-def get_downloaded_predictor():
- """
- Return a Class1AlleleSpecificPredictorLoader that uses downloaded models.
- """
- global CACHED_PREDICTOR, CACHED_PREDICTOR_PATH
-
- # Some of the unit tests manipulate the downloads directory configuration
- # so get_path here may return different results in the same Python process.
- # For this reason we check the path and invalidate the loader if it's
- # different.
- path = get_path("models_class1_allele_specific_ensemble")
- if CACHED_PREDICTOR_PATH != path:
- CACHED_PREDICTOR_PATH = path
- CACHED_PREDICTOR = (
- Class1EnsembleMultiAllelePredictor
- .load_from_download_directory(path))
- return CACHED_PREDICTOR
-
-
-class Class1EnsembleMultiAllelePredictor(object):
- """
- Multi-allele affinity predictor that uses ensembles of allele-specific
- models.
-
- The individual models are selected via hyperparameter selection over a fixed
- universe of models.
-
- Metadata for the individual models, including hyperparameters and test
- scores are stored in the "manifest_df" dataframe. This dataframe is generated
- in fit() and saved to / loaded from a CSV file when saving and loading
- models. The individual allele-specific models are stored as .pickle files in
- a directory, and named according to the `model_name` column in the manifest
- dataframe. They are loaded lazily as they are needed and cached in the
- `allele_to_models` attribute.
- """
- @staticmethod
- def load_from_download_directory(directory):
- """
- Instantiate a Class1EnsembleMultiAllelePredictor from a directory with
- structure:
-
- selected_models.csv
- Manifest file describing selected models
-
- models/
- Subdir with model pickle files
-
- Parameters
- -----------
- directory : string
- Path to directory
-
- Returns
- -----------
- Class1EnsembleMultiAllelePredictor
- """
- return Class1EnsembleMultiAllelePredictor.load_fit(
- os.path.join(directory, "models"),
- os.path.join(directory, "selected_models.csv"),
- )
-
- @staticmethod
- def load_fit(path_to_models_dir, path_to_manifest):
- """
- Instantiate a Class1EnsembleMultiAllelePredictor from the given manifest
- file and models directory.
-
- Parameters
- -----------
- path_to_models_dir : string
- Path to models/ directory
-
- path_to_manifest : string
- Path to manifest csv file
-
- Returns
- -----------
- Class1EnsembleMultiAllelePredictor
- """
- manifest_df = pandas.read_csv(path_to_manifest, index_col="model_name")
- # Convert string-serialized dicts into Python objects.
- manifest_df["hyperparameters"] = [
- eval(s) for s in manifest_df.hyperparameters
- ]
- hyperparameters_to_search = list(dict(
- (row.hyperparameters_architecture_num, row.hyperparameters)
- for (_, row) in manifest_df.iterrows()
- ).values())
- (ensemble_size,) = list(manifest_df.ensemble_size.unique())
- assert (
- manifest_df.ix[manifest_df.weight > 0]
- .groupby("allele")
- .weight
- .count() == ensemble_size).all()
- result = Class1EnsembleMultiAllelePredictor(
- ensemble_size=ensemble_size,
- hyperparameters_to_search=hyperparameters_to_search)
- result.manifest_df = manifest_df
- result.allele_to_models = {}
- result.models_dir = os.path.abspath(path_to_models_dir)
- return result
-
- def __init__(self, ensemble_size, hyperparameters_to_search):
- """
- Parameters
- -----------
- ensemble_size : int
- Number of models in each allele's ensemble
-
- hyperparameters_to_search : list of dict
- List of model architectures to perform model selection over
- """
- self.imputation_hyperparameters = None # None indicates no imputation
- self.hyperparameters_to_search = []
- for (num, params) in enumerate(hyperparameters_to_search):
- params = dict(params)
- params["architecture_num"] = num
- params = HYPERPARAMETER_DEFAULTS.with_defaults(params)
- self.hyperparameters_to_search.append(params)
-
- if params['impute']:
- imputation_args = IMPUTE_HYPERPARAMETER_DEFAULTS.subselect(
- params)
- if self.imputation_hyperparameters is None:
- self.imputation_hyperparameters = imputation_args
- if self.imputation_hyperparameters != imputation_args:
- raise NotImplementedError(
- "Only one set of imputation parameters is supported: "
- "%s != %s" % (
- str(self.imputation_hyperparameters),
- str(imputation_args)))
-
- self.ensemble_size = ensemble_size
- self.manifest_df = None
- self.allele_to_models = None
- self.models_dir = None
-
- @property
- def supported_alleles(self):
- """
- List of alleles this predictor has models for.
- """
- return list(
- self.manifest_df.ix[self.manifest_df.weight > 0].allele.unique())
-
- def description(self):
- """
- Human readable description of this model.
-
- Returns
- -----------
- str
- """
- lines = []
- kvs = []
-
- def kv(key, value):
- kvs.append((key, value))
-
- kv("ensemble size", self.ensemble_size)
- kv("num architectures considered",
- len(self.hyperparameters_to_search))
- if self.allele_to_models is not None:
- kv("supported alleles", " ".join(self.supported_alleles))
- kv("models dir", self.models_dir)
-
- lines.append("%s Ensemble: %s" % (
- "Untrained" if self.allele_to_models is None else "Trained",
- self))
- for (key, value) in kvs:
- lines.append("* %s: %s" % (key, value))
-
- if self.manifest_df is not None:
- models_used = self.manifest_df.ix[self.manifest_df.weight > 0]
-
- ignored_properties = set(['hyperparameters', 'scores'])
- lines.append("* Attributes common to all models:")
- unique = None
- for col in models_used.columns:
- unique = models_used[col].map(str).unique()
- if len(unique) == 1:
- lines.append("\t%s: %s" % (col, unique[0]))
- ignored_properties.add(col)
- if unique is None:
- lines.append("\t(none)")
-
- for (allele, manifest_rows) in models_used.groupby("allele"):
- lines.append("***")
- for (i, (name, row)) in enumerate(manifest_rows.iterrows()):
- lines.append("* %s model %d: %s" % (
- allele, i + 1, name))
- for (k, v) in row.iteritems():
- if k not in ignored_properties:
- lines.append("\t%s: %s" % (k, v))
- lines.append("")
- return "\n".join(lines)
-
- def models_for_allele(self, allele):
- """
- Return the single-allele models in the ensemble for the given allele.
-
- Parameters
- -----------
- allele : str
-
- Returns
- -----------
- list of Class1BindingPredictor instances
- """
- allele = normalize_allele_name(allele)
- if allele not in self.allele_to_models:
- model_names = self.manifest_df.ix[
- (self.manifest_df.weight > 0) &
- (self.manifest_df.allele == allele)
- ].index
- if len(model_names) == 0:
- raise UnsupportedAllele(
- "Unsupported allele: %s. Supported alleles: %s" % (
- allele,
- ", ".join(self.supported_alleles)))
- assert len(model_names) == self.ensemble_size
- models = []
- for name in model_names:
- filename = os.path.join(
- self.models_dir, "%s.pickle" % name)
- with open(filename, 'rb') as fd:
- model = pickle.load(fd)
- assert model.name == name
- models.append(model)
- self.allele_to_models[allele] = models
- result = self.allele_to_models[allele]
- assert len(result) == self.ensemble_size
- return result
-
- def write_fit(
- self,
- models_dir=None,
- selected_models_csv=None,
- all_models_csv=None):
- """
- Write the models and metadata to disk.
-
- Any arguments left unspecified result in the corresponding file not
- being written.
-
- The manifest CSV has a 'weight' column. The weight is set to 1.0 for
- selected models and 0.0 for non-selected models.
-
- Parameters
- -----------
- models_dir : str, optional
- Path to dir in which to write models.
-
- selected_models_csv : str, optional
- Path to selected models manifest csv. Descriptions and scores for
- the selected models are written here.
-
- all_models_csv : str, optional
- Path to manifest csv. Descriptions and scores for all models, both
- selected and unselected, are written here.
- """
- if all_models_csv:
- self.manifest_df.to_csv(all_models_csv)
- logging.debug("Wrote: %s" % all_models_csv)
- if selected_models_csv:
- self.manifest_df.ix[
- self.manifest_df.weight > 0
- ].to_csv(selected_models_csv)
- logging.debug("Wrote: %s" % selected_models_csv)
-
- if models_dir:
- models_written = []
- for (allele, models) in self.allele_to_models.items():
- for model in models:
- filename = os.path.join(
- models_dir, "%s.pickle" % model.name)
- with open(filename, 'wb') as fd:
- pickle.dump(model, fd)
- logging.debug("Wrote: %s" % filename)
- models_written.append(model.name)
- assert set(models_written) == set(
- self.manifest_df.ix[self.manifest_df.weight > 0].index)
-
- def predict_measurement_collection(self, measurement_collection):
- """
- Return affinity predictions for the (allele, peptide) pairs given in
- the specified measurement collection.
-
- Parameters
- -----------
- measurement_collection : MeasurementCollection
-
- Returns
- -----------
- pandas.Series of predictions corresponding to each row in
- measurement_collection.df.
- """
- result = pandas.Series(
- index=measurement_collection.df.index)
- for (allele, sub_df) in measurement_collection.df.groupby("allele"):
- result.loc[sub_df.index] = self.predict_for_allele(
- allele, sub_df.peptide.values)
- assert not result.isnull().any()
- return result
-
- def predict_for_allele(self, allele, peptides):
- """
- Return affinity predictions for a list of peptides on a single allele.
-
- Parameters
- -----------
- allele : string
-
- peptides : list of string
-
- Returns
- -----------
- numpy array of predictions for each peptide
- """
- encoded = encode_peptides(peptides)
- values = [
- model.predict(encoded)
- for model in self.models_for_allele(allele)
- ]
-
- # Geometric mean
- result = numpy.exp(numpy.nanmean(numpy.log(values), axis=0))
- assert len(result) == len(peptides)
- return result
-
- def fit(
- self,
- measurement_collection,
- parallel_backend=None,
- target_tasks=1):
- """
- Fit the predictor for any number of alleles. This method supports
- parallel execution and works as follows.
-
- (1) Split the dataset into `ensemble_size` random (test, train) splits.
- Stratify by allele so each allele has about the same fraction of
- points in each split.
- (2) Run imputation on each split (if any models require imputation)
- (3) For each allele and each split, perform model selection over all
- len(hyperparameters_to_search) architectures to pick the best model.
-
- The final predictor for allele is then an ensemble of the
- `ensemble_size` best models, which in general have different
- hyperparameters.
-
- Parameters
- -----------
- measurement_collection : MeasurementCollection
- training data
-
- parallel_backend : mhcflurry.parallelism.ParallelBackend instance
- Implementation to use for parallel execution
-
- target_tasks : int, optional
- Approximate number of parallel tasks to split the work into
- """
- if parallel_backend is None:
- parallel_backend = parallelism.get_default_backend()
-
- # Unique name for this fit to be used in the model filenames.
- fit_name = time.asctime().replace(" ", "_")
- assert len(measurement_collection.df) > 0
-
- # (1) Split the data
- splits = measurement_collection.half_splits(
- self.ensemble_size, random_state=0)
-
- # (2) perform imputation if necessary
- if self.imputation_hyperparameters is not None:
- logging.info("Imputing: %d tasks, imputation args: %s" % (
- len(splits), str(self.imputation_hyperparameters)))
- imputed_trains = list(parallel_backend.map(
- partial(
- impute, parallel_backend, self.imputation_hyperparameters),
- [train for (train, test) in splits]))
- logging.info("Imputation completed.")
- else:
- logging.info("No imputation required.")
- imputed_trains = None
-
- assert len(splits) == self.ensemble_size, len(splits)
-
- alleles = set(measurement_collection.df.allele.unique())
-
- # (3) Train and select models
- total_work = (
- len(alleles) *
- self.ensemble_size *
- len(self.hyperparameters_to_search))
- work_per_task = int(math.ceil(total_work / target_tasks))
-
- # tasks is a list of tuples. Each tuple represents a task and is the
- # arguments to the fit_and_test() top-level function.
- tasks = []
- for (fold_num, (train_split, test_split)) in enumerate(splits):
- assert len(train_split.df) > 0
- assert len(test_split.df) > 0
-
- # For efficiency, we pass around RemoteObject references to the
- # train and test data, so they are only uploaded once.
- train_remote_object = parallel_backend.remote_object(train_split)
- test_remote_object = parallel_backend.remote_object(test_split)
- imputed_train_remote_object = None
- if imputed_trains is not None:
- imputed_train_remote_object = imputed_trains[fold_num]
-
- # Buffer of alleles and models to use in the next task.
- task_allele_model_pairs = []
-
- # This function appends a new task to the tasks list and resets
- # the task_allele_model_pairs buffer.
