From da7b6e7d39628dc432dd845d9f95aa51c6b9fa06 Mon Sep 17 00:00:00 2001 From: xuhongzuo Date: Mon, 4 Sep 2023 10:39:04 +0800 Subject: [PATCH] add AnomalyTransformer --- .../models/time_series/anomalytransformer.py | 56 +- deepod/utils/utility.py | 1 + examples/data/feature.csv | 554 ------------------ examples/data/target.csv | 200 ------- examples/model_selection_fmms_example.py | 36 -- examples/utils.py | 256 -------- testbed/configs.py | 89 --- testbed/configs.yaml | 43 ++ testbed/testbed_unsupervised_tsad.py | 26 +- 9 files changed, 93 insertions(+), 1168 deletions(-) delete mode 100644 examples/data/feature.csv delete mode 100644 examples/data/target.csv delete mode 100644 examples/model_selection_fmms_example.py delete mode 100644 examples/utils.py delete mode 100644 testbed/configs.py create mode 100644 testbed/configs.yaml diff --git a/deepod/models/time_series/anomalytransformer.py b/deepod/models/time_series/anomalytransformer.py index 79322ae..4a93d68 100644 --- a/deepod/models/time_series/anomalytransformer.py +++ b/deepod/models/time_series/anomalytransformer.py @@ -4,9 +4,11 @@ import torch.nn.functional as F import numpy as np from torch.utils.data import DataLoader import math +import time from deepod.utils.utility import get_sub_seqs from deepod.core.base_model import BaseDeepAD + def my_kl_loss(p, q): res = p * (torch.log(p + 0.0001) - torch.log(q + 0.0001)) return torch.mean(torch.sum(res, dim=-1), dim=1) @@ -15,7 +17,7 @@ def my_kl_loss(p, q): class AnomalyTransformer(BaseDeepAD): def __init__(self, seq_len=100, stride=1, lr=0.0001, epochs=10, batch_size=32, epoch_steps=20, prt_steps=1, device='cuda', - k=3, input_c=25, output_c=25, anomaly_ratio=1, + k=3, anomaly_ratio=1, verbose=2, random_state=42): super(AnomalyTransformer, self).__init__( model_name='AnomalyTransformer', data_type='ts', epochs=epochs, batch_size=batch_size, lr=lr, @@ -24,31 +26,35 @@ class AnomalyTransformer(BaseDeepAD): verbose=verbose, random_state=random_state ) self.k = k - self.input_c = input_c - self.output_c = output_c self.anomaly_ratio = anomaly_ratio def fit(self, X, y=None): self.n_features = X.shape[1] train_seqs = get_sub_seqs(X, seq_len=self.seq_len, stride=self.stride) - self.model = AnomalyTransformerModel( + self.net = AnomalyTransformerModel( win_size=self.seq_len, - enc_in=self.input_c, - c_out=self.output_c, - e_layers=3).to(self.device) + enc_in=self.n_features, + c_out=self.n_features, + e_layers=3, + device=self.device + ).to(self.device) dataloader = DataLoader(train_seqs, batch_size=self.batch_size, shuffle=True, pin_memory=True) - self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.lr, weight_decay=1e-5) + self.optimizer = torch.optim.AdamW(self.net.parameters(), lr=self.lr, weight_decay=1e-5) self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=5, gamma=0.5) - self.model.train() + self.net.train() for e in range(self.epochs): + t1 = time.time() loss = self.training(dataloader) - print(f'Epoch {e + 1},\t L1 = {loss}') + if self.verbose >= 1 and (e == 0 or (e + 1) % self.prt_steps == 0): + print(f'epoch{e + 1:3d}, ' + f'training loss: {loss:.6f}, ' + f'time: {time.time() - t1:.1f}s') self.decision_scores_ = self.decision_function(X) self.labels_ = self._process_decision_scores() # in base model return @@ -58,7 +64,7 @@ class AnomalyTransformer(BaseDeepAD): dataloader = DataLoader(seqs, batch_size=self.batch_size, shuffle=False, drop_last=False) - self.model.eval() + self.net.eval() loss, _ = self.inference(dataloader) # (n,d) loss_final = np.mean(loss, axis=1) # (n,) @@ -76,7 +82,7 @@ class AnomalyTransformer(BaseDeepAD): self.optimizer.zero_grad() input = batch_x.float().to(self.device) - output, series, prior, _ = self.model(input) + output, series, prior, _ = self.net(input) # calculate Association discrepancy series_loss = 0.0 @@ -118,14 +124,14 @@ class AnomalyTransformer(BaseDeepAD): return np.average(loss_list) def inference(self, dataloader): - criterion = nn.MSELoss(reduce=False) + criterion = nn.MSELoss(reduction='none') temperature = 50 attens_energy = [] preds = [] for input_data in dataloader: # test_set input = input_data.float().to(self.device) - output, series, prior, _ = self.model(input) + output, series, prior, _ = self.net(input) loss = torch.mean(criterion(input, output), dim=-1) @@ -175,10 +181,9 @@ class AnomalyTransformer(BaseDeepAD): return -# Proposed Model class AnomalyTransformerModel(nn.Module): def __init__(self, win_size, enc_in, c_out, d_model=512, n_heads=8, e_layers=3, d_ff=512, - dropout=0.0, activation='gelu', output_attention=True): + dropout=0.0, activation='gelu', output_attention=True, device='cuda'): super(AnomalyTransformerModel, self).__init__() self.output_attention = output_attention @@ -190,7 +195,10 @@ class AnomalyTransformerModel(nn.Module): [ EncoderLayer( AttentionLayer( - AnomalyAttention(win_size, False, attention_dropout=dropout, output_attention=output_attention), + AnomalyAttention(win_size, False, + attention_dropout=dropout, output_attention=output_attention, + device=device + ), d_model, n_heads), d_model, d_ff, @@ -323,16 +331,17 @@ class TriangularCausalMask(): class AnomalyAttention(nn.Module): - def __init__(self, win_size, mask_flag=True, scale=None, attention_dropout=0.0, output_attention=False): + def __init__(self, win_size, mask_flag=True, scale=None, + attention_dropout=0.0, output_attention=False, device='cuda'): super(AnomalyAttention, self).__init__() self.scale = scale self.mask_flag = mask_flag self.output_attention = output_attention self.dropout = nn.Dropout(attention_dropout) - window_size = win_size - self.distances = torch.zeros((window_size, window_size)).cuda() - for i in range(window_size): - for j in range(window_size): + self.device = device + self.distances = torch.zeros((win_size, win_size)).to(device) + for i in range(win_size): + for j in range(win_size): self.distances[i][j] = abs(i - j) def forward(self, queries, keys, values, sigma, attn_mask): @@ -352,9 +361,8 @@ class AnomalyAttention(nn.Module): sigma = torch.sigmoid(sigma * 5) + 1e-5 sigma = torch.pow(3, sigma) - 1 sigma = sigma.unsqueeze(-1).repeat(1, 1, 1, window_size) # B H L L - prior = self.distances.unsqueeze(0).unsqueeze(0).repeat(sigma.shape[0], sigma.shape[1], 1, 1).cuda() + prior = self.distances.unsqueeze(0).unsqueeze(0).repeat(sigma.shape[0], sigma.shape[1], 1, 1) prior = 1.0 / (math.sqrt(2 * math.pi) * sigma) * torch.exp(-prior ** 2 / 2 / (sigma ** 2)) - series = self.dropout(torch.softmax(attn, dim=-1)) V = torch.einsum("bhls,bshd->blhd", series, values) diff --git a/deepod/utils/utility.py b/deepod/utils/utility.py index dea2e7e..f3c5260 100644 --- a/deepod/utils/utility.py +++ b/deepod/utils/utility.py @@ -27,6 +27,7 @@ def get_sub_seqs(x_arr, seq_len=100, stride=1): return x_seqs + def get_sub_seqs_label(y, seq_len=100, stride=1): """ diff --git a/examples/data/feature.csv b/examples/data/feature.csv deleted file mode 100644 index b0361c1..0000000 --- a/examples/data/feature.csv +++ /dev/null @@ -1,554 +0,0 @@ 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diff --git a/examples/model_selection_fmms_example.py b/examples/model_selection_fmms_example.py deleted file mode 100644 index 0c9b453..0000000 --- a/examples/model_selection_fmms_example.py +++ /dev/null @@ -1,36 +0,0 @@ -from deepod.model_selection.fmms import FMMS -import os -import numpy as np -import pandas as pd -from sklearn.impute import SimpleImputer - - -def get_data(feature_file, target_file): - df = pd.read_csv(target_file) - Y = df.values[:, 1:].astype(np.float64) - imp = SimpleImputer(missing_values=np.nan, strategy='mean') - Y = imp.fit(Y).transform(Y).T - - df = pd.read_csv(feature_file) - df = df.drop(['Data'], axis=1) - df = df.fillna(0) - - df = (df - df.min()) / (df.max() - df.min()) - df = df.fillna(0) - X = df.values.astype(np.float32) - - return Y, X - - -if __name__ == '__main__': - random_state = 0 - feature_f = os.path.join("data/feature.csv") - target_f = os.path.join("data/target.csv") - p_train, f_train = get_data(feature_f, target_f) - f_test = np.array([[1.227677502, 0.547297297, 0.25, 0.013513514, 0.234693979, 0.121621622, 8.222222222, 28.71360895, 9.512437578, - -0.52749456, 13.58201658, 0.817380952, 0.758373016, 0.843551587, 0.829662698, 0.61265873, 0.54952381, -2.106840516, - 2.106840516, 2.890371758, 4.997212274, 15, 4, 18, 0, 148, 0, 0, 3, 0.446808511, -0.350354369, -0.558601676, - 0, 0, 0, 5, 0.2, 5.387046595, 2.302843498, 0.330402533, 2.208985222, 8, 2.933333333, 2, 1.569146973, 44]]) - rfmms = FMMS() - rfmms.fit(f_train, p_train, save_path='./data/fmms.pt') - rfmms.predict(f=f_test, topn=5, load_path='./data/fmms.pt') diff --git a/examples/utils.py b/examples/utils.py deleted file mode 100644 index 4037c61..0000000 --- a/examples/utils.py +++ /dev/null @@ -1,256 +0,0 @@ -import os -import sys -import numpy as np -import pandas as pd -from tqdm import tqdm -from sklearn.preprocessing import MinMaxScaler -from sklearn import metrics -from sklearn.model_selection import train_test_split -from scipy.io import arff -from glob import glob -import datetime - - -def read_data(file, split='50%-normal', normalization='z-score', seed=42): - """ - read data from files, normalization, and perform train-test splitting - - Parameters - ---------- - file: str - file path of dataset - - split: str (default='50%-normal', choice=['50%-normal', '60%', 'none']) - training-testing set splitting methods: - - if '50%-normal': use half of the normal data as training set, - and the other half attached with anomalies as testing set, - this splitting method is used in self-supervised studies GOAD [ICLR'20], NeuTraL [ICML'21] - - if 'none': use the whole set as both the training and testing set - This is commonly used in traditional methods. - - if '60%': use 60% data during training and the rest 40% data in testing, - while keeping the original anomaly ratio. - - normalization: str (default='z-score', choice=['z-score', 'min-max']) - - seed: int (default=42) - random seed - """ - - if file.endswith('.npz'): - data = np.load(file, allow_pickle=True) - x, y = data['X'], data['y'] - y = np.array(y, dtype=int) - else: - if file.endswith('pkl'): - func = pd.read_pickle - elif file.endswith('csv'): - func = pd.read_csv - elif file.endswith('arff'): - def func(f): - df_ = pd.DataFrame(arff.loadarff(f)[0]) - df_ = df_.replace({b'no': 0, b'yes': 1}) - df_ = df_.drop('id', axis=1) - return df_ - else: - raise NotImplementedError('') - - df = func(file) - df.replace([np.inf, -np.inf], np.nan, inplace=True) - df.fillna(method='ffill', inplace=True) - x = df.values[:, :-1] - y = np.array(df.values[:, -1], dtype=int) - - # train-test splitting - if split == '50%-normal': - rng = np.random.RandomState(seed) - idx = rng.permutation(np.arange(len(x))) - x, y = x[idx], y[idx] - - norm_idx = np.where(y==0)[0] - anom_idx = np.where(y==1)[0] - split = int(0.5 * len(norm_idx)) - train_norm_idx, test_norm_idx = norm_idx[:split], norm_idx[split:] - - x_train = x[train_norm_idx] - y_train = y[train_norm_idx] - - x_test = x[np.hstack([test_norm_idx, anom_idx])] - y_test = y[np.hstack([test_norm_idx, anom_idx])] - - print(f'Original size: [{x.shape}], Normal/Anomaly: [{len(norm_idx)}/{len(anom_idx)}] \n' - f'After splitting: training/testing [{len(x_train)}/{len(x_test)}]') - - elif split == '60%': - x_train, y_train, x_test, y_test = train_test_split(x, y, shuffle=True, random_state=seed, - test_size=0.4, stratify=y) - - else: - x_train, x_test = x.copy(), x.copy() - y_train, y_test = y.copy(), y.copy() - - # normalization - if normalization == 'min-max': - minmax_scaler = MinMaxScaler() - minmax_scaler.fit(x_train) - x_train = minmax_scaler.transform(x_train) - x_test = minmax_scaler.transform(x_test) - - elif normalization == 'z-score': - mus = np.mean(x_train, axis=0) - sds = np.std(x_train, axis=0) - sds[sds == 0] = 1 - x_train = np.array([(xx - mus) / sds for xx in x_train]) - x_test = np.array([(xx - mus) / sds for xx in x_test]) - - elif normalization == 'scale': - x_train = x_train / 255 - x_test = x_test / 255 - - return x_train, y_train, x_test, y_test - - -def min_max_normalize(x): - filter_lst = [] - for k in range(x.shape[1]): - s = np.unique(x[:, k]) - if len(s) <= 1: - filter_lst.append(k) - if len(filter_lst) > 0: - print('remove features', filter_lst) - x = np.delete(x, filter_lst, 1) - - from sklearn.preprocessing import MinMaxScaler - - scaler = MinMaxScaler() - scaler.fit(x) - x = scaler.transform(x) - - return x - - -def evaluate(y_true, scores): - """calculate evaluation metrics""" - roc_auc = metrics.roc_auc_score(y_true, scores) - ap = metrics.average_precision_score(y_true, scores) - - # F1@k, using real percentage to calculate F1-score - ratio = 100.0 * len(np.where(y_true==0)[0]) / len(y_true) - thresh = np.percentile(scores, ratio) - y_pred = (scores >= thresh).astype(int) - y_true = y_true.astype(int) - precision, recall, f_score, support = metrics.precision_recall_fscore_support(y_true, y_pred, average='binary') - - return roc_auc, ap, f_score - - -def get_data_lst(dataset_dir, dataset): - if dataset == 'FULL': - print(os.path.join(dataset_dir, '*.*')) - data_lst = glob(os.path.join(dataset_dir, '*.*')) - else: - name_lst = dataset.split(',') - data_lst = [] - for d in name_lst: - data_lst.extend(glob(os.path.join(dataset_dir, d + '.*'))) - data_lst = sorted(data_lst) - return data_lst - - -def add_irrelevant_features(x, ratio, seed=None): - n_samples, n_f = x.shape - size = int(ratio * n_f) - - irr_new = np.zeros([n_samples, size]) - np.random.seed(seed) - for i in tqdm(range(size)): - irr_new[:, i] = np.random.rand(n_samples) - - # irr_new = np.zeros([n_samples, size]) - # np.random.seed(seed) - # for i in range(size): - # array = x[:, np.random.choice(x.shape[1], 1)] - # new_array = array[np.random.permutation(n_samples)].flatten() - # irr_new[:, i] = new_array - - x_new = np.hstack([x, irr_new]) - - return x_new - - -def adjust_contamination(x, y, contamination_r, swap_ratio=0.05, random_state=42): - """ - add/remove anomalies in training data to replicate anomaly contaminated data sets. - randomly swap 5% features