diff --git a/.travis.yml b/.travis.yml
index f8168067e6d160fa5c835826737f87bd4b7a8c9f..bdd72f8ec4c81fdb7b4d921580359e21c6764ffe 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -45,6 +45,8 @@ env:
script:
# download data and models, then run tests
- mhcflurry-downloads fetch
+ - mhcflurry-downloads fetch models_class1_allele_specific_ensemble
+ - mhcflurry-downloads fetch models_class1_allele_specific_single
- mhcflurry-downloads info # just to test this command works
- nosetests test --with-coverage --cover-package=mhcflurry && ./lint.sh
after_success:
diff --git a/Dockerfile b/Dockerfile
index a704f2f04e1580abe00ac810db5c5947165ab15d..986d828c1fc522eb850ab23fce7abf3f84a99b75 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -42,14 +42,11 @@ WORKDIR /home/user
# Setup virtual envs and install convenience packages. Note: installing
# cherrypy as part of the mhcflurry installation weirdly fails on a unicode
# issue in python2, but installing it separately seems to work.
-# We also install bokeh so that dask distributed will have an admin web interface.
RUN virtualenv venv-py3 --python=python3 && \
venv-py3/bin/pip install --upgrade pip && \
venv-py3/bin/pip install --upgrade \
numpy \
- bokeh \
cherrypy \
- git+https://github.com/dask/distributed.git \
jupyter \
lxml \
scipy \
@@ -59,9 +56,10 @@ RUN virtualenv venv-py3 --python=python3 && \
ENV KERAS_BACKEND theano
# RUN venv-py3/bin/pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp35-cp35m-linux_x86_64.whl
-# Install mhcflurry and download data and models.
+# Install mhcflurry and latest kubeface and download data and models.
COPY . ./mhcflurry
-RUN venv-py3/bin/pip install ./mhcflurry && venv-py3/bin/mhcflurry-downloads fetch
+RUN venv-py3/bin/pip install --upgrade ./mhcflurry git+https://github.com/hammerlab/kubeface.git \
+ && venv-py3/bin/mhcflurry-downloads fetch
EXPOSE 8888
CMD venv-py3/bin/jupyter notebook --no-browser
diff --git a/README.md b/README.md
index ad119b3a3f0bc0d1f9c9b26766ce96d3043cea01..09d8bb48b85f38159e9f4727334673b1dc76609a 100644
--- a/README.md
+++ b/README.md
@@ -5,15 +5,32 @@ Open source neural network models for peptide-MHC binding affinity prediction
The [adaptive immune system](https://en.wikipedia.org/wiki/Adaptive_immune_system) depends on the presentation of protein fragments by [MHC](https://en.wikipedia.org/wiki/Major_histocompatibility_complex) molecules. Machine learning models of this interaction are used in studies of infectious diseases, autoimmune diseases, vaccine development, and cancer immunotherapy.
-MHCflurry currently supports peptide / [MHC class I](https://en.wikipedia.org/wiki/MHC_class_I) affinity prediction using one model per MHC allele. The predictors may be trained on data that has been augmented with data imputed based on other alleles (see [Rubinsteyn 2016](http://biorxiv.org/content/early/2016/06/07/054775)). We anticipate adding additional models, including pan-allele and class II predictors.
+MHCflurry currently supports allele-specific peptide / [MHC class I](https://en.wikipedia.org/wiki/MHC_class_I) affinity prediction using two approaches:
-You can fit MHCflurry models to your own data or download trained models that we provide. Our models are trained on data from [IEDB](http://www.iedb.org/home_v3.php) and [Kim 2014](http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241). See [here](https://github.com/hammerlab/mhcflurry/tree/master/downloads-generation/data_combined_iedb_kim2014) for details on the training data preparation. The steps we use to train predictors on this data, including hyperparameter selection using cross validation, are [here](https://github.com/hammerlab/mhcflurry/tree/master/downloads-generation/models_class1_allele_specific_single).
+ * Ensembles of predictors trained on random halves of the training data (the default)
+ * Single-model predictors for each allele trained on all data
+
+For both kinds of predictors, you can fit models to your own data or download
+trained models that we provide.
+
+The downloadable models were trained on data from
+[IEDB](http://www.iedb.org/home_v3.php) and [Kim 2014](http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241).
+The ensemble predictors include models trained on data that has been
+augmented with values imputed from other alleles (see
+[Rubinsteyn 2016](http://biorxiv.org/content/early/2016/06/07/054775)).
+
+In validation experiments using presented peptides identified by mass-spec,
+the ensemble models perform best. We are working on a performance comparison of
+these models with other predictors such as netMHCpan, which we hope to make
+available soon.
+
+We anticipate adding additional models, including pan-allele and class II predictors.
-The MHCflurry predictors are implemented in Python using [keras](https://keras.io).
## Setup
-To configure keras, the neural network library used by MHCflurry, you'll need to set an environment variable in your shell:
+The MHCflurry predictors are implemented in Python using [keras](https://keras.io).
+To configure keras you'll need to set an environment variable in your shell:
```
export KERAS_BACKEND=theano
@@ -73,25 +90,28 @@ The predictions returned by `predict` are affinities (KD) in nM.
## Training your own models
-See the [class1_allele_specific_models.ipynb](https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb) notebook for an overview of the Python API, including predicting, fitting, and scoring models.
+See the [class1_allele_specific_models.ipynb](https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb) notebook for an overview of the Python API, including predicting, fitting, and scoring single-model predictors. There is also a script called `mhcflurry-class1-allele-specific-cv-and-train` that will perform cross validation and model selection given a CSV file of training data. Try `mhcflurry-class1-allele-specific-cv-and-train --help` for details.
-There is also a script called `mhcflurry-class1-allele-specific-cv-and-train` that will perform cross validation and model selection given a CSV file of training data. Try `mhcflurry-class1-allele-specific-cv-and-train --help` for details.
+The ensemble predictors are trained similarly using the `mhcflurry-class1-allele-specific-ensemble-train` command.
-## Details on the downloaded class I allele-specific models
+## Details on the downloadable models
-Besides the actual model weights, the data downloaded with `mhcflurry-downloads fetch` also includes a CSV file giving the hyperparameters used for each predictor. Another CSV gives the cross validation results used to select these hyperparameters.
+The scripts we use to train predictors, including hyperparameter selection
+using cross validation, are
+[here](downloads-generation/models_class1_allele_specific_ensemble)
+for the ensemble predictors and [here](downloads-generation/models_class1_allele_specific_single)
+for the single-model predictors.
-To see the hyperparameters for the production models, run:
+For the ensemble predictors, we also generate a [report](http://htmlpreview.github.io/?https://github.com/hammerlab/mhcflurry/blob/master/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.html)
+that describes the hyperparameters selected and the test performance of each
+model.
-```
-open "$(mhcflurry-downloads path models_class1_allele_specific_single)/production.csv"
-```
-
-To see the cross validation results:
+Besides the model weights, the data downloaded when you run
+`mhcflurry-downloads fetch` also includes a CSV file giving the
+hyperparameters used for each predictor. Run `mhcflurry-downloads path
+models_class1_allele_specific_ensemble` or `mhcflurry-downloads path
+models_class1_allele_specific_single` to get the directory where these files are stored.
-```
-open "$(mhcflurry-downloads path models_class1_allele_specific_single)/cv.csv"
-```
## Problems and Solutions
### undefined symbol
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
index fed1103c933b265759aade5767173177c9cad445..07b7f1d9e5d2f036bc646178281e04a4faf8e086 100755
--- a/downloads-generation/data_combined_iedb_kim2014/create-combined-class1-dataset.py
+++ b/downloads-generation/data_combined_iedb_kim2014/create-combined-class1-dataset.py
@@ -17,7 +17,7 @@
"""
Combine 2013 Kim/Peters NetMHCpan dataset[*] with more recent IEDB entries
-* = "Dataset size and composition impact the reliability..."
+* = "AffinityMeasurementDataset size and composition impact the reliability..."
"""
from __future__ import (
diff --git a/downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh b/downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh
new file mode 100755
index 0000000000000000000000000000000000000000..0e45c61f6372454dee16d4d309569da0cf820570
--- /dev/null
+++ b/downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh
@@ -0,0 +1,53 @@
+#!/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
new file mode 100644
index 0000000000000000000000000000000000000000..604852a62546e4c30b49cd5fa544a6dae50feed2
--- /dev/null
+++ b/downloads-generation/models_class1_allele_specific_ensemble/README.md
@@ -0,0 +1,29 @@
+# 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
new file mode 100644
index 0000000000000000000000000000000000000000..3cf13db5c9f6094e9092fd200e5f2e6017e4479d
--- /dev/null
+++ b/downloads-generation/models_class1_allele_specific_ensemble/models-summary/README.md
@@ -0,0 +1,12 @@
+# 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
new file mode 100644
index 0000000000000000000000000000000000000000..a98510c0b35b773203a55da5b25e865a63d0858f
--- /dev/null
+++ b/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.html
@@ -0,0 +1,18543 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>report</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+ /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.6 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+ font-family: sans-serif;
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+ -webkit-text-size-adjust: 100%;
+}
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+}
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+ top: -0.5em;
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+ border: 1px solid #c0c0c0;
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+.glyphicon-align-left:before {
+ content: "\e052";
+}
+.glyphicon-align-center:before {
+ content: "\e053";
+}
+.glyphicon-align-right:before {
+ content: "\e054";
+}
+.glyphicon-align-justify:before {
+ content: "\e055";
+}
+.glyphicon-list:before {
+ content: "\e056";
+}
+.glyphicon-indent-left:before {
+ content: "\e057";
+}
+.glyphicon-indent-right:before {
+ content: "\e058";
+}
+.glyphicon-facetime-video:before {
+ content: "\e059";
+}
+.glyphicon-picture:before {
+ content: "\e060";
+}
+.glyphicon-map-marker:before {
+ content: "\e062";
+}
+.glyphicon-adjust:before {
+ content: "\e063";
+}
+.glyphicon-tint:before {
+ content: "\e064";
+}
+.glyphicon-edit:before {
+ content: "\e065";
+}
+.glyphicon-share:before {
+ content: "\e066";
+}
+.glyphicon-check:before {
+ content: "\e067";
+}
+.glyphicon-move:before {
+ content: "\e068";
+}
+.glyphicon-step-backward:before {
+ content: "\e069";
+}
+.glyphicon-fast-backward:before {
+ content: "\e070";
+}
+.glyphicon-backward:before {
+ content: "\e071";
+}
+.glyphicon-play:before {
+ content: "\e072";
+}
+.glyphicon-pause:before {
+ content: "\e073";
+}
+.glyphicon-stop:before {
+ content: "\e074";
+}
+.glyphicon-forward:before {
+ content: "\e075";
+}
+.glyphicon-fast-forward:before {
+ content: "\e076";
+}
+.glyphicon-step-forward:before {
+ content: "\e077";
+}
+.glyphicon-eject:before {
+ content: "\e078";
+}
+.glyphicon-chevron-left:before {
+ content: "\e079";
+}
+.glyphicon-chevron-right:before {
+ content: "\e080";
+}
+.glyphicon-plus-sign:before {
+ content: "\e081";
+}
+.glyphicon-minus-sign:before {
+ content: "\e082";
+}
+.glyphicon-remove-sign:before {
+ content: "\e083";
+}
+.glyphicon-ok-sign:before {
+ content: "\e084";
+}
+.glyphicon-question-sign:before {
+ content: "\e085";
+}
+.glyphicon-info-sign:before {
+ content: "\e086";
+}
+.glyphicon-screenshot:before {
+ content: "\e087";
+}
+.glyphicon-remove-circle:before {
+ content: "\e088";
+}
+.glyphicon-ok-circle:before {
+ content: "\e089";
+}
+.glyphicon-ban-circle:before {
+ content: "\e090";
+}
+.glyphicon-arrow-left:before {
+ content: "\e091";
+}
+.glyphicon-arrow-right:before {
+ content: "\e092";
+}
+.glyphicon-arrow-up:before {
+ content: "\e093";
+}
+.glyphicon-arrow-down:before {
+ content: "\e094";
+}
+.glyphicon-share-alt:before {
+ content: "\e095";
+}
+.glyphicon-resize-full:before {
+ content: "\e096";
+}
+.glyphicon-resize-small:before {
+ content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+ content: "\e101";
+}
+.glyphicon-gift:before {
+ content: "\e102";
+}
+.glyphicon-leaf:before {
+ content: "\e103";
+}
+.glyphicon-fire:before {
+ content: "\e104";
+}
+.glyphicon-eye-open:before {
+ content: "\e105";
+}
+.glyphicon-eye-close:before {
+ content: "\e106";
+}
+.glyphicon-warning-sign:before {
+ content: "\e107";
+}
+.glyphicon-plane:before {
+ content: "\e108";
+}
+.glyphicon-calendar:before {
+ content: "\e109";
+}
+.glyphicon-random:before {
+ content: "\e110";
+}
+.glyphicon-comment:before {
+ content: "\e111";
+}
+.glyphicon-magnet:before {
+ content: "\e112";
+}
+.glyphicon-chevron-up:before {
+ content: "\e113";
+}
+.glyphicon-chevron-down:before {
+ content: "\e114";
+}
+.glyphicon-retweet:before {
+ content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+ content: "\e116";
+}
+.glyphicon-folder-close:before {
+ content: "\e117";
+}
+.glyphicon-folder-open:before {
+ content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+ content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+ content: "\e120";
+}
+.glyphicon-hdd:before {
+ content: "\e121";
+}
+.glyphicon-bullhorn:before {
+ content: "\e122";
+}
+.glyphicon-bell:before {
+ content: "\e123";
+}
+.glyphicon-certificate:before {
+ content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+ content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+ content: "\e126";
+}
+.glyphicon-hand-right:before {
+ content: "\e127";
+}
+.glyphicon-hand-left:before {
+ content: "\e128";
+}
+.glyphicon-hand-up:before {
+ content: "\e129";
+}
+.glyphicon-hand-down:before {
+ content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+ content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+ content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+ content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+ content: "\e134";
+}
+.glyphicon-globe:before {
+ content: "\e135";
+}
+.glyphicon-wrench:before {
+ content: "\e136";
+}
+.glyphicon-tasks:before {
+ content: "\e137";
+}
+.glyphicon-filter:before {
+ content: "\e138";
+}
+.glyphicon-briefcase:before {
+ content: "\e139";
+}
+.glyphicon-fullscreen:before {
+ content: "\e140";
+}
+.glyphicon-dashboard:before {
+ content: "\e141";
+}
+.glyphicon-paperclip:before {
+ content: "\e142";
+}
+.glyphicon-heart-empty:before {
+ content: "\e143";
+}
+.glyphicon-link:before {
+ content: "\e144";
+}
+.glyphicon-phone:before {
+ content: "\e145";
+}
+.glyphicon-pushpin:before {
+ content: "\e146";
+}
+.glyphicon-usd:before {
+ content: "\e148";
+}
+.glyphicon-gbp:before {
+ content: "\e149";
+}
+.glyphicon-sort:before {
+ content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+ content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+ content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+ content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+ content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+ content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+ content: "\e156";
+}
+.glyphicon-unchecked:before {
+ content: "\e157";
+}
+.glyphicon-expand:before {
+ content: "\e158";
+}
+.glyphicon-collapse-down:before {
+ content: "\e159";
+}
+.glyphicon-collapse-up:before {
+ content: "\e160";
+}
+.glyphicon-log-in:before {
+ content: "\e161";
+}
+.glyphicon-flash:before {
+ content: "\e162";
+}
+.glyphicon-log-out:before {
+ content: "\e163";
+}
+.glyphicon-new-window:before {
+ content: "\e164";
+}
+.glyphicon-record:before {
+ content: "\e165";
+}
+.glyphicon-save:before {
+ content: "\e166";
+}
+.glyphicon-open:before {
+ content: "\e167";
+}
+.glyphicon-saved:before {
+ content: "\e168";
+}
+.glyphicon-import:before {
+ content: "\e169";
+}
+.glyphicon-export:before {
+ content: "\e170";
+}
+.glyphicon-send:before {
+ content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+ content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+ content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+ content: "\e174";
+}
+.glyphicon-floppy-save:before {
+ content: "\e175";
+}
+.glyphicon-floppy-open:before {
+ content: "\e176";
+}
+.glyphicon-credit-card:before {
+ content: "\e177";
+}
+.glyphicon-transfer:before {
+ content: "\e178";
+}
+.glyphicon-cutlery:before {
+ content: "\e179";
+}
+.glyphicon-header:before {
+ content: "\e180";
+}
+.glyphicon-compressed:before {
+ content: "\e181";
+}
+.glyphicon-earphone:before {
+ content: "\e182";
+}
+.glyphicon-phone-alt:before {
+ content: "\e183";
+}
+.glyphicon-tower:before {
+ content: "\e184";
+}
+.glyphicon-stats:before {
+ content: "\e185";
+}
+.glyphicon-sd-video:before {
+ content: "\e186";
+}
+.glyphicon-hd-video:before {
+ content: "\e187";
+}
+.glyphicon-subtitles:before {
+ content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+ content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+ content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+ content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+ content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+ content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+ content: "\e194";
+}
+.glyphicon-registration-mark:before {
+ content: "\e195";
+}
+.glyphicon-cloud-download:before {
+ content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+ content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+ content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+ content: "\e200";
+}
+.glyphicon-cd:before {
+ content: "\e201";
+}
+.glyphicon-save-file:before {
+ content: "\e202";
+}
+.glyphicon-open-file:before {
+ content: "\e203";
+}
+.glyphicon-level-up:before {
+ content: "\e204";
+}
+.glyphicon-copy:before {
+ content: "\e205";
+}
+.glyphicon-paste:before {
+ content: "\e206";
+}
+.glyphicon-alert:before {
+ content: "\e209";
+}
+.glyphicon-equalizer:before {
+ content: "\e210";
+}
+.glyphicon-king:before {
+ content: "\e211";
+}
+.glyphicon-queen:before {
+ content: "\e212";
+}
+.glyphicon-pawn:before {
+ content: "\e213";
+}
+.glyphicon-bishop:before {
+ content: "\e214";
+}
+.glyphicon-knight:before {
+ content: "\e215";
+}
+.glyphicon-baby-formula:before {
+ content: "\e216";
+}
+.glyphicon-tent:before {
+ content: "\26fa";
+}
+.glyphicon-blackboard:before {
+ content: "\e218";
+}
+.glyphicon-bed:before {
+ content: "\e219";
+}
+.glyphicon-apple:before {
+ content: "\f8ff";
+}
+.glyphicon-erase:before {
+ content: "\e221";
+}
+.glyphicon-hourglass:before {
+ content: "\231b";
+}
+.glyphicon-lamp:before {
+ content: "\e223";
+}
+.glyphicon-duplicate:before {
+ content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+ content: "\e225";
+}
+.glyphicon-scissors:before {
+ content: "\e226";
+}
+.glyphicon-bitcoin:before {
+ content: "\e227";
+}
+.glyphicon-btc:before {
+ content: "\e227";
+}
+.glyphicon-xbt:before {
+ content: "\e227";
+}
+.glyphicon-yen:before {
+ content: "\00a5";
+}
+.glyphicon-jpy:before {
+ content: "\00a5";
+}
+.glyphicon-ruble:before {
+ content: "\20bd";
+}
+.glyphicon-rub:before {
+ content: "\20bd";
+}
+.glyphicon-scale:before {
+ content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+ content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+ content: "\e232";
+}
+.glyphicon-education:before {
+ content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+ content: "\e234";
+}
+.glyphicon-option-vertical:before {
+ content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+ content: "\e236";
+}
+.glyphicon-modal-window:before {
+ content: "\e237";
+}
+.glyphicon-oil:before {
+ content: "\e238";
+}
+.glyphicon-grain:before {
+ content: "\e239";
+}
+.glyphicon-sunglasses:before {
+ content: "\e240";
+}
+.glyphicon-text-size:before {
+ content: "\e241";
+}
+.glyphicon-text-color:before {
+ content: "\e242";
+}
+.glyphicon-text-background:before {
+ content: "\e243";
+}
+.glyphicon-object-align-top:before {
+ content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+ content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+ content: "\e246";
+}
+.glyphicon-object-align-left:before {
+ content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+ content: "\e248";
+}
+.glyphicon-object-align-right:before {
+ content: "\e249";
+}
+.glyphicon-triangle-right:before {
+ content: "\e250";
+}
+.glyphicon-triangle-left:before {
+ content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+ content: "\e252";
+}
+.glyphicon-triangle-top:before {
+ content: "\e253";
+}
+.glyphicon-console:before {
+ content: "\e254";
+}
+.glyphicon-superscript:before {
+ content: "\e255";
+}
+.glyphicon-subscript:before {
+ content: "\e256";
+}
+.glyphicon-menu-left:before {
+ content: "\e257";
+}
+.glyphicon-menu-right:before {
+ content: "\e258";
+}
+.glyphicon-menu-down:before {
+ content: "\e259";
+}
+.glyphicon-menu-up:before {
+ content: "\e260";
+}
+* {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+*:before,
+*:after {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+html {
+ font-size: 10px;
+ -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #000;
+ background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+ font-family: inherit;
+ font-size: inherit;
+ line-height: inherit;
+}
+a {
+ color: #337ab7;
+ text-decoration: none;
+}
+a:hover,
+a:focus {
+ color: #23527c;
+ text-decoration: underline;
+}
+a:focus {
+ outline: thin dotted;
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+figure {
+ margin: 0;
+}
+img {
+ vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+ display: block;
+ max-width: 100%;
+ height: auto;
+}
+.img-rounded {
+ border-radius: 3px;
+}
+.img-thumbnail {
+ padding: 4px;
+ line-height: 1.42857143;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+ -webkit-transition: all 0.2s ease-in-out;
+ -o-transition: all 0.2s ease-in-out;
+ transition: all 0.2s ease-in-out;
+ display: inline-block;
+ max-width: 100%;
+ height: auto;
+}
+.img-circle {
+ border-radius: 50%;
+}
+hr {
+ margin-top: 18px;
+ margin-bottom: 18px;
+ border: 0;
+ border-top: 1px solid #eeeeee;
+}
+.sr-only {
+ position: absolute;
+ width: 1px;
+ height: 1px;
+ margin: -1px;
+ padding: 0;
+ overflow: hidden;
+ clip: rect(0, 0, 0, 0);
+ border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+ position: static;
+ width: auto;
+ height: auto;
+ margin: 0;
+ overflow: visible;
+ clip: auto;
+}
+[role="button"] {
+ cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+ font-family: inherit;
+ font-weight: 500;
+ line-height: 1.1;
+ color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+ font-weight: normal;
+ line-height: 1;
+ color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+ margin-top: 18px;
+ margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+ font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+ margin-top: 9px;
+ margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+ font-size: 75%;
+}
+h1,
+.h1 {
+ font-size: 33px;
+}
+h2,
+.h2 {
+ font-size: 27px;
+}
+h3,
+.h3 {
+ font-size: 23px;
+}
+h4,
+.h4 {
+ font-size: 17px;
+}
+h5,
+.h5 {
+ font-size: 13px;
+}
+h6,
+.h6 {
+ font-size: 12px;
+}
+p {
+ margin: 0 0 9px;
+}
+.lead {
+ margin-bottom: 18px;
+ font-size: 14px;
+ font-weight: 300;
+ line-height: 1.4;
+}
+@media (min-width: 768px) {
+ .lead {
+ font-size: 19.5px;
+ }
+}
+small,
+.small {
+ font-size: 92%;
+}
+mark,
+.mark {
+ background-color: #fcf8e3;
+ padding: .2em;
+}
+.text-left {
+ text-align: left;
+}
+.text-right {
+ text-align: right;
+}
+.text-center {
+ text-align: center;
+}
+.text-justify {
+ text-align: justify;
+}
+.text-nowrap {
+ white-space: nowrap;
+}
+.text-lowercase {
+ text-transform: lowercase;
+}
+.text-uppercase {
+ text-transform: uppercase;
+}
+.text-capitalize {
+ text-transform: capitalize;
+}
+.text-muted {
+ color: #777777;
+}
+.text-primary {
+ color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+ color: #286090;
+}
+.text-success {
+ color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+ color: #2b542c;
+}
+.text-info {
+ color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+ color: #245269;
+}
+.text-warning {
+ color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+ color: #66512c;
+}
+.text-danger {
+ color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+ color: #843534;
+}
+.bg-primary {
+ color: #fff;
+ background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+ background-color: #286090;
+}
+.bg-success {
+ background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+ background-color: #c1e2b3;
+}
+.bg-info {
+ background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+ background-color: #afd9ee;
+}
+.bg-warning {
+ background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+ background-color: #f7ecb5;
+}
+.bg-danger {
+ background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+ background-color: #e4b9b9;
+}
+.page-header {
+ padding-bottom: 8px;
+ margin: 36px 0 18px;
+ border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+ margin-top: 0;
+ margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+ margin-bottom: 0;
+}
+.list-unstyled {
+ padding-left: 0;
+ list-style: none;
+}
+.list-inline {
+ padding-left: 0;
+ list-style: none;
+ margin-left: -5px;
+}
+.list-inline > li {
+ display: inline-block;
+ padding-left: 5px;
+ padding-right: 5px;
+}
+dl {
+ margin-top: 0;
+ margin-bottom: 18px;
+}
+dt,
+dd {
+ line-height: 1.42857143;
+}
+dt {
+ font-weight: bold;
+}
+dd {
+ margin-left: 0;
+}
+@media (min-width: 541px) {
+ .dl-horizontal dt {
+ float: left;
+ width: 160px;
+ clear: left;
+ text-align: right;
+ overflow: hidden;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+ }
+ .dl-horizontal dd {
+ margin-left: 180px;
+ }
+}
+abbr[title],
+abbr[data-original-title] {
+ cursor: help;
+ border-bottom: 1px dotted #777777;
+}
+.initialism {
+ font-size: 90%;
+ text-transform: uppercase;
+}
+blockquote {
+ padding: 9px 18px;
+ margin: 0 0 18px;
+ font-size: inherit;
+ border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+ margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+ display: block;
+ font-size: 80%;
+ line-height: 1.42857143;
+ color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+ content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+ padding-right: 15px;
+ padding-left: 0;
+ border-right: 5px solid #eeeeee;
+ border-left: 0;
+ text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+ content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+ content: '\00A0 \2014';
+}
+address {
+ margin-bottom: 18px;
+ font-style: normal;
+ line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+ font-family: monospace;
+}
+code {
+ padding: 2px 4px;
+ font-size: 90%;
+ color: #c7254e;
+ background-color: #f9f2f4;
+ border-radius: 2px;
+}
+kbd {
+ padding: 2px 4px;
+ font-size: 90%;
+ color: #888;
+ background-color: transparent;
+ border-radius: 1px;
+ box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+ padding: 0;
+ font-size: 100%;
+ font-weight: bold;
+ box-shadow: none;
+}
+pre {
+ display: block;
+ padding: 8.5px;
+ margin: 0 0 9px;
+ font-size: 12px;
+ line-height: 1.42857143;
+ word-break: break-all;
+ word-wrap: break-word;
+ color: #333333;
+ background-color: #f5f5f5;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+}
+pre code {
+ padding: 0;
+ font-size: inherit;
+ color: inherit;
+ white-space: pre-wrap;
+ background-color: transparent;
+ border-radius: 0;
+}
+.pre-scrollable {
+ max-height: 340px;
+ overflow-y: scroll;
+}
+.container {
+ margin-right: auto;
+ margin-left: auto;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+@media (min-width: 768px) {
+ .container {
+ width: 768px;
+ }
+}
+@media (min-width: 992px) {
+ .container {
+ width: 940px;
+ }
+}
+@media (min-width: 1200px) {
+ .container {
+ width: 1140px;
+ }
+}
+.container-fluid {
+ margin-right: auto;
+ margin-left: auto;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.row {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+ position: relative;
+ min-height: 1px;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+ float: left;
+}
+.col-xs-12 {
+ width: 100%;
+}
+.col-xs-11 {
+ width: 91.66666667%;
+}
+.col-xs-10 {
+ width: 83.33333333%;
+}
+.col-xs-9 {
+ width: 75%;
+}
+.col-xs-8 {
+ width: 66.66666667%;
+}
+.col-xs-7 {
+ width: 58.33333333%;
+}
+.col-xs-6 {
+ width: 50%;
+}
+.col-xs-5 {
+ width: 41.66666667%;
+}
+.col-xs-4 {
+ width: 33.33333333%;
+}
+.col-xs-3 {
+ width: 25%;
+}
+.col-xs-2 {
+ width: 16.66666667%;
+}
+.col-xs-1 {
+ width: 8.33333333%;
+}
+.col-xs-pull-12 {
+ right: 100%;
+}
+.col-xs-pull-11 {
+ right: 91.66666667%;
+}
+.col-xs-pull-10 {
+ right: 83.33333333%;
+}
+.col-xs-pull-9 {
+ right: 75%;
+}
+.col-xs-pull-8 {
+ right: 66.66666667%;
+}
+.col-xs-pull-7 {
+ right: 58.33333333%;
+}
+.col-xs-pull-6 {
+ right: 50%;
+}
+.col-xs-pull-5 {
+ right: 41.66666667%;
+}
+.col-xs-pull-4 {
+ right: 33.33333333%;
+}
+.col-xs-pull-3 {
+ right: 25%;
+}
+.col-xs-pull-2 {
+ right: 16.66666667%;
+}
+.col-xs-pull-1 {
+ right: 8.33333333%;
+}
+.col-xs-pull-0 {
+ right: auto;
+}
+.col-xs-push-12 {
+ left: 100%;
+}
+.col-xs-push-11 {
+ left: 91.66666667%;
+}
+.col-xs-push-10 {
+ left: 83.33333333%;
+}
+.col-xs-push-9 {
+ left: 75%;
+}
+.col-xs-push-8 {
+ left: 66.66666667%;
+}
+.col-xs-push-7 {
+ left: 58.33333333%;
+}
+.col-xs-push-6 {
+ left: 50%;
+}
+.col-xs-push-5 {
+ left: 41.66666667%;
+}
+.col-xs-push-4 {
+ left: 33.33333333%;
+}
+.col-xs-push-3 {
+ left: 25%;
+}
+.col-xs-push-2 {
+ left: 16.66666667%;
+}
+.col-xs-push-1 {
+ left: 8.33333333%;
+}
+.col-xs-push-0 {
+ left: auto;
+}
+.col-xs-offset-12 {
+ margin-left: 100%;
+}
+.col-xs-offset-11 {
+ margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+ margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+ margin-left: 75%;
+}
+.col-xs-offset-8 {
+ margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+ margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+ margin-left: 50%;
+}
+.col-xs-offset-5 {
+ margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+ margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+ margin-left: 25%;
+}
+.col-xs-offset-2 {
+ margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+ margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+ margin-left: 0%;
+}
+@media (min-width: 768px) {
+ .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+ float: left;
+ }
+ .col-sm-12 {
+ width: 100%;
+ }
+ .col-sm-11 {
+ width: 91.66666667%;
+ }
+ .col-sm-10 {
+ width: 83.33333333%;
+ }
+ .col-sm-9 {
+ width: 75%;
+ }
+ .col-sm-8 {
+ width: 66.66666667%;
+ }
+ .col-sm-7 {
+ width: 58.33333333%;
+ }
+ .col-sm-6 {
+ width: 50%;
+ }
+ .col-sm-5 {
+ width: 41.66666667%;
+ }
+ .col-sm-4 {
+ width: 33.33333333%;
+ }
+ .col-sm-3 {
+ width: 25%;
+ }
+ .col-sm-2 {
+ width: 16.66666667%;
+ }
+ .col-sm-1 {
+ width: 8.33333333%;
+ }
+ .col-sm-pull-12 {
+ right: 100%;
+ }
+ .col-sm-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-sm-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-sm-pull-9 {
+ right: 75%;
+ }
+ .col-sm-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-sm-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-sm-pull-6 {
+ right: 50%;
+ }
+ .col-sm-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-sm-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-sm-pull-3 {
+ right: 25%;
+ }
+ .col-sm-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-sm-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-sm-pull-0 {
+ right: auto;
+ }
+ .col-sm-push-12 {
+ left: 100%;
+ }
+ .col-sm-push-11 {
+ left: 91.66666667%;
+ }
+ .col-sm-push-10 {
+ left: 83.33333333%;
+ }
+ .col-sm-push-9 {
+ left: 75%;
+ }
+ .col-sm-push-8 {
+ left: 66.66666667%;
+ }
+ .col-sm-push-7 {
+ left: 58.33333333%;
+ }
+ .col-sm-push-6 {
+ left: 50%;
+ }
+ .col-sm-push-5 {
+ left: 41.66666667%;
+ }
+ .col-sm-push-4 {
+ left: 33.33333333%;
+ }
+ .col-sm-push-3 {
+ left: 25%;
+ }
+ .col-sm-push-2 {
+ left: 16.66666667%;
+ }
+ .col-sm-push-1 {
+ left: 8.33333333%;
+ }
+ .col-sm-push-0 {
+ left: auto;
+ }
+ .col-sm-offset-12 {
+ margin-left: 100%;
+ }
+ .col-sm-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-sm-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-sm-offset-9 {
+ margin-left: 75%;
+ }
+ .col-sm-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-sm-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-sm-offset-6 {
+ margin-left: 50%;
+ }
+ .col-sm-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-sm-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-sm-offset-3 {
+ margin-left: 25%;
+ }
+ .col-sm-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-sm-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-sm-offset-0 {
+ margin-left: 0%;
+ }
+}
+@media (min-width: 992px) {
+ .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+ float: left;
+ }
+ .col-md-12 {
+ width: 100%;
+ }
+ .col-md-11 {
+ width: 91.66666667%;
+ }
+ .col-md-10 {
+ width: 83.33333333%;
+ }
+ .col-md-9 {
+ width: 75%;
+ }
+ .col-md-8 {
+ width: 66.66666667%;
+ }
+ .col-md-7 {
+ width: 58.33333333%;
+ }
+ .col-md-6 {
+ width: 50%;
+ }
+ .col-md-5 {
+ width: 41.66666667%;
+ }
+ .col-md-4 {
+ width: 33.33333333%;
+ }
+ .col-md-3 {
+ width: 25%;
+ }
+ .col-md-2 {
+ width: 16.66666667%;
+ }
+ .col-md-1 {
+ width: 8.33333333%;
+ }
+ .col-md-pull-12 {
+ right: 100%;
+ }
+ .col-md-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-md-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-md-pull-9 {
+ right: 75%;
+ }
+ .col-md-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-md-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-md-pull-6 {
+ right: 50%;
+ }
+ .col-md-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-md-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-md-pull-3 {
+ right: 25%;
+ }
+ .col-md-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-md-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-md-pull-0 {
+ right: auto;
+ }
+ .col-md-push-12 {
+ left: 100%;
+ }
+ .col-md-push-11 {
+ left: 91.66666667%;
+ }
+ .col-md-push-10 {
+ left: 83.33333333%;
+ }
+ .col-md-push-9 {
+ left: 75%;
+ }
+ .col-md-push-8 {
+ left: 66.66666667%;
+ }
+ .col-md-push-7 {
+ left: 58.33333333%;
+ }
+ .col-md-push-6 {
+ left: 50%;
+ }
+ .col-md-push-5 {
+ left: 41.66666667%;
+ }
+ .col-md-push-4 {
+ left: 33.33333333%;
+ }
+ .col-md-push-3 {
+ left: 25%;
+ }
+ .col-md-push-2 {
+ left: 16.66666667%;
+ }
+ .col-md-push-1 {
+ left: 8.33333333%;
+ }
+ .col-md-push-0 {
+ left: auto;
+ }
+ .col-md-offset-12 {
+ margin-left: 100%;
+ }
+ .col-md-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-md-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-md-offset-9 {
+ margin-left: 75%;
+ }
+ .col-md-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-md-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-md-offset-6 {
+ margin-left: 50%;
+ }
+ .col-md-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-md-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-md-offset-3 {
+ margin-left: 25%;
+ }
+ .col-md-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-md-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-md-offset-0 {
+ margin-left: 0%;
+ }
+}
+@media (min-width: 1200px) {
+ .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+ float: left;
+ }
+ .col-lg-12 {
+ width: 100%;
+ }
+ .col-lg-11 {
+ width: 91.66666667%;
+ }
+ .col-lg-10 {
+ width: 83.33333333%;
+ }
+ .col-lg-9 {
+ width: 75%;
+ }
+ .col-lg-8 {
+ width: 66.66666667%;
+ }
+ .col-lg-7 {
+ width: 58.33333333%;
+ }
+ .col-lg-6 {
+ width: 50%;
+ }
+ .col-lg-5 {
+ width: 41.66666667%;
+ }
+ .col-lg-4 {
+ width: 33.33333333%;
+ }
+ .col-lg-3 {
+ width: 25%;
+ }
+ .col-lg-2 {
+ width: 16.66666667%;
+ }
+ .col-lg-1 {
+ width: 8.33333333%;
+ }
+ .col-lg-pull-12 {
+ right: 100%;
+ }
+ .col-lg-pull-11 {
+ right: 91.66666667%;
+ }
+ .col-lg-pull-10 {
+ right: 83.33333333%;
+ }
+ .col-lg-pull-9 {
+ right: 75%;
+ }
+ .col-lg-pull-8 {
+ right: 66.66666667%;
+ }
+ .col-lg-pull-7 {
+ right: 58.33333333%;
+ }
+ .col-lg-pull-6 {
+ right: 50%;
+ }
+ .col-lg-pull-5 {
+ right: 41.66666667%;
+ }
+ .col-lg-pull-4 {
+ right: 33.33333333%;
+ }
+ .col-lg-pull-3 {
+ right: 25%;
+ }
+ .col-lg-pull-2 {
+ right: 16.66666667%;
+ }
+ .col-lg-pull-1 {
+ right: 8.33333333%;
+ }
+ .col-lg-pull-0 {
+ right: auto;
+ }
+ .col-lg-push-12 {
+ left: 100%;
+ }
+ .col-lg-push-11 {
+ left: 91.66666667%;
+ }
+ .col-lg-push-10 {
+ left: 83.33333333%;
+ }
+ .col-lg-push-9 {
+ left: 75%;
+ }
+ .col-lg-push-8 {
+ left: 66.66666667%;
+ }
+ .col-lg-push-7 {
+ left: 58.33333333%;
+ }
+ .col-lg-push-6 {
+ left: 50%;
+ }
+ .col-lg-push-5 {
+ left: 41.66666667%;
+ }
+ .col-lg-push-4 {
+ left: 33.33333333%;
+ }
+ .col-lg-push-3 {
+ left: 25%;
+ }
+ .col-lg-push-2 {
+ left: 16.66666667%;
+ }
+ .col-lg-push-1 {
+ left: 8.33333333%;
+ }
+ .col-lg-push-0 {
+ left: auto;
+ }
+ .col-lg-offset-12 {
+ margin-left: 100%;
+ }
+ .col-lg-offset-11 {
+ margin-left: 91.66666667%;
+ }
+ .col-lg-offset-10 {
+ margin-left: 83.33333333%;
+ }
+ .col-lg-offset-9 {
+ margin-left: 75%;
+ }
+ .col-lg-offset-8 {
+ margin-left: 66.66666667%;
+ }
+ .col-lg-offset-7 {
+ margin-left: 58.33333333%;
+ }
+ .col-lg-offset-6 {
+ margin-left: 50%;
+ }
+ .col-lg-offset-5 {
+ margin-left: 41.66666667%;
+ }
+ .col-lg-offset-4 {
+ margin-left: 33.33333333%;
+ }
+ .col-lg-offset-3 {
+ margin-left: 25%;
+ }
+ .col-lg-offset-2 {
+ margin-left: 16.66666667%;
+ }
+ .col-lg-offset-1 {
+ margin-left: 8.33333333%;
+ }
+ .col-lg-offset-0 {
+ margin-left: 0%;
+ }
+}
+table {
+ background-color: transparent;
+}
+caption {
+ padding-top: 8px;
+ padding-bottom: 8px;
+ color: #777777;
+ text-align: left;
+}
+th {
+ text-align: left;
+}
+.table {
+ width: 100%;
+ max-width: 100%;
+ margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+ padding: 8px;
+ line-height: 1.42857143;
+ vertical-align: top;
+ border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+ vertical-align: bottom;
+ border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+ border-top: 0;
+}
+.table > tbody + tbody {
+ border-top: 2px solid #ddd;
+}
+.table .table {
+ background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+ padding: 5px;
+}
+.table-bordered {
+ border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+ border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+ border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+ background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+ background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+ position: static;
+ float: none;
+ display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+ position: static;
+ float: none;
