This folder contains a python3 file, a license file and several datasets on whch we can run SPOT and its variants. The python file spot.py contains 4 classes : SPOT, DSPOT, biSPOT and biDSPOT. The classical algorithms (SPOT and DSPOT) compute only upper threshold although biSPOT and biDSPOT computes upper and lower thresholds.
The following packages are needed to run the algorithm
scipy[optimization]numpy[array class]pandas[dataframe]matplotlib[plot]tqdm[progress bar]
These libraries can be downloaded through pip3 :
pip3 install scipy numpy pandas matplotlib tqdm --upgrade
The folder contains several datasets on which (D)SPOT can be run
-
mawi_170812_50_50.csvandmawi_180812_50_50.csvThese .csv gather aggregated NetFlow measures of MAWI network captures (17/08/2012 and 18/08/2012) -
physics.datNorth-South Component of the magnetic field (in nT) from the ACE satellite between 1/1/1995 and 1/6/1995 -
edf_stocks.csvEDF stocks prices from 23/01/2017 to 10/02/2017 -
rain.datDaily precipitation rate at (71.4262, -58.1250) from 1947 and 2007 in mm/s
Here some examples are given to test the algorithms. We advice to use the ipython3 shell with matplotlib to run these script and display plots.
To install ipython3
sudo apt-get install ipython3
To run from the command line
ipython3 --matplotlib
Then you can copy/paste the scripts you want to run.
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from spot import bidSPOT
f = './physics.dat'
r = open(f,'r').read().split(',')
X = np.array(list(map(float,r)))
n_init = 2000
init_data = X[:n_init] # initial batch
data = X[n_init:] # stream
q = 1e-3 # risk parameter
d = 450 # depth parameter
s = bidSPOT(q,d) # biDSPOT object
s.fit(init_data,data) # data import
s.initialize() # initialization step
results = s.run() # run
s.plot(results) # plot#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from spot import SPOT
f = './rain.dat'
r = open(f,'r').read().split(',')
X = np.array(list(map(float,r)))
n_init = 1000
init_data = X[:n_init] # initial batch
data = X[n_init:] # stream
q = 1e-4 # risk parameter
s = SPOT(q) # biDSPOT object
s.fit(init_data,data) # data import
s.initialize() # initialization step
results = s.run() # run
s.plot(results) # plot#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from spot import SPOT
import pandas as pd
f17 = './mawi_170812_50_50.csv'
f18 = './mawi_180812_50_50.csv'
P17 = pd.DataFrame.from_csv(f17)
P18 = pd.DataFrame.from_csv(f18)
X17 = P17['rSYN'].values
X18 = P18['rSYN'].values
n_init = 1000
init_data = X17[-n_init:] # initial batch
data = X18 # stream
q = 1e-4 # risk parameter
s = SPOT(q) # SPOT object
s.fit(init_data,data) # data import
s.initialize() # initialization step
results = s.run() # run
s.plot(results) # plot#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from spot import bidSPOT,dSPOT
import pandas as pd
f = './edf_stocks.csv'
P = pd.DataFrame.from_csv(f)
# stream
u_data = (P['DATE'] == '2017-02-09')
data = P['LOW'][u_data].values
# initial batch
u_init_data = (P['DATE'] == '2017-02-08') | (P['DATE'] == '2017-02-07') | (P['DATE'] == '2017-02-06')
init_data = P['LOW'][u_init_data].values
q = 1e-5 # risk parameter
d = 10 # depth
s = bidSPOT(q,d) # bidSPOT object
s.fit(init_data,data) # data import
s.initialize() # initialization step
results = s.run() # run
#del results['upper_thresholds'] # we can delete the upper thresholds
s.plot(results) # plot
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent