def batch_predict(
config_in,
model_in,
groups_in,
requests,
debug=False,
profile=None):
root = os.path.abspath(os.path.curdir)
with tempdir(cleanup_on_error=(not debug)):
requests_in = os.path.abspath('requests.pbs.gz')
responses_out = os.path.abspath('responses.pbs.gz')
protobuf_stream_dump(
(q.SerializeToString() for q in requests),
requests_in)
os.chdir(root)
loom.runner.query(
config_in=config_in,
model_in=model_in,
groups_in=groups_in,
requests_in=requests_in,
responses_out=responses_out,
debug=debug,
profile=profile)
return map(parse_response, protobuf_stream_load(responses_out))
def crossvalidate_one(seed, test_count, train_count, inputs, results,
extra_passes, debug):
LOG('running seed {}:'.format(seed))
results['train'] = os.path.join(results['root'], 'train', 'diffs.pbs.gz')
results['test'] = os.path.join(results['root'], 'test', 'rows.pbs.gz')
results['scores'] = os.path.join(results['root'], 'scores.json.gz')
config = {
'seed': seed,
'schedule': {
'extra_passes': extra_passes
},
}
loom.config.config_dump(config, results['samples'][0]['config'])
numpy.random.seed(seed)
split = [True] * train_count + [False] * test_count
numpy.random.shuffle(split)
diffs_in = protobuf_stream_load(inputs['ingest']['diffs'])
protobuf_stream_dump((row for s, row in izip(split, diffs_in) if s),
results['train'])
rows_in = protobuf_stream_load(inputs['ingest']['rows'])
protobuf_stream_dump((row for s, row in izip(split, rows_in) if not s),
results['test'])
LOG(' shuffle')
loom.runner.shuffle(rows_in=results['train'],
rows_out=results['samples'][0]['shuffled'],
seed=seed,
debug=debug)
LOG(' init')
loom.generate.generate_init(encoding_in=inputs['ingest']['encoding'],
model_out=results['samples'][0]['init'],
seed=seed)
LOG(' infer')
loom.runner.infer(config_in=results['samples'][0]['config'],
rows_in=results['samples'][0]['shuffled'],
tares_in=inputs['ingest']['tares'],
model_in=results['samples'][0]['init'],
model_out=results['samples'][0]['model'],
groups_out=results['samples'][0]['groups'],
debug=debug)
LOG(' query')
rows = loom.query.load_data_rows(results['test'])
loom.config.config_dump({}, results['query']['config'])
with loom.query.get_server(results['root'], debug=debug) as query:
scores = [query.score(row) for row in rows]
json_dump(scores, results['scores'])
LOG(' done\n')
return numpy.mean(scores)
def dump_rows(table, rows_name):
protobuf_stream_dump(serialize_rows(table), rows_name)
def import_tares(schema):
tare = ProductValue()
tare.observed.sparsity = ProductValue.Observed.ALL
tare.booleans[:] = [False] * len(schema)
protobuf_stream_dump([tare.SerializeToString()], TARES)
def import_tares(schema):
tare = ProductValue()
tare.observed.sparsity = ProductValue.Observed.ALL
tare.booleans[:] = [False] * len(schema)
protobuf_stream_dump([tare.SerializeToString()], TARES)
def crossvalidate_one(
seed,
test_count,
train_count,
inputs,
results,
extra_passes,
debug):
LOG('running seed {}:'.format(seed))
results['train'] = os.path.join(
results['root'],
'train',
'diffs.pbs.gz')
results['test'] = os.path.join(results['root'], 'test', 'rows.pbs.gz')
results['scores'] = os.path.join(results['root'], 'scores.json.gz')
config = {
'seed': seed,
'schedule': {'extra_passes': extra_passes},
}
loom.config.config_dump(config, results['samples'][0]['config'])
numpy.random.seed(seed)
split = [True] * train_count + [False] * test_count
numpy.random.shuffle(split)
diffs_in = protobuf_stream_load(inputs['ingest']['diffs'])