+ display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+ background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+ background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+ background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+ background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+ background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+ background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+ background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+ background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+ background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+ background-color: #ebcccc;
+}
+.table-responsive {
+ overflow-x: auto;
+ min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+ .table-responsive {
+ width: 100%;
+ margin-bottom: 13.5px;
+ overflow-y: hidden;
+ -ms-overflow-style: -ms-autohiding-scrollbar;
+ border: 1px solid #ddd;
+ }
+ .table-responsive > .table {
+ margin-bottom: 0;
+ }
+ .table-responsive > .table > thead > tr > th,
+ .table-responsive > .table > tbody > tr > th,
+ .table-responsive > .table > tfoot > tr > th,
+ .table-responsive > .table > thead > tr > td,
+ .table-responsive > .table > tbody > tr > td,
+ .table-responsive > .table > tfoot > tr > td {
+ white-space: nowrap;
+ }
+ .table-responsive > .table-bordered {
+ border: 0;
+ }
+ .table-responsive > .table-bordered > thead > tr > th:first-child,
+ .table-responsive > .table-bordered > tbody > tr > th:first-child,
+ .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+ .table-responsive > .table-bordered > thead > tr > td:first-child,
+ .table-responsive > .table-bordered > tbody > tr > td:first-child,
+ .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+ border-left: 0;
+ }
+ .table-responsive > .table-bordered > thead > tr > th:last-child,
+ .table-responsive > .table-bordered > tbody > tr > th:last-child,
+ .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+ .table-responsive > .table-bordered > thead > tr > td:last-child,
+ .table-responsive > .table-bordered > tbody > tr > td:last-child,
+ .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+ border-right: 0;
+ }
+ .table-responsive > .table-bordered > tbody > tr:last-child > th,
+ .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+ .table-responsive > .table-bordered > tbody > tr:last-child > td,
+ .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+ border-bottom: 0;
+ }
+}
+fieldset {
+ padding: 0;
+ margin: 0;
+ border: 0;
+ min-width: 0;
+}
+legend {
+ display: block;
+ width: 100%;
+ padding: 0;
+ margin-bottom: 18px;
+ font-size: 19.5px;
+ line-height: inherit;
+ color: #333333;
+ border: 0;
+ border-bottom: 1px solid #e5e5e5;
+}
+label {
+ display: inline-block;
+ max-width: 100%;
+ margin-bottom: 5px;
+ font-weight: bold;
+}
+input[type="search"] {
+ -webkit-box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+ margin: 4px 0 0;
+ margin-top: 1px \9;
+ line-height: normal;
+}
+input[type="file"] {
+ display: block;
+}
+input[type="range"] {
+ display: block;
+ width: 100%;
+}
+select[multiple],
+select[size] {
+ height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+ outline: thin dotted;
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+output {
+ display: block;
+ padding-top: 7px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+}
+.form-control {
+ display: block;
+ width: 100%;
+ height: 32px;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+ background-color: #fff;
+ background-image: none;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+ border-color: #66afe9;
+ outline: 0;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+ box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+ color: #999;
+ opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+ color: #999;
+}
+.form-control::-webkit-input-placeholder {
+ color: #999;
+}
+.form-control::-ms-expand {
+ border: 0;
+ background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+ background-color: #eeeeee;
+ opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+ cursor: not-allowed;
+}
+textarea.form-control {
+ height: auto;
+}
+input[type="search"] {
+ -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+ input[type="date"].form-control,
+ input[type="time"].form-control,
+ input[type="datetime-local"].form-control,
+ input[type="month"].form-control {
+ line-height: 32px;
+ }
+ input[type="date"].input-sm,
+ input[type="time"].input-sm,
+ input[type="datetime-local"].input-sm,
+ input[type="month"].input-sm,
+ .input-group-sm input[type="date"],
+ .input-group-sm input[type="time"],
+ .input-group-sm input[type="datetime-local"],
+ .input-group-sm input[type="month"] {
+ line-height: 30px;
+ }
+ input[type="date"].input-lg,
+ input[type="time"].input-lg,
+ input[type="datetime-local"].input-lg,
+ input[type="month"].input-lg,
+ .input-group-lg input[type="date"],
+ .input-group-lg input[type="time"],
+ .input-group-lg input[type="datetime-local"],
+ .input-group-lg input[type="month"] {
+ line-height: 45px;
+ }
+}
+.form-group {
+ margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+ position: relative;
+ display: block;
+ margin-top: 10px;
+ margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+ min-height: 18px;
+ padding-left: 20px;
+ margin-bottom: 0;
+ font-weight: normal;
+ cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+ position: absolute;
+ margin-left: -20px;
+ margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+ margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+ position: relative;
+ display: inline-block;
+ padding-left: 20px;
+ margin-bottom: 0;
+ vertical-align: middle;
+ font-weight: normal;
+ cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+ margin-top: 0;
+ margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+ cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+ cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+ cursor: not-allowed;
+}
+.form-control-static {
+ padding-top: 7px;
+ padding-bottom: 7px;
+ margin-bottom: 0;
+ min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+ padding-left: 0;
+ padding-right: 0;
+}
+.input-sm {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+select.input-sm {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+ height: auto;
+}
+.form-group-sm .form-control {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.form-group-sm select.form-control {
+ height: 30px;
+ line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+ height: auto;
+}
+.form-group-sm .form-control-static {
+ height: 30px;
+ min-height: 30px;
+ padding: 6px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+}
+.input-lg {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+select.input-lg {
+ height: 45px;
+ line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+ height: auto;
+}
+.form-group-lg .form-control {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+.form-group-lg select.form-control {
+ height: 45px;
+ line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+ height: auto;
+}
+.form-group-lg .form-control-static {
+ height: 45px;
+ min-height: 35px;
+ padding: 11px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+}
+.has-feedback {
+ position: relative;
+}
+.has-feedback .form-control {
+ padding-right: 40px;
+}
+.form-control-feedback {
+ position: absolute;
+ top: 0;
+ right: 0;
+ z-index: 2;
+ display: block;
+ width: 32px;
+ height: 32px;
+ line-height: 32px;
+ text-align: center;
+ pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+ width: 45px;
+ height: 45px;
+ line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+ width: 30px;
+ height: 30px;
+ line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+ color: #3c763d;
+}
+.has-success .form-control {
+ border-color: #3c763d;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+ border-color: #2b542c;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+ color: #3c763d;
+ border-color: #3c763d;
+ background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+ color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+ color: #8a6d3b;
+}
+.has-warning .form-control {
+ border-color: #8a6d3b;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+ border-color: #66512c;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+ color: #8a6d3b;
+ border-color: #8a6d3b;
+ background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+ color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+ color: #a94442;
+}
+.has-error .form-control {
+ border-color: #a94442;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+ border-color: #843534;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+ color: #a94442;
+ border-color: #a94442;
+ background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+ color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+ top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+ top: 0;
+}
+.help-block {
+ display: block;
+ margin-top: 5px;
+ margin-bottom: 10px;
+ color: #404040;
+}
+@media (min-width: 768px) {
+ .form-inline .form-group {
+ display: inline-block;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .form-control {
+ display: inline-block;
+ width: auto;
+ vertical-align: middle;
+ }
+ .form-inline .form-control-static {
+ display: inline-block;
+ }
+ .form-inline .input-group {
+ display: inline-table;
+ vertical-align: middle;
+ }
+ .form-inline .input-group .input-group-addon,
+ .form-inline .input-group .input-group-btn,
+ .form-inline .input-group .form-control {
+ width: auto;
+ }
+ .form-inline .input-group > .form-control {
+ width: 100%;
+ }
+ .form-inline .control-label {
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .radio,
+ .form-inline .checkbox {
+ display: inline-block;
+ margin-top: 0;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .form-inline .radio label,
+ .form-inline .checkbox label {
+ padding-left: 0;
+ }
+ .form-inline .radio input[type="radio"],
+ .form-inline .checkbox input[type="checkbox"] {
+ position: relative;
+ margin-left: 0;
+ }
+ .form-inline .has-feedback .form-control-feedback {
+ top: 0;
+ }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+ margin-top: 0;
+ margin-bottom: 0;
+ padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+ min-height: 25px;
+}
+.form-horizontal .form-group {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+@media (min-width: 768px) {
+ .form-horizontal .control-label {
+ text-align: right;
+ margin-bottom: 0;
+ padding-top: 7px;
+ }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+ right: 0px;
+}
+@media (min-width: 768px) {
+ .form-horizontal .form-group-lg .control-label {
+ padding-top: 11px;
+ font-size: 17px;
+ }
+}
+@media (min-width: 768px) {
+ .form-horizontal .form-group-sm .control-label {
+ padding-top: 6px;
+ font-size: 12px;
+ }
+}
+.btn {
+ display: inline-block;
+ margin-bottom: 0;
+ font-weight: normal;
+ text-align: center;
+ vertical-align: middle;
+ touch-action: manipulation;
+ cursor: pointer;
+ background-image: none;
+ border: 1px solid transparent;
+ white-space: nowrap;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ border-radius: 2px;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+ outline: thin dotted;
+ outline: 5px auto -webkit-focus-ring-color;
+ outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+ color: #333;
+ text-decoration: none;
+}
+.btn:active,
+.btn.active {
+ outline: 0;
+ background-image: none;
+ -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+ box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+ cursor: not-allowed;
+ opacity: 0.65;
+ filter: alpha(opacity=65);
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+ pointer-events: none;
+}
+.btn-default {
+ color: #333;
+ background-color: #fff;
+ border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+.btn-default:hover {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+ color: #333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+ background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+ background-color: #fff;
+ border-color: #ccc;
+}
+.btn-default .badge {
+ color: #fff;
+ background-color: #333;
+}
+.btn-primary {
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+ color: #fff;
+ background-color: #286090;
+ border-color: #122b40;
+}
+.btn-primary:hover {
+ color: #fff;
+ background-color: #286090;
+ border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+ color: #fff;
+ background-color: #286090;
+ border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+ color: #fff;
+ background-color: #204d74;
+ border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+ background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+ background-color: #337ab7;
+ border-color: #2e6da4;
+}
+.btn-primary .badge {
+ color: #337ab7;
+ background-color: #fff;
+}
+.btn-success {
+ color: #fff;
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #255625;
+}
+.btn-success:hover {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+ color: #fff;
+ background-color: #398439;
+ border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+ background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.btn-success .badge {
+ color: #5cb85c;
+ background-color: #fff;
+}
+.btn-info {
+ color: #fff;
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #1b6d85;
+}
+.btn-info:hover {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+ color: #fff;
+ background-color: #269abc;
+ border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+ background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.btn-info .badge {
+ color: #5bc0de;
+ background-color: #fff;
+}
+.btn-warning {
+ color: #fff;
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #985f0d;
+}
+.btn-warning:hover {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+ color: #fff;
+ background-color: #d58512;
+ border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+ background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.btn-warning .badge {
+ color: #f0ad4e;
+ background-color: #fff;
+}
+.btn-danger {
+ color: #fff;
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #761c19;
+}
+.btn-danger:hover {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+ color: #fff;
+ background-color: #ac2925;
+ border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+ background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.btn-danger .badge {
+ color: #d9534f;
+ background-color: #fff;
+}
+.btn-link {
+ color: #337ab7;
+ font-weight: normal;
+ border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+ background-color: transparent;
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+ border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+ color: #23527c;
+ text-decoration: underline;
+ background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+ color: #777777;
+ text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+ padding: 1px 5px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+.btn-block {
+ display: block;
+ width: 100%;
+}
+.btn-block + .btn-block {
+ margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+ width: 100%;
+}
+.fade {
+ opacity: 0;
+ -webkit-transition: opacity 0.15s linear;
+ -o-transition: opacity 0.15s linear;
+ transition: opacity 0.15s linear;
+}
+.fade.in {
+ opacity: 1;
+}
+.collapse {
+ display: none;
+}
+.collapse.in {
+ display: block;
+}
+tr.collapse.in {
+ display: table-row;
+}
+tbody.collapse.in {
+ display: table-row-group;
+}
+.collapsing {
+ position: relative;
+ height: 0;
+ overflow: hidden;
+ -webkit-transition-property: height, visibility;
+ transition-property: height, visibility;
+ -webkit-transition-duration: 0.35s;
+ transition-duration: 0.35s;
+ -webkit-transition-timing-function: ease;
+ transition-timing-function: ease;
+}
+.caret {
+ display: inline-block;
+ width: 0;
+ height: 0;
+ margin-left: 2px;
+ vertical-align: middle;
+ border-top: 4px dashed;
+ border-top: 4px solid \9;
+ border-right: 4px solid transparent;
+ border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+ position: relative;
+}
+.dropdown-toggle:focus {
+ outline: 0;
+}
+.dropdown-menu {
+ position: absolute;
+ top: 100%;
+ left: 0;
+ z-index: 1000;
+ display: none;
+ float: left;
+ min-width: 160px;
+ padding: 5px 0;
+ margin: 2px 0 0;
+ list-style: none;
+ font-size: 13px;
+ text-align: left;
+ background-color: #fff;
+ border: 1px solid #ccc;
+ border: 1px solid rgba(0, 0, 0, 0.15);
+ border-radius: 2px;
+ -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+ box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+ background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+ right: 0;
+ left: auto;
+}
+.dropdown-menu .divider {
+ height: 1px;
+ margin: 8px 0;
+ overflow: hidden;
+ background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+ display: block;
+ padding: 3px 20px;
+ clear: both;
+ font-weight: normal;
+ line-height: 1.42857143;
+ color: #333333;
+ white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+ text-decoration: none;
+ color: #262626;
+ background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+ color: #fff;
+ text-decoration: none;
+ outline: 0;
+ background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+ color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+ text-decoration: none;
+ background-color: transparent;
+ background-image: none;
+ filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+ cursor: not-allowed;
+}
+.open > .dropdown-menu {
+ display: block;
+}
+.open > a {
+ outline: 0;
+}
+.dropdown-menu-right {
+ left: auto;
+ right: 0;
+}
+.dropdown-menu-left {
+ left: 0;
+ right: auto;
+}
+.dropdown-header {
+ display: block;
+ padding: 3px 20px;
+ font-size: 12px;
+ line-height: 1.42857143;
+ color: #777777;
+ white-space: nowrap;
+}
+.dropdown-backdrop {
+ position: fixed;
+ left: 0;
+ right: 0;
+ bottom: 0;
+ top: 0;
+ z-index: 990;
+}
+.pull-right > .dropdown-menu {
+ right: 0;
+ left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+ border-top: 0;
+ border-bottom: 4px dashed;
+ border-bottom: 4px solid \9;
+ content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+ top: auto;
+ bottom: 100%;
+ margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+ .navbar-right .dropdown-menu {
+ left: auto;
+ right: 0;
+ }
+ .navbar-right .dropdown-menu-left {
+ left: 0;
+ right: auto;
+ }
+}
+.btn-group,
+.btn-group-vertical {
+ position: relative;
+ display: inline-block;
+ vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+ position: relative;
+ float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+ z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+ margin-left: -1px;
+}
+.btn-toolbar {
+ margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+ float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+ margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+ border-radius: 0;
+}
+.btn-group > .btn:first-child {
+ margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+ float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+ border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+ outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+ padding-left: 8px;
+ padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+ padding-left: 12px;
+ padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+ -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+ box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+ -webkit-box-shadow: none;
+ box-shadow: none;
+}
+.btn .caret {
+ margin-left: 0;
+}
+.btn-lg .caret {
+ border-width: 5px 5px 0;
+ border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+ border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+ display: block;
+ float: none;
+ width: 100%;
+ max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+ float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+ margin-top: -1px;
+ margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+ border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+ border-bottom-right-radius: 2px;
+ border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+ border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.btn-group-justified {
+ display: table;
+ width: 100%;
+ table-layout: fixed;
+ border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+ float: none;
+ display: table-cell;
+ width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+ width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+ left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+ position: absolute;
+ clip: rect(0, 0, 0, 0);
+ pointer-events: none;
+}
+.input-group {
+ position: relative;
+ display: table;
+ border-collapse: separate;
+}
+.input-group[class*="col-"] {
+ float: none;
+ padding-left: 0;
+ padding-right: 0;
+}
+.input-group .form-control {
+ position: relative;
+ z-index: 2;
+ float: left;
+ width: 100%;
+ margin-bottom: 0;
+}
+.input-group .form-control:focus {
+ z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+ height: 45px;
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+ border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+ height: 45px;
+ line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+ height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+ height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+ display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+ border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+ width: 1%;
+ white-space: nowrap;
+ vertical-align: middle;
+}
+.input-group-addon {
+ padding: 6px 12px;
+ font-size: 13px;
+ font-weight: normal;
+ line-height: 1;
+ color: #555555;
+ text-align: center;
+ background-color: #eeeeee;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+}
+.input-group-addon.input-sm {
+ padding: 5px 10px;
+ font-size: 12px;
+ border-radius: 1px;
+}
+.input-group-addon.input-lg {
+ padding: 10px 16px;
+ font-size: 17px;
+ border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+ margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+ border-bottom-right-radius: 0;
+ border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+ border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+ border-bottom-left-radius: 0;
+ border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+ border-left: 0;
+}
+.input-group-btn {
+ position: relative;
+ font-size: 0;
+ white-space: nowrap;
+}
+.input-group-btn > .btn {
+ position: relative;
+}
+.input-group-btn > .btn + .btn {
+ margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+ z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+ margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+ z-index: 2;
+ margin-left: -1px;
+}
+.nav {
+ margin-bottom: 0;
+ padding-left: 0;
+ list-style: none;
+}
+.nav > li {
+ position: relative;
+ display: block;
+}
+.nav > li > a {
+ position: relative;
+ display: block;
+ padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+ text-decoration: none;
+ background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+ color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+ color: #777777;
+ text-decoration: none;
+ background-color: transparent;
+ cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+ background-color: #eeeeee;
+ border-color: #337ab7;
+}
+.nav .nav-divider {
+ height: 1px;
+ margin: 8px 0;
+ overflow: hidden;
+ background-color: #e5e5e5;
+}
+.nav > li > a > img {
+ max-width: none;
+}
+.nav-tabs {
+ border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+ float: left;
+ margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+ margin-right: 2px;
+ line-height: 1.42857143;
+ border: 1px solid transparent;
+ border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+ border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+ color: #555555;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-bottom-color: transparent;
+ cursor: default;
+}
+.nav-tabs.nav-justified {
+ width: 100%;
+ border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+ float: none;
+}
+.nav-tabs.nav-justified > li > a {
+ text-align: center;
+ margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+ top: auto;
+ left: auto;
+}
+@media (min-width: 768px) {
+ .nav-tabs.nav-justified > li {
+ display: table-cell;
+ width: 1%;
+ }
+ .nav-tabs.nav-justified > li > a {
+ margin-bottom: 0;
+ }
+}
+.nav-tabs.nav-justified > li > a {
+ margin-right: 0;
+ border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+ border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+ .nav-tabs.nav-justified > li > a {
+ border-bottom: 1px solid #ddd;
+ border-radius: 2px 2px 0 0;
+ }
+ .nav-tabs.nav-justified > .active > a,
+ .nav-tabs.nav-justified > .active > a:hover,
+ .nav-tabs.nav-justified > .active > a:focus {
+ border-bottom-color: #fff;
+ }
+}
+.nav-pills > li {
+ float: left;
+}
+.nav-pills > li > a {
+ border-radius: 2px;
+}
+.nav-pills > li + li {
+ margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+ color: #fff;
+ background-color: #337ab7;
+}
+.nav-stacked > li {
+ float: none;
+}
+.nav-stacked > li + li {
+ margin-top: 2px;
+ margin-left: 0;
+}
+.nav-justified {
+ width: 100%;
+}
+.nav-justified > li {
+ float: none;
+}
+.nav-justified > li > a {
+ text-align: center;
+ margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+ top: auto;
+ left: auto;
+}
+@media (min-width: 768px) {
+ .nav-justified > li {
+ display: table-cell;
+ width: 1%;
+ }
+ .nav-justified > li > a {
+ margin-bottom: 0;
+ }
+}
+.nav-tabs-justified {
+ border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+ margin-right: 0;
+ border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+ border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+ .nav-tabs-justified > li > a {
+ border-bottom: 1px solid #ddd;
+ border-radius: 2px 2px 0 0;
+ }
+ .nav-tabs-justified > .active > a,
+ .nav-tabs-justified > .active > a:hover,
+ .nav-tabs-justified > .active > a:focus {
+ border-bottom-color: #fff;
+ }
+}
+.tab-content > .tab-pane {
+ display: none;
+}
+.tab-content > .active {
+ display: block;
+}
+.nav-tabs .dropdown-menu {
+ margin-top: -1px;
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.navbar {
+ position: relative;
+ min-height: 30px;
+ margin-bottom: 18px;
+ border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+ .navbar {
+ border-radius: 2px;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-header {
+ float: left;
+ }
+}
+.navbar-collapse {
+ overflow-x: visible;
+ padding-right: 0px;
+ padding-left: 0px;
+ border-top: 1px solid transparent;
+ box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+ -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+ overflow-y: auto;
+}
+@media (min-width: 541px) {
+ .navbar-collapse {
+ width: auto;
+ border-top: 0;
+ box-shadow: none;
+ }
+ .navbar-collapse.collapse {
+ display: block !important;
+ height: auto !important;
+ padding-bottom: 0;
+ overflow: visible !important;
+ }
+ .navbar-collapse.in {
+ overflow-y: visible;
+ }
+ .navbar-fixed-top .navbar-collapse,
+ .navbar-static-top .navbar-collapse,
+ .navbar-fixed-bottom .navbar-collapse {
+ padding-left: 0;
+ padding-right: 0;
+ }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+ max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+ .navbar-fixed-top .navbar-collapse,
+ .navbar-fixed-bottom .navbar-collapse {
+ max-height: 200px;
+ }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+ margin-right: 0px;
+ margin-left: 0px;
+}
+@media (min-width: 541px) {
+ .container > .navbar-header,
+ .container-fluid > .navbar-header,
+ .container > .navbar-collapse,
+ .container-fluid > .navbar-collapse {
+ margin-right: 0;
+ margin-left: 0;
+ }
+}
+.navbar-static-top {
+ z-index: 1000;
+ border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+ .navbar-static-top {
+ border-radius: 0;
+ }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+ position: fixed;
+ right: 0;
+ left: 0;
+ z-index: 1030;
+}
+@media (min-width: 541px) {
+ .navbar-fixed-top,
+ .navbar-fixed-bottom {
+ border-radius: 0;
+ }
+}
+.navbar-fixed-top {
+ top: 0;
+ border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+ bottom: 0;
+ margin-bottom: 0;
+ border-width: 1px 0 0;
+}
+.navbar-brand {
+ float: left;
+ padding: 6px 0px;
+ font-size: 17px;
+ line-height: 18px;
+ height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+ text-decoration: none;
+}
+.navbar-brand > img {
+ display: block;
+}
+@media (min-width: 541px) {
+ .navbar > .container .navbar-brand,
+ .navbar > .container-fluid .navbar-brand {
+ margin-left: 0px;
+ }
+}
+.navbar-toggle {
+ position: relative;
+ float: right;
+ margin-right: 0px;
+ padding: 9px 10px;
+ margin-top: -2px;
+ margin-bottom: -2px;
+ background-color: transparent;
+ background-image: none;
+ border: 1px solid transparent;
+ border-radius: 2px;
+}
+.navbar-toggle:focus {
+ outline: 0;
+}
+.navbar-toggle .icon-bar {
+ display: block;
+ width: 22px;
+ height: 2px;
+ border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+ margin-top: 4px;
+}
+@media (min-width: 541px) {
+ .navbar-toggle {
+ display: none;
+ }
+}
+.navbar-nav {
+ margin: 3px 0px;
+}
+.navbar-nav > li > a {
+ padding-top: 10px;
+ padding-bottom: 10px;
+ line-height: 18px;
+}
+@media (max-width: 540px) {
+ .navbar-nav .open .dropdown-menu {
+ position: static;
+ float: none;
+ width: auto;
+ margin-top: 0;
+ background-color: transparent;
+ border: 0;
+ box-shadow: none;
+ }
+ .navbar-nav .open .dropdown-menu > li > a,
+ .navbar-nav .open .dropdown-menu .dropdown-header {
+ padding: 5px 15px 5px 25px;
+ }
+ .navbar-nav .open .dropdown-menu > li > a {
+ line-height: 18px;
+ }
+ .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-nav .open .dropdown-menu > li > a:focus {
+ background-image: none;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-nav {
+ float: left;
+ margin: 0;
+ }
+ .navbar-nav > li {
+ float: left;
+ }
+ .navbar-nav > li > a {
+ padding-top: 6px;
+ padding-bottom: 6px;
+ }
+}
+.navbar-form {
+ margin-left: 0px;
+ margin-right: 0px;
+ padding: 10px 0px;
+ border-top: 1px solid transparent;
+ border-bottom: 1px solid transparent;
+ -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+ box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+ margin-top: -1px;
+ margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+ .navbar-form .form-group {
+ display: inline-block;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .form-control {
+ display: inline-block;
+ width: auto;
+ vertical-align: middle;
+ }
+ .navbar-form .form-control-static {
+ display: inline-block;
+ }
+ .navbar-form .input-group {
+ display: inline-table;
+ vertical-align: middle;
+ }
+ .navbar-form .input-group .input-group-addon,
+ .navbar-form .input-group .input-group-btn,
+ .navbar-form .input-group .form-control {
+ width: auto;
+ }
+ .navbar-form .input-group > .form-control {
+ width: 100%;
+ }
+ .navbar-form .control-label {
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .radio,
+ .navbar-form .checkbox {
+ display: inline-block;
+ margin-top: 0;
+ margin-bottom: 0;
+ vertical-align: middle;
+ }
+ .navbar-form .radio label,
+ .navbar-form .checkbox label {
+ padding-left: 0;
+ }
+ .navbar-form .radio input[type="radio"],
+ .navbar-form .checkbox input[type="checkbox"] {
+ position: relative;
+ margin-left: 0;
+ }
+ .navbar-form .has-feedback .form-control-feedback {
+ top: 0;
+ }
+}
+@media (max-width: 540px) {
+ .navbar-form .form-group {
+ margin-bottom: 5px;
+ }
+ .navbar-form .form-group:last-child {
+ margin-bottom: 0;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-form {
+ width: auto;
+ border: 0;
+ margin-left: 0;
+ margin-right: 0;
+ padding-top: 0;
+ padding-bottom: 0;
+ -webkit-box-shadow: none;
+ box-shadow: none;
+ }
+}
+.navbar-nav > li > .dropdown-menu {
+ margin-top: 0;
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+ margin-bottom: 0;
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+ border-bottom-right-radius: 0;
+ border-bottom-left-radius: 0;
+}
+.navbar-btn {
+ margin-top: -1px;
+ margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+ margin-top: 0px;
+ margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+ margin-top: 4px;
+ margin-bottom: 4px;
+}
+.navbar-text {
+ margin-top: 6px;
+ margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+ .navbar-text {
+ float: left;
+ margin-left: 0px;
+ margin-right: 0px;
+ }
+}
+@media (min-width: 541px) {
+ .navbar-left {
+ float: left !important;
+ float: left;
+ }
+ .navbar-right {
+ float: right !important;
+ float: right;
+ margin-right: 0px;
+ }
+ .navbar-right ~ .navbar-right {
+ margin-right: 0;
+ }
+}
+.navbar-default {
+ background-color: #f8f8f8;
+ border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+ color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+ color: #5e5e5e;
+ background-color: transparent;
+}
+.navbar-default .navbar-text {
+ color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+ color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+ color: #333;
+ background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+ color: #555;
+ background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+ color: #ccc;
+ background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+ border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+ background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+ background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+ border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+ background-color: #e7e7e7;
+ color: #555;
+}
+@media (max-width: 540px) {
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+ color: #777;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+ color: #333;
+ background-color: transparent;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+ color: #555;
+ background-color: #e7e7e7;
+ }
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+ .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+ color: #ccc;
+ background-color: transparent;
+ }
+}
+.navbar-default .navbar-link {
+ color: #777;
+}
+.navbar-default .navbar-link:hover {
+ color: #333;
+}
+.navbar-default .btn-link {
+ color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+ color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+ color: #ccc;
+}
+.navbar-inverse {
+ background-color: #222;
+ border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+ color: #fff;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+ color: #fff;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+ color: #fff;
+ background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+ color: #444;
+ background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+ border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+ background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+ background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+ border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+ background-color: #080808;
+ color: #fff;
+}
+@media (max-width: 540px) {
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+ border-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+ background-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+ color: #9d9d9d;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+ color: #fff;
+ background-color: transparent;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+ color: #fff;
+ background-color: #080808;
+ }
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+ .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+ color: #444;
+ background-color: transparent;
+ }
+}
+.navbar-inverse .navbar-link {
+ color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+ color: #fff;
+}
+.navbar-inverse .btn-link {
+ color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+ color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+ color: #444;
+}
+.breadcrumb {
+ padding: 8px 15px;
+ margin-bottom: 18px;
+ list-style: none;
+ background-color: #f5f5f5;
+ border-radius: 2px;
+}
+.breadcrumb > li {
+ display: inline-block;
+}
+.breadcrumb > li + li:before {
+ content: "/\00a0";
+ padding: 0 5px;
+ color: #5e5e5e;
+}
+.breadcrumb > .active {
+ color: #777777;
+}
+.pagination {
+ display: inline-block;
+ padding-left: 0;
+ margin: 18px 0;
+ border-radius: 2px;
+}
+.pagination > li {
+ display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+ position: relative;
+ float: left;
+ padding: 6px 12px;
+ line-height: 1.42857143;
+ text-decoration: none;
+ color: #337ab7;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+ margin-left: 0;
+ border-bottom-left-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+ border-bottom-right-radius: 2px;
+ border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+ z-index: 2;
+ color: #23527c;
+ background-color: #eeeeee;
+ border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+ z-index: 3;
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+ cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+ color: #777777;
+ background-color: #fff;
+ border-color: #ddd;
+ cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+ padding: 10px 16px;
+ font-size: 17px;
+ line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+ border-bottom-left-radius: 3px;
+ border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+ border-bottom-right-radius: 3px;
+ border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+ border-bottom-left-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+ border-bottom-right-radius: 1px;
+ border-top-right-radius: 1px;
+}
+.pager {
+ padding-left: 0;
+ margin: 18px 0;
+ list-style: none;
+ text-align: center;
+}
+.pager li {
+ display: inline;
+}
+.pager li > a,
+.pager li > span {
+ display: inline-block;
+ padding: 5px 14px;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+ text-decoration: none;
+ background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+ float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+ float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+ color: #777777;
+ background-color: #fff;
+ cursor: not-allowed;
+}
+.label {
+ display: inline;
+ padding: .2em .6em .3em;
+ font-size: 75%;
+ font-weight: bold;
+ line-height: 1;
+ color: #fff;
+ text-align: center;
+ white-space: nowrap;
+ vertical-align: baseline;
+ border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+ color: #fff;
+ text-decoration: none;
+ cursor: pointer;
+}
+.label:empty {
+ display: none;
+}
+.btn .label {
+ position: relative;
+ top: -1px;
+}
+.label-default {
+ background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+ background-color: #5e5e5e;
+}
+.label-primary {
+ background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+ background-color: #286090;
+}
+.label-success {
+ background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+ background-color: #449d44;
+}
+.label-info {
+ background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+ background-color: #31b0d5;
+}
+.label-warning {
+ background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+ background-color: #ec971f;
+}
+.label-danger {
+ background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+ background-color: #c9302c;
+}
+.badge {
+ display: inline-block;
+ min-width: 10px;
+ padding: 3px 7px;
+ font-size: 12px;
+ font-weight: bold;
+ color: #fff;
+ line-height: 1;
+ vertical-align: middle;
+ white-space: nowrap;
+ text-align: center;
+ background-color: #777777;
+ border-radius: 10px;
+}
+.badge:empty {
+ display: none;
+}
+.btn .badge {
+ position: relative;
+ top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+ top: 0;
+ padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+ color: #fff;
