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)