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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] |