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FEAWAD_Torch_core.py
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FEAWAD_Torch_core.py
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import torch
import numpy as np
from sklearn.model_selection import train_test_split
from torch.utils.data import Subset
from torch.utils.data import Dataset
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import os
from pytorch_lightning.callbacks import Callback
from sklearn.metrics import average_precision_score, roc_auc_score
from sklearn.datasets import load_svmlight_file
from scipy.sparse import vstack
from datetime import datetime
import numpy as np
import csv
# from FEAWAD_Unchanged import inject_noise, inject_noise_sparse
from scipy.sparse import vstack, csc_matrix
def inject_noise_sparse(seed, n_out, random_seed):
'''
add anomalies to training data to replicate anomaly contaminated data sets.
we randomly swape 5% features of anomalies to avoid duplicate contaminated anomalies.
This is for sparse data.
'''
rng = np.random.RandomState(random_seed)
n_sample, dim = seed.shape
swap_ratio = 0.05
n_swap_feat = int(swap_ratio * dim)
seed = seed.tocsc()
noise = csc_matrix((n_out, dim))
print(noise.shape)
for i in np.arange(n_out):
outlier_idx = rng.choice(n_sample, 2, replace = False)
o1 = seed[outlier_idx[0]]
o2 = seed[outlier_idx[1]]
swap_feats = rng.choice(dim, n_swap_feat, replace = False)
noise[i] = o1.copy()
noise[i, swap_feats] = o2[0, swap_feats]
return noise.tocsr()
def inject_noise(seed, n_out, random_seed):
'''
add anomalies to training data to replicate anomaly contaminated data sets.
we randomly swape 5% features of anomalies to avoid duplicate contaminated anomalies.
this is for dense data
'''
rng = np.random.RandomState(random_seed)
n_sample, dim = seed.shape
swap_ratio = 0.05
n_swap_feat = int(swap_ratio * dim)
noise = np.empty((n_out, dim))
for i in np.arange(n_out):
outlier_idx = rng.choice(n_sample, 2, replace = False)
o1 = seed[outlier_idx[0]]
o2 = seed[outlier_idx[1]]
swap_feats = rng.choice(dim, n_swap_feat, replace = False)
noise[i] = o1.copy()
noise[i, swap_feats] = o2[swap_feats]
return noise
# Functions
def flatten(x):
return x.view(784)
def filter_dataset(dataset, target_class, include=True):
"""
Returns datasubset object of dataset.
If include = True, only data with target = target_class (anomalies)
else: only data without target = target_class (normal)
"""
targets = dataset.targets
idx = np.arange(0, len(dataset))
target_class = 0
if include:
idx_new = targets[idx]==target_class
else:
idx_new = targets[idx]!=target_class
# Only keep your desired classes
idx_new = idx[idx_new]
return Subset(dataset, idx_new)
def test_dataset_purity(dataset, anomaly_class):
anom = dataset[0][1] == anomaly_class
for i in range(len(dataset)):
if (dataset[i][1] == anomaly_class) != anom:
print("Not all data are anomalous/normal")
print("Found digit {} at index {}".format(dataset[i][1], i))
return
print("Dataset contains {} only".format("Anomalies" if anom else "normal data"))
def get_ckpt_path(checkpoint_dir, version_num=-1):
version = os.listdir(checkpoint_dir +"/lightning_logs")[version_num]
ckpt = os.listdir(checkpoint_dir +"/lightning_logs/" + version +"/checkpoints")[-1]
ckpt_path = checkpoint_dir +"/lightning_logs/" + version + "/checkpoints/" + ckpt
return ckpt_path
def aucPerformance(mse, labels):
roc_auc = roc_auc_score(labels, mse)
ap = average_precision_score(labels, mse)
print("AUC-ROC: %.4f, AUC-PR: %.4f" % (roc_auc, ap))
return roc_auc, ap;
def write_details(results_dir=os.getcwd(), initial_write=False, **kwargs):
# Create details.txt file to store experiment details
# res = experiment_folder(results_dir)
with open(results_dir + 'Details.txt', 'a') as the_file:
if initial_write:
the_file.write("Experimental details: ")
the_file.write('\n\n\n')
for arg,value in kwargs.items():
the_file.write(arg + '=' + str(value) + '\n')
return results_dir
def make_checkpoint_dir(results_dir):
# results_dir = experiment_folder(results_dir)
if "CheckpointsAE" not in os.listdir(results_dir):
os.mkdir(results_dir + "/CheckpointsAE")
if "CheckpointsAS" not in os.listdir(results_dir):
os.mkdir(results_dir + "/CheckpointsAS")
return results_dir+"CheckpointsAE", results_dir+"CheckpointsAS"
def experiment_folder(res):
pth = res + "/" + str(datetime.now()).split()[0] + " || "+ str(datetime.now()).split()[1].split(":")[0] + " | " + str(datetime.now()).split()[1].split(":")[1] + "/"
if os.path.isdir(pth) == True:
print("Results directory exists, creating temporary sub directory")
pth = pth + str(datetime.now()).split('.')[-1] + "/"
os.mkdir(pth)
print('Make Experiment Directory')
return pth
# Functions for .csv data loading from original FEAWAD and toolsdev files, from FEAWAD repo
# These have been packaged into a single function csv_data_setup, which returns everything Torch needs to build a dataset class
def get_data_dim(dataset, data_type):
"""
Takes dataset Str and data_type (currently only datatype 0 is accepted)
returns length of first item in dataset
"""
if '_normalization' not in dataset:
dataset += '_normalization'
if "spambase" in dataset:
labels_dim=1
else:
labels_dim=2
if data_type == 0 or '0':
if '.csv' not in dataset:
dataset += '.csv'
if './dataset/' not in dataset:
dataset = './dataset/' + dataset
try:
with open(dataset, 'r') as file:
print('Dataset found')
reader = csv.reader(file)
return len(next(iter(reader))) - labels_dim
except:
raise ValueError("File not found: {}".format(dataset))
def csv_data_setup(dataset_name, random_seed, data_format, input_path, cont_rate, known_outliers):
"""
This takes care of the data generation steps in the original FEAWAD implementation.
It returns numpy arrays of data and labels, in a format ready for a custom Torch dataset class to use
"""
nm = dataset_name
filename = nm.strip()
data_dim = get_data_dim(nm, 0) #### custom, was previously an argument
if data_format == 0 or data_format == '0':
x, labels = dataLoading(input_path + filename + ".csv", byte_num=data_dim) ####
else:
x, labels = get_data_from_svmlight_file(input_path + filename + ".svm")
x = x.tocsr()
outlier_indices = np.where(labels == 1)[0]
outliers = x[outlier_indices]
n_outliers_org = outliers.shape[0]
print("dataLoading input:", input_path + filename + ".csv", data_dim)
# print("train_test_split input labels:", labels)
train_x, test_x, train_label, test_label = train_test_split(x, labels, test_size=0.2, random_state=42, stratify = labels)
# print(filename + ': round ' + str(i))
outlier_indices = np.where(train_label == 1)[0]
inlier_indices = np.where(train_label == 0)[0]
n_outliers = len(outlier_indices)
print("Original training size: %d, Number of outliers in Train data:: %d" % (train_x.shape[0], n_outliers))
n_noise = len(np.where(train_label == 0)[0]) * cont_rate / (1. - cont_rate)
n_noise = int(n_noise)
rng = np.random.RandomState(random_seed)
if data_format == 0 or data_format == '0':
if n_outliers > known_outliers:
mn = n_outliers - known_outliers
remove_idx = rng.choice(outlier_indices, mn, replace=False)
train_x = np.delete(train_x, remove_idx, axis=0)
train_label = np.delete(train_label, remove_idx, axis=0)
#ae_label = train_x
noises = inject_noise(outliers, n_noise, random_seed)
train_x = np.append(train_x, noises, axis = 0)
train_label = np.append(train_label, np.zeros((noises.shape[0], 1)))
else: # Only format 0 is supported currently
if n_outliers > known_outliers:
mn = n_outliers - known_outliers
remove_idx = rng.choice(outlier_indices, mn, replace=False)