+ text-decoration: none;
+ cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+ color: #337ab7;
+ background-color: #fff;
+}
+.list-group-item > .badge {
+ float: right;
+}
+.list-group-item > .badge + .badge {
+ margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+ margin-left: 3px;
+}
+.jumbotron {
+ padding-top: 30px;
+ padding-bottom: 30px;
+ margin-bottom: 30px;
+ color: inherit;
+ background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+ color: inherit;
+}
+.jumbotron p {
+ margin-bottom: 15px;
+ font-size: 20px;
+ font-weight: 200;
+}
+.jumbotron > hr {
+ border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+ border-radius: 3px;
+ padding-left: 0px;
+ padding-right: 0px;
+}
+.jumbotron .container {
+ max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+ .jumbotron {
+ padding-top: 48px;
+ padding-bottom: 48px;
+ }
+ .container .jumbotron,
+ .container-fluid .jumbotron {
+ padding-left: 60px;
+ padding-right: 60px;
+ }
+ .jumbotron h1,
+ .jumbotron .h1 {
+ font-size: 59px;
+ }
+}
+.thumbnail {
+ display: block;
+ padding: 4px;
+ margin-bottom: 18px;
+ line-height: 1.42857143;
+ background-color: #fff;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+ -webkit-transition: border 0.2s ease-in-out;
+ -o-transition: border 0.2s ease-in-out;
+ transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+ margin-left: auto;
+ margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+ border-color: #337ab7;
+}
+.thumbnail .caption {
+ padding: 9px;
+ color: #000;
+}
+.alert {
+ padding: 15px;
+ margin-bottom: 18px;
+ border: 1px solid transparent;
+ border-radius: 2px;
+}
+.alert h4 {
+ margin-top: 0;
+ color: inherit;
+}
+.alert .alert-link {
+ font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+ margin-bottom: 0;
+}
+.alert > p + p {
+ margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+ padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+ position: relative;
+ top: -2px;
+ right: -21px;
+ color: inherit;
+}
+.alert-success {
+ background-color: #dff0d8;
+ border-color: #d6e9c6;
+ color: #3c763d;
+}
+.alert-success hr {
+ border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+ color: #2b542c;
+}
+.alert-info {
+ background-color: #d9edf7;
+ border-color: #bce8f1;
+ color: #31708f;
+}
+.alert-info hr {
+ border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+ color: #245269;
+}
+.alert-warning {
+ background-color: #fcf8e3;
+ border-color: #faebcc;
+ color: #8a6d3b;
+}
+.alert-warning hr {
+ border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+ color: #66512c;
+}
+.alert-danger {
+ background-color: #f2dede;
+ border-color: #ebccd1;
+ color: #a94442;
+}
+.alert-danger hr {
+ border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+ color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+ from {
+ background-position: 40px 0;
+ }
+ to {
+ background-position: 0 0;
+ }
+}
+@keyframes progress-bar-stripes {
+ from {
+ background-position: 40px 0;
+ }
+ to {
+ background-position: 0 0;
+ }
+}
+.progress {
+ overflow: hidden;
+ height: 18px;
+ margin-bottom: 18px;
+ background-color: #f5f5f5;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+ box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+ float: left;
+ width: 0%;
+ height: 100%;
+ font-size: 12px;
+ line-height: 18px;
+ color: #fff;
+ text-align: center;
+ background-color: #337ab7;
+ -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+ box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+ -webkit-transition: width 0.6s ease;
+ -o-transition: width 0.6s ease;
+ transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+ -webkit-animation: progress-bar-stripes 2s linear infinite;
+ -o-animation: progress-bar-stripes 2s linear infinite;
+ animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+ background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+ background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+ background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+ background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+ background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+ background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+ margin-top: 15px;
+}
+.media:first-child {
+ margin-top: 0;
+}
+.media,
+.media-body {
+ zoom: 1;
+ overflow: hidden;
+}
+.media-body {
+ width: 10000px;
+}
+.media-object {
+ display: block;
+}
+.media-object.img-thumbnail {
+ max-width: none;
+}
+.media-right,
+.media > .pull-right {
+ padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+ padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+ display: table-cell;
+ vertical-align: top;
+}
+.media-middle {
+ vertical-align: middle;
+}
+.media-bottom {
+ vertical-align: bottom;
+}
+.media-heading {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
+.media-list {
+ padding-left: 0;
+ list-style: none;
+}
+.list-group {
+ margin-bottom: 20px;
+ padding-left: 0;
+}
+.list-group-item {
+ position: relative;
+ display: block;
+ padding: 10px 15px;
+ margin-bottom: -1px;
+ background-color: #fff;
+ border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+ border-top-right-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+ margin-bottom: 0;
+ border-bottom-right-radius: 2px;
+ border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+ color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+ color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+ text-decoration: none;
+ color: #555;
+ background-color: #f5f5f5;
+}
+button.list-group-item {
+ width: 100%;
+ text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+ background-color: #eeeeee;
+ color: #777777;
+ cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+ color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+ color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+ z-index: 2;
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+ color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+ color: #c7ddef;
+}
+.list-group-item-success {
+ color: #3c763d;
+ background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+ color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+ color: #3c763d;
+ background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+ color: #fff;
+ background-color: #3c763d;
+ border-color: #3c763d;
+}
+.list-group-item-info {
+ color: #31708f;
+ background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+ color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+ color: #31708f;
+ background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+ color: #fff;
+ background-color: #31708f;
+ border-color: #31708f;
+}
+.list-group-item-warning {
+ color: #8a6d3b;
+ background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+ color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+ color: #8a6d3b;
+ background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+ color: #fff;
+ background-color: #8a6d3b;
+ border-color: #8a6d3b;
+}
+.list-group-item-danger {
+ color: #a94442;
+ background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+ color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+ color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+ color: #a94442;
+ background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+ color: #fff;
+ background-color: #a94442;
+ border-color: #a94442;
+}
+.list-group-item-heading {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
+.list-group-item-text {
+ margin-bottom: 0;
+ line-height: 1.3;
+}
+.panel {
+ margin-bottom: 18px;
+ background-color: #fff;
+ border: 1px solid transparent;
+ border-radius: 2px;
+ -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+ box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+ padding: 15px;
+}
+.panel-heading {
+ padding: 10px 15px;
+ border-bottom: 1px solid transparent;
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+ color: inherit;
+}
+.panel-title {
+ margin-top: 0;
+ margin-bottom: 0;
+ font-size: 15px;
+ color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+ color: inherit;
+}
+.panel-footer {
+ padding: 10px 15px;
+ background-color: #f5f5f5;
+ border-top: 1px solid #ddd;
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+ margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+ border-width: 1px 0;
+ border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+ border-top: 0;
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+ border-bottom: 0;
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+ border-top-right-radius: 0;
+ border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+ border-top-width: 0;
+}
+.list-group + .panel-footer {
+ border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+ margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+ padding-left: 15px;
+ padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+ border-top-right-radius: 1px;
+ border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+ border-top-left-radius: 1px;
+ border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+ border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+ border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+ border-bottom-right-radius: 1px;
+ border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+ border-bottom-left-radius: 1px;
+ border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+ border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+ border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+ border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+ border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+ border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+ border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+ border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+ border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+ border-bottom: 0;
+}
+.panel > .table-responsive {
+ border: 0;
+ margin-bottom: 0;
+}
+.panel-group {
+ margin-bottom: 18px;
+}
+.panel-group .panel {
+ margin-bottom: 0;
+ border-radius: 2px;
+}
+.panel-group .panel + .panel {
+ margin-top: 5px;
+}
+.panel-group .panel-heading {
+ border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+ border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+ border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+ border-bottom: 1px solid #ddd;
+}
+.panel-default {
+ border-color: #ddd;
+}
+.panel-default > .panel-heading {
+ color: #333333;
+ background-color: #f5f5f5;
+ border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+ color: #f5f5f5;
+ background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #ddd;
+}
+.panel-primary {
+ border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+ color: #fff;
+ background-color: #337ab7;
+ border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+ color: #337ab7;
+ background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #337ab7;
+}
+.panel-success {
+ border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+ color: #3c763d;
+ background-color: #dff0d8;
+ border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+ color: #dff0d8;
+ background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #d6e9c6;
+}
+.panel-info {
+ border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+ color: #31708f;
+ background-color: #d9edf7;
+ border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+ color: #d9edf7;
+ background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #bce8f1;
+}
+.panel-warning {
+ border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+ color: #8a6d3b;
+ background-color: #fcf8e3;
+ border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+ color: #fcf8e3;
+ background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #faebcc;
+}
+.panel-danger {
+ border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+ color: #a94442;
+ background-color: #f2dede;
+ border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+ border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+ color: #f2dede;
+ background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+ border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+ position: relative;
+ display: block;
+ height: 0;
+ padding: 0;
+ overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+ position: absolute;
+ top: 0;
+ left: 0;
+ bottom: 0;
+ height: 100%;
+ width: 100%;
+ border: 0;
+}
+.embed-responsive-16by9 {
+ padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+ padding-bottom: 75%;
+}
+.well {
+ min-height: 20px;
+ padding: 19px;
+ margin-bottom: 20px;
+ background-color: #f5f5f5;
+ border: 1px solid #e3e3e3;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+ border-color: #ddd;
+ border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+ padding: 24px;
+ border-radius: 3px;
+}
+.well-sm {
+ padding: 9px;
+ border-radius: 1px;
+}
+.close {
+ float: right;
+ font-size: 19.5px;
+ font-weight: bold;
+ line-height: 1;
+ color: #000;
+ text-shadow: 0 1px 0 #fff;
+ opacity: 0.2;
+ filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+ color: #000;
+ text-decoration: none;
+ cursor: pointer;
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+}
+button.close {
+ padding: 0;
+ cursor: pointer;
+ background: transparent;
+ border: 0;
+ -webkit-appearance: none;
+}
+.modal-open {
+ overflow: hidden;
+}
+.modal {
+ display: none;
+ overflow: hidden;
+ position: fixed;
+ top: 0;
+ right: 0;
+ bottom: 0;
+ left: 0;
+ z-index: 1050;
+ -webkit-overflow-scrolling: touch;
+ outline: 0;
+}
+.modal.fade .modal-dialog {
+ -webkit-transform: translate(0, -25%);
+ -ms-transform: translate(0, -25%);
+ -o-transform: translate(0, -25%);
+ transform: translate(0, -25%);
+ -webkit-transition: -webkit-transform 0.3s ease-out;
+ -moz-transition: -moz-transform 0.3s ease-out;
+ -o-transition: -o-transform 0.3s ease-out;
+ transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+ -webkit-transform: translate(0, 0);
+ -ms-transform: translate(0, 0);
+ -o-transform: translate(0, 0);
+ transform: translate(0, 0);
+}
+.modal-open .modal {
+ overflow-x: hidden;
+ overflow-y: auto;
+}
+.modal-dialog {
+ position: relative;
+ width: auto;
+ margin: 10px;
+}
+.modal-content {
+ position: relative;
+ background-color: #fff;
+ border: 1px solid #999;
+ border: 1px solid rgba(0, 0, 0, 0.2);
+ border-radius: 3px;
+ -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+ box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+ background-clip: padding-box;
+ outline: 0;
+}
+.modal-backdrop {
+ position: fixed;
+ top: 0;
+ right: 0;
+ bottom: 0;
+ left: 0;
+ z-index: 1040;
+ background-color: #000;
+}
+.modal-backdrop.fade {
+ opacity: 0;
+ filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+}
+.modal-header {
+ padding: 15px;
+ border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+ margin-top: -2px;
+}
+.modal-title {
+ margin: 0;
+ line-height: 1.42857143;
+}
+.modal-body {
+ position: relative;
+ padding: 15px;
+}
+.modal-footer {
+ padding: 15px;
+ text-align: right;
+ border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+ margin-left: 5px;
+ margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+ margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+ margin-left: 0;
+}
+.modal-scrollbar-measure {
+ position: absolute;
+ top: -9999px;
+ width: 50px;
+ height: 50px;
+ overflow: scroll;
+}
+@media (min-width: 768px) {
+ .modal-dialog {
+ width: 600px;
+ margin: 30px auto;
+ }
+ .modal-content {
+ -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+ box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+ }
+ .modal-sm {
+ width: 300px;
+ }
+}
+@media (min-width: 992px) {
+ .modal-lg {
+ width: 900px;
+ }
+}
+.tooltip {
+ position: absolute;
+ z-index: 1070;
+ display: block;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-style: normal;
+ font-weight: normal;
+ letter-spacing: normal;
+ line-break: auto;
+ line-height: 1.42857143;
+ text-align: left;
+ text-align: start;
+ text-decoration: none;
+ text-shadow: none;
+ text-transform: none;
+ white-space: normal;
+ word-break: normal;
+ word-spacing: normal;
+ word-wrap: normal;
+ font-size: 12px;
+ opacity: 0;
+ filter: alpha(opacity=0);
+}
+.tooltip.in {
+ opacity: 0.9;
+ filter: alpha(opacity=90);
+}
+.tooltip.top {
+ margin-top: -3px;
+ padding: 5px 0;
+}
+.tooltip.right {
+ margin-left: 3px;
+ padding: 0 5px;
+}
+.tooltip.bottom {
+ margin-top: 3px;
+ padding: 5px 0;
+}
+.tooltip.left {
+ margin-left: -3px;
+ padding: 0 5px;
+}
+.tooltip-inner {
+ max-width: 200px;
+ padding: 3px 8px;
+ color: #fff;
+ text-align: center;
+ background-color: #000;
+ border-radius: 2px;
+}
+.tooltip-arrow {
+ position: absolute;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+ bottom: 0;
+ left: 50%;
+ margin-left: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+ bottom: 0;
+ right: 5px;
+ margin-bottom: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+ bottom: 0;
+ left: 5px;
+ margin-bottom: -5px;
+ border-width: 5px 5px 0;
+ border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+ top: 50%;
+ left: 0;
+ margin-top: -5px;
+ border-width: 5px 5px 5px 0;
+ border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+ top: 50%;
+ right: 0;
+ margin-top: -5px;
+ border-width: 5px 0 5px 5px;
+ border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+ top: 0;
+ left: 50%;
+ margin-left: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+ top: 0;
+ right: 5px;
+ margin-top: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+ top: 0;
+ left: 5px;
+ margin-top: -5px;
+ border-width: 0 5px 5px;
+ border-bottom-color: #000;
+}
+.popover {
+ position: absolute;
+ top: 0;
+ left: 0;
+ z-index: 1060;
+ display: none;
+ max-width: 276px;
+ padding: 1px;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+ font-style: normal;
+ font-weight: normal;
+ letter-spacing: normal;
+ line-break: auto;
+ line-height: 1.42857143;
+ text-align: left;
+ text-align: start;
+ text-decoration: none;
+ text-shadow: none;
+ text-transform: none;
+ white-space: normal;
+ word-break: normal;
+ word-spacing: normal;
+ word-wrap: normal;
+ font-size: 13px;
+ background-color: #fff;
+ background-clip: padding-box;
+ border: 1px solid #ccc;
+ border: 1px solid rgba(0, 0, 0, 0.2);
+ border-radius: 3px;
+ -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+ box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+ margin-top: -10px;
+}
+.popover.right {
+ margin-left: 10px;
+}
+.popover.bottom {
+ margin-top: 10px;
+}
+.popover.left {
+ margin-left: -10px;
+}
+.popover-title {
+ margin: 0;
+ padding: 8px 14px;
+ font-size: 13px;
+ background-color: #f7f7f7;
+ border-bottom: 1px solid #ebebeb;
+ border-radius: 2px 2px 0 0;
+}
+.popover-content {
+ padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+ position: absolute;
+ display: block;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+}
+.popover > .arrow {
+ border-width: 11px;
+}
+.popover > .arrow:after {
+ border-width: 10px;
+ content: "";
+}
+.popover.top > .arrow {
+ left: 50%;
+ margin-left: -11px;
+ border-bottom-width: 0;
+ border-top-color: #999999;
+ border-top-color: rgba(0, 0, 0, 0.25);
+ bottom: -11px;
+}
+.popover.top > .arrow:after {
+ content: " ";
+ bottom: 1px;
+ margin-left: -10px;
+ border-bottom-width: 0;
+ border-top-color: #fff;
+}
+.popover.right > .arrow {
+ top: 50%;
+ left: -11px;
+ margin-top: -11px;
+ border-left-width: 0;
+ border-right-color: #999999;
+ border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+ content: " ";
+ left: 1px;
+ bottom: -10px;
+ border-left-width: 0;
+ border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+ left: 50%;
+ margin-left: -11px;
+ border-top-width: 0;
+ border-bottom-color: #999999;
+ border-bottom-color: rgba(0, 0, 0, 0.25);
+ top: -11px;
+}
+.popover.bottom > .arrow:after {
+ content: " ";
+ top: 1px;
+ margin-left: -10px;
+ border-top-width: 0;
+ border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+ top: 50%;
+ right: -11px;
+ margin-top: -11px;
+ border-right-width: 0;
+ border-left-color: #999999;
+ border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+ content: " ";
+ right: 1px;
+ border-right-width: 0;
+ border-left-color: #fff;
+ bottom: -10px;
+}
+.carousel {
+ position: relative;
+}
+.carousel-inner {
+ position: relative;
+ overflow: hidden;
+ width: 100%;
+}
+.carousel-inner > .item {
+ display: none;
+ position: relative;
+ -webkit-transition: 0.6s ease-in-out left;
+ -o-transition: 0.6s ease-in-out left;
+ transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+ line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+ .carousel-inner > .item {
+ -webkit-transition: -webkit-transform 0.6s ease-in-out;
+ -moz-transition: -moz-transform 0.6s ease-in-out;
+ -o-transition: -o-transform 0.6s ease-in-out;
+ transition: transform 0.6s ease-in-out;
+ -webkit-backface-visibility: hidden;
+ -moz-backface-visibility: hidden;
+ backface-visibility: hidden;
+ -webkit-perspective: 1000px;
+ -moz-perspective: 1000px;
+ perspective: 1000px;
+ }
+ .carousel-inner > .item.next,
+ .carousel-inner > .item.active.right {
+ -webkit-transform: translate3d(100%, 0, 0);
+ transform: translate3d(100%, 0, 0);
+ left: 0;
+ }
+ .carousel-inner > .item.prev,
+ .carousel-inner > .item.active.left {
+ -webkit-transform: translate3d(-100%, 0, 0);
+ transform: translate3d(-100%, 0, 0);
+ left: 0;
+ }
+ .carousel-inner > .item.next.left,
+ .carousel-inner > .item.prev.right,
+ .carousel-inner > .item.active {
+ -webkit-transform: translate3d(0, 0, 0);
+ transform: translate3d(0, 0, 0);
+ left: 0;
+ }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+ display: block;
+}
+.carousel-inner > .active {
+ left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+ position: absolute;
+ top: 0;
+ width: 100%;
+}
+.carousel-inner > .next {
+ left: 100%;
+}
+.carousel-inner > .prev {
+ left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+ left: 0;
+}
+.carousel-inner > .active.left {
+ left: -100%;
+}
+.carousel-inner > .active.right {
+ left: 100%;
+}
+.carousel-control {
+ position: absolute;
+ top: 0;
+ left: 0;
+ bottom: 0;
+ width: 15%;
+ opacity: 0.5;
+ filter: alpha(opacity=50);
+ font-size: 20px;
+ color: #fff;
+ text-align: center;
+ text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+ background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+ background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+ background-repeat: repeat-x;
+ filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+ left: auto;
+ right: 0;
+ background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+ background-repeat: repeat-x;
+ filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+ outline: 0;
+ color: #fff;
+ text-decoration: none;
+ opacity: 0.9;
+ filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+ position: absolute;
+ top: 50%;
+ margin-top: -10px;
+ z-index: 5;
+ display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+ left: 50%;
+ margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+ right: 50%;
+ margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+ width: 20px;
+ height: 20px;
+ line-height: 1;
+ font-family: serif;
+}
+.carousel-control .icon-prev:before {
+ content: '\2039';
+}
+.carousel-control .icon-next:before {
+ content: '\203a';
+}
+.carousel-indicators {
+ position: absolute;
+ bottom: 10px;
+ left: 50%;
+ z-index: 15;
+ width: 60%;
+ margin-left: -30%;
+ padding-left: 0;
+ list-style: none;
+ text-align: center;
+}
+.carousel-indicators li {
+ display: inline-block;
+ width: 10px;
+ height: 10px;
+ margin: 1px;
+ text-indent: -999px;
+ border: 1px solid #fff;
+ border-radius: 10px;
+ cursor: pointer;
+ background-color: #000 \9;
+ background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+ margin: 0;
+ width: 12px;
+ height: 12px;
+ background-color: #fff;
+}
+.carousel-caption {
+ position: absolute;
+ left: 15%;
+ right: 15%;
+ bottom: 20px;
+ z-index: 10;
+ padding-top: 20px;
+ padding-bottom: 20px;
+ color: #fff;
+ text-align: center;
+ text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+ text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+ .carousel-control .glyphicon-chevron-left,
+ .carousel-control .glyphicon-chevron-right,
+ .carousel-control .icon-prev,
+ .carousel-control .icon-next {
+ width: 30px;
+ height: 30px;
+ margin-top: -10px;
+ font-size: 30px;
+ }
+ .carousel-control .glyphicon-chevron-left,
+ .carousel-control .icon-prev {
+ margin-left: -10px;
+ }
+ .carousel-control .glyphicon-chevron-right,
+ .carousel-control .icon-next {
+ margin-right: -10px;
+ }
+ .carousel-caption {
+ left: 20%;
+ right: 20%;
+ padding-bottom: 30px;
+ }
+ .carousel-indicators {
+ bottom: 20px;
+ }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+ content: " ";
+ display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+ clear: both;
+}
+.center-block {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+.pull-right {
+ float: right !important;
+}
+.pull-left {
+ float: left !important;
+}
+.hide {
+ display: none !important;
+}
+.show {
+ display: block !important;
+}
+.invisible {
+ visibility: hidden;
+}
+.text-hide {
+ font: 0/0 a;
+ color: transparent;
+ text-shadow: none;
+ background-color: transparent;
+ border: 0;
+}
+.hidden {
+ display: none !important;
+}
+.affix {
+ position: fixed;
+}
+@-ms-viewport {
+ width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+ display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+ display: none !important;
+}
+@media (max-width: 767px) {
+ .visible-xs {
+ display: block !important;
+ }
+ table.visible-xs {
+ display: table !important;
+ }
+ tr.visible-xs {
+ display: table-row !important;
+ }
+ th.visible-xs,
+ td.visible-xs {
+ display: table-cell !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-block {
+ display: block !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-inline {
+ display: inline !important;
+ }
+}
+@media (max-width: 767px) {
+ .visible-xs-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm {
+ display: block !important;
+ }
+ table.visible-sm {
+ display: table !important;
+ }
+ tr.visible-sm {
+ display: table-row !important;
+ }
+ th.visible-sm,
+ td.visible-sm {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-block {
+ display: block !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .visible-sm-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md {
+ display: block !important;
+ }
+ table.visible-md {
+ display: table !important;
+ }
+ tr.visible-md {
+ display: table-row !important;
+ }
+ th.visible-md,
+ td.visible-md {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-block {
+ display: block !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .visible-md-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg {
+ display: block !important;
+ }
+ table.visible-lg {
+ display: table !important;
+ }
+ tr.visible-lg {
+ display: table-row !important;
+ }
+ th.visible-lg,
+ td.visible-lg {
+ display: table-cell !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-block {
+ display: block !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-inline {
+ display: inline !important;
+ }
+}
+@media (min-width: 1200px) {
+ .visible-lg-inline-block {
+ display: inline-block !important;
+ }
+}
+@media (max-width: 767px) {
+ .hidden-xs {
+ display: none !important;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ .hidden-sm {
+ display: none !important;
+ }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+ .hidden-md {
+ display: none !important;
+ }
+}
+@media (min-width: 1200px) {
+ .hidden-lg {
+ display: none !important;
+ }
+}
+.visible-print {
+ display: none !important;
+}
+@media print {
+ .visible-print {
+ display: block !important;
+ }
+ table.visible-print {
+ display: table !important;
+ }
+ tr.visible-print {
+ display: table-row !important;
+ }
+ th.visible-print,
+ td.visible-print {
+ display: table-cell !important;
+ }
+}
+.visible-print-block {
+ display: none !important;
+}
+@media print {
+ .visible-print-block {
+ display: block !important;
+ }
+}
+.visible-print-inline {
+ display: none !important;
+}
+@media print {
+ .visible-print-inline {
+ display: inline !important;
+ }
+}
+.visible-print-inline-block {
+ display: none !important;
+}
+@media print {
+ .visible-print-inline-block {
+ display: inline-block !important;
+ }
+}
+@media print {
+ .hidden-print {
+ display: none !important;
+ }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ * Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+ font-family: 'FontAwesome';
+ src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+ src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+ font-weight: normal;
+ font-style: normal;
+}
+.fa {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+ font-size: 1.33333333em;
+ line-height: 0.75em;
+ vertical-align: -15%;
+}
+.fa-2x {
+ font-size: 2em;
+}
+.fa-3x {
+ font-size: 3em;
+}
+.fa-4x {
+ font-size: 4em;
+}
+.fa-5x {
+ font-size: 5em;
+}
+.fa-fw {
+ width: 1.28571429em;
+ text-align: center;
+}
+.fa-ul {
+ padding-left: 0;
+ margin-left: 2.14285714em;
+ list-style-type: none;
+}
+.fa-ul > li {
+ position: relative;
+}
+.fa-li {
+ position: absolute;
+ left: -2.14285714em;
+ width: 2.14285714em;
+ top: 0.14285714em;
+ text-align: center;
+}
+.fa-li.fa-lg {
+ left: -1.85714286em;
+}
+.fa-border {
+ padding: .2em .25em .15em;
+ border: solid 0.08em #eee;
+ border-radius: .1em;
+}
+.pull-right {
+ float: right;
+}
+.pull-left {
+ float: left;
+}
+.fa.pull-left {
+ margin-right: .3em;
+}
+.fa.pull-right {
+ margin-left: .3em;
+}
+.fa-spin {
+ -webkit-animation: fa-spin 2s infinite linear;
+ animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+ 0% {
+ -webkit-transform: rotate(0deg);
+ transform: rotate(0deg);
+ }
+ 100% {
+ -webkit-transform: rotate(359deg);
+ transform: rotate(359deg);
+ }
+}
+@keyframes fa-spin {
+ 0% {
+ -webkit-transform: rotate(0deg);
+ transform: rotate(0deg);
+ }
+ 100% {
+ -webkit-transform: rotate(359deg);
+ transform: rotate(359deg);
+ }
+}
+.fa-rotate-90 {
+ filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+ -webkit-transform: rotate(90deg);
+ -ms-transform: rotate(90deg);
+ transform: rotate(90deg);
+}
+.fa-rotate-180 {
+ filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+ -webkit-transform: rotate(180deg);
+ -ms-transform: rotate(180deg);
+ transform: rotate(180deg);
+}
+.fa-rotate-270 {
+ filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+ -webkit-transform: rotate(270deg);
+ -ms-transform: rotate(270deg);
+ transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+ filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+ -webkit-transform: scale(-1, 1);
+ -ms-transform: scale(-1, 1);
+ transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+ filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+ -webkit-transform: scale(1, -1);
+ -ms-transform: scale(1, -1);
+ transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+ filter: none;
+}
+.fa-stack {
+ position: relative;
+ display: inline-block;
+ width: 2em;
+ height: 2em;
+ line-height: 2em;
+ vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+ position: absolute;
+ left: 0;
+ width: 100%;
+ text-align: center;
+}
+.fa-stack-1x {
+ line-height: inherit;
+}
+.fa-stack-2x {
+ font-size: 2em;
+}
+.fa-inverse {
+ color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+ readers do not read off random characters that represent icons */
+.fa-glass:before {
+ content: "\f000";
+}
+.fa-music:before {
+ content: "\f001";
+}
+.fa-search:before {
+ content: "\f002";
+}
+.fa-envelope-o:before {
+ content: "\f003";
+}
+.fa-heart:before {
+ content: "\f004";
+}
+.fa-star:before {
+ content: "\f005";
+}
+.fa-star-o:before {
+ content: "\f006";
+}
+.fa-user:before {
+ content: "\f007";
+}
+.fa-film:before {
+ content: "\f008";
+}
+.fa-th-large:before {
+ content: "\f009";
+}
+.fa-th:before {
+ content: "\f00a";
+}
+.fa-th-list:before {
+ content: "\f00b";
+}
+.fa-check:before {
+ content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+ content: "\f00d";
+}
+.fa-search-plus:before {
+ content: "\f00e";
+}
+.fa-search-minus:before {
+ content: "\f010";
+}
+.fa-power-off:before {
+ content: "\f011";
+}
+.fa-signal:before {
+ content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+ content: "\f013";
+}
+.fa-trash-o:before {
+ content: "\f014";
+}
+.fa-home:before {
+ content: "\f015";
+}
+.fa-file-o:before {
+ content: "\f016";
+}
+.fa-clock-o:before {
+ content: "\f017";
+}
+.fa-road:before {
+ content: "\f018";
+}
+.fa-download:before {
+ content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+ content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+ content: "\f01b";
+}
+.fa-inbox:before {
+ content: "\f01c";
+}
+.fa-play-circle-o:before {
+ content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+ content: "\f01e";
+}
+.fa-refresh:before {
+ content: "\f021";
+}
+.fa-list-alt:before {
+ content: "\f022";
+}
+.fa-lock:before {
+ content: "\f023";
+}
+.fa-flag:before {
+ content: "\f024";
+}
+.fa-headphones:before {
+ content: "\f025";
+}
+.fa-volume-off:before {
+ content: "\f026";
+}
+.fa-volume-down:before {
+ content: "\f027";
+}
+.fa-volume-up:before {
+ content: "\f028";
+}
+.fa-qrcode:before {
+ content: "\f029";
+}
+.fa-barcode:before {
+ content: "\f02a";
+}
+.fa-tag:before {
+ content: "\f02b";
+}
+.fa-tags:before {
+ content: "\f02c";
+}
+.fa-book:before {
+ content: "\f02d";
+}
+.fa-bookmark:before {
+ content: "\f02e";
+}
+.fa-print:before {
+ content: "\f02f";
+}
+.fa-camera:before {
+ content: "\f030";
+}
+.fa-font:before {
+ content: "\f031";
+}
+.fa-bold:before {
+ content: "\f032";
+}
+.fa-italic:before {
+ content: "\f033";
+}
+.fa-text-height:before {
+ content: "\f034";
+}
+.fa-text-width:before {
+ content: "\f035";
+}
+.fa-align-left:before {
+ content: "\f036";
+}
+.fa-align-center:before {
+ content: "\f037";
+}
+.fa-align-right:before {
+ content: "\f038";
+}
+.fa-align-justify:before {
+ content: "\f039";
+}
+.fa-list:before {
+ content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+ content: "\f03b";
+}
+.fa-indent:before {
+ content: "\f03c";
+}
+.fa-video-camera:before {
+ content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+ content: "\f03e";
+}
+.fa-pencil:before {
+ content: "\f040";
+}
+.fa-map-marker:before {
+ content: "\f041";
+}
+.fa-adjust:before {
+ content: "\f042";
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+ content: "\f1ad";
+}
+.fa-child:before {
+ content: "\f1ae";
+}
+.fa-paw:before {
+ content: "\f1b0";
+}
+.fa-spoon:before {
+ content: "\f1b1";
+}
+.fa-cube:before {
+ content: "\f1b2";
+}
+.fa-cubes:before {
+ content: "\f1b3";
+}
+.fa-behance:before {
+ content: "\f1b4";
+}
+.fa-behance-square:before {
+ content: "\f1b5";
+}
+.fa-steam:before {
+ content: "\f1b6";
+}
+.fa-steam-square:before {
+ content: "\f1b7";
+}
+.fa-recycle:before {
+ content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+ content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+ content: "\f1ba";
+}
+.fa-tree:before {
+ content: "\f1bb";
+}
+.fa-spotify:before {
+ content: "\f1bc";
+}
+.fa-deviantart:before {
+ content: "\f1bd";
+}
+.fa-soundcloud:before {
+ content: "\f1be";
+}
+.fa-database:before {
+ content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+ content: "\f1c1";
+}
+.fa-file-word-o:before {
+ content: "\f1c2";
+}
+.fa-file-excel-o:before {
+ content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+ content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+ content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+ content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+ content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+ content: "\f1c8";
+}
+.fa-file-code-o:before {
+ content: "\f1c9";
+}
+.fa-vine:before {
+ content: "\f1ca";
+}
+.fa-codepen:before {
+ content: "\f1cb";
+}
+.fa-jsfiddle:before {
+ content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+ content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+ content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+ content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+ content: "\f1d1";
+}
+.fa-git-square:before {
+ content: "\f1d2";
+}
+.fa-git:before {
+ content: "\f1d3";
+}
+.fa-hacker-news:before {
+ content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+ content: "\f1d5";
+}
+.fa-qq:before {
+ content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+ content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+ content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+ content: "\f1d9";
+}
+.fa-history:before {
+ content: "\f1da";
+}
+.fa-circle-thin:before {
+ content: "\f1db";
+}
+.fa-header:before {
+ content: "\f1dc";
+}
+.fa-paragraph:before {
+ content: "\f1dd";
+}
+.fa-sliders:before {
+ content: "\f1de";
+}
+.fa-share-alt:before {
+ content: "\f1e0";
+}
+.fa-share-alt-square:before {
+ content: "\f1e1";
+}
+.fa-bomb:before {
+ content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+ content: "\f1e3";
+}
+.fa-tty:before {
+ content: "\f1e4";
+}
+.fa-binoculars:before {
+ content: "\f1e5";
+}
+.fa-plug:before {
+ content: "\f1e6";
+}
+.fa-slideshare:before {
+ content: "\f1e7";
+}
+.fa-twitch:before {
+ content: "\f1e8";
+}
+.fa-yelp:before {
+ content: "\f1e9";
+}
+.fa-newspaper-o:before {
+ content: "\f1ea";
+}
+.fa-wifi:before {
+ content: "\f1eb";
+}
+.fa-calculator:before {
+ content: "\f1ec";
+}
+.fa-paypal:before {
+ content: "\f1ed";
+}
+.fa-google-wallet:before {
+ content: "\f1ee";
+}
+.fa-cc-visa:before {
+ content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+ content: "\f1f1";
+}
+.fa-cc-discover:before {
+ content: "\f1f2";
+}
+.fa-cc-amex:before {
+ content: "\f1f3";
+}
+.fa-cc-paypal:before {
+ content: "\f1f4";
+}
+.fa-cc-stripe:before {
+ content: "\f1f5";
+}
+.fa-bell-slash:before {
+ content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+ content: "\f1f7";
+}
+.fa-trash:before {
+ content: "\f1f8";
+}
+.fa-copyright:before {
+ content: "\f1f9";
+}
+.fa-at:before {
+ content: "\f1fa";
+}
+.fa-eyedropper:before {
+ content: "\f1fb";
+}
+.fa-paint-brush:before {
+ content: "\f1fc";
+}
+.fa-birthday-cake:before {
+ content: "\f1fd";
+}
+.fa-area-chart:before {
+ content: "\f1fe";
+}
+.fa-pie-chart:before {
+ content: "\f200";
+}
+.fa-line-chart:before {
+ content: "\f201";
+}
+.fa-lastfm:before {
+ content: "\f202";
+}
+.fa-lastfm-square:before {
+ content: "\f203";
+}
+.fa-toggle-off:before {
+ content: "\f204";
+}
+.fa-toggle-on:before {
+ content: "\f205";
+}
+.fa-bicycle:before {
+ content: "\f206";
+}
+.fa-bus:before {
+ content: "\f207";
+}
+.fa-ioxhost:before {
+ content: "\f208";
+}
+.fa-angellist:before {
+ content: "\f209";
+}
+.fa-cc:before {
+ content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+ content: "\f20b";
+}
+.fa-meanpath:before {
+ content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+ -webkit-transform: translate(0, 0);
+ -ms-transform: translate(0, 0);
+ -o-transform: translate(0, 0);
+ transform: translate(0, 0);
+}
+code {
+ color: #000;
+}
+pre {
+ font-size: inherit;
+ line-height: inherit;
+}
+label {
+ font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+.corner-all {
+ border-radius: 2px;
+}
+.no-padding {
+ padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* 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
+`Old browsers` comments below.
