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preprocess.py
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preprocess.py
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import os
import sys
import pandas as pd
import numpy as np
import pickle
import json
from src.folderconstants import *
from shutil import copyfile
datasets = ['synthetic', 'SMD', 'SWaT', 'SMAP', 'MSL', 'WADI', 'MSDS', 'UCR', 'MBA', 'NAB']
wadi_drop = ['2_LS_001_AL', '2_LS_002_AL','2_P_001_STATUS','2_P_002_STATUS']
def load_and_save(category, filename, dataset, dataset_folder):
temp = np.genfromtxt(os.path.join(dataset_folder, category, filename),
dtype=np.float64,
delimiter=',')
print(dataset, category, filename, temp.shape)
np.save(os.path.join(output_folder, f"SMD/{dataset}_{category}.npy"), temp)
return temp.shape
def load_and_save2(category, filename, dataset, dataset_folder, shape):
temp = np.zeros(shape)
with open(os.path.join(dataset_folder, 'interpretation_label', filename), "r") as f:
ls = f.readlines()
for line in ls:
pos, values = line.split(':')[0], line.split(':')[1].split(',')
start, end, indx = int(pos.split('-')[0]), int(pos.split('-')[1]), [int(i)-1 for i in values]
temp[start-1:end-1, indx] = 1
print(dataset, category, filename, temp.shape)
np.save(os.path.join(output_folder, f"SMD/{dataset}_{category}.npy"), temp)
def normalize(a):
a = a / np.maximum(np.absolute(a.max(axis=0)), np.absolute(a.min(axis=0)))
return (a / 2 + 0.5)
def normalize2(a, min_a = None, max_a = None):
if min_a is None: min_a, max_a = min(a), max(a)
return (a - min_a) / (max_a - min_a), min_a, max_a
def normalize3(a, min_a = None, max_a = None):
if min_a is None: min_a, max_a = np.min(a, axis = 0), np.max(a, axis = 0)
return (a - min_a) / (max_a - min_a + 0.0001), min_a, max_a
def convertNumpy(df):
x = df[df.columns[3:]].values[::10, :]
return (x - x.min(0)) / (x.ptp(0) + 1e-4)
def load_data(dataset):
folder = os.path.join(output_folder, dataset)
os.makedirs(folder, exist_ok=True)
if dataset == 'synthetic':
train_file = os.path.join(data_folder, dataset, 'synthetic_data_with_anomaly-s-1.csv')
test_labels = os.path.join(data_folder, dataset, 'test_anomaly.csv')
dat = pd.read_csv(train_file, header=None)
split = 10000
train = normalize(dat.values[:, :split].reshape(split, -1))
test = normalize(dat.values[:, split:].reshape(split, -1))
lab = pd.read_csv(test_labels, header=None)
lab[0] -= split
labels = np.zeros(test.shape)
for i in range(lab.shape[0]):
point = lab.values[i][0]
labels[point-30:point+30, lab.values[i][1:]] = 1
test += labels * np.random.normal(0.75, 0.1, test.shape)
for file in ['train', 'test', 'labels']:
np.save(os.path.join(folder, f'{file}.npy'), eval(file))
elif dataset == 'SMD':
dataset_folder = 'data/SMD'
file_list = os.listdir(os.path.join(dataset_folder, "train"))
for filename in file_list:
if filename.endswith('.txt'):
load_and_save('train', filename, filename.strip('.txt'), dataset_folder)
s = load_and_save('test', filename, filename.strip('.txt'), dataset_folder)
load_and_save2('labels', filename, filename.strip('.txt'), dataset_folder, s)
elif dataset == 'UCR':
dataset_folder = 'data/UCR'
file_list = os.listdir(dataset_folder)
for filename in file_list:
if not filename.endswith('.txt'): continue
vals = filename.split('.')[0].split('_')
dnum, vals = int(vals[0]), vals[-3:]
vals = [int(i) for i in vals]
temp = np.genfromtxt(os.path.join(dataset_folder, filename),
dtype=np.float64,
delimiter=',')
min_temp, max_temp = np.min(temp), np.max(temp)
temp = (temp - min_temp) / (max_temp - min_temp)
train, test = temp[:vals[0]], temp[vals[0]:]
labels = np.zeros_like(test)
labels[vals[1]-vals[0]:vals[2]-vals[0]] = 1
train, test, labels = train.reshape(-1, 1), test.reshape(-1, 1), labels.reshape(-1, 1)
for file in ['train', 'test', 'labels']:
np.save(os.path.join(folder, f'{dnum}_{file}.npy'), eval(file))
elif dataset == 'NAB':
dataset_folder = 'data/NAB'
file_list = os.listdir(dataset_folder)
with open(dataset_folder + '/labels.json') as f:
labeldict = json.load(f)
for filename in file_list:
if not filename.endswith('.csv'): continue
df = pd.read_csv(dataset_folder+'/'+filename)
vals = df.values[:,1]
labels = np.zeros_like(vals, dtype=np.float64)
for timestamp in labeldict['realKnownCause/'+filename]:
tstamp = timestamp.replace('.000000', '')
index = np.where(((df['timestamp'] == tstamp).values + 0) == 1)[0][0]
labels[index-4:index+4] = 1
min_temp, max_temp = np.min(vals), np.max(vals)
