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import numpy as np
import torch
from torch.utils.data import Dataset
from preprocessing.augmentation.gaussiannoise import GaussianNoise
from preprocessing.transformation.transformation import Transformation
import torch.nn.functional as F
from sklearn import preprocessing
import torch
class DataSetBuilder(Dataset):
def __init__(self, x, y, labels, transform_method=None, scaler=None, noise=None, classification=None):
self.x = x
self.y = y
self.labels = labels
self.y_label = []
self.transform_method = transform_method
self.scaler = scaler
self.noise = noise
self.classification = classification
self._preprocess()
if self.classification:
self._run_label_encoding()
self.n_sample = len(y)
# x = np.transpose(self.x, (0, 2, 1))
self.x = torch.from_numpy(x).double()
self.y = torch.from_numpy(self.y).double()
def _run_label_encoding(self):
le = preprocessing.LabelEncoder()
y_label = le.fit_transform(self.labels[:, 0, 3])
y_label = torch.as_tensor(y_label)
# self.y_label = F.one_hot(y_label.to(torch.int64))
self.y_label = y_label.to(torch.int64)
def _preprocess(self):
if self.transform_method['data_transformer_method'] is not None:
self._run_transform()
if self.noise is not None:
self._run_noise()
def _run_transform(self):
transform_handler = Transformation(method=self.transform_method['data_transformer_method'], by=self.transform_method['data_transformer_by'])
if self.scaler is None:
self.scaler, self.x = transform_handler.run_transform(train=self.x, scaler_fit=self.scaler)
else:
self.x = transform_handler.run_transform(val=self.x, scaler_fit=self.scaler)
def _run_noise(self, ):
gaussiannoise_handler = GaussianNoise(mean=0, std=1)
self.x, self.y, self.labels = gaussiannoise_handler.run_add_noise(self.x, self.y, self.labels)
def __len__(self):
return self.n_sample
def __getitem__(self, item):
if self.classification:
return self.x[item], self.y[item], self.y_label[item]
else:
return self.x[item], self.y[item] |