+*/
+.hbox {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+.hbox > * {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+.vbox {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+.vbox > * {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+ /* Old browsers */
+ -webkit-box-direction: reverse;
+ -moz-box-direction: reverse;
+ box-direction: reverse;
+ /* Modern browsers */
+ flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+ /* Old browsers */
+ -webkit-box-flex: 0;
+ -moz-box-flex: 0;
+ box-flex: 0;
+ /* Modern browsers */
+ flex: none;
+ width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+ /* Old browsers */
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+ /* Old browsers */
+ -webkit-box-flex: 2;
+ -moz-box-flex: 2;
+ box-flex: 2;
+ /* Modern browsers */
+ flex: 2;
+}
+.box-group1 {
+ /* Deprecated */
+ -webkit-box-flex-group: 1;
+ -moz-box-flex-group: 1;
+ box-flex-group: 1;
+}
+.box-group2 {
+ /* Deprecated */
+ -webkit-box-flex-group: 2;
+ -moz-box-flex-group: 2;
+ box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+ /* Old browsers */
+ -webkit-box-pack: start;
+ -moz-box-pack: start;
+ box-pack: start;
+ /* Modern browsers */
+ justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+ /* Old browsers */
+ -webkit-box-pack: end;
+ -moz-box-pack: end;
+ box-pack: end;
+ /* Modern browsers */
+ justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+ /* Old browsers */
+ -webkit-box-pack: center;
+ -moz-box-pack: center;
+ box-pack: center;
+ /* Modern browsers */
+ justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+ /* Old browsers */
+ -webkit-box-pack: baseline;
+ -moz-box-pack: baseline;
+ box-pack: baseline;
+ /* Modern browsers */
+ justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+ /* Old browsers */
+ -webkit-box-pack: stretch;
+ -moz-box-pack: stretch;
+ box-pack: stretch;
+ /* Modern browsers */
+ justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+ /* Old browsers */
+ -webkit-box-align: start;
+ -moz-box-align: start;
+ box-align: start;
+ /* Modern browsers */
+ align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+ /* Old browsers */
+ -webkit-box-align: end;
+ -moz-box-align: end;
+ box-align: end;
+ /* Modern browsers */
+ align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+ /* Old browsers */
+ -webkit-box-align: center;
+ -moz-box-align: center;
+ box-align: center;
+ /* Modern browsers */
+ align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+ /* Old browsers */
+ -webkit-box-align: baseline;
+ -moz-box-align: baseline;
+ box-align: baseline;
+ /* Modern browsers */
+ align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+ /* Old browsers */
+ -webkit-box-align: stretch;
+ -moz-box-align: stretch;
+ box-align: stretch;
+ /* Modern browsers */
+ align-items: stretch;
+}
+div.error {
+ margin: 2em;
+ text-align: center;
+}
+div.error > h1 {
+ font-size: 500%;
+ line-height: normal;
+}
+div.error > p {
+ font-size: 200%;
+ line-height: normal;
+}
+div.traceback-wrapper {
+ text-align: left;
+ max-width: 800px;
+ margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+ background-color: #fff;
+ /* This makes sure that the body covers the entire window and needs to
+ be in a different element than the display: box in wrapper below */
+ position: absolute;
+ left: 0px;
+ right: 0px;
+ top: 0px;
+ bottom: 0px;
+ overflow: visible;
+}
+body > #header {
+ /* Initially hidden to prevent FLOUC */
+ display: none;
+ background-color: #fff;
+ /* Display over codemirror */
+ position: relative;
+ z-index: 100;
+}
+body > #header #header-container {
+ padding-bottom: 5px;
+ padding-top: 5px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+ width: 100%;
+ height: 1px;
+ background: #e7e7e7;
+ margin-bottom: -1px;
+}
+@media print {
+ body > #header {
+ display: none !important;
+ }
+}
+#header-spacer {
+ width: 100%;
+ visibility: hidden;
+}
+@media print {
+ #header-spacer {
+ display: none;
+ }
+}
+#ipython_notebook {
+ padding-left: 0px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+ #ipython_notebook {
+ margin-left: 10px;
+ }
+}
+#noscript {
+ width: auto;
+ padding-top: 16px;
+ padding-bottom: 16px;
+ text-align: center;
+ font-size: 22px;
+ color: red;
+ font-weight: bold;
+}
+#ipython_notebook img {
+ height: 28px;
+}
+#site {
+ width: 100%;
+ display: none;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ overflow: auto;
+}
+@media print {
+ #site {
+ height: auto !important;
+ }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+ padding: 0.2em 0.8em;
+ font-size: 77%;
+}
+input.ui-button {
+ padding: 0.3em 0.9em;
+}
+span#login_widget {
+ float: right;
+}
+span#login_widget > .button,
+#logout {
+ color: #333;
+ background-color: #fff;
+ border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+ color: #333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+ background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+ background-color: #fff;
+ border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+ color: #fff;
+ background-color: #333;
+}
+.nav-header {
+ text-transform: none;
+}
+#header > span {
+ margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+ max-height: calc(100vh - 200px);
+ overflow: auto;
+ flex: 1;
+}
+@media (min-width: 768px) {
+ .modal .modal-dialog {
+ width: 700px;
+ }
+}
+@media (min-width: 768px) {
+ select.form-control {
+ margin-left: 12px;
+ margin-right: 12px;
+ }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+ display: inline-block;
+ margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+ background-color: none;
+ display: inline;
+}
+.alternate_upload.form {
+ padding: 0;
+ margin: 0;
+}
+.alternate_upload input.fileinput {
+ text-align: center;
+ vertical-align: middle;
+ display: inline;
+ opacity: 0;
+ z-index: 2;
+ width: 12ex;
+ margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+ height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+ul#tabs {
+ margin-bottom: 4px;
+}
+ul#tabs a {
+ padding-top: 6px;
+ padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+ text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+ font-size: 16px;
+ margin-right: 4px;
+}
+ul.breadcrumb span {
+ color: #5e5e5e;
+}
+.list_toolbar {
+ padding: 4px 0 4px 0;
+ vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+ padding-top: 1px;
+}
+.dynamic-buttons {
+ padding-top: 3px;
+ display: inline-block;
+}
+.list_toolbar [class*="span"] {
+ min-height: 24px;
+}
+.list_header {
+ font-weight: bold;
+ background-color: #EEE;
+}
+.list_placeholder {
+ font-weight: bold;
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+}
+.list_container {
+ margin-top: 4px;
+ margin-bottom: 20px;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+}
+.list_container > div {
+ border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+ background-color: red;
+}
+.list_container > div:last-child {
+ border: none;
+}
+.list_item:hover .list_item {
+ background-color: #ddd;
+}
+.list_item a {
+ text-decoration: none;
+}
+.list_item:hover {
+ background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+ line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+ margin-right: 7px;
+ margin-left: 14px;
+ vertical-align: baseline;
+ line-height: 22px;
+ position: relative;
+ top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+ margin-left: -1px;
+ vertical-align: baseline;
+ line-height: 22px;
+}
+.new-file input[type=checkbox] {
+ visibility: hidden;
+}
+.item_name {
+ line-height: 22px;
+ height: 24px;
+}
+.item_icon {
+ font-size: 14px;
+ color: #5e5e5e;
+ margin-right: 7px;
+ margin-left: 7px;
+ line-height: 22px;
+ vertical-align: baseline;
+}
+.item_buttons {
+ line-height: 1em;
+ margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+ float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+ margin-left: 5px;
+}
+.item_buttons .btn {
+ min-width: 13ex;
+}
+.item_buttons .running-indicator {
+ padding-top: 4px;
+ color: #5cb85c;
+}
+.item_buttons .kernel-name {
+ padding-top: 4px;
+ color: #5bc0de;
+ margin-right: 7px;
+ float: left;
+}
+.toolbar_info {
+ height: 24px;
+ line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+ padding-top: 3px;
+ padding-bottom: 3px;
+ height: 22px;
+ line-height: 14px;
+ margin: 0px;
+}
+.highlight_text {
+ color: blue;
+}
+#project_name {
+ display: inline-block;
+ padding-left: 7px;
+ margin-left: -2px;
+}
+#project_name > .breadcrumb {
+ padding: 0px;
+ margin-bottom: 0px;
+ background-color: transparent;
+ font-weight: bold;
+}
+#tree-selector {
+ padding-right: 0px;
+}
+#button-select-all {
+ min-width: 50px;
+}
+#select-all {
+ margin-left: 7px;
+ margin-right: 2px;
+}
+.menu_icon {
+ margin-right: 2px;
+}
+.tab-content .row {
+ margin-left: 0px;
+ margin-right: 0px;
+}
+.folder_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f114";
+}
+.folder_icon:before.pull-left {
+ margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+ margin-left: .3em;
+}
+.notebook_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f02d";
+ position: relative;
+ top: -1px;
+}
+.notebook_icon:before.pull-left {
+ margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+ margin-left: .3em;
+}
+.running_notebook_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f02d";
+ position: relative;
+ top: -1px;
+ color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+ margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+ margin-left: .3em;
+}
+.file_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f016";
+ position: relative;
+ top: -2px;
+}
+.file_icon:before.pull-left {
+ margin-right: .3em;
+}
+.file_icon:before.pull-right {
+ margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+ padding-top: 0px;
+ margin-right: -1px;
+}
+ul#new-menu {
+ left: auto;
+ right: 0;
+}
+.kernel-menu-icon {
+ padding-right: 12px;
+ width: 24px;
+ content: "\f096";
+}
+.kernel-menu-icon:before {
+ content: "\f096";
+}
+.kernel-menu-icon-current:before {
+ content: "\f00c";
+}
+#tab_content {
+ padding-top: 20px;
+}
+#running .panel-group .panel {
+ margin-top: 3px;
+ margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+ background-color: #EEE;
+ padding-top: 4px;
+ padding-bottom: 4px;
+ padding-left: 7px;
+ padding-right: 7px;
+ line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+ text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+ padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+ margin-top: 0px;
+ margin-bottom: 0px;
+ border: 0px;
+ border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+ border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+ border-bottom: 0px;
+}
+.delete-button {
+ display: none;
+}
+.duplicate-button {
+ display: none;
+}
+.rename-button {
+ display: none;
+}
+.shutdown-button {
+ display: none;
+}
+.dynamic-instructions {
+ display: inline-block;
+ padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+ padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+ content: "\f00c";
+}
+#mode-menu {
+ overflow: auto;
+ max-height: 20em;
+}
+.edit_app #header {
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+ /* Use a negative 1 bottom margin, so the border overlaps the border of the
+ header */
+ margin-bottom: -1px;
+}
+.dirty-indicator {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-dirty {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+ margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+ margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+ margin-left: .3em;
+}
+#filename {
+ font-size: 16pt;
+ display: table;
+ padding: 0px 5px;
+}
+#current-mode {
+ padding-left: 5px;
+ padding-right: 5px;
+}
+#texteditor-backdrop {
+ padding-top: 20px;
+ padding-bottom: 20px;
+}
+@media not print {
+ #texteditor-backdrop {
+ background-color: #EEE;
+ }
+}
+@media print {
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+ background-color: #fff;
+ }
+}
+@media not print {
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+ #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+ background-color: #fff;
+ }
+}
+@media not print {
+ #texteditor-backdrop #texteditor-container {
+ padding: 0px;
+ background-color: #fff;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+ font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+ color: black;
+}
+.ansired {
+ color: darkred;
+}
+.ansigreen {
+ color: darkgreen;
+}
+.ansiyellow {
+ color: #c4a000;
+}
+.ansiblue {
+ color: darkblue;
+}
+.ansipurple {
+ color: darkviolet;
+}
+.ansicyan {
+ color: steelblue;
+}
+.ansigray {
+ color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+ background-color: black;
+}
+.ansibgred {
+ background-color: red;
+}
+.ansibggreen {
+ background-color: green;
+}
+.ansibgyellow {
+ background-color: yellow;
+}
+.ansibgblue {
+ background-color: blue;
+}
+.ansibgpurple {
+ background-color: magenta;
+}
+.ansibgcyan {
+ background-color: cyan;
+}
+.ansibggray {
+ background-color: gray;
+}
+div.cell {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ border-radius: 2px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ border-width: 1px;
+ border-style: solid;
+ border-color: transparent;
+ width: 100%;
+ padding: 5px;
+ /* This acts as a spacer between cells, that is outside the border */
+ margin: 0px;
+ outline: none;
+ border-left-width: 1px;
+ padding-left: 5px;
+ background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+ border-left-color: #90CAF9;
+ border-left-color: #E3F2FD;
+ border-left-width: 1px;
+ padding-left: 5px;
+ border-right-color: #E3F2FD;
+ border-right-width: 1px;
+ background: #E3F2FD;
+}
+@media print {
+ div.cell.jupyter-soft-selected {
+ border-color: transparent;
+ }
+}
+div.cell.selected {
+ border-color: #ababab;
+ border-left-width: 0px;
+ padding-left: 6px;
+ background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+ div.cell.selected {
+ border-color: transparent;
+ }
+}
+div.cell.selected.jupyter-soft-selected {
+ border-left-width: 0;
+ padding-left: 6px;
+ background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+ border-color: #66BB6A;
+ border-left-width: 0px;
+ padding-left: 6px;
+ background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+ .edit_mode div.cell.selected {
+ border-color: transparent;
+ }
+}
+.prompt {
+ /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+ min-width: 14ex;
+ /* This padding is tuned to match the padding on the CodeMirror editor. */
+ padding: 0.4em;
+ margin: 0px;
+ font-family: monospace;
+ text-align: right;
+ /* This has to match that of the the CodeMirror class line-height below */
+ line-height: 1.21429em;
+ /* Don't highlight prompt number selection */
+ -webkit-touch-callout: none;
+ -webkit-user-select: none;
+ -khtml-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+ /* Use default cursor */
+ cursor: default;
+}
+@media (max-width: 540px) {
+ .prompt {
+ text-align: left;
+ }
+}
+div.inner_cell {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+@-moz-document url-prefix() {
+ div.inner_cell {
+ overflow-x: hidden;
+ }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+ border: 1px solid #cfcfcf;
+ border-radius: 2px;
+ background: #f7f7f7;
+ line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+ is no content in the output_subarea and the prompt. The main purpose of this is
+ to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+ padding-top: 0;
+ padding-bottom: 0;
+}
+div.unrecognized_cell {
+ padding: 5px 5px 5px 0px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+ border-radius: 2px;
+ padding: 5px;
+ font-weight: bold;
+ color: red;
+ border: 1px solid #cfcfcf;
+ background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+ color: inherit;
+ text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+ color: inherit;
+ text-decoration: none;
+}
+@media (max-width: 540px) {
+ div.unrecognized_cell > div.prompt {
+ display: none;
+ }
+}
+div.code_cell {
+ /* avoid page breaking on code cells when printing */
+}
+@media print {
+ div.code_cell {
+ page-break-inside: avoid;
+ }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+ page-break-inside: avoid;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+@media (max-width: 540px) {
+ div.input {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+ color: #303F9F;
+ border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+ margin: 0.4em;
+ border: none;
+ padding: 0px;
+ background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+ margin: 0px;
+ border: none;
+ padding: 0px;
+ background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height. See
+ * notebookmain.js. Such fonts will have keywords vertically offset with
+ * respect to the rest of the text. The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ * vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+ line-height: 1.21429em;
+ /* Changed from 1em to our global default */
+ font-size: 14px;
+ height: auto;
+ /* Changed to auto to autogrow */
+ background: none;
+ /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+ /* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+ /* We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+ overflow-y: hidden;
+ overflow-x: auto;
+}
+.CodeMirror-lines {
+ /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+ /* we have set a different line-height and want this to scale with that. */
+ padding: 0.4em;
+}
+.CodeMirror-linenumber {
+ padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+ border-bottom-left-radius: 2px;
+ border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+ /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+ /* .CodeMirror-lines */
+ padding: 0;
+ border: 0;
+ border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+ color: #000;
+}
+.highlight-variable {
+ color: #000;
+}
+.highlight-variable-2 {
+ color: #1a1a1a;
+}
+.highlight-variable-3 {
+ color: #333333;
+}
+.highlight-string {
+ color: #BA2121;
+}
+.highlight-comment {
+ color: #408080;
+ font-style: italic;
+}
+.highlight-number {
+ color: #080;
+}
+.highlight-atom {
+ color: #88F;
+}
+.highlight-keyword {
+ color: #008000;
+ font-weight: bold;
+}
+.highlight-builtin {
+ color: #008000;
+}
+.highlight-error {
+ color: #f00;
+}
+.highlight-operator {
+ color: #AA22FF;
+ font-weight: bold;
+}
+.highlight-meta {
+ color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+ color: #00f;
+}
+.highlight-string-2 {
+ color: #f50;
+}
+.highlight-qualifier {
+ color: #555;
+}
+.highlight-bracket {
+ color: #997;
+}
+.highlight-tag {
+ color: #170;
+}
+.highlight-attribute {
+ color: #00c;
+}
+.highlight-header {
+ color: blue;
+}
+.highlight-quote {
+ color: #090;
+}
+.highlight-link {
+ color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+ color: #008000;
+ font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+ color: #88F;
+}
+.cm-s-ipython span.cm-number {
+ color: #080;
+}
+.cm-s-ipython span.cm-def {
+ color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+ color: #000;
+}
+.cm-s-ipython span.cm-operator {
+ color: #AA22FF;
+ font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+ color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+ color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+ color: #408080;
+ font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+ color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+ color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+ color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+ color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+ color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+ color: #997;
+}
+.cm-s-ipython span.cm-tag {
+ color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+ color: #00c;
+}
+.cm-s-ipython span.cm-header {
+ color: blue;
+}
+.cm-s-ipython span.cm-quote {
+ color: #090;
+}
+.cm-s-ipython span.cm-link {
+ color: #00c;
+}
+.cm-s-ipython span.cm-error {
+ color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+ background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+ background-position: right;
+ background-repeat: no-repeat;
+}
+div.output_wrapper {
+ /* this position must be relative to enable descendents to be absolute within it */
+ position: relative;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+ /* ideally, this would be max-height, but FF barfs all over that */
+ height: 24em;
+ /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+ width: 100%;
+ overflow: auto;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+ box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+ display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+ margin: 0px;
+ padding: 0px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+div.out_prompt_overlay {
+ height: 100%;
+ padding: 0px 0.4em;
+ position: absolute;
+ border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+ /* use inner shadow to get border that is computed the same on WebKit/FF */
+ -webkit-box-shadow: inset 0 0 1px #000;
+ box-shadow: inset 0 0 1px #000;
+ background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+ color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+ padding: 0px;
+ page-break-inside: avoid;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+div.output_area .MathJax_Display {
+ text-align: left !important;
+}
+div.output_area .rendered_html table {
+ margin-left: 0;
+ margin-right: 0;
+}
+div.output_area .rendered_html img {
+ margin-left: 0;
+ margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+ max-width: 100%;
+ height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+ max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+ as that of bootstrap */
+.output {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+@media (max-width: 540px) {
+ div.output_area {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: vertical;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: vertical;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: vertical;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+ }
+}
+div.output_area pre {
+ margin: 0;
+ padding: 0;
+ border: 0;
+ vertical-align: baseline;
+ color: black;
+ background-color: transparent;
+ border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+ the prompt div. */
+div.output_subarea {
+ overflow-x: auto;
+ padding: 0.4em;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+ max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+ overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+ output types */
+/* all text output has this class: */
+div.output_text {
+ text-align: left;
+ color: #000;
+ /* This has to match that of the the CodeMirror class line-height below */
+ line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+ background: #fdd;
+ /* very light red background for stderr */
+}
+div.output_latex {
+ text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+ padding: 0;
+}
+.js-error {
+ color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+ line-height: 1.21429em;
+ padding-top: 5px;
+}
+pre.raw_input_prompt {
+ /* nothing needed here. */
+}
+input.raw_input {
+ font-family: monospace;
+ font-size: inherit;
+ color: inherit;
+ width: auto;
+ /* make sure input baseline aligns with prompt */
+ vertical-align: baseline;
+ /* padding + margin = 0.5em between prompt and cursor */
+ padding: 0em 0.25em;
+ margin: 0em 0.25em;
+}
+input.raw_input:focus {
+ box-shadow: none;
+}
+p.p-space {
+ margin-bottom: 10px;
+}
+div.output_unrecognized {
+ padding: 5px;
+ font-weight: bold;
+ color: red;
+}
+div.output_unrecognized a {
+ color: inherit;
+ text-decoration: none;
+}
+div.output_unrecognized a:hover {
+ color: inherit;
+ text-decoration: none;
+}
+.rendered_html {
+ color: #000;
+ /* any extras will just be numbers: */
+}
+.rendered_html em {
+ font-style: italic;
+}
+.rendered_html strong {
+ font-weight: bold;
+}
+.rendered_html u {
+ text-decoration: underline;
+}
+.rendered_html :link {
+ text-decoration: underline;
+}
+.rendered_html :visited {
+ text-decoration: underline;
+}
+.rendered_html h1 {
+ font-size: 185.7%;
+ margin: 1.08em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h2 {
+ font-size: 157.1%;
+ margin: 1.27em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h3 {
+ font-size: 128.6%;
+ margin: 1.55em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h4 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+}
+.rendered_html h5 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+ font-style: italic;
+}
+.rendered_html h6 {
+ font-size: 100%;
+ margin: 2em 0 0 0;
+ font-weight: bold;
+ line-height: 1.0;
+ font-style: italic;
+}
+.rendered_html h1:first-child {
+ margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+ margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+ margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+ margin-top: 1em;
+}
+.rendered_html h5:first-child {
+ margin-top: 1em;
+}
+.rendered_html h6:first-child {
+ margin-top: 1em;
+}
+.rendered_html ul {
+ list-style: disc;
+ margin: 0em 2em;
+ padding-left: 0px;
+}
+.rendered_html ul ul {
+ list-style: square;
+ margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+ list-style: circle;
+ margin: 0em 2em;
+}
+.rendered_html ol {
+ list-style: decimal;
+ margin: 0em 2em;
+ padding-left: 0px;
+}
+.rendered_html ol ol {
+ list-style: upper-alpha;
+ margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+ list-style: lower-alpha;
+ margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+ list-style: lower-roman;
+ margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+ list-style: decimal;
+ margin: 0em 2em;
+}
+.rendered_html * + ul {
+ margin-top: 1em;
+}
+.rendered_html * + ol {
+ margin-top: 1em;
+}
+.rendered_html hr {
+ color: black;
+ background-color: black;
+}
+.rendered_html pre {
+ margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+ border: 0;
+ background-color: #fff;
+ color: #000;
+ font-size: 100%;
+ padding: 0px;
+}
+.rendered_html blockquote {
+ margin: 1em 2em;
+}
+.rendered_html table {
+ margin-left: auto;
+ margin-right: auto;
+ border: 1px solid black;
+ border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+ border: 1px solid black;
+ border-collapse: collapse;
+ margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+ text-align: left;
+ vertical-align: middle;
+ padding: 4px;
+}
+.rendered_html th {
+ font-weight: bold;
+}
+.rendered_html * + table {
+ margin-top: 1em;
+}
+.rendered_html p {
+ text-align: left;
+}
+.rendered_html * + p {
+ margin-top: 1em;
+}
+.rendered_html img {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+.rendered_html * + img {
+ margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+ max-width: 100%;
+ height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+ max-width: none;
+}
+div.text_cell {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+}
+@media (max-width: 540px) {
+ div.text_cell > div.prompt {
+ display: none;
+ }
+}
+div.text_cell_render {
+ /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+ outline: none;
+ resize: none;
+ width: inherit;
+ border-style: none;
+ padding: 0.5em 0.5em 0.5em 0.4em;
+ color: #000;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+ text-decoration: none;
+ padding: 0px 20px;
+ visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+ visibility: visible;
+}
+.text_cell.rendered .input_area {
+ display: none;
+}
+.text_cell.rendered .rendered_html {
+ overflow-x: auto;
+ overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+ display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+ font-weight: bold;
+ font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+ font-size: 185.7%;
+}
+.cm-header-2 {
+ font-size: 157.1%;
+}
+.cm-header-3 {
+ font-size: 128.6%;
+}
+.cm-header-4 {
+ font-size: 110%;
+}
+.cm-header-5 {
+ font-size: 100%;
+ font-style: italic;
+}
+.cm-header-6 {
+ font-size: 100%;
+ font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+ .notebook_app {
+ padding-left: 0px;
+ padding-right: 0px;
+ }
+}
+#ipython-main-app {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ height: 100%;
+}
+div#notebook_panel {
+ margin: 0px;
+ padding: 0px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ height: 100%;
+}
+div#notebook {
+ font-size: 14px;
+ line-height: 20px;
+ overflow-y: hidden;
+ overflow-x: auto;
+ width: 100%;
+ /* This spaces the page away from the edge of the notebook area */
+ padding-top: 20px;
+ margin: 0px;
+ outline: none;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ min-height: 100%;
+}
+@media not print {
+ #notebook-container {
+ padding: 15px;
+ background-color: #fff;
+ min-height: 0;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ }
+}
+@media print {
+ #notebook-container {
+ width: 100%;
+ }
+}
+div.ui-widget-content {
+ border: 1px solid #ababab;
+ outline: none;
+}
+pre.dialog {
+ background-color: #f7f7f7;
+ border: 1px solid #ddd;
+ border-radius: 2px;
+ padding: 0.4em;
+ padding-left: 2em;
+}
+p.dialog {
+ padding: 0.2em;
+}