- def make_task():
- if task_allele_model_pairs:
- tasks.append((
- parallel_backend,
- fold_num,
- train_remote_object,
- imputed_train_remote_object,
- test_remote_object,
- list(task_allele_model_pairs)))
- task_allele_model_pairs[:] = []
-
- assert all(
- allele in set(train_split.df.allele.unique())
- for allele in alleles), (
- "%s not in %s" % (
- alleles, set(train_split.df.allele.unique())))
- assert all(
- allele in set(test_split.df.allele.unique())
- for allele in alleles), (
- "%s not in %s" % (
- alleles, set(test_split.df.allele.unique())))
-
- # Loop through models and alleles and generate new tasks whenever
- # the current task's work exceeds work_per_task.
- for model in self.hyperparameters_to_search:
- for allele in alleles:
- task_allele_model_pairs.append((allele, model))
- if len(task_allele_model_pairs) > work_per_task:
- make_task()
- make_task()
- assert not task_allele_model_pairs
-
- allele_models_per_task = numpy.array([
- len(task[-1]) for task in tasks
- ])
- logging.info(
- "Training and scoring models: %d tasks (target was %d), "
- "total work: %d alleles * %d ensemble size * %d models = %d, "
- "allele/models per task: (min=%d mean=%f max=%d)" % (
- len(tasks),
- target_tasks,
- len(alleles),
- self.ensemble_size,
- len(self.hyperparameters_to_search),
- total_work,
- allele_models_per_task.min(),
- allele_models_per_task.max(),
- allele_models_per_task.mean()))
-
- assert len(tasks) > 0
- results = parallel_backend.map(call_fit_and_test, tasks)
-
- # Iterate over results, keeping track of the best model seen so far for
- # each (fold, allele) pair.
- # Also track metadata for all models in manifest_rows, which will be
- # used to generate the manifest dataframe.
-
- # fold number -> allele -> best model
- results_per_fold = [
- {}
- for _ in range(len(splits))
- ]
- next_model_num = 1
- manifest_rows = []
- for result in results:
- logging.debug("Received task result with %d items." % len(result))
- for item in result:
- item['model_name'] = "%s.%d.%s" % (
- item['allele'], next_model_num, fit_name)
- next_model_num += 1
-
- scores = pandas.Series(item['scores'])
- item['summary_score'] = scores.fillna(0).sum()
- fold_results = results_per_fold[item['fold_num']]
- allele = item['allele']
- current_best = float('-inf')
- if allele in fold_results:
- current_best = fold_results[allele]['summary_score']
-
- if item['summary_score'] > current_best:
- logging.info("Updating current best: %s" % str(item))
- fold_results[allele] = item
-
- manifest_entry = dict(item)
- del manifest_entry['model']
- for key in ['hyperparameters', 'scores']:
- for (sub_key, value) in item[key].items():
- manifest_entry["%s_%s" % (key, sub_key)] = value
- manifest_rows.append(manifest_entry)
-
- assert len(manifest_rows) > 0
- manifest_df = pandas.DataFrame(manifest_rows)
- manifest_df.index = manifest_df.model_name
- del manifest_df["model_name"]
- manifest_df["weight"] = 0.0
- manifest_df["ensemble_size"] = self.ensemble_size
-
- logging.info("Done collecting results.")
-
- self.allele_to_models = collections.defaultdict(list)
- for fold_results in results_per_fold:
- assert set(fold_results) == set(alleles), (
- "%s != %s" % (set(fold_results), set(alleles)))
- for (allele, item) in fold_results.items():
- model = item['model'].value
- model.name = item['model_name']
- self.allele_to_models[allele].append(model)
- manifest_df.loc[model.name, "weight"] = 1.0
-
- self.manifest_df = manifest_df
-
-def call_fit_and_test(args):
- """
- Call fit_and_test with the given arguments and return the result.
-
- This top-level function exists as a convenience for parallel execution of
- fit_and_test using a parallel map.
- """
- return fit_and_test(*args)
-
-
-def fit_and_test(
- parallel_backend,
- fold_num,
- train_mc_remote_object,
- imputed_mc_remote_object,
- test_mc_remote_object,
- allele_and_hyperparameter_pairs):
- """
- Fit and test one or more models on one or more alleles. When running
- parallel jobs, this function is the entry point for each task.
-
- The input measurement collections and output trained models are passed
- as kubeface.RemoteObject instances instead of by value. This is an
- optimization that enables the master node to upload each measurement
- collection only once and run many tasks that use it. For the returned models,
- it allows the master to download only the models that actually perform best.
-
- Parameters
- -----------
-
- parallel_backend : mhcflurry.parallelism.ParallelBackend instance
- Implementation to use for parallel execution
-
- fold_num : int
- Which split of the data is being fit. For an ensemble of size 16,
- 0 <= fold_num < 16.
-
- train_mc_remote_object : kubeface.RemoteObject of MeasurementCollection
- train MeasurementCollection
-
- imputed_mc_remote_object : kubeface.RemoteObject of MeasurementCollection
- imputed (pre-training) MeasurementCollection
-
- test_mc_remote_object : kubeface.RemoteObject of MeasurementCollection
- test MeasurementCollection
-
- allele_and_hyperparameter_pairs : list of (string, dict) pairs
- The "work" of the task: the alleles and model hyperparameters to train
- and evaluate.
-
- Returns
- -----------
- list of dict giving scores, metadata, and remote objects pointing to the
- trained models
- """
-
- logging.info(
- "Fit and test: fold=%d train=%s,%s test=%s alleles/models [%d]=%s" % (
- fold_num,
- train_mc_remote_object.value,
- imputed_mc_remote_object,
- test_mc_remote_object.value,
- len(allele_and_hyperparameter_pairs),
- "\n".join("Allele: %s, hyperparameters: %s" % (
- allele, hyperparameters)
- for (allele, hyperparameters)
- in allele_and_hyperparameter_pairs)))
-
- assert len(train_mc_remote_object.value.df) > 0
- assert len(test_mc_remote_object.value.df) > 0
-
- train_mc_hash = common.dataframe_cryptographic_hash(
- train_mc_remote_object.value.df)
-
- imputed_mc_hash = None
- if imputed_mc_remote_object is not None:
- imputed_mc_hash = common.dataframe_cryptographic_hash(
- imputed_mc_remote_object.value.df)
- test_mc_hash = common.dataframe_cryptographic_hash(
- test_mc_remote_object.value.df)
-
- common_result_entries = {
- 'fold_num': fold_num,
- 'all_alleles_train_data_hash': train_mc_hash,
- 'all_alleles_imputed_data_hash': imputed_mc_hash,
- 'all_alleles_test_data_hash': test_mc_hash,
- }
-
- results = []
- for (i, (allele, all_hyperparameters)) in enumerate(
- allele_and_hyperparameter_pairs):
- logging.info("Model %d / %d: allele=%s hyperparameters=%s" % (
- i + 1,
- len(allele_and_hyperparameter_pairs),
- allele,
- str(all_hyperparameters)))
-
- start = time.time()
- measurement_collection_hyperparameters = (
- MEASUREMENT_COLLECTION_HYPERPARAMETER_DEFAULTS.subselect(
- all_hyperparameters))
- model_hyperparameters = (
- Class1BindingPredictor.hyperparameter_defaults.subselect(
- all_hyperparameters))
-
- train_dataset = (
- train_mc_remote_object
- .value
- .select_allele(allele)
- .to_dataset(**measurement_collection_hyperparameters))
- if all_hyperparameters['impute'] and (
- allele in
- imputed_mc_remote_object.value.alleles):
- imputed_train_dataset = (
- imputed_mc_remote_object
- .value
- .select_allele(allele)
- .to_dataset(**measurement_collection_hyperparameters))
- else:
- imputed_train_dataset = None
- test_dataset = (
- test_mc_remote_object
- .value
- .select_allele(allele)
- .to_dataset(**measurement_collection_hyperparameters))
-
- assert len(train_dataset) > 0
- assert len(test_dataset) > 0
-
- model = Class1BindingPredictor(**model_hyperparameters)
-
- train_start = time.time()
- model.fit_dataset(
- train_dataset,
- pretraining_dataset=imputed_train_dataset)
- train_end = time.time()
- predictions = model.predict(test_dataset.peptides)
- test_end = time.time()
- assert len(test_dataset.affinities) == len(predictions)
- scores = scoring.make_scores(
- test_dataset.affinities, predictions)
-
- result = dict(common_result_entries)
- result.update({
- 'allele': allele,
- 'hyperparameters': all_hyperparameters,
- 'model': parallel_backend.remote_object(model),
- 'scores': scores,
- 'train_size': len(train_dataset),
- 'test_size': len(test_dataset),
- 'pretrain_size': (
- 0 if imputed_train_dataset is None
- else len(imputed_train_dataset)),
- 'train_time': train_end - train_start,
- 'predict_time': test_end - train_end,
- })
- results.append(result)
- logging.info("Done training model in %0.2f sec" % (
- time.time() - start))
- return results
-
-
-def impute(parallel_backend, hyperparameters, measurement_collection):
- """
- Run imputation on a measurement collection and return the resulting
- measurement collection. When running parallel jobs, this function is the
- entry point for each imputation task.
-
- Parameters
- -----------
-
- parallel_backend : mhcflurry.parallelism.ParallelBackend instance
- Implementation to use for parallel execution
-
- hyperparameters : dict
- Imputation hyperparameters. See IMPUTE_HYPERPARAMETER_DEFAULTS.
-
- measurement_collection : MeasurementCollection
- data to run imputation on
-
- Returns
- -----------
- RemoteObject of MeasurementCollection
-
- the imputed data
- """
- return parallel_backend.remote_object(
- measurement_collection.impute(**hyperparameters))
\ No newline at end of file
diff --git a/mhcflurry/class1_allele_specific_ensemble/train_command.py b/mhcflurry/class1_allele_specific_ensemble/train_command.py
deleted file mode 100644
index cabe40cd..00000000
--- a/mhcflurry/class1_allele_specific_ensemble/train_command.py
+++ /dev/null
@@ -1,232 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-'''
-Ensemble class1 allele-specific model selection and training script.
-
-Procedure
- - N (ensemble size) times:
- - Split full dataset (all alleles) into 50/50 train and test splits.
- Stratify by allele.
- - Perform imputation on train subset.
- - For each allele and architecture, train and test on splits.
-
- The final predictor is an ensemble of the N best predictors for each
- allele.
-
-The parallelization is primary intended to be used with an
-alternative concurrent.futures Executor such as dask-distributed that supports
-multi-node parallelization. Theano in particular seems to have deadlocks
-when running with single-node parallelization.
-'''
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-import sys
-import argparse
-import json
-import logging
-import os
-import traceback
-import signal
-
-from .. import parallelism
-from ..affinity_measurement_dataset import AffinityMeasurementDataset
-
-from .class1_ensemble_multi_allele_predictor import (
- Class1EnsembleMultiAllelePredictor)
-from ..measurement_collection import MeasurementCollection
-
-parser = argparse.ArgumentParser(
- description=__doc__,
- formatter_class=argparse.RawDescriptionHelpFormatter)
-
-parser.add_argument(
- "--train-data",
- metavar="X.csv",
- required=True,
- help="Training data")
-
-parser.add_argument(
- "--model-architectures",
- metavar="X.json",
- type=argparse.FileType('r'),
- required=True,
- help="JSON file giving model architectures to assess in cross validation."
- " Can be - to read from stdin")
-
-parser.add_argument(
- "--alleles",
- metavar="ALLELE",
- nargs="+",
- default=None,
- help="Use only the specified alleles")
-
-parser.add_argument(
- "--out-manifest",
- metavar="X.csv",
- help="Write descriptions of selected models to given file")
-
-parser.add_argument(
- "--out-model-selection-manifest",
- metavar="X.csv",
- help="Write complete results of all models to the given file")
-
-parser.add_argument(
- "--out-models-dir",
- metavar="DIR",
- help="Write production models to files in this dir")
-
-parser.add_argument(
- "--max-models",
- type=int,
- metavar="N",
- help="Use only the first N models")
-
-parser.add_argument(
- "--ensemble-size",
- type=int,
- metavar="N",
- required=True,
- help="Number of models to use per allele")
-
-parser.add_argument(
- "--target-tasks",
- type=int,
- metavar="N",
- required=True,
- help="Target number of tasks to submit")
-
-parser.add_argument(
- "--min-samples-per-allele",
- default=100,
- metavar="N",
- help="Don't train predictors for alleles with fewer than N samples. "
- "Set to 0 to disable filtering. Default: %(default)s",
- type=int)
-
-parser.add_argument(
- "--quiet",
- action="store_true",
- default=False,
- help="Output less info")
-
-parser.add_argument(
- "--verbose",
- action="store_true",
- default=False,
- help="Output more info")
-
-parser.add_argument(
- "--dask-scheduler",
- metavar="HOST:PORT",
- help="Host and port of dask distributed scheduler")
-
-parser.add_argument(
- "--parallel-backend",
- choices=("local-threads", "local-processes", "kubeface", "dask"),
- default="local-threads",
- help="Backend to use, default: %(default)s")
-
-parser.add_argument(
- "--num-local-processes",
- metavar="N",
- type=int,
- help="Processes (exclusive with --dask-scheduler and --num-local-threads)")
-
-parser.add_argument(
- "--num-local-threads",
- metavar="N",
- type=int,
- default=1,
- help="Threads (exclusive with --dask-scheduler and --num-local-processes)")
-
-try:
- import kubeface
- kubeface.Client.add_args(parser)
-except ImportError:
- logging.error("Kubeface support disabled, not installed.")
