From f9f0119d8c480608c90350807a185bfddcb8c3f6 Mon Sep 17 00:00:00 2001
From: Joerg Martin <joerg.martin@ptb.de>
Date: Fri, 28 Jan 2022 09:32:04 +0100
Subject: [PATCH] repeated sampling updated
---
EIVPackage/EIVData/repeated_sampling.py | 53 +++++++++++++++++++++----
1 file changed, 45 insertions(+), 8 deletions(-)
diff --git a/EIVPackage/EIVData/repeated_sampling.py b/EIVPackage/EIVData/repeated_sampling.py
index bff0c9f..f6339df 100644
--- a/EIVPackage/EIVData/repeated_sampling.py
+++ b/EIVPackage/EIVData/repeated_sampling.py
@@ -7,7 +7,8 @@ import sys
import torch
from torch.utils.data import TensorDataset
-from EIVGeneral.manipulate_tensors import add_noise
+from EIVGeneral.manipulate_tensors import add_noise, normalize_tensor,\
+ unnormalize_tensor
class repeated_sampling():
"""
@@ -25,17 +26,35 @@ class repeated_sampling():
"""
def __init__(self, dataclass, fixed_seed=0):
self.dataclass = dataclass
+ self.func = dataclass.func
self.fixed_seed = fixed_seed
self.x_noise_strength = dataclass.x_noise_strength
self.y_noise_strength = dataclass.y_noise_strength
def __call__(self,seed=0, splitting_part=0.8, normalize=True,
- return_ground_truth=False):
+ return_ground_truth=False,
+ return_normalized_func=False):
_, _, true_trainset, true_testset\
= self.dataclass.load_data(
seed=self.fixed_seed, splitting_part=splitting_part,
normalize=False,
return_ground_truth=True)
+ """
+ Loads repeated sampling data
+ :param seed: Seed for the used noise
+ :param splitting_part: Which fraction of the data to use as training
+ data. Defaults to 0.8.
+ :param normalize: Whether to normalize the data, defaults to True.
+ :param return_ground_truth: Boolean. If True, the unnoisy ground truth will
+ also be returned. Defaults to False.
+ :param return_normalized_func: Boolean (default False). If True, the
+ normalized version of the used function is returned as a last element.
+ :returns: trainset, testset, (, normalized_func) if
+ return_ground_truth is False,
+ else trainset, testset, true_trainset,
+ true_testset, (, normalized_func). The "true" datasets each return
+ **four tensors**: The true x,y and their noisy counterparts.
+ """
full_noisy_x = torch.concat((true_trainset.tensors[2],
true_testset.tensors[2]), dim=0)
full_noisy_y = torch.concat((true_trainset.tensors[3],
@@ -46,23 +65,41 @@ class repeated_sampling():
# draw different seeds for noise and splitting
seeds = [int(t) for t in torch.randint(0,sys.maxsize,(2,),\
generator=random_generator)]
- (noisy_train_x, noisy_train_y), (true_train_x, true_train_y) =\
- add_noise((true_train_x, true_train_y),
+ # use same normalization for train and test
+ (noisy_train_x, noisy_train_y), (true_train_x, true_train_y),\
+ normalization_list= add_noise((true_train_x, true_train_y),
(self.x_noise_strength, self.y_noise_strength), seeds,
normalize=normalize,
- normalization_list=[full_noisy_x, full_noisy_y])
+ normalization_list=[full_noisy_x, full_noisy_y],
+ return_normalization=True)
(noisy_test_x, noisy_test_y), (true_test_x, true_test_y) =\
add_noise((true_test_x, true_test_y),
(self.x_noise_strength, self.y_noise_strength), seeds,
normalize=normalize,
- normalization_list=[full_noisy_x, full_noisy_y])
+ normalization_list=[full_noisy_x, full_noisy_y],
+ return_normalization=False) # same normalization
+ def normalized_func(x):
+ unnormalized_x = unnormalize_tensor(x, normalization_list[0])
+ y = self.func(unnormalized_x)
+ normalized_y = normalize_tensor(y, normalization_list[1])
+ return normalized_y
trainset = TensorDataset(noisy_train_x, noisy_train_y)
testset = TensorDataset(noisy_test_x, noisy_test_y)
true_trainset = TensorDataset(true_train_x, true_train_y,
noisy_train_x, noisy_train_y)
true_testset = TensorDataset(true_test_x, true_test_y,
noisy_test_x, noisy_test_y)
+ # return different objects, depending on Booleans
if not return_ground_truth:
- return trainset, testset
+ if not return_normalized_func:
+ return trainset, testset
+ else:
+ return trainset, testset, normalized_func
else:
- return trainset, testset, true_trainset, true_testset
+ if not return_normalized_func:
+ return trainset, testset, true_trainset,\
+ true_testset
+ else:
+ return trainset, testset, true_trainset,\
+ true_testset, normalized_func
+
--
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