of two anomalies to avoid duplicate contaminated anomalies. - """ - rng = np.random.RandomState(random_state) - - anom_idx = np.where(y == 1)[0] - norm_idx = np.where(y == 0)[0] - n_cur_anom = len(anom_idx) - n_adj_anom = int(len(norm_idx) * contamination_r / (1. - contamination_r)) - - # x_train = np.delete(x_train, unknown_anom_idx, axis=0) - # y_train = np.delete(y_train, unknown_anom_idx, axis=0) - # noises = inject_noise(true_anoms, n_adj_noise, 42) - # x_train = np.append(x_train, noises, axis=0) - # y_train = np.append(y_train, np.zeros((noises.shape[0], 1))) - - # inject noise - if n_cur_anom < n_adj_anom: - n_inj_noise = n_adj_anom - n_cur_anom - print(f'Control Contamination Rate: injecting [{n_inj_noise}] Noisy samples') - - seed_anomalies = x[anom_idx] - - n_sample, dim = seed_anomalies.shape - n_swap_feat = int(swap_ratio * dim) - inj_noise = np.empty((n_inj_noise, dim)) - for i in np.arange(n_inj_noise): - idx = rng.choice(n_sample, 2, replace=False) - o1 = seed_anomalies[idx[0]] - o2 = seed_anomalies[idx[1]] - swap_feats = rng.choice(dim, n_swap_feat, replace=False) - inj_noise[i] = o1.copy() - inj_noise[i, swap_feats] = o2[swap_feats] - - x = np.append(x, inj_noise, axis=0) - y = np.append(y, np.ones(n_inj_noise)) - - # remove noise - elif n_cur_anom > n_adj_anom: - n_remove = n_cur_anom - n_adj_anom - print(f'Control Contamination Rate: Removing [{n_remove}] Noise') - - remove_id = anom_idx[rng.choice(n_cur_anom, n_remove, replace=False)] - print(x.shape) - - x = np.delete(x, remove_id, 0) - y = np.delete(y, remove_id, 0) - print(x.shape) - - return x, y - - -def make_print_to_file(path='./'): - - class Logger(object): - def __init__(self, filename="Default.log", path="./"): - self.terminal = sys.stdout - self.log = open(os.path.join(path, filename), "a", encoding='utf8', ) - - def write(self, message): - self.terminal.write(message) - self.log.write(message) - - def flush(self): - pass - - fileName = datetime.datetime.now().strftime('day' + '%Y_%m_%d') - sys.stdout = Logger(fileName + '.log', path=path) - - ############################################################# - # 这里输出之后的所有的输出的print 内容即将写入日志 - ############################################################# - print(fileName.center(60, '*')) - - diff --git a/testbed/configs.py b/testbed/configs.py deleted file mode 100644 index c26ad28..0000000 --- a/testbed/configs.py +++ /dev/null @@ -1,89 +0,0 @@ -from deepod.models.time_series import * -# from deepod.models.time_series.seq_reg_ad import SeqRegAD - - -def get_model_class(name): - if name == 'dif': - return DeepIsolationForestTS - elif name == 'dsvdd': - return DeepSVDDTS - elif name == 'tranad': - return TranAD - elif name == 'usad': - return USAD - elif name == 'couta': - return COUTA - elif name == 'anomalytransformer': - return AnomalyTransformer - # elif name == 'seqregad': - # return SeqRegAD - - -def get_additional_configs(model_name, ds_name): - n_dims = {"SMAP": 25, "SMD": 38, "ASD": 19} - config_dict = { - # 'seqregad': { - # 'seq_len_lst': [10, 30, 50], - # 'epochs': 10, - # 'batch_size': 64, - # 'lr': 1e-4, - # 'rep_dim': 128, - # }, - - 'dif': { - 'rep_dim': 20, - 'hidden_dims': 32, - 'n_ensemble': 50, - 'n_estimators': 6, - }, - - 'dsvdd': { - 'network': 'Transformer', - 'rep_dim': 64, - 'hidden_dims': '512', - 'act': 'GELU', - 'lr': 1e-5, - 'epochs': 20, - 'batch_size': 128, - 'epoch_steps': -1, - }, - - 'tranad': { - 'lr': 1e-3, - 'epochs': 10, - 'batch_size': 128, - 'epoch_steps': -1, - }, - - 'usad': { - 'hidden_dims': 100, - 'lr': 