-
-
-def run(argv=sys.argv[1:]):
- # On sigusr1 print stack trace
- print("To show stack trace, run:\nkill -s USR1 %d" % os.getpid())
- signal.signal(signal.SIGUSR1, lambda sig, frame: traceback.print_stack())
-
- args = parser.parse_args(argv)
- if args.verbose:
- logging.root.setLevel(level="DEBUG")
- elif not args.quiet:
- logging.root.setLevel(level="INFO")
-
- logging.info("Running with arguments: %s" % args)
-
- # Set parallel backend
- if args.parallel_backend == "dask":
- backend = parallelism.DaskDistributedParallelBackend(
- args.dask_scheduler)
- elif args.parallel_backend == "kubeface":
- backend = parallelism.KubefaceParallelBackend(args)
- elif args.parallel_backend == "local-threads":
- backend = parallelism.ConcurrentFuturesParallelBackend(
- args.num_local_threads,
- processes=False)
- elif args.parallel_backend == "local-processes":
- backend = parallelism.ConcurrentFuturesParallelBackend(
- args.num_local_processes,
- processes=True)
- else:
- assert False, args.parallel_backend
-
- parallelism.set_default_backend(backend)
- print("Using parallel backend: %s" % backend)
- go(args)
-
-
-def go(args):
- model_architectures = json.loads(args.model_architectures.read())
- logging.info("Read %d model architectures" % len(model_architectures))
- if args.max_models:
- model_architectures = model_architectures[:args.max_models]
- logging.info(
- "Subselected to %d model architectures" % len(model_architectures))
-
- train_dataset = AffinityMeasurementDataset.from_csv(args.train_data)
- logging.info("Loaded training data: %s" % train_dataset)
-
- if args.alleles:
- train_dataset = train_dataset.get_alleles(args.alleles)
- logging.info(
- "Filtered training dataset by allele to: %s" % train_dataset)
-
- if args.min_samples_per_allele:
- train_dataset = train_dataset.filter_alleles_by_count(
- args.min_samples_per_allele)
- logging.info(
- "Filtered training dataset to alleles with >= %d observations: %s"
- % (args.min_samples_per_allele, train_dataset))
-
- train_mc = MeasurementCollection.from_dataset(train_dataset)
- model = Class1EnsembleMultiAllelePredictor(
- args.ensemble_size,
- model_architectures)
- model.fit(train_mc, target_tasks=args.target_tasks)
- logging.info("Done fitting.")
-
- model.write_fit(
- selected_models_csv=args.out_manifest,
- all_models_csv=args.out_model_selection_manifest,
- models_dir=args.out_models_dir)
diff --git a/mhcflurry/common.py b/mhcflurry/common.py
index da205c34..42aced01 100644
--- a/mhcflurry/common.py
+++ b/mhcflurry/common.py
@@ -15,16 +15,18 @@
from __future__ import print_function, division, absolute_import
from math import exp, log
import itertools
-from collections import defaultdict
+import collections
import logging
import hashlib
import time
import sys
from os import environ
-import numpy as np
+import numpy
import pandas
+from . import amino_acid
+
class UnsupportedAllele(Exception):
pass
@@ -110,7 +112,7 @@ def groupby_indices(iterable, key_fn=lambda x: x):
Returns dictionary mapping unique values to list of indices that
had those values.
"""
- index_groups = defaultdict(list)
+ index_groups = collections.defaultdict(list)
for i, x in enumerate(key_fn(x) for x in iterable):
index_groups[x].append(i)
return index_groups
@@ -240,3 +242,63 @@ def drop_nulls_and_warn(df, related_df_with_same_index_to_describe=None):
new_df.shape,
describe_nulls(df, related_df_with_same_index_to_describe)))
return new_df
+
+
+def from_ic50(ic50):
+ x = 1.0 - (numpy.log(ic50) / numpy.log(50000))
+ return numpy.minimum(
+ 1.0,
+ numpy.maximum(0.0, x))
+
+
+def to_ic50(x):
+ return 50000.0 ** (1.0 - x)
+
+
+def amino_acid_distribution(peptides, smoothing=0.0):
+ peptides = pandas.Series(peptides)
+ aa_counts = pandas.Series(peptides.map(collections.Counter).sum())
+ normalized = aa_counts / aa_counts.sum()
+ if smoothing:
+ normalized += smoothing
+ normalized /= normalized.sum()
+ return normalized
+
+
+def random_peptides(num, length=9, distribution=None):
+ """
+ Generate random peptides (kmers).
+
+ Parameters
+ ----------
+ num : int
+ Number of peptides to return
+
+ length : int
+ Length of each peptide
+
+ distribution : pandas.Series
+ Maps 1-letter amino acid abbreviations to
+ probabilities. If not specified a uniform
+ distribution is used.
+
+ Returns
+ ----------
+ list of string
+
+ """
+ if num == 0:
+ return []
+ if distribution is None:
+ distribution = pandas.Series(
+ 1, index=amino_acid.common_amino_acid_letters)
+ distribution /= distribution.sum()
+
+ return [
+ ''.join(peptide_sequence)
+ for peptide_sequence in
+ numpy.random.choice(
+ distribution.index,
+ p=distribution.values,
+ size=(int(num), int(length)))
+ ]
diff --git a/mhcflurry/dataset_helpers.py b/mhcflurry/dataset_helpers.py
deleted file mode 100644
index dc509371..00000000
--- a/mhcflurry/dataset_helpers.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-
-from typechecks import require_instance
-import numpy as np
-import pandas as pd
-
-from .common import normalize_allele_name
-from .peptide_encoding import (
- indices_to_hotshot_encoding,
- fixed_length_index_encoding,
- check_valid_index_encoding_array,
-)
-
-
-def check_pMHC_affinity_arrays(alleles, peptides, affinities, sample_weights):
- """
- Make sure that we have the same number of peptides, affinity values,
- and weights.
- """
- require_instance(alleles, np.ndarray)
- require_instance(peptides, np.ndarray)
- require_instance(affinities, np.ndarray)
- require_instance(sample_weights, np.ndarray)
-
- if len(alleles.shape) != 1:
- raise ValueError("Expected 1d array of alleles but got shape %s" % (
- alleles.shape,))
- if len(peptides.shape) != 1:
- raise ValueError("Expected 1d array of peptides but got shape %s" % (
- peptides.shape,))
- if len(affinities.shape) != 1:
- raise ValueError(
- "Expected 1d array of affinity values but got shape %s" % (
- alleles.shape,))
- if len(sample_weights.shape) != 1:
- raise ValueError(
- "Expected 1d array of sample weights but got shape %s" % (
- sample_weights.shape,))
-
- n = len(alleles)
- if len(peptides) != n:
- raise ValueError(
- "Expected %d peptides but got %d" % (n, len(peptides)))
- if len(affinities) != n:
- raise ValueError(
- "Expected %d affinity values but got %d" % (n, len(affinities)))
- if len(sample_weights) != n:
- raise ValueError(
- "Expected %d sample weights but got %d" % (n, len(sample_weights)))
-
-
-def prepare_pMHC_affinity_arrays(alleles, peptides, affinities, sample_weights=None):
- """
- Converts every sequence to an array and if sample_weights is missing then
- create an array of ones.
- """
- alleles = np.asarray(alleles)
- peptides = np.asarray(peptides)
- affinities = np.asarray(affinities)
- if sample_weights is None:
- sample_weights = np.ones(len(alleles), dtype=float)
- check_pMHC_affinity_arrays(
- alleles=alleles,
- peptides=peptides,
- affinities=affinities,
- sample_weights=sample_weights)
- return alleles, peptides, affinities, sample_weights
-
-
-def infer_csv_separator(filename):
- """
- Determine if file is separated by comma, tab, or whitespace.
- Default to whitespace if the others are not detected.
-
- Returns (sep, delim_whitespace)
- """
- for candidate in [",", "\t"]:
- with open(filename, "r") as f:
- for line in f:
- if line.startswith("#"):
- continue
- if candidate in line:
- return candidate, False
- return None, True
-
-def load_dataframe(
- filename,
- sep=None,
- allele_column_name=None,
- peptide_column_name=None,
- affinity_column_name=None,
- filter_peptide_length=None,
- normalize_allele_names=True):
- """
- Load a dataframe of peptide-MHC affinity measurements
-
- filename : str
- TSV filename with columns:
- - 'species'
- - 'mhc'
- - 'peptide_length'
- - 'sequence'
- - 'meas'
-
- sep : str, optional
- Separator in CSV file, default is to let Pandas infer
-
- allele_column_name : str, optional
- Default behavior is to try {"mhc", "allele", "hla"}
-
- peptide_column_name : str, optional
- Default behavior is to try {"sequence", "peptide", "peptide_sequence"}
-
- affinity_column_name : str, optional
- Default behavior is to try {"meas", "ic50", "affinity", "aff"}
-
- filter_peptide_length : int, optional
- Which length peptides to use (default=load all lengths)
-
- normalize_allele_names : bool
- Normalize MHC names or leave them alone
-
- Returns:
- - DataFrame
- - peptide column name
- - allele column name
- - affinity column name
- """
- if sep is None:
- sep, delim_whitespace = infer_csv_separator(filename)
- else:
- delim_whitespace = False
-
- df = pd.read_csv(
- filename,
- sep=sep,
- delim_whitespace=delim_whitespace,
- engine="c")
-
- columns = set(df.keys())
-
- if allele_column_name is None:
- for candidate in ["mhc", "allele", "hla"]:
- if candidate in columns:
- allele_column_name = candidate
- break
- if allele_column_name is None:
- raise ValueError(
- "Couldn't find alleles, available columns: %s" % (
- columns,))
-
- if peptide_column_name is None:
- for candidate in ["sequence", "peptide", "peptide_sequence"]:
- if candidate in columns:
- peptide_column_name = candidate
- break
- if peptide_column_name is None:
- raise ValueError(
- "Couldn't find peptides, available columns: %s" % (
- columns,))
-
- if affinity_column_name is None:
- for candidate in ["meas", "ic50", "affinity"]:
- if candidate in columns:
- affinity_column_name = candidate
- break
- if affinity_column_name is None:
- raise ValueError(
- "Couldn't find affinity values, available columns: %s" % (
- columns,))
- if filter_peptide_length:
- length_mask = df[peptide_column_name].str.len() == filter_peptide_length
- df = df[length_mask]
- df[allele_column_name] = df[allele_column_name].map(normalize_allele_name)
- return df, allele_column_name, peptide_column_name, affinity_column_name
-
-
-def encode_peptide_to_affinity_dict(
- peptide_to_affinity_dict,
- peptide_length=9,
- flatten_binary_encoding=True,
- allow_unknown_amino_acids=True):
- """
- Given a dictionary mapping from peptide sequences to affinity values, return
- both index and binary encodings of fixed length peptides, and
- a vector of their affinities.
-
- Parameters
- ----------
- peptide_to_affinity_dict : dict
- Keys are peptide strings (of multiple lengths), each mapping to a
- continuous affinity value.
-
- peptide_length : int
- Length of vector encoding
-
- flatten_binary_encoding : bool
- Should the binary encoding of a peptide be two-dimensional (9x20)
- or a flattened 1d vector
-
- allow_unknown_amino_acids : bool
- When extending a short vector to the desired peptide length, should
- we insert every possible amino acid or a designated character "X"
- indicating an unknown amino acid.