protobuf_stream_dump(
(row for s, row in izip(split, diffs_in) if s),
results['train'])
rows_in = protobuf_stream_load(inputs['ingest']['rows'])
protobuf_stream_dump(
(row for s, row in izip(split, rows_in) if not s),
results['test'])
LOG(' shuffle')
loom.runner.shuffle(
rows_in=results['train'],
rows_out=results['samples'][0]['shuffled'],
seed=seed,
debug=debug)
LOG(' init')
loom.generate.generate_init(
encoding_in=inputs['ingest']['encoding'],
model_out=results['samples'][0]['init'],
seed=seed)
LOG(' infer')
loom.runner.infer(
config_in=results['samples'][0]['config'],
rows_in=results['samples'][0]['shuffled'],
tares_in=inputs['ingest']['tares'],
model_in=results['samples'][0]['init'],
model_out=results['samples'][0]['model'],
groups_out=results['samples'][0]['groups'],
debug=debug)
LOG(' query')
rows = loom.query.load_data_rows(results['test'])
loom.config.config_dump({}, results['query']['config'])
with loom.query.get_server(results['root'], debug=debug) as query:
scores = [query.score(row) for row in rows]
json_dump(scores, results['scores'])
LOG(' done\n')
return numpy.mean(scores)
def dump_rows(table, rows_name):
protobuf_stream_dump(serialize_rows(table), rows_name)
def generate_one((name, sample_count, force, debug)):
paths = loom.store.get_paths(name, sample_count=sample_count)
if not force and all(os.path.exists(f) for f in paths.itervalues()):
with open_compressed(paths['ingest']['version']) as f:
version = f.read().strip()
if version == loom.__version__:
return
print 'generating', name
mkdir_p(paths['root'])
with open_compressed(paths['ingest']['version'], 'w') as f:
f.write(loom.__version__)
config = CONFIGS[name]
chunk_size = max(10, (config['row_count'] + 7) / 8)
loom.transforms.make_fake_transforms(
transforms_out=paths['ingest']['transforms'])
loom.generate.generate(
init_out=paths['samples'][0]['init'],
rows_out=paths['ingest']['rows'],
model_out=paths['samples'][0]['model'],
groups_out=paths['samples'][0]['groups'],
assign_out=paths['samples'][0]['assign'],
**config)
loom.format.make_schema(
model_in=paths['samples'][0]['model'],
schema_out=paths['ingest']['schema'])
loom.format.make_fake_encoding(
schema_in=paths['ingest']['schema'],
model_in=paths['samples'][0]['model'],
encoding_out=paths['ingest']['encoding'])
loom.format.make_schema_row(
schema_in=paths['ingest']['schema'],
schema_row_out=paths['ingest']['schema_row'])
loom.runner.tare(
schema_row_in=paths['ingest']['schema_row'],
rows_in=paths['ingest']['rows'],
tares_out=paths['ingest']['tares'],
debug=debug)
loom.runner.sparsify(
schema_row_in=paths['ingest']['schema_row'],
tares_in=paths['ingest']['tares'],
rows_in=paths['ingest']['rows'],
rows_out=paths['ingest']['diffs'],
debug=debug)
loom.format.export_rows(
encoding_in=paths['ingest']['encoding'],
rows_in=paths['ingest']['rows'],
rows_csv_out=paths['ingest']['rows_csv'],
chunk_size=chunk_size)
loom.format.import_rowids(
rows_csv_in=paths['ingest']['rows_csv'],
rowids_out=paths['ingest']['rowids'],
id_field='_id')
protobuf_stream_dump([], paths['query']['query_log'])
loom.config.config_dump({}, paths['query']['config'])
for seed, sample in enumerate(paths['samples']):
loom.config.config_dump({'seed': seed}, sample['config'])
loom.generate.generate_init(
encoding_in=paths['ingest']['encoding'],
model_out=sample['init'],
seed=seed)
loom.runner.shuffle(
rows_in=paths['ingest']['diffs'],
rows_out=sample['shuffled'],
seed=seed,
debug=debug)