vals = (vals - min_temp) / (max_temp - min_temp)
train, test = vals.astype(float), vals.astype(float)
train, test, labels = train.reshape(-1, 1), test.reshape(-1, 1), labels.reshape(-1, 1)
fn = filename.replace('.csv', '')
for file in ['train', 'test', 'labels']:
np.save(os.path.join(folder, f'{fn}_{file}.npy'), eval(file))
elif dataset == 'MSDS':
dataset_folder = 'data/MSDS'
df_train = pd.read_csv(os.path.join(dataset_folder, 'train.csv'))
df_test = pd.read_csv(os.path.join(dataset_folder, 'test.csv'))
df_train, df_test = df_train.values[::5, 1:], df_test.values[::5, 1:]
_, min_a, max_a = normalize3(np.concatenate((df_train, df_test), axis=0))
train, _, _ = normalize3(df_train, min_a, max_a)
test, _, _ = normalize3(df_test, min_a, max_a)
labels = pd.read_csv(os.path.join(dataset_folder, 'labels.csv'))
labels = labels.values[::1, 1:]
for file in ['train', 'test', 'labels']:
np.save(os.path.join(folder, f'{file}.npy'), eval(file).astype('float64'))
elif dataset == 'SWaT':
dataset_folder = 'data/SWaT'
file = os.path.join(dataset_folder, 'series.json')
df_train = pd.read_json(file, lines=True)[['val']][3000:6000]
df_test = pd.read_json(file, lines=True)[['val']][7000:12000]
train, min_a, max_a = normalize2(df_train.values)
test, _, _ = normalize2(df_test.values, min_a, max_a)
labels = pd.read_json(file, lines=True)[['noti']][7000:12000] + 0
for file in ['train', 'test', 'labels']:
np.save(os.path.join(folder, f'{file}.npy'), eval(file))
elif dataset in ['SMAP', 'MSL']:
dataset_folder = 'data/SMAP_MSL'
file = os.path.join(dataset_folder, 'labeled_anomalies.csv')
values = pd.read_csv(file)
values = values[values['spacecraft'] == dataset]
filenames = values['chan_id'].values.tolist()
for fn in filenames:
train = np.load(f'{dataset_folder}/train/{fn}.npy')
test = np.load(f'{dataset_folder}/test/{fn}.npy')
train, min_a, max_a = normalize3(train)
test, _, _ = normalize3(test, min_a, max_a)
np.save(f'{folder}/{fn}_train.npy', train)
np.save(f'{folder}/{fn}_test.npy', test)
labels = np.zeros(test.shape)
indices = values[values['chan_id'] == fn]['anomaly_sequences'].values[0]
indices = indices.replace(']', '').replace('[', '').split(', ')
indices = [int(i) for i in indices]
for i in range(0, len(indices), 2):
labels[indices[i]:indices[i+1], :] = 1
np.save(f'{folder}/{fn}_labels.npy', labels)
elif dataset == 'WADI':
dataset_folder = 'data/WADI'
ls = pd.read_csv(os.path.join(dataset_folder, 'WADI_attacklabels.csv'))
train = pd.read_csv(os.path.join(dataset_folder, 'WADI_14days.csv'), skiprows=1000, nrows=2e5)
test = pd.read_csv(os.path.join(dataset_folder, 'WADI_attackdata.csv'))
train.dropna(how='all', inplace=True); test.dropna(how='all', inplace=True)
train.fillna(0, inplace=True); test.fillna(0, inplace=True)
test['Time'] = test['Time'].astype(str)
test['Time'] = pd.to_datetime(test['Date'] + ' ' + test['Time'])
labels = test.copy(deep = True)
for i in test.columns.tolist()[3:]: labels[i] = 0
for i in ['Start Time', 'End Time']:
ls[i] = ls[i].astype(str)
ls[i] = pd.to_datetime(ls['Date'] + ' ' + ls[i])
for index, row in ls.iterrows():
to_match = row['Affected'].split(', ')
matched = []
for i in test.columns.tolist()[3:]:
for tm in to_match:
if tm in i:
matched.append(i); break
st, et = str(row['Start Time']), str(row['End Time'])
labels.loc[(labels['Time'] >= st) & (labels['Time'] <= et), matched] = 1
train, test, labels = convertNumpy(train), convertNumpy(test), convertNumpy(labels)
print(train.shape, test.shape, labels.shape)
for file in ['train', 'test', 'labels']:
np.save(os.path.join(folder, f'{file}.npy'), eval(file))
elif dataset == 'MBA':
dataset_folder = 'data/MBA'
ls = pd.read_excel(os.path.join(dataset_folder, 'labels.xlsx'))
train = pd.read_excel(os.path.join(dataset_folder, 'train.xlsx'))
test = pd.read_excel(os.path.join(dataset_folder, 'test.xlsx'))
train, test = train.values[1:,1:].astype(float), test.values[1:,1:].astype(float)
train, min_a, max_a = normalize3(train)
test, _, _ = normalize3(test, min_a, max_a)
ls = ls.values[:,1].astype(int)
labels = np.zeros_like(test)
for i in range(-20, 20):
labels[ls + i, :] = 1
for file in ['train', 'test', 'labels']:
np.save(os.path.join(folder, f'{file}.npy'), eval(file))
else:
raise Exception(f'Not Implemented. Check one of {datasets}')
if __name__ == '__main__':
commands = sys.argv[1:]
load = []
if len(commands) > 0:
for d in commands:
load_data(d)
else:
print("Usage: python preprocess.py <datasets>")
print(f"where <datasets> is space separated list of {datasets}")