diff --git a/docs/README.md b/docs/README.md
index de9abd942f5f8ade4178a6b1d1f3cd0b16059955..e7b6e9539acfb51f11816e180f7001a05d0c2981 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -7,8 +7,16 @@ $ pip install -r requirements.txt # for the first time you generate docs
$ make generate html
```
-The above command will also regenerate `docs/package_readme/readme.generated.md`. The main
-[README.md](../README.md) is symlinked to this file.
+Documentation is written to the _build/ directory. These files should not be
+checked into the repo.
+
+We use the documentation system to generate the mhcflurry package level README.
+To build this file, run:
+
+```
+$ make readme
+```
+
+This will write `docs/package_readme/readme.generated.rst`. The main
+[README.rst](../README.rst) is symlinked to this file.
-Many other files are also generated by the command above and output to
-the _build/ directory. These files should not be checked into the repo.
\ No newline at end of file
diff --git a/docs/package_readme/readme.generated.md b/docs/package_readme/readme.generated.md
deleted file mode 100644
index 8280de9d8083ce885113acaaf11f351062b55ce9..0000000000000000000000000000000000000000
--- a/docs/package_readme/readme.generated.md
+++ /dev/null
@@ -1,316 +0,0 @@
-[](https://travis-ci.org/hammerlab/mhcflurry) [](https://coveralls.io/github/hammerlab/mhcflurry?branch=master)
-
-# mhcflurry
-Open source neural network models for peptide-MHC binding affinity prediction
-
-MHCflurry is a Python package for peptide/MHC I binding affinity
-prediction. It provides competitive accuracy with a fast, documented,
-open source implementation.
-
-You can download pre-trained MHCflurry models fit to affinity
-measurements deposited in IEDB. See the
-"downloads\_generation/models\_class1" directory in the repository for
-the workflow used to train these predictors. Users with their own data
-can also fit their own MHCflurry models.
-
-Currently only allele-specific prediction is implemented, in which
-separate models are trained for each allele. The released models
-therefore support a fixed set of common class I alleles for which
-sufficient published training data is available.
-
-MHCflurry supports Python versions 2.7 and 3.4+. It uses the Keras
-neural network library via either the Tensorflow or Theano backends.
-GPUs may optionally be used for a generally modest speed improvement.
-
-If you find MHCflurry useful in your research please cite:
-
-> O'Donnell, T. et al., 2017. MHCflurry: open-source class I MHC binding
-> affinity prediction. bioRxiv. Available at:
-> <http://www.biorxiv.org/content/early/2017/08/09/174243>.
-
-## Installation (pip)
-
-Install the package:
-
-> pip install mhcflurry
-
-Then download our datasets and trained models:
-
-> mhcflurry-downloads fetch
-
-From a checkout you can run the unit tests with:
-
-> pip install nose nosetests .
-
-## Using conda
-
-You can alternatively get up and running with a conda environment as
-follows. Some users have reported that this can avoid problems
-installing tensorflow.
-
-> conda create -q -n mhcflurry-env python=3.6 'tensorflow\>=1.1.2'
-> source activate mhcflurry-env
-
-Then continue as above:
-
-> pip install mhcflurry mhcflurry-downloads fetch
-
-### Command-line usage
-
-## Downloading models
-
-Most users will use pre-trained MHCflurry models that we release. These
-models are distributed separately from the source code and may be
-downloaded with the following command:
-
-We also release other "downloads," such as curated training data and
-some experimental models. To see what you have downloaded, run:
-
-## mhcflurry-predict
-
-The "mhcflurry-predict" command generates predictions from the
-command-line. It defaults to using the pre-trained models you downloaded
-above but this can be customized with the "--models" argument. See
-"mhcflurry-predict -h" for details.
