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Tim O'Donnell authored
Lazily putting this all in one commit. * infrastructure for downloading datasets and published trained models (the `mhcflurry-downloads` command) * docs and scripts (in `downloads-generation`) to generate the pubilshed datsets and trained models * parallelized cross validation and model training implementation, including support for imputation (based on the old mhcflurry-cloud repo, which is now gone) * a single front-end script for class1 allele-specific cross validation and model training / testing (`mhcflurry-class1-allele-specific-cv-and-train`) * refactor how we deal with hyper-parameters and how we instantiate Class1BindingPredictors * make Class1BindingPredictor pickleable and remove old serialization code * move code particular to class 1 allele-specific predictors into its own submodule * remove unused code including arg parsing, plotting, and ensembles * had to bump the binding prediction threshold for the Titin1 epitope from 500 to 700, as this test was sporadically failing for me (see test_known_class1_epitopes.py) * Attempt to make tests involving randomness somewhat more reproducible by setting numpy random seed * update README
Tim O'Donnell authoredLazily putting this all in one commit. * infrastructure for downloading datasets and published trained models (the `mhcflurry-downloads` command) * docs and scripts (in `downloads-generation`) to generate the pubilshed datsets and trained models * parallelized cross validation and model training implementation, including support for imputation (based on the old mhcflurry-cloud repo, which is now gone) * a single front-end script for class1 allele-specific cross validation and model training / testing (`mhcflurry-class1-allele-specific-cv-and-train`) * refactor how we deal with hyper-parameters and how we instantiate Class1BindingPredictors * make Class1BindingPredictor pickleable and remove old serialization code * move code particular to class 1 allele-specific predictors into its own submodule * remove unused code including arg parsing, plotting, and ensembles * had to bump the binding prediction threshold for the Titin1 epitope from 500 to 700, as this test was sporadically failing for me (see test_known_class1_epitopes.py) * Attempt to make tests involving randomness somewhat more reproducible by setting numpy random seed * update README
models.py 457 B
import sys
from mhcflurry.class1_allele_specific.train import HYPERPARAMETER_DEFAULTS
import json
models = HYPERPARAMETER_DEFAULTS.models_grid(
impute=[False, True],
activation=["tanh"],
layer_sizes=[[12], [64], [128]],
embedding_output_dim=[8, 32, 64],
dropout_probability=[0, .1, .25],
fraction_negative=[0, .1, .2],
n_training_epochs=[250])
sys.stderr.write("Models: %d\n" % len(models))
print(json.dumps(models, indent=4))