diff --git a/config.ini b/config.ini new file mode 100644 index 0000000..c34dd78 --- /dev/null +++ b/config.ini @@ -0,0 +1,50 @@ +[Dataset] +# 填写数据集名称（文件夹名称），同时训练测试多个数据集，请用逗号隔开 +# 请确保数据集已经按照要求格式放置在./dataset路径下 +name=SWAT + +[Method] +# 填写模型名称（文件名称，不用写".py"），同时训练测试多个算法，请用逗号隔开 +# 请确保模型文件已经按照要求格式放置在./model路径下 +# 模型名称请全部由字母组成 +name=AnomalyTransformer + +[Preprocess] +# 填写预处理方法名称（文件名称，不用写".py"） +# 请确保预处理方法文件已经按照要求格式放置在./preprocess路径下 +name=standardization + +[Evaluation] +# 填写评估方法名称（文件名称，不用写".py"），同时使用多个评估方法，请用逗号隔开 +# 请确保评估方法文件已经按照要求格式放置在./evaluation路径下 +name=f1,f1pa,ftad,affiliation + +[ModelPath] +# 模型加载先前参数，请用"模型名_数据集名"作为变量名 +# 路径名称从TSAD文件夹下开始写相对路径，或者直接写绝对路径 +MtadGatAtt_SWAT=none + +[BaseParameters] +# train表示是否训练模型，请填写true或者false，为false时，仅测试模型 +train=true +# epoch表示训练轮数，请填写正整数 +epochs=20 +# batch-size表示每个batch的尺寸，请填写正整数 +batch_size=32 +# learning-rate表示学习率，建议不要高于0.001 +learning_rate=1e-4 +# device表示使用的设备，可以选auto、cpu、cuda，其中auto代表自动判断，有gpu使用gpu，没有则使用cpu；选gpu时需要选择gpu序号，例如cuda:0 +device=auto + +[CustomParameters] +# 此处可以填入自定义参数，数量不限，用于填写特殊需求输入，例如模型文件所需的所有外部参数，构建数据集的部分参数 +# 输入数据长度input_size，即截取数据的窗口长度 +input_size=100 +# 输出数据长度output_size +output_size=1 +# 截取数据的窗口的移动步长 +step=1 +# 数据集文件（csv）中，第一行是否需要忽略（第一行为列名的话需要忽略） +del_column_name=true +# 数据集文件（csv）中，第一列是否是时间戳 +time_index=true diff --git a/daemon.py b/daemon.py new file mode 100644 index 0000000..4b3cf0b --- /dev/null +++ b/daemon.py @@ -0,0 +1,250 @@ +''' +*** +Modified generic daemon class +*** + +Author: http://www.jejik.com/articles/2007/02/ + a_simple_unix_linux_daemon_in_python/www.boxedice.com + +License: http://creativecommons.org/licenses/by-sa/3.0/ + +Changes: 23rd Jan 2009 (David Mytton ) + - Replaced hard coded '/dev/null in __init__ with os.devnull + - Added OS check to conditionally remove code that doesn't + work on OS X + - Added output to console on completion + - Tidied up formatting + 11th Mar 2009 (David Mytton ) + - Fixed problem with daemon exiting on Python 2.4 + (before SystemExit was part of the Exception base) + 13th Aug 2010 (David Mytton + - Fixed unhandled exception if PID file is empty +''' + +# Core modules +from __future__ import print_function +import atexit +import errno +import os +import sys +import time +import signal + + +class Daemon(object): + """ + A generic daemon class. + + Usage: subclass the Daemon class and override the run() method + """ + def __init__(self, pidfile, stdin=os.devnull, + stdout=os.devnull, stderr=os.devnull, + home_dir='.', umask=0o22, verbose=1, + use_gevent=False, use_eventlet=False): + self.stdin = stdin + self.stdout = stdout + self.stderr = stderr + self.pidfile = pidfile + self.home_dir = home_dir + self.verbose = verbose + self.umask = umask + self.daemon_alive = True + self.use_gevent = use_gevent + self.use_eventlet = use_eventlet + + def log(self, *args): + if self.verbose >= 1: + print(*args) + + def daemonize(self): + """ + Do the UNIX double-fork magic, see Stevens' "Advanced + Programming in the UNIX Environment" for details (ISBN 0201563177) + http://www.erlenstar.demon.co.uk/unix/faq_2.html#SEC16 + """ + if self.use_eventlet: + import eventlet.tpool + eventlet.tpool.killall() + try: + pid = os.fork() + if pid > 0: + # Exit first parent + sys.exit(0) + except OSError as e: + sys.stderr.write( + "fork #1 failed: %d (%s)\n" % (e.errno, e.strerror)) + sys.exit(1) + + # Decouple from parent environment + os.chdir(self.home_dir) + os.setsid() + os.umask(self.umask) + + # Do second fork + try: + pid = os.fork() + if pid > 0: + # Exit from second parent + sys.exit(0) + except OSError as e: + sys.stderr.write( + "fork #2 failed: %d (%s)\n" % (e.errno, e.strerror)) + sys.exit(1) + + if sys.platform != 'darwin': # This block breaks on OS X + # Redirect standard file descriptors + sys.stdout.flush() + sys.stderr.flush() + si = open(self.stdin, 'r') + so = open(self.stdout, 'a+') + if self.stderr: + try: + se = open(self.stderr, 'a+', 0) + except ValueError: + # Python 3 can't have unbuffered text I/O + se = open(self.stderr, 'a+', 1) + else: + se = so + os.dup2(si.fileno(), sys.stdin.fileno()) + os.dup2(so.fileno(), sys.stdout.fileno()) + os.dup2(se.fileno(), sys.stderr.fileno()) + + def sigtermhandler(signum, frame): + self.daemon_alive = False + sys.exit() + + if self.use_gevent: + import gevent + gevent.reinit() + gevent.signal(signal.SIGTERM, sigtermhandler, signal.SIGTERM, None) + gevent.signal(signal.SIGINT, sigtermhandler, signal.SIGINT, None) + else: + signal.signal(signal.SIGTERM, sigtermhandler) + signal.signal(signal.SIGINT, sigtermhandler) + + self.log("Started") + + # Write pidfile + atexit.register( + self.delpid) # Make sure pid file is removed if we quit + pid = str(os.getpid()) + open(self.pidfile, 'w+').write("%s\n" % pid) + + def delpid(self): + try: + # the process may fork itself again + pid = int(open(self.pidfile, 'r').read().strip()) + if pid == os.getpid(): + os.remove(self.pidfile) + except OSError as e: + if e.errno == errno.ENOENT: + pass + else: + raise + + def start(self, *args, **kwargs): + """ + Start the daemon + """ + + self.log("Starting...") + + # Check for a pidfile to see if the daemon already runs + try: + pf = open(self.pidfile, 'r') + pid = int(pf.read().strip()) + pf.close() + except IOError: + pid = None + except SystemExit: + pid = None + + if pid: + message = "pidfile %s already exists. Is it already running?\n" + sys.stderr.write(message % self.pidfile) + sys.exit(1) + + # Start the daemon + self.daemonize() + self.run(*args, **kwargs) + + def stop(self): + """ + Stop the daemon + """ + + if self.verbose >= 1: + self.log("Stopping...") + + # Get the pid from the pidfile + pid = self.get_pid() + + if not pid: + message = "pidfile %s does not exist. Not running?\n" + sys.stderr.write(message % self.pidfile) + + # Just to be sure. A ValueError might occur if the PID file is + # empty but does actually exist + if os.path.exists(self.pidfile): + os.remove(self.pidfile) + + sys.exit(0) # Not an error in a restart + + # Try killing the daemon process + try: + i = 0 + while 1: + os.kill(pid, signal.SIGTERM) + time.sleep(0.1) + i = i + 1 + if i % 10 == 0: + os.kill(pid, signal.SIGHUP) + except OSError as err: + if err.errno == errno.ESRCH: + if os.path.exists(self.pidfile): + os.remove(self.pidfile) + else: + print(str(err)) + sys.exit(1) + + self.log("Stopped") + + + def restart(self): + """ + Restart the daemon + """ + self.stop() + self.start() + + def get_pid(self): + try: + pf = open(self.pidfile, 'r') + pid = int(pf.read().strip()) + pf.close() + except IOError: + pid = None + except SystemExit: + pid = None + return pid + + def is_running(self): + pid = self.get_pid() + + if pid is None: + self.log('Process is stopped') + return False + elif os.path.exists('/proc/%d' % pid): + self.log('Process (pid %d) is running...' % pid) + return True + else: + self.log('Process (pid %d) is killed' % pid) + return False + + def run(self): + """ + You should override this method when you subclass Daemon. + It will be called after the process has been + daemonized by start() or restart(). + """ + raise NotImplementedError diff --git a/evaluation/__init__.py b/evaluation/__init__.py new file mode 100644 index 0000000..95af832 --- /dev/null +++ b/evaluation/__init__.py @@ -0,0 +1,4 @@ +from .ftad import evaluate +from .f1pa import evaluate +from .affiliation import evaluate +from .f1 import evaluate diff --git a/evaluation/affiliation.py b/evaluation/affiliation.py new file mode 100644 index 0000000..362330e --- /dev/null +++ b/evaluation/affiliation.py @@ -0,0 +1,23 @@ +from .affiliation_bin.generics import convert_vector_to_events +from .affiliation_bin.metrics import pr_from_events + + +def evaluate(y_true: list, y_pred: list) -> float: + """ + F1PA评估方法，经过point adjust调整标签后再用F1评分 + :param y_true: 真实标签 + :param y_pred: 检测标签 + :return: affiliation的三个score + """ + true, pred = y_true.copy(), y_pred.copy() + events_pred = convert_vector_to_events(pred) + events_gt = convert_vector_to_events(true) + Trange = (0, len(pred)) + + res = pr_from_events(events_pred, events_gt, Trange) + aff_precision = res["precision"] + aff_recall = res["recall"] + if aff_recall == 0 or aff_precision == 0: + return 0 + aff_f1 = 2 * aff_precision * aff_recall / (aff_precision + aff_recall) + return aff_f1 \ No newline at end of file diff --git a/evaluation/affiliation_bin/__init__.py b/evaluation/affiliation_bin/__init__.py new file mode 100644 index 0000000..5c6c8d7 --- /dev/null +++ b/evaluation/affiliation_bin/__init__.py @@ -0,0 +1 @@ +from .metrics import pr_from_events \ No newline at end of file diff --git a/evaluation/affiliation_bin/_affiliation_zone.py b/evaluation/affiliation_bin/_affiliation_zone.py new file mode 100644 index 0000000..7e6370b --- /dev/null +++ b/evaluation/affiliation_bin/_affiliation_zone.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +from ._integral_interval import interval_intersection + + +def t_start(j, Js=[(1, 2), (3, 4), (5, 6)], Trange=(1, 10)): + """ + Helper for `E_gt_func` + + :param j: index from 0 to len(Js) (included) on which to get the start + :param Js: ground truth events, as a list of couples + :param Trange: range of the series where Js is included + :return: generalized start such that the middle of t_start and t_stop + always gives the affiliation_bin zone + """ + b = max(Trange) + n = len(Js) + if j == n: + return (2 * b - t_stop(n - 1, Js, Trange)) + else: + return (Js[j][0]) + + +def t_stop(j, Js=[(1, 2), (3, 4), (5, 6)], Trange=(1, 10)): + """ + Helper for `E_gt_func` + + :param j: index from 0 to len(Js) (included) on which to get the stop + :param Js: ground truth events, as a list of couples + :param Trange: range of the series where Js is included + :return: generalized stop such that the middle of t_start and t_stop + always gives the affiliation_bin zone + """ + if j == -1: + a = min(Trange) + return (2 * a - t_start(0, Js, Trange)) + else: + return (Js[j][1]) + + +def E_gt_func(j, Js, Trange): + """ + Get the affiliation_bin zone of element j of the ground truth + + :param j: index from 0 to len(Js) (excluded) on which to get the zone + :param Js: ground truth events, as a list of couples + :param Trange: range of the series where Js is included, can + be (-math.inf, math.inf) for distance measures + :return: affiliation_bin zone of element j of the ground truth represented + as a couple + """ + range_left = (t_stop(j - 1, Js, Trange) + t_start(j, Js, Trange)) / 2 + range_right = (t_stop(j, Js, Trange) + t_start(j + 1, Js, Trange)) / 2 + return ((range_left, range_right)) + + +def get_all_E_gt_func(Js, Trange): + """ + Get the affiliation_bin partition from the ground truth point of view + + :param Js: ground truth events, as a list of couples + :param Trange: range of the series where Js is included, can + be (-math.inf, math.inf) for distance measures + :return: affiliation_bin partition of the events + """ + # E_gt is the limit of affiliation_bin/attraction for each ground truth event + E_gt = [E_gt_func(j, Js, Trange) for j in range(len(Js))] + return (E_gt) + + +def affiliation_partition(Is=[(1, 1.5), (2, 5), (5, 6), (8, 9)], E_gt=[(1, 2.5), (2.5, 4.5), (4.5, 10)]): + """ + Cut the events into the affiliation_bin zones + The presentation given here is from the ground truth point of view, + but it is also used in the reversed direction in the main function. + + :param Is: events as a list of couples + :param E_gt: range of the affiliation_bin zones + :return: a list of list of intervals (each interval represented by either + a couple or None for empty interval). The outer list is indexed by each + affiliation_bin zone of `E_gt`. The inner list is indexed by the events of `Is`. + """ + out = [None] * len(E_gt) + for j in range(len(E_gt)): + E_gt_j = E_gt[j] + discarded_idx_before = [I[1] < E_gt_j[0] for I in Is] # end point of predicted I is before the begin of E + discarded_idx_after = [I[0] > E_gt_j[1] for I in Is] # start of predicted I is after the end of E + kept_index = [not (a or b) for a, b in zip(discarded_idx_before, discarded_idx_after)] + Is_j = [x for x, y in zip(Is, kept_index)] + out[j] = [interval_intersection(I, E_gt[j]) for I in Is_j] + return (out) \ No newline at end of file diff --git a/evaluation/affiliation_bin/_integral_interval.py b/evaluation/affiliation_bin/_integral_interval.py new file mode 100644 index 0000000..6584c01 --- /dev/null +++ b/evaluation/affiliation_bin/_integral_interval.py @@ -0,0 +1,493 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import math +from .generics import _sum_wo_nan + +""" +In order to shorten the length of the variables, +the general convention in this file is to let: + - I for a predicted event (start, stop), + - Is for a list of predicted events, + - J for a ground truth event, + - Js for a list of ground truth events. +""" + + +def interval_length(J=(1, 2)): + """ + Length of an interval + + :param J: couple representating the start and stop of an interval, or None + :return: length of the interval, and 0 for a None interval + """ + if J is None: + return (0) + return (J[1] - J[0]) + + +def sum_interval_lengths(Is=[(1, 2), (3, 4), (5, 6)]): + """ + Sum of length of the intervals + + :param Is: list of intervals represented by starts and stops + :return: sum of the interval length + """ + return (sum([interval_length(I) for I in Is])) + + +def interval_intersection(I=(1, 3), J=(2, 4)): + """ + Intersection between two intervals I and J + I and J should be either empty or represent a positive interval (no point) + + :param I: an interval represented by start and stop + :param J: a second interval of the same form + :return: an interval representing the start and stop of the intersection (or None if empty) + """ + if I is None: + return (None) + if J is None: + return (None) + + I_inter_J = (max(I[0], J[0]), min(I[1], J[1])) + if I_inter_J[0] >= I_inter_J[1]: + return (None) + else: + return (I_inter_J) + + +def interval_subset(I=(1, 3), J=(0, 6)): + """ + Checks whether I is a subset of J + + :param I: an non empty interval represented by start and stop + :param J: a second non empty interval of the same form + :return: True if I is a subset of J + """ + if (I[0] >= J[0]) and (I[1] <= J[1]): + return True + else: + return False + + +def cut_into_three_func(I, J): + """ + Cut an interval I into a partition of 3 subsets: + the elements before J, + the elements belonging to J, + and the elements after J + + :param I: an interval represented by start and stop, or None for an empty one + :param J: a non empty interval + :return: a triplet of three intervals, each represented by either (start, stop) or None + """ + if I is None: + return ((None, None, None)) + + I_inter_J = interval_intersection(I, J) + if I == I_inter_J: + I_before = None + I_after = None + elif I[1] <= J[0]: + I_before = I + I_after = None + elif I[0] >= J[1]: + I_before = None + I_after = I + elif (I[0] <= J[0]) and (I[1] >= J[1]): + I_before = (I[0], I_inter_J[0]) + I_after = (I_inter_J[1], I[1]) + elif I[0] <= J[0]: + I_before = (I[0], I_inter_J[0]) + I_after = None + elif I[1] >= J[1]: + I_before = None + I_after = (I_inter_J[1], I[1]) + else: + raise ValueError('unexpected unconsidered case') + return (I_before, I_inter_J, I_after) + + +def get_pivot_j(I, J): + """ + Get the single point of J that is the closest to I, called 'pivot' here, + with the requirement that I should be outside J + + :param I: a non empty interval (start, stop) + :param J: another non empty interval, with empty intersection with I + :return: the element j of J that is the closest to I + """ + if interval_intersection(I, J) is