def download(filename,
load=True,
n_tries=100,
sleep_seconds=300,
verbose=False,
include_train=True,
single_precision=False):
"""
Download current Numerai dataset; overwrites if file exists.
If `load` is True (default) then return data object; otherwise return
None. It will include train data if `include_train` is True as well.
Set `single_precision` to True in order to load data in float32 precision.
If download fails then retry download `n_tries` times, pausing
`sleep_seconds` between each try.
Unlike nx.download() this function loads and returns the data object.
"""
# line below expands e.g. ~/tmp to /home/me/tmp...
filename = os.path.expanduser(filename)
count = 0
while count < n_tries:
try:
if verbose:
print("Download dataset {}".format(filename))
napi = NumerAPI()
url = napi.get_dataset_url(tournament=8)
download_file(url, filename)
break
except: # noqa
print('download failed')
time.sleep(sleep_seconds)
count += 1
if load:
data = nx.load_zip(filename,
verbose=verbose,
include_train=include_train,
single_precision=single_precision)
else:
data = None
return data
def download_validation_data(self, dest_path: str = ".",
dest_filename: str = None) -> str:
"""download CSV file with historical targets and ticker universe
Returns:
str: path to csv file
Example:
>>> SignalsAPI().download_validation_data()
signals_train_val_bbg.csv
"""
# set up download path
if dest_filename is None:
dest_filename = "numerai_signals_historical.csv"
path = os.path.join(dest_path, dest_filename)
# create parent folder if necessary
utils.ensure_directory_exists(dest_path)
utils.download_file(
self.HISTORICAL_DATA_URL, path, self.show_progress_bars)
return path