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hl.py
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import numpy as np
import matplotlib.pyplot as plt
from numpy import genfromtxt
import datetime
#The HardLimits class is capable of fetching data, performing anomaly detection on said data, calculates the score and plot the result.
class hl():
#Function that loads data into a numpy matrix from csv file.
#These files are generated by running ./garch/garch_long.py
def getData(datatype="D1", size="100"):
D = genfromtxt('./garch/' + datatype + '_' + size + '.csv', delimiter=',')
D_no_anomalies = genfromtxt('./garch/' + datatype + '_unpolluted_' + size + '.csv', delimiter=',')
D_truth = genfromtxt('./garch/' + datatype + '_truth_' + size + '.csv', delimiter=',')
return D, D_no_anomalies, D_truth
def HL(self, data):
λ = .07
UCL = np.empty_like(data)
LCL = np.empty_like(data)
for i in range(0,len(data)):
UCL[i] = data[i] + λ*abs(data[i])
LCL[i] = data[i] - λ*abs(data[i])
return UCL, LCL
#Expected three tensorflow matrices of datapoints.
#Runs the AD and prints results.
def runAnomalyDetection(self, D, D_truth, D_unpolluted, dataType, dataSize):
FP = 0.
FN = 0.
TP = 0.
TN = 0.
FN_ALO = 0.
TP_ALO = 0.
FN_LS = 0.
TP_LS = 0.
FN_TC = 0.
TP_TC = 0.
FN_LTO = 0.
TP_LTO = 0.
FN_SALO = 0.
TP_SALO = 0.
reaction_counter = 0.
tot_reaction = 0.
print("Running Hardlimits-AD")
guessed_anomalies = np.zeros((len(D), len(D[0])))
for i in range(len(D)):
UCL, LCL = self.HL(D[i])
for t in range(int(0.1*len(D[i])), len(D[i])):
if UCL[t-1] < D[i][t] or D[i][t] < LCL[t-1]:
guessed_anomalies[i][t] = 1 #This is an anomaly guess
#Evaluate
in_anomaly_window = False
anomaly_window_type = 0
has_flagged_anomaly_window = False
for t in range(1, len(D[i])):
#Exiting an anomaly window. Check if it has been flagged.
if D_truth[i][t] == 0 and in_anomaly_window:
in_anomaly_window = False
if not has_flagged_anomaly_window:
FN += 1 #Failed to flag the entire anomaly window.
if int(anomaly_window_type) == 1:
FN_ALO += 1 #Failed to flag the entire anomaly window.
elif int(anomaly_window_type) == 2:
FN_LS += 1 #Failed to flag the entire anomaly window.
elif int(anomaly_window_type) == 3:
FN_TC += 1 #Failed to flag the entire anomaly window.
elif int(anomaly_window_type) == 4:
FN_LTO += 1 #Failed to flag the entire anomaly window.
elif int(anomaly_window_type) == 5:
FN_SALO += 1 #Failed to flag the entire anomaly window.
else:
print("ERROR ANOMALY WINDOW HAD NO TYPE!")
print(int(anomaly_window_type))
else:
has_flagged_anomaly_window = False
reaction_counter = 0.
anomaly_window_type = 0
#Entering an anomaly window.
if D_truth[i][t] != 0 and not in_anomaly_window:
in_anomaly_window = True
anomaly_window_type = D_truth[i][t]
if guessed_anomalies[i][t] == 0 and not in_anomaly_window:
TN += 1 #Correct to not guess for an anomaly.
if guessed_anomalies[i][t] != 0 and in_anomaly_window and not has_flagged_anomaly_window:
TP += 1 #Correct guess, within anomaly window.
tot_reaction += reaction_counter #Add reaction counter to av
reaction_counter = 0.
has_flagged_anomaly_window = True
if int(anomaly_window_type) == 1:
TP_ALO += 1
elif int(anomaly_window_type) == 2:
TP_LS += 1
elif int(anomaly_window_type) == 3:
TP_TC += 1
elif int(anomaly_window_type) == 4:
TP_LTO += 1
elif int(anomaly_window_type) == 5:
TP_SALO += 1
else:
print("ERROR ANOMALY WINDOW HAD NO TYPE!")
print(int(anomaly_window_type))
if guessed_anomalies[i][t] != 0 and not in_anomaly_window:
FP += 1 #Erroneous guess, outside anomaly window.
if guessed_anomalies[i][t] == 0 and in_anomaly_window and not has_flagged_anomaly_window:
reaction_counter += 1 #Failed to react to an anomaly which was present in the current timestep.
