diff --git a/downloads-generation/models_class1/GENERATE.sh b/downloads-generation/models_class1/GENERATE.sh
index cf77bcf77d667e7b9266d02b6516bb4fe9015964..79a48cf7ce151913f8648043276efd56c4442b40 100755
--- a/downloads-generation/models_class1/GENERATE.sh
+++ b/downloads-generation/models_class1/GENERATE.sh
@@ -29,7 +29,7 @@ cd $SCRATCH_DIR/$DOWNLOAD_NAME
mkdir models
-cp $SCRIPT_DIR/hyperparameters.yaml .
+python $SCRIPT_DIR/generate_hyperparameters.py > hyperparameters.yaml
time mhcflurry-class1-train-allele-specific-models \
--data "$(mhcflurry-downloads path data_curated)/curated_training_data.with_mass_spec.csv.bz2" \
@@ -37,7 +37,7 @@ time mhcflurry-class1-train-allele-specific-models \
--out-models-dir models \
--percent-rank-calibration-num-peptides-per-length 1000000 \
--min-measurements-per-allele 75 \
- --num-jobs 0
+ --num-jobs 32
cp $SCRIPT_ABSOLUTE_PATH .
bzip2 LOG.txt
diff --git a/downloads-generation/models_class1/generate_hyperparameters.py b/downloads-generation/models_class1/generate_hyperparameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae249a95e4d8c0b223fc0aca6aba12f167ec2494
--- /dev/null
+++ b/downloads-generation/models_class1/generate_hyperparameters.py
@@ -0,0 +1,76 @@
+"""
+Generate grid of hyperparameters
+"""
+
+from sys import stdout
+from copy import deepcopy
+from yaml import dump
+
+base_hyperparameters = {
+ ##########################################
+ # ENSEMBLE SIZE
+ ##########################################
+ "n_models": 1,
+
+ ##########################################
+ # OPTIMIZATION
+ ##########################################
+ "max_epochs": 500,
+ "patience": 20,
+ "early_stopping": True,
+ "validation_split": 0.1,
+ "minibatch_size": 128,
+ "loss": "custom:mse_with_inequalities",
+
+ ##########################################
+ # RANDOM NEGATIVE PEPTIDES
+ ##########################################
+ "random_negative_rate": 0.2,
+ "random_negative_constant": 25,
+ "random_negative_affinity_min": 20000.0,
+ "random_negative_affinity_max": 50000.0,
+
+ ##########################################
+ # PEPTIDE REPRESENTATION
+ ##########################################
+ # One of "one-hot", "embedding", or "BLOSUM62".
+ "peptide_amino_acid_encoding": "BLOSUM62",
+ "use_embedding": False, # maintained for backward compatability
+ "embedding_output_dim": 8, # only used if using embedding
+ "kmer_size": 15,
+
+ ##########################################
+ # NEURAL NETWORK ARCHITECTURE
+ ##########################################
+ "locally_connected_layers": [
+ {
+ "filters": 8,
+ "activation": "tanh",
+ "kernel_size": 3
+ }
+ ],
+ "activation": "relu",
+ "output_activation": "sigmoid",
+ "layer_sizes": [16],
+ "dense_layer_l1_regularization": 0.001,
+ "batch_normalization": False,
+ "dropout_probability": 0.0,
+}
+
+grid = []
+for dense_layer_size in [64, 16]:
+ for num_lc in [0, 1, 2]:
+ for lc_kernel_size in [3, 5]:
+ new = deepcopy(base_hyperparameters)
+ new["layer_sizes"] = [dense_layer_size]
+ (lc_layer,) = new["locally_connected_layers"]
+ lc_layer['kernel_size'] = lc_kernel_size
+ if num_lc == 0:
+ new["locally_connected_layers"] = []
+ elif num_lc == 1:
+ new["locally_connected_layers"] = [lc_layer]
+ elif num_lc == 2:
+ new["locally_connected_layers"] = [lc_layer, deepcopy(lc_layer)]
+ grid.append(new)
+
+dump(grid, stdout)
\ No newline at end of file
diff --git a/downloads-generation/models_class1/hyperparameters.yaml b/downloads-generation/models_class1/hyperparameters.yaml