not None: + raise ValueError('I and J should have a void intersection') + + j_pivot = None # j_pivot is a border of J + if max(I) <= min(J): + j_pivot = min(J) + elif min(I) >= max(J): + j_pivot = max(J) + else: + raise ValueError('I should be outside J') + return (j_pivot) + + +def integral_mini_interval(I, J): + """ + In the specific case where interval I is located outside J, + integral of distance from x to J over the interval x \in I. + This is the *integral* i.e. the sum. + It's not the mean (not divided by the length of I yet) + + :param I: a interval (start, stop), or None + :param J: a non empty interval, with empty intersection with I + :return: the integral of distances d(x, J) over x \in I + """ + if I is None: + return (0) + + j_pivot = get_pivot_j(I, J) + a = min(I) + b = max(I) + return ((b - a) * abs((j_pivot - (a + b) / 2))) + + +def integral_interval_distance(I, J): + """ + For any non empty intervals I, J, compute the + integral of distance from x to J over the interval x \in I. + This is the *integral* i.e. the sum. + It's not the mean (not divided by the length of I yet) + The interval I can intersect J or not + + :param I: a interval (start, stop), or None + :param J: a non empty interval + :return: the integral of distances d(x, J) over x \in I + """ + + # I and J are single intervals (not generic sets) + # I is a predicted interval in the range of affiliation_bin of J + + def f(I_cut): + return (integral_mini_interval(I_cut, J)) + + # If I_middle is fully included into J, it is + # the distance to J is always 0 + def f0(I_middle): + return (0) + + cut_into_three = cut_into_three_func(I, J) + # Distance for now, not the mean: + # Distance left: Between cut_into_three[0] and the point min(J) + d_left = f(cut_into_three[0]) + # Distance middle: Between cut_into_three[1] = I inter J, and J + d_middle = f0(cut_into_three[1]) + # Distance right: Between cut_into_three[2] and the point max(J) + d_right = f(cut_into_three[2]) + # It's an integral so summable + return (d_left + d_middle + d_right) + + +def integral_mini_interval_P_CDFmethod__min_piece(I, J, E): + """ + Helper of `integral_mini_interval_Pprecision_CDFmethod` + In the specific case where interval I is located outside J, + compute the integral $\int_{d_min}^{d_max} \min(m, x) dx$, with: + - m the smallest distance from J to E, + - d_min the smallest distance d(x, J) from x \in I to J + - d_max the largest distance d(x, J) from x \in I to J + + :param I: a single predicted interval, a non empty interval (start, stop) + :param J: ground truth interval, a non empty interval, with empty intersection with I + :param E: the affiliation_bin/influence zone for J, represented as a couple (start, stop) + :return: the integral $\int_{d_min}^{d_max} \min(m, x) dx$ + """ + if interval_intersection(I, J) is not None: + raise ValueError('I and J should have a void intersection') + if not interval_subset(J, E): + raise ValueError('J should be included in E') + if not interval_subset(I, E): + raise ValueError('I should be included in E') + + e_min = min(E) + j_min = min(J) + j_max = max(J) + e_max = max(E) + i_min = min(I) + i_max = max(I) + + d_min = max(i_min - j_max, j_min - i_max) + d_max = max(i_max - j_max, j_min - i_min) + m = min(j_min - e_min, e_max - j_max) + A = min(d_max, m) ** 2 - min(d_min, m) ** 2 + B = max(d_max, m) - max(d_min, m) + C = (1 / 2) * A + m * B + return (C) + + +def integral_mini_interval_Pprecision_CDFmethod(I, J, E): + """ + Integral of the probability of distances over the interval I. + In the specific case where interval I is located outside J, + compute the integral $\int_{x \in I} Fbar(dist(x,J)) dx$. + This is the *integral* i.e. the sum (not the mean) + + :param I: a single predicted interval, a non empty interval (start, stop) + :param J: ground truth interval, a non empty interval, with empty intersection with I + :param E: the affiliation_bin/influence zone for J, represented as a couple (start, stop) + :return: the integral $\int_{x \in I} Fbar(dist(x,J)) dx$ + """ + integral_min_piece = integral_mini_interval_P_CDFmethod__min_piece(I, J, E) + + e_min = min(E) + j_min = min(J) + j_max = max(J) + e_max = max(E) + i_min = min(I) + i_max = max(I) + d_min = max(i_min - j_max, j_min - i_max) + d_max = max(i_max - j_max, j_min - i_min) + integral_linear_piece = (1 / 2) * (d_max ** 2 - d_min ** 2) + integral_remaining_piece = (j_max - j_min) * (i_max - i_min) + + DeltaI = i_max - i_min + DeltaE = e_max - e_min + + output = DeltaI - (1 / DeltaE) * (integral_min_piece + integral_linear_piece + integral_remaining_piece) + return (output) + + +def integral_interval_probaCDF_precision(I, J, E): + """ + Integral of the probability of distances over the interval I. + Compute the integral $\int_{x \in I} Fbar(dist(x,J)) dx$. + This is the *integral* i.e. the sum (not the mean) + + :param I: a single (non empty) predicted interval in the zone of affiliation_bin of J + :param J: ground truth interval + :param E: affiliation_bin/influence zone for J + :return: the integral $\int_{x \in I} Fbar(dist(x,J)) dx$ + """ + + # I and J are single intervals (not generic sets) + def f(I_cut): + if I_cut is None: + return (0) + else: + return (integral_mini_interval_Pprecision_CDFmethod(I_cut, J, E)) + + # If I_middle is fully included into J, it is + # integral of 1 on the interval I_middle, so it's |I_middle| + def f0(I_middle): + if I_middle is None: + return (0) + else: + return (max(I_middle) - min(I_middle)) + + cut_into_three = cut_into_three_func(I, J) + # Distance for now, not the mean: + # Distance left: Between cut_into_three[0] and the point min(J) + d_left = f(cut_into_three[0]) + # Distance middle: Between cut_into_three[1] = I inter J, and J + d_middle = f0(cut_into_three[1]) + # Distance right: Between cut_into_three[2] and the point max(J) + d_right = f(cut_into_three[2]) + # It's an integral so summable + return (d_left + d_middle + d_right) + + +def cut_J_based_on_mean_func(J, e_mean): + """ + Helper function for the recall. + Partition J into two intervals: before and after e_mean + (e_mean represents the center element of E the zone of affiliation_bin) + + :param J: ground truth interval + :param e_mean: a float number (center value of E) + :return: a couple partitionning J into (J_before, J_after) + """ + if J is None: + J_before = None + J_after = None + elif e_mean >= max(J): + J_before = J + J_after = None + elif e_mean <= min(J): + J_before = None + J_after = J + else: # e_mean is across J + J_before = (min(J), e_mean) + J_after = (e_mean, max(J)) + + return ((J_before, J_after)) + + +def integral_mini_interval_Precall_CDFmethod(I, J, E): + """ + Integral of the probability of distances over the interval J. + In the specific case where interval J is located outside I, + compute the integral $\int_{y \in J} Fbar_y(dist(y,I)) dy$. + This is the *integral* i.e. the sum (not the mean) + + :param I: a single (non empty) predicted interval + :param J: ground truth (non empty) interval, with empty intersection with I + :param E: the affiliation_bin/influence zone for J, represented as a couple (start, stop) + :return: the integral $\int_{y \in J} Fbar_y(dist(y,I)) dy$ + """ + # The interval J should be located outside I + # (so it's either the left piece or the right piece w.r.t I) + i_pivot = get_pivot_j(J, I) + e_min = min(E) + e_max = max(E) + e_mean = (e_min + e_max) / 2 + + # If i_pivot is outside E (it's possible), then + # the distance is worst that any random element within E, + # so we set the recall to 0 + if i_pivot <= min(E): + return (0) + elif i_pivot >= max(E): + return (0) + # Otherwise, we have at least i_pivot in E and so d < M so min(d,M)=d + + cut_J_based_on_e_mean = cut_J_based_on_mean_func(J, e_mean) + J_before = cut_J_based_on_e_mean[0] + J_after = cut_J_based_on_e_mean[1] + + iemin_mean = (e_min + i_pivot) / 2 + cut_Jbefore_based_on_iemin_mean = cut_J_based_on_mean_func(J_before, iemin_mean) + J_before_closeE = cut_Jbefore_based_on_iemin_mean[ + 0] # before e_mean and closer to e_min than i_pivot ~ J_before_before + J_before_closeI = cut_Jbefore_based_on_iemin_mean[ + 1] # before e_mean and closer to i_pivot than e_min ~ J_before_after + + iemax_mean = (e_max + i_pivot) / 2 + cut_Jafter_based_on_iemax_mean = cut_J_based_on_mean_func(J_after, iemax_mean) + J_after_closeI = cut_Jafter_based_on_iemax_mean[0] # after e_mean and closer to i_pivot than e_max ~ J_after_before + J_after_closeE = cut_Jafter_based_on_iemax_mean[1] # after e_mean and closer to e_max than i_pivot ~ J_after_after + + if J_before_closeE is not None: + j_before_before_min = min(J_before_closeE) # == min(J) + j_before_before_max = max(J_before_closeE) + else: + j_before_before_min = math.nan + j_before_before_max = math.nan + + if J_before_closeI is not None: + j_before_after_min = min(J_before_closeI) # == j_before_before_max if existing + j_before_after_max = max(J_before_closeI) # == max(J_before) + else: + j_before_after_min = math.nan + j_before_after_max = math.nan + + if J_after_closeI is not None: + j_after_before_min = min(J_after_closeI) # == min(J_after) + j_after_before_max = max(J_after_closeI) + else: + j_after_before_min = math.nan + j_after_before_max = math.nan + + if J_after_closeE is not None: + j_after_after_min = min(J_after_closeE) # == j_after_before_max if existing + j_after_after_max = max(J_after_closeE) # == max(J) + else: + j_after_after_min = math.nan + j_after_after_max = math.nan + + # <-- J_before_closeE --> <-- J_before_closeI --> <-- J_after_closeI --> <-- J_after_closeE --> + # j_bb_min j_bb_max j_ba_min j_ba_max j_ab_min j_ab_max j_aa_min j_aa_max + # (with `b` for before and `a` for after in the previous variable names) + + # vs e_mean m = min(t-e_min, e_max-t) d=|i_pivot-t| min(d,m) \int min(d,m)dt \int d dt \int_(min(d,m)+d)dt \int_{t \in J}(min(d,m)+d)dt + # Case J_before_closeE & i_pivot after J before t-e_min i_pivot-t min(i_pivot-t,t-e_min) = t-e_min t^2/2-e_min*t i_pivot*t-t^2/2 t^2/2-e_min*t+i_pivot*t-t^2/2 = (i_pivot-e_min)*t (i_pivot-e_min)*tB - (i_pivot-e_min)*tA = (i_pivot-e_min)*(tB-tA) + # Case J_before_closeI & i_pivot after J before t-e_min i_pivot-t min(i_pivot-t,t-e_min) = i_pivot-t i_pivot*t-t^2/2 i_pivot*t-t^2/2 i_pivot*t-t^2/2+i_pivot*t-t^2/2 = 2*i_pivot*t-t^2 2*i_pivot*tB-tB^2 - 2*i_pivot*tA + tA^2 = 2*i_pivot*(tB-tA) - (tB^2 - tA^2) + # Case J_after_closeI & i_pivot after J after e_max-t i_pivot-t min(i_pivot-t,e_max-t) = i_pivot-t i_pivot*t-t^2/2 i_pivot*t-t^2/2 i_pivot*t-t^2/2+i_pivot*t-t^2/2 = 2*i_pivot*t-t^2 2*i_pivot*tB-tB^2 - 2*i_pivot*tA + tA^2 = 2*i_pivot*(tB-tA) - (tB^2 - tA^2) + # Case J_after_closeE & i_pivot after J after e_max-t i_pivot-t min(i_pivot-t,e_max-t) = e_max-t e_max*t-t^2/2 i_pivot*t-t^2/2 e_max*t-t^2/2+i_pivot*t-t^2/2 = (e_max+i_pivot)*t-t^2 (e_max+i_pivot)*tB-tB^2 - (e_max+i_pivot)*tA + tA^2 = (e_max+i_pivot)*(tB-tA) - (tB^2 - tA^2) + # + # Case J_before_closeE & i_pivot before J before t-e_min t-i_pivot min(t-i_pivot,t-e_min) = t-e_min t^2/2-e_min*t t^2/2-i_pivot*t t^2/2-e_min*t+t^2/2-i_pivot*t = t^2-(e_min+i_pivot)*t tB^2-(e_min+i_pivot)*tB - tA^2 + (e_min+i_pivot)*tA = (tB^2 - tA^2) - (e_min+i_pivot)*(tB-tA) + # Case J_before_closeI & i_pivot before J before t-e_min t-i_pivot min(t-i_pivot,t-e_min) = t-i_pivot t^2/2-i_pivot*t t^2/2-i_pivot*t t^2/2-i_pivot*t+t^2/2-i_pivot*t = t^2-2*i_pivot*t tB^2-2*i_pivot*tB - tA^2 + 2*i_pivot*tA = (tB^2 - tA^2) - 2*i_pivot*(tB-tA) + # Case J_after_closeI & i_pivot before J after e_max-t t-i_pivot min(t-i_pivot,e_max-t) = t-i_pivot t^2/2-i_pivot*t t^2/2-i_pivot*t t^2/2-i_pivot*t+t^2/2-i_pivot*t = t^2-2*i_pivot*t tB^2-2*i_pivot*tB - tA^2 + 2*i_pivot*tA = (tB^2 - tA^2) - 2*i_pivot*(tB-tA) + # Case J_after_closeE & i_pivot before J after e_max-t t-i_pivot min(t-i_pivot,e_max-t) = e_max-t e_max*t-t^2/2 t^2/2-i_pivot*t e_max*t-t^2/2+t^2/2-i_pivot*t = (e_max-i_pivot)*t (e_max-i_pivot)*tB - (e_max-i_pivot)*tA = (e_max-i_pivot)*(tB-tA) + + if i_pivot >= max(J): + part1_before_closeE = (i_pivot - e_min) * ( + j_before_before_max - j_before_before_min) # (i_pivot-e_min)*(tB-tA) # j_before_before_max - j_before_before_min + part2_before_closeI = 2 * i_pivot * (j_before_after_max - j_before_after_min) - ( + j_before_after_max ** 2 - j_before_after_min ** 2) # 2*i_pivot*(tB-tA) - (tB^2 - tA^2) # j_before_after_max - j_before_after_min + part3_after_closeI = 2 * i_pivot * (j_after_before_max - j_after_before_min) - ( + j_after_before_max ** 2 - j_after_before_min ** 2) # 2*i_pivot*(tB-tA) - (tB^2 - tA^2) # j_after_before_max - j_after_before_min + part4_after_closeE = (e_max + i_pivot) * (j_after_after_max - j_after_after_min) - ( + j_after_after_max ** 2 - j_after_after_min ** 2) # (e_max+i_pivot)*(tB-tA) - (tB^2 - tA^2) # j_after_after_max - j_after_after_min + out_parts = [part1_before_closeE, part2_before_closeI, part3_after_closeI, part4_after_closeE] + elif i_pivot <= min(J): + part1_before_closeE = (j_before_before_max ** 2 - j_before_before_min ** 2) - (e_min + i_pivot) * ( + j_before_before_max - j_before_before_min) # (tB^2 - tA^2) - (e_min+i_pivot)*(tB-tA) # j_before_before_max - j_before_before_min + part2_before_closeI = (j_before_after_max ** 2 - j_before_after_min ** 2) - 2 * i_pivot * ( + j_before_after_max - j_before_after_min) # (tB^2 - tA^2) - 2*i_pivot*(tB-tA) # j_before_after_max - j_before_after_min + part3_after_closeI = (j_after_before_max ** 2 - j_after_before_min ** 2) - 2 * i_pivot * ( + j_after_before_max - j_after_before_min) # (tB^2 - tA^2) - 2*i_pivot*(tB-tA) # j_after_before_max - j_after_before_min + part4_after_closeE = (e_max - i_pivot) * ( + j_after_after_max - j_after_after_min) # (e_max-i_pivot)*(tB-tA) # j_after_after_max - j_after_after_min + out_parts = [part1_before_closeE, part2_before_closeI, part3_after_closeI, part4_after_closeE] + else: + raise ValueError('The i_pivot should be outside J') + + out_integral_min_dm_plus_d = _sum_wo_nan(out_parts) # integral on all J, i.e. sum of the disjoint parts + + # We have for each point t of J: + # \bar{F}_{t, recall}(d) = 1 - (1/|E|) * (min(d,m) + d) + # Since t is a single-point here, and we are in the case where i_pivot is inside E. + # The integral is then given by: + # C = \int_{t \in J} \bar{F}_{t, recall}(D(t)) dt + # = \int_{t \in J} 1 - (1/|E|) * (min(d,m) + d) dt + # = |J| - (1/|E|) * [\int_{t \in J} (min(d,m) + d) dt] + # = |J| - (1/|E|) * out_integral_min_dm_plus_d + DeltaJ = max(J) - min(J) + DeltaE = max(E) - min(E) + C = DeltaJ - (1 / DeltaE) * out_integral_min_dm_plus_d + + return (C) + + +def integral_interval_probaCDF_recall(I, J, E): + """ + Integral of the probability of distances over the interval J. + Compute the integral $\int_{y \in J} Fbar_y(dist(y,I)) dy$. + This is the *integral* i.e. the sum (not the mean) + + :param I: a single (non empty) predicted interval + :param J: ground truth (non empty) interval + :param E: the affiliation_bin/influence zone for J + :return: the integral $\int_{y \in J} Fbar_y(dist(y,I)) dy$ + """ + + # I and J are single intervals (not generic sets) + # E is the outside affiliation_bin interval of J (even for recall!) + # (in particular J \subset E) + # + # J is the portion of the ground truth affiliated to I + # I is a predicted interval (can be outside E possibly since it's recall) + def f(J_cut): + if J_cut is None: + return (0) + else: + return integral_mini_interval_Precall_CDFmethod(I, J_cut, E) + + # If J_middle is fully included into I, it is + # integral of 1 on the interval J_middle, so it's |J_middle| + def f0(J_middle): + if J_middle is None: + return (0) + else: + return (max(J_middle) - min(J_middle)) + + cut_into_three = cut_into_three_func(J, I) # it's J that we cut into 3, depending on the position w.r.t I + # since we integrate over J this time. + # + # Distance for now, not the mean: + # Distance left: Between cut_into_three[0] and the point min(I) + d_left = f(cut_into_three[0]) + # Distance middle: Between cut_into_three[1] = J inter I, and I + d_middle = f0(cut_into_three[1]) + # Distance right: Between cut_into_three[2] and the point max(I) + d_right = f(cut_into_three[2]) + # It's an integral so summable + return (d_left + d_middle + d_right) \ No newline at end of file diff --git a/evaluation/affiliation_bin/_single_ground_truth_event.py b/evaluation/affiliation_bin/_single_ground_truth_event.py new file mode 100644 index 0000000..9c82e95 --- /dev/null +++ b/evaluation/affiliation_bin/_single_ground_truth_event.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import math +from ._affiliation_zone import ( + get_all_E_gt_func, + affiliation_partition) +from ._integral_interval import ( + integral_interval_distance, + integral_interval_probaCDF_precision, + integral_interval_probaCDF_recall, + interval_length, + sum_interval_lengths) + + +def affiliation_precision_distance(Is=[(1, 2), (3, 4), (5, 6)], J=(2, 5.5)): + """ + Compute the individual average distance from Is to a single ground truth J + + :param Is: list of predicted events within the affiliation_bin zone of J + :param J: couple representating the start and stop of a ground truth interval + :return: individual average precision directed distance number + """ + if all([I is None for I in Is]): # no prediction in the current area + return (math.nan) # undefined + return (sum([integral_interval_distance(I, J) for I in Is]) / sum_interval_lengths(Is)) + + +def affiliation_precision_proba(Is=[(1, 2), (3, 4), (5, 6)], J=(2, 5.5), E=(0, 8)): + """ + Compute the individual precision probability from Is to a single ground truth J + + :param Is: list of predicted events within the affiliation_bin zone of J + :param J: couple representating the start and stop of a ground truth interval + :param E: couple representing the start and stop of the zone of affiliation_bin of J + :return: individual precision probability in [0, 1], or math.nan if undefined + """ + if all([I is None for I in Is]): # no prediction in the current area + return (math.nan) # undefined + return (sum([integral_interval_probaCDF_precision(I, J, E) for I in Is]) / sum_interval_lengths(Is)) + + +def affiliation_recall_distance(Is=[(1, 2), (3, 4), (5, 6)], J=(2, 5.5)): + """ + Compute the individual average distance from a single J to the predictions Is + + :param Is: list of predicted events within the affiliation_bin zone of J + :param J: couple representating the start and stop of a ground truth interval + :return: individual average recall directed distance number + """ + Is = [I for I in Is if I is not None] # filter possible None in Is + if len(Is) == 0: # there is no prediction in the current area + return (math.inf) + E_gt_recall = get_all_E_gt_func(Is, (-math.inf, math.inf)) # here from the point of view of the predictions + Js = affiliation_partition([J], E_gt_recall) # partition of J depending of proximity with Is + return (sum([integral_interval_distance(J[0], I) for I, J in zip(Is, Js)]) / interval_length(J)) + + +def affiliation_recall_proba(Is=[(1, 2), (3, 4), (5, 6)], J=(2, 5.5), E=(0, 8)): + """ + Compute the individual recall probability from a single ground truth J to Is + + :param Is: list of predicted events within the affiliation_bin zone of J + :param J: couple representating the start and stop of a ground truth interval + :param E: couple representing the start and stop of the zone of affiliation_bin of J + :return: individual recall probability in [0, 1] + """ + Is = [I for I in Is if I is not None] # filter possible None in Is + if len(Is) == 0: # there is no prediction in the current area + return (0) + E_gt_recall = get_all_E_gt_func(Is, E) # here from the point of view of the predictions + Js = affiliation_partition([J], E_gt_recall) # partition of J depending of proximity with Is + return (sum([integral_interval_probaCDF_recall(I, J[0], E) for I, J in zip(Is, Js)]) / interval_length(J)) \ No newline at end of file diff --git a/evaluation/affiliation_bin/generics.py b/evaluation/affiliation_bin/generics.py new file mode 100644 index 0000000..a2f84be --- /dev/null +++ b/evaluation/affiliation_bin/generics.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +from itertools import groupby +from operator import itemgetter +import math +import gzip +import glob +import os + + +def convert_vector_to_events(vector=[0, 1, 1, 0, 0, 1, 0]): + """ + Convert a binary vector (indicating 1 for the anomalous instances) + to a list of events. The events are considered as durations, + i.e. setting 1 at index i corresponds to an anomalous interval [i, i+1). + + :param vector: a list of elements belonging to {0, 1} + :return: a list of couples, each couple representing the start and stop of + each event + """ + positive_indexes = [idx for idx, val in enumerate(vector) if val > 0] + events = [] + for k, g in groupby(enumerate(positive_indexes), lambda ix: ix[0] - ix[1]): + cur_cut = list(map(itemgetter(1), g)) + events.append((cur_cut[0], cur_cut[-1])) + + # Consistent conversion in case of range anomalies (for indexes): + # A positive index i is considered as the interval [i, i+1), + # so the last index should be moved by 1 + events = [(x, y + 1) for (x, y) in events] + + return (events) + + +def infer_Trange(events_pred, events_gt): + """ + Given the list of events events_pred and events_gt, get the + smallest possible Trange corresponding to the start and stop indexes + of the whole series. + Trange will not influence the measure of distances, but will impact the + measures of probabilities. + + :param events_pred: a list of couples corresponding to predicted events + :param events_gt: a list of couples corresponding to ground truth events + :return: a couple corresponding to the smallest range containing the events + """ + if len(events_gt) == 0: + raise ValueError('The gt events should contain at least one event') + if len(events_pred) == 0: + # empty prediction, base Trange only on events_gt (which is non empty) + return (infer_Trange(events_gt, events_gt)) + + min_pred = min([x[0] for x in events_pred]) + min_gt = min([x[0] for x in events_gt]) + max_pred = max([x[1] for x in events_pred]) + max_gt = max([x[1] for x in events_gt]) + Trange = (min(min_pred, min_gt), max(max_pred, max_gt)) + return (Trange) + + +def has_point_anomalies(events): + """ + Checking whether events contain point anomalies, i.e. + events starting and stopping at the same time. + + :param events: a list of couples corresponding to predicted events + :return: True is the events have any point anomalies, False otherwise + """ + if len(events) == 0: + return (False) + return (min([x[1] - x[0] for x in events]) == 0) + + +def _sum_wo_nan(vec): + """ + Sum of elements, ignoring math.isnan ones + + :param vec: vector of floating numbers + :return: sum of the elements, ignoring math.isnan ones + """ + vec_wo_nan = [e for e in vec if not math.isnan(e)] + return (sum(vec_wo_nan)) + + +def _len_wo_nan(vec): + """ + Count of elements, ignoring math.isnan ones + + :param vec: vector of floating numbers + :return: count of the elements, ignoring math.isnan ones + """ + vec_wo_nan = [e for e in vec if not math.isnan(e)] + return (len(vec_wo_nan)) + + +def read_gz_data(filename='data/machinetemp_groundtruth.gz'): + """ + Load a file compressed with gz, such that each line of the + file is either 0 (representing a normal instance) or 1 (representing) + an anomalous instance. + :param filename: file path to the gz compressed file + :return: list of integers with either 0 or 1 + """ + with gzip.open(filename, 'rb') as f: + content = f.read().splitlines() + content = [int(x) for x in content] + return (content) + + +def read_all_as_events(): + """ + Load the files contained in the folder `data/` and convert + to events. The length of the series is kept. + The convention for the file name is: `dataset_algorithm.gz` + :return: two dictionaries: + - the first containing the list of events for each dataset and algorithm, + - the second containing the range of the series for each dataset + """ + filepaths = glob.glob('data/*.gz') + datasets = dict() + Tranges = dict() + for filepath in filepaths: + vector = read_gz_data(filepath) + events = convert_vector_to_events(vector) + # ad hoc cut for those files + cut_filepath = (os.path.split(filepath)[1]).split('_') + data_name = cut_filepath[0] + algo_name = (cut_filepath[1]).split('.')[0] + if not data_name in datasets: + datasets[data_name] = dict() + Tranges[data_name] = (0, len(vector)) + datasets[data_name][algo_name] = events + return (datasets, Tranges) + + +def f1_func(p, r): + """ + Compute the f1 function + :param p: precision numeric value + :param r: recall numeric value + :return: f1 numeric value + """ + return (2 * p * r / (p + r)) \ No newline at end of file diff --git a/evaluation/affiliation_bin/metrics.py b/evaluation/affiliation_bin/metrics.py new file mode 100644 index 0000000..a15caa5 --- /dev/null +++ b/evaluation/affiliation_bin/metrics.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +from .generics import ( + infer_Trange, + has_point_anomalies, + _len_wo_nan, + _sum_wo_nan, + read_all_as_events) +from ._affiliation_zone import ( + get_all_E_gt_func, + affiliation_partition) +from ._single_ground_truth_event import ( + affiliation_precision_distance, + affiliation_recall_distance, + affiliation_precision_proba, + affiliation_recall_proba) + + +def test_events(events): + """ + Verify the validity of the input events + :param events: list of events, each represented by a couple (start, stop) + :return: None. Raise an error for incorrect formed or non ordered events + """ + if type(events) is not list: + raise TypeError('Input `events` should be a list of couples') + if not all([type(x) is tuple for x in events]): + raise TypeError('Input `events` should be a list of tuples') + if not all([len(x) == 2 for x in events]): + raise ValueError('Input `events` should be a list of couples (start, stop)') + if not all([x[0] <= x[1] for x in events]): + raise ValueError('Input `events` should be a list of couples (start, stop) with start <= stop') + if not all([events[i][1] < events[i + 1][0] for i in range(len(events) - 1)]): + raise ValueError('Couples of input `events` should be disjoint and ordered') + + +def pr_from_events(events_pred, events_gt, Trange): + """ + Compute the affiliation_bin metrics including the precision/recall in [0,1], + along with the individual precision/recall distances and probabilities + + :param events_pred: list of predicted events, each represented by a couple + indicating the start and the stop of the event + :param events_gt: list of ground truth events, each represented by a couple + indicating the start and the stop of the event + :param Trange: range of the series where events_pred and events_gt are included, + represented as a couple (start, stop) + :return: dictionary with precision, recall, and the individual metrics + """ + # testing the inputs + test_events(events_pred) + test_events(events_gt) + + # other tests + minimal_Trange = infer_Trange(events_pred, events_gt) + if not Trange[0] <= minimal_Trange[0]: + raise ValueError('`Trange` should include all the events') + if not minimal_Trange[1] <= Trange[1]: + raise ValueError('`Trange` should include all the events') + + if len(events_gt) == 0: + raise ValueError('Input `events_gt` should have at least one event') + + if has_point_anomalies(events_pred) or has_point_anomalies(events_gt): + raise ValueError('Cannot manage point anomalies currently') + + if Trange is None: + # Set as default, but Trange should be indicated if probabilities are used + raise ValueError('Trange should be indicated (or inferred with the `infer_Trange` function') + + E_gt = get_all_E_gt_func(events_gt, Trange) + aff_partition = affiliation_partition(events_pred, E_gt) + + # Computing precision distance + d_precision = [affiliation_precision_distance(Is, J) for Is, J in zip(aff_partition, events_gt)] + + # Computing recall distance + d_recall = [affiliation_recall_distance(Is, J) for Is, J in zip(aff_partition, events_gt)] + + # Computing precision + p_precision = [affiliation_precision_proba(Is, J, E) for Is, J, E in zip(aff_partition, events_gt, E_gt)] + + # Computing recall + p_recall = [affiliation_recall_proba(Is, J, E) for Is, J, E in zip(aff_partition, events_gt, E_gt)] + + if _len_wo_nan(p_precision) > 0: + p_precision_average = _sum_wo_nan(p_precision) / _len_wo_nan(p_precision) + else: + p_precision_average = p_precision[0] # math.nan + p_recall_average = sum(p_recall) / len(p_recall) + + dict_out = dict({'precision': p_precision_average, + 'recall': p_recall_average, + 'individual_precision_probabilities': p_precision, + 'individual_recall_probabilities': p_recall, + 'individual_precision_distances': d_precision, + 'individual_recall_distances': d_recall}) + return (dict_out) + + +def produce_all_results(): + """ + Produce the affiliation_bin precision/recall for all files + contained in the `data` repository + :return: a dictionary indexed by data names, each containing a dictionary + indexed by algorithm names, each containing the results of the affiliation_bin + metrics (precision, recall, individual probabilities and distances) + """ + datasets, Tranges = read_all_as_events() # read all the events in folder `data` + results = dict() + for data_name in datasets.keys(): + results_data = dict() + for algo_name in datasets[data_name].keys(): + if algo_name != 'groundtruth': + results_data[algo_name] = pr_from_events(datasets[data_name][algo_name], + datasets[data_name]['groundtruth'], + Tranges[data_name]) + results[data_name] = results_data + return (results) \ No newline at end of file diff --git a/evaluation/f1.py b/evaluation/f1.py new file mode 100644 index 0000000..94a8568 --- /dev/null +++ b/evaluation/f1.py @@ -0,0 +1,12 @@ +from sklearn.metrics import f1_score, precision_score, recall_score + + +def evaluate(y_true: list, y_pred: list) -> float: + """ + F1评估方法 + :param y_true: 真实标签 + :param y_pred: 检测标签 + :return: f1、recall、precision + """ + f1 = f1_score(y_true, y_pred) + return f1 \ No newline at end of file diff --git a/evaluation/f1pa.py b/evaluation/f1pa.py new file mode 100644 index 0000000..be11f98 --- /dev/null +++ b/evaluation/f1pa.py @@ -0,0 +1,66 @@ +import numpy as np +from sklearn.metrics import f1_score, precision_score, recall_score + + +def adjust_predicts(score, label, + threshold=None, + pred=None, + calc_latency=False): + """ + 该函数是point-adjust官方源码 + Calculate adjusted predict labels using given `score`, `threshold` (or given `pred`) and `label`. + Args: + score =: The anomaly score + label : The ground-truth label + threshold (float): The threshold of anomaly score. + A point is labeled as "anomaly" if its score is lower than the threshold. + pred : if not None, adjust `pred` and ignore `score` and `threshold`, + calc_latency (bool): + Returns: + np.ndarray: predict labels + """ + if len(score) != len(label): + raise ValueError("score and label must have the same length") + score = np.asarray(score) + label = np.asarray(label) + latency = 0 + if pred is None: + predict = score < threshold + else: + predict = pred + actual = label > 0.1 + anomaly_state = False + anomaly_count = 