-
-```
-> $ mhcflurry-predict --alleles HLA-A0201 HLA-A0301 --peptides SIINFEKL
-> SIINFEKD SIINFEKQ
-> allele,peptide,mhcflurry\_prediction,mhcflurry\_prediction\_low,mhcflurry\_prediction\_high
-> HLA-A0201,SIINFEKL,5326.541919062165,3757.86675352994,7461.37693353508
-> HLA-A0201,SIINFEKD,18763.70298522213,13140.82000240037,23269.82139560844
-> HLA-A0201,SIINFEKQ,18620.10057358322,13096.425874678192,23223.148184869413
-> HLA-A0301,SIINFEKL,24481.726678691946,21035.52779725433,27245.371837497867
-> HLA-A0301,SIINFEKD,24687.529360239587,21582.590014592537,27749.39869616437
-> HLA-A0301,SIINFEKQ,25923.062203902562,23522.5793450799,28079.456657427705
-```
-
-The predictions returned are affinities (KD) in nM. The
-"prediction\_low" and "prediction\_high" fields give the 5-95 percentile
-predictions across the models in the ensemble. The predictions above
-were generated with MHCflurry 0.9.2.
-
-Your exact predictions may vary slightly from these (up to about 1 nM)
-depending on the Keras backend in use and other numerical details.
-Different versions of MHCflurry can of course give results considerably
-different from these.
-
-You can also specify the input and output as CSV files. Run
-"mhcflurry-predict -h" for details.
-
-## Fitting your own models
-
-### Library usage
-
-The MHCflurry Python API exposes additional options and features beyond
-those supported by the commandline tools. This tutorial gives a basic
-overview of the most important functionality. See the API Documentation
-for further details.
-
-The "Class1AffinityPredictor" class is the primary user-facing
-interface.
-
-> \>\>\> import mhcflurry \>\>\> print("MHCflurry version: %s" %
-> (mhcflurry.\_\_version\_\_)) MHCflurry version: 1.0.0 \>\>\> \>\>\> \#
-> Load downloaded predictor \>\>\> predictor =
-> mhcflurry.Class1AffinityPredictor.load() \>\>\>
-> print(predictor.supported\_alleles) \['BoLA-6\*13:01',
-> 'Eqca-1\*01:01', 'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk',
-> 'H-2-Ld', 'HLA-A\*01:01', 'HLA-A\*02:01', 'HLA-A\*02:02',
-> 'HLA-A\*02:03', 'HLA-A\*02:05', 'HLA-A\*02:06', 'HLA-A\*02:07',
-> 'HLA-A\*02:11', 'HLA-A\*02:12', 'HLA-A\*02:16', 'HLA-A\*02:17',
-> 'HLA-A\*02:19', 'HLA-A\*02:50', 'HLA-A\*03:01', 'HLA-A\*11:01',
-> 'HLA-A\*23:01', 'HLA-A\*24:01', 'HLA-A\*24:02', 'HLA-A\*24:03',
-> 'HLA-A\*25:01', 'HLA-A\*26:01', 'HLA-A\*26:02', 'HLA-A\*26:03',
-> 'HLA-A\*29:02', 'HLA-A\*30:01', 'HLA-A\*30:02', 'HLA-A\*31:01',
-> 'HLA-A\*32:01', 'HLA-A\*32:07', 'HLA-A\*33:01', 'HLA-A\*66:01',
-> 'HLA-A\*68:01', 'HLA-A\*68:02', 'HLA-A\*68:23', 'HLA-A\*69:01',
-> 'HLA-A\*80:01', 'HLA-B\*07:01', 'HLA-B\*07:02', 'HLA-B\*08:01',
-> 'HLA-B\*08:02', 'HLA-B\*08:03', 'HLA-B\*14:02', 'HLA-B\*15:01',
-> 'HLA-B\*15:02', 'HLA-B\*15:03', 'HLA-B\*15:09', 'HLA-B\*15:17',
-> 'HLA-B\*15:42', 'HLA-B\*18:01', 'HLA-B\*27:01', 'HLA-B\*27:03',
-> 'HLA-B\*27:04', 'HLA-B\*27:05', 'HLA-B\*27:06', 'HLA-B\*27:20',
-> 'HLA-B\*35:01', 'HLA-B\*35:03', 'HLA-B\*35:08', 'HLA-B\*37:01',
-> 'HLA-B\*38:01', 'HLA-B\*39:01', 'HLA-B\*40:01', 'HLA-B\*40:02',
-> 'HLA-B\*42:01', 'HLA-B\*44:01', 'HLA-B\*44:02', 'HLA-B\*44:03',
-> 'HLA-B\*45:01', 'HLA-B\*45:06', 'HLA-B\*46:01', 'HLA-B\*48:01',
-> 'HLA-B\*51:01', 'HLA-B\*53:01', 'HLA-B\*54:01', 'HLA-B\*57:01',
-> 'HLA-B\*58:01', 'HLA-B\*73:01', 'HLA-B\*83:01', 'HLA-C\*03:03',
-> 'HLA-C\*03:04', 'HLA-C\*04:01', 'HLA-C\*05:01', 'HLA-C\*06:02',
-> 'HLA-C\*07:01', 'HLA-C\*07:02', 'HLA-C\*08:02', 'HLA-C\*12:03',
-> 'HLA-C\*14:02', 'HLA-C\*15:02', 'Mamu-A\*01:01', 'Mamu-A\*02:01',