print("|" + "TP: " + str(TP) + "|TN: " + str(TN) + "|" + "FP: " + str(FP) + "|" + "FN: " + str(FN) + "|")
precision = TP / (TP + FP + 0.000000001)
recall = TP / (TP + FN)
avg_reaction = tot_reaction/(TP + 0.000000001)
if precision == 0 and recall == 0:
print("Somethings is wrong. No TP's")
precision = 0.0000001
recall = 0.00000001
F1 = 2* (precision * recall) / (precision + recall)
print("Anomalies: " + str(TP + FN))
print("Guesses: " + str(TP + FP))
print("precision: " + str(precision))
print("recall: " + str(recall))
print("F1: " + str(F1))
print("Reaction: " + str(avg_reaction))
print("TP_ALO: " + str(TP_ALO))
print("FN_ALO: " + str(FN_ALO))
print("TP_LS: " + str(TP_LS))
print("FN_LS: " + str(FN_LS))
print("TP_TC: " + str(TP_TC))
print("FN_TC: " + str(FN_TC))
print("TP_LTO: " + str(TP_LTO))
print("FN_LTO: " + str(FN_LTO))
print("TP_SALO: " + str(TP_SALO))
print("FN_SALO: " + str(FN_SALO))
with open(str(dataType) + "_" + str(dataSize) + '_result_hl.txt', 'a') as the_result_file:
the_result_file.write("\n\n\n")
the_result_file.write(datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))
the_result_file.write("\n")
the_result_file.write("|" + "TP: " + str(TP) + "|TN: " + str(TN) + "|" + "FP: " + str(FP) + "|" + "FN: " + str(FN) + "|")
the_result_file.write("\nAnomalies: " + str(TP + FN))
the_result_file.write("\nGuesses: " + str(TP + FP))
the_result_file.write("\nprecision: " + str(precision))
the_result_file.write("\nrecall: " + str(recall))
the_result_file.write("\nF1: " + str(F1))
the_result_file.write("\nReaction: " + str(avg_reaction))
the_result_file.write("\nTP_ALO: " + str(TP_ALO))
the_result_file.write("\nFN_ALO: " + str(FN_ALO))
the_result_file.write("\nTP_LS: " + str(TP_LS))
the_result_file.write("\nFN_LS: " + str(FN_LS))
the_result_file.write("\nTP_TC: " + str(TP_TC))
the_result_file.write("\nFN_TC: " + str(FN_TC))
the_result_file.write("\nTP_LTO: " + str(TP_LTO))
the_result_file.write("\nFN_LTO: " + str(FN_LTO))
the_result_file.write("\nTP_SALO: " + str(TP_SALO))
the_result_file.write("\nFN_SALO: " + str(FN_SALO))
return guessed_anomalies
def plotAnomalyDetection(self, D, D_truth, D_unpolluted, D_guesses = None):
if D_guesses is None:
D_guesses = np.zeros((len(D), len(D[0])))
for sample_nr in range(min(len(D),10)):
UCL, LCL = self.HL(D[sample_nr])
x = range(len(D[sample_nr]))
at = np.trim_zeros(D_truth[sample_nr])
UCL = np.insert(UCL, 0, np.mean(UCL))
UCL = np.delete(UCL, -1)
LCL = np.insert(LCL, 0, np.mean(LCL))
LCL = np.delete(LCL, -1)
plt.fill_between(x, UCL, LCL, facecolor='green', alpha=0.4)
all_gi = np.argwhere(D_guesses[sample_nr] != 0) #all guesses
all_ai = np.argwhere(D_truth[sample_nr] != 0) #all actuall anomalies
for gi in all_gi:
dot_wrong, = plt.plot(gi, D[sample_nr][gi], 'yo', label='Incorrect Guess')
#plt.axvspan(ai-0.1, ai+0.1, facecolor='#990000', alpha=0.8)
for ai in all_ai:
dot_actual, = plt.plot(ai, D[sample_nr][ai], 'ro', label='Anomaly')
if ai in all_gi:
dot_correct, = plt.plot(ai, D[sample_nr][ai], 'ko', label='Correct Guess')
#plt.axvspan(ai-0.1, ai+0.1, facecolor='#990000', alpha=0.8)
line_actual, = plt.plot(D[sample_nr], 'r-', label='True Values')
line_unpolluted, = plt.plot(D_unpolluted[sample_nr], label='True Values without Anomalies')
plt.axvline(x=int(len(D[sample_nr])*0.1), color='grey')
plt.title("HL on anomlies that are present in this serie: " + str(np.unique(at)))
plt.legend(handles=[line_actual, line_unpolluted, dot_wrong, dot_actual, dot_correct])
plt.show()