forked from Alqatf/AnomalyDetection
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generate_plots.py
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generate_plots.py
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# -*- coding: utf-8 -*-
"""
http://www.astroml.org/sklearn_tutorial/dimensionality_reduction.html
"""
print (__doc__)
import numpy as np
import copy
import cPickle as pickle
import matplotlib
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from matplotlib import gridspec
import nslkdd.preprocessing as preprocessing
import nslkdd.data.model as model
from nslkdd.get_kdd_dataframe import attack_types
from nslkdd.get_kdd_dataframe import df_by_attack_type
import colorhex
import util
import logger
today = util.make_today_folder('./results')
today = "./results/2015-02-09"
plot_lim_max = 21
plot_lim_min = -21
def plot_true_labels(ax, data_per_true_labels, title="", highlight_point = None):
ax.set_title("True labels")
for i, p in enumerate(data_per_true_labels) :
x = np.array([t[0] for t in p])
y = np.array([t[1] for t in p])
if i == model.attack_normal:
colors = ['g'] * len(x)
ax.scatter(x, y, c=colors)
elif i != model.attack_normal and i != highlight_point:
colors = ['r'] * len(x)
ax.scatter(x, y, c=colors)
if highlight_point != None :
p = data_per_true_labels[highlight_point]
x = np.array([t[0] for t in p])
y = np.array([t[1] for t in p])
colors = ['y'] * len(x)
ax.scatter(x, y, c=colors)
def plot_normal_label(ax, data_per_true_labels, title=""):
ax.set_title(title)
for i, p in enumerate(data_per_true_labels) :
x = [t[0] for t in p]
y = [t[1] for t in p]
x = np.array(x)
y = np.array(y)
if i == model.attack_normal:
ax.scatter(x, y, c='g')
logger.debug("* mean/std of normal")
logger.debug(len(x))
logger.debug(np.mean(x))
logger.debug(np.mean(y))
logger.debug(np.std(x))
logger.debug(np.std(y))
def plot_abnormal_label(ax, data_per_true_labels, title=""):
ax.set_title(title)
for i, p in enumerate(data_per_true_labels) :
x = [t[0] for t in p]
y = [t[1] for t in p]
x = np.array(x)
y = np.array(y)
if i != model.attack_normal:
ax.scatter(x, y, c='r')
def get_data(title):
with open(today+'/'+title+'_cproj.pkl','rb') as input:
cproj = pickle.load(input)
with open(today+'/'+title+'_res.pkl','rb') as input:
res = pickle.load(input)
with open(today+'/'+title+'_df.pkl','rb') as input:
df = pickle.load(input)
with open(today+'/'+title+'_highlight_point.pkl','rb') as input:
highlight_point = pickle.load(input)
return cproj, res, df, highlight_point
def gen_plot(cproj, res, df, highlight_point, title):
_, attacks = preprocessing.get_header_data()
# figure setting
fig, axarr = plt.subplots(4, 4, sharex='col', sharey='row')
plt.subplots_adjust(wspace=0.4, hspace=0.4)
plt.xlim(plot_lim_min, plot_lim_max)
plt.ylim(plot_lim_min, plot_lim_max)
# plt.xlabel('interval')
# plt.ylabel('log(probability) + k')
# plt.title('Convergence plot')
# plt.grid(True)
data_per_true_labels = []
for i in range( len(attacks) ):
data_per_true_labels.append([])
true_attack_types = df["attack"].values.tolist()
for i, d in enumerate(cproj):
data_per_true_labels[true_attack_types[i]].append(d)
k = int( len(cproj) * 12/500.0)
clusters = [0] * k
cluster_xs = []
cluster_ys = []
for i in range(k):
cluster_xs.append([])
cluster_ys.append([])
cluster_xmeans = [0] * k
cluster_ymeans = [0] * k
cluster_xstds = [0] * k
cluster_ystds = [0] * k
for i, p in enumerate(cproj):
true_label = true_attack_types[i]
if true_label == model.attack_normal :
clusters[ res[i] ] = clusters[ res[i] ] + 1
else :
clusters[ res[i] ] = clusters[ res[i] ] - 1
cluster_xs[ res[i] ].append(p[0])
cluster_ys[ res[i] ].append(p[1])
logger.debug("* mean/std of cluster")
for i, cluster in enumerate(clusters) :
cluster_xmeans[i] = np.mean(cluster_xs[i])
cluster_ymeans[i] = np.mean(cluster_ys[i])
cluster_xstds[i] = np.std(cluster_xs[i])
cluster_ystds[i] = np.std(cluster_ys[i])
logger.debug("cluster : " + str(i))
logger.debug("- size [" + str(len(cluster_xs[i])) + "]")
logger.debug("- xmin [" + str(cluster_xmeans[i]) + "]")
logger.debug("- ymin [" + str(cluster_ymeans[i]) + "]")
logger.debug("- xstd [" + str(cluster_xstds[i]) + "]")
logger.debug("- ystd [" + str(cluster_ystds[i]) + "]")
ax1 = axarr[0, 0]
ax2 = axarr[0, 1]
ax3 = axarr[0, 2]
ax4 = axarr[0, 3]
ax5 = axarr[1, 0]
ax6 = axarr[1, 1]
ax7 = axarr[1, 2]
ax8 = axarr[1, 3]
ax9 = axarr[2, 0]
ax10 = axarr[2, 1]
ax11 = axarr[2, 2]
ax12 = axarr[2, 3]
ax13 = axarr[3, 0]
ax14 = axarr[3, 1]
ax15 = axarr[3, 2]
ax16 = axarr[3, 3]
plot_true_labels(ax1, data_per_true_labels, "True labels", highlight_point)
plot_normal_label(ax2, data_per_true_labels, "True normals")
plot_abnormal_label(ax3, data_per_true_labels, "True abnormal")
ax4.set_title("k-means")
for i, p in enumerate(cproj):
ax4.scatter(p[0], p[1], c=colorhex.codes[ res[i] ])
##############################################################
ax5.set_title("Normal res")
for i, p in enumerate(cproj):
if clusters[ res[i] ] >= 0 :
ax5.scatter(p[0], p[1], c='g')
##############################################################
ax6.set_title("Abnormal res")
for i, p in enumerate(cproj):
if clusters[ res[i] ] < 0 :
ax6.scatter(p[0], p[1], c='r')
##############################################################
ax7.set_title("Cluster 1")
for i, p in enumerate(cproj):
if res[i] == 0 :
ax7.scatter(p[0], p[1], c='g')
##############################################################
ax8.set_title("Cluster 2")
for i, p in enumerate(cproj):
if res[i] == 1 :
ax8.scatter(p[0], p[1], c='g')
##############################################################
# ax9.set_title("kmeans")
# kmean_plot(title, ax9)
##############################################################
ax9.set_title("Cluster 3")
for i, p in enumerate(cproj):
if res[i] == 2 :
ax9.scatter(p[0], p[1], c='g')
##############################################################
ax10.set_title("Cluster 4")
for i, p in enumerate(cproj):
if res[i] == 3 :
ax10.scatter(p[0], p[1], c='g')
##############################################################
ax11.set_title("Cluster 5")
for i, p in enumerate(cproj):
if res[i] == 4 :
ax11.scatter(p[0], p[1], c='g')
##############################################################
ax12.set_title("Cluster 6")
for i, p in enumerate(cproj):
if res[i] == 5 :
ax12.scatter(p[0], p[1], c='g')
##############################################################
ax13.set_title("Cluster 7")
for i, p in enumerate(cproj):
if res[i] == 6 :
ax13.scatter(p[0], p[1], c='g')
##############################################################
ax14.set_title("Cluster 8")
for i, p in enumerate(cproj):
if res[i] == 7 :
ax14.scatter(p[0], p[1], c='g')
##############################################################
ax15.set_title("Cluster 9")
for i, p in enumerate(cproj):
if res[i] == 8 :
ax15.scatter(p[0], p[1], c='g')
##############################################################
ax16.set_title("Cluster 10")
for i, p in enumerate(cproj):
if res[i] == 9 :
ax16.scatter(p[0], p[1], c='g')
##############################################################
print title + " has been saved"
fig.savefig(today + "/" + title + ".png")
plt.close()
fig, ax = plt.subplots(1, 1, sharex='col', sharey='row')
plt.subplots_adjust(wspace=0.4, hspace=0.4)
plt.xlim(plot_lim_min, plot_lim_max)
plt.ylim(plot_lim_min, plot_lim_max)
for i, p in enumerate(data_per_true_labels) :
x = np.array([t[0] for t in p])
y = np.array([t[1] for t in p])
if i == model.attack_normal:
colors = ['g'] * len(x)
ax.scatter(x, y, c=colors)
elif i != model.attack_normal and i != highlight_point:
colors = ['r'] * len(x)
ax.scatter(x, y, c=colors)
if highlight_point != None :
p = data_per_true_labels[highlight_point]
x = np.array([t[0] for t in p])
y = np.array([t[1] for t in p])
colors = ['y'] * len(x)
ax.scatter(x, y, c=colors)
plt.xlabel('Similarity score to normal')
plt.ylabel('Similarity score to abnormal')
plt.title('True labels')
plt.grid(True)
fig.savefig(today + "/" + title + "_true_.png")
plt.close()
fig, ax = plt.subplots(1, 1, sharex='col', sharey='row')
plt.subplots_adjust(wspace=0.4, hspace=0.4)
plt.xlim(plot_lim_min, plot_lim_max)
plt.ylim(plot_lim_min, plot_lim_max)
for i, p in enumerate(cproj):
if clusters[ res[i] ] >= 0 :
ax.scatter(p[0], p[1], c='g')
else :
ax.scatter(p[0], p[1], c='r')
plt.xlabel('Similarity score to normal')
plt.ylabel('Similarity score to abnormal')
plt.title('Prediected labels')
plt.grid(True)
fig.savefig(today + "/" + title + "_prediction_.png")
plt.close()
def gen_plots():
dataset_description = "training20_only"
title = dataset_description