-> 'Mamu-A\*02:0102', 'Mamu-A\*07:01', 'Mamu-A\*07:0103',
-> 'Mamu-A\*11:01', 'Mamu-A\*22:01', 'Mamu-A\*26:01', 'Mamu-B\*01:01',
-> 'Mamu-B\*03:01', 'Mamu-B\*08:01', 'Mamu-B\*10:01', 'Mamu-B\*17:01',
-> 'Mamu-B\*17:04', 'Mamu-B\*39:01', 'Mamu-B\*52:01', 'Mamu-B\*66:01',
-> 'Mamu-B\*83:01', 'Mamu-B\*87:01', 'Patr-A\*01:01', 'Patr-A\*03:01',
-> 'Patr-A\*04:01', 'Patr-A\*07:01', 'Patr-A\*09:01', 'Patr-B\*01:01',
-> 'Patr-B\*13:01', 'Patr-B\*24:01'\]
->
-> \# coding: utf-8
->
-> \# In\[22\]:
->
-> import pandas import numpy import seaborn import logging from
-> matplotlib import pyplot
->
-> import mhcflurry
->
-> \# \# Download data and models
->
-> \# In\[2\]:
->
-> get\_ipython().system('mhcflurry-downloads fetch')
->
-> \# \# Making predictions with Class1AffinityPredictor
->
-> \# In\[3\]:
->
-> help(mhcflurry.Class1AffinityPredictor)
->
-> \# In\[4\]:
->
-> downloaded\_predictor = mhcflurry.Class1AffinityPredictor.load()
->
-> \# In\[5\]:
->
-> downloaded\_predictor.predict(allele="HLA-A0201",
-> peptides=\["SIINFEKL", "SIINFEQL"\])
->
-> \# In\[6\]:
->
-> downloaded\_predictor.predict\_to\_dataframe(allele="HLA-A0201",
-> peptides=\["SIINFEKL", "SIINFEQL"\])
->
-> \# In\[7\]:
->
-> downloaded\_predictor.predict\_to\_dataframe(alleles=\["HLA-A0201",
-> "HLA-B\*57:01"\], peptides=\["SIINFEKL", "SIINFEQL"\])
->
-> \# In\[8\]:
->
-> - downloaded\_predictor.predict\_to\_dataframe(
-> allele="HLA-A0201", peptides=\["SIINFEKL", "SIINFEQL"\],
-> include\_individual\_model\_predictions=True)
->
-> \# In\[9\]:
->
-> - downloaded\_predictor.predict\_to\_dataframe(
-> allele="HLA-A0201", peptides=\["SIINFEKL", "SIINFEQL",
-> "TAAAALANGGGGGGGG"\], throw=False) \# Without throw=False, you'll
-> get a ValueError for invalid peptides or alleles
->
-> \# \# Instantiating a Class1AffinityPredictor from a saved model on
-> disk
->
-> \# In\[10\]:
->
-> models\_dir = mhcflurry.downloads.get\_path("models\_class1",
-> "models") models\_dir
->
-> \# In\[11\]:
->
-> \# This will be the same predictor we instantiated above. We're just
-> being explicit about what models to load. downloaded\_predictor =
-> mhcflurry.Class1AffinityPredictor.load(models\_dir)
-> downloaded\_predictor.predict(\["SIINFEKL", "SIQNPEKP", "SYNFPEPI"\],
-> allele="HLA-A0301")
->
-> \# \# Fit a model: first load some data
->
-> \# In\[12\]:
->
-> \# This is the data the downloaded models were trained on data\_path =
-> mhcflurry.downloads.get\_path("data\_curated",
-> "curated\_training\_data.csv.bz2") data\_path
->
-> \# In\[13\]:
->
-> data\_df = pandas.read\_csv(data\_path) data\_df
->
-> \# \# Fit a model: Low level Class1NeuralNetwork interface
->
-> \# In\[14\]:
->
-> \# We'll use mostly the default hyperparameters here. Could also
-> specify them as kwargs. new\_model =
-> mhcflurry.Class1NeuralNetwork(layer\_sizes=\[16\])
-> new\_model.hyperparameters
->
-> \# In\[16\]:
->
-> - train\_data = data\_df.loc\[
-> (data\_df.allele == "HLA-B\*57:01") & (data\_df.peptide.str.len()
-> \>= 8) & (data\_df.peptide.str.len() \<= 15)
->
-> \] get\_ipython().magic('time
-> new\_model.fit(train\_data.peptide.values,
-> train\_data.measurement\_value.values)')
->
-> \# In\[17\]:
->
-> new\_model.predict(\["SYNPEPII"\])
->
-> \# \# Fit a model: high level Class1AffinityPredictor interface
->
-> \# In\[18\]:
->
-> affinity\_predictor = mhcflurry.Class1AffinityPredictor()
->
-> \# This can be called any number of times, for example on different
-> alleles, to build up the ensembles.