retain_idx = set(np.arange(train_x.shape[0])) - set(remove_idx)
retain_idx = list(retain_idx)
train_x = train_x[retain_idx]
train_label = train_label[retain_idx]
noises = inject_noise_sparse(outliers, n_noise, random_seed)
train_x = vstack([train_x, noises])
train_label = np.append(train_label, np.zeros((noises.shape[0], 1)))
outlier_indices = np.where(train_label == 1)[0]
inlier_indices = np.where(train_label == 0)[0]
return train_x, inlier_indices, outlier_indices, data_dim, test_x, test_label
# From toolsdev.py
def get_data_from_svmlight_file(path):
data = load_svmlight_file(path)
return data[0], data[1]
def dataLoading(path, byte_num):
# loading data
x=[]
labels=[]
with (open(path,'r')) as data_from:
csv_reader=csv.reader(data_from)
for i in csv_reader:
x.append(i[0:byte_num])
labels.append(i[byte_num])
for i in range(len(x)):
for j in range(byte_num):
x[i][j] = float(x[i][j])
for i in range(len(labels)):
labels[i] = float(labels[i])
x = np.array(x)
labels = np.array(labels)
return x, labels;
def aucPerformance(mse, labels):
try:
roc_auc = roc_auc_score(labels, mse)
except:
print('labels = ',labels, 'preds =', mse)
raise ValueError("roc_auc fails")
ap = average_precision_score(labels, mse)
# print("AUC-ROC: %.4f, AUC-PR: %.4f" % (roc_auc, ap))
return roc_auc, ap;
def writeResults(name, n_samples_trn, n_outliers, n_samples_test,test_outliers ,test_inliers, avg_AUC_ROC, avg_AUC_PR, std_AUC_ROC,std_AUC_PR, path):
csv_file = open(path, 'a')
row = name + "," + n_samples_trn + ','+n_outliers + ','+n_samples_test+','+test_outliers+','+test_inliers+','+avg_AUC_ROC+','+avg_AUC_PR+','+std_AUC_ROC+','+std_AUC_PR + "\n"
csv_file.write(row)
# Classes
class ae_unlabeled(Dataset):
def __init__(self, dataset_norm, dataset_anom, contaminant_prob, transform=None, target_transform=None):
self.dataset_norm = dataset_norm
self.dataset_anom = dataset_anom
self.contaminant_prob = contaminant_prob
def __len__(self):
return len(self.dataset_norm)
def __getitem__(self, idx):
if self.with_prob():
idx = np.random.randint(len(self.dataset_anom))
data = self.dataset_anom[idx][0]
else:
data = self.dataset_norm[idx][0]
# Autoencoders require target = input
return data, data
def with_prob(self):
if np.random.rand() < self.contaminant_prob:
return True
else:
return False
class anomaly_score(Dataset):
def __init__(self, dataset_norm, dataset_anom, known_anoms, contaminant_prob, transform=None, target_transform=None):
self.dataset_norm = dataset_norm
self.dataset_anom = dataset_anom
# Create list of k indices corresponding to elements in dataset_anom
self.eval = True
if known_anoms != 0:
self.eval = False
self.anom_indices = np.random.choice(np.arange(len(dataset_anom)), known_anoms, replace=False)
self.anom_pseudodataset = np.random.choice(self.anom_indices, len(dataset_norm), replace=True)
else:
self.anom_indices, self.anom_pseudodataset = [],[]
self.contaminant_prob = contaminant_prob
def __len__(self):
if self.eval:
return len(self.dataset_norm)
else:
return len(self.dataset_norm)*2
def __getitem__(self, idx):
# sample alternately from unlabeled and labeled_anomaly set
# unlabeled is a mix of normal data and a small probability of incorrectly labeled anomaly data
# labeled anomalies are found by first fixing a subset of known_anoms. Then creating an oversampled dataset by choosing len(dataset_norm) items from this list (with replacement)
if self.eval:
return self.get_item_eval(idx)
if idx//2 == idx/2:
if self.with_prob():
idx = self.get_contaminant()
data = self.dataset_anom[idx][0]
# print("Contaminant: {}".format(idx))
else:
data = self.dataset_norm[idx//2][0]
# print("norm: {}".format(idx))
return data, 0.0
else:
# print("anom: {}".format(self.anom_pseudodataset[idx]))
return self.dataset_anom[self.anom_pseudodataset[idx//2]][0], 1.0
def get_item_eval(self, idx):