-
- Returns tuple with the following fields:
- - kmer_peptides: fixed length peptide strings
- - original_peptides: variable length peptide strings
- - counts: how many fixed length peptides were made from this original
- - X_index: index encoding of fixed length peptides
- - X_binary: binary encoding of fixed length peptides
- - Y: affinity values associated with original peptides
- """
- raw_peptides = list(sorted(peptide_to_affinity_dict.keys()))
- X_index, kmer_peptides, original_peptides, counts = \
- fixed_length_index_encoding(
- peptides=raw_peptides,
- desired_length=peptide_length,
- start_offset_shorten=0,
- end_offset_shorten=0,
- start_offset_extend=0,
- end_offset_extend=0,
- allow_unknown_amino_acids=allow_unknown_amino_acids)
-
- n_samples = len(kmer_peptides)
-
- assert n_samples == len(original_peptides), \
- "Mismatch between # of samples (%d) and # of peptides (%d)" % (
- n_samples, len(original_peptides))
- assert n_samples == len(counts), \
- "Mismatch between # of samples (%d) and # of counts (%d)" % (
- n_samples, len(counts))
- assert n_samples == len(X_index), \
- "Mismatch between # of sample (%d) and index feature vectors (%d)" % (
- n_samples, len(X_index))
- X_index = check_valid_index_encoding_array(X_index, allow_unknown_amino_acids)
- n_indices = 20 + allow_unknown_amino_acids
- X_binary = indices_to_hotshot_encoding(
- X_index,
- n_indices=n_indices)
-
- assert X_binary.shape[0] == X_index.shape[0], \
- ("Mismatch between number of samples for index encoding (%d)"
- " vs. binary encoding (%d)") % (
- X_binary.shape[0],
- X_index.shape[0])
-
- if flatten_binary_encoding:
- # collapse 3D input into 2D matrix
- n_binary_features = peptide_length * n_indices
- X_binary = X_binary.reshape((n_samples, n_binary_features))
-
- # easier to work with counts when they're an array instead of list
- counts = np.array(counts)
-
- Y = np.array([peptide_to_affinity_dict[p] for p in original_peptides])
- assert n_samples == len(Y), \
- "Mismatch between # peptides %d and # regression outputs %d" % (
- n_samples, len(Y))
- return (kmer_peptides, original_peptides, counts, X_index, X_binary, Y)
diff --git a/mhcflurry/encodable_sequences.py b/mhcflurry/encodable_sequences.py
new file mode 100644
index 00000000..15e7ced7
--- /dev/null
+++ b/mhcflurry/encodable_sequences.py
@@ -0,0 +1,263 @@
+# Copyright (c) 2016. Mount Sinai School of Medicine
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import (
+ print_function,
+ division,
+ absolute_import,
+)
+
+import math
+
+import pandas
+import numpy
+
+from . import amino_acid
+
+
+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 one_hot_encoding(index_encoded, alphabet_size):
+ """
+ Given an n * k array of integers in the range [0, alphabet_size), return
+ an n * k * alphabet_size array where element (i, k, j) is 1 if element
+ (i, k) == j in the input array and zero otherwise.
+
+ Parameters
+ ----------
+ index_encoded : numpy.array of integers with shape (n, k)
+ alphabet_size : int
+
+ Returns
+ -------
+ numpy.array of integers of shape (n, k, alphabet_size)
+
+ """
+ (num_sequences, sequence_length) = index_encoded.shape
+ result = numpy.zeros(
+ (num_sequences, sequence_length, alphabet_size),
+ dtype='int32')
+ for position in range(sequence_length):
+ result[:, position, index_encoded[:, position]] = 1
+ return result
+
+
+class EncodableSequences(object):
+ """
+ Sequences of amino acids.
+
+ This class caches various encodings of a list of sequences.
+ """
+ unknown_character = "X"
+
+ @classmethod
+ def create(klass, sequences):
+ """
+ Factory that returns an EncodableSequences given a list of
+ strings. As a convenience, you can also pass it an EncodableSequences
+ instance, in which case the object is returned unchanged.
+ """
+ if isinstance(sequences, klass):
+ return sequences
+ return klass(sequences)
+
+ def __init__(self, sequences):
+ self.sequences = sequences
+ self.encoding_cache = {}
+ self.fixed_sequence_length = None
+ if sequences and all(len(s) == len(sequences[0]) for s in sequences):
+ self.fixed_sequence_length = len(sequences[0])
+
+ def __len__(self):
+ return len(self.sequences)
+
+ def fixed_length_to_categorical(self):
+ """
+ Returns a categorical encoding (i.e. integers 0 <= x < 21) of the
+ sequences, which must already be all the same length.
+
+ Returns
+ -------
+ numpy.array of integers
+ """
+ cache_key = ("categorical",)
+ if cache_key not in self.encoding_cache:
+ assert self.fixed_sequence_length
+ self.encoding_cache[cache_key] = index_encoding(
+ self.sequences, amino_acid.AMINO_ACID_INDEX)
+ return self.encoding_cache[cache_key]
+
+ def fixed_length_one_hot(self):
+ """
+ Returns a binary one-hot encoding of the sequences, which must already
+ be all the same length.
+
+ Returns
+ -------
+ numpy.array of integers
+ """
+ cache_key = ("one_hot",)
+ if cache_key not in self.encoding_cache:
+ assert self.fixed_sequence_length
+ encoded = self.categorical_encoding()
+ result = one_hot_encoding(
+ encoded, alphabet_size=len(amino_acid.AMINO_ACID_INDEX))
+ self.encoding_cache[cache_key] = result
+ return self.encoding_cache[cache_key]
+
+ def variable_length_to_fixed_length_categorical(
+ self, left_edge=4, right_edge=4, max_length=15):
+ """
+ Encode variable-length sequences using a fixed-length encoding designed
+ for preserving the anchor positions of class I peptides.
+
+ The sequences must be of length at least left_edge + right_edge, and at
+ most max_length.
+
+ Parameters
+ ----------
+ left_edge : int, size of fixed-position left side
+ right_edge : int, size of the fixed-position right side
+ max_length : sequence length of the resulting encoding
+
+ Returns
+ -------
+ numpy.array of integers with shape (num sequences, max_length)
+ """
+
+ cache_key = (
+ "fixed_length_categorical",
+ left_edge,
+ right_edge,
+ max_length)
+
+ if cache_key not in self.encoding_cache:
+ fixed_length_sequences = [
+ self.sequence_to_fixed_length_string(
+ sequence,
+ left_edge=left_edge,
+ right_edge=right_edge,
+ max_length=max_length)
+ for sequence in self.sequences
+ ]
+ self.encoding_cache[cache_key] = index_encoding(
+ fixed_length_sequences, amino_acid.AMINO_ACID_INDEX)
+ return self.encoding_cache[cache_key]
+
+ def variable_length_to_fixed_length_one_hot(
+ self, left_edge=4, right_edge=4, max_length=15):
+ """
+ Encode variable-length sequences using a fixed-length encoding designed
+ for preserving the anchor positions of class I peptides.
+
+ The sequences must be of length at least left_edge + right_edge, and at
+ most max_length.
+
+ Parameters
+ ----------
+ left_edge : int, size of fixed-position left side
+ right_edge : int, size of the fixed-position right side
+ max_length : sequence length of the resulting encoding
+
+ Returns
+ -------
+ binary numpy.array with shape (num sequences, max_length, 21)
+ """
+
+ cache_key = (
+ "fixed_length_one_hot",
+ left_edge,
+ right_edge,
+ max_length)
+
+ if cache_key not in self.encoding_cache:
+ encoded = self.fixed_length_categorical_encoding(
+ left_edge=left_edge,
+ right_edge=right_edge,
+ max_length=max_length)
+ result = one_hot_encoding(
+ encoded, alphabet_size=len(amino_acid.AMINO_ACID_INDEX))
+ assert result.shape == (
+ len(self.sequences),
+ encoded.shape[1],
+ len(amino_acid.AMINO_ACID_INDEX))
+ self.encoding_cache[cache_key] = result
+ return self.encoding_cache[cache_key]
+
+ @classmethod
+ def sequence_to_fixed_length_string(
+ klass, sequence, left_edge=4, right_edge=4, max_length=15):
+ """
+ Transform a string of length at least left_edge + right_edge and at
+ most max_length into a string of length max_length using a scheme
+ designed to preserve the anchor positions of class I peptides.
+
+ The first left_edge characters in the input always map to the first
+ left_edge characters in the output. Similarly for the last right_edge
+ characters. The middle characters are filled in based on the length,
+ with the X character filling in the blanks.
+
+ For example, using defaults:
+
+ AAAACDDDD -> AAAAXXXCXXXDDDD
+
+
+ Parameters
+ ----------
+ sequence : string
+ left_edge : int
+ right_edge : int
+ max_length : int
+
+ Returns
+ -------
+ string of length max_length
+
+ """
+ assert len(klass.unknown_character) == 1
+ assert len(sequence) >= left_edge + right_edge, sequence
+ assert len(sequence) <= max_length, sequence
+
+ middle_length = max_length - left_edge - right_edge
+
+ num_null = max_length - len(sequence)
+ num_null_left = int(math.ceil(num_null / 2))
+ num_null_right = int(math.floor(num_null / 2))
+ num_not_null_middle = middle_length - num_null
+ string_encoding = "".join([
+ sequence[:left_edge],
+ klass.unknown_character * num_null_left,
+ sequence[left_edge:left_edge + num_not_null_middle],
+ klass.unknown_character * num_null_right,
+ sequence[-right_edge:],
+ ])
+ assert len(string_encoding) == max_length
+ return string_encoding
diff --git a/mhcflurry/feedforward.py b/mhcflurry/feedforward.py
deleted file mode 100644
index f8296c28..00000000
--- a/mhcflurry/feedforward.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-
-from keras.models import Sequential
-from keras.layers.core import Dense, Activation, Flatten, Dropout
-from keras.layers.embeddings import Embedding
-from keras.layers.normalization import BatchNormalization
-
-import theano
-
-theano.config.exception_verbosity = 'high'
-
-
-def make_network(
- input_size,
- embedding_input_dim=None,
- embedding_output_dim=None,
- layer_sizes=[100],
- activation="tanh",
- init="glorot_uniform",
- output_activation="sigmoid",
- dropout_probability=0.0,
- batch_normalization=True,
- initial_embedding_weights=None,
- embedding_init_method="glorot_uniform",
- model=None,
- optimizer="rmsprop",
- loss="mse"):
-
- if model is None:
- model = Sequential()
-
- if embedding_input_dim:
- if not embedding_output_dim:
- raise ValueError(
- "Both embedding_input_dim and embedding_output_dim must be "
- "set")
-
- if initial_embedding_weights:
- n_rows, n_cols = initial_embedding_weights.shape
- if n_rows != embedding_input_dim or n_cols != embedding_output_dim:
- raise ValueError(
- "Wrong shape for embedding: expected (%d, %d) but got "
- "(%d, %d)" % (
- embedding_input_dim, embedding_output_dim,
- n_rows, n_cols))
- model.add(Embedding(
- input_dim=embedding_input_dim,
- output_dim=embedding_output_dim,
- input_length=input_size,
- weights=[initial_embedding_weights],
- dropout=dropout_probability))
- else:
- model.add(Embedding(
- input_dim=embedding_input_dim,
- output_dim=embedding_output_dim,
- input_length=input_size,
- init=embedding_init_method,
- dropout=dropout_probability))
- model.add(Flatten())
-
- input_size = input_size * embedding_output_dim
-
- layer_sizes = (input_size,) + tuple(layer_sizes)
-
- for i, dim in enumerate(layer_sizes):
- if i == 0:
- # input is only conceptually a layer of the network,
- # don't need to actually do anything
- continue
-
- previous_dim = layer_sizes[i - 1]
-
- # hidden layer fully connected layer
- model.add(
- Dense(
- input_dim=previous_dim,
- output_dim=dim,
- init=init))
- model.add(Activation(activation))
-
- if batch_normalization:
- model.add(BatchNormalization())
-
- if dropout_probability > 0:
- model.add(Dropout(dropout_probability))
-
- # output
- model.add(Dense(
- input_dim=layer_sizes[-1],
- output_dim=1,
- init=init))
- model.add(Activation(output_activation))
- model.compile(loss=loss, optimizer=optimizer)
- return model
-
-
-def make_hotshot_network(
- peptide_length=9,
- n_amino_acids=20,
- **kwargs):
- """
- Construct a feed-forward neural network whose inputs are binary vectors
- representing a "one-hot" or "hot-shot" encoding of a fixed length amino
- acid sequence.
- """
- return make_network(input_size=peptide_length * n_amino_acids, **kwargs)
-
-
-def make_embedding_network(
- peptide_length=9,
- n_amino_acids=20,
- embedding_output_dim=20,
- **kwargs):
- """
- Construct a feed-forward neural network whose inputs are vectors of integer
- indices.
