def do_makesamples(args):
settings = Settings(
num_samples=args.num_samples,
block_variables=args.block,
condition_variables=args.condition,
stat='f')
schema = load_schema(args.schema)
input = Input.from_raw_file(args.infile, schema, limit=1)
job = Job(input=input,
settings=settings,
schema=schema)
res = an.new_sample_indexes(job)
if args.output is None:
output = sys.stdout
else:
output = args.output
output.write("# Block layout: " + str(job.block_layout) + "\n")
output.write("# Condition layout: " + str(job.condition_layout) + "\n")
test = GroupSymbols(job.condition_layout)
for row in res:
for x in row:
output.write(' {:3d}'.format(x))
output.write(" # " + test(np.array(row)) + "\n")
def copy_input(path, input_path, schema, settings, job_id):
logging.info("Loading input for job from {0}".format(input_path))
input = Input.from_raw_file(input_path, schema)
logging.info("Saving input, settings, and schema to " + str(path))
with h5py.File(path, 'w') as db:
# Save the input object
ids = input.feature_ids
# Saving feature ids is tricky because they are strings
dt = h5py.special_dtype(vlen=str)
db.create_dataset("table", data=input.table)
db.create_dataset("feature_ids", (len(ids), ), dt)
for i, fid in enumerate(ids):
input.feature_ids[i] = fid
db['feature_ids'][i] = fid
print('block vars are', settings.block_variables)
# Save the settings object
db.create_dataset("tuning_params", data=settings.tuning_params)
db.attrs['job_id'] = job_id
db.attrs['stat'] = settings.stat,
db.attrs['glm_family'] = settings.glm_family,
db.attrs['num_bins'] = settings.num_bins
db.attrs['num_samples'] = settings.num_samples
db.attrs['sample_from_residuals'] = settings.sample_from_residuals
db.attrs['sample_with_replacement'] = settings.sample_with_replacement
db.attrs['condition_variables'] = map(str,
settings.condition_variables)
if settings.block_variables != []:
db.attrs['block_variables'] = settings.block_variables
db.attrs['summary_min_conf'] = settings.summary_min_conf
db.attrs['summary_step_size'] = settings.summary_step_size
db.attrs['equalize_means'] = settings.equalize_means
db.attrs['shrink'] = settings.shrink
# Save the schema object
schema_str = StringIO()
schema.save(schema_str)
db.attrs['schema'] = str(schema_str.getvalue())
if settings.equalize_means_ids is not None:
db['equalize_means_ids'] = settings.equalize_means_ids
def copy_input(path, input_path, schema, settings, job_id):
logging.info("Loading input for job from {0}".format(input_path))
input = Input.from_raw_file(input_path, schema)
logging.info("Saving input, settings, and schema to " + str(path))
with h5py.File(path, 'w') as db:
# Save the input object
ids = input.feature_ids
# Saving feature ids is tricky because they are strings
dt = h5py.special_dtype(vlen=str)
db.create_dataset("table", data=input.table)
db.create_dataset("feature_ids", (len(ids),), dt)
for i, fid in enumerate(ids):
input.feature_ids[i] = fid
db['feature_ids'][i] = fid
print('block vars are', settings.block_variables)
# Save the settings object
db.create_dataset("tuning_params", data=settings.tuning_params)
db.attrs['job_id'] = job_id
db.attrs['stat'] = settings.stat,
db.attrs['glm_family'] = settings.glm_family,
db.attrs['num_bins'] = settings.num_bins
db.attrs['num_samples'] = settings.num_samples
db.attrs['sample_from_residuals'] = settings.sample_from_residuals
db.attrs['sample_with_replacement'] = settings.sample_with_replacement
db.attrs['condition_variables'] = map(str, settings.condition_variables)
if settings.block_variables != []:
db.attrs['block_variables'] = settings.block_variables
db.attrs['summary_min_conf'] = settings.summary_min_conf
db.attrs['summary_step_size'] = settings.summary_step_size
db.attrs['equalize_means'] = settings.equalize_means
db.attrs['shrink'] = settings.shrink
# Save the schema object
schema_str = StringIO()
schema.save(schema_str)
db.attrs['schema'] = str(schema_str.getvalue())
if settings.equalize_means_ids is not None:
db['equalize_means_ids'] = settings.equalize_means_ids
def load_input(db):
return Input(db['table'][...], db['feature_ids'][...])