cproj, res, df, highlight_point = get_data(title)
gen_plot(cproj, res, df, highlight_point, title)
dataset_description = "training20_test20"
for attack_type_index, attack_type in enumerate(model.attack_types) :
if attack_type_index == model.attack_normal : # why <= instead of !=
continue
title = dataset_description + "_" + attack_type
cproj, res, df, highlight_point = get_data(title)
gen_plot(cproj, res, df, highlight_point, title)
def gen_one_plot():
dataset_description = "training20_test20_guess_passwd"
title = dataset_description
cproj, res, df, highlight_point = get_data(title)
gen_plot(cproj, res, df, highlight_point, title)
def kmean_plot(title, ax):
_, attacks = preprocessing.get_header_data()
cproj, res, df, highlight_point = get_data(title)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
# plt.xlim(plot_lim_min, plot_lim_max)
# plt.ylim(plot_lim_min, plot_lim_max)
# ax = axarr
# ax.set_title("plot")
data_per_true_labels = []
for i in range( len(attacks) ):
data_per_true_labels.append([])
true_attack_types = df["attack"].values.tolist()
for i, d in enumerate(cproj):
data_per_true_labels[true_attack_types[i]].append(d)
k = 10
clusters = [0] * k
for i, p in enumerate(cproj):
true_label = true_attack_types[i]
if true_label == model.attack_normal :
clusters[ res[i] ] = clusters[ res[i] ] + 1
else :
clusters[ res[i] ] = clusters[ res[i] ] - 1
x = []
y = []
p = []
for ii, pp in enumerate(cproj):
if clusters[ res[ii] ] > 0 :
x.append(pp[0])
y.append(pp[1])
p.append(pp)
from sklearn.cluster import KMeans
data = p
h = .02
estimator = KMeans(init='k-means++', n_clusters=3)
estimator.fit(data)
centroids = estimator.cluster_centers_
x_min, x_max = min(x) + 1, max(x) - 1
y_min, y_max = min(y) + 1, max(y) - 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='w', zorder=10)
colors = ['g'] * len(x)
ax.scatter(x, y, c=colors)
ax.scatter(np.mean(x), np.mean(y), c='r')
ax.scatter(np.median(x), np.median(y), c='b')
delta = 0.025
X = np.arange(plot_lim_min, plot_lim_max, delta)
Y = np.arange(plot_lim_min, plot_lim_max, delta)
X,Y = np.meshgrid(X,Y)
Z = mlab.bivariate_normal(X, Y, np.std(x), np.std(y), np.mean(x), np.mean(y))
plt.contour(X,Y,Z)
def test():
_, attacks = preprocessing.get_header_data()
dataset_description = "training20_only"
title = dataset_description
cproj, res, df, highlight_point = get_data(title)
fig, axarr = plt.subplots(1, 1, sharex='col', sharey='row')
plt.subplots_adjust(wspace=0.4, hspace=0.4)
plt.xlim(plot_lim_min, plot_lim_max)
plt.ylim(plot_lim_min, plot_lim_max)
ax = axarr
ax.set_title("plot")
data_per_true_labels = []
for i in range( len(attacks) ):
data_per_true_labels.append([])
true_attack_types = df["attack"].values.tolist()
for i, d in enumerate(cproj):
data_per_true_labels[true_attack_types[i]].append(d)
for i, p in enumerate(data_per_true_labels) :
x = np.array([t[0] for t in p])
y = np.array([t[1] for t in p])
if i == model.attack_normal:
from sklearn.cluster import KMeans
data = p
h = .02
estimator = KMeans(init='k-means++', n_clusters=3)
estimator.fit(data)
centroids = estimator.cluster_centers_
x_min, x_max = min(x) + 1, max(x) - 1
y_min, y_max = min(y) + 1, max(y) - 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# plt.figure(1)
# plt.clf()
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='w', zorder=10)
colors = ['g'] * len(x)
ax.scatter(x, y, c=colors)
ax.scatter(np.mean(x), np.mean(y), c='r')
ax.scatter(np.median(x), np.median(y), c='b')
delta = 0.025
X = np.arange(plot_lim_min, plot_lim_max, delta)
Y = np.arange(plot_lim_min, plot_lim_max, delta)
X,Y = np.meshgrid(X,Y)
Z = mlab.bivariate_normal(X, Y, np.std(x), np.std(y), np.mean(x), np.mean(y))
plt.contour(X,Y,Z)
# for i, r in df.iterrows() :
# if r['attack']
# for i, p in enumerate(cproj):
# if res[i] == 8 :
# ax1.scatter(p[0], p[1], c='g')
# plt.xticks(())
# plt.yticks(())
plt.show()
plt.close()
if __name__ == '__main__':
""" Anomaly detection with spectral clustering algorithm.
First training set only, to see what would happen with only known classes
Next with test set, to see what would happen with only unknown classes
"""
import time
start = time.time()
logger.set_file(today + "/log_plots.txt")
gen_plots()
# gen_one_plot()
# test()
elapsed = (time.time() - start)
print "done in %s seconds" % (elapsed)