0 + for i in range(len(score)): + if actual[i] and predict[i] and not anomaly_state: + anomaly_state = True + anomaly_count += 1 + for j in range(i, 0, -1): + if not actual[j]: + break + else: + if not predict[j]: + predict[j] = True + latency += 1 + elif not actual[i]: + anomaly_state = False + if anomaly_state: + predict[i] = True + if calc_latency: + return predict, latency / (anomaly_count + 1e-4) + else: + return predict + + +def evaluate(y_true: list, y_pred: list) -> float: + """ + F1PA评估方法，经过point adjust调整标签后再用F1评分 + :param y_true: 真实标签 + :param y_pred: 检测标签 + :return: 经过pa调整后的f1、recall、precision + """ + y_true, y_pred = y_true.copy(), y_pred.copy() + adjust_y_pred = adjust_predicts(score=np.array([0] * len(y_true)), label=y_true, pred=y_pred) + f1 = f1_score(y_true, adjust_y_pred) + return f1 + diff --git a/evaluation/ftad.py b/evaluation/ftad.py new file mode 100644 index 0000000..80e048f --- /dev/null +++ b/evaluation/ftad.py @@ -0,0 +1,156 @@ +import math +import numpy as np + + +def evaluate(y_true: [int], y_pred: [int], pos_label: int = 1, max_segment: int = 0) -> float: + """ + 基于异常段计算F值 + + :param y_true: 真实标签 + :param y_pred: 检测标签 + :param pos_label: 检测的目标数值，即指定哪个数为异常数值 + :param max_segment: 异常段最大长度 + :return: 段F值 + """ + p_tad = precision_tad(y_true=y_true, y_pred=y_pred, pos_label=pos_label, max_segment=max_segment) + r_tad = recall_tad(y_true=y_true, y_pred=y_pred, pos_label=pos_label, max_segment=max_segment) + score = 0 + if p_tad and r_tad: + score = 2 * p_tad * r_tad / (p_tad + r_tad) + return score + + +def recall_tad(y_true: [int], y_pred: [int], pos_label: int = 1, max_segment: int = 0) -> float: + """ + 基于异常段计算召回率 + + :param y_true: 真实标签 + :param y_pred: 检测标签 + :param pos_label: 检测的目标数值，即指定哪个数为异常数值 + :param max_segment: 异常段最大长度 + :return: 段召回率 + """ + if max_segment == 0: + max_segment = get_max_segment(y_true=y_true, pos_label=pos_label) + score = tp_count(y_true, y_pred, pos_label=pos_label, max_segment=max_segment) + return score + + +def precision_tad(y_true: [int], y_pred: [int], pos_label: int = 1, max_segment: int = 0) -> float: + """ + 基于异常段计算精确率 + + :param y_true: 真实标签 + :param y_pred: 检测标签 + :param pos_label: 检测的目标数值，即指定哪个数为异常数值 + :param max_segment: 异常段最大长度 + :return: 段精确率 + """ + if max_segment == 0: + max_segment = get_max_segment(y_true=y_true, pos_label=pos_label) + score = tp_count(y_pred, y_true, pos_label=pos_label, max_segment=max_segment) + return score + + +def tp_count(y_true: [int], y_pred: [int], max_segment: int = 0, pos_label: int = 1) -> float: + """ + 计算段的评分，交换y_true和y_pred可以分别表示召回率与精确率。 + + :param y_true: 真实标签 + :param y_pred: 检测标签 + :param pos_label: 检测的目标数值，即指定哪个数为异常数值 + :param max_segment: 异常段最大长度 + :return: 分数 + """ + if len(y_true) != len(y_pred): + raise ValueError("y_true and y_pred should have the same length.") + neg_label = 1 - pos_label + position = 0 + tp_list = [] + if max_segment == 0: + raise ValueError("max segment length is 0") + while position < len(y_true): + if y_true[position] == neg_label: + position += 1 + continue + elif y_true[position] == pos_label: + start = position + while position < len(y_true) and y_true[position] == pos_label and position - start < max_segment: + position += 1 + end = position + true_window = [weight_line(i/(end-start)) for i in range(end-start)] + true_window = softmax(true_window) + pred_window = np.array(y_pred[start:end]) + pred_window = np.where(pred_window == pos_label, true_window, 0) + tp_list.append(sum(pred_window)) + else: + raise ValueError("label value must be 0 or 1") + score = sum(tp_list) / len(tp_list) if len(tp_list) > 0 else 0 + return score + + +def weight_line(position: float) -> float: + """ + 按照权重曲线，给不同位置的点赋值 + + :param position: 点在段中的相对位置，取值范围[0,1] + :return: 权重值 + """ + if position < 0 or position > 1: + raise ValueError(f"point position in segment need between 0 and 1, {position} is error position") + sigma = 1 / (1 + math.exp(10*(position-0.5))) + return sigma + + +def softmax(x: [float]) -> [float]: + """ + softmax函数 + :param x: 一个异常段的数据 + :return: 经过softmax的一段数据 + """ + ret = np.exp(x)/np.sum(np.exp(x), axis=0) + return ret.tolist() + + +def get_max_segment(y_true: [int], pos_label: int = 1) -> int: + """ + 获取最大的异常段的长度 + :param y_true: 真实标签 + :param pos_label: 异常标签的取值 + :return: 最大长度 + """ + max_num, i = 0, 0 + neg_label = 1 - pos_label + while i < len(y_true): + if y_true[i] == neg_label: + i += 1 + continue + elif y_true[i] == pos_label: + start = i + while i < len(y_true) and y_true[i] == pos_label: + i += 1 + end = i + max_num = max(max_num, end-start) + else: + raise ValueError("label value must be 0 or 1") + return max_num + + +if __name__ == "__main__": + + # y_true = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + # 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + # y_pred = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + # 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + import pandas as pd + data = pd.read_csv("../records/2023-04-10_10-30-27/detection_result/MtadGatAtt_SWAT.csv") + y_true = data["true"].tolist() + y_pred = data["ftad"].tolist() + + print(evaluate(y_true, y_pred)) + # print(precision_tad(y_true, y_pred)) + # print(recall_tad(y_true, y_pred)) + # from sklearn.metrics import f1_score, precision_score, recall_score + # print(f1_score(y_true, y_pred)) + # print(precision_score(y_true, y_pred)) + # print(recall_score(y_true, y_pred)) diff --git a/evaluation/template.py b/evaluation/template.py new file mode 100644 index 0000000..bcaf172 --- /dev/null +++ b/evaluation/template.py @@ -0,0 +1,10 @@ + + +def evaluate(y_true: [int], y_pred: [int]) -> float: + """ + 评估方法 + :param y_true: 真实标签 + :param y_pred: 检测标签 + :return: 分数，float类型 + """ + return 0.0 diff --git a/main.py b/main.py new file mode 100644 index 0000000..f0a3533 --- /dev/null +++ b/main.py @@ -0,0 +1,169 @@ +# -*- coding:utf-8 -*- +import configparser +from daemon import Daemon +import sys +from torch.cuda import is_available +from loguru import logger +from datetime import datetime +from utils import create_all_dataloader, train_and_test_model +import method +import gc +import traceback +import pandas +import os + + +class Config(configparser.ConfigParser): + def __init__(self, defaults=None): + configparser.ConfigParser.__init__(self, defaults=defaults) + + def optionxform(self, optionstr): + return optionstr + + def as_dict(self): + d = dict(self._sections) + for k in d: + d[k] = dict(d[k]) + return d + + +class Main(Daemon): + def __init__(self, pidfile): + super(Main, self).__init__(pidfile=pidfile) + current = datetime.now() + self.start_time = current.strftime("%Y-%m-%d_%H-%M-%S") + + if len(sys.argv) == 1 or sys.argv[1] == "start": + self.run() + elif sys.argv[1] == "stop": + self.stop() + elif sys.argv[1] == "daemon": + self.start() + else: + print("Input format error. Please input: python3 main.py start|stop|daemon") + sys.exit(0) + + def run(self): + # 读取配置文件参数 + cf = Config() + cf.read("./config.ini", encoding='utf8') + cf_dict = cf.as_dict() + # 读取数据集名称 + dataset_names = cf.get("Dataset", "name") + datasets = dataset_names.split(",") + datasets = [name.strip() for name in datasets] + # 读取模型名称 + model_names = cf.get("Method", "name") + models = model_names.split(",") + models = [name.strip() for name in models] + # 读取预处理方法 + preprocess_name = cf.get("Preprocess", "name") + # 读取评估方法 + evaluation_names = cf.get("Evaluation", "name") + evaluations = evaluation_names.split(",") + evaluations = [name.strip() for name in evaluations] + # 读取模型参数文件路径 + model_path = cf_dict["ModelPath"] + # 读取训练参数 + train = cf.getboolean("BaseParameters", "train") + epochs = cf.getint("BaseParameters", "epochs") + batch_size = cf.getint("BaseParameters", "batch_size") + learning_rate = cf.getfloat("BaseParameters", "learning_rate") + device = cf.get("BaseParameters", "device") + if device == "auto": + device = 'cuda:0' if is_available() else 'cpu' + # 读取自定义参数 + customs = cf_dict["CustomParameters"] + + # 建立本次实验记录的路径 + os.makedirs(f"./records", exist_ok=True) + os.makedirs(f"./records/{self.start_time}", exist_ok=True) + os.makedirs(f"./records/{self.start_time}/detection_result", exist_ok=True) + os.makedirs(f"./records/{self.start_time}/model", exist_ok=True) + + # 初始化日志 + logger.add(f"./records/{self.start_time}/log", + level='DEBUG', + format='{time:YYYY-MM-DD HH:mm:ss} - {level} - {file} - {line} - {message}', + rotation="100 MB") + + # 核心程序 + self.core(models, datasets, preprocess_name, evaluations, model_path, train, epochs, batch_size, learning_rate, + device, customs) + logger.info(f"实验结束，关闭进程") + + def core(self, models: [str], datasets: [str], preprocess_name: str, evaluations: [str], model_path: {}, train: bool, + epochs: int, batch_size: int, learning_rate: float, device: str, customs: {}): + """ + 初始化数据集与模型，并开始训练与测试 + :param models: 训练的模型名称，可包含多个 + :param datasets: 使用的数据集名称，可包含多个 + :param preprocess_name: 预处理方法名称 + :param evaluations: 评估方法名称，可包含多个 + :param model_path: 需要加载模型参数的路径，可包含多个 + :param train: 是否训练，如果为False，则仅测试模型 + :param epochs: 总训练轮数 + :param batch_size: batch的尺寸 + :param learning_rate: 学习率 + :param device: 设备 + :param customs: 自定义参数 + """ + logger.info(f"加载数据集") + try: + # 初始化所有数据集 + all_dataloader = create_all_dataloader(datasets=datasets, input_size=int(customs["input_size"]), + output_size=int(customs["output_size"]), step=int(customs["step"]), + batch_size=batch_size, time_index=customs["time_index"] == "true", + del_column_name=customs["del_column_name"] == "true", + preprocess_name=preprocess_name) + except RuntimeError: + logger.error(traceback.format_exc()) + return + + # 开始训练与测试 + + for model_name in models: + try: + logger.info(f"------------华丽丽的分界线：{model_name} 实验开始------------") + for i in range(len(all_dataloader)): + dataloader = all_dataloader[i] + all_score = {} + for sub_dataloader in dataloader: + dataset_name = sub_dataloader["dataset_name"] + normal_dataloader = sub_dataloader["normal"] + attack_dataloader = sub_dataloader["attack"] + logger.info(f"初始化模型 {model_name}") + model = eval(f"method.{model_name}.Model")(customs=customs, dataloader=normal_dataloader) + model = model.to(device) + logger.info(f"模型初始化完成") + pth_name = model_path[f"{model_name}_{dataset_name}"] if f"{model_name}_{dataset_name}" \ + in model_path else None + best_score, best_detection = train_and_test_model(start_time=self.start_time, epochs=epochs, + normal_dataloader=normal_dataloader, + attack_dataloader=attack_dataloader, + model=model, evaluations=evaluations, + device=device, lr=learning_rate, + model_path=pth_name, train=train) + # 保存最佳检测结果的标签为csv文件 + best_detection = pandas.DataFrame(best_detection) + best_detection.to_csv(f"./records/{self.start_time}/detection_result/{model_name}_{dataset_name}.csv", index=False) + for evaluation_name in evaluations: + if evaluation_name not in all_score: + all_score[evaluation_name] = [] + all_score[evaluation_name].append(best_score[evaluation_name]) + gc.collect() + logger.info(f"------------------------") + logger.info(f"{model_name} / {datasets[i]} 实验完毕") + for evaluation_name in all_score: + logger.info(f"{evaluation_name}: {'{:.3f}'.format(sum(all_score[evaluation_name]) / len(all_score[evaluation_name]))}") + logger.info(f"------------------------") + logger.info(f"------------华丽丽的分界线：{model_name} 实验结束------------") + except RuntimeError: + logger.error(traceback.format_exc()) + return + + +if __name__ == '__main__': + pidpath = "/tmp/command_detection.pid" + app = Main(pidpath) + diff --git a/method/AnomalyTransformer.py b/method/AnomalyTransformer.py new file mode 100644 index 0000000..9dba21a --- /dev/null +++ b/method/AnomalyTransformer.py @@ -0,0 +1,305 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import math +from math import sqrt +import torch.utils.data as tud + + +class PositionalEmbedding(nn.Module): + def __init__(self, d_model, max_len=5000): + super(PositionalEmbedding, self).__init__() + # Compute the positional encodings once in log space. + pe = torch.zeros(max_len, d_model).float() + pe.require_grad = False + + position = torch.arange(0, max_len).float().unsqueeze(1) + div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp() + + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + pe = pe.unsqueeze(0) + self.register_buffer('pe', pe) + + def forward(self, x): + return self.pe[:, :x.size(1)] + + +class TokenEmbedding(nn.Module): + def __init__(self, c_in, d_model): + super(TokenEmbedding, self).__init__() + padding = 1 if torch.__version__ >= '1.5.0' else 2 + self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model, + kernel_size=3, padding=padding, padding_mode='circular', bias=False) + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='leaky_relu') + + def forward(self, x): + x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2) + return x + + +class DataEmbedding(nn.Module): + def __init__(self, c_in, d_model, dropout=0.0): + super(DataEmbedding, self).__init__() + + self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) + self.position_embedding = PositionalEmbedding(d_model=d_model) + + self.dropout = nn.Dropout(p=dropout) + + def forward(self, x): + x = self.value_embedding(x) + self.position_embedding(x) + return self.dropout(x) + + +class TriangularCausalMask(): + def __init__(self, B, L, device="cpu"): + mask_shape = [B, 1, L, L] + with torch.no_grad(): + self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device) + + @property + def mask(self): + return self._mask + + +class AnomalyAttention(nn.Module): + def __init__(self, win_size, mask_flag=True, scale=None, attention_dropout=0.0, output_attention=False): + 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.distances[i][j] = abs(i - j) + + def forward(self, queries, keys, values, sigma, attn_mask): + B, L, H, E = queries.shape + _, S, _, D = values.shape + scale = self.scale or 1. / sqrt(E) + + scores = torch.einsum("blhe,bshe->bhls", queries, keys) + if self.mask_flag: + if attn_mask is None: + attn_mask = TriangularCausalMask(B, L, device=queries.device) + scores.masked_fill_(attn_mask.mask, -np.inf) + attn = scale * scores + + sigma = sigma.transpose(1, 2) # B L H -> B H L + window_size = attn.shape[-1] + 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 = 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) + + if self.output_attention: + return (V.contiguous(), series, prior, sigma) + else: + return (V.contiguous(), None) + + +class AttentionLayer(nn.Module): + def __init__(self, attention, d_model, n_heads, d_keys=None, + d_values=None): + super(AttentionLayer, self).__init__() + + d_keys = d_keys or (d_model // n_heads) + d_values = d_values or (d_model // n_heads) + self.norm = nn.LayerNorm(d_model) + self.inner_attention = attention + self.query_projection = nn.Linear(d_model, + d_keys * n_heads) + self.key_projection = nn.Linear(d_model, + d_keys * n_heads) + self.value_projection = nn.Linear(d_model, + d_values * n_heads) + self.sigma_projection = nn.Linear(d_model, + n_heads) + self.out_projection = nn.Linear(d_values * n_heads, d_model) + + self.n_heads = n_heads + + def forward(self, queries, keys, values, attn_mask): + B, L, _ = queries.shape + _, S, _ = keys.shape + H = self.n_heads + x = queries + queries = self.query_projection(queries).view(B, L, H, -1) + keys = self.key_projection(keys).view(B, S, H, -1) + values = self.value_projection(values).view(B, S, H, -1) + sigma = self.sigma_projection(x).view(B, L, H) + + out, series, prior, sigma = self.inner_attention( + queries, + keys, + values, + sigma, + attn_mask + ) + out = out.view(B, L, -1) + + return self.out_projection(out), series, prior, sigma + + +class EncoderLayer(nn.Module): + def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"): + super(EncoderLayer, self).