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import numpy as np
import torch.utils.data as data
class DijkprofileDataset(data.Dataset):
"""Pytorch custom dataset class to use with the pytorch dataloader."""
def __init__(self, profile_dict, partition, custom_scaler_path=None):
"""Dijkprofile Dataset, provides profiles and labels to pytorch model.
Args:
profile_dict (dict): dict containing the profiles and labels
partition (list): list used to split the dataset into train and test
sets. list contains ids to use for this dataset, format is
as returned by sklearn.model_selection.train_test_split
"""
self.data_dict = profile_dict
self.list_IDs = partition
print("scaler in dataset class is depracated and moved to preprocessing")
# load scaler
# if custom_scaler_path:
# self.scaler = joblib.load(custom_scaler_path)
# else:
# self.scaler = joblib.load(os.path.join(dir, config.SCALER_PATH))
# # rescale all profiles profiles
# for key in profile_dict.keys():
# profile_dict[key]['profile'] = self.scaler.transform(
# profile_dict[key]['profile'].reshape(-1, 1)).reshape(-1)
# profile_dict[key]['profile'] = profile_dict[key]['profile'] / 10
def __len__(self):
return len(self.list_IDs)
def __getitem__(self, index):
id = self.list_IDs[index]
X = self.data_dict[id]['profile'].reshape(1,-1).astype(np.float32)
y = self.data_dict[id]['label'].reshape(1,-1)
return X, y
def __str__(self):
return "<Dijkprofile dataset: datapoints={}>".format(len(self.list_IDs))
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