-> affinity\_predictor.fit\_allele\_specific\_predictors( n\_models=1,
-> architecture\_hyperparameters={"layer\_sizes": \[16\], "max\_epochs":
-> 10}, peptides=train\_data.peptide.values,
-> affinities=train\_data.measurement\_value.values,
-> allele="HLA-B\*57:01", )
->
-> \# In\[19\]:
->
-> affinity\_predictor.predict(\["SYNPEPII"\], allele="HLA-B\*57:01")
->
-> \# \# Save and restore the fit model
->
-> \# In\[20\]:
->
-> get\_ipython().system('mkdir /tmp/saved-affinity-predictor')
-> affinity\_predictor.save("/tmp/saved-affinity-predictor")
-> get\_ipython().system('ls /tmp/saved-affinity-predictor')
->
-> \# In\[21\]:
->
-> affinity\_predictor2 =
-> mhcflurry.Class1AffinityPredictor.load("/tmp/saved-affinity-predictor")
-> affinity\_predictor2.predict(\["SYNPEPII"\], allele="HLA-B\*57:01")
-
-### Supported alleles and peptide lengths
-
-Models released with the current version of MHCflurry (1.0.0) support
-peptides of length 8-15 and the following 124 alleles:
-
-> BoLA-6\*13:01, Eqca-1\*01:01, H-2-Db, H-2-Dd, H-2-Kb, H-2-Kd, H-2-Kk,
-> H-2-Ld, HLA-A\*01:01, HLA-A\*02:01, HLA-A\*02:02, HLA-A\*02:03,
-> HLA-A\*02:05, HLA-A\*02:06, HLA-A\*02:07, HLA-A\*02:11, HLA-A\*02:12,
-> HLA-A\*02:16, HLA-A\*02:17, HLA-A\*02:19, HLA-A\*02:50, HLA-A\*03:01,
-> HLA-A\*11:01, HLA-A\*23:01, HLA-A\*24:01, HLA-A\*24:02, HLA-A\*24:03,
-> HLA-A\*25:01, HLA-A\*26:01, HLA-A\*26:02, HLA-A\*26:03, HLA-A\*29:02,
-> HLA-A\*30:01, HLA-A\*30:02, HLA-A\*31:01, HLA-A\*32:01, HLA-A\*32:07,
-> HLA-A\*33:01, HLA-A\*66:01, HLA-A\*68:01, HLA-A\*68:02, HLA-A\*68:23,
-> HLA-A\*69:01, HLA-A\*80:01, HLA-B\*07:01, HLA-B\*07:02, HLA-B\*08:01,
-> HLA-B\*08:02, HLA-B\*08:03, HLA-B\*14:02, HLA-B\*15:01, HLA-B\*15:02,
-> HLA-B\*15:03, HLA-B\*15:09, HLA-B\*15:17, HLA-B\*15:42, HLA-B\*18:01,
-> HLA-B\*27:01, HLA-B\*27:03, HLA-B\*27:04, HLA-B\*27:05, HLA-B\*27:06,
-> HLA-B\*27:20, HLA-B\*35:01, HLA-B\*35:03, HLA-B\*35:08, HLA-B\*37:01,
-> HLA-B\*38:01, HLA-B\*39:01, HLA-B\*40:01, HLA-B\*40:02, HLA-B\*42:01,
-> HLA-B\*44:01, HLA-B\*44:02, HLA-B\*44:03, HLA-B\*45:01, HLA-B\*45:06,
-> HLA-B\*46:01, HLA-B\*48:01, HLA-B\*51:01, HLA-B\*53:01, HLA-B\*54:01,
-> HLA-B\*57:01, HLA-B\*58:01, HLA-B\*73:01, HLA-B\*83:01, HLA-C\*03:03,
-> HLA-C\*03:04, HLA-C\*04:01, HLA-C\*05:01, HLA-C\*06:02, HLA-C\*07:01,
-> HLA-C\*07:02, HLA-C\*08:02, HLA-C\*12:03, HLA-C\*14:02, HLA-C\*15:02,
-> Mamu-A\*01:01, Mamu-A\*02:01, Mamu-A\*02:0102, Mamu-A\*07:01,
-> Mamu-A\*07:0103, Mamu-A\*11:01, Mamu-A\*22:01, Mamu-A\*26:01,
-> Mamu-B\*01:01, Mamu-B\*03:01, Mamu-B\*08:01, Mamu-B\*10:01,