+/* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems
+ to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+ white-space: pre-wrap;
+}
+#fonttest {
+ font-family: monospace;
+}
+p {
+ margin-bottom: 0;
+}
+.end_space {
+ min-height: 100px;
+ transition: height .2s ease;
+}
+.notebook_app > #header {
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+ .notebook_app {
+ background-color: #EEE;
+ }
+}
+kbd {
+ border-style: solid;
+ border-width: 1px;
+ box-shadow: none;
+ margin: 2px;
+ padding-left: 2px;
+ padding-right: 2px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+ border: thin solid #CFCFCF;
+ border-bottom: none;
+ background: #EEE;
+ border-radius: 2px 2px 0px 0px;
+ width: 100%;
+ height: 29px;
+ padding-right: 4px;
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ /* Old browsers */
+ -webkit-box-pack: end;
+ -moz-box-pack: end;
+ box-pack: end;
+ /* Modern browsers */
+ justify-content: flex-end;
+ display: -webkit-flex;
+}
+@media print {
+ .celltoolbar {
+ display: none;
+ }
+}
+.ctb_hideshow {
+ display: none;
+ vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+ Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+ display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+ border-top-right-radius: 0px;
+ border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+ border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+ font-size: 87%;
+ padding-top: 3px;
+}
+.celltoolbar select {
+ display: block;
+ width: 100%;
+ height: 32px;
+ padding: 6px 12px;
+ font-size: 13px;
+ line-height: 1.42857143;
+ color: #555555;
+ background-color: #fff;
+ background-image: none;
+ border: 1px solid #ccc;
+ border-radius: 2px;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+ -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+ height: 30px;
+ padding: 5px 10px;
+ font-size: 12px;
+ line-height: 1.5;
+ border-radius: 1px;
+ width: inherit;
+ font-size: inherit;
+ height: 22px;
+ padding: 0px;
+ display: inline-block;
+}
+.celltoolbar select:focus {
+ border-color: #66afe9;
+ outline: 0;
+ -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+ box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+ color: #999;
+ opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+ color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+ color: #999;
+}
+.celltoolbar select::-ms-expand {
+ border: 0;
+ background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+ background-color: #eeeeee;
+ opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+ cursor: not-allowed;
+}
+textarea.celltoolbar select {
+ height: auto;
+}
+select.celltoolbar select {
+ height: 30px;
+ line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+ height: auto;
+}
+.celltoolbar label {
+ margin-left: 5px;
+ margin-right: 5px;
+}
+.completions {
+ position: absolute;
+ z-index: 110;
+ overflow: hidden;
+ border: 1px solid #ababab;
+ border-radius: 2px;
+ -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+ box-shadow: 0px 6px 10px -1px #adadad;
+ line-height: 1;
+}
+.completions select {
+ background: white;
+ outline: none;
+ border: none;
+ padding: 0px;
+ margin: 0px;
+ overflow: auto;
+ font-family: monospace;
+ font-size: 110%;
+ color: #000;
+ width: auto;
+}
+.completions select option.context {
+ color: #286090;
+}
+#kernel_logo_widget {
+ float: right !important;
+ float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+ display: none;
+ margin-top: -1px;
+ margin-bottom: -1px;
+ width: 32px;
+ height: 32px;
+}
+#menubar {
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+ margin-top: 1px;
+}
+#menubar .navbar {
+ border-top: 1px;
+ border-radius: 0px 0px 2px 2px;
+ margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+ float: left;
+ padding-top: 7px;
+ padding-bottom: 7px;
+ border: none;
+}
+#menubar .navbar-collapse {
+ clear: left;
+}
+.nav-wrapper {
+ border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+ padding-top: 4px;
+}
+ul#help_menu li a {
+ overflow: hidden;
+ padding-right: 2.2em;
+}
+ul#help_menu li a i {
+ margin-right: -1.2em;
+}
+.dropdown-submenu {
+ position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+ top: 0;
+ left: 100%;
+ margin-top: -6px;
+ margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+ display: block;
+}
+.dropdown-submenu > a:after {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ display: block;
+ content: "\f0da";
+ float: right;
+ color: #333333;
+ margin-top: 2px;
+ margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+ margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+ margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+ color: #262626;
+}
+.dropdown-submenu.pull-left {
+ float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+ left: -100%;
+ margin-left: 10px;
+}
+#notification_area {
+ float: right !important;
+ float: right;
+ z-index: 10;
+}
+.indicator_area {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+}
+#kernel_indicator {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+ border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+ padding-left: 5px;
+ padding-right: 5px;
+}
+#modal_indicator {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+}
+#readonly-indicator {
+ float: right !important;
+ float: right;
+ color: #777;
+ margin-left: 5px;
+ margin-right: 5px;
+ width: 11px;
+ z-index: 10;
+ text-align: center;
+ width: auto;
+ margin-top: 2px;
+ margin-bottom: 0px;
+ margin-left: 0px;
+ margin-right: 0px;
+ display: none;
+}
+.modal_indicator:before {
+ width: 1.28571429em;
+ text-align: center;
+}
+.edit_mode .modal_indicator:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+ margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+ margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+ margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_idle_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_busy_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_dead_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+ margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+ display: inline-block;
+ font: normal normal normal 14px/1 FontAwesome;
+ font-size: inherit;
+ text-rendering: auto;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+ content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+ margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+ margin-left: .3em;
+}
+.notification_widget {
+ color: #777;
+ z-index: 10;
+ background: rgba(240, 240, 240, 0.5);
+ margin-right: 4px;
+ color: #333;
+ background-color: #fff;
+ border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #8c8c8c;
+}
+.notification_widget:hover {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+ color: #333;
+ background-color: #e6e6e6;
+ border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+ color: #333;
+ background-color: #d4d4d4;
+ border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+ background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+ background-color: #fff;
+ border-color: #ccc;
+}
+.notification_widget .badge {
+ color: #fff;
+ background-color: #333;
+}
+.notification_widget.warning {
+ color: #fff;
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+ color: #fff;
+ background-color: #ec971f;
+ border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+ color: #fff;
+ background-color: #d58512;
+ border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+ background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+ background-color: #f0ad4e;
+ border-color: #eea236;
+}
+.notification_widget.warning .badge {
+ color: #f0ad4e;
+ background-color: #fff;
+}
+.notification_widget.success {
+ color: #fff;
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #255625;
+}
+.notification_widget.success:hover {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+ color: #fff;
+ background-color: #449d44;
+ border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+ color: #fff;
+ background-color: #398439;
+ border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+ background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+ background-color: #5cb85c;
+ border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+ color: #5cb85c;
+ background-color: #fff;
+}
+.notification_widget.info {
+ color: #fff;
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+ color: #fff;
+ background-color: #31b0d5;
+ border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+ color: #fff;
+ background-color: #269abc;
+ border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+ background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+ background-color: #5bc0de;
+ border-color: #46b8da;
+}
+.notification_widget.info .badge {
+ color: #5bc0de;
+ background-color: #fff;
+}
+.notification_widget.danger {
+ color: #fff;
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #761c19;
+}
+.notification_widget.danger:hover {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+ color: #fff;
+ background-color: #c9302c;
+ border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+ color: #fff;
+ background-color: #ac2925;
+ border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+ background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+ background-color: #d9534f;
+ border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+ color: #d9534f;
+ background-color: #fff;
+}
+div#pager {
+ background-color: #fff;
+ font-size: 14px;
+ line-height: 20px;
+ overflow: hidden;
+ display: none;
+ position: fixed;
+ bottom: 0px;
+ width: 100%;
+ max-height: 50%;
+ padding-top: 8px;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ /* Display over codemirror */
+ z-index: 100;
+ /* Hack which prevents jquery ui resizable from changing top. */
+ top: auto !important;
+}
+div#pager pre {
+ line-height: 1.21429em;
+ color: #000;
+ background-color: #f7f7f7;
+ padding: 0.4em;
+}
+div#pager #pager-button-area {
+ position: absolute;
+ top: 8px;
+ right: 20px;
+}
+div#pager #pager-contents {
+ position: relative;
+ overflow: auto;
+ width: 100%;
+ height: 100%;
+}
+div#pager #pager-contents #pager-container {
+ position: relative;
+ padding: 15px 0px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+ top: 0px;
+ height: 8px;
+ background: #f7f7f7;
+ border-top: 1px solid #cfcfcf;
+ border-bottom: 1px solid #cfcfcf;
+ /* This injects handle bars (a short, wide = symbol) for
+ the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+ content: '';
+ top: 2px;
+ left: 50%;
+ height: 3px;
+ width: 30px;
+ margin-left: -15px;
+ position: absolute;
+ border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+ /* Old browsers */
+ display: -webkit-box;
+ -webkit-box-orient: horizontal;
+ -webkit-box-align: stretch;
+ display: -moz-box;
+ -moz-box-orient: horizontal;
+ -moz-box-align: stretch;
+ display: box;
+ box-orient: horizontal;
+ box-align: stretch;
+ /* Modern browsers */
+ display: flex;
+ flex-direction: row;
+ align-items: stretch;
+ line-height: 1.8em;
+}
+.shortcut_key {
+ display: inline-block;
+ width: 20ex;
+ text-align: right;
+ font-family: monospace;
+}
+.shortcut_descr {
+ display: inline-block;
+ /* Old browsers */
+ -webkit-box-flex: 1;
+ -moz-box-flex: 1;
+ box-flex: 1;
+ /* Modern browsers */
+ flex: 1;
+}
+span.save_widget {
+ margin-top: 6px;
+}
+span.save_widget span.filename {
+ height: 1em;
+ line-height: 1em;
+ padding: 3px;
+ margin-left: 16px;
+ border: none;
+ font-size: 146.5%;
+ border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+ background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+ font-size: small;
+}
+@media (max-width: 767px) {
+ span.save_widget {
+ font-size: small;
+ }
+ span.checkpoint_status,
+ span.autosave_status {
+ display: none;
+ }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+ span.checkpoint_status {
+ display: none;
+ }
+ span.autosave_status {
+ font-size: x-small;
+ }
+}
+.toolbar {
+ padding: 0px;
+ margin-left: -5px;
+ margin-top: 2px;
+ margin-bottom: 5px;
+ box-sizing: border-box;
+ -moz-box-sizing: border-box;
+ -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+ width: auto;
+ vertical-align: middle;
+ margin-right: 2px;
+ margin-bottom: 0px;
+ display: inline;
+ font-size: 92%;
+ margin-left: 0.3em;
+ margin-right: 0.3em;
+ padding: 0px;
+ padding-top: 3px;
+}
+.toolbar .btn {
+ padding: 2px 8px;
+}
+.toolbar .btn-group {
+ margin-top: 0px;
+ margin-left: 5px;
+}
+#maintoolbar {
+ margin-bottom: -3px;
+ margin-top: -8px;
+ border: 0px;
+ min-height: 27px;
+ margin-left: 0px;
+ padding-top: 11px;
+ padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+ float: none;
+ vertical-align: middle;
+ text-align: right;
+ margin-left: 5px;
+ margin-right: 0px;
+ margin-top: 0px;
+}
+.select-xs {
+ height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+ background-color: #F37626;
+ color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+ from {
+ opacity: 1;
+ }
+ to {
+ opacity: 0;
+ }
+}
+@-webkit-keyframes fadeOut {
+ from {
+ opacity: 1;
+ }
+ to {
+ opacity: 0;
+ }
+}
+@-moz-keyframes fadeIn {
+ from {
+ opacity: 0;
+ }
+ to {
+ opacity: 1;
+ }
+}
+@-webkit-keyframes fadeIn {
+ from {
+ opacity: 0;
+ }
+ to {
+ opacity: 1;
+ }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+ overflow: auto;
+ height: 200px;
+ -webkit-transition-property: height;
+ -webkit-transition-duration: 500ms;
+ -moz-transition-property: height;
+ -moz-transition-duration: 500ms;
+ transition-property: height;
+ transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+ -webkit-transition-property: height;
+ -webkit-transition-duration: 500ms;
+ -moz-transition-property: height;
+ -moz-transition-duration: 500ms;
+ transition-property: height;
+ transition-duration: 500ms;
+ text-overflow: ellipsis;
+ overflow: hidden;
+ height: 80px;
+}
+.tooltipbuttons {
+ position: absolute;
+ padding-right: 15px;
+ top: 0px;
+ right: 0px;
+}
+.tooltiptext {
+ /*avoid the button to overlap on some docstring*/
+ padding-right: 30px;
+}
+.ipython_tooltip {
+ max-width: 700px;
+ /*fade-in animation when inserted*/
+ -webkit-animation: fadeOut 400ms;
+ -moz-animation: fadeOut 400ms;
+ animation: fadeOut 400ms;
+ -webkit-animation: fadeIn 400ms;
+ -moz-animation: fadeIn 400ms;
+ animation: fadeIn 400ms;
+ vertical-align: middle;
+ background-color: #f7f7f7;
+ overflow: visible;
+ border: #ababab 1px solid;
+ outline: none;
+ padding: 3px;
+ margin: 0px;
+ padding-left: 7px;
+ font-family: monospace;
+ min-height: 50px;
+ -moz-box-shadow: 0px 6px 10px -1px #adadad;
+ -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+ box-shadow: 0px 6px 10px -1px #adadad;
+ border-radius: 2px;
+ position: absolute;
+ z-index: 1000;
+}
+.ipython_tooltip a {
+ float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+ border: 0;
+ border-radius: 0;
+ font-size: 100%;
+ background-color: #f7f7f7;
+}
+.pretooltiparrow {
+ left: 0px;
+ margin: 0px;
+ top: -16px;
+ width: 40px;
+ height: 16px;
+ overflow: hidden;
+ position: absolute;
+}
+.pretooltiparrow:before {
+ background-color: #f7f7f7;
+ border: 1px #ababab solid;
+ z-index: 11;
+ content: "";
+ position: absolute;
+ left: 15px;
+ top: 10px;
+ width: 25px;
+ height: 25px;
+ -webkit-transform: rotate(45deg);
+ -moz-transform: rotate(45deg);
+ -ms-transform: rotate(45deg);
+ -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+ margin-left: -10px;
+ width: 18px;
+}
+ul.typeahead-list {
+ max-height: 80vh;
+ overflow: auto;
+}
+ul.typeahead-list > li > a {
+ /** Firefox bug **/
+ /* see https://github.com/jupyter/notebook/issues/559 */
+ white-space: normal;
+}
+.cmd-palette .modal-body {
+ padding: 7px;
+}
+.cmd-palette form {
+ background: white;
+}
+.cmd-palette input {
+ outline: none;
+}
+.no-shortcut {
+ display: none;
+}
+.command-shortcut:before {
+ content: "(command)";
+ padding-right: 3px;
+ color: #777777;
+}
+.edit-shortcut:before {
+ content: "(edit)";
+ padding-right: 3px;
+ color: #777777;
+}
+#find-and-replace #replace-preview .match,
+#find-and-replace #replace-preview .insert {
+ background-color: #BBDEFB;
+ border-color: #90CAF9;
+ border-style: solid;
+ border-width: 1px;
+ border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .match {
+ background-color: #FFCDD2;
+ border-color: #EF9A9A;
+ border-radius: 0px;
+}
+#find-and-replace #replace-preview .replace .insert {
+ background-color: #C8E6C9;
+ border-color: #A5D6A7;
+ border-radius: 0px;
+}
+#find-and-replace #replace-preview {
+ max-height: 60vh;
+ overflow: auto;
+}
+#find-and-replace #replace-preview pre {
+ padding: 5px 10px;
+}
+.terminal-app {
+ background: #EEE;
+}
+.terminal-app #header {
+ background: #fff;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.terminal-app .terminal {
+ float: left;
+ font-family: monospace;
+ color: white;
+ background: black;
+ padding: 0.4em;
+ border-radius: 2px;
+ -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+ box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+}
+.terminal-app .terminal,
+.terminal-app .terminal dummy-screen {
+ line-height: 1em;
+ font-size: 14px;
+}
+.terminal-app .terminal-cursor {
+ color: black;
+ background: white;
+}
+.terminal-app #terminado-container {
+ margin-top: 20px;
+}
+/*# sourceMappingURL=style.min.css.map */
+ </style>
+<style type="text/css">
+ .highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
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+ </style>
+<style type="text/css">
+
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+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-bold { font-weight: bold; }
+
+ </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+ overflow: visible;
+ padding: 8px;
+}
+
+div#notebook {
+ overflow: visible;
+ border-top: none;
+}
+
+@media print {
+ div.cell {
+ display: block;
+ page-break-inside: avoid;
+ }
+ div.output_wrapper {
+ display: block;
+ page-break-inside: avoid;
+ }
+ div.output {
+ display: block;
+ page-break-inside: avoid;
+ }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+ <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+ <!-- MathJax configuration -->
+ <script type="text/x-mathjax-config">
+ MathJax.Hub.Config({
+ tex2jax: {
+ inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+ displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+ processEscapes: true,
+ processEnvironments: true
+ },
+ // Center justify equations in code and markdown cells. Elsewhere
+ // we use CSS to left justify single line equations in code cells.
+ displayAlign: 'center',
+ "HTML-CSS": {
+ styles: {'.MathJax_Display': {"margin": 0}},
+ linebreaks: { automatic: true }
+ }
+ });
+ </script>
+ <!-- End of mathjax configuration --></head>
+<body>
+ <div tabindex="-1" id="notebook" class="border-box-sizing">
+ <div class="container" id="notebook-container">
+
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<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>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [2]:</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">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>
+
+</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 ">
+<script>jQuery(function() {if (jQuery("body.notebook_app").length == 0) { jQuery(".input_area").toggle(); jQuery(".prompt").toggle(); jQuery("div.output_stderr").toggle();}});</script>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<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>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [3]:</div>
+<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>
+
+<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>
+
+</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>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>
+
+</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>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<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>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [6]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[6]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+<div>
+<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>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [7]:</div>
+<div class="inner_cell">
+ <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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[7]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre><matplotlib.text.Text at 0x1177537b8></pre>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[8]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>(0.5, 1.0)</pre>
+</div>
+
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[9]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>(0.5, 1)</pre>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [10]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<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>
+
+<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>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[10]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre><matplotlib.text.Text at 0x11e086dd8></pre>
+</div>
+
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [11]:</div>
+<div class="inner_cell">
+ <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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[11]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre><matplotlib.text.Text at 0x11977ab00></pre>
+</div>
+
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre><matplotlib.text.Text at 0x11f7f0c50></pre>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[13]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre><matplotlib.text.Text at 0x11f7e65c0></pre>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[14]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre><matplotlib.text.Text at 0x12214b940></pre>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<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>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[15]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre><matplotlib.text.Text at 0x12214b710></pre>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
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+kf8PCsz+fJ3oB8IAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<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>
+</div>
+
+</div>
+
+</div>
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+</div>
+ </div>
+ </div>
+</body>
+</html>
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
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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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\nzxERERFdo1eC1vZfSYz3q4nFwEXACuDo8dqVNB3YGfgt8AvKjGr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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x11b990860>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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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\nMBqrM2cCQ9z9KDObDFwTn8s7FDg31m6I2x8C4O4nJHoHIiLS7ZJ0mI/JBw4Ad78dGJ1w+9OA++N6\ns4HD2rx+KHC5mc00sy/G5w4Easzsb2b2QAw6klA2l2PmvGXc8sBzzJy3jGyuw6k6IiJdliR4bDWz\nQ/IPzOxQYHPC7Y9iW/MTQMbMCvd5C/BR4HhgmpmdCtQD33H3twIfA37XZh0pIp+yZFHdembMXcqs\n+ct7ukgi0g8labb6NHCHma0lTBocDbwv4fY3ACMLHqfdPVvw+Dp33wBgZvcQbm/7D+AFgNj/sQYY\nBywttqPa2pHFXh4w1tQ3Uj0o3epxV46Njmf56FiWl45n75BktNXjZrYvsC+hpuLu3phw+7OA04E/\nmtmRwIL8C2Y2CnjazPYDGoATgOuBi4ADgEvNbBdC8On08nnVqo0Ji9S/7VgzmKZMttXjUo9Nbe1I\nHc8y0bEsLx3P8nojgTjJaCsjZNHdoeA52sw678idwMlmNis+vsDMzgFq3P1XZnY58CBhJNZ0d7/f\nzKqBG81sJpAFLmxTW5EiiqUsUQp3ESmXVK6TDlUzewa4FXi58Hl3v7mC5SpVTlcjHcsHjdnPrmDF\n2gZGDK8G4ISDx7eb3kRXd+WjY1leOp7lVVs7sstXj0n6PNa7+1Vd3YH0vHwn+toNW9ja2AzAiOHV\nZU/hrpqNyMCRJHjcZGbfINwEqiWnlbs/XLFSSVnlg8TgQVVsbWymMdMMVJc9hbtuTiUycCQJHscB\nhwNHFTyXI3RwSx+Qv+9HzbDw5x47ehiTJ44tewp33ZxKZOBIEjwOc/d9Kl4SKZu2zUdTDtgZoOLN\nSbo5lcjAkWTy3QIzm1TxkkjZFE4UnP5UHTffu5C6VfWMHzOcHHDb9OcrMvt8ygE7M2FMDblsjglj\ntgUtEel/ktQ89gLmmtlyoJEwUTCn29CWX7k6nAubi+obMsxfvIbRo4by1KJVQOgsr0SfxGMLXqVu\ndT2pdIq61fU8tuBV9XmI9FNJgseZnS8i5VCuDufC5qPGTDODB1W1/B6Eobrl6JMoDHhLV29q9Vrd\nqnqNwBLpp5LMMH+5s2WkPMrV4Vw4UXDzlibqVm8bbVWoHH0ShQFv0+YmgJZ5JBNqazQCS6SfSlLz\nkG5Srg7nwnubF175jx8zHFIplrYz+7yrCgNczbBBjBhWzfgxI1r6V2bMqaN+S6YloGgElkj/0GHw\nMLOz3f3AaDVMAAAZPklEQVQ2Mxvj7qu7s1ADVbHUIl3VXiApp8KAl0qlWoYA33jPs8xfvIZsNteS\na2vE8PLPLRGRnlGs5nGlmd0B/B04pMhyUiaFJ/pKqEQTUnsB75F5y5izaBWNmSzkcgwbMoiaoYM4\n4eDxTJ00Tv0gIv1AseDxKLCVcOvZtokJc+5e1c460st01qH9RreZbwor9MTClTRlsuSyYShwczbH\niYdOaAlUM+ctY/pTddQ3ZHj8mVdZtGQ9F5w2UQFEpA/pMHjErLkXmtlf3P2d3VgmKaNiHdrjxwxn\n5rxlJdcA8tvM5XI8+vRymrM5aoZWt+qvSafCP7lcjh1GDmnVBLdk5SbWvLaFhsZmUsD8xWuYNX+5\nOtJF+pAko63eaWZvB06My89w97sqXjIpi446tCfU1oQO7S40Y+W3Wd+QoWFrBlIpspsbW147YuJY\nVqxtaBkm/NYjdmsVlBq2hvVyuZDnJpvNqSNdpI/pdIa5mV0GfB14BXgR+IqZfanC5ZIyKeygzndo\nn3PSPhx94C4s7eLQ4Pw2GzPNpFIp8mGhMdPMhNoapk0axxlH7c6uO41g7OhhAK1msw8bOohhQwaR\nSqdIxx91pIv0LUmG6p4LTHb3BgAz+yUwB/i/ShZMyqPYCK6uDg3Ob2P2syt4dc1mUqkUjZlmJu21\nY0vTVyqVon5LSML8z7lLSbGtVrNr7QgWbTeU+oZMq/XUkS7SdyQJHul84Ii2UJCaXXq3YiO42gaW\nKQfszMx5y1hT38iONYM7PHnntzl10rgOT/bFJjy2F9DSqRQz5y3ThEKRPiJJ8Jgeh+zeFB+fD8yo\nWImk27QNLPmTd/WgdMvcjGIn72KBqVitpqP1lNJdpO9IEjw+DXwUOI/QRzID+HklCyU9I3+yzuVy\nbNrcxPQ5dQBdaj7qyoRHpXQX6TuSjLbKAT+NP9KP5U/eGzc3sTGOnso3I5XafNSVCY+VmGEvIpWh\n3FYDSGcd0vmT9UPzl9HcnOv2fFSVnmEvIuWj4DGAdJaeJH/yHjlyKH95+IWW57ur+UijrUT6jkTB\nw8xGAdtBy5B+3P2VShVKKqOzDun8yXv1pq1MGFPDsKGD2LV2RLc1Hyl9u0jf0WnwiBMCvwisKXg6\nR7jDoPQhnXVI50/e+dFWJxw8vltP3kmDm2omIj0vSc3jImBvd19V6cJIZXXWId3TQ2U7Cm75oDH7\n2RWsWNtQsdvoikhySYLHK8DaShdEKq+jm0TlA0mSobKVvPrvKLjla0RrN2xha2O4le6I4dWaByLS\ng5IEj+eAR8zsn4TZ5QC4+1UVK5VUXHv9C/mTdeEM8yTrlevqv7PJg4MHVbG1sTnei103lhLpSUmC\nx9L4AwUd5tK3tddElT9519aOZNWqjYnXq7R8jahmWPi4jh09jCP224lsLsd3b50LwBETx3LUATvz\n2IJX1Sci0g2STBK80sxqgclx+cfcfUXFSyYV1dXZ3D0xC7y95qxZ85fz11kvtUxmXLG2geeWrKdu\ndQhmhbWp9prZ1Pku8sYkGW31VuAG4HFCepKfm9lF7n53pQsnldPV2dw9MQu8veasulX1NGaayeVy\nNGdzrN+0lScXrWLE0Gpqhg2iviHD9Dl1LGonoBx94C4aFizyBiVptvoGMM3dXwQws72APwEKHn1Y\nV2dz95ZZ4BNqaxg8qIrNWzJkc0AuR7apmfWZLFsaMy2JHecvXsPgQVWvmy1fmMcrH2iga3m8RAai\nTm8GBVTnAweAuy9OuJ5IxUyNN5zabsRgqtIp0qltHXKZbI6RwwczYng1gwdVtdRQNm1uYunqTcyc\nt4zxsbmtviHDxs2N1G/JMGPuUmbNX55o/9lcjpnzlnHLA88xc96yVje7EhkIEg3VNbNPA9fHxxcD\nL1euSCKdS6dSHHPQeFKpFHfNeon1m7aSzeZIpWBQelvNoWbYIPar3Z51m7ZS35BpCRLHH7QLJxw8\nvqXGka+ZLFm5qeW+7uPHDIdUirqVm2jYmmHokCq2bG1m2NBBNGzJULe6nlwux1OLVjH72RVMnjhW\nNRcZMJJOEvwh8GVCjWM68JFKFkoqr790GE+dNI5sNssfH1rMlsZmhg6uYsdRQxgxfHDLvdqnThrH\nbdOfZ1NDE5s2N9GYaQ4n+zfvzIjh1S13PIRwf/XpT9WxaXMT/5zbSIoUg6vDjPv8zPuRwwe33J8d\nYEP9Vuq3NLFk5SYWLVnPBadN7JPHUqQUSUZbrQTO7oaySDfqLx3G6VSKdDrN0MGDaMpkacpkqd/S\nzImHjm31fibU1vDUolUto7NeWbGJleteahn+WzN0EJMnjmXJqk3UN2R4rb6R5tCZQqY5SzqdIpfJ\nkoKWwBHmm0A2B9lMlmw2x/zFa5g1f3mXj2V/CerS/3UYPMzsbnc/3cxeJOSyasXdlduqD+vpVCTl\nVLeqviUINGaaGTt62OtGgU2dNI7Zz65odeKv39LU8niXMTUcfeAuzJy3jNnPrCBX0IeRgxBIckAq\nTFYsbA5bvGwDzc1ZmrM5mjJZZj+7oksn/Wwux433PNvSyb+obj25XI5UKqVgIr1OsZrHh+P/x3VD\nOaSb9ae79uXfS+i3qGbyxLGvO8GmUykmTxzLpoYm6hsyNGzNkM3myGZzbG1spiE2XU2dNI5FS9Yz\nZ9EqGpuaiQO5SBFO7tVVacbuMIzJbx7bEqBuvOdZ5ixa1VL7WLG2oUu1j1nzlzN/8Rq2Nja3pGGZ\n/ewKVq7b0hLkstks6XSaJbEfpm3m47a1lvaeU/CRcugweLh7ftjJNe5+VuFrZjYdOLGzjZtZCvgJ\ncCAhtcnFcbRW/vVPEzrgV8anLgGeL7aOlEd/umtf0veSDwzzF6+hKp0il4N0OkXN0GqGDQlfhXQq\nxQWnTWSfCdvxxMKVvLp2cxgOnA01kSGDqxhfO6JVYLjgtIms27SVJSs3tQwLLqUml8lmufnehcx/\nYQ1bm5pbnm/MNLN+01bWb9pKDthMhr8/Wcfg6io2xbs9jhw+mOfqXiOXy/Fc3Wutai15/aF5Unqf\nYs1WdxJO4OPNrPDkXU1IlpjEmcAQdz/KzCYD18Tn8g4FznX3uQX7fVcn60gZ9Jb5GuWQ9L2kUymG\nD61m9KihLSff6kFpRgyvZtedRrRa7piDxnPMQeOZOW8ZdxXMZB88qOp1tbR8raaw472UmtzN9y7k\nXwtX0pwNEx4HD0ozZHAVk/bakcXLN7QErhywob6RMdsPa+lvyef5emLhSpas3NSq1tJeAOvLzZPS\nuxSbr3E+cAJwP6Hp6vj4cyRwbMLtT4vr4+6zgcPavH4ocLmZzTSzLyRcR6TL8if1mmGDGDl8MLvu\nNIITDh5ftLZyxlG786YJ2/GmCdtxxtQ92l12ygE7M2FMDblsjgljaphywM6dliU/V2T+C2tozuZI\np6AqnWJIdRXvOXZvLjhtItuPHEI6nSKVTpFOpxhVMxigZaRX/v+2vzdmmplQW8OE2hqy2Syr1zew\nbHU9Ly5/jUw2m/yAiXSgWLPVBmCDmX0c+JS7f8HM9gS+CXyObU1NxYwCXit4nDGztLvnP723AD8G\nNgB3mtnTCdYR6bL2mriK9QEU1kKKeWzBq9StrieVTlG3up7HFrzaaW0oP+Itmwt9L6RTVKVTTNp7\nx5Z1J++3EyvXNrT0eZxy2IR2+zxywIynwpyVxkwzk/baseW9zpy/jIbGZlKEUWY337uQi05/c8Ij\nJtK+JPM8fgvcGn9fBsyMz52SYN0NwMiCx22DwHUxSGFm9wIHEwJHsXXaVVs7srNFpAT9+Xi++6RR\nZd/mmvrQBAYh5cncF1azpr6RPXYexYmH70Y63TpAZbM55j6/hvWbtjJsSFWoWaRSHD5xLJ9470EM\nitt614nGqFHDeOnVDR1uK7+9USOHtrtcljBxMhvzgHndenbccUS72+kL+vNnsy9JEjx2dPefA7j7\nVuCXZvaxhNufBZwO/NHMjgQW5F+I90V/2sz2AxoITWTXA8OBM9pbp5iOUohL6YqlZJf2jR5ezdrX\ntrB5a4ZMc5Yh1VWs27CVZxavYePGLa+7Cdfjz6zghaWvkcnmSAHbjxjCO6buwdEH7sK6da37JQ7a\nazQH7TUagDVrNnVYho6WG7fDcF5atoHYdcKGTY3cOd07rU31RvpsltcbCcRJgsdmM3u7u98HYGYn\nAkl73e4ETjazWfHxBWZ2DlDj7r8ys8uBBwmjqqa7+/1xhFardZK+GZEek0qxtamZxkyWXDbHllwz\nmzY3scOoIS2d1Pl5HHMWrWJrU3MYAhwv/gdXpys24u38U/fj6RfXsqG+kRzQ1JzllhnP81zda5x/\n6n4MSrfu+swHuMKmsQljaiCVYqmG/EqUynWS0M3MDiI0U+1MGO7+CmGE1NOVL15iOV2NlI+u7kp3\nywPP8fgzr7K1sZnmbI4cMHxIFcOHVrPjqCHsMGIILyx7jRXrtrRaLwVUD0rzpgnb8bn3HVyx8n33\n1rk8t2Q9Tc25Vvs9fL+duOC0iTwybxlPLAzdmDuMGELd6vpWw4GBlgmLjZlmDthzNPvutsPrgkk2\nl2u1rSMmjmVaGQONPpvlVVs7sst/mCTpSf4NvMXMdgSa8n0UIrLNhNoaqgelY4r4MJlw5PDBNGdz\nrFjXwMJX1tPedVqOcBI/Yr+dKlq+IyaO5eVXN9LUnGnZb3M2x5KVm3hk3jL+8OALLUONUykYPmRQ\nS59IflhwUybbMpP+sWdW8K+FqxhVM/h190m569GXWL9xK9kcLHx5HffPfpm9d9mu0wmNbyTAKK1L\n90tyM6hpwGXACCBlZlXA7u6+R4XLJtJnTJ00joUvr+Vfvoo0KaoHpdl+xGCamsMJuqMK/tDBVey+\n80imVXjOzbRJ43ji2RUsWrKeTHPoZ8kBu+40gicWrmRzwRyVXA7qt2QYHDvt80OAmzLZkMcrvpem\n5izrN20FWt8npeUeK4RlX13bwIp1DQyprqJmaHVLDaackxf7S662viTJfTl+BfyZEGh+DDxH6MsQ\nkSidSvHa5ibSqTDctimTZf2mMLGwubn9wYJDqtOMHT283XQqlSjf5IljGbfjcGqGDmLQoDR7jRvJ\n+afu1+E62VyO3caO4OB9x3DGUbtz6L61Yc5JwTI5ts0pgY4nR+Zy0JjJsnFzI08sXFn23Gr9KVdb\nX5Gkw7zB3W80sz2AdYScV3MqWiqRfmD7EYM5efIe/ObeZ1gXAwlAVRr2HDeK3XcexW47jei21DAd\nzXE5YuJYXlq+gc1bt6VGqUqn2K5mCHuO245zTtoHoKV2lM/7lUqFGlbhnJKpk8bhr6zjiYUryTS3\nrm4VBp1y51brT7na+ookwWOLmY0GHDjS3WeYmf4yIm0cMXEsKwom9E1+886cPHl3Nmxo4K+Pvtzy\n/BlH7d4jw2Q7SuMybdI4yOWY/ewKlq3ezNamZmqGVjNieHWrk3DbvF/w+g7xdCrFhae/mX133Z7Z\nz66Ita8cG+qbSKdTDB5UxRETx5Y9t1p/ytXWVyQZbfUeQsLCdwP/ApqBf7v7BypfvMQ02qqMNKKl\na9rrtB270yhWrNzQZzpzK9HxXM5t6rNZXhUdbUWYwHeKu+fM7FBgX2BeV3co0l91dGXfl5JQVqKs\nfen9S3JJgsfV7n4PgLvXA3M7WV5ERPq5JMHjBTO7AZhNqIUA4O6/rlipRESkV0sSPNYQBkocWfBc\nDlDwEBEZoIrdDGq8uy91d+WWEhGRVopNEvxr/hcz+2w3lEVERPqIYsGjcAhXbxqWKyIiPaxY8Cic\nANI7B6WLiEiPSJLbCloHEhERGeCKjbba38wWx9/HF/yeAnLuvldliyYiIr1VseCxb7eVQkRE+pQO\ng4e7v9ydBRERkb4jaZ+HiIhICwUPEREpmYKHiIiUTMFDRERKpuAhIiIlU/AQEZGSKXiIiEjJFDxE\nRKRkCh4iIlIyBQ8RESmZgoeIiJRMwUNEREqm4CEiIiVT8BARkZIpeIiISMkUPEREpGQKHiIiUjIF\nDxERKZmCh4iIlKzDe5iXg5mlgJ8ABwJbgIvdfXE7y/0cWOPuX4qP5wCvxZdfdPeLKllOEREpTUWD\nB3AmMMTdjzKzycA18bkWZnYJ8Bbgofh4CIC7n1DhsomISBdVutlqGnA/gLvPBg4rfNHMpgCHAz8v\nePpAoMbM/mZmD8SgIyIivUilax6j2Nb8BJAxs7S7Z81sZ+BrhJrI2QXLbAa+4+7Xm9k+wH1mtq+7\nZ4vsJ1VbO7LshR/IdDzLR8eyvHQ8e4dKB48NQOFfOl0QBN4L7AjcC4wDhpnZQuBW4HkAd3/OzNbE\n15dWuKwiIpJQpZutZgGnApjZkcCC/Avu/kN3Pzz2bXwL+L27/xq4EPheXGcXQvBZXuFyiohICSpd\n87gTONnMZsXHF5jZOUCNu/+qg3WuB240s5lAFriwkyYrERHpZqlcLtfTZRARkT5GkwRFRKRkCh4i\nIlIyBQ8RESmZgoeIiJRMwUNEREpW6aG6PcbMjgfe7+4f7umy9GVmdgLwPmAYcLW7L+hkFSnCzA4B\nPhkfft7dV/Vkefo6MxsL3O3uh/d0Wfo6M5sE/BBYDNzk7g8VW75f1jzMbG/gYGBIT5elHxjm7h8h\nTNw8pacL0w8MAf6bkFlhSg+XpT+4DHippwvRT0wmTMjOAP/pbOE+V/OIiRK/5e7Hd5Ty3d1fAK4x\ns1/3ZFl7u4TH8h4zG064Wv5CDxa310t4PB+L2RY+C/xXDxa3V0tyLM3so8BvCcdSikhyPIFHCOmh\nxhKCctHve5+qeZjZZcAv2VajaEn5DlxOSPleKNWNxetTkh5LMxtDqMpe4e6re6KsfUEJx/MwYA4h\nbY9Oeu0o4Xt+MnAJcISZndXtBe0jSjieBwFVwPr4f1F9KngQEia+q+Bx0ZTvgKbPd6yzY3lofP57\nwM7AN83s3d1awr4l6fEcBdwAXA38rjsL2Ick+p67+1nu/jFgtrvf0e2l7DuSfjZfIlwofjv+X1Sf\narZy9zvNbPeCpzpM+R6XP69bC9iHJDiWzfFYnt/NReuTSjieM4AZ3Vu6vkXf8/Iq4bP5GPBY0u32\ntZpHW8VSvktpdCzLS8ezfHQsy6ssx7OvB48OU75LyXQsy0vHs3x0LMurLMezTzVbteN1Kd97sjB9\nnI5leel4lo+OZXmV5XgqJbuIiJSsrzdbiYhID1DwEBGRkil4iIhIyRQ8RESkZAoeIiJSMgUPEREp\nmYKHiIiUrK9PEpR+zMyy7p6OeXkWEe4xkM+UnAN+6e4/NbOXgE1AIyFz6FrgM+7+r/x2gH/Hdavj\n7xe5+9Y2+/s6cAUwJSaMyz9/LfApd+/XF1tmNgq42d3f1enCMuApeEhvVjiDdam7H9LBclng7e6+\nBMDMTgXuNTNz97VArnBdM7uDMKv2Z+3sbwnwHmB2XDYFHMPAyNA8mnCPB5FOKXhIf5Ci4N4t7n6v\nmT0BvB/4UeFrZjYYGA6s6GBbdwHvINwMB0L66seIJ1UzSwPfAY4l3PPgJne/zsyqgJ8C+xNupuPA\nu4HBwC3xOYAr3f1uM/sn8DV3fzjWrB509z3N7EZgR2Bv4POxnN8n3AZ4NXCJu78c158LnAQMBT4V\nf94MXOvu15pZDfDjWKYq4NvufpuZnQ+8jRAs9gL+5u6fAK4DdonB9UPtlbvI30AGmH5dDZd+ZbyZ\nPRV/5sb/9y+y/NPAfvkH+fWApYT7k0zvYL3VwItmlr/HwdnAbQWvf5hQkzmMcNvOM81sKnAUsNXd\npwL7EALUqYT7KLwY77F9LnB0B/strNmsdvf9gb8DvwLOifu7Jj5uWcfdJxHupveDuK9jCE1vAF8B\nnoz7Phb4ipntEV+bEpefBLwjHstPAcvc/awSyi0DlGoe0lcUa7ZqTw5oyD9o02z1LeB2wtV3e+vd\nDrwnBpsphFvw5p0EHGhmJ8bHNcAB7v4zM1tjZh8nBK03ASOAR4FvmNkE4B7gfxOUPd/fsi+hBnJX\nbD4jbjPvvvj/y8DjsQ/nFTPbrqCsw8zsovh4GKEWAvCou2+Ox2MxoRayqWDbXSm3DCCqeUh/NYnQ\nwQ6v76/4PaGm0JE/E27VeRzwsLsXrl8FfN7dD3b3gwnB5UYzewfhzoCbCHcKnAmk3P15QjD5LeHq\n/V8FZcoHhOo2+88HvSrgBXc/JO7rEFrXABoLfs+08z6qgA8WlPUo4G/xtS0FyxWWBYAi5RYBFDyk\nd0t18HtRZnYG4X7Mt3ew7knAUx2tHzvZXyZcbd/aZhszgI+Y2SAzGwE8Qmi+OhG4zd1/DawkNB9V\nmdmlwFXxNqmXArVxVNNqttUCOhrdtBAYbWbT4uOLCYGvM4Vl/TiAmY0D5gO7FlkvQ2yNKFJuEUDN\nVtK7FV7xjzOztif8h9390/H3e82skXDiXAW8Ld8sA+TiuvmhuquAj3Sy79uBKwqG7ObL8jNCk9Rc\nwpX99bHTey3wezN7L7CV0Mm+J+Fe5bea2XxCTeFr7r7BzK4GbjazCwk1nde9Z3dvjNv7gZkNIdwB\n7ry2y7Uj/9qVwE/MbAHhQvFz7v6imR3TwfIrgCVmNh14Z3vlLrJPGWB0Pw8RESmZmq1ERKRkCh4i\nIlIyBQ8RESmZgoeIiJRMwUNEREqm4CEiIiVT8BARkZL9f4YPu/xKp8+bAAAAAElFTkSuQmCC\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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NiWjZcghiFBAtX6Orz7csQucuhYpte4dy5b/HzzOxINdAxdCWew3NilASpmHw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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
new file mode 100644
index 0000000000000000000000000000000000000000..08be52528c05610c02e5808e0fde405d33306903
--- /dev/null
+++ b/downloads-generation/models_class1_allele_specific_ensemble/models.py
@@ -0,0 +1,24 @@
+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/examples/class1_allele_specific_models.ipynb b/examples/class1_allele_specific_models.ipynb
index 0fda9cd6f10d65a21810e10db8abeec646015ad3..e6af34dd630f60ce653788e291ce59a2c3995b9f 100644
--- a/examples/class1_allele_specific_models.ipynb
+++ b/examples/class1_allele_specific_models.ipynb
@@ -13,7 +13,9 @@
"text": [
"Using Theano backend.\n",
"/Users/tim/miniconda3/envs/py3k/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
- " warnings.warn(self.msg_depr % (key, alt_key))\n"
+ " warnings.warn(self.msg_depr % (key, alt_key))\n",
+ "/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.\n",
+ " \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
@@ -56,7 +58,7 @@
"text": [
"Help on function predict in module mhcflurry.predict:\n",
"\n",
- "predict(alleles, peptides, loaders=None)\n",
+ "predict(alleles, peptides, predictor=None)\n",
" Make predictions across all combinations of the specified alleles and\n",
" peptides.\n",
" \n",
@@ -68,8 +70,7 @@
" peptides : list of str\n",
" Peptide amino acid sequences.\n",
" \n",
- " loaders : list of Class1AlleleSpecificPredictorLoader, optional\n",
- " Loaders to try. Will be tried in the order given.\n",
+ " predictor : Predictor to use. Defaults to downloaded Class1SingleModelMultiAllelePredictor.\n",
" \n",
" Returns DataFrame with columns \"Allele\", \"Peptide\", and \"Prediction\"\n",
"\n"
@@ -165,7 +166,7 @@
}
],
"source": [
- "model = mhcflurry.class1_allele_specific.load.from_allele_name(\"HLA-A0201\")\n",
+ "model = mhcflurry.class1_allele_specific.class1_single_model_multi_allele_predictor.from_allele_name(\"HLA-A0201\")\n",
"model.predict([\"SIINFEKL\", \"SIQNPEKP\", \"SYNFPEPI\"])\n"
]
},
@@ -193,7 +194,7 @@
{
"data": {
"text/plain": [
- "'/Users/tim/Library/Application Support/mhcflurry/4/0.0.8/models_class1_allele_specific_single/'"
+ "'/Users/tim/Library/Application Support/mhcflurry/4/0.1.0/models_class1_allele_specific_single/'"
]
},
"execution_count": 5,
@@ -226,8 +227,9 @@
],
"source": [
"# Make a Loader first\n",
- "loader = mhcflurry.class1_allele_specific.load.Class1AlleleSpecificPredictorLoader(models_dir)\n",
- "model = loader.from_allele_name(\"HLA-A0201\")\n",
+ "loader = mhcflurry.Class1SingleModelMultiAllelePredictor.load_from_download_directory(\n",
+ " models_dir)\n",
+ "model = loader.predictor_for_allele(\"HLA-A0201\")\n",
"model.predict([\"SIINFEKL\", \"SIQNPEKP\", \"SYNFPEPI\"])"
]
},
@@ -255,7 +257,7 @@
{
"data": {
"text/plain": [
- "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'])"
+ "AffinityMeasurementDataset(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'])"
]
},
"execution_count": 7,
@@ -264,7 +266,7 @@
}
],
"source": [
- "full_training_data = mhcflurry.dataset.Dataset.from_csv(\n",
+ "full_training_data = mhcflurry.AffinityMeasurementDataset.from_csv(\n",
" mhcflurry.downloads.get_path(\"data_combined_iedb_kim2014\", \"combined_human_class1_dataset.csv\"))\n",
"full_training_data"
]
@@ -288,12 +290,12 @@
}
],
"source": [
- "kim2014_full = mhcflurry.dataset.Dataset.from_csv(\n",
+ "kim2014_full = mhcflurry.AffinityMeasurementDataset.from_csv(\n",
" mhcflurry.downloads.get_path(\"data_kim2014\", \"bdata.20130222.mhci.public.1.txt\"))\n",
"\n",
- "kim2014_train = mhcflurry.dataset.Dataset.from_csv(\n",
+ "kim2014_train = mhcflurry.AffinityMeasurementDataset.from_csv(\n",
" mhcflurry.downloads.get_path(\"data_kim2014\", \"bdata.2009.mhci.public.1.txt\"))\n",
- "kim2014_test = mhcflurry.dataset.Dataset.from_csv(\n",
+ "kim2014_test = mhcflurry.AffinityMeasurementDataset.from_csv(\n",
" mhcflurry.downloads.get_path(\"data_kim2014\", \"bdata.2013.mhci.public.blind.1.txt\"))\n",
"\n",
"len(kim2014_full), len(kim2014_train), len(kim2014_test)"
@@ -333,7 +335,7 @@
}
],
"source": [
- "model = mhcflurry.class1_allele_specific.load.from_allele_name(\"HLA-A0201\")\n",
+ "model = loader.predictor_for_allele(\"HLA-A0201\")\n",
"model.predict(kim2014_train.get_allele(\"HLA-A0201\").peptides)\n"
]
},
@@ -464,26 +466,18 @@
" | Return str(self).\n",
" | \n",
" | encode_peptides(self, peptides)\n",
- " | Parameters\n",
- " | ----------\n",
- " | peptides : str list\n",
- " | Peptide strings of any length\n",
- " | \n",
- " | Encode peptides of any length into fixed length vectors.\n",
- " | Returns 2d array of encoded peptides and 1d array indicating the\n",
- " | original peptide index for each row.\n",
" | \n",
" | fit_dataset(self, dataset, pretraining_dataset=None, sample_censored_affinities=False, **kwargs)\n",
" | Fit the model parameters on the given training data.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
- " | dataset : Dataset\n",
+ " | dataset : AffinityMeasurementDataset\n",
" | \n",
- " | pretraining_dataset : Dataset\n",
+ " | pretraining_dataset : AffinityMeasurementDataset\n",
" | \n",
" | sample_censored_affinities : bool\n",
- " | If a column named 'inequality' is in the Dataset then every\n",
+ " | If a column named 'inequality' is in the AffinityMeasurementDataset then every\n",
" | peptide with a value of '>' on each training epoch, gets a\n",
" | randomly sampled IC50 between its observed value and the\n",
" | max_ic50 of the predictor. Default is False.\n",
@@ -492,19 +486,14 @@
" | Extra arguments are passed on to the fit_encoded_kmer_arrays()\n",
" | method.\n",
" | \n",
- " | predict_ic50_for_kmer_peptides(self, peptides)\n",
- " | \n",
- " | predict_scores(self, peptides, combine_fn=<function mean at 0x109180ae8>)\n",
+ " | predict_scores(self, peptides)\n",
" | Given a list of peptides of any length, returns an array of predicted\n",
" | normalized affinity values. Unlike IC50, a higher value here\n",
" | means a stronger affinity. Peptides of lengths other than 9 are\n",
" | transformed into a set of k-mers either by deleting or inserting\n",
- " | amino acid characters. The prediction for a single peptide will be\n",
+ " | amino acid characters. The prediction for a single peptides will be\n",
" | the average of expanded k-mers.\n",
" | \n",
- " | predict_scores_for_kmer_peptides(self, peptides)\n",
- " | Predict binding affinity for 9mer peptides\n",
- " | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from mhcflurry.class1_allele_specific.class1_allele_specific_kmer_ic50_predictor_base.Class1AlleleSpecificKmerIC50PredictorBase:\n",
" | \n",
@@ -557,7 +546,7 @@
{
"data": {
"text/plain": [
- "Dataset(n=3040, alleles=['HLA-A3301'])"
+ "AffinityMeasurementDataset(n=3040, alleles=['HLA-A3301'])"
]
},
"execution_count": 11,
@@ -619,8 +608,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 1min 22s, sys: 1.13 s, total: 1min 24s\n",
- "Wall time: 45.8 s\n"
+ "CPU times: user 1min 11s, sys: 5.66 s, total: 1min 16s\n",
+ "Wall time: 1min 12s\n"
]
}
],
@@ -660,7 +649,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.text.Text at 0x122e50400>"
+ "<matplotlib.text.Text at 0x121829dd8>"
]
},
"execution_count": 14,
@@ -669,9 +658,9 @@
},
{
"data": {
- "image/png": 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olntaXO/SaLpG1MKSq+xAsau3goQKAsWMwhpjw37On+xJZQQrz+T0CuNTK0iy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\nu+2GKFkpMMPKOutfAd6pquprjW5MMYqN6Cu9oe0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kE+laJVPpOq1dvYQdew9P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\nQmCC\n",
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WmOd44eIDxiZq1xIXmk89Yv2CUuqXgL8G8lMJtdZfX2O/TwCmUup9wFngD5RS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\nBieumzNQAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x122384da0>"
+ "<matplotlib.figure.Figure at 0x11d2d7da0>"
]
},
"metadata": {},
@@ -756,9 +745,9 @@
{
"data": {
"text/plain": [
- "{'auc': 0.84099099099099106,\n",
- " 'f1': 0.65531914893617027,\n",
- " 'tau': 0.43387627983717181}"
+ "{'auc': 0.88567942942942945,\n",
+ " 'f1': 0.73814432989690715,\n",
+ " 'tau': 0.4912073299323329}"
]
},
"execution_count": 16,
@@ -798,12 +787,12 @@
"Help on function cross_validation_folds in module mhcflurry.class1_allele_specific.cross_validation:\n",
"\n",
"cross_validation_folds(train_data, alleles=None, n_folds=3, drop_similar_peptides=False, imputer=None, impute_kwargs={'min_observations_per_allele': 2, 'min_observations_per_peptide': 2}, parallel_backend=None)\n",
- " Split a Dataset into n_folds cross validation folds for each allele,\n",
+ " Split a AffinityMeasurementDataset into n_folds cross validation folds for each allele,\n",
" optionally performing imputation.\n",
" \n",
" Parameters\n",
" -----------\n",
- " train_data : mhcflurry.Dataset\n",
+ " train_data : mhcflurry.AffinityMeasurementDataset\n",
" \n",
" alleles : string list, optional\n",
" Alleles to run cross validation on. Default: all alleles in\n",
@@ -821,7 +810,7 @@
" Imputer to use. If not specified, no imputation is done.\n",
" \n",
" impute_kwargs : dict, optional\n",
- " Additional kwargs to pass to mhcflurry.Dataset.impute_missing_values.\n",
+ " Additional kwargs to pass to mhcflurry.AffinityMeasurementDataset.impute_missing_values.\n",
" \n",
" parallel_backend : mhcflurry.parallelism.ParallelBackend, optional\n",
" Futures implementation to use for running on multiple threads,\n",
@@ -848,9 +837,9 @@
{
"data": {
"text/plain": [
- "[AlleleSpecificTrainTestFold(allele='HLA-A3301', train=Dataset(n=2026, alleles=['HLA-A3301']), imputed_train=None, test=Dataset(n=1014, alleles=['HLA-A3301'])),\n",
- " AlleleSpecificTrainTestFold(allele='HLA-A3301', train=Dataset(n=2027, alleles=['HLA-A3301']), imputed_train=None, test=Dataset(n=1013, alleles=['HLA-A3301'])),\n",
- " AlleleSpecificTrainTestFold(allele='HLA-A3301', train=Dataset(n=2027, alleles=['HLA-A3301']), imputed_train=None, test=Dataset(n=1013, alleles=['HLA-A3301']))]"
+ "[AlleleSpecificTrainTestFold(allele='HLA-A3301', train=AffinityMeasurementDataset(n=2026, alleles=['HLA-A3301']), imputed_train=None, test=AffinityMeasurementDataset(n=1014, alleles=['HLA-A3301'])),\n",
+ " AlleleSpecificTrainTestFold(allele='HLA-A3301', train=AffinityMeasurementDataset(n=2027, alleles=['HLA-A3301']), imputed_train=None, test=AffinityMeasurementDataset(n=1013, alleles=['HLA-A3301'])),\n",
+ " AlleleSpecificTrainTestFold(allele='HLA-A3301', train=AffinityMeasurementDataset(n=2027, alleles=['HLA-A3301']), imputed_train=None, test=AffinityMeasurementDataset(n=1013, alleles=['HLA-A3301']))]"
]
},
"execution_count": 18,
@@ -914,7 +903,7 @@
"text": [
"Searching over 2 models.\n",
"First model: \n",
- "{'output_activation': 'sigmoid', 'pretrain_decay': 'numpy.exp(-epoch)', 'n_training_epochs': 250, 'embedding_output_dim': 32, 'optimizer': 'rmsprop', 'loss': 'mse', 'fraction_negative': 0.1, 'batch_normalization': True, 'dropout_probability': 0.0, 'init': 'glorot_uniform', 'activation': 'tanh', 'batch_size': 128, 'impute': False, 'kmer_size': 9, 'max_ic50': 50000.0, 'layer_sizes': [8]}\n"
+ "{'kmer_size': 9, 'n_training_epochs': 250, 'loss': 'mse', 'max_ic50': 50000.0, 'fraction_negative': 0.1, 'layer_sizes': [8], 'batch_size': 128, 'activation': 'tanh', 'init': 'glorot_uniform', 'dropout_probability': 0.0, 'pretrain_decay': 'numpy.exp(-epoch)', 'optimizer': 'rmsprop', 'impute': False, 'embedding_output_dim': 32, 'batch_normalization': True, 'output_activation': 'sigmoid'}\n"
]
}
],
@@ -995,20 +984,20 @@
" <th>test_size</th>\n",
" <th>imputed_train_size</th>\n",
" <th>train_tau</th>\n",
- " <th>train_auc</th>\n",
" <th>train_f1</th>\n",
+ " <th>train_auc</th>\n",
" <th>test_tau</th>\n",
" <th>...</th>\n",
- " <th>model_fraction_negative</th>\n",
- " <th>model_batch_normalization</th>\n",
- " <th>model_dropout_probability</th>\n",
- " <th>model_init</th>\n",
- " <th>model_activation</th>\n",
" <th>model_batch_size</th>\n",
+ " <th>model_activation</th>\n",
+ " <th>model_init</th>\n",
+ " <th>model_dropout_probability</th>\n",
+ " <th>model_pretrain_decay</th>\n",
+ " <th>model_optimizer</th>\n",
" <th>model_impute</th>\n",
- " <th>model_kmer_size</th>\n",
- " <th>model_max_ic50</th>\n",
- " <th>model_layer_sizes</th>\n",
+ " <th>model_embedding_output_dim</th>\n",
+ " <th>model_batch_normalization</th>\n",
+ " <th>model_output_activation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -1020,21 +1009,21 @@
" <td>2026</td>\n",
" <td>1014</td>\n",
" <td>None</td>\n",
- " <td>0.710233</td>\n",
- " <td>0.989589</td>\n",
- " <td>0.902256</td>\n",
- " <td>0.429803</td>\n",
+ " <td>0.705331</td>\n",
+ " <td>0.887640</td>\n",
+ " <td>0.988062</td>\n",
+ " <td>0.408410</td>\n",
" <td>...</td>\n",
- " <td>0.1</td>\n",
- " <td>True</td>\n",
- " <td>0.0</td>\n",
- " <td>glorot_uniform</td>\n",
- " <td>tanh</td>\n",
" <td>128</td>\n",
+ " <td>tanh</td>\n",
+ " <td>glorot_uniform</td>\n",
+ " <td>0.0</td>\n",
+ " <td>numpy.exp(-epoch)</td>\n",
+ " <td>rmsprop</td>\n",
" <td>False</td>\n",
- " <td>9</td>\n",
- " <td>50000.0</td>\n",
- " <td>[8]</td>\n",
+ " <td>32</td>\n",
+ " <td>True</td>\n",
+ " <td>sigmoid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -1044,21 +1033,21 @@
" <td>2026</td>\n",
" <td>1014</td>\n",
" <td>None</td>\n",
- " <td>0.747597</td>\n",
- " <td>0.993938</td>\n",
- " <td>0.919708</td>\n",
- " <td>0.425610</td>\n",
+ " <td>0.743330</td>\n",
+ " <td>0.907407</td>\n",
+ " <td>0.994428</td>\n",
+ " <td>0.425253</td>\n",
" <td>...</td>\n",
- " <td>0.1</td>\n",
- " <td>True</td>\n",
- " <td>0.0</td>\n",
- " <td>glorot_uniform</td>\n",
- " <td>tanh</td>\n",
" <td>128</td>\n",
+ " <td>tanh</td>\n",
+ " <td>glorot_uniform</td>\n",
+ " <td>0.0</td>\n",
+ " <td>numpy.exp(-epoch)</td>\n",
+ " <td>rmsprop</td>\n",
" <td>False</td>\n",
- " <td>9</td>\n",
- " <td>50000.0</td>\n",
- " <td>[12]</td>\n",
+ " <td>32</td>\n",
+ " <td>True</td>\n",
+ " <td>sigmoid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -1068,21 +1057,21 @@
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
- " <td>0.705507</td>\n",
- " <td>0.990185</td>\n",
- " <td>0.882466</td>\n",
- " <td>0.430678</td>\n",
+ " <td>0.698517</td>\n",
+ " <td>0.877470</td>\n",
+ " <td>0.990193</td>\n",
+ " <td>0.434371</td>\n",
" <td>...</td>\n",
- " <td>0.1</td>\n",
- " <td>True</td>\n",
- " <td>0.0</td>\n",
- " <td>glorot_uniform</td>\n",
- " <td>tanh</td>\n",
" <td>128</td>\n",
+ " <td>tanh</td>\n",
+ " <td>glorot_uniform</td>\n",
+ " <td>0.0</td>\n",
+ " <td>numpy.exp(-epoch)</td>\n",
+ " <td>rmsprop</td>\n",
" <td>False</td>\n",
- " <td>9</td>\n",
- " <td>50000.0</td>\n",
- " <td>[8]</td>\n",
+ " <td>32</td>\n",
+ " <td>True</td>\n",
+ " <td>sigmoid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
@@ -1092,21 +1081,21 @@
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
- " <td>0.745532</td>\n",
- " <td>0.993875</td>\n",
- " <td>0.924812</td>\n",
- " <td>0.395103</td>\n",
+ " <td>0.745956</td>\n",
+ " <td>0.874227</td>\n",
+ " <td>0.994672</td>\n",
+ " <td>0.444289</td>\n",
" <td>...</td>\n",
- " <td>0.1</td>\n",
- " <td>True</td>\n",
- " <td>0.0</td>\n",
- " <td>glorot_uniform</td>\n",
- " <td>tanh</td>\n",
" <td>128</td>\n",
+ " <td>tanh</td>\n",
+ " <td>glorot_uniform</td>\n",
+ " <td>0.0</td>\n",
+ " <td>numpy.exp(-epoch)</td>\n",
+ " <td>rmsprop</td>\n",
" <td>False</td>\n",
- " <td>9</td>\n",
- " <td>50000.0</td>\n",
- " <td>[12]</td>\n",
+ " <td>32</td>\n",
+ " <td>True</td>\n",
+ " <td>sigmoid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
@@ -1116,21 +1105,21 @@
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
- " <td>0.709275</td>\n",
- " <td>0.992395</td>\n",
- " <td>0.894531</td>\n",
- " <td>0.441365</td>\n",
+ " <td>0.703742</td>\n",
+ " <td>0.859961</td>\n",
+ " <td>0.988574</td>\n",
+ " <td>0.440792</td>\n",
" <td>...</td>\n",
- " <td>0.1</td>\n",
- " <td>True</td>\n",
- " <td>0.0</td>\n",
- " <td>glorot_uniform</td>\n",
- " <td>tanh</td>\n",
" <td>128</td>\n",
+ " <td>tanh</td>\n",
+ " <td>glorot_uniform</td>\n",
+ " <td>0.0</td>\n",
+ " <td>numpy.exp(-epoch)</td>\n",
+ " <td>rmsprop</td>\n",
" <td>False</td>\n",
- " <td>9</td>\n",
- " <td>50000.0</td>\n",
- " <td>[8]</td>\n",
+ " <td>32</td>\n",
+ " <td>True</td>\n",
+ " <td>sigmoid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
@@ -1140,21 +1129,21 @@
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
- " <td>0.743498</td>\n",
- " <td>0.994674</td>\n",
- " <td>0.873518</td>\n",
- " <td>0.439221</td>\n",
+ " <td>0.741095</td>\n",
+ " <td>0.912801</td>\n",
+ " <td>0.993722</td>\n",
+ " <td>0.443505</td>\n",
" <td>...</td>\n",
- " <td>0.1</td>\n",
- " <td>True</td>\n",
- " <td>0.0</td>\n",
- " <td>glorot_uniform</td>\n",
- " <td>tanh</td>\n",
" <td>128</td>\n",
+ " <td>tanh</td>\n",
+ " <td>glorot_uniform</td>\n",
+ " <td>0.0</td>\n",
+ " <td>numpy.exp(-epoch)</td>\n",
+ " <td>rmsprop</td>\n",
" <td>False</td>\n",
- " <td>9</td>\n",
- " <td>50000.0</td>\n",
- " <td>[12]</td>\n",
+ " <td>32</td>\n",
+ " <td>True</td>\n",
+ " <td>sigmoid</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1170,45 +1159,45 @@
"4 HLA-A3301 2 0 2027 1013 None \n",
"5 HLA-A3301 2 1 2027 1013 None \n",
"\n",
- " train_tau train_auc train_f1 test_tau ... \\\n",
- "0 0.710233 0.989589 0.902256 0.429803 ... \n",
- "1 0.747597 0.993938 0.919708 0.425610 ... \n",
- "2 0.705507 0.990185 0.882466 0.430678 ... \n",
- "3 0.745532 0.993875 0.924812 0.395103 ... \n",
- "4 0.709275 0.992395 0.894531 0.441365 ... \n",
- "5 0.743498 0.994674 0.873518 0.439221 ... \n",
+ " train_tau train_f1 train_auc test_tau ... \\\n",
+ "0 0.705331 0.887640 0.988062 0.408410 ... \n",
+ "1 0.743330 0.907407 0.994428 0.425253 ... \n",
+ "2 0.698517 0.877470 0.990193 0.434371 ... \n",
+ "3 0.745956 0.874227 0.994672 0.444289 ... \n",
+ "4 0.703742 0.859961 0.988574 0.440792 ... \n",
+ "5 0.741095 0.912801 0.993722 0.443505 ... \n",
"\n",
- " model_fraction_negative model_batch_normalization \\\n",
- "0 0.1 True \n",
- "1 0.1 True \n",
- "2 0.1 True \n",
- "3 0.1 True \n",
- "4 0.1 True \n",
- "5 0.1 True \n",
+ " model_batch_size model_activation model_init \\\n",
+ "0 128 tanh glorot_uniform \n",
+ "1 128 tanh glorot_uniform \n",
+ "2 128 tanh glorot_uniform \n",
+ "3 128 tanh glorot_uniform \n",
+ "4 128 tanh glorot_uniform \n",
+ "5 128 tanh glorot_uniform \n",
"\n",
- " model_dropout_probability model_init model_activation \\\n",
- "0 0.0 glorot_uniform tanh \n",
- "1 0.0 glorot_uniform tanh \n",
- "2 0.0 glorot_uniform tanh \n",
- "3 0.0 glorot_uniform tanh \n",
- "4 0.0 glorot_uniform tanh \n",
- "5 0.0 glorot_uniform tanh \n",
+ " model_dropout_probability model_pretrain_decay model_optimizer \\\n",
+ "0 0.0 numpy.exp(-epoch) rmsprop \n",
+ "1 0.0 numpy.exp(-epoch) rmsprop \n",
+ "2 0.0 numpy.exp(-epoch) rmsprop \n",
+ "3 0.0 numpy.exp(-epoch) rmsprop \n",
+ "4 0.0 numpy.exp(-epoch) rmsprop \n",
+ "5 0.0 numpy.exp(-epoch) rmsprop \n",
"\n",
- " model_batch_size model_impute model_kmer_size model_max_ic50 \\\n",
- "0 128 False 9 50000.0 \n",
- "1 128 False 9 50000.0 \n",
- "2 128 False 9 50000.0 \n",
- "3 128 False 9 50000.0 \n",
- "4 128 False 9 50000.0 \n",
- "5 128 False 9 50000.0 \n",
+ " model_impute model_embedding_output_dim model_batch_normalization \\\n",
+ "0 False 32 True \n",
+ "1 False 32 True \n",
+ "2 False 32 True \n",
+ "3 False 32 True \n",
+ "4 False 32 True \n",
+ "5 False 32 True \n",
"\n",
- " model_layer_sizes \n",
- "0 [8] \n",
- "1 [12] \n",
- "2 [8] \n",
- "3 [12] \n",
- "4 [8] \n",
- "5 [12] \n",
+ " model_output_activation \n",
+ "0 sigmoid \n",
+ "1 sigmoid \n",
+ "2 sigmoid \n",
+ "3 sigmoid \n",
+ "4 sigmoid \n",
+ "5 sigmoid \n",
"\n",
"[6 rows x 31 columns]"
]
@@ -1266,8 +1255,8 @@
"data": {
"text/plain": [
"model_num\n",
- "0 0.859859\n",
- "1 0.847004\n",
+ "0 0.841541\n",
+ "1 0.849979\n",
"Name: test_auc, dtype: float64"
]
},
diff --git a/mhcflurry/__init__.py b/mhcflurry/__init__.py
index a2b1d664cabb8f417700ef252abf27c73d01353c..bb50dabcd968bf7ce5460a0668b9bae8b5c322ca 100644
--- a/mhcflurry/__init__.py
+++ b/mhcflurry/__init__.py
@@ -14,7 +14,11 @@
from .class1_allele_specific.class1_binding_predictor import (
Class1BindingPredictor)
-from .predict import predict
+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
__version__ = "0.2.0"
@@ -23,5 +27,9 @@ __all__ = [
"Class1BindingPredictor",
"predict",
"parallelism",
+ "AffinityMeasurementDataset",
+ "Class1EnsembleMultiAllelePredictor",
+ "Class1SingleModelMultiAllelePredictor",
+ "MeasurementCollection",
"__version__",
]
diff --git a/mhcflurry/dataset.py b/mhcflurry/affinity_measurement_dataset.py
similarity index 92%
rename from mhcflurry/dataset.py
rename to mhcflurry/affinity_measurement_dataset.py
index 43fff5c96eff9ea0d25f896098254aab711c9029..757e51ed7895924a7f040873f43b7f76fb09b52b 100644
--- a/mhcflurry/dataset.py
+++ b/mhcflurry/affinity_measurement_dataset.py
@@ -37,12 +37,15 @@ from .imputation_helpers import (
)
-class Dataset(object):
-
+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)
@@ -50,7 +53,7 @@ class Dataset(object):
"""
def __init__(self, df):
"""
- Constructs a DataSet from a pandas DataFrame with the following
+ Constructs a AffinityMeasurementDataset from a pandas DataFrame with the following
columns:
- allele
- peptide
@@ -89,7 +92,7 @@ class Dataset(object):
def to_dataframe(self):
"""
- Returns DataFrame representation of data contained in Dataset
+ Returns DataFrame representation of data contained in AffinityMeasurementDataset
"""
return self._df
@@ -125,7 +128,7 @@ class Dataset(object):
return len(self.to_dataframe())
def __str__(self):
- return "Dataset(n=%d, alleles=%s)" % (
+ return "AffinityMeasurementDataset(n=%d, alleles=%s)" % (
len(self), list(sorted(self.unique_alleles())))
def __repr__(self):
@@ -136,7 +139,7 @@ class Dataset(object):
Two datasets are equal if they contain the same number of samples
with the same properties and values.
"""
- if type(other) is not Dataset:
+ if type(other) is not AffinityMeasurementDataset:
return False
elif len(self) != len(other):
return False
@@ -180,13 +183,13 @@ class Dataset(object):
def unique_alleles(self):
"""
- Returns the set of allele names contained in this Dataset.
+ 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 Dataset.
+ Returns the set of peptide sequences contained in this AffinityMeasurementDataset.
"""
return set(self.peptides)
@@ -202,11 +205,11 @@ class Dataset(object):
entries just for that allele.
"""
for (allele_name, group_df) in self.to_dataframe().groupby("allele"):
- yield (allele_name, Dataset(group_df))
+ yield (allele_name, AffinityMeasurementDataset(group_df))
def groupby_allele_dictionary(self):
"""
- Returns dictionary mapping each allele name to a Dataset containing
+ Returns dictionary mapping each allele name to a AffinityMeasurementDataset containing
only entries from that allele.
"""
return dict(self.groupby_allele())
@@ -314,7 +317,7 @@ class Dataset(object):
@classmethod
def from_single_allele_dataframe(cls, allele_name, single_allele_df):
"""
- Construct a Dataset from a single MHC allele's DataFrame
+ Construct a AffinityMeasurementDataset from a single MHC allele's DataFrame
"""
df = single_allele_df.copy()
df["allele"] = allele_name
@@ -326,7 +329,7 @@ class Dataset(object):
allele_to_peptide_to_affinity_dict):
"""
Given nested dictionaries mapping allele -> peptide -> affinity,
- construct a Dataset with uniform sample weights.
+ construct a AffinityMeasurementDataset with uniform sample weights.
"""
alleles = []
peptides = []
@@ -344,7 +347,7 @@ class Dataset(object):
@classmethod
def create_empty(cls):
"""
- Returns an empty Dataset containing no pMHC entries.
+ Returns an empty AffinityMeasurementDataset containing no pMHC entries.
"""
return cls.from_nested_dictionary({})
@@ -355,7 +358,7 @@ class Dataset(object):
peptide_to_affinity_dict):
"""
Given a peptide->affinity dictionary for a single allele,
- create a Dataset.
+ create a AffinityMeasurementDataset.
"""
return cls.from_nested_dictionary({allele_name: peptide_to_affinity_dict})
@@ -382,7 +385,7 @@ class Dataset(object):
def get_allele(self, allele_name):
"""
- Get Dataset for a single allele
+ Get AffinityMeasurementDataset for a single allele
"""
if allele_name not in self.unique_alleles():
raise KeyError("Allele '%s' not found, available alleles: %s" % (
@@ -393,7 +396,7 @@ class Dataset(object):
def get_alleles(self, allele_names):
"""
- Restrict Dataset to several allele names.
+ Restrict AffinityMeasurementDataset to several allele names.
