From d1aa3d76b1184f387c49e2c20f23950e72d73b93 Mon Sep 17 00:00:00 2001
From: Alex Rubinsteyn <alex.rubinsteyn@gmail.com>
Date: Tue, 10 May 2016 19:01:51 -0400
Subject: [PATCH] working on liberating the titration script from the
experiments directory
---
experiments/dataset-size-sensitivity.py | 84 +++++++++++--------
...llele_specific_kmer_ic50_predictor_base.py | 7 +-
mhcflurry/dataset.py | 4 +-
mhcflurry/ic50_predictor_base.py | 3 +-
...rry-train-class1-allele-specific-models.py | 14 ++--
5 files changed, 63 insertions(+), 49 deletions(-)
diff --git a/experiments/dataset-size-sensitivity.py b/experiments/dataset-size-sensitivity.py
index d6e67a93..4635a13b 100644
--- a/experiments/dataset-size-sensitivity.py
+++ b/experiments/dataset-size-sensitivity.py
@@ -21,12 +21,13 @@ Plot AUC and F1 score of predictors as a function of dataset size
from argparse import ArgumentParser
import numpy as np
-import mhcflurry
import sklearn
import sklearn.metrics
-from sklearn.linear_model import LinearRegression
import seaborn
+from mhcflurry.dataset import Dataset
+from mhcflurry.class1_binding_predictor import Class1BindingPredictor
+
from dataset_paths import PETERS2009_CSV_PATH
parser = ArgumentParser()
@@ -42,7 +43,7 @@ parser.add_argument(
parser.add_argument(
"--max-ic50",
type=float,
- default=20000.0)
+ default=50000.0)
parser.add_argument(
"--hidden-layer-size",
@@ -50,6 +51,12 @@ parser.add_argument(
default=10,
help="Hidden layer size for neural network, if 0 use linear regression")
+parser.add_argument(
+ "--embedding-dim",
+ type=int,
+ default=50,
+ help="Number of dimensions for vector embedding of amino acids")
+
parser.add_argument(
"--activation",
default="tanh")
@@ -71,35 +78,41 @@ parser.add_argument(
help="How many times to train model for same dataset size")
-def binary_encode(X, n_indices=20):
- n_cols = X.shape[1]
- X_encode = np.zeros((len(X), n_indices * n_cols), dtype=float)
- for i in range(len(X)):
- for col_idx in range(n_cols):
- X_encode[i, col_idx * n_indices + X[i, col_idx]] = True
- return X_encode
-
-
def subsample_performance(
- X,
- Y,
- max_ic50,
+ dataset,
+ allele,
+ pretraining=False,
model_fn=None,
- fractions=np.arange(0.01, 1, 0.03),
+ n_training_samples=[
+ 25,
+ 50,
+ 100,
+ 200,
+ 300,
+ 400,
+ 500,
+ 600,
+ 800,
+ 1000,
+ 1200,
+ 1400,
+ 1600,
+ 1800,
+ 2000],
niters=10,
- fraction_test=0.2,
- nb_epoch=50,
+ n_training_epochs=200,
batch_size=32):
- n = len(Y)
+ n_total = len(dataset)
+
xs = []
aucs = []
f1s = []
+
for iternum in range(niters):
- if model_fn is None:
- model = LinearRegression()
- else:
- model = model_fn()
- initial_weights = model.get_weights()
+
+ model = model_fn()
+ initial_weights = model.get_weights()
+
mask = np.random.rand(n) > fraction_test
X_train = X[mask]
X_test = X[~mask]
@@ -139,22 +152,21 @@ def subsample_performance(
if __name__ == "__main__":
args = parser.parse_args()
- print(args)
- datasets, _ = mhcflurry.data_helpers.load_data(
- args.training_csv,
- binary_encoding=True,
- flatten_binary_encoding=True,
- max_ic50=args.max_ic50)
- dataset = datasets[args.allele]
+ dataset = Dataset.load_csv(args.training_csv)
X = dataset.X
Y = dataset.Y
print("Total # of samples for %s: %d" % (args.allele, len(Y)))
- if args.hidden_layer_size > 0:
- model_fn = lambda: mhcflurry.feedforward.make_hotshot_network(
- layer_sizes=[args.hidden_layer_size],
- activation=args.activation)
+ if args.hidden_layer_size == 0:
+ hidden_layer_sizes = []
else:
- model_fn = None
+ hidden_layer_sizes = [args.hidden_layer_size]
+
+ def make_model():
+ return Class1BindingPredictor.from_hyperparameters(
+ layer_sizes=[args.hidden_layer_size],
+ activation=args.activation,
+ embedding_dim=args.embedding_dim)
+
xs, aucs, f1s = subsample_performance(
X=X,
Y=Y,
diff --git a/mhcflurry/class1_allele_specific_kmer_ic50_predictor_base.py b/mhcflurry/class1_allele_specific_kmer_ic50_predictor_base.py
index 3a242d8b..5f1886c6 100644
--- a/mhcflurry/class1_allele_specific_kmer_ic50_predictor_base.py
+++ b/mhcflurry/class1_allele_specific_kmer_ic50_predictor_base.py
@@ -43,10 +43,11 @@ class Class1AlleleSpecificKmerIC50PredictorBase(IC50PredictorBase):
verbose,
max_ic50=MAX_IC50,
kmer_size=9):
- self.name = name
- self.max_ic50 = max_ic50
+ IC50PredictorBase.__init__(
+ name=name,
+ max_ic50=max_ic50,
+ verbose=verbose)
self.allow_unknown_amino_acids = allow_unknown_amino_acids
- self.verbose = verbose
self.kmer_size = kmer_size
def __repr__(self):
diff --git a/mhcflurry/dataset.py b/mhcflurry/dataset.py
index d58180d6..ea0dc87d 100644
--- a/mhcflurry/dataset.py
+++ b/mhcflurry/dataset.py
@@ -254,7 +254,9 @@ class Dataset(object):
if len(column) != len(alleles):
raise ValueError(
"Wrong length for column '%s', expected %d but got %d" % (
- column_name, column))
+ column_name,
+ len(alleles),
+ len(column)))
df[column_name] = np.asarray(column)
return cls(df)
diff --git a/mhcflurry/ic50_predictor_base.py b/mhcflurry/ic50_predictor_base.py
index fe93cfc2..6b32d8ae 100644
--- a/mhcflurry/ic50_predictor_base.py
+++ b/mhcflurry/ic50_predictor_base.py
@@ -32,7 +32,7 @@ class IC50PredictorBase(object):
def __init__(
self,
name,
- verbose,
+ verbose=False,
max_ic50=MAX_IC50):
self.name = name
self.max_ic50 = max_ic50
@@ -42,7 +42,6 @@ class IC50PredictorBase(object):
return "%s(name=%s, max_ic50=%f)" % (
self.__class__.__name__,
self.name,
- self.model,
self.max_ic50)
def __str__(self):
diff --git a/script/mhcflurry-train-class1-allele-specific-models.py b/script/mhcflurry-train-class1-allele-specific-models.py
index b9225505..81f60782 100755
--- a/script/mhcflurry-train-class1-allele-specific-models.py
+++ b/script/mhcflurry-train-class1-allele-specific-models.py
@@ -167,6 +167,13 @@ if __name__ == "__main__":
print("-- too few data points, skipping")
continue
+ model.fit_dataset(
+ allele_dataset,
+ pretraining_dataset=imputed_dataset_allele,
+ n_training_epochs=args.training_epochs,
+ n_random_negative_samples=args.random_negative_samples,
+ verbose=True)
+
if exists(json_path):
print("-- removing old model description %s" % json_path)
remove(json_path)
@@ -175,13 +182,6 @@ if __name__ == "__main__":
print("-- removing old weights file %s" % hdf_path)
remove(hdf_path)
- model.fit_dataset(
- allele_dataset,
- pretraining_dataset=imputed_dataset_allele,
- n_training_epochs=args.training_epochs,
- n_random_negative_samples=args.random_negative_samples,
- verbose=True)
-
model.to_disk(
model_json_path=json_path,
weights_hdf_path=hdf_path,
--
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