- """
- return make_network(
- input_size=peptide_length,
- embedding_input_dim=n_amino_acids,
- embedding_output_dim=embedding_output_dim,
- **kwargs)
diff --git a/mhcflurry/ic50_predictor_base.py b/mhcflurry/ic50_predictor_base.py
deleted file mode 100644
index 38f6fa49..00000000
--- a/mhcflurry/ic50_predictor_base.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-
-import numpy as np
-
-from .regression_target import regression_target_to_ic50, MAX_IC50
-from .affinity_measurement_dataset import AffinityMeasurementDataset
-from .hyperparameters import HyperparameterDefaults
-
-
-class IC50PredictorBase(object):
- """
- Base class for all mhcflurry predictors which predict IC50 values
- (using any representation of peptides)
- """
- hyperparameter_defaults = HyperparameterDefaults(max_ic50=MAX_IC50)
-
- def __init__(
- self,
- name,
- verbose=False,
- max_ic50=hyperparameter_defaults.defaults["max_ic50"]):
- self.name = name
- self.max_ic50 = max_ic50
- self.verbose = verbose
-
- def __repr__(self):
- return "%s(name=%s, max_ic50=%f)" % (
- self.__class__.__name__,
- self.name,
- self.max_ic50)
-
- def __str__(self):
- return repr(self)
-
- def predict_scores(self, peptides, combine_fn=np.mean):
- raise NotImplementedError(
- "predict_scores expected to be implemented in sub-class")
-
- def predict(self, peptides):
- """
- Predict IC50 affinities for peptides of any length
- """
- scores = self.predict_scores(peptides)
- return regression_target_to_ic50(scores, max_ic50=self.max_ic50)
-
- def fit_dictionary(self, peptide_to_ic50_dict, **kwargs):
- """
- Fit the model parameters using the given peptide->IC50 dictionary,
- all samples are given the same weight.
-
- Parameters
- ----------
- peptide_to_ic50_dict : dict
- Dictionary that maps peptides to IC50 values.
- """
- dataset = AffinityMeasurementDataset.from_peptide_to_affinity_dictionary(
- allele_name=self.name,
- peptide_to_affinity_dict=peptide_to_ic50_dict)
- return self.fit_dataset(dataset, **kwargs)
-
- def fit_sequences(
- self,
- peptides,
- affinities,
- sample_weights=None,
- alleles=None, **kwargs):
- if alleles is None:
- alleles = [self.name] * len(peptides)
- dataset = AffinityMeasurementDataset.from_sequences(
- alleles=alleles,
- peptides=peptides,
- affinities=affinities,
- sample_weights=sample_weights)
- return self.fit_dataset(dataset, **kwargs)
-
- def fit_dataset(self, dataset, pretraining_dataset=None, *args, **kwargs):
- raise NotImplementedError(
- "fit_dataset expected to be implemented in sub-class")
diff --git a/mhcflurry/imputation_helpers.py b/mhcflurry/imputation_helpers.py
deleted file mode 100644
index b03d75e3..00000000
--- a/mhcflurry/imputation_helpers.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-from collections import defaultdict
-
-import numpy as np
-from fancyimpute.knn import KNN
-from fancyimpute.iterative_svd import IterativeSVD
-from fancyimpute.simple_fill import SimpleFill
-from fancyimpute.soft_impute import SoftImpute
-from fancyimpute.mice import MICE
-from fancyimpute.biscaler import BiScaler
-
-
-def check_dense_pMHC_array(X, peptide_list, allele_list):
- if len(peptide_list) != len(set(peptide_list)):
- raise ValueError("Duplicate peptides detected in peptide list")
- if len(allele_list) != len(set(allele_list)):
- raise ValueError("Duplicate alleles detected in allele list")
- n_rows, n_cols = X.shape
- if n_rows != len(peptide_list):
- raise ValueError(
- "Expected dense array with shape %s to have %d rows" % (
- X.shape, len(peptide_list)))
- if n_cols != len(allele_list):
- raise ValueError(
- "Expected dense array with shape %s to have %d columns" % (
- X.shape, len(allele_list)))
-
-
-def prune_dense_matrix_and_labels(
- X,
- peptide_list,
- allele_list,
- min_observations_per_peptide=1,
- min_observations_per_allele=1):
- """
- Filter the dense matrix of pMHC binding affinities according to
- the given minimum number of row/column observations.
-
- Parameters
- ----------
- X : numpy.ndarray
- Incomplete dense matrix of pMHC affinity with n_peptides rows and
- n_alleles columns.
-
- peptide_list : list of str
- Expected to have n_peptides entries
-
- allele_list : list of str
- Expected to have n_alleles entries
-
- min_observations_per_peptide : int
- Drop peptide rows with fewer than this number of observed values.
-
- min_observations_per_allele : int
- Drop allele columns with fewer than this number of observed values.
- """
- observed_mask = np.isfinite(X)
- n_observed_per_peptide = observed_mask.sum(axis=1)
- too_few_peptide_observations = (
- n_observed_per_peptide < min_observations_per_peptide)
- if too_few_peptide_observations.any():
- drop_peptide_indices = np.where(too_few_peptide_observations)[0]
- keep_peptide_indices = np.where(~too_few_peptide_observations)[0]
- print("Dropping %d peptides with <%d observations" % (
- len(drop_peptide_indices),
- min_observations_per_peptide))
- X = X[keep_peptide_indices]
- observed_mask = observed_mask[keep_peptide_indices]
- peptide_list = [peptide_list[i] for i in keep_peptide_indices]
-
- n_observed_per_allele = observed_mask.sum(axis=0)
- too_few_allele_observations = (
- n_observed_per_allele < min_observations_per_peptide)
- if too_few_peptide_observations.any():
- drop_allele_indices = np.where(too_few_allele_observations)[0]
- keep_allele_indices = np.where(~too_few_allele_observations)[0]
- print("Dropping %d alleles with <%d observations: %s" % (
- len(drop_allele_indices),
- min_observations_per_allele,
- [allele_list[i] for i in drop_allele_indices]))
- X = X[:, keep_allele_indices]
- observed_mask = observed_mask[:, keep_allele_indices]
- allele_list = [allele_list[i] for i in keep_allele_indices]
- check_dense_pMHC_array(X, peptide_list, allele_list)
- return X, peptide_list, allele_list
-
-
-def dense_pMHC_matrix_to_nested_dict(X, peptide_list, allele_list):
- """
- Converts a dense matrix of (n_peptides, n_alleles) floats to a nested
- dictionary from allele -> peptide -> affinity.
- """
- allele_to_peptide_to_ic50_dict = defaultdict(dict)
- for row_index, peptide in enumerate(peptide_list):
- for column_index, allele_name in enumerate(allele_list):
- affinity = X[row_index, column_index]
- if np.isfinite(affinity):
- allele_to_peptide_to_ic50_dict[allele_name][peptide] = affinity
- return allele_to_peptide_to_ic50_dict
-
-
-def imputer_from_name(imputation_method_name, **kwargs):
- """
- Helper function for constructing an imputation object from a name given
- typically from a commandline argument.
- """
- imputation_method_name = imputation_method_name.strip().lower()
- if imputation_method_name == "mice":
- kwargs["n_burn_in"] = kwargs.get("n_burn_in", 5)
- kwargs["n_imputations"] = kwargs.get("n_imputations", 25)
- kwargs["n_nearest_columns"] = kwargs.get("n_nearest_columns", 25)
- return MICE(**kwargs)
- elif imputation_method_name == "knn":
- kwargs["k"] = kwargs.get("k", 3)
- kwargs["orientation"] = kwargs.get("orientation", "columns")
- kwargs["print_interval"] = kwargs.get("print_interval", 10)
- return KNN(**kwargs)
- elif imputation_method_name == "svd":
- kwargs["rank"] = kwargs.get("rank", 10)
- return IterativeSVD(**kwargs)
- elif imputation_method_name in ("svt", "softimpute"):
- kwargs["init_fill_method"] = kwargs.get("init_fill_method", "min")
- kwargs["normalizer"] = kwargs.get("normalizer", BiScaler())
- return SoftImpute(**kwargs)
- elif imputation_method_name == "mean":
- return SimpleFill("mean", **kwargs)
- elif imputation_method_name == "none":
- return None
- else:
- raise ValueError(
- "Invalid imputation method: %s" % imputation_method_name)
diff --git a/mhcflurry/keras_layers/drop_mask.py b/mhcflurry/keras_layers/drop_mask.py
deleted file mode 100644
index 8ea799ca..00000000
--- a/mhcflurry/keras_layers/drop_mask.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from keras.layers import Layer
-
-class DropMask(Layer):
- """
- Sometimes we know that a mask is always going to contain 1s (and never 0s)
- due to e.g. slicing the beginning of a sequence with a known min length.
- In that case it can be useful to drop the sequence mask and feed the
- activations to a layer which does not support masking (e.g. Dense).
- """
- supports_masking = True
-
- def call(self, x, mask):
- return x
-
- def compute_mask(self, x, mask):
- return None
diff --git a/mhcflurry/keras_layers/masked_global_average_pooling.py b/mhcflurry/keras_layers/masked_global_average_pooling.py
deleted file mode 100644
index 187cc9c8..00000000
--- a/mhcflurry/keras_layers/masked_global_average_pooling.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import keras.layers
-import keras.backend as K
-
-class MaskedGlobalAveragePooling1D(keras.layers.pooling._GlobalPooling1D):
- """
- Takes an embedded representation of a sentence with dims
- (n_samples, max_length, n_dims)
- where each sample is masked to allow for variable-length inputs.
- Returns a tensor of shape (n_samples, n_dims) after averaging across
- time in a mask-sensitive fashion.
- """
- supports_masking = True
-
- def call(self, x, mask):
- expanded_mask = K.expand_dims(mask)
- # zero embedded vectors which come from masked characters
- x_masked = x * expanded_mask
- # how many non-masked characters are in each row?
- mask_counts = K.sum(mask, axis=-1)
- # add up the vector representations along the time dimension
- # the result should have dimension (n_samples, n_embedding_dims)
- x_sums = K.sum(x_masked, axis=1)
- # cast the number of non-zero elements to float32 and
- # give it an extra dimension so it can broadcast properly in
- # an elementwise divsion
- counts_cast = K.expand_dims(K.cast(mask_counts, "float32"))
- return x_sums / counts_cast
-
- def compute_mask(self, x, mask):
- return None
-
diff --git a/mhcflurry/keras_layers/masked_global_max_pooling.py b/mhcflurry/keras_layers/masked_global_max_pooling.py
deleted file mode 100644
index 5bec252a..00000000
--- a/mhcflurry/keras_layers/masked_global_max_pooling.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import keras.layers
-import keras.backend as K
-
-class MaskedGlobalMaxPooling1D(keras.layers.pooling._GlobalPooling1D):
- """
- Takes an embedded representation of a sentence with dims
- (n_samples, max_length, n_dims)
- where each sample is masked to allow for variable-length inputs.
- Returns a tensor of shape (n_samples, n_dims) after averaging across
- time in a mask-sensitive fashion.
- """
- supports_masking = True
-
- def call(self, x, mask):
- expanded_mask = K.expand_dims(mask)
- # zero embedded vectors which come from masked characters
- x_masked = x * expanded_mask
-
- # one flaw here is that we're returning max(0, max(x[:, i])) instead of
- # max(x[:, i])
- return K.max(x_masked, axis=1)
-
- def compute_mask(self, x, mask):
- return None
diff --git a/mhcflurry/keras_layers/masked_slice.py b/mhcflurry/keras_layers/masked_slice.py
deleted file mode 100644
index 022c7f79..00000000
--- a/mhcflurry/keras_layers/masked_slice.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import keras.layers
-
-class MaskedSlice(keras.layers.Lambda):
- """
- Takes an embedded representation of a sentence with dims
- (n_samples, max_length, n_dims)
- where each sample is masked to allow for variable-length inputs.
- Returns a tensor of shape (n_samples, n_dims) which are the first
- and last vectors in each sentence.
- """
- supports_masking = True
-
- def __init__(
- self,
- time_start,
- time_end,
- *args,
- **kwargs):
- assert time_start >= 0
- assert time_end >= 0
- self.time_start = time_start
- self.time_end = time_end
- super(MaskedSlice, self).__init__(*args, **kwargs)
-
- def call(self, x, mask):
- return x[:, self.time_start:self.time_end, :]
-
- def compute_mask(self, x, mask):
- return mask[:, self.time_start:self.time_end, :]
-
- def get_output_shape_for(self, input_shape):
- assert len(input_shape) == 3
- output_shape = (
- input_shape[0],
- self.time_end - self.time_start + 1,
- input_shape[2])
- return output_shape
diff --git a/mhcflurry/measurement_collection.py b/mhcflurry/measurement_collection.py
deleted file mode 100644
index 3bcc6863..00000000
--- a/mhcflurry/measurement_collection.py
+++ /dev/null
@@ -1,217 +0,0 @@
-from sklearn.model_selection import StratifiedKFold
-import pandas
-
-from .affinity_measurement_dataset import AffinityMeasurementDataset
-from .imputation_helpers import imputer_from_name
-
-COLUMNS = [
- "allele",
- "peptide",
- "measurement_type",
- "measurement_source",
- "measurement_value",
- "weight",
-]
-
-MEASUREMENT_TYPES = [
- "affinity",
- "ms_hit",
-]
-
-MEASUREMENT_SOURCES = [
- "in_vitro_affinity_assay",
- "imputed",
- "ms_hit",
- "ms_decoy",
-]
-
-
-class MeasurementCollection(object):
- """
- A measurement collection is a set of observations for allele/peptide pairs.
