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plot_features.py
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plot_features.py
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#!/usr/bin/env python3
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.colors as mc
# from matplotlib import rcParams
# rcParams.update({'figure.autolayout': True})
import colorsys
import numpy as np
import sys
import os
import json
import pickle
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
# from learn import numpy_sigmoid
def numpy_sigmoid(x):
return 1/(1+np.exp(-x))
plt.rcParams['font.family'] = 'serif'
plt.rcParams['savefig.format'] = 'pdf'
DIR_NAME = "plots/plot_features"
dataroot_basename = sys.argv[1].split('_')[0]
with open(dataroot_basename + "_categories_mapping.json", "r") as f:
categories_mapping_content = json.load(f)
categories_mapping, mapping = categories_mapping_content["categories_mapping"], categories_mapping_content["mapping"]
reverse_mapping = {v: k for k, v in mapping.items()}
# print("reverse_mapping", reverse_mapping)
with open(dataroot_basename+"_normalization_data.pickle", "rb") as f:
means, stds = pickle.load(f)
file_name = sys.argv[1]
with open(file_name, "rb") as f:
loaded = pickle.load(f)
results_by_attack_number = loaded["results_by_attack_number"]
flows_by_attack_number = loaded["flows_by_attack_number"]
result_ranges_by_attack_number = loaded["result_ranges_by_attack_number"]
sample_indices_by_attack_number = loaded["sample_indices_by_attack_number"]
features = loaded["features"]
# print("features", features)
def brighten(rgb, how_much=0.0):
hls = list(colorsys.rgb_to_hls(*rgb))
hls[1] = hls[1] + how_much*(1.0-hls[1])
return colorsys.hls_to_rgb(*hls)
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
colors_rgb = [matplotlib.colors.to_rgb(item) for item in plt.rcParams['axes.prop_cycle'].by_key()['color']]
COLOR_MAP_ELEMENTS = 100
brightness_map = list(np.linspace(1.0, 0.5, num=COLOR_MAP_ELEMENTS))
# print("brightness_map", brightness_map)
colors_rgb_ranges = [matplotlib.colors.ListedColormap([brighten(color, item) for item in brightness_map]) for color in colors_rgb]
# print("colors", colors)
# print("colors_rgb_ranges[0]", colors_rgb_ranges[0])
# quit()
FEATURE_NAMES = ["Pkt. length (B)", "IAT (ms)"]
for attack_type, (results_by_attack_number_item, flows_by_attack_number_item, result_ranges_by_attack_number_item, sample_indices_by_attack_number_item) in enumerate(zip(results_by_attack_number, flows_by_attack_number, result_ranges_by_attack_number, sample_indices_by_attack_number)):
assert len(results_by_attack_number_item) == len(flows_by_attack_number_item) == len(result_ranges_by_attack_number_item) == len(sample_indices_by_attack_number_item)
if len(results_by_attack_number_item) <= 0:
continue
sorted_seq_indices = [item[0] for item in sorted(enumerate(flows_by_attack_number_item), key=lambda x: x[1].shape[0], reverse=True)]
max_length = flows_by_attack_number_item[sorted_seq_indices[0]].shape[0]
print("max_length", max_length)
indices_by_length = []
for i in range(max_length):
indices_by_length.append([])
for index in sorted_seq_indices:
if flows_by_attack_number_item[index].shape[0] < i+1:
break
indices_by_length[i].append(index)
# for i in range(len(indices_by_length)):
# indices_by_length[i] = np.concatenate(indices_by_length[i], axis=1)
# print("shape of values", [item.shape for item in values_by_length])
# means = np.array([np.mean(item, axis=0) for item in values_by_length])
# medians = np.array([np.median(np.array([results_by_attack_number_item[index][position] for index in item]), axis=0) for position, item in enumerate(indices_by_length)])
# actual_flow_medians = np.stack([np.median(np.stack(np.array([flows_by_attack_number_item[index][position,:] for index in item])), axis=0) for position, item in enumerate(indices_by_length)])
# print("actual_flow_medians.shape", actual_flow_medians.shape)
actual_flow_medians = np.stack([np.median(np.concatenate([flows_by_attack_number_item[index][position:position+1,:] for index in item]), axis=0) for position, item in enumerate(indices_by_length)])
actual_flow_first_quartiles = np.stack([np.quantile(np.concatenate([flows_by_attack_number_item[index][position:position+1,:] for index in item]), 0.25, axis=0) for position, item in enumerate(indices_by_length)])
actual_flow_third_quartiles = np.stack([np.quantile(np.concatenate([flows_by_attack_number_item[index][position:position+1,:] for index in item]), 0.75, axis=0) for position, item in enumerate(indices_by_length)])
# print("flow shapes", actual_flow_medians.shape, actual_flow_first_quartiles.shape, actual_flow_third_quartiles.shape)
# for i in range(medians.shape[1]):
# plt.figure(figsize=(5,4))
plt.title(reverse_mapping[attack_type])
fig, ax1 = plt.subplots(figsize=(5,2))
ax2 = ax1.twinx()
all_legends = []
for feature_index_from_zero, (feature_name, feature_index, ax) in enumerate(zip(FEATURE_NAMES, (3, 4), (ax1, ax2))):
# if feature_index_from_zero > 0:
# continue
# plt.subplot("{}{}{}".format(len(FEATURE_NAMES), 1, feature_index_from_zero+1))
ax.set_ylabel(feature_name) #, color=colors[feature_index_from_zero])
ax.set_ylabel_legend(Rectangle((0,0),1,1, color=colors[feature_index_from_zero], alpha=0.5), handlelength=1)
legend = "{}".format(feature_name)
ret = ax.plot(range(max_length), actual_flow_medians[:,feature_index]*stds[feature_index]+means[feature_index], label=legend, color=colors[feature_index_from_zero])
ret2 = ax.fill_between(range(max_length), actual_flow_first_quartiles[:,feature_index]*stds[feature_index]+means[feature_index], actual_flow_third_quartiles[:,feature_index]*stds[feature_index]+means[feature_index], alpha=0.5, edgecolor=colors[feature_index_from_zero], facecolor=colors[feature_index_from_zero], label=legend+" 1st and 3rd quartile")
ylim1,ylim2 = ax.get_ylim()
ax.set_ylim((ylim1,ylim2+(ylim2-ylim1)*0.3)) # move plots away from legend
# print("ret", ret)
# print("ret2", ret2)
all_legends += ret
all_legends += [ret2]
# all_legends += [legend, legend+" 1st and 3rd quartile"]
all_labels = [item.get_label() for item in all_legends]
# ax2.legend(all_legends, all_labels, loc="upper left")
leg = plt.legend(ret +[ret2], ["Median", "1st and 3rd quartile"])
leg.legendHandles[0].set_color('gray')
leg.legendHandles[1].set_color('gray')
ax1.set_xlabel('Time step $t$')
# plt.title(reverse_mapping[attack_type])
plt.tight_layout()
os.makedirs(DIR_NAME, exist_ok=True)
plt.savefig(DIR_NAME+'/{}_{}_{}.pdf'.format(file_name.split("/")[-1], attack_type, reverse_mapping[attack_type].replace("/", "-").replace(":", "-")), bbox_inches='tight', pad_inches=0)
plt.close()