diff --git a/downloads-generation/models_class1/GENERATE.sh b/downloads-generation/models_class1/GENERATE.sh
index 275e5c1b2efa49142bea2c690c718789890f7c7d..642c5d9628abb606b6279a038b111ed31960e6f4 100755
--- a/downloads-generation/models_class1/GENERATE.sh
+++ b/downloads-generation/models_class1/GENERATE.sh
@@ -1,8 +1,6 @@
#!/bin/bash
#
-# Train standard MHCflurry Class I models.
-# Calls mhcflurry-class1-train-allele-specific-models on curated training data
-# using the hyperparameters in "hyperparameters.yaml".
+# Model select standard MHCflurry Class I models.
#
set -e
set -x
@@ -29,15 +27,24 @@ cd $SCRATCH_DIR/$DOWNLOAD_NAME
mkdir models
-python $SCRIPT_DIR/generate_hyperparameters.py > hyperparameters.yaml
+GPUS=$(nvidia-smi -L 2> /dev/null | wc -l) || GPUS=0
+echo "Detected GPUS: $GPUS"
-time mhcflurry-class1-train-allele-specific-models \
- --data "$(mhcflurry-downloads path data_curated)/curated_training_data.with_mass_spec.csv.bz2" \
- --hyperparameters hyperparameters.yaml \
+PROCESSORS=$(getconf _NPROCESSORS_ONLN)
+echo "Detected processors: $PROCESSORS"
+
+time mhcflurry-class1-select-allele-specific-models \
+ --models-dir "$(mhcflurry-downloads path models_class1_unselected)/models" \
--out-models-dir models \
- --percent-rank-calibration-num-peptides-per-length 100000 \
- --min-measurements-per-allele 75 \
- --num-jobs 32 16
+ --scoring mass-spec consensus \
+ --consensus-num-peptides-per-length 10000 \
+ --min-models 8 \
+ --num-jobs $(expr $PROCESSORS \* 2) --gpus $GPUS --max-workers-per-gpu 2 --max-tasks-per-worker 50
+
+time mhcflurry-calibrate-percentile-ranks \
+ --models-dir models \
+ --num-peptides-per-length 100000 \
+ --num-jobs $(expr $PROCESSORS \* 2) --gpus $GPUS --max-workers-per-gpu 2 --max-tasks-per-worker 50
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
deleted file mode 100644
index 4cbd1f2b43562c3f055b2cc729182e724477fd49..0000000000000000000000000000000000000000
--- a/downloads-generation/models_class1/generate_hyperparameters.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""
-Generate grid of hyperparameters
-"""
-
-from sys import stdout
-from copy import deepcopy
-from yaml import dump
-
-base_hyperparameters = {
- ##########################################
- # ENSEMBLE SIZE
- ##########################################
- "n_models": 4,
-
- ##########################################
- # OPTIMIZATION
- ##########################################
- "max_epochs": 500,
- "patience": 20,
- "early_stopping": True,
- "validation_split": 0.1,
- "minibatch_size": None,
- "loss": "custom:mse_with_inequalities",
-
- ##########################################
- # RANDOM NEGATIVE PEPTIDES
- ##########################################
- "random_negative_rate": 0.0,
- "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": "tanh",
- "output_activation": "sigmoid",
- "layer_sizes": [16],
- "dense_layer_l1_regularization": None,
- "batch_normalization": False,
- "dropout_probability": 0.0,
-
- ##########################################
- # TRAINING Data
- ##########################################
- "train_data": {"subset": "all", "pretrain_min_points": 1000},
-}
-
-grid = []
-for train_subset in ["all", "quantitative"]:
- for minibatch_size in [128]:
- for dense_layer_size in [8, 16, 32, 64]:
- for l1 in [0.0, 0.001]:
- for num_lc in [0, 1, 2]:
- for lc_kernel_size in [3, 5]:
- new = deepcopy(base_hyperparameters)
- new["minibatch_size"] = minibatch_size
- new["train_data"]["subset"] = train_subset
- new["layer_sizes"] = [dense_layer_size]
- new["dense_layer_l1_regularization"] = l1
- (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)]
- if not grid or new not in grid:
- grid.append(new)
-
-dump(grid, stdout)
diff --git a/downloads-generation/models_class1_unselected/GENERATE.sh b/downloads-generation/models_class1_unselected/GENERATE.sh
index ea9ba8a47496435975f99589c0eacfbf20795fc6..19dc08b47ec00c5bf79aabee31da2e9990c4a62b 100755
--- a/downloads-generation/models_class1_unselected/GENERATE.sh
+++ b/downloads-generation/models_class1_unselected/GENERATE.sh
@@ -45,7 +45,8 @@ time mhcflurry-class1-train-allele-specific-models \
--hyperparameters hyperparameters.yaml \
--out-models-dir models \
--percent-rank-calibration-num-peptides-per-length 0 \
- --min-measurements-per-allele 75 \
+ --held-out-fraction-reciprocal 10 \
+ --min-measurements-per-allele 50 \
--num-jobs $(expr $PROCESSORS \* 2) --gpus $GPUS --max-workers-per-gpu 2 --max-tasks-per-worker 50
cp $SCRIPT_ABSOLUTE_PATH .