-> Mamu-B\*17:01, Mamu-B\*17:04, Mamu-B\*39:01, Mamu-B\*52:01,
-> Mamu-B\*66:01, Mamu-B\*83:01, Mamu-B\*87:01, Patr-A\*01:01,
-> Patr-A\*03:01, Patr-A\*04:01, Patr-A\*07:01, Patr-A\*09:01,
-> Patr-B\*01:01, Patr-B\*13:01, Patr-B\*24:01
diff --git a/docs/package_readme/readme_header.md b/docs/package_readme/readme_header.md
deleted file mode 100644
index 00ecddc608a47394478f68a0f9be3ce3cfa72e3d..0000000000000000000000000000000000000000
--- a/docs/package_readme/readme_header.md
+++ /dev/null
@@ -1,5 +0,0 @@
-[](https://travis-ci.org/hammerlab/mhcflurry) [](https://coveralls.io/github/hammerlab/mhcflurry?branch=master)
-
-# mhcflurry
-Open source neural network models for peptide-MHC binding affinity prediction
-
diff --git a/docs/readme.template.rst b/docs/readme.template.rst
deleted file mode 100644
index 674dd28e996c5fde81355e9b9f0c29fefe6d77f3..0000000000000000000000000000000000000000
--- a/docs/readme.template.rst
+++ /dev/null
@@ -1,315 +0,0 @@
-MHCflurry is a Python package for peptide/MHC I binding affinity
-prediction. It provides competitive accuracy with a fast, documented,
-open source implementation.
-
-You can download pre-trained MHCflurry models fit to affinity
-measurements deposited in IEDB. See the
-"downloads_generation/models_class1" directory in the repository for
-the workflow used to train these predictors. Users with their own data
-can also fit their own MHCflurry models.
-
-Currently only allele-specific prediction is implemented, in which
-separate models are trained for each allele. The released models
-therefore support a fixed set of common class I alleles for which
-sufficient published training data is available.
-
-MHCflurry supports Python versions 2.7 and 3.4+. It uses the Keras
-neural network library via either the Tensorflow or Theano backends.
-GPUs may optionally be used for a generally modest speed improvement.
-
-If you find MHCflurry useful in your research please cite:
-
- O'Donnell, T. et al., 2017. MHCflurry: open-source class I MHC
- binding affinity prediction. bioRxiv. Available at:
- http://www.biorxiv.org/content/early/2017/08/09/174243.
-
-
-Installation (pip)
-******************
-
-Install the package:
-
- pip install mhcflurry
-
-Then download our datasets and trained models:
-
- mhcflurry-downloads fetch
-
-From a checkout you can run the unit tests with:
-
- pip install nose
- nosetests .
-
-
-Using conda
-***********
-
-You can alternatively get up and running with a conda environment as
-follows. Some users have reported that this can avoid problems
-installing tensorflow.