__init__() + d_ff = d_ff or 4 * d_model + self.attention = attention + self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1) + self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1) + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + self.activation = F.relu if activation == "relu" else F.gelu + + def forward(self, x, attn_mask=None): + new_x, attn, mask, sigma = self.attention( + x, x, x, + attn_mask=attn_mask + ) + x = x + self.dropout(new_x) + y = x = self.norm1(x) + y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1)))) + y = self.dropout(self.conv2(y).transpose(-1, 1)) + + return self.norm2(x + y), attn, mask, sigma + + +class Encoder(nn.Module): + def __init__(self, attn_layers, norm_layer=None): + super(Encoder, self).__init__() + self.attn_layers = nn.ModuleList(attn_layers) + self.norm = norm_layer + + def forward(self, x, attn_mask=None): + # x [B, L, D] + series_list = [] + prior_list = [] + sigma_list = [] + for attn_layer in self.attn_layers: + x, series, prior, sigma = attn_layer(x, attn_mask=attn_mask) + series_list.append(series) + prior_list.append(prior) + sigma_list.append(sigma) + + if self.norm is not None: + x = self.norm(x) + + return x, series_list, prior_list, sigma_list + + +class Model(nn.Module): + def __init__(self, customs: {}, dataloader: tud.DataLoader): + super(Model, self).__init__() + win_size = int(customs["input_size"]) + enc_in = c_out = dataloader.dataset.train_inputs.shape[-1] + d_model = 512 + n_heads = 8 + e_layers = 3 + d_ff = 512 + dropout = 0.0 + activation = 'gelu' + output_attention = True + self.k = 3 + self.win_size = win_size + + self.name = "AnomalyTransformer" + # Encoding + self.embedding = DataEmbedding(enc_in, d_model, dropout) + + # Encoder + self.encoder = Encoder( + [ + EncoderLayer( + AttentionLayer( + AnomalyAttention(win_size, False, attention_dropout=dropout, output_attention=output_attention), + d_model, n_heads), + d_model, + d_ff, + dropout=dropout, + activation=activation + ) for l in range(e_layers) + ], + norm_layer=torch.nn.LayerNorm(d_model) + ) + + self.projection = nn.Linear(d_model, c_out, bias=True) + + def forward(self, x): + enc_out = self.embedding(x) + enc_out, series, prior, sigmas = self.encoder(enc_out) + enc_out = self.projection(enc_out) + return enc_out, series, prior, sigmas + + @staticmethod + 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) + + def loss(self, x, y_true, epoch: int = None, device: str = "cpu"): + output, series, prior, _ = self.forward(x) + series_loss = 0.0 + prior_loss = 0.0 + for u in range(len(prior)): + series_loss += (torch.mean(self.my_kl_loss(series[u], (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size)).detach())) + + torch.mean(self.my_kl_loss((prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size)).detach(), series[u]))) + + prior_loss += (torch.mean(self.my_kl_loss((prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size)), series[u].detach())) + + torch.mean(self.my_kl_loss(series[u].detach(), (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, self.win_size))))) + series_loss = series_loss / len(prior) + prior_loss = prior_loss / len(prior) + rec_loss = nn.MSELoss()(output, x) + + loss1 = rec_loss - self.k * series_loss + loss2 = rec_loss + self.k * prior_loss + + # Minimax strategy + loss1.backward(retain_graph=True) + loss2.backward() + + return loss1.item() + + def detection(self, x, y_true, epoch: int = None, device: str = "cpu"): + temperature = 50 + output, series, prior, _ = self.forward(x) + + loss = torch.mean(nn.MSELoss()(x, output), dim=-1) + + series_loss = 0.0 + prior_loss = 0.0 + for u in range(len(prior)): + if u == 0: + series_loss = self.my_kl_loss(series[u], ( + prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, + self.win_size)).detach()) * temperature + prior_loss = self.my_kl_loss( + (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, + self.win_size)), + series[u].detach()) * temperature + else: + series_loss += self.my_kl_loss(series[u], ( + prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, + self.win_size)).detach()) * temperature + prior_loss += self.my_kl_loss( + (prior[u] / torch.unsqueeze(torch.sum(prior[u], dim=-1), dim=-1).repeat(1, 1, 1, + self.win_size)), + series[u].detach()) * temperature + metric = torch.softmax((-series_loss - prior_loss), dim=-1) + + cri = metric * loss + cri = cri.mean(dim=-1) + return cri, None + + diff --git a/method/MtadGat.py b/method/MtadGat.py new file mode 100644 index 0000000..1ed679f --- /dev/null +++ b/method/MtadGat.py @@ -0,0 +1,414 @@ +""" +github找的第三方实现的Mtad-Gat源码 +由于源码使用了torch.empty，经常导致loss为nan，因此替换为了torch.zeros +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data as tud + + +class ConvLayer(nn.Module): + """1-D Convolution layer to extract high-level features of each time-series input + :param n_features: Number of input features/nodes + :param window_size: length of the input sequence + :param kernel_size: size of kernel to use in the convolution operation + """ + + def __init__(self, n_features, kernel_size=7): + super(ConvLayer, self).__init__() + self.padding = nn.ConstantPad1d((kernel_size - 1) // 2, 0.0) + self.conv = nn.Conv1d(in_channels=n_features, out_channels=n_features, kernel_size=kernel_size) + self.relu = nn.ReLU() + + def forward(self, x): + x = x.permute(0, 2, 1) + x = self.padding(x) + x = self.relu(self.conv(x)) + return x.permute(0, 2, 1) # Permute back + + +class FeatureAttentionLayer(nn.Module): + """Single Graph Feature/Spatial Attention Layer + :param n_features: Number of input features/nodes + :param window_size: length of the input sequence + :param dropout: percentage of nodes to dropout + :param alpha: negative slope used in the leaky rely activation function + :param embed_dim: embedding dimension (output dimension of linear transformation) + :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT + :param use_bias: whether to include a bias term in the attention layer + """ + + def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True): + super(FeatureAttentionLayer, self).__init__() + self.n_features = n_features + self.window_size = window_size + self.dropout = dropout + self.embed_dim = embed_dim if embed_dim is not None else window_size + self.use_gatv2 = use_gatv2 + self.num_nodes = n_features + self.use_bias = use_bias + + # Because linear transformation is done after concatenation in GATv2 + if self.use_gatv2: + self.embed_dim *= 2 + lin_input_dim = 2 * window_size + a_input_dim = self.embed_dim + else: + lin_input_dim = window_size + a_input_dim = 2 * self.embed_dim + + self.lin = nn.Linear(lin_input_dim, self.embed_dim) + # self.a = nn.Parameter(torch.empty((a_input_dim, 1))) + self.a = nn.Parameter(torch.zeros((a_input_dim, 1))) + nn.init.xavier_uniform_(self.a.data, gain=1.414) + + if self.use_bias: + # self.bias = nn.Parameter(torch.empty(n_features, n_features)) + self.bias = nn.Parameter(torch.zeros(n_features, n_features)) + + self.leakyrelu = nn.LeakyReLU(alpha) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x shape (b, n, k): b - batch size, n - window size, k - number of features + # For feature attention we represent a node as the values of a particular feature across all timestamps + + x = x.permute(0, 2, 1) + + # 'Dynamic' GAT attention + # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf) + # Linear transformation applied after concatenation and attention layer applied after leakyrelu + if self.use_gatv2: + a_input = self._make_attention_input(x) # (b, k, k, 2*window_size) + a_input = self.leakyrelu(self.lin(a_input)) # (b, k, k, embed_dim) + e = torch.matmul(a_input, self.a).squeeze(3) # (b, k, k, 1) + + # Original GAT attention + else: + Wx = self.lin(x) # (b, k, k, embed_dim) + a_input = self._make_attention_input(Wx) # (b, k, k, 2*embed_dim) + e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3) # (b, k, k, 1) + + if self.use_bias: + e += self.bias + + # Attention weights + attention = torch.softmax(e, dim=2) + attention = torch.dropout(attention, self.dropout, train=self.training) + + # Computing new node features using the attention + h = self.sigmoid(torch.matmul(attention, x)) + + return h.permute(0, 2, 1) + + def _make_attention_input(self, v): + """Preparing the feature attention mechanism. + Creating matrix with all possible combinations of concatenations of node. + Each node consists of all values of that node within the window + v1 || v1, + ... + v1 || vK, + v2 || v1, + ... + v2 || vK, + ... + ... + vK || v1, + ... + vK || vK, + """ + + K = self.num_nodes + blocks_repeating = v.repeat_interleave(K, dim=1) # Left-side of the matrix + blocks_alternating = v.repeat(1, K, 1) # Right-side of the matrix + combined = torch.cat((blocks_repeating, blocks_alternating), dim=2) # (b, K*K, 2*window_size) + + if self.use_gatv2: + return combined.view(v.size(0), K, K, 2 * self.window_size) + else: + return combined.view(v.size(0), K, K, 2 * self.embed_dim) + + +class TemporalAttentionLayer(nn.Module): + """Single Graph Temporal Attention Layer + :param n_features: number of input features/nodes + :param window_size: length of the input sequence + :param dropout: percentage of nodes to dropout + :param alpha: negative slope used in the leaky rely activation function + :param embed_dim: embedding dimension (output dimension of linear transformation) + :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT + :param use_bias: whether to include a bias term in the attention layer + + """ + + def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True): + super(TemporalAttentionLayer, self).__init__() + self.n_features = n_features + self.window_size = window_size + self.dropout = dropout + self.use_gatv2 = use_gatv2 + self.embed_dim = embed_dim if embed_dim is not None else n_features + self.num_nodes = window_size + self.use_bias = use_bias + + # Because linear transformation is performed after concatenation in GATv2 + if self.use_gatv2: + self.embed_dim *= 2 + lin_input_dim = 2 * n_features + a_input_dim = self.embed_dim + else: + lin_input_dim = n_features + a_input_dim = 2 * self.embed_dim + + self.lin = nn.Linear(lin_input_dim, self.embed_dim) + # self.a = nn.Parameter(torch.empty((a_input_dim, 1))) + self.a = nn.Parameter(torch.zeros((a_input_dim, 1))) + nn.init.xavier_uniform_(self.a.data, gain=1.414) + + if self.use_bias: + # self.bias = nn.Parameter(torch.empty(window_size, window_size)) + self.bias = nn.Parameter(torch.zeros(window_size, window_size)) + self.leakyrelu = nn.LeakyReLU(alpha) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x shape (b, n, k): b - batch size, n - window size, k - number of features + # For temporal attention a node is represented as all feature values at a specific timestamp + + # 'Dynamic' GAT attention + # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf) + # Linear transformation applied after concatenation and attention layer applied after leakyrelu + if self.use_gatv2: + a_input = self._make_attention_input(x) # (b, n, n, 2*n_features) + a_input = self.leakyrelu(self.lin(a_input)) # (b, n, n, embed_dim) + e = torch.matmul(a_input, self.a).squeeze(3) # (b, n, n, 1) + + # Original GAT attention + else: + Wx = self.lin(x) # (b, n, n, embed_dim) + a_input = self._make_attention_input(Wx) # (b, n, n, 2*embed_dim) + e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3) # (b, n, n, 1) + + if self.use_bias: + e += self.bias # (b, n, n, 1) + + # Attention weights + attention = torch.softmax(e, dim=2) + attention = torch.dropout(attention, self.dropout, train=self.training) + + h = self.sigmoid(torch.matmul(attention, x)) # (b, n, k) + + return h + + def _make_attention_input(self, v): + """Preparing the temporal attention mechanism. + Creating matrix with all possible combinations of concatenations of node values: + (v1, v2..)_t1 || (v1, v2..)_t1 + (v1, v2..)_t1 || (v1, v2..)_t2 + + ... + ... + + (v1, v2..)_tn || (v1, v2..)_t1 + (v1, v2..)_tn || (v1, v2..)_t2 + + """ + + K = self.num_nodes + blocks_repeating = v.repeat_interleave(K, dim=1) # Left-side of the matrix + blocks_alternating = v.repeat(1, K, 1) # Right-side of the matrix + combined = torch.cat((blocks_repeating, blocks_alternating), dim=2) + + if self.use_gatv2: + return combined.view(v.size(0), K, K, 2 * self.n_features) + else: + return combined.view(v.size(0), K, K, 2 * self.embed_dim) + + +class GRULayer(nn.Module): + """Gated Recurrent Unit (GRU) Layer + :param in_dim: number of input features + :param hid_dim: hidden size of the GRU + :param n_layers: number of layers in GRU + :param dropout: dropout rate + """ + + def __init__(self, in_dim, hid_dim, n_layers, dropout): + super(GRULayer, self).__init__() + self.hid_dim = hid_dim + self.n_layers = n_layers + self.dropout = 0.0 if n_layers == 1 else dropout + self.gru = nn.GRU(in_dim, hid_dim, num_layers=n_layers, batch_first=True, dropout=self.dropout) + + def forward(self, x): + out, h = self.gru(x) + out, h = out[-1, :, :], h[-1, :, :] # Extracting from last layer + return out, h + + +class RNNDecoder(nn.Module): + """GRU-based Decoder network that converts latent vector into output + :param in_dim: number of input features + :param n_layers: number of layers in RNN + :param hid_dim: hidden size of the RNN + :param dropout: dropout rate + """ + + def __init__(self, in_dim, hid_dim, n_layers, dropout): + super(RNNDecoder, self).__init__() + self.in_dim = in_dim + self.dropout = 0.0 if n_layers == 1 else dropout + self.rnn = nn.GRU(in_dim, hid_dim, n_layers, batch_first=True, dropout=self.dropout) + + def forward(self, x): + decoder_out, _ = self.rnn(x) + return decoder_out + + +class ReconstructionModel(nn.Module): + """Reconstruction Model + :param window_size: length of the input sequence + :param in_dim: number of input features + :param n_layers: number of layers in RNN + :param hid_dim: hidden size of the RNN + :param in_dim: number of output features + :param dropout: dropout rate + """ + + def __init__(self, window_size, in_dim, hid_dim, out_dim, n_layers, dropout): + super(ReconstructionModel, self).__init__() + self.window_size = window_size + self.decoder = RNNDecoder(in_dim, hid_dim, n_layers, dropout) + self.fc = nn.Linear(hid_dim, out_dim) + + def forward(self, x): + # x will be last hidden state of the GRU layer + h_end = x + h_end_rep = h_end.repeat_interleave(self.window_size, dim=1).view(x.size(0), self.window_size, -1) + + decoder_out = self.decoder(h_end_rep) + out = self.fc(decoder_out) + return out + + +class Forecasting_Model(nn.Module): + """Forecasting model (fully-connected network) + :param in_dim: number of input features + :param hid_dim: hidden size of the FC network + :param out_dim: number of output features + :param n_layers: number of FC layers + :param dropout: dropout rate + """ + + def __init__(self, in_dim, hid_dim, out_dim, n_layers, dropout): + super(Forecasting_Model, self).