diff --git a/downloads-generation/models_class1_unselected/generate_hyperparameters.py b/downloads-generation/models_class1_unselected/generate_hyperparameters.py
deleted file mode 120000
index 5f2599b4b2b4c418d1c296b43c55be87bb4d85ce..0000000000000000000000000000000000000000
--- a/downloads-generation/models_class1_unselected/generate_hyperparameters.py
+++ /dev/null
@@ -1 +0,0 @@
-../models_class1/generate_hyperparameters.py
\ No newline at end of file
diff --git a/downloads-generation/models_class1_unselected/generate_hyperparameters.py b/downloads-generation/models_class1_unselected/generate_hyperparameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cbd1f2b43562c3f055b2cc729182e724477fd49
--- /dev/null
+++ b/downloads-generation/models_class1_unselected/generate_hyperparameters.py
@@ -0,0 +1,88 @@
+"""
+Generate grid of hyperparameters
+"""
+
+from sys import stdout
+from copy import deepcopy
+from yaml import dump
+
+base_hyperparameters = {
+ ##########################################
+ # ENSEMBLE SIZE
+ ##########################################
+ "n_models": 4,
+
+ ##########################################
+ # OPTIMIZATION
+ ##########################################
+ "max_epochs": 500,
+ "patience": 20,
+ "early_stopping": True,
+ "validation_split": 0.1,
+ "minibatch_size": None,
+ "loss": "custom:mse_with_inequalities",
+
+ ##########################################
+ # RANDOM NEGATIVE PEPTIDES
+ ##########################################
+ "random_negative_rate": 0.0,
+ "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": "tanh",
+ "output_activation": "sigmoid",
+ "layer_sizes": [16],
+ "dense_layer_l1_regularization": None,
+ "batch_normalization": False,
+ "dropout_probability": 0.0,
+
+ ##########################################
+ # TRAINING Data
+ ##########################################
+ "train_data": {"subset": "all", "pretrain_min_points": 1000},
+}
+
+grid = []
+for train_subset in ["all", "quantitative"]:
+ for minibatch_size in [128]:
+ for dense_layer_size in [8, 16, 32, 64]:
+ for l1 in [0.0, 0.001]:
+ for num_lc in [0, 1, 2]:
+ for lc_kernel_size in [3, 5]:
+ new = deepcopy(base_hyperparameters)
+ new["minibatch_size"] = minibatch_size
+ new["train_data"]["subset"] = train_subset
+ new["layer_sizes"] = [dense_layer_size]
+ new["dense_layer_l1_regularization"] = l1
+ (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)]
+ if not grid or new not in grid:
+ grid.append(new)
+
+dump(grid, stdout)
diff --git a/mhcflurry/train_allele_specific_models_command.py b/mhcflurry/train_allele_specific_models_command.py
index fdc1ae229375c4b939f32e2bdbb6b09d50ce7121..6084e874049a4ded0533f0b91ee200f313e4d086 100644
--- a/mhcflurry/train_allele_specific_models_command.py
+++ b/mhcflurry/train_allele_specific_models_command.py
@@ -60,14 +60,12 @@ parser.add_argument(
metavar="FILE.json",
required=True,
help="JSON or YAML of hyperparameters")
-
parser.add_argument(
"--allele",
default=None,
nargs="+",
help="Alleles to train models for. If not specified, all alleles with "
"enough measurements will be used.")
-
parser.add_argument(
"--min-measurements-per-allele",
type=int,