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/mhcflurry/class1_affinity_predictor.py b/mhcflurry/class1_affinity_predictor.py
index fa2023eb9e8f661b3d31b78ce25f735bbb2aa505..46e8d080e5e48b0188e05d7e20015d9f4536c23b 100644
--- a/mhcflurry/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_predictor.py
@@ -9,12 +9,10 @@ from os.path import join, exists
from os import mkdir
from socket import gethostname
from getpass import getuser
-from functools import partial
import mhcnames
import numpy
import pandas
-import tqdm # progress bars
from numpy.testing import assert_equal
from six import string_types
@@ -25,6 +23,7 @@ from .encodable_sequences import EncodableSequences
from .percent_rank_transform import PercentRankTransform
from .regression_target import to_ic50
from .version import __version__
+from .ensemble_centrality import CENTRALITY_MEASURES
class Class1AffinityPredictor(object):
@@ -672,7 +671,8 @@ class Class1AffinityPredictor(object):
allele=None,
throw=True,
include_individual_model_predictions=False,
- include_percentile_ranks=True):
+ include_percentile_ranks=True,
+ centrality_measure="robust_mean"):
"""
Predict nM binding affinities. Gives more detailed output than `predict`
method, including 5-95% prediction intervals.
@@ -701,6 +701,9 @@ class Class1AffinityPredictor(object):
If True, a "prediction_percentile" column will be included giving the
percentile ranks. If no percentile rank information is available,
this will be ignored with a warning.
+ centrality_measure : string or callable
+ Measure of central tendency to use to combine predictions in the
+ ensemble.
Returns
-------
@@ -817,9 +820,15 @@ class Class1AffinityPredictor(object):
df_predictions = df[
[c for c in df.columns if c.startswith("model_")]
]
+
+ if callable(centrality_measure):
+ centrality_function = centrality_measure
+ else:
+ centrality_function = CENTRALITY_MEASURES[centrality_measure]
+
logs = numpy.log(df_predictions)
- log_means = logs.mean(1)
- df["prediction"] = numpy.exp(log_means)
+ log_centers = centrality_function(logs.values)
+ df["prediction"] = numpy.exp(log_centers)
df["prediction_low"] = numpy.exp(logs.quantile(0.05, axis=1))
df["prediction_high"] = numpy.exp(logs.quantile(0.95, axis=1))
diff --git a/mhcflurry/ensemble_centrality.py b/mhcflurry/ensemble_centrality.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a812d7656b40099f8bec67e7a0d5832c9cc677f
--- /dev/null
+++ b/mhcflurry/ensemble_centrality.py
@@ -0,0 +1,37 @@
+"""
+Measures of centrality (e.g. mean) used to combine predictions across an
+ensemble. The input to these functions are log affinities, and they are expected
+to return a centrality measure also in log-space.
+"""
+
+import numpy
+from functools import partial
+
+
+def robust_mean(log_values):
+ """
+ Mean of values falling within the 25-75 percentiles.
+
+ Parameters
+ ----------
+ log_values : 2-d numpy.array
+ Center is computed along the second axis (i.e. per row).
+
+ Returns
+ -------
+ center : numpy.array of length log_values.shape[1]
+
+ """
+ if log_values.shape[1] <= 3:
+ # Too few values to use robust mean.
+ return numpy.nanmean(log_values, axis=1)
+ mask = (
+ (log_values <= numpy.nanpercentile(log_values, 75, axis=1).reshape((-1, 1))) &
+ (log_values >= numpy.nanpercentile(log_values, 25, axis=1).reshape((-1, 1))))
+ return (log_values * mask.astype(float)).sum(1) / mask.sum(1)
+
+CENTRALITY_MEASURES = {
+ "mean": partial(numpy.nanmean, axis=1),
+ "median": partial(numpy.nanmedian, axis=1),
+ "robust_mean": robust_mean,
+}
\ No newline at end of file