def twice(*args):
'Run a command twice'
argv = [sys.argv[0]] + list(args)
print('first time:')
parsable(argv)
print('second time:')
parsable(argv)
@parsable
def extract(filename):
"""Extract code from markdown file."""
TODO()
@parsable
def annotate(*files):
'''
Annotate markdown files with high-latency results of analyst.
'''
TODO()
@parsable
def suggest(filename):
'''
Get real-time feedback from low-latency results of analyst.
'''
TODO()
@parsable
def precommit():
"""Reformat and annotate all code before a git commit."""
reformat(CORPUS)
if __name__ == '__main__':
parsable()
npred_held_out,
) for idx in xrange(epochs)
]
filename = get_results_filename(shortname, n_test, iters, epochs, schedule,
seed)
filepath = '%s.json' % (os.path.join(PATH_RESULTS, filename), )
with open(filepath, 'w') as fptr:
json.dump(
{
'path_dataset': path_dataset,
'n_test': n_test,
'xs_train': xs_train.tolist(),
'ys_train': ys_train.tolist(),
'xs_test': xs_test.tolist(),
'ys_test': ys_test.tolist(),
'xs_probe': xs_probe.tolist(),
'n_iters': iters,
'n_epochs': epochs,
'nprobe_held_in': nprobe_held_in,
'npred_held_in': npred_held_in,
'npred_held_out': npred_held_out,
'seed': seed,
'statistics': statistics,
'schedule': schedule,
}, fptr)
print filepath
if __name__ == '__main__':
parsable()
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
from parsable import parsable
from treecat.config import make_config
from treecat.format import guess_schema
from treecat.format import import_data
from treecat.format import pickle_dump
from treecat.format import pickle_load
parsable = parsable.Parsable()
parsable(guess_schema)
parsable(import_data)
@parsable
def train(dataset_in, ensemble_out, **options):
"""Train a TreeCat ensemble model on imported data."""
from treecat.training import train_ensemble
dataset = pickle_load(dataset_in)
table = dataset['table']
tree_prior = dataset['schema']['tree_prior']
config = make_config(**options)
ensemble = train_ensemble(table, tree_prior, config)
pickle_dump(ensemble, ensemble_out)