if self.with_prob():
idx = self.get_contaminant()
data = self.dataset_anom[idx][0]
label = 1.0
# print("Contaminant: {}".format(idx))
else:
data = self.dataset_norm[idx][0]
label = 0.0
# print("norm: {}".format(idx))
return data, label
def with_prob(self):
if np.random.rand() < self.contaminant_prob:
return True
else:
return False
def get_contaminant(self):
idx = np.random.randint(len(self.dataset_anom))
while idx in self.anom_indices:
idx = np.random.randint(len(self.dataset_anom))
return idx
class AutoEncoder(pl.LightningModule):
def __init__(self, input_len):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(input_len, 400),
nn.ReLU(),
nn.Linear(400, 300),
nn.ReLU(),
nn.Linear(300, 200),
nn.ReLU(),
nn.Linear(200, 100),
nn.ReLU(),
nn.Linear(100, 50))
self.decoder = nn.Sequential(
nn.Linear(50, 100),
nn.ReLU(),
nn.Linear(100, 200),
nn.ReLU(),
nn.Linear(200, 300),
nn.ReLU(),
nn.Linear(300, 400),
nn.ReLU(),
nn.Linear(400, input_len),
)
self.preds, self.labels = np.array([]), np.array([])
def forward(self, data):
data = data.view(data.size(0), -1)
embedding = self.encoder(data)
recon = self.decoder(embedding)
return recon
def get_embedding(self, x):
return self.encoder(x)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def training_step(self, train_batch, batch_idx):
data, _ = train_batch
recon = self.forward(data)
loss = F.mse_loss(recon.view(recon.size(0), -1), data.view(data.size(0), -1))
self.log('ae_train_loss', loss)
return loss
def anomaly_prediction(self, batch):
recon = self.forward(batch)
square_error = (batch - recon)**2
mse = torch.mean(square_error, dim=list(range(1, len(square_error.shape))))
return mse
def validation_step(self, eval_batch, eval_batch_idx):
data, label = eval_batch
mse = self.anomaly_prediction(data).detach().cpu()
self.preds = np.append(self.preds, mse)
self.labels = np.append(self.labels, label.detach().cpu().numpy())
def get_aucs(self, log=True, return_current_vals=False):
if return_current_vals: # Does not interfere with val procedure, can be used seperately
return self.auc_roc, self.auc_pr
# Must be called after eval epoch to reset preds and labels
self.auc_roc, self.auc_pr = aucPerformance(mse=self.preds, labels=self.labels)
if log:
self.log("AUC_ROC", self.auc_roc)
self.log("AUC_pr", self.auc_pr)
self.preds = np.array([])
self.labels = np.array([])
class AnomalyScoreModel(pl.LightningModule):
def __init__(self, input_len, ae_hidden, ckpt_path, AEmodel_class):
super().__init__()
self.AEmodel = AEmodel_class.load_from_checkpoint(ckpt_path, input_len=input_len)
self.dense1 = nn.Linear(input_len+ae_hidden+1, 256)
self.dense2 = nn.Linear(256 + 1, 32)
self.dense3 = nn.Linear(32 + 1, 1)
self.relu = nn.ReLU()
self.preds = np.array([])
self.labels = np.array([])
self.auc_roc, self.auc_pr = 0,0
def get_encoding_and_recon(self, x):
x = x.view(x.size(0), -1)
enc = self.AEmodel.encoder(x)
rec = self.AEmodel.decoder(enc)
return enc, rec
def forward(self, data):
# running through AE is handled with seperate function for easy modification by inherited classes
enc, rec = self.get_encoding_and_recon(data)
residual = data - rec
residual = residual.view(residual.size(0), -1)
# print(residual.shape)
if len(enc.shape) == 1:
# Single item, not batch
recon_error = torch.linalg.norm(residual).unsqueeze(0)
else:
recon_error = torch.linalg.norm(residual, dim=1).unsqueeze(1)
# print(recon_error.shape)
# print("residual:{} | recon_error: {}".format(residual.shape, recon_error.shape))
residual_normalised = residual/recon_error #torch.div(residual, recon_error)
# print("residual_normalised:{}, enc:{}, recon:{}".format(residual_normalised.shape, enc.shape, recon_error.shape))
combined_input = torch.cat((residual_normalised.T, enc.T, recon_error.T)).T
out = self.dense1(combined_input)
