566 lines
25 KiB
Python
566 lines
25 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import patches
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from matplotlib.font_manager import FontProperties
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from matplotlib.ticker import FuncFormatter
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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import matplotlib.patheffects as pe
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# 设置字体为 Times New Roman，并调整字体大小
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plt.rcParams['font.family'] = 'Times New Roman'
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plt.rcParams['font.size'] = 22 # 设置默认字体大小
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plt.rcParams['axes.titlesize'] = 26 # 设置标题字体大小
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plt.rcParams['axes.labelsize'] = 26 # 设置轴标签字体大小
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plt.rcParams['xtick.labelsize'] = 26 # 设置x轴刻度字体大小
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plt.rcParams['ytick.labelsize'] = 26 # 设置y轴刻度字体大小
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plt.rcParams['legend.fontsize'] = 20 # 设置图例字体大小
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import matplotlib
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from matplotlib.patches import FancyBboxPatch
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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from analyzeTool import generate_heat_maps_array, count_classes_samples
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matplotlib.use('MacOSX') # 使用MacOSX后端以兼容Mac
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# 绘制原始和扰动时间序列的函数
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def draw_ori_perturbed(ori_data, pert_data, dashed_lists=None, title=None, filename='comp_ori_pert_result.pdf',
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loc_sample='upper left', loc_sub='upper left', inset_range=None, xy=(0, 0), xytext=(0, 0)):
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color_1 = '#4B0082'
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color_2 = '#FF8C00'
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# 将字符串数据转换为浮点数列表
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ori_ts = list(map(float, ori_data.split()))
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perturbed_ts = list(map(float, pert_data.split()))
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length = len(ori_ts)
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x = np.arange(0, length, 1) # 生成从0到数据长度的x值
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# 绘制数据
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plt.figure(figsize=(10, 5))
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plt.plot(x, ori_ts, label='Original', color=color_1, linewidth=5)
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plt.plot(x, perturbed_ts, label='Perturbed', color=color_2, linewidth=5)
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# 添加虚线框
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if dashed_lists is None:
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dashed_lists = []
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if len(dashed_lists) > 0:
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for start, end in dashed_lists:
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max_len = max(max(ori_ts[start:end]), max(perturbed_ts[start:end]))
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min_len = min(min(ori_ts[start:end]), min(perturbed_ts[start:end]))
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plt.plot(
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[start, start, end, end, start],
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[min_len, max_len, max_len, min_len, min_len],
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linestyle='--', # 使用linestyle来指定虚线样式
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linewidth=4, # 线宽
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color='black'
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)
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plt.xlim(-10, length + 10) # 设置x轴范围
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plt.legend(loc=loc_sample)
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plt.grid(True)
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# 获取当前轴并设置边框宽度
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ax = plt.gca()
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for spine in ax.spines.values():
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spine.set_linewidth(2) # 设置边框线宽
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# 添加局部放大
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if inset_range is not None:
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plt.annotate('', xy=xy, xytext=xytext,
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arrowprops=dict(arrowstyle='->', color='black', lw=3))
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inset_start, inset_end = inset_range
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ax_inset = inset_axes(plt.gca(), width="30%", height="30%", loc=loc_sub, borderpad=1) # 调整宽度和高度
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ax_inset.plot(x[inset_start:inset_end], ori_ts[inset_start:inset_end], label='Original', color=color_1,
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linewidth=4)
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ax_inset.plot(x[inset_start:inset_end], perturbed_ts[inset_start:inset_end], label='Perturbed', color=color_2,
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linewidth=4)
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ax_inset.set_xlim(inset_start, inset_end)
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ax_inset.grid(True)
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# 移除插入图的坐标轴刻度
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ax_inset.set_xticks([])
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ax_inset.set_yticks([])
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for spine in ax_inset.spines.values():
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spine.set_linewidth(2) # 设置边框线宽
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if title is not None:
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# 使用 tight_layout 和 subplots_adjust 调整布局
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plt.tight_layout()
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plt.subplots_adjust(bottom=0.15) # 调整底部边距
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# 在下方添加标题
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plt.figtext(0.5, 0.02, title, ha='center', fontsize=12)
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plt.savefig(filename)
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plt.show()
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# 绘制原始和扰动时间序列的函数，带有放大视图
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def draw_ori_perturbed_with_inset(ori_data, pert_data, inset_start, inset_end):
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# 将字符串数据转换为浮点数列表