"""
datasets = []
for allele_name in allele_names:
@@ -446,7 +449,7 @@ class Dataset(object):
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 Dataset
+ # the provenance of entries in the new AffinityMeasurementDataset
original_peptide = row.get("original_peptide")
if original_peptide is None:
original_peptide = ()
@@ -569,14 +572,14 @@ class Dataset(object):
def slice(self, indices):
"""
- Create a new Dataset by slicing through all columns of this dataset
+ 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 Dataset of size %d" % (
+ raise ValueError("Invalid index %d for AffinityMeasurementDataset of size %d" % (
max_index, n_total))
df = self.to_dataframe()
@@ -587,7 +590,7 @@ class Dataset(object):
def random_split(self, n=None, stratify_fn=None):
"""
- Randomly split the Dataset into smaller Dataset objects.
+ Randomly split the AffinityMeasurementDataset into smaller AffinityMeasurementDataset objects.
Parameters
----------
@@ -598,7 +601,7 @@ class Dataset(object):
Function that takes a row and returns bool, stratifying sampling
into two groups.
- Returns a pair of Dataset objects.
+ Returns a pair of AffinityMeasurementDataset objects.
"""
n_total = len(self)
if n is None:
@@ -646,7 +649,7 @@ class Dataset(object):
test_samples = self
test_sample_indices = np.arange(n_total)
elif test_allele not in self.unique_alleles():
- raise ValueError("Allele '%s' not in Dataset" % test_allele)
+ raise ValueError("Allele '%s' not in AffinityMeasurementDataset" % test_allele)
else:
test_sample_indices = np.where(self.alleles == test_allele)[0]
@@ -685,7 +688,7 @@ class Dataset(object):
"""
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 Dataset.
+ in this AffinityMeasurementDataset.
(apologies for the wordy name, this turns out to be a common operation)
@@ -698,13 +701,13 @@ class Dataset(object):
Size of the training set to return for this allele.
stratify_fn : function
- Function mapping from rows of the Dataset to booleans for stratifying
+ Function mapping from rows of the AffinityMeasurementDataset to booleans for stratifying
by two groups.
**kwargs : dict
- Extra keyword arguments passed to Dataset.impute_missing_values
+ Extra keyword arguments passed to AffinityMeasurementDataset.impute_missing_values
- Returns three Dataset objects:
+ Returns three AffinityMeasurementDataset objects:
- training set with original pMHC affinities for given allele
- larger imputed training set for given allele
- test set
@@ -729,7 +732,7 @@ class Dataset(object):
The two arguments are assumed to be the same length.
- Returns Dataset of equal or smaller size.
+ Returns AffinityMeasurementDataset of equal or smaller size.
"""
if len(alleles) != len(peptides):
raise ValueError(
@@ -746,7 +749,7 @@ class Dataset(object):
allele_peptide_pairs : list of (str, str) tuples
The two arguments are assumed to be the same length.
- Returns Dataset of equal or smaller size.
+ Returns AffinityMeasurementDataset of equal or smaller size.
"""
require_iterable_of(allele_peptide_pairs, tuple)
keys_to_remove_set = set(allele_peptide_pairs)
@@ -763,7 +766,7 @@ class Dataset(object):
Parameters
----------
- other_dataset : Dataset
+ other_dataset : AffinityMeasurementDataset
Returns a new Dataset object of equal or lesser size.
"""
@@ -792,7 +795,7 @@ class Dataset(object):
of floats and replaces some or all NaN values with synthetic
affinities.
- log_transform : function
+ log_transform : bool
Transform affinities with to log10 values before imputation
(and then transform back afterward).
@@ -802,7 +805,7 @@ class Dataset(object):
min_observations_per_allele : int
Drop allele columns with fewer than this number of observed values.
- Returns Dataset with original pMHC affinities and additional
+ Returns AffinityMeasurementDataset with original pMHC affinities and additional
synthetic samples.
"""
if isinstance(imputation_method, string_types):
diff --git a/mhcflurry/antigen_presentation/__init__.py b/mhcflurry/antigen_presentation/__init__.py
index 0b544c71ac10883f5ce56a506171a020bfd88b2d..7406a57d6d863243870d0e09b0594a88e5828602 100644
--- a/mhcflurry/antigen_presentation/__init__.py
+++ b/mhcflurry/antigen_presentation/__init__.py
@@ -1,9 +1,10 @@
-from .presentation_model import PresentationModel
+from .presentation_model import PresentationModel, build_presentation_models
from .percent_rank_transform import PercentRankTransform
from . import presentation_component_models, decoy_strategies
__all__ = [
"PresentationModel",
+ "build_presentation_models",
"PercentRankTransform",
"presentation_component_models",
"decoy_strategies",
diff --git a/mhcflurry/antigen_presentation/presentation_component_models/expression.py b/mhcflurry/antigen_presentation/presentation_component_models/expression.py
index 0c4b516ac9fb82b500247dc89413349dff57d54b..22e1abb0d34b38a2e7dd9f270771bb47be8f7e27 100644
--- a/mhcflurry/antigen_presentation/presentation_component_models/expression.py
+++ b/mhcflurry/antigen_presentation/presentation_component_models/expression.py
@@ -25,7 +25,7 @@ class Expression(PresentationComponentModel):
group in expression_values.columns
for group in experiment_to_expression_group.values())
- assert_no_null(experiment_to_expression_group)
+ assert_no_null(expression_values)
self.experiment_to_expression_group = experiment_to_expression_group
self.expression_values = expression_values
diff --git a/mhcflurry/antigen_presentation/presentation_component_models/fixed_affinity_predictions.py b/mhcflurry/antigen_presentation/presentation_component_models/fixed_affinity_predictions.py
index 3a4a29b10e6e638c68cb29bba4361df0aa26abd2..0680dde4abe83d588c9ea24e7941fb934ba8bbe7 100644
--- a/mhcflurry/antigen_presentation/presentation_component_models/fixed_affinity_predictions.py
+++ b/mhcflurry/antigen_presentation/presentation_component_models/fixed_affinity_predictions.py
@@ -16,6 +16,9 @@ class FixedAffinityPredictions(PresentationComponentModel):
- "value", "percentile_rank" (IC50 and percent rank)
- peptide (string)
- allele (string)
+
+ name : string
+ Used to name output columns and in debug messages
"""
def __init__(
diff --git a/mhcflurry/antigen_presentation/presentation_component_models/mhc_binding_component_model_base.py b/mhcflurry/antigen_presentation/presentation_component_models/mhc_binding_component_model_base.py
index a5a3d3c10122261be5f377293503eb5dea82bb97..fdceb8833cfb65ab70ac7413d85247278625cb17 100644
--- a/mhcflurry/antigen_presentation/presentation_component_models/mhc_binding_component_model_base.py
+++ b/mhcflurry/antigen_presentation/presentation_component_models/mhc_binding_component_model_base.py
@@ -47,7 +47,7 @@ class MHCBindingComponentModelBase(PresentationComponentModel):
fallback_predictor : function: (allele, peptides) -> predictions
Used when missing an allele.
- iedb_dataset : mhcflurry.Dataset
+ iedb_dataset : mhcflurry.AffinityMeasurementDataset
IEDB data for this allele. If not specified no iedb data is used.
decoys_per_hit : int
diff --git a/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_released.py b/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_released.py
index fc401e32a20a1d12e8226a8536dc2557f148b47a..d05aeddd2606b723e4373150e0c95ea68644ffa6 100644
--- a/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_released.py
+++ b/mhcflurry/antigen_presentation/presentation_component_models/mhcflurry_released.py
@@ -4,8 +4,9 @@ import numpy
import pandas
from ...common import normalize_allele_name
-from ...predict import predict
+from ...prediction import predict
from ..percent_rank_transform import PercentRankTransform
+from ...peptide_encoding import encode_peptides
from .presentation_component_model import PresentationComponentModel
@@ -21,15 +22,22 @@ class MHCflurryReleased(PresentationComponentModel):
random_peptides_for_percent_rank : list of string
If specified, then percentile rank will be calibrated and emitted
using the given peptides.
+
+ predictor : Class1EnsembleMultiAllelePredictor-like object
+ Predictor to use.
"""
def __init__(
self,
experiment_to_alleles,
random_peptides_for_percent_rank=None,
+ predictor=None,
+ predictor_name="mhcflurry_released",
**kwargs):
PresentationComponentModel.__init__(self, **kwargs)
self.experiment_to_alleles = experiment_to_alleles
+ self.predictor = predictor
+ self.predictor_name = predictor_name
if random_peptides_for_percent_rank is None:
self.percent_rank_transforms = None
self.random_peptides_for_percent_rank = None
@@ -39,9 +47,9 @@ class MHCflurryReleased(PresentationComponentModel):
random_peptides_for_percent_rank)
def column_names(self):
- columns = ['mhcflurry_released_affinity']
+ columns = [self.predictor_name + '_affinity']
if self.percent_rank_transforms is not None:
- columns.append('mhcflurry_released_percentile_rank')
+ columns.append(self.predictor_name + '_percentile_rank')
return columns
def requires_fitting(self):
@@ -59,15 +67,18 @@ class MHCflurryReleased(PresentationComponentModel):
.Prediction.values)
def predict_min_across_alleles(self, alleles, peptides):
- alleles = [
+ alleles = list(set([
normalize_allele_name(allele)
for allele in alleles
- ]
- df = predict(alleles, numpy.unique(numpy.array(peptides)))
+ ]))
+ df = predict(
+ alleles,
+ peptides,
+ predictor=self.predictor)
pivoted = df.pivot(index='Peptide', columns='Allele')
pivoted.columns = pivoted.columns.droplevel()
result = {
- 'mhcflurry_released_affinity': (
+ self.predictor_name + '_affinity': (
pivoted.min(axis=1).ix[peptides].values)
}
if self.percent_rank_transforms is not None:
@@ -77,8 +88,11 @@ class MHCflurryReleased(PresentationComponentModel):
percentile_ranks[allele] = (
self.percent_rank_transforms[allele]
.transform(pivoted[allele].values))
- result['mhcflurry_released_percentile_rank'] = (
+ result[self.predictor_name + '_percentile_rank'] = (
percentile_ranks.min(axis=1).ix[peptides].values)
+
+ for (key, value) in result.items():
+ assert len(value) == len(peptides), (len(peptides), result)
return result
def predict_for_experiment(self, experiment_name, peptides):
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 0f0177e03cf4abbd344b4935cefb8aa01d15a4e3..47be4dca4959cc169c4110ba0c6c17ecae9f696f 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
@@ -3,7 +3,7 @@ from copy import copy
import pandas
from numpy import log, exp, nanmean
-from ...dataset import Dataset
+from ...affinity_measurement_dataset import AffinityMeasurementDataset
from ...class1_allele_specific import Class1BindingPredictor
from ...common import normalize_allele_name
@@ -22,7 +22,7 @@ class MHCflurryTrainedOnHits(MHCBindingComponentModelBase):
Parameters
------------
- iedb_dataset : mhcflurry.Dataset
+ iedb_dataset : mhcflurry.AffinityMeasurementDataset
IEDB data for this allele. If not specified no iedb data is used.
mhcflurry_hyperparameters : dict
@@ -96,7 +96,7 @@ class MHCflurryTrainedOnHits(MHCBindingComponentModelBase):
if self.iedb_dataset is not None:
df = df.append(iedb_dataset_df, ignore_index=True)
- dataset = Dataset(
+ dataset = AffinityMeasurementDataset(
df.sample(frac=1)) # shuffle dataframe
print("Train data: ", dataset)
model = Class1BindingPredictor(
diff --git a/mhcflurry/antigen_presentation/presentation_component_models/presentation_component_model.py b/mhcflurry/antigen_presentation/presentation_component_models/presentation_component_model.py
index eb20fbd5d436747c779099084216b88177b736b7..00bc5a8df6f6fa0e04eff682e01cde4f38c55fac 100644
--- a/mhcflurry/antigen_presentation/presentation_component_models/presentation_component_model.py
+++ b/mhcflurry/antigen_presentation/presentation_component_models/presentation_component_model.py
@@ -187,7 +187,12 @@ class PresentationComponentModel(object):
self.predict_for_experiment(
str(peptides_df.iloc[0].experiment_name),
peptides_df.peptide.values))
- assert len(return_value) == len(peptides_df), str(self)
+ assert len(return_value) == len(peptides_df), (
+ "%d != %d" % (len(return_value), len(peptides_df)),
+ str(self),
+ peptides_df.peptide.nunique(),
+ return_value,
+ peptides_df)
assert_no_null(return_value, str(self))
else:
peptides_df = (
@@ -202,7 +207,9 @@ class PresentationComponentModel(object):
result_df.loc[sub_df.index, "experiment_name"] ==
experiment_name).all()
- unique_peptides = numpy.unique(sub_df.peptide.values)
+ unique_peptides = list(set(sub_df.peptide))
+ if len(unique_peptides) == 0:
+ continue
result_dict = self.predict_for_experiment(
experiment_name, unique_peptides)
diff --git a/mhcflurry/class1_allele_specific/__init__.py b/mhcflurry/class1_allele_specific/__init__.py
index f66cf684c55738064c3a660a81f905d56f578506..228b33d9b11be41a360acd50510c6038c1ed1730 100644
--- a/mhcflurry/class1_allele_specific/__init__.py
+++ b/mhcflurry/class1_allele_specific/__init__.py
@@ -3,7 +3,11 @@ 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 .load import from_allele_name, supported_alleles
+from .class1_single_model_multi_allele_predictor import (
+ from_allele_name,
+ supported_alleles,
+ get_downloaded_predictor,
+ Class1SingleModelMultiAllelePredictor)
__all__ = [
'Class1BindingPredictor',
@@ -12,4 +16,6 @@ __all__ = [
'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
index 875730c16c37935b4120302b6d8210e58e5d2c37..b309df00af2067a4db33b570555d2cab358a05f4 100644
--- 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
@@ -17,17 +17,14 @@ from __future__ import (
division,
absolute_import,
)
-from collections import defaultdict
-import numpy as np
from six import string_types
-from ..peptide_encoding import fixed_length_index_encoding
+from ..peptide_encoding import encode_peptides
from ..amino_acid import (
amino_acids_with_unknown,
common_amino_acids
)
-from ..regression_target import regression_target_to_ic50
from ..ic50_predictor_base import IC50PredictorBase
from ..hyperparameters import HyperparameterDefaults
@@ -88,74 +85,25 @@ class Class1AlleleSpecificKmerIC50PredictorBase(IC50PredictorBase):
return len(self.amino_acids)
def encode_peptides(self, peptides):
- """
- Parameters
- ----------
- peptides : str list
- Peptide strings of any length
-
- Encode peptides of any length into fixed length vectors.
- Returns 2d array of encoded peptides and 1d array indicating the
- original peptide index for each row.
- """
- indices = []
- encoded_matrices = []
- for i, peptide in enumerate(peptides):
- matrix, _, _, _ = fixed_length_index_encoding(
- peptides=[peptide],
- desired_length=self.kmer_size,
- allow_unknown_amino_acids=self.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), self.kmer_size)
- assert combined_matrix.shape == expected_shape, \
- "Expected shape %s but got %s" % (
- expected_shape, combined_matrix.shape)
- return combined_matrix, index_array
-
- def predict_scores_for_kmer_peptides(self, peptides):
- """
- Predict binding affinity for 9mer peptides
- """
- if any(len(peptide) != self.kmer_size for peptide in peptides):
- raise ValueError("Can only predict 9mer peptides")
- X, _ = self.encode_peptides(peptides)
- return self.predict_scores_for_kmer_encoded_array(X)
-
- def predict_ic50_for_kmer_peptides(self, peptides):
- scores = self.predict_scores_for_kmer_peptides(peptides)
- return regression_target_to_ic50(scores, max_ic50=self.max_ic50)
+ return encode_peptides(
+ peptides, kmer_size=self.kmer_size, allow_unknown_amino_acids=self.allow_unknown_amino_acids)
- def predict_scores(self, peptides, combine_fn=np.mean):
+ 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 peptide will be
+ 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)))
- input_matrix, original_peptide_indices = self.encode_peptides(peptides)
- # peptides of lengths other than self.kmer_size get multiple
- # predictions, which are then combined with the combine_fn argument
- multiple_predictions_dict = defaultdict(list)
- fixed_length_predictions = \
- self.predict_scores_for_kmer_encoded_array(input_matrix)
- for i, yi in enumerate(fixed_length_predictions):
- original_peptide_index = original_peptide_indices[i]
- original_peptide = peptides[original_peptide_index]
- multiple_predictions_dict[original_peptide].append(yi)
- combined_predictions_dict = {
- p: combine_fn(ys) if len(ys) > 1 else ys[0]
- for (p, ys) in multiple_predictions_dict.items()
- }
- return np.array([combined_predictions_dict[p] for p in 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,
@@ -168,12 +116,12 @@ class Class1AlleleSpecificKmerIC50PredictorBase(IC50PredictorBase):
Parameters
----------
- dataset : Dataset
+ dataset : AffinityMeasurementDataset
- pretraining_dataset : Dataset
+ pretraining_dataset : AffinityMeasurementDataset
sample_censored_affinities : bool
- If a column named 'inequality' is in the Dataset then every
+ 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.
diff --git a/mhcflurry/class1_allele_specific/class1_binding_predictor.py b/mhcflurry/class1_allele_specific/class1_binding_predictor.py
index 3f68385c81e0f976712e0a5060ad2de434dc3898..31024b679e78ed058468fe480cfd73b9e216df52 100644
--- a/mhcflurry/class1_allele_specific/class1_binding_predictor.py
+++ b/mhcflurry/class1_allele_specific/class1_binding_predictor.py
@@ -114,24 +114,34 @@ class Class1BindingPredictor(Class1AlleleSpecificKmerIC50PredictorBase):
def __getstate__(self):
result = dict(self.__dict__)
del result['model']
- with tempfile.NamedTemporaryFile(suffix='.hdf5', delete=True) as fd:
- keras.models.save_model(self.model, fd.name, overwrite=True)
- result['model_bytes'] = fd.read()
+ result['model_json'] = self.model.to_json()
+ result['model_weights'] = self.get_weights()
return result
def __setstate__(self, state):
- model_bytes = state.pop('model_bytes')
- self.__dict__.update(state)
- fd = tempfile.NamedTemporaryFile(suffix='.hdf5', delete=False)
+ model_bytes = model_json = model_weights = None
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)
+ 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):
"""
diff --git a/mhcflurry/class1_allele_specific/load.py b/mhcflurry/class1_allele_specific/class1_single_model_multi_allele_predictor.py
similarity index 62%
rename from mhcflurry/class1_allele_specific/load.py
rename to mhcflurry/class1_allele_specific/class1_single_model_multi_allele_predictor.py
index 619bd0f314ac817cf005668643241b3e156b1b6c..1dca8766306e3cc4faebfee478fc1dadf19176c7 100644
--- a/mhcflurry/class1_allele_specific/load.py
+++ b/mhcflurry/class1_allele_specific/class1_single_model_multi_allele_predictor.py
@@ -27,12 +27,12 @@ import pandas
from ..downloads import get_path
from ..common import normalize_allele_name, UnsupportedAllele
-CACHED_LOADER = None
+CACHED_PREDICTOR = None
def from_allele_name(allele_name):
"""
- Load a predictor for an allele using the default loader.
+ Load a single-allele predictor.
Parameters
----------
@@ -42,7 +42,7 @@ def from_allele_name(allele_name):
----------
Class1BindingPredictor
"""
- return get_loader_for_downloaded_models().from_allele_name(allele_name)
+ return get_downloaded_predictor().predictor_for_allele(allele_name)
def supported_alleles():
@@ -50,26 +50,28 @@ def supported_alleles():
Return a list of the names of the alleles for which there are trained
predictors in the default laoder.
"""
- return get_loader_for_downloaded_models().supported_alleles
+ return get_downloaded_predictor().supported_alleles
-def get_loader_for_downloaded_models():
+def get_downloaded_predictor():
"""
Return a Class1AlleleSpecificPredictorLoader that uses downloaded models.
"""
- global CACHED_LOADER
+ 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_LOADER is None or path != CACHED_LOADER.path:
- CACHED_LOADER = Class1AlleleSpecificPredictorLoader(path)
- return CACHED_LOADER
+ if CACHED_PREDICTOR is None or path != CACHED_PREDICTOR.path:
+ CACHED_PREDICTOR = (
+ Class1SingleModelMultiAllelePredictor
+ .load_from_download_directory(path))
+ return CACHED_PREDICTOR
-class Class1AlleleSpecificPredictorLoader(object):
+class Class1SingleModelMultiAllelePredictor(object):
"""
Factory for Class1BindingPredictor instances that are stored on disk
using this directory structure:
@@ -82,6 +84,10 @@ class Class1AlleleSpecificPredictorLoader(object):
...
"""
+ @staticmethod
+ def load_from_download_directory(directory):
+ return Class1SingleModelMultiAllelePredictor(directory)
+
def __init__(self, path):
"""
Parameters
@@ -96,31 +102,49 @@ class Class1AlleleSpecificPredictorLoader(object):
self.supported_alleles = list(sorted(self.df.allele))
self.predictors_cache = {}
- def from_allele_name(self, allele_name):
+ def predictor_for_allele(self, allele):
"""
Load a predictor for an allele.
Parameters
----------
- allele_name : class I allele name
+ allele : class I allele name
Returns
----------
Class1BindingPredictor
"""
- allele_name = normalize_allele_name(allele_name)
- if allele_name not in self.predictors_cache:
+ allele = normalize_allele_name(allele)
+ if allele not in self.predictors_cache:
try:
- predictor_name = self.df.ix[allele_name].predictor_name
+ 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_name,
+ 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_name] = pickle.load(fd)
- return self.predictors_cache[allele_name]
+ 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
index d2189377e0d9f28d4d608bda9186b9a6095b06e5..c819bc4e69fb589324db01f98f84f20ad453552b 100644
--- a/mhcflurry/class1_allele_specific/cross_validation.py
+++ b/mhcflurry/class1_allele_specific/cross_validation.py
@@ -102,12 +102,12 @@ def cross_validation_folds(
},
parallel_backend=None):
'''
- Split a Dataset into n_folds cross validation folds for each allele,
+ Split a AffinityMeasurementDataset into n_folds cross validation folds for each allele,
optionally performing imputation.
Parameters
-----------
- train_data : mhcflurry.Dataset
+ train_data : mhcflurry.AffinityMeasurementDataset
alleles : string list, optional
Alleles to run cross validation on. Default: all alleles in
@@ -125,7 +125,7 @@ def cross_validation_folds(
Imputer to use. If not specified, no imputation is done.
impute_kwargs : dict, optional
- Additional kwargs to pass to mhcflurry.Dataset.impute_missing_values.
+ 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,
@@ -193,8 +193,8 @@ def cross_validation_folds(
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.
+ # 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(
diff --git a/mhcflurry/class1_allele_specific/cv_and_train_command.py b/mhcflurry/class1_allele_specific/cv_and_train_command.py
index dac1c4e1a5f02db16ef0e9eb5907a2db80cd04e2..3d0159f4b1d1f6bb3c37783a92dae7a89fe40bd2 100644
--- a/mhcflurry/class1_allele_specific/cv_and_train_command.py
+++ b/mhcflurry/class1_allele_specific/cv_and_train_command.py
@@ -49,7 +49,7 @@ import pickle
import numpy
from .. import parallelism
-from ..dataset import Dataset
+from ..affinity_measurement_dataset import AffinityMeasurementDataset
from ..imputation_helpers import imputer_from_name
from .cross_validation import cross_validation_folds
from .train import (
@@ -216,12 +216,12 @@ def go(args):
logging.info(
"Subselected to %d model architectures" % len(model_architectures))
- train_data = Dataset.from_csv(args.train_data)
+ train_data = AffinityMeasurementDataset.from_csv(args.train_data)
logging.info("Loaded training dataset: %s" % train_data)
test_data = None
if args.test_data:
- test_data = Dataset.from_csv(args.test_data)
+ test_data = AffinityMeasurementDataset.from_csv(args.test_data)
logging.info("Loaded testing dataset: %s" % test_data)
if args.min_samples_per_allele:
diff --git a/mhcflurry/class1_allele_specific/scoring.py b/mhcflurry/class1_allele_specific/scoring.py
index 485d4303af62ea5a57e4e9b08e763640b74224a4..12cccc7fde4e308a0d16c5fa334272454538fbae 100644
--- a/mhcflurry/class1_allele_specific/scoring.py
+++ b/mhcflurry/class1_allele_specific/scoring.py
@@ -3,6 +3,7 @@ from __future__ import (
division,
absolute_import,
)
+import logging
import sklearn
import numpy
import scipy
@@ -44,18 +45,21 @@ def make_scores(
ic50_y <= threshold_nm,
y_pred,
sample_weight=sample_weight)
- except ValueError:
+ except ValueError as e:
+ logging.warning(e)
auc = numpy.nan
try:
f1 = sklearn.metrics.f1_score(
ic50_y <= threshold_nm,
ic50_y_pred <= threshold_nm,
sample_weight=sample_weight)
- except ValueError:
+ except ValueError as e:
+ logging.warning(e)
f1 = numpy.nan
try:
tau = scipy.stats.kendalltau(ic50_y_pred, ic50_y)[0]
- except ValueError:
+ except ValueError as e:
+ logging.warning(e)
tau = numpy.nan
return dict(
diff --git a/mhcflurry/class1_allele_specific/train.py b/mhcflurry/class1_allele_specific/train.py
index 2c6d651924be67ecd43d2d638781e1b37461a9dd..cfc2234463ca6fe7e2aae6acca5fb98ac19fc248 100644
--- a/mhcflurry/class1_allele_specific/train.py
+++ b/mhcflurry/class1_allele_specific/train.py
@@ -52,10 +52,10 @@ def impute_and_select_allele(dataset, imputer, allele=None, **kwargs):
Parameters
-----------
- dataset : mhcflurry.Dataset
+ dataset : mhcflurry.AffinityMeasurementDataset
imputer : object or string
- See Dataset.impute_missing_values
+ See AffinityMeasurementDataset.impute_missing_values
allele : string [optional]
Allele name to subselect to after imputation
@@ -121,14 +121,14 @@ def train_and_test_one_model_one_fold(
-----------
model_description : dict of model parameters
- train_dataset : mhcflurry.Dataset
- Dataset to train on. Must include only one allele.
+ train_dataset : mhcflurry.AffinityMeasurementDataset
+ AffinityMeasurementDataset to train on. Must include only one allele.
- test_dataset : mhcflurry.Dataset, optional
- Dataset to test on. Must include only one allele. If not specified
+ 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.Dataset, optional
+ imputed_train_dataset : mhcflurry.AffinityMeasurementDataset, optional
Required only if model_description["impute"] == True
return_train_scores : boolean
diff --git a/mhcflurry/class1_allele_specific_ensemble/__init__.py b/mhcflurry/class1_allele_specific_ensemble/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bc674059ab9c78731518cf12ea7aa7d5d4fba6b
--- /dev/null
+++ b/mhcflurry/class1_allele_specific_ensemble/__init__.py
@@ -0,0 +1,12 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..c3ec0ae7f4c5b874ea09b1527958f68979520436
--- /dev/null
+++ b/mhcflurry/class1_allele_specific_ensemble/class1_ensemble_multi_allele_predictor.py
@@ -0,0 +1,791 @@
+# 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
new file mode 100644
index 0000000000000000000000000000000000000000..cabe40cd962b6a595fe17052534238c270c1b26b
--- /dev/null
+++ b/mhcflurry/class1_allele_specific_ensemble/train_command.py
@@ -0,0 +1,232 @@
+# 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 1a8c5a7185de68e392a0180c0854ae0195053679..cc21280eea4e3e5ccd8a2b3ab1007a4292ccc2da 100644
--- a/mhcflurry/common.py
+++ b/mhcflurry/common.py
@@ -150,7 +150,8 @@ def dataframe_cryptographic_hash(df):
"""
start = time.time()
result = hashlib.sha1(df.to_msgpack()).hexdigest()
- print("Generated dataframe hash in %0.2f sec" % (time.time() - start))
+ logging.info(
+ "Generated dataframe hash in %0.2f sec" % (time.time() - start))
return result
diff --git a/mhcflurry/downloads.yml b/mhcflurry/downloads.yml
index eed77bac6dd2ac7415fa92837315009e2986959a..91c3100090fcf2d32b463c1ca42da517d173ba69 100644
--- a/mhcflurry/downloads.yml
+++ b/mhcflurry/downloads.yml
@@ -8,7 +8,7 @@
# by name, the downloads with "default=true" are downloaded.
# This should usually be the latest release.
-current-release: 0.0.8
+current-release: 0.1.0
# An integer indicating what models the current MHCflurry code base is compatible
# with. Increment this integer when changes are made to MHCflurry that would break
@@ -17,6 +17,25 @@ current-compatibility-version: 1
# Add new releases here as they are made.
releases:
+ 0.1.0:
+ compatibility-version: 1
+ downloads:
+ - name: models_class1_allele_specific_ensemble
+ url: http://github.com/hammerlab/mhcflurry/releases/download/0.1.0-alpha1/models_class1_allele_specific_ensemble.tar.bz2
+ default: true
+
+ - name: models_class1_allele_specific_single
+ url: http://github.com/hammerlab/mhcflurry/releases/download/0.1.0-alpha1/models_class1_allele_specific_single.tar.bz2
+ default: false
+
+ - name: data_kim2014
+ url: http://github.com/hammerlab/mhcflurry/releases/download/0.0.8/data_kim2014.tar.bz2
+ default: true
+
+ - name: data_combined_iedb_kim2014
+ url: http://github.com/hammerlab/mhcflurry/releases/download/0.0.8/data_combined_iedb_kim2014.tar.bz2
+ default: true
+
0.0.8:
compatibility-version: 1
downloads:
diff --git a/mhcflurry/downloads_command.py b/mhcflurry/downloads_command.py
index 1f162357134c6a15f3ddee2c6877e9c1857f3392..d5ad018db4e102ada1f06d4bd70567d647792314 100644
--- a/mhcflurry/downloads_command.py
+++ b/mhcflurry/downloads_command.py
@@ -197,7 +197,7 @@ def fetch_subcommand(args):
logging.debug("Extracting: %s" % names)
bad_names = [
n for n in names
- if n.strip().startswith("/") or n.strip().startswith(".")
+ if n.strip().startswith("/") or n.strip().startswith("..")
]
if bad_names:
raise RuntimeError(
diff --git a/mhcflurry/ic50_predictor_base.py b/mhcflurry/ic50_predictor_base.py
index 6166bf6df2e622076e3690b55dcf3e09bfc3b412..38f6fa49e617cc6feb5c02211989572280fff72a 100644
--- a/mhcflurry/ic50_predictor_base.py
+++ b/mhcflurry/ic50_predictor_base.py
@@ -21,7 +21,7 @@ from __future__ import (
import numpy as np
from .regression_target import regression_target_to_ic50, MAX_IC50
-from .dataset import Dataset
+from .affinity_measurement_dataset import AffinityMeasurementDataset
from .hyperparameters import HyperparameterDefaults
@@ -71,7 +71,7 @@ class IC50PredictorBase(object):
peptide_to_ic50_dict : dict
Dictionary that maps peptides to IC50 values.
"""
- dataset = Dataset.from_peptide_to_affinity_dictionary(
+ 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)
@@ -84,7 +84,7 @@ class IC50PredictorBase(object):
alleles=None, **kwargs):
if alleles is None:
alleles = [self.name] * len(peptides)
- dataset = Dataset.from_sequences(
+ dataset = AffinityMeasurementDataset.from_sequences(
alleles=alleles,
peptides=peptides,
affinities=affinities,
diff --git a/mhcflurry/measurement_collection.py b/mhcflurry/measurement_collection.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bcc686394082286a585657cab5c1d7d0153e5ee
--- /dev/null
+++ b/mhcflurry/measurement_collection.py
@@ -0,0 +1,217 @@
+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
index df8dcfab8ff6b054ee65a2b1fc71df85f204b7bc..f89d17d82d3bf4198d5dc2aa80850f42d747ceaa 100644
--- a/mhcflurry/parallelism.py
+++ b/mhcflurry/parallelism.py
@@ -4,6 +4,11 @@ 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
@@ -14,6 +19,9 @@ class ParallelBackend(object):
self.module = module
self.verbose = verbose
+ def remote_object(self, value):
+ return RemoteObjectStub(value)
+
class KubefaceParallelBackend(ParallelBackend):
"""
@@ -26,6 +34,9 @@ class KubefaceParallelBackend(ParallelBackend):
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
diff --git a/mhcflurry/peptide_encoding.py b/mhcflurry/peptide_encoding.py
index dc39de38f4648b6763ed6fed174fee1af0c3b11d..0738037160be0d716177a3b977cd2a6d1c836fcf 100644
--- a/mhcflurry/peptide_encoding.py
+++ b/mhcflurry/peptide_encoding.py
@@ -16,6 +16,7 @@ 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
@@ -23,6 +24,95 @@ 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
@@ -313,3 +403,5 @@ def check_valid_index_encoding_array(X, allow_unknown_amino_acids=True):
X.max(),
max_expected_index))
return X
+
+
diff --git a/mhcflurry/predict.py b/mhcflurry/predict.py
deleted file mode 100644
index 6534971ef67c20c15c0645800c6ab8af828f6ccb..0000000000000000000000000000000000000000
--- a/mhcflurry/predict.py
+++ /dev/null
@@ -1,72 +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 as pd
-
-from .class1_allele_specific import load
-from .common import normalize_allele_name, UnsupportedAllele
-
-
-def predict(alleles, peptides, loaders=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.
-
- loaders : list of Class1AlleleSpecificPredictorLoader, optional
- Loaders to try. Will be tried in the order given.
-
- Returns DataFrame with columns "Allele", "Peptide", and "Prediction"
- """
- if loaders is None:
- loaders = [
- load.get_loader_for_downloaded_models(),
- ]
- result_dict = OrderedDict([
- ("Allele", []),
- ("Peptide", []),
- ("Prediction", []),
- ])
- for allele in alleles:
- allele = normalize_allele_name(allele)
- exceptions = {} # loader -> UnsupportedAllele exception
- model = None
- for loader in loaders:
- try:
- model = loader.from_allele_name(allele)
- break
- except UnsupportedAllele as e:
- exceptions[loader] = e
- if model is None:
- raise UnsupportedAllele(
- "No loaders support allele '%s'. Errors were:\n%s" % (
- allele,
- "\n".join(
- ("\t%-20s : %s" % (k, v))
- for (k, v) in exceptions.items())))
-
- for i, ic50 in enumerate(model.predict(peptides)):
- result_dict["Allele"].append(allele)
- result_dict["Peptide"].append(peptides[i])
- result_dict["Prediction"].append(ic50)
- return pd.DataFrame(result_dict)
diff --git a/mhcflurry/predict_command.py b/mhcflurry/predict_command.py
index edcc6af020e175bd5077f1e1a84e9e9b6d61553c..03c92a133295762936b4737fcef472d64c97e09a 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
+from . import class1_allele_specific, class1_allele_specific_ensemble
parser = argparse.ArgumentParser(
description=__doc__,
@@ -93,12 +93,25 @@ parser.add_argument(
default="mhcflurry_prediction",
help="Output column name for predictions. Default: '%(default)s'")
+parser.add_argument(
+ "--predictor",
+ choices=("class1-allele-specific-single", "class1-allele-specific-ensemble"),
+ default="class1-allele-specific-single",
+ help="Predictor to use. Default: %(default)s.")