1e-3, - 'epochs': 10, - 'batch_size': 128, - }, - - 'couta': { - 'neg_batch_ratio': 0.2, - 'alpha': 0.1, - 'rep_dim': 16, - 'hidden_dims': 16, - 'lr': 1e-4, - 'epochs': 20, - 'batch_size': 64, - }, - - 'anomalytransformer': { - 'lr': 1e-4, - 'epochs': 10, - 'batch_size': 32, - 'k': 3, - 'input_c': n_dims[ds_name], - 'output_c': n_dims[ds_name], - 'anomaly_ratio': 1 - } - } - - try: - return config_dict[model_name] - except KeyError: - return {} diff --git a/testbed/configs.yaml b/testbed/configs.yaml new file mode 100644 index 0000000..5fba530 --- /dev/null +++ b/testbed/configs.yaml @@ -0,0 +1,43 @@ +DIF: + rep_dim: 20 + hidden_dims: 32 + n_ensemble: 50 + n_estimators: 6 + +DeepSVDDTS: + network: Transformer + rep_dim: 64 + hidden_dims: 512 + act: GELU + lr: 0.00001 + epochs: 20 + batch_size: 128 + epoch_steps: -1 + +TranAD: + lr: 0.001 + epochs: 10 + batch_size: 128 + epoch_steps: -1 + +USAD: + hidden_dims: 100 + lr: 1e-3 + epochs: 10 + batch_size: 128 + +COUTA: + neg_batch_ratio: 0.2 + alpha: 0.1 + rep_dim: 16 + hidden_dims: 16 + lr: 0.0001 + epochs: 20 + batch_size: 64 + +AnomalyTransformer: + lr: 0.0001 + epochs: 10 + batch_size: 32 + k: 3 + anomaly_ratio: 1 diff --git a/testbed/testbed_unsupervised_tsad.py b/testbed/testbed_unsupervised_tsad.py index 3ba2305..3518c80 100644 --- a/testbed/testbed_unsupervised_tsad.py +++ b/testbed/testbed_unsupervised_tsad.py @@ -7,10 +7,11 @@ testbed of unsupervised time series anomaly detection import os import argparse import getpass +import yaml import time +import importlib as imp import numpy as np -from testbed import utils -import configs +import utils dataset_root = f'/home/{getpass.getuser()}/dataset/5-TSdata/_processed_data/' @@ -21,17 +22,15 @@ parser.add_argument("--runs", type=int, default=5, help="how many times we repeat the experiments to obtain the average performance") parser.add_argument("--output_dir", type=str, default='@records/', help="the output file path") - parser.add_argument("--dataset", type=str, default='ASD,SMAP,MSL', ) - parser.add_argument("--entities", type=str, default='FULL', help='FULL represents all the csv file in the folder, or a list of entity names split by comma' ) parser.add_argument("--entity_combined", type=int, default=1) -parser.add_argument("--model", type=str, default='anomalytransformer', help="") # anomalytransformer +parser.add_argument("--model", type=str, default='AnomalyTransformer', help="") parser.add_argument('--silent_header', action='store_true') parser.add_argument("--flag", type=str, default='') @@ -42,8 +41,17 @@ parser.add_argument('--stride', type=int, default=10) args = parser.parse_args() -model_class = configs.get_model_class(args.model) -model_configs = configs.get_additional_configs(args.model, args.dataset) +module = imp.import_module('deepod.models.time_series') +model_class = getattr(module, args.model) + +path = 'configs.yaml' +with open(path) as f: + d = yaml.safe_load(f) + try: + model_configs = d[args.model] + except KeyError: + print(f'config file does not contain default parameter settings of {args.model}') + model_configs = {} model_configs['seq_len'] = args.seq_len model_configs['stride'] = args.stride @@ -91,8 +99,8 @@ for dataset in dataset_name_lst: t1 = time.time() clf = model_class(**model_configs, random_state=42+i) - clf.fit(train_data) # 主要改这里 - scores = clf.decision_function(test_data) # scores(n,) test_data(n,d) + clf.fit(train_data) + scores = clf.decision_function(test_data) t = time.time() - t1 eval_metrics = utils.get_metrics(labels, scores)