-
- conda create -q -n mhcflurry-env python=3.6 'tensorflow>=1.1.2'
- source activate mhcflurry-env
-
-Then continue as above:
-
- pip install mhcflurry
- mhcflurry-downloads fetch
-
-
-Command-line usage
-==================
-
-
-Downloading models
-******************
-
-Most users will use pre-trained MHCflurry models that we release.
-These models are distributed separately from the source code and may
-be downloaded with the following command:
-
-We also release other "downloads," such as curated training data and
-some experimental models. To see what you have downloaded, run:
-
-
-mhcflurry-predict
-*****************
-
-The "mhcflurry-predict" command generates predictions from the
-command-line. It defaults to using the pre-trained models you
-downloaded above but this can be customized with the "--models"
-argument. See "mhcflurry-predict -h" for details.
-
- $ mhcflurry-predict --alleles HLA-A0201 HLA-A0301 --peptides SIINFEKL SIINFEKD SIINFEKQ
- allele,peptide,mhcflurry_prediction,mhcflurry_prediction_low,mhcflurry_prediction_high
- HLA-A0201,SIINFEKL,5326.541919062165,3757.86675352994,7461.37693353508
- HLA-A0201,SIINFEKD,18763.70298522213,13140.82000240037,23269.82139560844
- HLA-A0201,SIINFEKQ,18620.10057358322,13096.425874678192,23223.148184869413
- HLA-A0301,SIINFEKL,24481.726678691946,21035.52779725433,27245.371837497867
- HLA-A0301,SIINFEKD,24687.529360239587,21582.590014592537,27749.39869616437
- HLA-A0301,SIINFEKQ,25923.062203902562,23522.5793450799,28079.456657427705
-
-The predictions returned are affinities (KD) in nM. The
-"prediction_low" and "prediction_high" fields give the 5-95 percentile
-predictions across the models in the ensemble. The predictions above
-were generated with MHCflurry 0.9.2.
-
-Your exact predictions may vary slightly from these (up to about 1 nM)
-depending on the Keras backend in use and other numerical details.
-Different versions of MHCflurry can of course give results
-considerably different from these.
-
-You can also specify the input and output as CSV files. Run
-"mhcflurry-predict -h" for details.
-
-
-Fitting your own models
-***********************
-
-
-Library usage
-=============
-
-The MHCflurry Python API exposes additional options and features
-beyond those supported by the commandline tools. This tutorial gives a
-basic overview of the most important functionality. See the API
-Documentation for further details.
-
-The "Class1AffinityPredictor" class is the primary user-facing
-interface.
-
-
- >>> import mhcflurry
- >>> print("MHCflurry version: %s" % (mhcflurry.__version__))
- MHCflurry version: 1.0.0
- >>>
- >>> # Load downloaded predictor
- >>> predictor = mhcflurry.Class1AffinityPredictor.load()
- >>> print(predictor.supported_alleles)
- ['BoLA-6*13:01', 'Eqca-1*01:01', 'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk', 'H-2-Ld', 'HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:05', 'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:11', 'HLA-A*02:12', 'HLA-A*02:16', 'HLA-A*02:17', 'HLA-A*02:19', 'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01', 'HLA-A*24:01', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-A*25:01', 'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*29:02', 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*32:01', 'HLA-A*32:07', 'HLA-A*33:01', 'HLA-A*66:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:23', 'HLA-A*69:01', 'HLA-A*80:01', 'HLA-B*07:01', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*14:02', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*15:42', 'HLA-B*18:01', 'HLA-B*27:01', 'HLA-B*27:03', 'HLA-B*27:04', 'HLA-B*27:05', 'HLA-B*27:06', 'HLA-B*27:20', 'HLA-B*35:01', 'HLA-B*35:03', 'HLA-B*35:08', 'HLA-B*37:01', 