protobuf_stream_dump([], sample['infer_log'])
sample0 = paths['samples'][0]
for seed, sample in enumerate(paths['samples'][1:]):
if LOOM_DEBUG_MIX:
cp_ns(sample0['model'], sample['model'])
cp_ns(sample0['groups'], sample['groups'])
cp_ns(sample0['assign'], sample['assign'])
else:
loom.runner.mix(
config_in=sample['config'],
rows_in=paths['ingest']['rows'],
model_in=sample0['model'],
groups_in=sample0['groups'],
assign_in=sample0['assign'],
model_out=sample['model'],
groups_out=sample['groups'],
assign_out=sample['assign'],
debug=debug)
loom.consensus.make_fake_consensus(
paths=paths,
debug=debug)
def generate_one((name, sample_count, force, debug)):
paths = loom.store.get_paths(name, sample_count=sample_count)
if not force and all(os.path.exists(f) for f in paths.itervalues()):
with open_compressed(paths['ingest']['version']) as f:
version = f.read().strip()
if version == loom.__version__:
return
print 'generating', name
mkdir_p(paths['root'])
with open_compressed(paths['ingest']['version'], 'w') as f:
f.write(loom.__version__)
config = CONFIGS[name]
chunk_size = max(10, (config['row_count'] + 7) / 8)
loom.transforms.make_fake_transforms(
transforms_out=paths['ingest']['transforms'])
loom.generate.generate(
init_out=paths['samples'][0]['init'],
rows_out=paths['ingest']['rows'],
model_out=paths['samples'][0]['model'],
groups_out=paths['samples'][0]['groups'],
assign_out=paths['samples'][0]['assign'],
**config)
loom.format.make_schema(
model_in=paths['samples'][0]['model'],
schema_out=paths['ingest']['schema'])
loom.format.make_fake_encoding(
schema_in=paths['ingest']['schema'],
model_in=paths['samples'][0]['model'],
encoding_out=paths['ingest']['encoding'])
loom.format.make_schema_row(
schema_in=paths['ingest']['schema'],
schema_row_out=paths['ingest']['schema_row'])
loom.runner.tare(
schema_row_in=paths['ingest']['schema_row'],
rows_in=paths['ingest']['rows'],
tares_out=paths['ingest']['tares'],
debug=debug)
loom.runner.sparsify(
schema_row_in=paths['ingest']['schema_row'],
tares_in=paths['ingest']['tares'],
rows_in=paths['ingest']['rows'],
rows_out=paths['ingest']['diffs'],
debug=debug)
loom.format.export_rows(
encoding_in=paths['ingest']['encoding'],
rows_in=paths['ingest']['rows'],
rows_csv_out=paths['ingest']['rows_csv'],
chunk_size=chunk_size)
loom.format.import_rowids(
rows_csv_in=paths['ingest']['rows_csv'],
rowids_out=paths['ingest']['rowids'],
id_field='_id')
protobuf_stream_dump([], paths['query']['query_log'])
loom.config.config_dump({}, paths['query']['config'])
for seed, sample in enumerate(paths['samples']):
loom.config.config_dump({'seed': seed}, sample['config'])
loom.generate.generate_init(
encoding_in=paths['ingest']['encoding'],
model_out=sample['init'],
seed=seed)
loom.runner.shuffle(
rows_in=paths['ingest']['diffs'],
rows_out=sample['shuffled'],
seed=seed,
debug=debug)
protobuf_stream_dump([], sample['infer_log'])
sample0 = paths['samples'][0]
for seed, sample in enumerate(paths['samples'][1:]):
if LOOM_DEBUG_MIX:
cp_ns(sample0['model'], sample['model'])
cp_ns(sample0['groups'], sample['groups'])
cp_ns(sample0['assign'], sample['assign'])
else:
loom.runner.mix(
config_in=sample['config'],
rows_in=paths['ingest']['rows'],
model_in=sample0['model'],
groups_in=sample0['groups'],
assign_in=sample0['assign'],
model_out=sample['model'],
groups_out=sample['groups'],
assign_out=sample['assign'],
debug=debug)
loom.consensus.make_fake_consensus(
paths=paths,
debug=debug)