- A single measurement collection may have both MS hits and affinity measurements.
-
- This is more general than a AffinityMeasurementDataset since it supports MS hits. It is also
- simpler, as the user is expected to manipulate the underlying dataframe.
- Later we may want to retire AffinityMeasurementDataset or combine it with this class.
- """
-
- def __init__(self, df, check=True):
- if check:
- for col in COLUMNS:
- assert col in df.columns, col
-
- for measurement_type in df.measurement_type.unique():
- assert measurement_type in MEASUREMENT_TYPES, measurement_type
- self.df = df[COLUMNS]
- self.alleles = set(df.allele)
-
- @staticmethod
- def from_dataset(dataset):
- """
- Given a AffinityMeasurementDataset, return a MeasurementCollection
- """
- dataset_df = dataset.to_dataframe()
- df = dataset_df.reset_index(drop=True)[["allele", "peptide"]].copy()
- df["measurement_type"] = "affinity"
- df["measurement_source"] = "in_vitro_affinity_assay"
- df["measurement_value"] = dataset_df.affinity.values
- df["weight"] = dataset_df.sample_weight.values
- return MeasurementCollection(df)
-
- def select_measurement_type(self, kind):
- """
- Return a new MeasurementCollection containing only measurements of the
- given type.
-
- Parameters
- -----------
- kind : string
- "affinity" or "ms_hit"
-
- Returns
- -----------
- MeasurementCollection instance
- """
- if kind not in MEASUREMENT_TYPES:
- raise ValueError(
- "Unknown measurement type: %s. Supported types: %s" % (
- kind, ", ".join(MEASUREMENT_TYPES)))
- return MeasurementCollection(
- self.df.ix[self.df.measurement_type == kind],
- check=False)
-
- def select_allele(self, allele):
- """
- Return a new MeasurementCollection containing only observations for the
- specified allele.
- """
- assert isinstance(allele, str), type(allele)
- assert len(self.df) > 0
- alleles = set(self.df.allele.unique())
- assert allele in alleles, "%s not in %s" % (allele, alleles)
- return MeasurementCollection(
- self.df.ix[self.df.allele == allele],
- check=False)
-
- def half_splits(self, num, random_state=None):
- """
- Split the MeasurementCollection into disjoint pairs of
- MeasurementCollection instances, each containing half the observations.
-
- Parameters
- -------------
- num : int
- Number of pairs to return
-
- random_state : int, optional
-
- Returns
- -------------
- list of (MeasurementCollection, MeasurementCollection) pairs
- Each pair gives a disjoint train and test split.
- """
- assert num > 0
- results = []
- while True:
- cv = StratifiedKFold(
- n_splits=2,
- shuffle=True,
- random_state=(
- None if random_state is None
- else random_state + len(results)))
- stratification_groups = self.df.allele + self.df.measurement_type
- #assert len(stratification_groups.unique()) > 1, (
- # stratification_groups.unique())
- (indices1, indices2) = next(
- cv.split(self.df.values, stratification_groups))
- assert len(indices1) > 0
- assert len(indices2) > 0
- mc1 = MeasurementCollection(self.df.iloc[indices1], check=False)
- mc2 = MeasurementCollection(self.df.iloc[indices2], check=False)
- for pair in [(mc1, mc2), (mc2, mc1)]:
- results.append(pair)
- if len(results) == num:
- return results
-
- def to_dataset(
- self,
- include_ms=False,
- ms_hit_affinity=1.0,
- ms_decoy_affinity=20000):
- """
- Return a AffinityMeasurementDataset containing the observations in the collection.
- Mass-spec data are converted to affinities according to
- ms_hit_affinity and ms_decoy_affinity.
-
- Parameters
- -------------
- include_ms : bool
- If True then mass spec data is included; otherwise it is dropped
-
- ms_hit_affinity : float
- nM affinity to assign to mass-spec hits (relevant only if
- include_ms=True)
-
- ms_decoy_affinity : float
- nM affinity to assign to mass-spec decoys (relevant only if
- include_ms=True)
-
- Returns
- -------------
- AffinityMeasurementDataset instance
- """
- if include_ms:
- dataset = AffinityMeasurementDataset(pandas.DataFrame({
- "allele": self.df.allele,
- "peptide": self.df.peptide,
- "affinity": [
- row.measurement_value if row.measurement_type == "affinity"
- else (
- ms_hit_affinity if row.value > 0
- else ms_decoy_affinity)
- for (_, row) in self.df.iterrows()
- ],
- "sample_weight": self.df.weight,
- }))
- else:
- df = self.df.ix[
- (self.df.measurement_type == "affinity") &
- (self.df.measurement_source == "in_vitro_affinity_assay")
- ]
- dataset = AffinityMeasurementDataset(pandas.DataFrame({
- "allele": df.allele,
- "peptide": df.peptide,
- "affinity": df.measurement_value,
- "sample_weight": df.weight,
- }))
- return dataset
-
- def impute(
- self,
- impute_method="mice",
- impute_log_transform=True,
- impute_min_observations_per_peptide=1,
- impute_min_observations_per_allele=1,
- imputer_args={}):
- """
- Return a new MeasurementCollection after applying imputation to
- this collection. The imputed collection will have the
- observations in the current collection plus the imputed data.
- """
- assert len(self.df) > 0
-
- dataset = self.to_dataset(include_ms=False)
- assert len(dataset) > 0
- imputer = imputer_from_name(impute_method, **imputer_args)
- result_df = dataset.impute_missing_values(
- imputation_method=imputer,
- log_transform=impute_log_transform,
- min_observations_per_peptide=impute_min_observations_per_peptide,
- min_observations_per_allele=impute_min_observations_per_allele
- ).to_dataframe()
- result_df["measurement_type"] = "affinity"
- result_df["measurement_source"] = "imputed"
- result_df["measurement_value"] = result_df.affinity
- result_df["weight"] = result_df.sample_weight
- return MeasurementCollection(result_df)
diff --git a/mhcflurry/parallelism.py b/mhcflurry/parallelism.py
deleted file mode 100644
index f89d17d8..00000000
--- a/mhcflurry/parallelism.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import logging
-from concurrent import futures
-
-DEFAULT_BACKEND = None
-
-
-class RemoteObjectStub(object):
- def __init__(self, value):
- self.value = value
-
-
-class ParallelBackend(object):
- """
- Thin wrapper of futures implementations. Designed to support
- concurrent.futures as well as dask.distributed's workalike implementation.
- """
- def __init__(self, executor, module, verbose=1):
- self.executor = executor
- self.module = module
- self.verbose = verbose
-
- def remote_object(self, value):
- return RemoteObjectStub(value)
-
-
-class KubefaceParallelBackend(ParallelBackend):
- """
- ParallelBackend that uses kubeface
- """
- def __init__(self, args):
- from kubeface import Client # pylint: disable=import-error
- self.client = Client.from_args(args)
-
- def map(self, func, iterable):
- return self.client.map(func, iterable)
-
- def remote_object(self, value):
- return self.client.remote_object(value)
-
- def __str__(self):
- return "<Kubeface backend, client=%s>" % self.client
-
-
-class DaskDistributedParallelBackend(ParallelBackend):
- """
- ParallelBackend that uses dask.distributed
- """
- def __init__(self, scheduler_ip_and_port, verbose=1):
- from dask import distributed # pylint: disable=import-error
- executor = distributed.Executor(scheduler_ip_and_port)
- ParallelBackend.__init__(self, executor, distributed, verbose=verbose)
- self.scheduler_ip_and_port = scheduler_ip_and_port
-
- def map(self, func, iterable):
- fs = [
- self.executor.submit(func, arg) for arg in iterable
- ]
- return self.wait(fs)
-
- def wait(self, fs):
- result_dict = {}
- for finished_future in self.module.as_completed(fs):
- result = finished_future.result()
- logging.info("%3d / %3d tasks completed" % (
- len(result_dict), len(fs)))
- result_dict[finished_future] = result
-
- return [result_dict[future] for future in fs]
-
- def __str__(self):
- return "<Dask distributed backend, scheduler=%s, total_cores=%d>" % (
- self.scheduler_ip_and_port,
- sum(self.executor.ncores().values()))
-
-
-class ConcurrentFuturesParallelBackend(ParallelBackend):
- """
- ParallelBackend that uses Python's concurrent.futures module.
- Can use either threads or processes.
- """
- def __init__(self, num_workers=1, processes=False, verbose=1):
- if processes:
- executor = futures.ProcessPoolExecutor(num_workers)
- else:
- executor = futures.ThreadPoolExecutor(num_workers)
- ParallelBackend.__init__(self, executor, futures, verbose=verbose)
- self.num_workers = num_workers
- self.processes = processes
-
- def __str__(self):
- return "<Concurrent futures %s parallel backend, num workers = %d>" % (
- ("processes" if self.processes else "threads"), self.num_workers)
-
- def map(self, func, iterable):
- fs = [
- self.executor.submit(func, arg) for arg in iterable
- ]
- return self.wait(fs)
-
- def wait(self, fs):
- result_dict = {}
- for finished_future in self.module.as_completed(fs):
- result = finished_future.result()
- logging.info("%3d / %3d tasks completed" % (
- len(result_dict), len(fs)))
- result_dict[finished_future] = result
-
- return [result_dict[future] for future in fs]
-
-
-def set_default_backend(backend):
- global DEFAULT_BACKEND
- DEFAULT_BACKEND = backend
-
-
-def get_default_backend():
- global DEFAULT_BACKEND
- if DEFAULT_BACKEND is None:
- set_default_backend(ConcurrentFuturesParallelBackend())
- return DEFAULT_BACKEND
diff --git a/mhcflurry/peptide_encoding.py b/mhcflurry/peptide_encoding.py
deleted file mode 100644
index 07380371..00000000
--- a/mhcflurry/peptide_encoding.py
+++ /dev/null
@@ -1,407 +0,0 @@
-# Copyright (c) 2015. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function, division, absolute_import
-import itertools
-import logging
-
-import pandas
-import numpy as np
-
-from .amino_acid import common_amino_acids, amino_acids_with_unknown
-
-common_amino_acid_letters = common_amino_acids.letters()
-
-
-class KmerEncodedPeptides(object):
- """
- Variable-length peptides encoded into a fixed length matrix using netmhc-style reduction to kmers (usually 9mers).
-
- Parameters
- ----------
- peptides : str list
- Peptide strings of any length
-
- encoded_matrix : int array of shape (R, k)
- The encoded peptides. R >= len(peptides) is the number of kmers needed to represent the peptides.
- If all peptides are length k then R == len(peptides).
-
- indices : int array of length R
- peptides[indices[i]] gives the peptide that gave rise to row encoded_matrix[i] for all 0 < i < R.
-
- kmer_size : int
- k (usually 9)
-
- allow_unknown_amino_acids : bool
- """
- def __init__(self, peptides, encoded_matrix, indices, kmer_size, allow_unknown_amino_acids):
- assert len(indices) == len(encoded_matrix)
- assert len(peptides) == 0 or len(peptides) == max(indices) + 1
- self.encoded_matrix = encoded_matrix
- self.kmer_size = kmer_size
- self.allow_unknown_amino_acids = allow_unknown_amino_acids
- self.peptides = peptides
- self.indices = indices
- self.unique_peptides = np.unique(peptides)
-
- def __len__(self):
- return len(self.peptides)
-
- def combine_predictions(self, predictions):
- assert len(predictions) == len(self.encoded_matrix)
- assert len(predictions) == len(self.indices)
- df = pandas.DataFrame({
- 'original_peptide_index': self.indices,
- 'prediction': predictions,
- })
- predictions_by_index = df.groupby("original_peptide_index").prediction.mean()
- return predictions_by_index[np.arange(0, max(self.indices) + 1)].values
-
-
-def encode_peptides(peptides, kmer_size=9, allow_unknown_amino_acids=True):
- """
- Encode peptides of any length into KmerEncodedPeptides instance
-
- Parameters
- ----------
- peptides : str list or KmerEncodedPeptides
- Peptide strings of any length. For convenience, if a KmerEncodedPeptides instance is passed then it is returned.
-
- """
- if isinstance(peptides, KmerEncodedPeptides):
- assert peptides.kmer_size == kmer_size
- assert peptides.allow_unknown_amino_acids == allow_unknown_amino_acids
- return peptides
-
- if len(peptides) == 0:
- combined_matrix = np.zeros((0, kmer_size))
- indices = []
- else:
- indices = []
- encoded_matrices = []
- for i, peptide in enumerate(peptides):
- matrix, _, _, _ = fixed_length_index_encoding(
- peptides=[peptide],
- desired_length=kmer_size,
- allow_unknown_amino_acids=allow_unknown_amino_acids)
- encoded_matrices.append(matrix)
- indices.extend([i] * len(matrix))
- combined_matrix = np.concatenate(encoded_matrices)
- index_array = np.array(indices)
- expected_shape = (len(index_array), kmer_size)
- assert combined_matrix.shape == expected_shape, \
- "Expected shape %s but got %s" % (
- expected_shape, combined_matrix.shape)
-
- return KmerEncodedPeptides(
- peptides,
- combined_matrix,
- index_array,
- kmer_size=kmer_size,
- allow_unknown_amino_acids=allow_unknown_amino_acids)
-
-
-
-def all_kmers(k, alphabet=common_amino_acid_letters):
- """
- Generates all k-mer peptide sequences
-
- Parameters
- ----------
- k : int
-
- alphabet : str | list of characters
- """
- alphabets = [alphabet] * k
- return [
- "".join(combination)
- for combination
- in itertools.product(*alphabets)
- ]
-
-
-class CombinatorialExplosion(Exception):
- pass
-
-
-def extend_peptide(
- peptide,
- desired_length,
- start_offset,
- end_offset,
- insert_amino_acid_letters=common_amino_acid_letters):
- """Extend peptide by inserting every possible amino acid combination
- if we're trying to e.g. turn an 8mer into 9mers.
-
- Parameters
- ----------
- peptide : str
-
- desired_length : int
-
- start_offset : int
- How many characters (from the position before the start of the string)
- to skip before inserting characters.
-
-
- end_offset : int
- Last character position from the end where we insert new characters,
- where 0 is the position after the last character.
-
- insert_alphabet : str | list of character
- """
- n = len(peptide)
- assert n < desired_length, \
- "%s (length = %d) is too long! Must be shorter than %d" % (
- peptide, n, desired_length)
- n_missing = desired_length - n
- if n_missing > 3:
- raise CombinatorialExplosion(
- "Cannot transform %s of length %d into a %d-mer peptide" % (
- peptide, n, desired_length))
- return [
- peptide[:i] + extra + peptide[i:]
- for i in range(start_offset, n - end_offset + 1)
- for extra in all_kmers(
- n_missing,
- alphabet=insert_amino_acid_letters)
- ]
-
-
-def shorten_peptide(
- peptide,
- desired_length,
- start_offset,
- end_offset,
- insert_amino_acid_letters=common_amino_acid_letters):
- """Shorten peptide if trying to e.g. turn 10mer into 9mers
-
- Parameters
- ----------
-
- peptide : str
-
- desired_length : int
-
- start_offset : int
-
- end_offset : int
-
- alphabet : str | list of characters
- """
- n = len(peptide)
- assert n > desired_length, \
- "%s (length = %d) is too short! Must be longer than %d" % (
- peptide, n, desired_length)
- n_skip = n - desired_length
- assert n_skip > 0, \
- "Expected length of peptide %s %d to be greater than %d" % (
- peptide, n, desired_length)
- end_range = n - end_offset - n_skip + 1
- return [
- peptide[:i] + peptide[i + n_skip:]
- for i in range(start_offset, end_range)
- ]
-
-def fixed_length_from_many_peptides(
- peptides,
- desired_length,
- start_offset_extend=2,
- end_offset_extend=1,
- start_offset_shorten=2,
- end_offset_shorten=0,
- insert_amino_acid_letters=common_amino_acid_letters):
- """
- Create a set of fixed-length k-mer peptides from a collection of varying
- length peptides.
-
- Shorter peptides are filled in using all possible amino acids at any
- insertion site between start_offset_shorten and -end_offset_shorten
- where start_offset_extend=0 represents insertions before the string
- and end_offset_extend=0 represents insertions after the string's ending.
-
- Longer peptides are shortened by deleting contiguous residues, starting
- from start_offset_shorten and ending with -end_offset_shorten. Unlike
- peptide extensions, the offsets for shortening a peptide range between
- the first and last positions (rather than between the positions *before*
- the string starts and the position *after*).
-
- We can recreate the methods from:
- Accurate approximation method for prediction of class I MHC
- affinities for peptides of length 8, 10 and 11 using prediction
- tools trained on 9mers.
- by Lundegaard et. al. (http://bioinformatics.oxfordjournals.org/content/24/11/1397)
- with the following settings:
- - desired_length = 9
- - start_offset_extend = 3
- - end_offset_extend = 2
- - start_offset_shorten = 3
- - end_offset_shorten = 1
-
- Returns three lists:
- - a list of fixed length peptides (all of length `desired_length`)
- - a list of indices of the original peptides from which subsequences
- were contracted or lengthened
- - a list of counts for each fixed length peptide indicating the
- number extracted from its corresponding shorter/longer peptide
-
- Example:
- kmers, original, counts = fixed_length_from_many_peptides(
- peptides=["ABC", "A"]
- desired_length=2,
- start_offset_extend=0,
- end_offset_extend=0,
- start_offset_shorten=0,
- end_offset_shorten=0,
- insert_amino_acid_letters="ABC")
- kmers == ["BC", "AC", "AB", "AA", "BA", "CA", "AA", "AB", "AC"]
- original == ["ABC", "ABC", "ABC", "A", "A", "A", "A", "A", "A"]
- counts == [3, 3, 3, 6, 6, 6, 6, 6, 6]
-
- Parameters
- ----------
- peptides : list of str
-
- desired_length : int
-
- start_offset_extend : int
-
- end_offset_extend : int
-
- start_offset_shorten : int
-
- end_offset_shorten : int
-
- insert_amino_acid_letters : str | list of characters
- """
- all_fixed_length_peptides = []
- indices = []
- counts = []
- for i, peptide in enumerate(peptides):
- n = len(peptide)
- if n == desired_length:
- fixed_length_peptides = [peptide]
- elif n < desired_length:
- try:
- fixed_length_peptides = extend_peptide(
- peptide=peptide,
- desired_length=desired_length,
- start_offset=start_offset_extend,
- end_offset=end_offset_extend,
- insert_amino_acid_letters=insert_amino_acid_letters)
- except CombinatorialExplosion:
- logging.warn(
- "Peptide %s is too short to be extended to length %d" % (
- peptide, desired_length))
- continue
- else:
- fixed_length_peptides = shorten_peptide(
- peptide=peptide,
- desired_length=desired_length,
- start_offset=start_offset_shorten,
- end_offset=end_offset_shorten,
- insert_amino_acid_letters=insert_amino_acid_letters)
- n_fixed_length = len(fixed_length_peptides)
- all_fixed_length_peptides.extend(fixed_length_peptides)
- indices.extend([i] * n_fixed_length)
- counts.extend([n_fixed_length] * n_fixed_length)
- return all_fixed_length_peptides, indices, counts
-
-
-def indices_to_hotshot_encoding(X, n_indices=None, first_index_value=0):
- """
- Given an (n_samples, peptide_length) integer matrix
- convert it to a binary encoding of shape:
- (n_samples, peptide_length * n_indices)
- """
- (n_samples, peptide_length) = X.shape
- if not n_indices:
- n_indices = X.max() - first_index_value + 1
- X_binary = np.zeros((n_samples, peptide_length * n_indices), dtype=bool)
- for i, row in enumerate(X):
- for j, xij in enumerate(row):
- X_binary[i, n_indices * j + xij - first_index_value] = 1
- return X_binary.astype(float)
-
-
-def fixed_length_index_encoding(
- peptides,
- desired_length,
- start_offset_shorten=0,
- end_offset_shorten=0,
- start_offset_extend=0,
- end_offset_extend=0,
- allow_unknown_amino_acids=True):
- """
- Take peptides of varying lengths, chop them into substrings of fixed
- length and apply index encoding to these substrings.
-
- If a string is longer than the desired length, then it's reduced to
- the desired length by deleting characters at all possible positions. When
- positions at the start or end of a string should be exempt from deletion
- then the number of exempt characters can be controlled via
- `start_offset_shorten` and `end_offset_shorten`.
-
- If a string is shorter than the desired length then it is filled
- with all possible characters of the alphabet at all positions. The
- parameters `start_offset_extend` and `end_offset_extend` control whether
- certain positions are excluded from insertion. The positions are
- in a "inter-residue" coordinate system, where `start_offset_extend` = 0
- refers to the position *before* the start of a peptide and, similarly,
- `end_offset_extend` = 0 refers to the position *after* the peptide.
-
- Returns tuple with the following fields:
- - index encoded feature matrix X
- - list of fixed length peptides
- - list of "original" peptides of varying lengths
- - list of integer counts indicating how many rows came from
- that original peptide.
-
- When two rows are expanded out of a single original peptide, they will both
- have a count of 2. These counts can be useful for down-weighting the
- importance of multiple feature vectors which originate from the same sample.
- """
- if allow_unknown_amino_acids:
- insert_letters = ["X"]
- index_encoding = amino_acids_with_unknown.index_encoding
- else:
- insert_letters = common_amino_acid_letters
- index_encoding = common_amino_acids.index_encoding
-
- fixed_length, original_peptide_indices, counts = \
- fixed_length_from_many_peptides(
- peptides=peptides,
- desired_length=desired_length,
- start_offset_shorten=start_offset_shorten,
- end_offset_shorten=end_offset_shorten,
- start_offset_extend=start_offset_extend,
- end_offset_extend=end_offset_extend,
- insert_amino_acid_letters=insert_letters)
- X = index_encoding(fixed_length, desired_length)
- return (X, fixed_length, original_peptide_indices, counts)
-
-def check_valid_index_encoding_array(X, allow_unknown_amino_acids=True):
- X = np.asarray(X)
- if len(X.shape) != 2:
- raise ValueError("Expected 2d input, got array with shape %s" % (
- X.shape,))
- max_expected_index = 20 if allow_unknown_amino_acids else 19
- if X.max() > max_expected_index:
- raise ValueError(
- "Got index %d in peptide encoding, max expected %d" % (
- X.max(),
- max_expected_index))
- return X
-
-
diff --git a/mhcflurry/predict_command.py b/mhcflurry/predict_command.py
index 05c4c809..2231c2a2 100644
--- a/mhcflurry/predict_command.py
+++ b/mhcflurry/predict_command.py
@@ -46,7 +46,7 @@ import pandas
import itertools
from .downloads import get_path
-from . import class1_allele_specific, class1_allele_specific_ensemble
+from . import class1_affinity_prediction, class1_allele_specific_ensemble
parser = argparse.ArgumentParser(
description=__doc__,
@@ -163,7 +163,7 @@ def run(argv=sys.argv[1:]):
# them to download the models if needed.
models_dir = get_path("models_class1_allele_specific_single")
predictor = (
- class1_allele_specific
+ class1_affinity_prediction
.class1_single_model_multi_allele_predictor
.Class1SingleModelMultiAllelePredictor
).load_from_download_directory(models_dir)
diff --git a/mhcflurry/prediction.py b/mhcflurry/prediction.py
deleted file mode 100644
index 36e1f0e7..00000000
--- a/mhcflurry/prediction.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) 2015. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from collections import OrderedDict
-
-import pandas
-import numpy
-
-from .class1_allele_specific_ensemble import class1_ensemble_multi_allele_predictor
-from .common import normalize_allele_name, UnsupportedAllele
-from .peptide_encoding import encode_peptides
-
-
-def predict(alleles, peptides, predictor=None):
- """
- Make predictions across all combinations of the specified alleles and
- peptides.
-
- Parameters
- ----------
- alleles : list of str
- Names of alleles to make predictions for.
-
- peptides : list of str
- Peptide amino acid sequences.
-
- predictor : Predictor to use. Defaults to downloaded Class1SingleModelMultiAllelePredictor.
-
- Returns DataFrame with columns "Allele", "Peptide", and "Prediction"
- """
- if predictor is None:
- predictor = class1_ensemble_multi_allele_predictor.get_downloaded_predictor()
-
- if len(peptides) == 0 or len(alleles) == 0:
- return pandas.DataFrame(columns=["Peptide", "Allele", "Prediction"])
-
- peptides = numpy.unique(peptides)
- encoded_peptides = encode_peptides(peptides)
- result_dfs = []
- result_df = pandas.DataFrame()
- result_df["Peptide"] = peptides
- for allele in alleles:
- allele = normalize_allele_name(allele)
- predictions = predictor.predict_for_allele(allele, encoded_peptides)
- result_df = result_df.copy()
- result_df["Allele"] = allele
- result_df["Prediction"] = predictions
- result_dfs.append(result_df)
- return pandas.concat(result_dfs, ignore_index=True)
diff --git a/mhcflurry/regression_target.py b/mhcflurry/regression_target.py
index 66ca26cd..cbd572cc 100644
--- a/mhcflurry/regression_target.py
+++ b/mhcflurry/regression_target.py
@@ -12,40 +12,39 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-import numpy as np
+import numpy
-MAX_IC50 = 50000.0
-def ic50_to_regression_target(ic50, max_ic50=MAX_IC50):
+def from_ic50(ic50):
"""
- Transform IC50 inhibitory binding concentrations to affinity values between
- [0,1] where 0 means a value greater or equal to max_ic50 and 1 means very
- strong binder.