'HLA-B*38:01', 'HLA-B*39:01', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*42:01', 'HLA-B*44:01', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01', 'HLA-B*45:06', 'HLA-B*46:01', 'HLA-B*48:01', 'HLA-B*51:01', 'HLA-B*53:01', 'HLA-B*54:01', 'HLA-B*57:01', 'HLA-B*58:01', 'HLA-B*73:01', 'HLA-B*83:01', 'HLA-C*03:03', 'HLA-C*03:04', 'HLA-C*04:01', 'HLA-C*05:01', 'HLA-C*06:02', 'HLA-C*07:01', 'HLA-C*07:02', 'HLA-C*08:02', 'HLA-C*12:03', 'HLA-C*14:02', 'HLA-C*15:02', 'Mamu-A*01:01', 'Mamu-A*02:01', 'Mamu-A*02:0102', 'Mamu-A*07:01', 'Mamu-A*07:0103', 'Mamu-A*11:01', 'Mamu-A*22:01', 'Mamu-A*26:01', 'Mamu-B*01:01', 'Mamu-B*03:01', 'Mamu-B*08:01', 'Mamu-B*10:01', 'Mamu-B*17:01', 'Mamu-B*17:04', 'Mamu-B*39:01', 'Mamu-B*52:01', 'Mamu-B*66:01', 'Mamu-B*83:01', 'Mamu-B*87:01', 'Patr-A*01:01', 'Patr-A*03:01', 'Patr-A*04:01', 'Patr-A*07:01', 'Patr-A*09:01', 'Patr-B*01:01', 'Patr-B*13:01', 'Patr-B*24:01']
-
- # coding: utf-8
-
- # In[22]:
-
- import pandas
- import numpy
- import seaborn
- import logging
- from matplotlib import pyplot
-
- import mhcflurry
-
-
-
- # # Download data and models
-
- # In[2]:
-
- get_ipython().system('mhcflurry-downloads fetch')
-
-
- # # Making predictions with `Class1AffinityPredictor`
-
- # In[3]:
-
- help(mhcflurry.Class1AffinityPredictor)
-
-
- # In[4]:
-
- downloaded_predictor = mhcflurry.Class1AffinityPredictor.load()
-
-
- # In[5]:
-
- downloaded_predictor.predict(allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"])
-
-
- # In[6]:
-
- downloaded_predictor.predict_to_dataframe(allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"])
-
-
- # In[7]:
-
- downloaded_predictor.predict_to_dataframe(alleles=["HLA-A0201", "HLA-B*57:01"], peptides=["SIINFEKL", "SIINFEQL"])
-
-
- # In[8]:
-
- downloaded_predictor.predict_to_dataframe(
- allele="HLA-A0201",
- peptides=["SIINFEKL", "SIINFEQL"],
- include_individual_model_predictions=True)
-
-
- # In[9]:
-
- downloaded_predictor.predict_to_dataframe(
- allele="HLA-A0201",
- peptides=["SIINFEKL", "SIINFEQL", "TAAAALANGGGGGGGG"],
- throw=False) # Without throw=False, you'll get a ValueError for invalid peptides or alleles
-
-
- # # Instantiating a `Class1AffinityPredictor` from a saved model on disk
-
- # In[10]:
-
- models_dir = mhcflurry.downloads.get_path("models_class1", "models")
- models_dir
-
-
- # In[11]:
-
- # This will be the same predictor we instantiated above. We're just being explicit about what models to load.
- downloaded_predictor = mhcflurry.Class1AffinityPredictor.load(models_dir)
- downloaded_predictor.predict(["SIINFEKL", "SIQNPEKP", "SYNFPEPI"], allele="HLA-A0301")
-
-
- # # Fit a model: first load some data
-
- # In[12]:
-
- # This is the data the downloaded models were trained on
- data_path = mhcflurry.downloads.get_path("data_curated", "curated_training_data.csv.bz2")
- data_path
-
-
- # In[13]:
-
- data_df = pandas.read_csv(data_path)
- data_df
-
-
- # # Fit a model: Low level `Class1NeuralNetwork` interface
-
- # In[14]:
-
- # We'll use mostly the default hyperparameters here. Could also specify them as kwargs.
- new_model = mhcflurry.Class1NeuralNetwork(layer_sizes=[16])
- new_model.hyperparameters
-
-
- # In[16]:
-
- train_data = data_df.loc[
- (data_df.allele == "HLA-B*57:01") &
- (data_df.peptide.str.len() >= 8) &
- (data_df.peptide.str.len() <= 15)
- ]
- get_ipython().magic('time new_model.fit(train_data.peptide.values, train_data.measurement_value.values)')
-
-
- # In[17]:
-
- new_model.predict(["SYNPEPII"])
-
-
- # # Fit a model: high level `Class1AffinityPredictor` interface
-
- # In[18]:
-
- affinity_predictor = mhcflurry.Class1AffinityPredictor()
-
- # This can be called any number of times, for example on different alleles, to build up the ensembles.