out = self.relu(out)
# print("out:{} recon_error:{} cat:{}".format(out.shape, recon_error.shape, torch.cat((out, recon_error), dim=1).shape))
if len(data.shape) == 1:
out = torch.cat((out, recon_error), dim=0)
else:
out = torch.cat((out, recon_error), dim=1)
# print("cat:{}".format(out.shape))
out = self.dense2(out)
out = self.relu(out)
if len(data.shape) == 1:
out = self.dense3(torch.cat((out, recon_error), dim=0))
else:
out = self.dense3(torch.cat((out, recon_error), dim=1))
return out, recon_error
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def training_step(self, train_batch, batch_idx):
data, label = train_batch
pred, recon_error = self.forward(data)
loss = self.loss(pred, label, recon_error)
self.log("ae_recon_loss", recon_error.mean())
self.log('as_train_loss', loss)
return loss
def validation_step(self, eval_batch, eval_batch_idx):
data, label = eval_batch
preds = self.forward(data)[0].detach().cpu().numpy()
self.preds = np.append(self.preds, preds)
self.labels = np.append(self.labels, label.detach().cpu().numpy())
def get_aucs(self, log=True, return_current_vals=False):
if return_current_vals: # Does not interfere with val procedure, can be used seperately
return self.auc_roc, self.auc_pr
# Must be called after eval epoch to reset preds and labels
self.auc_roc, self.auc_pr = aucPerformance(self.preds, self.labels)
if log:
self.log("AUC_ROC", self.auc_roc)
self.log("AUC_pr", self.auc_pr)
self.preds = np.array([])
self.labels = np.array([])
def loss(self, pred, true, recon_error):
ae_loss = (1-true)*recon_error + true * torch.max(torch.zeros_like(recon_error), 5 - recon_error)
ae_loss = ae_loss.sum()
anomaly_score_loss = (1-true)*torch.abs(pred) + true * torch.max(torch.zeros_like(pred), 5 - pred)
anomaly_score_loss = anomaly_score_loss.sum()
return ae_loss + anomaly_score_loss
class auc(Callback):
def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Called when the val epoch ends."""
pl_module.get_aucs(log=True)
#
class ae_unlabelled_csv_data(Dataset):
def __init__(self, train_x, inlier_indices):
super(Dataset).__init__()
random_seed = 42
self.rng = np.random.RandomState(random_seed)
self.train_x = train_x
self.inlier_indices = inlier_indices
self.len = len(self.inlier_indices)
self.random_indices = np.random.permutation(self.len)
def __len__(self):
return self.len
def __getitem__(self, index):
rand_inx = self.random_indices[index]
data = self.train_x[self.inlier_indices[rand_inx]] # choose the sid'th inlier index. Then get the training data corresponding
return torch.Tensor(data), torch.Tensor(data) # For autoencoders
class anomaly_score_csv_data(Dataset):
def __init__(self, train_x, outlier_indices, inlier_indices):
super(Dataset).__init__()
random_seed = 42
self.rng = np.random.RandomState(random_seed)
self.train_x = train_x
self.inlier_indices = inlier_indices
self.outlier_indices = outlier_indices
self.random_indices_inliers = np.random.permutation(len(self.inlier_indices))
self.random_indices_outliers = np.random.choice(outlier_indices, len(inlier_indices), replace=True)
def __len__(self):
return len(self.random_indices_inliers) + len(self.random_indices_outliers)
def __getitem__(self, index):
if(index % 2 == 0):
data = self.train_x[self.random_indices_inliers[index//2]]
training_label = [0.0]
else:
data = self.train_x[self.random_indices_outliers[index//2]]
training_label = [1.0]
return torch.Tensor(data), torch.Tensor(training_label)
class anomaly_score_csv_data_eval(Dataset):
def __init__(self, test_x, test_labels):
super(Dataset).__init__()
self.test_x = test_x
self.test_labels = test_labels
def __len__(self):
return len(self.test_x)
def __getitem__(self, index):
data = self.test_x[index]
label = self.test_labels[index]
return torch.Tensor(data), torch.Tensor([label])