-
+ Convert ic50s to regression targets in the range [0.0, 1.0].
+
Parameters
----------
- ic50 : numpy.ndarray
+ ic50 : numpy.array of float
+
+ Returns
+ -------
+ numpy.array of float
- max_ic50 : float
- """
- log_ic50 = np.log(ic50) / np.log(max_ic50)
- regression_target = 1.0 - log_ic50
- # clamp to values between 0, 1
- regression_target = np.maximum(regression_target, 0.0)
- regression_target = np.minimum(regression_target, 1.0)
- return regression_target
-
-def regression_target_to_ic50(y, max_ic50=MAX_IC50):
"""
- Transform values between [0,1] to IC50 inhibitory binding concentrations
- between [1.0, infinity]
+ x = 1.0 - (numpy.log(ic50) / numpy.log(50000))
+ return numpy.minimum(
+ 1.0,
+ numpy.maximum(0.0, x))
+
+def to_ic50(x):
+ """
+ Convert regression targets in the range [0.0, 1.0] to ic50s in the range
+ [0, 50000.0].
+
Parameters
----------
- y : numpy.ndarray of float
-
- max_ic50 : float
+ x : numpy.array of float
- Returns numpy.ndarray
+ Returns
+ -------
+ numpy.array of float
"""
- return max_ic50 ** (1.0 - y)
+ return 50000.0 ** (1.0 - x)
diff --git a/mhcflurry/training_helpers.py b/mhcflurry/training_helpers.py
deleted file mode 100644
index 5a3be5c4..00000000
--- a/mhcflurry/training_helpers.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Helper functions for training predictors on fixed length encoding of peptides
-along with vectors representing affinity and sample weights.
-
-Eventually we'll have to generalize or split this to work with sequence
-inputs for RNN predictors.
-"""
-
-from __future__ import (
- print_function,
- division,
- absolute_import,
-)
-
-import numpy as np
-
-def check_encoded_array_shapes(X, Y, sample_weights):
- """
- Check to make sure that the shapes of X, Y, and weights are all compatible.
- This function differs from check_pMHC_affinity_array_lengths in that the
- peptides are assumed to be encoded into a single 2d array of features X
- and the data is either for a single allele or allele features are included
- in X.
-
- Returns the numbers of rows and columns in X.
- """
- if len(X.shape) != 2:
- raise ValueError("Expected X to be 2d, got shape: %s" % (X.shape,))
-
- if len(Y.shape) != 1:
- raise ValueError("Expected Y to be 1d, got shape: %s" % (Y.shape,))
-
- if len(sample_weights.shape) != 1:
- raise ValueError("Expected weights to be 1d, got shape: %s" % (
- sample_weights.shape,))
-
- n_samples, n_dims = X.shape
- if len(Y) != n_samples:
- raise ValueError("Mismatch between len(X) = %d and len(Y) = %d" % (
- n_samples, len(Y)))
-
- if len(sample_weights) != n_samples:
- raise ValueError(
- "Length of sample_weights (%d) doesn't match number of samples (%d)" % (
- len(sample_weights),
- n_samples))
-
- return n_samples, n_dims
-
-def combine_training_arrays(
- X,
- Y,
- sample_weights,
- X_pretrain,
- Y_pretrain,
- sample_weights_pretrain):
- """
- Make sure the shapes of given training and pre-training data
- conform with each other. Then concatenate the pre-training and the
- training data.
-
- Returns (X_combined, Y_combined, weights_combined, n_pretrain_samples)
- """
- X = np.asarray(X)
- Y = np.asarray(Y)
- if sample_weights is None:
- sample_weights = np.ones_like(Y)
- else:
- sample_weights = np.asarray(sample_weights)
-
- n_samples, n_dims = check_encoded_array_shapes(X, Y, sample_weights)
-
- if X_pretrain is None or Y_pretrain is None:
- X_pretrain = np.zeros((0, n_dims), dtype=X.dtype)
- Y_pretrain = np.zeros((0,), dtype=Y.dtype)
- else:
- X_pretrain = np.asarray(X_pretrain)
- Y_pretrain = np.asarray(Y_pretrain)
-
- if sample_weights_pretrain is None:
- sample_weights_pretrain = np.ones_like(Y_pretrain)
- else:
- sample_weights_pretrain = np.asarray(sample_weights_pretrain)
-
- n_pretrain_samples, n_pretrain_dims = check_encoded_array_shapes(
- X_pretrain, Y_pretrain, sample_weights_pretrain)
-
- X_combined = np.vstack([X_pretrain, X])
- Y_combined = np.concatenate([Y_pretrain, Y])
- combined_weights = np.concatenate([
- sample_weights_pretrain,
- sample_weights,
- ])
- return X_combined, Y_combined, combined_weights, n_pretrain_samples
-
-
-def extend_with_negative_random_samples(
- X, Y, weights, n_random_negative_samples, max_amino_acid_encoding_value):
- """
- Extend training data with randomly generated negative samples. Assumes that
- X is an integer array of amino acid indices for fixed length peptides.
-
- Parameters
- ----------
- X : numpy.ndarray
- 2d array of integer amino acid encodings
-
- Y : numpy.ndarray
- 1d array of regression targets
-
- weights : numpy.ndarray
- 1d array of sample weights (must be same length as X and Y)
-
- n_random_negative_samples : int
- Number of random negative samplex to create
-
- max_amino_acid_encoding_value : int
- Typically 20 for the standard set of amino acids or 21 if we're
- including the null character "X" used to extend 8mers into 9mers
-
- Returns X, Y, weights (extended with random negative samples)
- """
- assert len(X) == len(Y) == len(weights)
- if n_random_negative_samples == 0:
- return X, Y, weights
- n_cols = X.shape[1]
- X_random = np.random.randint(
- low=0,
- high=max_amino_acid_encoding_value,
- size=(n_random_negative_samples, n_cols)).astype(X.dtype)
- Y_random = np.zeros(n_random_negative_samples, dtype=float)
- weights_random = np.ones(n_random_negative_samples, dtype=float)
- X_with_negative = np.vstack([X, X_random])
- Y_with_negative = np.concatenate([Y, Y_random])
- weights_with_negative = np.concatenate([
- weights,
- weights_random])
- assert len(X_with_negative) == len(X) + n_random_negative_samples
- assert len(Y_with_negative) == len(Y) + n_random_negative_samples
- assert len(weights_with_negative) == len(weights) + n_random_negative_samples
- return X_with_negative, Y_with_negative, weights_with_negative
diff --git a/requirements.txt b/requirements.txt
index 9b6ae49d..757af426 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,8 @@
numpy>= 1.11
pandas>=0.13.1
appdirs
-theano==0.8.2
-keras==1.2.0
-fancyimpute>=0.0.12
+theano
+keras
scikit-learn
h5py
typechecks
diff --git a/setup.py b/setup.py
index e4853ddd..42f50f45 100644
--- a/setup.py
+++ b/setup.py
@@ -52,9 +52,8 @@ if __name__ == '__main__':
'numpy>=1.11',
'pandas>=0.13.1',
'appdirs',
- 'theano==0.8.2',
- 'keras==1.2.0',
- 'fancyimpute>=0.0.12',
+ 'theano',
+ 'keras',
'scikit-learn',
'h5py',
'typechecks',
@@ -79,10 +78,9 @@ if __name__ == '__main__':
'console_scripts': [
'mhcflurry-downloads = mhcflurry.downloads_command:run',
'mhcflurry-predict = mhcflurry.predict_command:run',
- 'mhcflurry-class1-allele-specific-ensemble-train = '
- 'mhcflurry.class1_allele_specific_ensemble.train_command:run',
- 'mhcflurry-class1-allele-specific-cv-and-train = '
- 'mhcflurry.class1_allele_specific.cv_and_train_command:run',
+ 'mhcflurry-class1-train-allele-specific-models = '
+ 'mhcflurry.class1_affinity_prediction.'
+ 'train_allele_specific_models_command:run',
]
},
classifiers=[
@@ -101,7 +99,6 @@ if __name__ == '__main__':
long_description=readme,
packages=[
'mhcflurry',
- 'mhcflurry.class1_allele_specific',
- 'mhcflurry.class1_allele_specific_ensemble',
+ 'mhcflurry.class1_affinity_prediction',
],
)
diff --git a/test/test_class1_allele_specific_cv_and_train_command.py b/test/test_class1_allele_specific_cv_and_train_command.py
index eb722ffe..29e4e491 100644
--- a/test/test_class1_allele_specific_cv_and_train_command.py
+++ b/test/test_class1_allele_specific_cv_and_train_command.py
@@ -9,9 +9,9 @@ from os import mkdir, environ
import pandas
-from mhcflurry.class1_allele_specific import cv_and_train_command
-from mhcflurry import downloads, predict, class1_allele_specific
-from mhcflurry.class1_allele_specific.train import HYPERPARAMETER_DEFAULTS
+from mhcflurry.class1_affinity_prediction import cv_and_train_command
+from mhcflurry import downloads, predict, class1_affinity_prediction
+from mhcflurry.class1_affinity_prediction.train import HYPERPARAMETER_DEFAULTS
try:
import kubeface
@@ -116,7 +116,7 @@ def verify_trained_models(base_temp_dir):
data["prediction"] = predict(
data.allele,
data.peptide,
- predictor=class1_allele_specific.get_downloaded_predictor()
+ predictor=class1_affinity_prediction.get_downloaded_predictor()
).Prediction
print(data)
mean_binder = data.ix[data.binder].prediction.mean()
diff --git a/test/test_cross_validation.py b/test/test_cross_validation.py
index fdcd160f..dd577707 100644
--- a/test/test_cross_validation.py
+++ b/test/test_cross_validation.py
@@ -7,11 +7,11 @@ import fancyimpute
from mhcflurry.downloads import get_path
-from mhcflurry.class1_allele_specific import (
+from mhcflurry.class1_affinity_prediction import (
cross_validation_folds,
train_across_models_and_folds)
-from mhcflurry.class1_allele_specific.train import (
+from mhcflurry.class1_affinity_prediction.train import (
HYPERPARAMETER_DEFAULTS)
from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
diff --git a/test/test_ensemble.py b/test/test_ensemble.py
index 24ea46c9..210989e3 100644
--- a/test/test_ensemble.py
+++ b/test/test_ensemble.py
@@ -14,7 +14,7 @@ from nose.tools import eq_
from . import make_random_peptides
-from mhcflurry.class1_allele_specific import scoring
+from mhcflurry.class1_affinity_prediction import scoring
from mhcflurry.measurement_collection import MeasurementCollection
from mhcflurry.class1_allele_specific_ensemble import train_command
from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
diff --git a/test/test_hyperparameters.py b/test/test_hyperparameters.py
index afce49f8..a840bbf7 100644
--- a/test/test_hyperparameters.py
+++ b/test/test_hyperparameters.py
@@ -1,6 +1,6 @@
from numpy.testing import assert_equal
-from mhcflurry.class1_allele_specific import Class1BindingPredictor
+from mhcflurry.class1_affinity_prediction import Class1BindingPredictor
def test_all_combinations_of_hyperparameters():
diff --git a/test/test_known_class1_epitopes.py b/test/test_known_class1_epitopes.py
index a1c35794..50685d0a 100644
--- a/test/test_known_class1_epitopes.py
+++ b/test/test_known_class1_epitopes.py
@@ -15,12 +15,12 @@
import cProfile
import mhcflurry
-import mhcflurry.class1_allele_specific
+import mhcflurry.class1_affinity_prediction
import mhcflurry.class1_allele_specific_ensemble
predictors = [
- mhcflurry.class1_allele_specific.get_downloaded_predictor(),
+ mhcflurry.class1_affinity_prediction.get_downloaded_predictor(),
mhcflurry.class1_allele_specific_ensemble.get_downloaded_predictor(),
]
diff --git a/test/test_serialization.py b/test/test_serialization.py
index 494f5584..98870bb8 100644
--- a/test/test_serialization.py
+++ b/test/test_serialization.py
@@ -1,7 +1,7 @@
import pickle
import numpy as np
-from mhcflurry.class1_allele_specific import Class1BindingPredictor
+from mhcflurry.class1_affinity_prediction import Class1BindingPredictor
def test_predict_after_saving_model_to_disk():
--
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