+
+parser.add_argument(
+ "--models-class1-allele-specific-ensemble",
+ metavar="DIR",
+ default=None,
+ help="Directory containing class1 allele specific ensemble models. "
+ "Default: %s" % get_path("models_class1_allele_specific_ensemble", test_exists=False))
+
parser.add_argument(
"--models-class1-allele-specific-single",
metavar="DIR",
- default=get_path("models_class1_allele_specific_single"),
+ default=None,
help="Directory containing class1 allele specific single models. "
- "Default: '%(default)s'")
+ "Default: %s" % get_path("models_class1_allele_specific_single", test_exists=False))
def run(argv=sys.argv[1:]):
@@ -142,20 +155,39 @@ def run(argv=sys.argv[1:]):
print("Predicting for %d alleles and %d peptides = %d predictions" % (
len(args.alleles), len(args.peptides), len(df)))
- class1_allele_specific_loader = (
- class1_allele_specific.load.Class1AlleleSpecificPredictorLoader(
- args.models_class1_allele_specific_single))
+ if args.predictor == "class1-allele-specific-single":
+ models_dir = args.models_class1_allele_specific_single
+ if models_dir is None:
+ # The reason we set the default here instead of in the argument parser is that
+ # we want to test_exists at this point, so the user gets a message instructing
+ # them to download the models if needed.
+ models_dir = get_path("models_class1_allele_specific_single")
+ predictor = (
+ class1_allele_specific
+ .class1_single_model_multi_allele_predictor
+ .Class1SingleModelMultiAllelePredictor
+ ).load_from_download_directory(models_dir)
+ elif args.predictor == "class1-allele-specific-ensemble":
+ models_dir = args.models_class1_allele_specific_ensemble
+ if models_dir is None:
+ models_dir = get_path("models_class1_allele_specific_ensemble")
+ predictor = (
+ class1_allele_specific_ensemble
+ .class1_ensemble_multi_allele_predictor
+ .Class1EnsembleMultiAllelePredictor
+ ).load_from_download_directory(models_dir)
+ else:
+ assert False
predictions = {} # allele -> peptide -> value
for (allele, sub_df) in df.groupby(args.allele_column):
logging.info("Running %d predictions for allele %s" % (
len(sub_df), allele))
- model = class1_allele_specific_loader.from_allele_name(allele)
peptides = sub_df[args.peptide_column].values
predictions[allele] = dict(
(peptide, prediction)
for (peptide, prediction)
- in zip(peptides, model.predict(peptides)))
+ in zip(peptides, predictor.predict_for_allele(allele, peptides)))
logging.info("Collecting result")
df[args.prediction_column] = [
diff --git a/mhcflurry/prediction.py b/mhcflurry/prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..36e1f0e71534a3b92eb23d46c4dfa25d09fb33d0
--- /dev/null
+++ b/mhcflurry/prediction.py
@@ -0,0 +1,60 @@
+# 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/requirements.txt b/requirements.txt
index e1768825d305e148f3429f64c7b54c4f60dd06dd..9b6ae49d48bfaef191ec9d09c91ba3cd354907ba 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
numpy>= 1.11
pandas>=0.13.1
appdirs
-theano>=0.8.2
+theano==0.8.2
keras==1.2.0
fancyimpute>=0.0.12
scikit-learn
diff --git a/setup.py b/setup.py
index dd66f5813776311e7bcaa4269175373e26a8cfce..e4853dddd2ff8a74a43c18cdb1a04fcaa722b8b3 100644
--- a/setup.py
+++ b/setup.py
@@ -52,7 +52,7 @@ if __name__ == '__main__':
'numpy>=1.11',
'pandas>=0.13.1',
'appdirs',
- 'theano>=0.8.2',
+ 'theano==0.8.2',
'keras==1.2.0',
'fancyimpute>=0.0.12',
'scikit-learn',
@@ -79,8 +79,10 @@ 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_allele_specific.cv_and_train_command:run',
]
},
classifiers=[
@@ -97,5 +99,9 @@ if __name__ == '__main__':
},
install_requires=required_packages,
long_description=readme,
- packages=['mhcflurry', 'mhcflurry.class1_allele_specific'],
+ packages=[
+ 'mhcflurry',
+ 'mhcflurry.class1_allele_specific',
+ 'mhcflurry.class1_allele_specific_ensemble',
+ ],
)
diff --git a/test/__init__.py b/test/__init__.py
index 41d07bd4e26c104ac024a971f26edae1836f24f8..ea6085a3b822f81d2098e552f42cfa1b5c90d4f5 100644
--- a/test/__init__.py
+++ b/test/__init__.py
@@ -4,6 +4,17 @@ Utility functions for tests.
import os
+import numpy
+
+from mhcflurry import amino_acid
+
+def make_random_peptides(num, length=9):
+ return [
+ ''.join(peptide_sequence)
+ for peptide_sequence in
+ numpy.random.choice(
+ amino_acid.common_amino_acid_letters, size=(num, length))
+ ]
def data_path(name):
'''
diff --git a/test/test_dataset.py b/test/test_affinity_measurement_dataset.py
similarity index 73%
rename from test/test_dataset.py
rename to test/test_affinity_measurement_dataset.py
index 57e541738fe10b28cc661c3bcbf38853ace6775e..4ecc71d4372d6010ae68c36e4e92c8b762dba0a2 100644
--- a/test/test_dataset.py
+++ b/test/test_affinity_measurement_dataset.py
@@ -1,15 +1,15 @@
from nose.tools import eq_
-from mhcflurry.dataset import Dataset
+from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
def test_create_allele_data_from_single_allele_dict():
peptide_to_ic50_dict = {
("A" * 10): 1.2,
("C" * 9): 1000,
}
- dataset = Dataset.from_single_allele_dictionary(
+ dataset = AffinityMeasurementDataset.from_single_allele_dictionary(
allele_name="A0201",
peptide_to_affinity_dict=peptide_to_ic50_dict)
- assert isinstance(dataset, Dataset)
+ assert isinstance(dataset, AffinityMeasurementDataset)
eq_(len(peptide_to_ic50_dict), len(dataset))
expected_peptides = set([
@@ -22,7 +22,7 @@ def test_create_allele_data_from_single_allele_dict():
eq_(pi, pj)
def test_dataset_random_split():
- dataset = Dataset.from_nested_dictionary({
+ dataset = AffinityMeasurementDataset.from_nested_dictionary({
"H-2-Kb": {
"SIINFEKL": 10.0,
"FEKLSIIN": 20000.0,
@@ -33,15 +33,15 @@ def test_dataset_random_split():
assert len(right) == 1
def test_dataset_difference():
- dataset1 = Dataset.from_nested_dictionary({
+ dataset1 = AffinityMeasurementDataset.from_nested_dictionary({
"H-2-Kb": {
"SIINFEKL": 10.0,
"FEKLSIIN": 20000.0,
"SIFEKLIN": 50000.0,
}})
- dataset2 = Dataset.from_nested_dictionary({"H-2-Kb": {"SIINFEKL": 10.0}})
+ dataset2 = AffinityMeasurementDataset.from_nested_dictionary({"H-2-Kb": {"SIINFEKL": 10.0}})
dataset_diff = dataset1.difference(dataset2)
- expected_result = Dataset.from_nested_dictionary({
+ expected_result = AffinityMeasurementDataset.from_nested_dictionary({
"H-2-Kb": {
"FEKLSIIN": 20000.0,
"SIFEKLIN": 50000.0,
@@ -50,20 +50,20 @@ def test_dataset_difference():
def test_dataset_intersection():
- dataset1 = Dataset.from_nested_dictionary({
+ dataset1 = AffinityMeasurementDataset.from_nested_dictionary({
"H-2-Kb": {
"SIINFEKL": 10.0,
"FEKLSIIN": 20000.0,
"SIFEKLIN": 50000.0,
}})
- dataset2 = Dataset.from_nested_dictionary({"H-2-Kb": {"SIINFEKL": 30.0}})
+ dataset2 = AffinityMeasurementDataset.from_nested_dictionary({"H-2-Kb": {"SIINFEKL": 30.0}})
dataset_intersection = dataset1.intersection(dataset2)
- expected_result = Dataset.from_nested_dictionary({
+ expected_result = AffinityMeasurementDataset.from_nested_dictionary({
"H-2-Kb": {"SIINFEKL": 10.0}})
eq_(dataset_intersection, expected_result)
def test_dataset_cross_validation():
- dataset = Dataset.from_nested_dictionary({
+ dataset = AffinityMeasurementDataset.from_nested_dictionary({
"H-2-Kb": {
"SIINFEKL": 10.0,
"FEKLSIIN": 20000.0,
diff --git a/test/test_antigen_presentation.py b/test/test_antigen_presentation.py
index 4da4ba5c1f207e5b29954594c46575ed609c808c..1714929e88149716330e6e7ecf8a3c09a4bf9387 100644
--- a/test/test_antigen_presentation.py
+++ b/test/test_antigen_presentation.py
@@ -13,20 +13,12 @@ from mhcflurry.antigen_presentation import (
presentation_model)
from mhcflurry.amino_acid import common_amino_acid_letters
+from . import make_random_peptides
######################
# Helper functions
-def make_random_peptides(num, length=9):
- return [
- ''.join(peptide_sequence)
- for peptide_sequence in
- numpy.random.choice(
- amino_acid.common_amino_acid_letters, size=(num, length))
- ]
-
-
def hit_criterion(experiment_name, peptide):
# Peptides with 'A' are always hits. Easy for model to learn.
return 'A' in peptide
@@ -81,6 +73,9 @@ AA_COMPOSITION_DF.index = AA_COMPOSITION_DF.peptide
del AA_COMPOSITION_DF['peptide']
HITS_DF = PEPTIDES_DF.ix[PEPTIDES_DF.hit].reset_index().copy()
+
+# Add some duplicates:
+HITS_DF = pandas.concat([HITS_DF, HITS_DF.iloc[:10]], ignore_index=True)
del HITS_DF["hit"]
######################
@@ -107,6 +102,15 @@ def mhcflurry_basic_model():
)
+def mhcflurry_released_model():
+ return presentation_component_models.MHCflurryReleased(
+ predictor_name="mhcflurry_ensemble",
+ experiment_to_alleles=EXPERIMENT_TO_ALLELES,
+ random_peptides_for_percent_rank=OTHER_PEPTIDES,
+ fit_cache_policy="strong",
+ predictions_cache_policy="strong")
+
+
def test_mhcflurry_trained_on_hits():
mhcflurry_model = mhcflurry_basic_model()
mhcflurry_model.fit(HITS_DF)
@@ -144,6 +148,7 @@ def compare_predictions(peptides_df, model1, model2):
def test_presentation_model():
mhcflurry_model = mhcflurry_basic_model()
+ mhcflurry_ie_model = mhcflurry_released_model()
aa_content_model = (
presentation_component_models.FixedPerPeptideQuantity(
@@ -155,6 +160,9 @@ def test_presentation_model():
decoys_per_hit=50)
terms = {
+ 'A_ie': (
+ [mhcflurry_ie_model],
+ ["log1p(mhcflurry_ensemble_affinity)"]),
'A_ms': (
[mhcflurry_model],
["log1p(mhcflurry_affinity_value)"]),
@@ -166,10 +174,10 @@ def test_presentation_model():
for kwargs in [{}, {'ensemble_size': 3}]:
models = presentation_model.build_presentation_models(
terms,
- ["A_ms", "A_ms + P"],
+ ["A_ms", "A_ms + P", "A_ie + P"],
decoy_strategy=decoys,
**kwargs)
- eq_(len(models), 2)
+ eq_(len(models), 3)
unfit_model = models["A_ms"]
model = unfit_model.clone()
@@ -197,10 +205,14 @@ def test_presentation_model():
model = better_unfit_model.clone()
model.fit(HITS_DF.ix[HITS_DF.experiment_name == "exp1"])
peptides["prediction_better"] = model.predict(peptides)
-
assert_less(
peptides.prediction_better[~peptides.hit].mean(),
peptides.prediction[~peptides.hit].mean())
assert_less(
peptides.prediction[peptides.hit].mean(),
peptides.prediction_better[peptides.hit].mean())
+
+ another_unfit_model = models["A_ie + P"]
+ model = another_unfit_model.clone()
+ model.fit(HITS_DF.ix[HITS_DF.experiment_name == "exp1"])
+ model.predict(peptides)
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 bcc71db1e63e8f35e0e5be41f907943c1d7d650a..eb722ffeee727a9f3a09389dc6ea77bf89ed70fb 100644
--- a/test/test_class1_allele_specific_cv_and_train_command.py
+++ b/test/test_class1_allele_specific_cv_and_train_command.py
@@ -10,7 +10,7 @@ from os import mkdir, environ
import pandas
from mhcflurry.class1_allele_specific import cv_and_train_command
-from mhcflurry import downloads, predict
+from mhcflurry import downloads, predict, class1_allele_specific
from mhcflurry.class1_allele_specific.train import HYPERPARAMETER_DEFAULTS
try:
@@ -72,7 +72,7 @@ def test_small_run():
# If kubeface is installed, then this command will by default use it.
# In that case, we want to have the kubeface storage written to a
# local file and not assume the existence of a google storage bucket.
- args.extend(["--storage-prefix", "/tmp/"])
+ args.extend(["--kubeface-storage", "/tmp/"])
print("Running cv_and_train_command with args: %s " % str(args))
cv_and_train_command.run(args)
@@ -113,7 +113,11 @@ def verify_trained_models(base_temp_dir):
("HLA-A0201", "LLGLPAAEY", False),
], columns=("allele", "peptide", "binder"))
- data["prediction"] = predict(data.allele, data.peptide).Prediction
+ data["prediction"] = predict(
+ data.allele,
+ data.peptide,
+ predictor=class1_allele_specific.get_downloaded_predictor()
+ ).Prediction
print(data)
mean_binder = data.ix[data.binder].prediction.mean()
mean_nonbinder = data.ix[~data.binder].prediction.mean()
diff --git a/test/test_class1_binding_predictor_A0205.py b/test/test_class1_binding_predictor_A0205.py
index b9e9211549699ce5b7d56b922700a63435f3e080..fb3df4a7391c99055b65547e2a80a7b549e7f2bd 100644
--- a/test/test_class1_binding_predictor_A0205.py
+++ b/test/test_class1_binding_predictor_A0205.py
@@ -1,7 +1,7 @@
import numpy as np
np.random.seed(0)
-from mhcflurry.dataset import Dataset
+from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
from mhcflurry import Class1BindingPredictor
from nose.tools import eq_
@@ -11,10 +11,10 @@ from mhcflurry.downloads import get_path
def test_class1_binding_predictor_A0205_training_accuracy():
- dataset = Dataset.from_csv(get_path(
+ dataset = AffinityMeasurementDataset.from_csv(get_path(
"data_combined_iedb_kim2014", "combined_human_class1_dataset.csv"))
dataset_a0205_all_lengths = dataset.get_allele("HLA-A0205")
- dataset_a0205 = Dataset(
+ dataset_a0205 = AffinityMeasurementDataset(
dataset_a0205_all_lengths._df.ix[
dataset_a0205_all_lengths._df.peptide.str.len() == 9])
diff --git a/test/test_class1_binding_predictor_with_dummy_models.py b/test/test_class1_binding_predictor_with_dummy_models.py
index dc390f35b95426a544b1a515e91c793fdf264339..c558357271b0ef05dd959901c67d5c91e9b2ca93 100644
--- a/test/test_class1_binding_predictor_with_dummy_models.py
+++ b/test/test_class1_binding_predictor_with_dummy_models.py
@@ -68,24 +68,28 @@ def test_always_zero_10mer_inputs():
def test_encode_peptides_9mer():
- X, indices = always_zero_predictor_with_unknown_AAs.encode_peptides(["AAASSSYYY"])
- assert len(indices) == 1
- assert indices[0] == 0
- assert X.shape[0] == 1, X.shape
- assert X.shape[1] == 9, X.shape
+ encoded = always_zero_predictor_with_unknown_AAs.encode_peptides(["AAASSSYYY"])
+ assert len(encoded.indices) == 1
+ assert encoded.indices[0] == 0
+ assert encoded.encoded_matrix.shape[0] == 1, encoded.encoded_matrix.shape
+ assert encoded.encoded_matrix.shape[1] == 9, encoded.encoded_matrix.shape
def test_encode_peptides_8mer():
- X, indices = always_zero_predictor_with_unknown_AAs.encode_peptides(["AAASSSYY"])
- assert len(indices) == 9
- assert (indices == 0).all()
+ encoded = always_zero_predictor_with_unknown_AAs.encode_peptides(["AAASSSYY"])
+ assert len(encoded.indices) == 9
+ assert (encoded.indices == 0).all()
+
+ X = encoded.encoded_matrix
assert X.shape[0] == 9, (X.shape, X)
assert X.shape[1] == 9, (X.shape, X)
def test_encode_peptides_10mer():
- X, indices = always_zero_predictor_with_unknown_AAs.encode_peptides(["AAASSSYYFF"])
- assert len(indices) == 10
- assert (indices == 0).all()
+ encoded = always_zero_predictor_with_unknown_AAs.encode_peptides(["AAASSSYYFF"])
+ assert len(encoded.indices) == 10
+ assert (encoded.indices == 0).all()
+
+ X = encoded.encoded_matrix
assert X.shape[0] == 10, (X.shape, X)
assert X.shape[1] == 9, (X.shape, X)
diff --git a/test/test_cross_validation.py b/test/test_cross_validation.py
index c0d4296e815dad534d8cb8f46b799dd0343f9f69..fdcd160f97a6e36d057138fa7857714badebe80c 100644
--- a/test/test_cross_validation.py
+++ b/test/test_cross_validation.py
@@ -14,12 +14,14 @@ from mhcflurry.class1_allele_specific import (
from mhcflurry.class1_allele_specific.train import (
HYPERPARAMETER_DEFAULTS)
+from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
+
def test_imputation():
imputer = fancyimpute.MICE(
n_imputations=2, n_burn_in=1, n_nearest_columns=25)
train_data = (
- mhcflurry.dataset.Dataset.from_csv(
+ AffinityMeasurementDataset.from_csv(
get_path("data_kim2014", "bdata.2009.mhci.public.1.txt"))
.get_alleles(["HLA-A0201", "HLA-A0202", "HLA-A0301"]))
@@ -41,7 +43,7 @@ def test_imputation():
def test_cross_validation_no_imputation():
train_data = (
- mhcflurry.dataset.Dataset.from_csv(
+ AffinityMeasurementDataset.from_csv(
get_path("data_kim2014", "bdata.2009.mhci.public.1.txt"))
.get_alleles(["HLA-A0201", "HLA-A0202", "HLA-A0301"]))
@@ -76,7 +78,7 @@ def test_cross_validation_with_imputation():
imputer = fancyimpute.MICE(
n_imputations=2, n_burn_in=1, n_nearest_columns=25)
train_data = (
- mhcflurry.dataset.Dataset.from_csv(
+ AffinityMeasurementDataset.from_csv(
get_path("data_kim2014", "bdata.2009.mhci.public.1.txt"))
.get_alleles(["HLA-A0201", "HLA-A0202", "HLA-A0301"]))
diff --git a/test/test_ensemble.py b/test/test_ensemble.py
new file mode 100644
index 0000000000000000000000000000000000000000..24ea46c9f04b68ef367740e34603daaff3f94cb6
--- /dev/null
+++ b/test/test_ensemble.py
@@ -0,0 +1,203 @@
+import tempfile
+import shutil
+import os
+import time
+import cProfile
+
+import json
+from os.path import join
+from os import mkdir
+
+from numpy.testing import assert_allclose, assert_equal
+import numpy
+from nose.tools import eq_
+
+from . import make_random_peptides
+
+from mhcflurry.class1_allele_specific import scoring
+from mhcflurry.measurement_collection import MeasurementCollection
+from mhcflurry.class1_allele_specific_ensemble import train_command
+from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
+from mhcflurry.downloads import get_path
+from mhcflurry.amino_acid import common_amino_acid_letters
+from mhcflurry \
+ .class1_allele_specific_ensemble \
+ .class1_ensemble_multi_allele_predictor import (
+ Class1EnsembleMultiAllelePredictor,
+ get_downloaded_predictor,
+ HYPERPARAMETER_DEFAULTS)
+
+
+def test_single_allele():
+ model_hyperparameters = HYPERPARAMETER_DEFAULTS.models_grid(
+ impute=[False],
+ activation=["tanh"],
+ layer_sizes=[[4], [16]],
+ embedding_output_dim=[16],
+ dropout_probability=[.25],
+ n_training_epochs=[20])
+ model = Class1EnsembleMultiAllelePredictor(
+ ensemble_size=3,
+ hyperparameters_to_search=model_hyperparameters)
+ print(model)
+
+ dataset = AffinityMeasurementDataset.from_csv(get_path(
+ "data_combined_iedb_kim2014", "combined_human_class1_dataset.csv"))
+ sub_dataset = AffinityMeasurementDataset(
+ dataset._df.ix[
+ (dataset._df.allele.isin(["HLA-A0205"])) &
+ (dataset._df.peptide.str.len() == 9)
+ ])
+ mc = MeasurementCollection.from_dataset(sub_dataset)
+ model.fit(mc)
+ results = mc.df.copy()
+ results["prediction"] = model.predict_measurement_collection(mc)
+ score = results[["measurement_value", "prediction"]].corr('kendall').ix["measurement_value", "prediction"]
+ assert score > 0.3, score
+
+ repeated = model.predict_for_allele("HLA-A0205", ["AAAAAAAA","AAAAAAAA", "AAAAAAAA"])
+ assert len(repeated) == 3, repeated
+ assert numpy.std(repeated) < 1e-4, (repeated, numpy.std(repeated))
+
+
+def test_basic():
+ model_hyperparameters = HYPERPARAMETER_DEFAULTS.models_grid(
+ impute=[False, True],
+ activation=["tanh"],
+ layer_sizes=[[4], [16]],
+ embedding_output_dim=[16],
+ dropout_probability=[.25],
+ n_training_epochs=[20])
+ model = Class1EnsembleMultiAllelePredictor(
+ ensemble_size=3,
+ hyperparameters_to_search=model_hyperparameters)
+ print(model)
+
+ dataset = AffinityMeasurementDataset.from_csv(get_path(
+ "data_combined_iedb_kim2014", "combined_human_class1_dataset.csv"))
+ sub_dataset = AffinityMeasurementDataset(
+ dataset._df.ix[
+ (dataset._df.allele.isin(["HLA-A0101", "HLA-A0205"])) &
+ (dataset._df.peptide.str.len() == 9)
+ ])
+
+ mc = MeasurementCollection.from_dataset(sub_dataset)
+ print(model.description())
+ print("Now fitting.")
+ model.fit(mc)
+
+ model2 = Class1EnsembleMultiAllelePredictor(
+ ensemble_size=3,
+ hyperparameters_to_search=model_hyperparameters)
+ model2.fit(mc)
+ print(set(model.manifest_df.all_alleles_train_data_hash))
+ assert_equal(
+ set(model.manifest_df.all_alleles_train_data_hash),
+ set(model2.manifest_df.all_alleles_train_data_hash))
+ assert_equal(
+ set(model.manifest_df.all_alleles_test_data_hash),
+ set(model2.manifest_df.all_alleles_test_data_hash))
+ assert_equal(
+ set(model.manifest_df.all_alleles_imputed_data_hash),
+ set(model2.manifest_df.all_alleles_imputed_data_hash))
+
+ print(model.description())
+ ic50_pred = model.predict_measurement_collection(mc)
+ ic50_true = mc.df.measurement_value
+
+ scores = scoring.make_scores(ic50_true, ic50_pred)
+ print(scores)
+ assert scores['auc'] > 0.85, "Expected higher AUC"
+
+ # test save and restore
+ try:
+ tmpdir = tempfile.mkdtemp(prefix="mhcflurry-test")
+ model.write_fit(
+ tmpdir,
+ all_models_csv=os.path.join(tmpdir, "models.csv"))
+ model2 = Class1EnsembleMultiAllelePredictor.load_fit(
+ tmpdir,
+ os.path.join(tmpdir, "models.csv"))
+ finally:
+ shutil.rmtree(tmpdir)
+
+ eq_(model.ensemble_size, model2.ensemble_size)
+ eq_(model.supported_alleles, model2.supported_alleles)
+ eq_(model.hyperparameters_to_search, model2.hyperparameters_to_search)
+ ic50_pred2 = model.predict_measurement_collection(mc)
+ assert_allclose(ic50_pred, ic50_pred2, rtol=1e-04)
+
+
+def test_prediction_performance():
+ use_profiling = False
+
+ def evaluate(context, s):
+ if use_profiling:
+ return cProfile.runctx(s, globals(), context, sort="cumtime")
+ else:
+ return eval(s, globals(), context)
+
+ start = time.time()
+ predictor = get_downloaded_predictor()
+ print("\nInstantiated ensemble predictor in %0.2f sec" % (time.time() - start))
+
+ start = time.time()
+ evaluate({'predictor': predictor}, 'predictor.models_for_allele(("HLA-A*01:01"))')
+ print("Loaded ensemble for allele in %0.2f sec" % (time.time() - start))
+
+ start = time.time()
+ evaluate({'predictor': predictor}, 'predictor.predict_for_allele("HLA-A*01:01", ["ESDPIVAQY"])')
+ print("Generated 1 prediction in %0.2f sec" % (time.time() - start))
+
+ start = time.time()
+ peptides = make_random_peptides(10000)
+ evaluate(
+ {'predictor': predictor, 'peptides': peptides},
+ 'predictor.predict_for_allele("HLA-A*01:01", peptides)')
+ print("Generated %d predictions in %0.2f sec" % (
+ len(peptides), time.time() - start))
+
+
+def test_train_command():
+ base_temp_dir = tempfile.mkdtemp()
+ temp_dir = join(base_temp_dir, "models_class1_allele_specific_single")
+ mkdir(temp_dir)
+
+ def write_json(payload, filename):
+ path = join(temp_dir, filename)
+ with open(path, 'w') as fd:
+ json.dump(payload, fd)
+ return path
+
+ models = HYPERPARAMETER_DEFAULTS.models_grid(
+ impute=[False, True],
+ activation=["tanh"],
+ layer_sizes=[[4], [8]],
+ embedding_output_dim=[16],
+ dropout_probability=[.25],
+ n_training_epochs=[10],
+ imputer_args=[{"n_burn_in": 2, "n_imputations": 10}],
+ impute_min_observations_per_peptide=[1],
+ impute_min_observations_per_allele=[1],
+ )
+ print("Model selection will be over %d models" % len(models))
+
+ bdata2009 = get_path(
+ "data_kim2014", "bdata.2009.mhci.public.1.txt")
+ mkdir(join(temp_dir, "models"))
+
+ args = [
+ '--parallel-backend', 'local-threads',
+ "--model-architectures", write_json(models, "models.json"),
+ "--train-data", bdata2009,
+ "--out-manifest", join(temp_dir, "models.csv"),
+ "--out-models", join(temp_dir, "models"),
+ "--alleles", "HLA-A0201", "HLA-A0301",
+ "--verbose",
+ "--num-local-threads", "1",
+ "--ensemble-size", "2",
+ "--target-tasks", "1",
+ ]
+ print("Running train command with args: %s " % str(args))
+
+ train_command.run(args)
diff --git a/test/test_imputation.py b/test/test_imputation.py
index 81cea624cf21903a2d23c4b8c517fa0b97cc7ac4..d30f12f7229475ccbed003e6cda8ffef204450ec 100644
--- a/test/test_imputation.py
+++ b/test/test_imputation.py
@@ -2,7 +2,7 @@ import numpy as np
np.random.seed(0)
from mhcflurry.imputation_helpers import imputer_from_name
-from mhcflurry.dataset import Dataset
+from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
from mhcflurry import Class1BindingPredictor
from fancyimpute import MICE, KNN, SoftImpute, IterativeSVD
@@ -12,13 +12,13 @@ from mhcflurry.downloads import get_path
def test_create_imputed_datasets_empty():
- empty_dataset = Dataset.create_empty()
+ empty_dataset = AffinityMeasurementDataset.create_empty()
result = empty_dataset.impute_missing_values(MICE(n_imputations=25))
eq_(result, empty_dataset)
def test_create_imputed_datasets_two_alleles():
- dataset = Dataset.from_nested_dictionary({
+ dataset = AffinityMeasurementDataset.from_nested_dictionary({
"HLA-A*02:01": {
"A" * 9: 20.0,
"C" * 9: 40000.0,
@@ -38,7 +38,7 @@ def test_create_imputed_datasets_two_alleles():
def test_performance_improves_for_A0205_with_pretraining():
# test to make sure that imputation improves predictive accuracy after a
# small number of training iterations (5 epochs)
- dataset = Dataset.from_csv(
+ dataset = AffinityMeasurementDataset.from_csv(
get_path("data_combined_iedb_kim2014", "combined_human_class1_dataset.csv"))
print("Full dataset: %d pMHC entries" % len(dataset))
@@ -51,7 +51,7 @@ def test_performance_improves_for_A0205_with_pretraining():
a0205_data_without_imputation = dataset.get_allele("HLA-A0205")
- print("Dataset with only A0205, # entries: %d" % (
+ print("AffinityMeasurementDataset with only A0205, # entries: %d" % (
len(a0205_data_without_imputation)))
predictor_without_imputation = Class1BindingPredictor(
diff --git a/test/test_known_class1_epitopes.py b/test/test_known_class1_epitopes.py
index 7434c1a519dfc296c5cfbcede5910e0f0a7722a5..a1c35794c94aa0421bef39ba64cc2778717ee5b3 100644
--- a/test/test_known_class1_epitopes.py
+++ b/test/test_known_class1_epitopes.py
@@ -12,7 +12,24 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-from mhcflurry.class1_allele_specific import load
+import cProfile
+
+import mhcflurry
+import mhcflurry.class1_allele_specific
+import mhcflurry.class1_allele_specific_ensemble
+
+
+predictors = [
+ mhcflurry.class1_allele_specific.get_downloaded_predictor(),
+ mhcflurry.class1_allele_specific_ensemble.get_downloaded_predictor(),
+]
+
+
+def predict_and_check(allele, peptide, expected_range=(0, 500)):
+ for predictor in predictors:
+ (prediction,) = predictor.predict_for_allele(allele, [peptide])
+ assert prediction >= expected_range[0], (predictor, prediction)
+ assert prediction <= expected_range[1], (predictor, prediction)
def test_A1_Titin_epitope():
@@ -20,12 +37,7 @@ def test_A1_Titin_epitope():
# Identification of a Titin-Derived HLA-A1-Presented Peptide
# as a Cross-Reactive Target for Engineered MAGE A3-Directed
# T Cells
- model = load.from_allele_name("HLA-A*01:01")
- ic50s = model.predict(["ESDPIVAQY"])
- print(ic50s)
- assert len(ic50s) == 1
- ic50 = ic50s[0]
- assert ic50 <= 500, ic50
+ predict_and_check("HLA-A*01:01", "ESDPIVAQY")
def test_A1_MAGE_epitope():
@@ -33,23 +45,13 @@ def test_A1_MAGE_epitope():
# Identification of a Titin-Derived HLA-A1-Presented Peptide
# as a Cross-Reactive Target for Engineered MAGE A3-Directed
# T Cells
- model = load.from_allele_name("HLA-A*01:01")
- ic50s = model.predict(["EVDPIGHLY"])
- print(ic50s)
- assert len(ic50s) == 1
- ic50 = ic50s[0]
- assert ic50 <= 500, ic50
+ predict_and_check("HLA-A*01:01", "EVDPIGHLY")
def test_A2_HIV_epitope():
# Test the A2 HIV epitope SLYNTVATL from
# The HIV-1 HLA-A2-SLYNTVATL Is a Help-Independent CTL Epitope
- model = load.from_allele_name("HLA-A*02:01")
- ic50s = model.predict(["SLYNTVATL"])
- print(ic50s)
- assert len(ic50s) == 1
- ic50 = ic50s[0]
- assert ic50 <= 500, ic50
+ predict_and_check("HLA-A*02:01", "SLYNTVATL")
if __name__ == "__main__":
diff --git a/test/test_load_allele_datasets.py b/test/test_load_allele_datasets.py
index 7c76be9e6b761f063598cbcbb375fe6933078a8d..7103a18c4b7e6838f75c288a4594a9349395dba6 100644
--- a/test/test_load_allele_datasets.py
+++ b/test/test_load_allele_datasets.py
@@ -1,10 +1,10 @@
-from mhcflurry.dataset import Dataset
+from mhcflurry.affinity_measurement_dataset import AffinityMeasurementDataset
from nose.tools import eq_
from . import data_path
def load_csv(filename):
- return Dataset.from_csv(data_path(filename))
+ return AffinityMeasurementDataset.from_csv(data_path(filename))
def test_load_allele_datasets_8mer():
diff --git a/test/test_predict.py b/test/test_predict.py
index 12dd9ba6cce9cb09bbf0c26a96ae0db69c8f2b5c..cc67d065812354319b254b93c75382629d646384 100644
--- a/test/test_predict.py
+++ b/test/test_predict.py
@@ -7,3 +7,17 @@ def test_predict_A1_Titin_epitope():
row = result_df.ix[0]
ic50 = row["Prediction"]
assert ic50 <= 500
+
+
+def test_predict_duplicates_and_empty():
+ result_df = predict(
+ alleles=["HLA-A*01:01", "HLA-A01:01"],
+ peptides=["ESDPIVAQY", "ESDPIVAQY"])
+ assert len(set(result_df.Prediction)) == 1
+ assert len(set(result_df.Allele)) == 1
+ assert len(set(result_df.Peptide)) == 1
+
+ result_df = predict(
+ alleles=["HLA-A*01:01"],
+ peptides=[])
+ assert len(result_df) == 0
\ No newline at end of file