__init__() + layers = [nn.Linear(in_dim, hid_dim)] + for _ in range(n_layers - 1): + layers.append(nn.Linear(hid_dim, hid_dim)) + + layers.append(nn.Linear(hid_dim, out_dim)) + + self.layers = nn.ModuleList(layers) + self.dropout = nn.Dropout(dropout) + self.relu = nn.ReLU() + + def forward(self, x): + for i in range(len(self.layers) - 1): + x = self.relu(self.layers[i](x)) + x = self.dropout(x) + return self.layers[-1](x) + + +class Model(nn.Module): + """ MTAD_GAT model class. + + :param n_features: Number of input features + :param window_size: Length of the input sequence + :param out_dim: Number of features to output + :param kernel_size: size of kernel to use in the 1-D convolution + :param feat_gat_embed_dim: embedding dimension (output dimension of linear transformation) + in feat-oriented GAT layer + :param time_gat_embed_dim: embedding dimension (output dimension of linear transformation) + in time-oriented GAT layer + :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT + :param gru_n_layers: number of layers in the GRU layer + :param gru_hid_dim: hidden dimension in the GRU layer + :param forecast_n_layers: number of layers in the FC-based Forecasting Model + :param forecast_hid_dim: hidden dimension in the FC-based Forecasting Model + :param recon_n_layers: number of layers in the GRU-based Reconstruction Model + :param recon_hid_dim: hidden dimension in the GRU-based Reconstruction Model + :param dropout: dropout rate + :param alpha: negative slope used in the leaky rely activation function + + """ + + def __init__(self, customs: {}, dataloader: tud.DataLoader): + super(Model, self).__init__() + n_features = dataloader.dataset.train_inputs.shape[-1] + window_size = int(customs["input_size"]) + out_dim = n_features + kernel_size = 7 + feat_gat_embed_dim = None + time_gat_embed_dim = None + use_gatv2 = True + gru_n_layers = 1 + gru_hid_dim = 150 + forecast_n_layers = 1 + forecast_hid_dim = 150 + recon_n_layers = 1 + recon_hid_dim = 150 + dropout = 0.2 + alpha = 0.2 + + self.name = "MtadGat" + self.conv = ConvLayer(n_features, kernel_size) + self.feature_gat = FeatureAttentionLayer( + n_features, window_size, dropout, alpha, feat_gat_embed_dim, use_gatv2) + self.temporal_gat = TemporalAttentionLayer(n_features, window_size, dropout, alpha, time_gat_embed_dim, + use_gatv2) + self.gru = GRULayer(3 * n_features, gru_hid_dim, gru_n_layers, dropout) + self.forecasting_model = Forecasting_Model( + gru_hid_dim, forecast_hid_dim, out_dim, forecast_n_layers, dropout) + self.recon_model = ReconstructionModel(window_size, gru_hid_dim, recon_hid_dim, out_dim, recon_n_layers, + dropout) + + def forward(self, x): + # x shape (b, n, k): b - batch size, n - window size, k - number of features + + x = self.conv(x) + h_feat = self.feature_gat(x) + h_temp = self.temporal_gat(x) + + h_cat = torch.cat([x, h_feat, h_temp], dim=2) # (b, n, 3k) + + _, h_end = self.gru(h_cat) + h_end = h_end.view(x.shape[0], -1) # Hidden state for last timestamp + + predictions = self.forecasting_model(h_end) + recons = self.recon_model(h_end) + + return predictions, recons + + def loss(self, x, y_true, epoch: int = None, device: str = "cpu"): + preds, recons = self.forward(x) + if preds.ndim == 3: + preds = preds.squeeze(1) + if y_true.ndim == 3: + y_true = y_true.squeeze(1) + forecast_criterion = nn.MSELoss() + recon_criterion = nn.MSELoss() + forecast_loss = torch.sqrt(forecast_criterion(y_true, preds)) + recon_loss = torch.sqrt(recon_criterion(x, recons)) + loss = forecast_loss + recon_loss + loss.backward() + return loss.item() + + def detection(self, x, y_true, epoch: int = None, device: str = "cpu"): + preds, recons = self.forward(x) + score = F.pairwise_distance(recons.reshape(recons.size(0), -1), x.reshape(x.size(0), -1)) + \ + F.pairwise_distance(y_true.reshape(y_true.size(0), -1), preds.reshape(preds.size(0), -1)) + return score, None + + diff --git a/method/MtadGatAtt.py b/method/MtadGatAtt.py new file mode 100644 index 0000000..7ee50d5 --- /dev/null +++ b/method/MtadGatAtt.py @@ -0,0 +1,647 @@ +import torch +import torch.nn as nn +from math import sqrt +import torch.nn.functional as F +import numpy as np +import torch.utils.data as tud + + +class ConvLayer(nn.Module): + """1-D Convolution layer to extract high-level features of each time-series input + :param n_features: Number of input features/nodes + :param window_size: length of the input sequence + :param kernel_size: size of kernel to use in the convolution operation + """ + + def __init__(self, n_features, kernel_size=7): + super(ConvLayer, self).__init__() + self.padding = nn.ConstantPad1d((kernel_size - 1) // 2, 0.0) + self.conv = nn.Conv1d(in_channels=n_features, out_channels=n_features, kernel_size=kernel_size) + self.relu = nn.ReLU() + + def forward(self, x): + x = x.permute(0, 2, 1) + x = self.padding(x) + x = self.relu(self.conv(x)) + return x.permute(0, 2, 1) # Permute back + + +class FeatureAttentionLayer(nn.Module): + """Single Graph Feature/Spatial Attention Layer + :param n_features: Number of input features/nodes + :param window_size: length of the input sequence + :param dropout: percentage of nodes to dropout + :param alpha: negative slope used in the leaky rely activation function + :param embed_dim: embedding dimension (output dimension of linear transformation) + :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT + :param use_bias: whether to include a bias term in the attention layer + """ + + def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True, + use_softmax=True): + super(FeatureAttentionLayer, self).__init__() + self.n_features = n_features + self.window_size = window_size + self.dropout = dropout + self.embed_dim = embed_dim if embed_dim is not None else window_size + self.use_gatv2 = use_gatv2 + self.num_nodes = n_features + self.use_bias = use_bias + self.use_softmax = use_softmax + + # Because linear transformation is done after concatenation in GATv2 + if self.use_gatv2: + self.embed_dim *= 2 + lin_input_dim = 2 * window_size + a_input_dim = self.embed_dim + else: + lin_input_dim = window_size + a_input_dim = 2 * self.embed_dim + + self.lin = nn.Linear(lin_input_dim, self.embed_dim) + self.a = nn.Parameter(torch.empty((a_input_dim, 1))) + nn.init.xavier_uniform_(self.a.data, gain=1.414) + + if self.use_bias: + self.bias = nn.Parameter(torch.ones(n_features, n_features)) + + self.leakyrelu = nn.LeakyReLU(alpha) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x shape (b, n, k): b - batch size, n - window size, k - number of features + # For feature attention we represent a node as the values of a particular feature across all timestamps + + x = x.permute(0, 2, 1) + + # 'Dynamic' GAT attention + # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf) + # Linear transformation applied after concatenation and attention layer applied after leakyrelu + if self.use_gatv2: + a_input = self._make_attention_input(x) # (b, k, k, 2*window_size) + a_input = self.leakyrelu(self.lin(a_input)) # (b, k, k, embed_dim) + e = torch.matmul(a_input, self.a).squeeze(3) # (b, k, k, 1) + + # Original GAT attention + else: + Wx = self.lin(x) # (b, k, k, embed_dim) + a_input = self._make_attention_input(Wx) # (b, k, k, 2*embed_dim) + e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3) # (b, k, k, 1) + + if self.use_bias: + e += self.bias + + # Attention weights + if self.use_softmax: + e = torch.softmax(e, dim=2) + attention = torch.dropout(e, self.dropout, train=self.training) + + # Computing new node features using the attention + h = self.sigmoid(torch.matmul(attention, x)) + + return h.permute(0, 2, 1) + + def _make_attention_input(self, v): + """Preparing the feature attention mechanism. + Creating matrix with all possible combinations of concatenations of node. + Each node consists of all values of that node within the window + v1 || v1, + ... + v1 || vK, + v2 || v1, + ... + v2 || vK, + ... + ... + vK || v1, + ... + vK || vK, + """ + + K = self.num_nodes + blocks_repeating = v.repeat_interleave(K, dim=1) # Left-side of the matrix + blocks_alternating = v.repeat(1, K, 1) # Right-side of the matrix + combined = torch.cat((blocks_repeating, blocks_alternating), dim=2) # (b, K*K, 2*window_size) + + if self.use_gatv2: + return combined.view(v.size(0), K, K, 2 * self.window_size) + else: + return combined.view(v.size(0), K, K, 2 * self.embed_dim) + + +class TemporalAttentionLayer(nn.Module): + """Single Graph Temporal Attention Layer + :param n_features: number of input features/nodes + :param window_size: length of the input sequence + :param dropout: percentage of nodes to dropout + :param alpha: negative slope used in the leaky rely activation function + :param embed_dim: embedding dimension (output dimension of linear transformation) + :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT + :param use_bias: whether to include a bias term in the attention layer + + """ + + def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True, + use_softmax=True): + super(TemporalAttentionLayer, self).__init__() + self.n_features = n_features + self.window_size = window_size + self.dropout = dropout + self.use_gatv2 = use_gatv2 + self.embed_dim = embed_dim if embed_dim is not None else n_features + self.num_nodes = window_size + self.use_bias = use_bias + self.use_softmax = use_softmax + + # Because linear transformation is performed after concatenation in GATv2 + if self.use_gatv2: + self.embed_dim *= 2 + lin_input_dim = 2 * n_features + a_input_dim = self.embed_dim + else: + lin_input_dim = n_features + a_input_dim = 2 * self.embed_dim + + self.lin = nn.Linear(lin_input_dim, self.embed_dim) + self.a = nn.Parameter(torch.empty((a_input_dim, 1))) + nn.init.xavier_uniform_(self.a.data, gain=1.414) + + if self.use_bias: + self.bias = nn.Parameter(torch.ones(window_size, window_size)) + + self.leakyrelu = nn.LeakyReLU(alpha) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x shape (b, n, k): b - batch size, n - window size, k - number of features + # For temporal attention a node is represented as all feature values at a specific timestamp + + # 'Dynamic' GAT attention + # Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf) + # Linear transformation applied after concatenation and attention layer applied after leakyrelu + if self.use_gatv2: + a_input = self._make_attention_input(x) # (b, n, n, 2*n_features) + a_input = self.leakyrelu(self.lin(a_input)) # (b, n, n, embed_dim) + e = torch.matmul(a_input, self.a).squeeze(3) # (b, n, n, 1) + + # Original GAT attention + else: + Wx = self.lin(x) # (b, n, n, embed_dim) + a_input = self._make_attention_input(Wx) # (b, n, n, 2*embed_dim) + e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3) # (b, n, n, 1) + + if self.use_bias: + e += self.bias # (b, n, n, 1) + + # Attention weights + if self.use_softmax: + e = torch.softmax(e, dim=2) + attention = torch.dropout(e, self.dropout, train=self.training) + + h = self.sigmoid(torch.matmul(attention, x)) # (b, n, k) + + return h + + def _make_attention_input(self, v): + """Preparing the temporal attention mechanism. + Creating matrix with all possible combinations of concatenations of node values: + (v1, v2..)_t1 || (v1, v2..)_t1 + (v1, v2..)_t1 || (v1, v2..)_t2 + + ... + ... + + (v1, v2..)_tn || (v1, v2..)_t1 + (v1, v2..)_tn || (v1, v2..)_t2 + + """ + + K = self.num_nodes + blocks_repeating = v.repeat_interleave(K, dim=1) # Left-side of the matrix + blocks_alternating = v.repeat(1, K, 1) # Right-side of the matrix + combined = torch.cat((blocks_repeating, blocks_alternating), dim=2) + + if self.use_gatv2: + return combined.view(v.size(0), K, K, 2 * self.n_features) + else: + return combined.view(v.size(0), K, K, 2 * self.embed_dim) + + +class FullAttention(nn.Module): + def __init__(self, mask_flag=True, scale=None, attention_dropout=0.1, output_attention=False): + super(FullAttention, self).__init__() + self.scale = scale + self.mask_flag = mask_flag + self.output_attention = output_attention + self.dropout = nn.Dropout(attention_dropout) + self.relu_q = nn.ReLU() + self.relu_k = nn.ReLU() + + @staticmethod + def TriangularCausalMask(B, L, S, device='cpu'): + mask_shape = [B, 1, L, S] + with torch.no_grad(): + mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1) + return mask.to(device) + + def forward(self, queries, keys, values, attn_mask): + B, L, H, E = queries.shape + _, S, _, D = values.shape + scale = self.scale or 1. / sqrt(E) # scale相对于取多少比例，取前1/根号n + + scores = torch.einsum("blhe,bshe->bhls", queries, keys) + if self.mask_flag: + if attn_mask is None: + attn_mask = self.TriangularCausalMask(B, L, S, device=queries.device) + + scores.masked_fill_(attn_mask, 0) + + A = self.dropout(torch.softmax(scale * scores, dim=-1)) + V = torch.einsum("bhls,bshd->blhd", A, values) + + # queries = self.relu_q(queries) + # keys = self.relu_k(keys) + # KV = torch.einsum("blhe,bshe->bhls", keys, values) + # A = self.dropout(scale * KV) + # V = torch.einsum("bshd,bhls->blhd", queries, A) + + if self.output_attention: + return (V.contiguous(), A) + else: + return (V.contiguous(), None) + + +class ProbAttention(nn.Module): + def __init__(self, mask_flag=True, factor=2, scale=None, attention_dropout=0.1, output_attention=False): + super(ProbAttention, self).__init__() + self.factor = factor + self.scale = scale + self.mask_flag = mask_flag + self.output_attention = output_attention + + @staticmethod + def ProbMask(B, H, D, index, scores, device='cpu'): + _mask = torch.ones(D, scores.shape[-2], dtype=torch.bool).triu(1) + _mask_ex = _mask[None, None, :].expand(B, H, D, scores.shape[-2]) + indicator = _mask_ex.transpose(-2, -1)[torch.arange(B)[:, None, None], + torch.arange(H)[None, :, None], + index, :].transpose(-2, -1) + mask = indicator.view(scores.shape) + return mask.to(device) + + def _prob_KV(self, K, V, sample_v, n_top): # n_top: c*ln(L_q) + # Q [B, H, L, D] + B, H, L, E_V = V.shape + _, _, _, E_K = K.shape + + # calculate the sampled K_V + + V_expand = V.transpose(-2, -1).unsqueeze(-2).expand(B, H, E_V, E_K, L) + index_sample = torch.randint(E_V, (E_K, sample_v)) # real U = U_part(factor*ln(L_k))*L_q + V_sample = V_expand[:, :, torch.arange(E_V).unsqueeze(1), index_sample, :] + K_V_sample = torch.matmul(K.transpose(-2, -1).unsqueeze(-2), V_sample.transpose(-2, -1)).squeeze() + + # find the Top_k query with sparisty measurement + M = K_V_sample.max(-1)[0] - torch.div(K_V_sample.sum(-1), E_V) + M_top = M.topk(n_top, sorted=False)[1] + + # use the reduced Q to calculate Q_K + V_reduce = V.transpose(-2, -1)[torch.arange(B)[:, None, None], + torch.arange(H)[None, :, None], + M_top, :].transpose(-2, -1) # factor*ln(L_q) + K_V = torch.matmul(K.transpose(-2, -1), V_reduce) # factor*ln(L_q)*L_k + # + return K_V, M_top + + def _get_initial_context(self, V, L_Q): + B, H, L_V, D = V.shape + if not self.mask_flag: + # V_sum = V.sum(dim=-2) + V_sum = V.mean(dim=-2) + contex = V_sum.unsqueeze(-2).expand(B, H, L_Q, V_sum.shape[-1]).clone() + else: # use mask + assert (L_Q == L_V) # requires that L_Q == L_V, i.e. for self-attention only + contex = V.cumsum(dim=-2) + return contex + + def _update_context(self, context_in, Q, scores, index, D_K, attn_mask): + B, H, L, D_Q = Q.shape + + if self.mask_flag: + attn_mask = self.ProbMask(B, H, D_K, index, scores, device=Q.device) + scores.masked_fill_(attn_mask, -np.inf) + + attn = torch.softmax(scores, dim=-1) # nn.Softmax(dim=-1)(scores) + + context_in.transpose(-2, -1)[torch.arange(B)[:, None, None], + torch.arange(H)[None, :, None], + index, :] = torch.matmul(Q, attn).type_as(context_in).transpose(-2, -1) + if self.output_attention: + attns = (torch.ones([B, H, D_K, D_K]) / D_K).type_as(attn) + attns[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :] = attn + return (context_in, attns) + else: + return (context_in, None) + + def forward(self, queries, keys, values, attn_mask): + # B, L_Q, H, D = queries.shape + # _, L_K, _, _ = keys.shape + + B, L, H, D_K = keys.shape + _, _, _, D_V = values.shape + + queries = queries.transpose(2, 1) + keys = keys.transpose(2, 1) + values = values.transpose(2, 1) + + U_part = self.factor * np.ceil(np.log(D_V)).astype('int').item() # c*ln(L_k) + u = self.factor * np.ceil(np.log(D_K)).astype('int').item() # c*ln(L_q) + + U_part = U_part if U_part < D_V else D_V + u = u if u < D_K else D_K + + scores_top, index = self._prob_KV(keys, values, sample_v=U_part, n_top=u) + + # add scale factor + scale = self.scale or 1. / sqrt(D_K) + if scale is not None: + scores_top = scores_top * scale + # get the context + context = self._get_initial_context(queries, L) + # update the context with selected top_k queries + context, attn = self._update_context(context, queries, scores_top, index, D_K, attn_mask) + + return context.contiguous(), attn + + +class AttentionBlock(nn.Module): + def __init__(self, d_model, n_model, n_heads=8, d_keys=None, d_values=None): + super(AttentionBlock, self).