- affinity_predictor.fit_allele_specific_predictors(
- n_models=1,
- architecture_hyperparameters={"layer_sizes": [16], "max_epochs": 10},
- peptides=train_data.peptide.values,
- affinities=train_data.measurement_value.values,
- allele="HLA-B*57:01",
- )
-
-
- # In[19]:
-
- affinity_predictor.predict(["SYNPEPII"], allele="HLA-B*57:01")
-
-
- # # Save and restore the fit model
-
- # In[20]:
-
- get_ipython().system('mkdir /tmp/saved-affinity-predictor')
- affinity_predictor.save("/tmp/saved-affinity-predictor")
- get_ipython().system('ls /tmp/saved-affinity-predictor')
-
-
- # In[21]:
-
- affinity_predictor2 = mhcflurry.Class1AffinityPredictor.load("/tmp/saved-affinity-predictor")
- affinity_predictor2.predict(["SYNPEPII"], allele="HLA-B*57:01")
-
-
-Supported alleles and peptide lengths
-=====================================
-
-Models released with the current version of MHCflurry (1.0.0) support
-peptides of length 8-15 and the following 124 alleles:
-
- BoLA-6*13:01, Eqca-1*01:01, H-2-Db, H-2-Dd, H-2-Kb, H-2-Kd, H-2-Kk,
- H-2-Ld, HLA-A*01:01, HLA-A*02:01, HLA-A*02:02, HLA-A*02:03,
- HLA-A*02:05, HLA-A*02:06, HLA-A*02:07, HLA-A*02:11, HLA-A*02:12,
- HLA-A*02:16, HLA-A*02:17, HLA-A*02:19, HLA-A*02:50, HLA-A*03:01,
- HLA-A*11:01, HLA-A*23:01, HLA-A*24:01, HLA-A*24:02, HLA-A*24:03,
- HLA-A*25:01, HLA-A*26:01, HLA-A*26:02, HLA-A*26:03, HLA-A*29:02,
- HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*32:07,
- HLA-A*33:01, HLA-A*66:01, HLA-A*68:01, HLA-A*68:02, HLA-A*68:23,
- HLA-A*69:01, HLA-A*80:01, HLA-B*07:01, HLA-B*07:02, HLA-B*08:01,
- HLA-B*08:02, HLA-B*08:03, HLA-B*14:02, HLA-B*15:01, HLA-B*15:02,
- HLA-B*15:03, HLA-B*15:09, HLA-B*15:17, HLA-B*15:42, HLA-B*18:01,
- HLA-B*27:01, HLA-B*27:03, HLA-B*27:04, HLA-B*27:05, HLA-B*27:06,
- HLA-B*27:20, HLA-B*35:01, HLA-B*35:03, HLA-B*35:08, HLA-B*37:01,
- HLA-B*38:01, HLA-B*39:01, HLA-B*40:01, HLA-B*40:02, HLA-B*42:01,
- HLA-B*44:01, HLA-B*44:02, HLA-B*44:03, HLA-B*45:01, HLA-B*45:06,
- HLA-B*46:01, HLA-B*48:01, HLA-B*51:01, HLA-B*53:01, HLA-B*54:01,
- HLA-B*57:01, HLA-B*58:01, HLA-B*73:01, HLA-B*83:01, HLA-C*03:03,
- HLA-C*03:04, HLA-C*04:01, HLA-C*05:01, HLA-C*06:02, HLA-C*07:01,
- HLA-C*07:02, HLA-C*08:02, HLA-C*12:03, HLA-C*14:02, HLA-C*15:02,
- Mamu-A*01:01, Mamu-A*02:01, Mamu-A*02:0102, Mamu-A*07:01,
- Mamu-A*07:0103, Mamu-A*11:01, Mamu-A*22:01, Mamu-A*26:01,
- Mamu-B*01:01, Mamu-B*03:01, Mamu-B*08:01, Mamu-B*10:01, Mamu-B*17:01,
- Mamu-B*17:04, Mamu-B*39:01, Mamu-B*52:01, Mamu-B*66:01, Mamu-B*83:01,
- Mamu-B*87:01, Patr-A*01:01, Patr-A*03:01, Patr-A*04:01, Patr-A*07:01,
- Patr-A*09:01, Patr-B*01:01, Patr-B*13:01, Patr-B*24:01