From d3184f295f0841c30826cdca1af837b0dc82be99 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Fri, 4 Oct 2019 09:03:17 -0400
Subject: [PATCH] fix
---
.../run_predictors.py | 153 ++++++++++++------
1 file changed, 103 insertions(+), 50 deletions(-)
diff --git a/downloads-generation/data_mass_spec_benchmark/run_predictors.py b/downloads-generation/data_mass_spec_benchmark/run_predictors.py
index 7b58966f..3c1a747f 100644
--- a/downloads-generation/data_mass_spec_benchmark/run_predictors.py
+++ b/downloads-generation/data_mass_spec_benchmark/run_predictors.py
@@ -244,16 +244,20 @@ def run(argv=sys.argv[1:]):
# We rerun any alleles have nulls for any kind of values
# (e.g. affinity, percentile rank, elution score).
+ is_null_matrix = pandas.DataFrame(columns=alleles, dtype="int8")
+ for (allele, sub_df) in manifest_df.groupby("allele"):
+ is_null_matrix[allele] = result_df[sub_df.col.values].isnull().any(1)
+ print("Fraction null", is_null_matrix.values.mean())
+
print("Computing blocks.")
start = time.time()
- blocks = blocks_of_ones(result_df.isnull().values)
+ blocks = blocks_of_ones(is_null_matrix.values)
print("Found %d blocks in %f sec." % (
len(blocks), (time.time() - start)))
work_items = []
for (row_index1, col_index1, row_index2, col_index2) in blocks:
- block_cols = result_df.columns[col_index1 : col_index2 + 1]
- block_alleles = sorted(set([x.split()[0] for x in block_cols]))
+ block_alleles = is_null_matrix.columns[col_index1 : col_index2 + 1]
block_peptides = result_df.index[row_index1 : row_index2 + 1]
print("Block: ", row_index1, col_index1, row_index2, col_index2)
@@ -285,6 +289,26 @@ def run(argv=sys.argv[1:]):
for (i, work_item) in enumerate(work_items):
work_item["work_item_num"] = i
+ # Combine work items to form tasks.
+ tasks = []
+ peptides_in_last_task = None
+ # We sort work_items to put small items first so they get combined.
+ for work_item in sorted(work_items, key=lambda d: len(d['peptides'])):
+ if peptides_in_last_task is not None and (
+ len(work_item['peptides']) +
+ peptides_in_last_task < args.chunk_size):
+
+ # Add to last task.
+ tasks[-1]['work_item_dicts'].append(work_item)
+ peptides_in_last_task += len(work_item['peptides'])
+ else:
+ # New task
+ tasks.append({'work_item_dicts': [work_item]})
+ peptides_in_last_task = len(work_item['peptides'])
+
+ print("Collected %d work items into %d tasks" % (
+ len(work_items), len(tasks)))
+
if args.predictor == "mhcflurry":
do_predictions_function = do_predictions_mhcflurry
else:
@@ -296,14 +320,14 @@ def run(argv=sys.argv[1:]):
# Serial run
print("Running in serial.")
results = (
- do_predictions_function(**item) for item in work_items)
+ do_predictions_function(**task) for task in tasks)
elif args.cluster_parallelism:
# Run using separate processes HPC cluster.
print("Running on cluster.")
results = cluster_results_from_args(
args,
work_function=do_predictions_function,
- work_items=work_items,
+ work_items=tasks,
constant_data=GLOBAL_DATA,
input_serialization_method="dill",
result_serialization_method="pickle",
@@ -314,7 +338,7 @@ def run(argv=sys.argv[1:]):
assert worker_pool is not None
results = worker_pool.imap_unordered(
partial(call_wrapped_kwargs, do_predictions_function),
- work_items,
+ tasks,
chunksize=1)
allele_to_chunk_index_to_predictions = {}
@@ -332,16 +356,16 @@ def run(argv=sys.argv[1:]):
result_df[col].isnull().mean() * 100.0),
out_path)
- for (work_item_num, col_to_predictions) in tqdm.tqdm(
- results, total=len(work_items)):
- for (col, predictions) in col_to_predictions.items():
- result_df.loc[
- work_items[work_item_num]['peptides'],
- col
- ] = predictions
- if time.time() - last_write_time_per_column[col] > 180:
- write_col(col)
- last_write_time_per_column[col] = time.time()
+ for worker_results in tqdm.tqdm(results, total=len(work_items)):
+ for (work_item_num, col_to_predictions) in worker_results:
+ for (col, predictions) in col_to_predictions.items():
+ result_df.loc[
+ work_items[work_item_num]['peptides'],
+ col
+ ] = predictions
+ if time.time() - last_write_time_per_column[col] > 180:
+ write_col(col)
+ last_write_time_per_column[col] = time.time()
print("Done processing. Final write for each column.")