__init__() + + d_keys = d_keys or (d_model // n_heads) + d_values = d_values or (d_model // n_heads) + self.inner_attention = FullAttention() + # self.inner_attention = ProbAttention(device=device) + self.query_projection = nn.Linear(d_model, d_keys * n_heads) + self.key_projection = nn.Linear(d_model, d_keys * n_heads) + self.value_projection = nn.Linear(d_model, d_values * n_heads) + self.out_projection = nn.Linear(d_values * n_heads, d_model) + self.n_heads = n_heads + self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) + + def forward(self, queries, keys, values, attn_mask): + ''' + Q: [batch_size, len_q, d_k] + K: [batch_size, len_k, d_k] + V: [batch_size, len_v(=len_k), d_v] + attn_mask: [batch_size, seq_len, seq_len] + ''' + batch_size, len_q, _ = queries.shape + _, len_k, _ = keys.shape + + queries = self.query_projection(queries).view(batch_size, len_q, self.n_heads, -1) + keys = self.key_projection(keys).view(batch_size, len_k, self.n_heads, -1) + values = self.value_projection(values).view(batch_size, len_k, self.n_heads, -1) + + out, attn = self.inner_attention( + queries, + keys, + values, + attn_mask + ) + out = out.view(batch_size, len_q, -1) + out = self.out_projection(out) + out = self.layer_norm(out) + return out, attn + + +class GRULayer(nn.Module): + """Gated Recurrent Unit (GRU) Layer + :param in_dim: number of input features + :param hid_dim: hidden size of the GRU + :param n_layers: number of layers in GRU + :param dropout: dropout rate + """ + + def __init__(self, in_dim, hid_dim, n_layers, dropout): + super(GRULayer, self).__init__() + self.hid_dim = hid_dim + self.n_layers = n_layers + self.dropout = 0.0 if n_layers == 1 else dropout + self.gru = nn.GRU(in_dim, hid_dim, num_layers=n_layers, batch_first=True, dropout=self.dropout) + + def forward(self, x): + out, h = self.gru(x) + out, h = out[-1, :, :], h[-1, :, :] # Extracting from last layer + return out, h + + +class RNNDecoder(nn.Module): + """GRU-based Decoder network that converts latent vector into output + :param in_dim: number of input features + :param n_layers: number of layers in RNN + :param hid_dim: hidden size of the RNN + :param dropout: dropout rate + """ + + def __init__(self, in_dim, hid_dim, n_layers, dropout): + super(RNNDecoder, self).__init__() + self.in_dim = in_dim + self.dropout = 0.0 if n_layers == 1 else dropout + self.rnn = nn.GRU(in_dim, hid_dim, n_layers, batch_first=True, dropout=self.dropout) + + def forward(self, x): + decoder_out, _ = self.rnn(x) + return decoder_out + + +class ReconstructionModel(nn.Module): + """Reconstruction Model + :param window_size: length of the input sequence + :param in_dim: number of input features + :param n_layers: number of layers in RNN + :param hid_dim: hidden size of the RNN + :param in_dim: number of output features + :param dropout: dropout rate + """ + + def __init__(self, window_size, in_dim, hid_dim, out_dim, n_layers, dropout): + super(ReconstructionModel, self).__init__() + self.window_size = window_size + self.decoder = RNNDecoder(in_dim, hid_dim, n_layers, dropout) + self.fc = nn.Linear(hid_dim, out_dim) + + def forward(self, x): + # x will be last hidden state of the GRU layer + h_end = x + h_end_rep = h_end.repeat_interleave(self.window_size, dim=1).view(x.size(0), self.window_size, -1) + + decoder_out = self.decoder(h_end_rep) + out = self.fc(decoder_out) + return out + + +class Forecasting_Model(nn.Module): + """Forecasting model (fully-connected network) + :param in_dim: number of input features + :param hid_dim: hidden size of the FC network + :param out_dim: number of output features + :param n_layers: number of FC layers + :param dropout: dropout rate + """ + + def __init__(self, in_dim, hid_dim, out_dim, n_layers, dropout): + super(Forecasting_Model, self).__init__() + layers = [nn.Linear(in_dim, hid_dim)] + for _ in range(n_layers - 1): + layers.append(nn.Linear(hid_dim, hid_dim)) + + layers.append(nn.Linear(hid_dim, out_dim)) + + self.layers = nn.ModuleList(layers) + self.dropout = nn.Dropout(dropout) + self.relu = nn.ReLU() + + def forward(self, x): + for i in range(len(self.layers) - 1): + x = self.relu(self.layers[i](x)) + x = self.dropout(x) + return self.layers[-1](x) + + +class Model(nn.Module): + """ MTAD_GAT model class. + + :param n_features: Number of input features + :param window_size: Length of the input sequence + :param out_dim: Number of features to output + :param kernel_size: size of kernel to use in the 1-D convolution + :param feat_gat_embed_dim: embedding dimension (output dimension of linear transformation) + in feat-oriented GAT layer + :param time_gat_embed_dim: embedding dimension (output dimension of linear transformation) + in time-oriented GAT layer + :param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT + :param gru_n_layers: number of layers in the GRU layer + :param gru_hid_dim: hidden dimension in the GRU layer + :param forecast_n_layers: number of layers in the FC-based Forecasting Model + :param forecast_hid_dim: hidden dimension in the FC-based Forecasting Model + :param recon_n_layers: number of layers in the GRU-based Reconstruction Model + :param recon_hid_dim: hidden dimension in the GRU-based Reconstruction Model + :param dropout: dropout rate + :param alpha: negative slope used in the leaky rely activation function + + """ + + def __init__(self, customs: dict, dataloader: tud.DataLoader = None): + super(Model, self).__init__() + n_features = dataloader.dataset.train_inputs.shape[-1] + window_size = int(customs["input_size"]) + out_dim = n_features + kernel_size = 7 + feat_gat_embed_dim = None + time_gat_embed_dim = None + use_gatv2 = True + gru_n_layers = 1 + gru_hid_dim = 150 + forecast_n_layers = 1 + forecast_hid_dim = 150 + recon_n_layers = 1 + recon_hid_dim = 150 + dropout = 0.2 + alpha = 0.2 + optimize = True + + self.name = "MtadGatAtt" + self.optimize = optimize + use_softmax = not optimize + + self.conv = ConvLayer(n_features, kernel_size) + self.feature_gat = FeatureAttentionLayer( + n_features, window_size, dropout, alpha, feat_gat_embed_dim, use_gatv2, use_softmax=use_softmax) + self.temporal_gat = TemporalAttentionLayer(n_features, window_size, dropout, alpha, time_gat_embed_dim, + use_gatv2, use_softmax=use_softmax) + self.forecasting_model = Forecasting_Model( + gru_hid_dim, forecast_hid_dim, out_dim, forecast_n_layers, dropout) + if optimize: + self.encode = AttentionBlock(3 * n_features, window_size) + self.encode_feature = nn.Linear(3 * n_features * window_size, gru_hid_dim) + self.decode_feature = nn.Linear(gru_hid_dim, n_features * window_size) + self.decode = AttentionBlock(n_features, window_size) + else: + self.gru = GRULayer(3 * n_features, gru_hid_dim, gru_n_layers, dropout) + self.recon_model = ReconstructionModel(window_size, gru_hid_dim, recon_hid_dim, out_dim, recon_n_layers, + dropout) + + def forward(self, x): + x = self.conv(x) + h_feat = self.feature_gat(x) + h_temp = self.temporal_gat(x) + h_cat = torch.cat([x, h_feat, h_temp], dim=2) # (b, n, 3k) + + if self.optimize: + h_end, _ = self.encode(h_cat, h_cat, h_cat, None) + h_end = self.encode_feature(h_end.reshape(h_end.size(0), -1)) + else: + _, h_end = self.gru(h_cat) + h_end = h_end.view(x.shape[0], -1) # Hidden state for last timestamp + + predictions = self.forecasting_model(h_end) + + if self.optimize: + h_end = self.decode_feature(h_end) + h_end = h_end.reshape(x.shape[0], x.shape[1], x.shape[2]) + recons, _ = self.decode(h_end, h_end, h_end, None) + else: + recons = self.recon_model(h_end) + + return predictions, recons + + def loss(self, x, y_true, epoch: int = None, device: str = "cpu"): + preds, recons = self.forward(x) + + if preds.ndim == 3: + preds = preds.squeeze(1) + if y_true.ndim == 3: + y_true = y_true.squeeze(1) + forecast_criterion = nn.MSELoss() + recon_criterion = nn.MSELoss() + + forecast_loss = torch.sqrt(forecast_criterion(y_true, preds)) + recon_loss = torch.sqrt(recon_criterion(x, recons)) + + loss = forecast_loss + recon_loss + loss.backward() + return loss.item() + + def detection(self, x, y_true, epoch: int = None, device: str = "cpu"): + preds, recons = self.forward(x) + score = F.pairwise_distance(recons.reshape(recons.size(0), -1), x.reshape(x.size(0), -1)) + F.pairwise_distance(y_true.reshape(y_true.size(0), -1), preds.reshape(preds.size(0), -1)) + return score, None + + +if __name__ == "__main__": + from tqdm import tqdm + import time + epoch = 10000 + batch_size = 1 + # device = 'cuda:1' if torch.cuda.is_available() else 'cpu' + device = 'cpu' + input_len_list = [30, 60, 90, 120, 150, 180, 210, 240, 270, 300] + for input_len in input_len_list: + model = Model(52, input_len, 52, optimize=False, device=device).to(device) + a = torch.Tensor(torch.ones((batch_size, input_len, 52))).to(device) + start = time.time() + for i in tqdm(range(epoch)): + model(a) + end = time.time() + speed1 = batch_size * epoch / (end - start) + + model = Model(52, input_len, 52, optimize=True, device=device).to(device) + a = torch.Tensor(torch.ones((batch_size, input_len, 52))).to(device) + start = time.time() + for i in tqdm(range(epoch)): + model(a) + end = time.time() + speed2 = batch_size * epoch / (end - start) + print(input_len, (speed2 - speed1)/speed1, speed1, speed2) + diff --git a/method/__init__.py b/method/__init__.py new file mode 100644 index 0000000..39fe90b --- /dev/null +++ b/method/__init__.py @@ -0,0 +1,3 @@ +from .MtadGat import Model +from .MtadGatAtt import Model +from .AnomalyTransformer import Model \ No newline at end of file diff --git a/method/template.py b/method/template.py new file mode 100644 index 0000000..a608627 --- /dev/null +++ b/method/template.py @@ -0,0 +1,50 @@ +import torch.nn as nn +import torch.utils.data as tud +import torch + + +class Model(nn.Module): + def __init__(self, customs: dict, dataloader: tud.DataLoader = None): + """ + :param customs: 自定义参数，内容取自于config.ini文件的[CustomParameters]部分。 + :param dataloader: 数据集初始化完成的dataloader。在自定义的预处理方法文件中，可以增加内部变量或者方法，提供给模型。 + 例如：模型初始化需要数据的维度数量，可通过n_features = dataloader.dataset.train_inputs.shape[-1]获取 + 或在预处理方法的MyDataset类中，定义self.n_features = self.train_inputs.shape[-1]， + 通过n_features = dataloader.dataset.n_features获取 + """ + super(Model, self).__init__() + + def forward(self, x): + """ + :param x: 模型的输入，在本工具中为MyDataset类中__getitem__方法返回的三个变量中的第一个变量。 + :return: 模型的输出，可以自定义 + """ + return None + + def loss(self, x, y_true, epoch: int = None, device: str = "cpu"): + """ + 计算loss。注意，计算loss时如采用torch之外的库计算会造成梯度截断，请全部使用torch的方法 + :param x: 输入数据 + :param y_true: 真实输出数据 + :param epoch: 当前是第几个epoch + :param device: 设备，cpu或者cuda + :return: loss值 + """ + y_pred = self.forward(x) # 模型的输出 + loss = torch.Tensor([1]) # 示例，请修改 + loss.backward() + return loss.item() + + def detection(self, x, y_true, epoch: int = None, device: str = "cpu"): + """ + 检测方法，可以输出异常的分数，也可以输出具体的标签。 + 如输出异常分数，则后续会根据异常分数自动划分阈值，高于阈值的为异常，自动赋予标签；如输出标签，则直接进行评估。 + :param x: 输入数据 + :param y_true: 真实输出数据 + :param epoch: 当前是第几个epoch + :param device: 设备，cpu或者cuda + :return: score，label。如选择输出异常的分数，则输出score，label为None；如选择输出标签，则输出label，score为None。 + score的格式为torch的Tensor格式，尺寸为[batch_size]；label的格式为torch的IntTensor格式，尺寸为[batch_size] + """ + y_pred = self.forward(x) # 模型的输出 + return None, None \ No newline at end of file diff --git a/preprocess/__init__.py b/preprocess/__init__.py new file mode 100644 index 0000000..7ca1f60 --- /dev/null +++ b/preprocess/__init__.py @@ -0,0 +1 @@ +from .standardization import MyDataset \ No newline at end of file diff --git a/preprocess/standardization.py b/preprocess/standardization.py new file mode 100644 index 0000000..9b8b2be --- /dev/null +++ b/preprocess/standardization.py @@ -0,0 +1,119 @@ +from torch.utils.data import Dataset +from torch import float32, Tensor +from numpy import array, where + + +class MyDataset(Dataset): + def __init__(self, name: str, train_path: str = None, test_path: str = None, input_size: int = 1, + output_size: int = 1, step: int = 1, mode: str = 'train', time_index: bool = True, + del_column_name: bool = True): + """ + 可以将csv文件批量转成tensor + :param name: 数据集名称。 + :param train_path: 训练数据集路径。 + :param test_path: 测试数据集路径。 + :param input_size: 输入数据长度。 + :param output_size: 输出数据长度。 + :param step: 截取数据的窗口移动间隔。 + :param mode: train或者test，用于指示使用训练集数据还是测试集数据。 + :param time_index: True为第一列是时间戳，False为不。 + :param del_column_name: 文件中第一行为列名时，使用True。 + """ + self.name = name + self.input_size = input_size + self.output_size = output_size + self.del_column_name = del_column_name + self.step = step + self.mode = mode + self.time_index = time_index + self.train_inputs, self.train_labels, self.train_outputs, self.test_inputs, self.test_labels, self.test_outputs\ + = self.parse_data(train_path, test_path) + self.train_inputs = Tensor(self.train_inputs).to(float32) if self.train_inputs is not None else None + self.train_labels = Tensor(self.train_labels).to(float32) if self.train_labels is not None else None + self.train_outputs = Tensor(self.train_outputs).to(float32) if self.train_outputs is not None else None + self.test_inputs = Tensor(self.test_inputs).to(float32) if self.test_inputs is not None else None + self.test_labels = Tensor(self.test_labels).to(float32) if self.test_labels is not None else None + self.test_outputs = Tensor(self.test_outputs).to(float32) if self.test_outputs is not None else None + + def parse_data(self, train_path: str = None, test_path: str = None): + if train_path is None and test_path is None: + raise ValueError("train_path is None and test_path is None.") + + mean = None + deviation = None + train_data_input, train_label, train_data_output = None, None, None + test_data_input, test_label, test_data_output = None, None, None + + # 读取训练集数据 + if train_path: + train_data = [] + train_label = [] + with open(train_path, 'r', encoding='utf8') as f: + if self.del_column_name is True: + data = f.readlines()[1:] + else: + data = f.readlines() + train_data.extend([list(map(float, line.strip().split(','))) for line in data]) + train_label.extend([0 for _ in data]) + train_np = array(train_data) + if self.time_index: + train_np[:, 0] = train_np[:, 0] % 86400 + mean = train_np.mean(axis=0) # 计算平均数 + deviation = train_np.std(axis=0) # 计算标准差 + deviation = where(deviation != 0, deviation, 1) + train_np = (train_np - mean) / deviation # 标准化 + train_data = train_np.tolist() + train_data_input, train_data_output, train_label = self.cut_data(train_data, train_label) + + # 读取测试集数据 + if test_path: + test_data = [] + test_label = [] + with open(test_path, 'r', encoding='utf8') as f: + if self.del_column_name is True: + data = f.readlines()[1:] + else: + data = f.readlines() + test_data.extend([list(map(float, line.strip().split(',')))[:-1] for line in data]) + test_label.extend([int(line.strip().split(',')[-1]) for line in data]) + test_np = array(test_data) + if self.time_index: + test_np[:, 0] = test_np[:, 0] % 86400 + # mean = test_np.mean(axis=0) # 计算平均数 + # deviation = test_np.std(axis=0) # 计算标准差 + # deviation = where(deviation != 0, deviation, 1) + test_np = (test_np - mean) / deviation # 标准化 + test_data = test_np.tolist() + # 自动判断是否需要反转标签。