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This commit is contained in:
Jan Kowalczyk
2024-06-28 11:36:46 +02:00
parent d33c6b1e16
commit 71f9662022
40 changed files with 2938 additions and 1260 deletions
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77
Deep-SAD-PyTorch/src/datasets/preprocessing.py
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@@ -2,10 +2,17 @@ import torch
import numpy as np
def create_semisupervised_setting(labels, normal_classes, outlier_classes, known_outlier_classes,
ratio_known_normal, ratio_known_outlier, ratio_pollution):
def create_semisupervised_setting(
labels,
normal_classes,
outlier_classes,
known_outlier_classes,
ratio_known_normal,
ratio_known_outlier,
ratio_pollution,
):
"""
Create a semi-supervised data setting.
Create a semi-supervised data setting.
:param labels: np.array with labels of all dataset samples
:param normal_classes: tuple with normal class labels
:param outlier_classes: tuple with anomaly class labels
@@ -17,15 +24,31 @@ def create_semisupervised_setting(labels, normal_classes, outlier_classes, known
"""
idx_normal = np.argwhere(np.isin(labels, normal_classes)).flatten()
idx_outlier = np.argwhere(np.isin(labels, outlier_classes)).flatten()
idx_known_outlier_candidates = np.argwhere(np.isin(labels, known_outlier_classes)).flatten()
idx_known_outlier_candidates = np.argwhere(
np.isin(labels, known_outlier_classes)
).flatten()
n_normal = len(idx_normal)
# Solve system of linear equations to obtain respective number of samples
a = np.array([[1, 1, 0, 0],
[(1-ratio_known_normal), -ratio_known_normal, -ratio_known_normal, -ratio_known_normal],
[-ratio_known_outlier, -ratio_known_outlier, -ratio_known_outlier, (1-ratio_known_outlier)],
[0, -ratio_pollution, (1-ratio_pollution), 0]])
a = np.array(
[
[1, 1, 0, 0],
[
(1 - ratio_known_normal),
-ratio_known_normal,
-ratio_known_normal,
-ratio_known_normal,
],
[
-ratio_known_outlier,
-ratio_known_outlier,
-ratio_known_outlier,
(1 - ratio_known_outlier),
],
[0, -ratio_pollution, (1 - ratio_pollution), 0],
]
)
b = np.array([n_normal, 0, 0, 0])
x = np.linalg.solve(a, b)
@@ -41,9 +64,13 @@ def create_semisupervised_setting(labels, normal_classes, outlier_classes, known
perm_known_outlier = np.random.permutation(len(idx_known_outlier_candidates))
idx_known_normal = idx_normal[perm_normal[:n_known_normal]].tolist()
idx_unlabeled_normal = idx_normal[perm_normal[n_known_normal:n_known_normal+n_unlabeled_normal]].tolist()
idx_unlabeled_normal = idx_normal[
perm_normal[n_known_normal : n_known_normal + n_unlabeled_normal]
].tolist()
idx_unlabeled_outlier = idx_outlier[perm_outlier[:n_unlabeled_outlier]].tolist()
idx_known_outlier = idx_known_outlier_candidates[perm_known_outlier[:n_known_outlier]].tolist()
idx_known_outlier = idx_known_outlier_candidates[
perm_known_outlier[:n_known_outlier]
].tolist()
# Get original class labels
labels_known_normal = labels[idx_known_normal].tolist()
@@ -53,14 +80,32 @@ def create_semisupervised_setting(labels, normal_classes, outlier_classes, known
# Get semi-supervised setting labels
semi_labels_known_normal = np.ones(n_known_normal).astype(np.int32).tolist()
semi_labels_unlabeled_normal = np.zeros(n_unlabeled_normal).astype(np.int32).tolist()
semi_labels_unlabeled_outlier = np.zeros(n_unlabeled_outlier).astype(np.int32).tolist()
semi_labels_unlabeled_normal = (
np.zeros(n_unlabeled_normal).astype(np.int32).tolist()
)
semi_labels_unlabeled_outlier = (
np.zeros(n_unlabeled_outlier).astype(np.int32).tolist()
)
semi_labels_known_outlier = (-np.ones(n_known_outlier).astype(np.int32)).tolist()
# Create final lists
list_idx = idx_known_normal + idx_unlabeled_normal + idx_unlabeled_outlier + idx_known_outlier
list_labels = labels_known_normal + labels_unlabeled_normal + labels_unlabeled_outlier + labels_known_outlier
list_semi_labels = (semi_labels_known_normal + semi_labels_unlabeled_normal + semi_labels_unlabeled_outlier
+ semi_labels_known_outlier)
list_idx = (
idx_known_normal
+ idx_unlabeled_normal
+ idx_unlabeled_outlier
+ idx_known_outlier
)
list_labels = (
labels_known_normal
+ labels_unlabeled_normal
+ labels_unlabeled_outlier
+ labels_known_outlier
)
list_semi_labels = (
semi_labels_known_normal
+ semi_labels_unlabeled_normal
+ semi_labels_unlabeled_outlier
+ semi_labels_known_outlier
)
return list_idx, list_labels, list_semi_labels