for col in result_df.columns:
@@ -359,8 +383,14 @@ def run(argv=sys.argv[1:]):
prediction_time / 60.0))
-def do_predictions_mhctools(
- work_item_num, peptides, alleles, constant_data=None):
+def do_predictions_mhctools(work_item_dicts, constant_data=None):
+ """
+ Each tuple of work items consists of:
+
+ (work_item_num, peptides, alleles)
+
+ """
+
# This may run on the cluster in a way that misses all top level imports,
# so we have to re-import everything here.
import time
@@ -371,28 +401,43 @@ def do_predictions_mhctools(
if constant_data is None:
constant_data = GLOBAL_DATA
+ cols = constant_data['cols']
predictor_name = constant_data['args'].predictor
- if predictor_name == "netmhcpan4":
- predictor = mhctools.NetMHCpan4(
- alleles=alleles, program_name="netMHCpan-4.0")
- else:
- raise ValueError("Unsupported", predictor_name)
- cols = constant_data['cols']
+ results = []
+ for (i, d) in enumerate(work_item_dicts):
+ work_item_num = d['work_item_num']
+ peptides = d['peptides']
+ alleles = d['alleles']
- start = time.time()
- df = predictor.predict_peptides_dataframe(peptides)
- print("Generated predictions for %d peptides x %d alleles in %0.2f sec." % (
- len(peptides), len(alleles), (time.time() - start)))
+ print("Processing work item", i + 1, "of", len(work_item_dicts))
+ result = {}
+ results.append((work_item_num, result))
+
+ if predictor_name == "netmhcpan4":
+ predictor = mhctools.NetMHCpan4(
+ alleles=alleles, program_name="netMHCpan-4.0")
+ else:
+ raise ValueError("Unsupported", predictor_name)
+
+ start = time.time()
+ df = predictor.predict_peptides_dataframe(peptides)
+ print("Predicted for %d peptides x %d alleles in %0.2f sec." % (
+ len(peptides), len(alleles), (time.time() - start)))
+
+ for (allele, sub_df) in df.groupby("allele"):
+ for col in cols:
+ result["%s %s" % (allele, col)] = (
+ sub_df[col].values.astype('float32'))
+ return results
- results = {}
- for (allele, sub_df) in df.groupby("allele"):
- for col in cols:
- results["%s %s" % (allele, col)] = sub_df[col].values.astype('float32')
- return (work_item_num, results)
+def do_predictions_mhcflurry(work_item_dicts, constant_data=None):
+ """
+ Each dict of work items should have keys: work_item_num, peptides, alleles
+
+ """
-def do_predictions_mhcflurry(work_item_num, peptides, alleles, constant_data=None):
# This may run on the cluster in a way that misses all top level imports,
# so we have to re-import everything here.
import time
@@ -409,22 +454,30 @@ def do_predictions_mhcflurry(work_item_num, peptides, alleles, constant_data=Non
predictor = Class1AffinityPredictor.load(args.mhcflurry_models_dir)
- start = time.time()
- results = {}
- peptides = EncodableSequences.create(peptides)
- for (i, allele) in enumerate(alleles):
- print("Processing allele %d / %d: %0.2f sec elapsed" % (
- i + 1, len(alleles), time.time() - start))
- for col in ["affinity"]:
- results["%s %s" % (allele, col)] = predictor.predict(
- peptides=peptides,
- allele=allele,
- throw=False,
- model_kwargs={
- 'batch_size': args.mhcflurry_batch_size,
- }).astype('float32')
- print("Done predicting in", time.time() - start, "sec")
- return (work_item_num, results)
+ results = []
+ for (i, d) in enumerate(work_item_dicts):
+ work_item_num = d['work_item_num']
+ peptides = d['peptides']
+ alleles = d['alleles']
+
+ print("Processing work item", i + 1, "of", len(work_item_dicts))
+ result = {}
+ results.append((work_item_num, result))
+ start = time.time()
+ peptides = EncodableSequences.create(peptides)
+ for (i, allele) in enumerate(alleles):
+ print("Processing allele %d / %d: %0.2f sec elapsed" % (
+ i + 1, len(alleles), time.time() - start))
+ for col in ["affinity"]:
+ result["%s %s" % (allele, col)] = predictor.predict(
+ peptides=peptides,
+ allele=allele,
+ throw=False,
+ model_kwargs={
+ 'batch_size': args.mhcflurry_batch_size,
+ }).astype('float32')
+ print("Done predicting in", time.time() - start, "sec")
+ return results
if __name__ == '__main__':
--
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