异常标签统一认为是1，当异常标签超过一半时，需反转标签 + if sum(test_label) > 0.5*len(test_label): + test_label = (1-array(test_label)).tolist() + test_data_input, test_data_output, test_label = self.cut_data(test_data, test_label) + + return train_data_input, train_label, train_data_output, test_data_input, test_label, test_data_output + + def cut_data(self, data: [[float]], label: [int]): + n = 0 + input_data, output_data, anomaly_label = [], [], [] + while n + self.input_size + self.output_size <= len(data): + input_data.append(data[n: n + self.input_size]) + output_data.append(data[n + self.input_size: n + self.input_size + self.output_size]) + anomaly_label.append([max(label[n + self.input_size: n + self.input_size + self.output_size])]) + n = n + self.step + return input_data.copy(), output_data.copy(), anomaly_label.copy() + + def __len__(self): + if self.mode == 'train': + return self.train_inputs.shape[0] + elif self.mode == 'test': + return self.test_inputs.shape[0] + + def __getitem__(self, idx): + if self.mode == 'train': + return self.train_inputs[idx], self.train_labels[idx], self.train_outputs[idx] + elif self.mode == 'test': + return self.test_inputs[idx], self.test_labels[idx], self.test_outputs[idx] + + +if __name__ == "__main__": + app = MyDataset('../dataset/SWAT/train.csv', test_path='../dataset/SWAT/test.csv', input_size=3) + print(app) diff --git a/preprocess/template.py b/preprocess/template.py new file mode 100644 index 0000000..1c4d6a5 --- /dev/null +++ b/preprocess/template.py @@ -0,0 +1,61 @@ +""" +模板文件，有自定义预处理方法可以通过编辑本文件实现数据集预处理。 +编辑完成以后，请将文件名修改，写入config.ini，并在同级目录下init文件添加本文件 +""" + + +from torch.utils.data import Dataset + + +class DataSet(Dataset): + def __init__(self, train_path: str = None, test_path: str = None, input_size: int = 1, output_size: int = 1, + step: int = 1, mode: str = 'train', del_time: bool = True, del_column_name: bool = True, + reverse_label: bool = True): + """ + 可以将csv文件批量转成tensor + + 注意：必须包含以下变量或方法。 + 变量：self.train_inputs、self.train_labels、self.train_outputs + self.test_inputs、self.test_labels、self.test_outputs、self.mode + 方法：__len__()、__getitem__() + + :param train_path: str类型。训练数据集路径。 + :param test_path: str类型。测试数据集路径。 + :param input_size: int类型。输入数据长度。 + :param output_size: int类型。输出数据长度。 + :param step: int类型。截取数据的窗口移动间隔。 + :param mode: str类型。train或者test，用于指示使用训练集数据还是测试集数据。 + :param del_time: bool类型。True为删除时间戳列，False为不删除。 + :param del_column_name: bool类型。文件中第一行为列名时，使用True。 + :param reverse_label: bool类型。转化标签，0和1互换。标签统一采用正常为0异常为1的格式，若原文件中不符和该规定，使用True。 + """ + + self.mode = mode + self.train_inputs = None # 训练时的输入数据，Tensor格式，尺寸为[N,L,D]。N表示训练数据的数量，L表示每条数据的长度（由多少个时间点组成的数据），D表示数据维度数量 + self.train_labels = None # 训练时的数据标签，Tensor格式，尺寸为[N,1]。 + self.train_outputs = None # 训练时的输出数据，Tensor格式，尺寸为[N,L,D]。 + self.test_inputs = None # 测试时的输入数据，Tensor格式，尺寸为[N,L,D]。 + self.test_labels = None # 测试时的数据标签，Tensor格式，尺寸为[N,1]。 + self.test_outputs = None # 测试时的输出数据，Tensor格式，尺寸为[N,L,D]。 + + def __len__(self): + """ + 提供数据集长度 + :return: 测试集或者训练集数据长度N + """ + if self.mode == 'train': + return self.train_inputs.shape[0] + elif self.mode == 'test': + return self.test_inputs.shape[0] + + def __getitem__(self, idx): + """ + 获取数据 + :param idx: 数据序号 + :return: 对应的输入数据、标签、输出数据 + """ + if self.mode == 'train': + return self.train_inputs[idx], self.train_labels[idx], self.train_outputs[idx] + elif self.mode == 'test': + return self.test_inputs[idx], self.test_labels[idx], self.test_outputs[idx] + diff --git a/records/2023-04-06_14-37-45/log b/records/2023-04-06_14-37-45/log new file mode 100644 index 0000000..e69de29 diff --git a/records/2023-04-10_10-30-27/detection_result/MtadGatAtt_SWAT.csv b/records/2023-04-10_10-30-27/detection_result/MtadGatAtt_SWAT.csv new file mode 100644 index 0000000..1b1997c --- /dev/null +++ b/records/2023-04-10_10-30-27/detection_result/MtadGatAtt_SWAT.csv 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+++ b/requirements.txt @@ -0,0 +1,8 @@ +eventlet==0.33.3 +gevent==22.10.2 +loguru==0.6.0 +numpy==1.21.2 +pandas==1.4.1 +scikit_learn==1.2.2 +torch==1.10.2 +tqdm==4.63.1 diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..59202da --- /dev/null +++ b/utils.py @@ -0,0 +1,318 @@ +import os +import numpy +import torch.nn as nn +import torch +import torch.utils.data as tud +import preprocess +import evaluation +from loguru import logger +import time +from tqdm import tqdm + + +def create_dataloader(dataset_name: str, input_size: int = 1, output_size: int = 1, step: int = 1, batch_size: int = 1, + time_index: bool = True, del_column_name: bool = True, + preprocess_name: str = "standardization") -> (tud.DataLoader, tud.DataLoader): + """ + 针对一个数据集，构建Dataloader + :param dataset_name: 数据集名称 + :param input_size: 输入数据长度 + :param output_size: 输出数据长度 + :param step: 截取数据的窗口移动间隔 + :param batch_size: batch的大小 + :param time_index: True为第一列是时间戳，False为不 + :param del_column_name: 文件中第一行为列名时，使用True + :param preprocess_name: 预处理方法 + :return: 训练数据与测试数据 + """ + ds = eval(f"preprocess.{preprocess_name}.MyDataset")(name=dataset_name.replace("/", "-"), + train_path=f"./dataset/{dataset_name}/train.csv", + test_path=f"./dataset/{dataset_name}/test.csv", + input_size=input_size, output_size=output_size, + step=step, time_index=time_index, + del_column_name=del_column_name) + normal_dl = tud.DataLoader(dataset=ds, batch_size=batch_size, shuffle=True) + ds.mode = "test" + attack_dl = tud.DataLoader(dataset=ds, batch_size=batch_size, shuffle=False) + return normal_dl, attack_dl + + +def create_all_dataloader(datasets: [str], input_size: int = 1, output_size: int = 1, step: int = 1, + batch_size: int = 1, time_index: bool = True, del_column_name: bool = True, + preprocess_name: str = "standardization") -> [{}]: + """ + 对所有数据集构建dataloader + :param datasets: 数据集列表 + :param input_size: 输入数据长度 + :param output_size: 输出数据长度 + :param step: 截取数据的窗口移动间隔 + :param batch_size: batch的大小 + :param time_index: True为第一列是时间戳，False为不。 + :param del_column_name: 文件中第一行为列名时，使用True + :param preprocess_name: 预处理方法 + :return: 所有数据集的dataloader构建结果 + """ + all_dataloader = [] + for dataset_name in datasets: + logger.info(f'开始建立 dataloader {dataset_name}') + if "train.csv" in os.listdir(f"./dataset/{dataset_name}"): + normal_dl, attack_dl = create_dataloader(dataset_name=dataset_name, input_size=input_size, + output_size=output_size, step=step, batch_size=batch_size, + time_index=time_index, del_column_name=del_column_name, + preprocess_name=preprocess_name) + all_dataloader.append([{ + 'dataset_name': dataset_name, + 'normal': normal_dl, + 'attack': attack_dl + }]) + else: + all_sub_dataloader = [] + for sub_dataset_dir in os.listdir(f"./dataset/{dataset_name}"): + sub_dataset_name = f"{dataset_name}/{sub_dataset_dir}" + normal_dl, attack_dl = create_dataloader(dataset_name=sub_dataset_name, input_size=input_size, + output_size=output_size, step=step, batch_size=batch_size, + del_time=del_time, del_column_name=del_column_name) + all_sub_dataloader.append({ + 'dataset_name': sub_dataset_name.replace("/", "-"), + 'normal': normal_dl, + 'attack': attack_dl + }) + all_dataloader.append(all_sub_dataloader) + + logger.info(f'完成建立 dataloader {dataset_name}') + return all_dataloader + + +class EvaluationScore: + def __init__(self, evaluations: [str], attack=1): + """ + 用于自动划分阈值并进行批量评估 + :param evaluations: 使用的评估方法名称（需在evaluation文件夹中进行定义） + :param attack: 异常的标签，0 or 1 + """ + self.time = 0 + self.f1 = 0 + self.f1_pa = 0 + self.f_tad = 0 + self.attack = attack + self.normal = 1 - attack + self._total_y_loss = None + self._total_label = None + self._total_pred_label = None + self.true_pred_df = None + self.true_pred_dict = None + self.evaluations = evaluations + self.scores = {} + + def add_data(self, y_loss, true_label, pred_label=None): + """ + 添加每个batch的数据 + :param y_loss: 数据偏差 + :param true_label: 真实数据标签 + :param pred_label: 预测标签 + """ + if pred_label is not None: + if self._total_label is None and self._total_pred_label is None: + self._total_label = true_label + self._total_pred_label = pred_label + else: + self._total_label = torch.cat([self._total_label, true_label], dim=0) + self._total_pred_label = torch.cat([self._total_pred_label, pred_label], dim=0) + return + + y_loss = y_loss.view(-1).cpu().detach().numpy() + true_label = true_label.view(-1).cpu().detach().numpy() + + if self._total_y_loss is None and self._total_label is None: + self._total_y_loss = y_loss + self._total_label = true_label + return + self._total_y_loss = numpy.concatenate((self._total_y_loss, y_loss), axis=0) + self._total_label = numpy.concatenate((self._total_label, true_label), axis=0) + + def best_threshold(self, true_label: list, y_loss: list) -> dict: + ret = {} + for func_name in self.evaluations: + threshold_max = max(y_loss) + threshold_min = 0 + best_threshold = 0 + for _ in range(5): + threshold_list = [threshold_max - i * (threshold_max - threshold_min) / 10 for i in range(11)] + f1_list = [] + for threshold_one in threshold_list: + prediction_loss = numpy.where(numpy.array(y_loss) > threshold_one, self.attack, self.normal) + f1 = eval(f"evaluation.{func_name}.evaluate")(y_true=true_label, y_pred=prediction_loss.tolist()) + f1_list.append(f1) + ind = f1_list.index(max(f1_list)) + best_threshold = threshold_list[ind] + if ind == 0: + threshold_max = threshold_list[ind] + threshold_min = threshold_list[ind+1] + elif ind == len(threshold_list)-1: + threshold_max = threshold_list[ind-1] + threshold_min = threshold_list[ind] + else: + threshold_max = threshold_list[ind-1] + threshold_min = threshold_list[ind+1] + ret[func_name] = best_threshold + return ret + + def auto_threshold(self): + if self._total_pred_label is not None: + return + self._total_y_loss[numpy.isnan(self._total_y_loss)] = 0 + self._total_y_loss = self._total_y_loss / max(self._total_y_loss) + thresholds = self.best_threshold( + self._total_label.reshape(-1).data.tolist(), self._total_y_loss.reshape(-1).data.tolist()) + self.true_pred_dict = { + 'true': self._total_label.squeeze().tolist() + } + for func_name in thresholds: + self.true_pred_dict[func_name] = \ + numpy.where(self._total_y_loss > thresholds[func_name], self.attack, self.normal).squeeze().tolist() + # self.true_pred_df = pandas.DataFrame(self.true_pred_dict) + for func_name in self.true_pred_dict: + if func_name == "true": + continue + self.scores[func_name] = self.get_score(func_name) + + def get_score(self, func_name): + if self._total_pred_label is not None: + return eval(f"evaluation.{func_name}.evaluate")(self._total_label.reshape(-1).tolist(), + self._total_pred_label.reshape(-1).tolist()) + return eval(f"evaluation.{func_name}.evaluate")(self._total_label.reshape(-1).tolist(), + self.true_pred_dict[f"{func_name}"]) + + def __str__(self): + res = "" + for func_name in self.scores: + res += f"{func_name}={self.scores[func_name]:.3f} " + return res[:-1] + + +def train_model(epoch: int, optimizer: torch.optim, dataloader: tud.DataLoader, model: nn.Module, + device: str = "cpu") -> (nn.Module, str): + """ + 训练模型 + :param epoch: 当前训练轮数 + :param optimizer: 优化器 + :param dataloader: 数据集 + :param model: 模型 + :param device: 训练设备使用cpu还是gpu + :return: 训练完成的模型；训练完成需要输出的信息 + """ + model.train() + avg_loss = [] + dataloader.dataset.mode = "train" + start_time = time.time() + with tqdm(total=len(dataloader), ncols=150) as _tqdm: + _tqdm.set_description(f'（进度条部分不会写进本地日志）epoch:{epoch}，训练进度') + for data in dataloader: + x = data[0].to(device) + y_true = data[2].to(device) + optimizer.zero_grad() + loss = model.loss(x=x, y_true=y_true, epoch=epoch, device=device) + avg_loss.append(loss) + optimizer.step() + _tqdm.set_postfix(loss='{:.6f}'.format(sum(avg_loss) / len(avg_loss))) + _tqdm.update(1) + end_time = time.time() + info = f"epoch={epoch}, average loss={'{:.6f}'.format(sum(avg_loss) / len(avg_loss))}, " \ + f"train time={'{:.1f}'.format(end_time-start_time)}s" + return model, info + + +def test_model(dataloader: tud.DataLoader, model: nn.Module, evaluations: [str], device: str = "cpu") -> \ + (EvaluationScore, str): + """ + 测试模型 + :param dataloader: 数据集 + :param model: 模型 + :param device: 训练设备使用cpu还是gpu + :return: 评估分数；测试完成需要输出的信息 + """ + es = EvaluationScore(evaluations) + model.eval() + dataloader.dataset.mode = "test" + start_time = time.time() + with tqdm(total=len(dataloader), ncols=150) as _tqdm: + _tqdm.set_description(f'（进度条部分不会写进本地日志）测试进度') + with torch.no_grad(): + for data in dataloader: + x = data[0].to(device) + y_true = data[2].to(device) + label_true = data[1].int().to(device) + y_loss, label_pred = model.detection(x=x, y_true=y_true, device=device) + if label_pred is not None: + es.add_data(y_loss=None, true_label=label_true, pred_label=label_pred) + else: + es.add_data(y_loss=y_loss, true_label=label_true, pred_label=None) + _tqdm.update(1) + end_time = time.time() + es.auto_threshold() + es_score = es.__str__().replace(" ", ", ") + info = f"{es_score}, test time={'{:.1f}'.format(end_time-start_time)}s" + return es, info + + +def train_and_test_model(start_time: str, epochs: int, normal_dataloader: tud.DataLoader, attack_dataloader: tud.DataLoader, + model: nn.Module, evaluations: [str], device: str = "cpu", lr: float = 1e-4, + model_path: str = None, train: bool = True) -> (dict, dict): + """ + 训练与测试 + :param start_time: 实验的开始时间。此处用于寻找存放路径。 + :param epochs: 总共训练轮数 + :param normal_dataloader: 训练数据集 + :param attack_dataloader: 测试数据集 + :param model: 模型 + :param evaluations: 评估方法 + :param device: 设备 + :param lr: 学习率 + :param model_path: 模型参数文件路径 + :param train: 是否训练，如果为否，则仅进行测试 + :return: 各个评估方法的最佳分数、各个评估方法最佳情况下的检测标签 + """ + dataset_name = normal_dataloader.dataset.name + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + if model_path: + try: + checkpoint = torch.load(model_path) + model.load_state_dict(checkpoint['model_state_dict']) + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + logger.info(f"模型参数文件{model_path}加载成功") + except: + logger.warning(f"模型参数文件{model_path}加载失败，将训练新模型") + logger.info(f"模型：{model.name}，数据集：{dataset_name}，设备：{device}，训练开始") + best_score = {} + best_detection = {} + if train: + logger.info(f"模式：训练并测试") + for epoch in range(1, epochs+1): + model, train_info = train_model(epoch=epoch, optimizer=optimizer, dataloader=normal_dataloader, model=model, + device=device) + es, test_info = test_model(dataloader=attack_dataloader, model=model, evaluations=evaluations, + device=device) + logger.info(f"{train_info}, {test_info}") + es_score = es.__str__().replace(" ", "_") + torch.save({ + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict() + }, f'./records/{start_time}/model/model={model.name}_dataset={dataset_name}_epoch={epoch}_{es_score}.pth') + for func_name in es.scores: + if func_name not in best_score or es.scores[func_name] > best_score[func_name]: + best_score[func_name] = es.scores[func_name] + best_detection[func_name] = es.true_pred_dict[func_name] + best_detection["true"] = es.true_pred_dict["true"] + else: + logger.info(f"模式：仅进行测试") + es, test_info = test_model(dataloader=attack_dataloader, model=model, evaluations=evaluations, device=device) + logger.info(test_info) + for func_name in es.scores: + best_score[func_name] = es.scores[func_name] + best_detection[func_name] = es.true_pred_dict[func_name] + best_detection["true"] = es.true_pred_dict["true"] + return best_score, best_detection + + + +