def save_predictions_csv(
path_csv: str,
pred_records: List[Dict],
dataset: Any,
) -> None:
"""Saves csv with metadata of predictions.
Parameters
----------
path_csv
CSV save path.
pred_records
List of metadata for each prediction.
dataset
Dataset from where signal-target pairs were retrieved.
"""
df = pd.DataFrame(pred_records).set_index('index')
if isinstance(dataset, FnetDataset):
# For FnetDataset, add additional metadata
df = (df.rename_axis(dataset.df.index.name).join(dataset.df,
lsuffix='_pre'))
if os.path.exists(path_csv):
df_old = pd.read_csv(path_csv)
col_index = df_old.columns[0] # Assumes first col is index col
df_old = df_old.set_index(col_index)
df = df.combine_first(df_old)
df = df.sort_index(axis=1)
dirname = os.path.dirname(path_csv)
if not os.path.exists(dirname):
os.makedirs(dirname)
print('Created:', dirname)
retry_if_oserror(df.to_csv)(path_csv)
print('Saved:', path_csv)
def save_csv(path_csv, df: pd.DataFrame) -> None:
"""Saves dataframe as csv and merges with existing csv if necessary."""
if os.path.exists(path_csv):
df_old = pd.read_csv(path_csv)
col_index = df_old.columns[0] # Assumes first col is index col
df_old = df_old.set_index(col_index)
df = df.combine_first(df_old)
df = df.sort_index(axis=1)
retry_if_oserror(df.to_csv)(path_csv)
print('Saved:', path_csv)
def save(self, path_save: str):
"""Saves model to disk.
Parameters
----------
path_save
Filename to which model is saved.
"""
assert not os.path.isdir(path_save)
curr_gpu_ids = self.gpu_ids
self.to_gpu(-1)
retry_if_oserror(torch.save)(self.get_state(), path_save)
self.to_gpu(curr_gpu_ids)
def save(self, path_save: str):
"""Saves model to disk.
Parameters
----------
path_save
Filename to which model is saved.
"""
dirname = os.path.dirname(path_save)
if not os.path.exists(dirname):
os.makedirs(dirname)
logger.info(f"Created: {dirname}")
curr_gpu_ids = self.gpu_ids
self.to_gpu(-1)
retry_if_oserror(torch.save)(self.get_state(), path_save)
self.to_gpu(curr_gpu_ids)