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ori_ts = list(map(float, ori_data.split()))
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perturbed_ts = list(map(float, pert_data.split()))
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length = len(ori_ts)
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x = np.arange(0, length, 1) # 生成从0到数据长度的x值
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# 创建主图
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fig, ax = plt.subplots(figsize=(10, 5))
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ax.plot(x, ori_ts, label='Original: Class 0', color='royalblue', linewidth=2)
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ax.plot(x, perturbed_ts, label='Perturbed: Class 1', color='tomato', linewidth=2)
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ax.set_xlim(-20, length + 20) # 设置x轴范围
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ax.grid(True)
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# 创建放大视图
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes, mark_inset
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# 定义放大区域
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inset_axes_params = {
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'width': '40%', # 放大视图的宽度
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'height': '40%', # 放大视图的高度
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'bbox_to_anchor': (-0.2, -0.1, 0.8, 0.8), # 偏移，尺寸
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'bbox_transform': ax.transAxes # 使用轴的变换
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}
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# 创建放大视图轴
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ax_inset = inset_axes(ax, **inset_axes_params)
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# 设置放大视图的范围
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ax_inset.plot(x, ori_ts, color='royalblue', linewidth=2)
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ax_inset.plot(x, perturbed_ts, color='tomato', linewidth=2)
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ax_inset.set_xlim(inset_start, inset_end)
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ax_inset.set_ylim(min(ori_ts[inset_start:inset_end]) - 0.5, max(ori_ts[inset_start:inset_end]) + 0.5)
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# 移除放大视图中的刻度
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ax_inset.set_xticks([])
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ax_inset.set_yticks([])
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# 标记放大区域并设置边框粗细和zorder(层次位置，越大越靠前)，避免线条重叠在数据线上，loc1和loc2代表引导线的起始角
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mark_inset(ax, ax_inset, loc1=3, loc2=1, fc="none", ec="0.5", lw=2, zorder=10)
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# 添加阴影效果到原图的标记框
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bbox_shadow = FancyBboxPatch((inset_start, min(ori_ts[inset_start:inset_end]) - 0.5),
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inset_end - inset_start,
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max(ori_ts[inset_start:inset_end]) - min(ori_ts[inset_start:inset_end]) + 1,
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boxstyle="round,pad=0.1",
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ec="none", fc="black", alpha=0.2,
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transform=ax.transData, zorder=2)
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ax.add_patch(bbox_shadow)
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# 保存并展示图像
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plt.savefig('comp_ori_pert_inset_result.pdf')
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plt.show()
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def draw_times_diff(data1, data2, data3, data1_label, data2_label, data3_label):
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x = np.arange(1, 6)
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# 创建图表
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plt.figure(figsize=(10, 5))
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plt.plot(x, data1, label=data1_label, color='royalblue', marker='o')
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plt.plot(x, data2, label=data2_label, color='tomato', marker='o')
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plt.plot(x, data3, label=data3_label, color='sandybrown', marker='o')
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# 添加标签和图例
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plt.xlabel('Times of population size')
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plt.ylabel('MSE')
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plt.legend()
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plt.grid(True)
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# 调整布局以防止标签被裁剪
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plt.tight_layout()
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plt.legend(loc='center right')
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# 保存为PDF格式（矢量图）
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plt.savefig('line_chart_result.pdf', format='pdf')
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plt.show()
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def draw_times_diff_bar(data1, data2, data3, data4,
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line_data1, line_data2, line_data3, line_data4,
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data1_label, data2_label, data3_label, data4_label,
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filename='bar_chart_with_lines_and_legend_above.pdf', leftLabel='MSE (10^-3)', rightLabel='ANI',
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xlabel='Times of population size', x_value=None, times=1000):
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if x_value is None:
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x_value = ['1', '2', '3', '4', '5']
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x = np.arange(1, 6) # 假设有5个数据点
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# 设置柱状图的宽度
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bar_width = 0.15
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# 创建图表
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fig, ax1 = plt.subplots(figsize=(8, 5))
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color1 = '#05B9E2'
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color2 = '#54B345'
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color3 = '#F27970'
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color4 = '#8983BF'
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# 为每组数据创建柱状图，并添加黑色边框
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bars1 = ax1.bar(x - bar_width * 1.5, data1, width=bar_width, label=data1_label, color=color1, alpha=0.6,
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edgecolor='black')
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bars2 = ax1.bar(x - bar_width * 0.5, data2, width=bar_width, label=data2_label, color=color2, alpha=0.6,
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edgecolor='black')
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bars3 = ax1.bar(x + bar_width * 0.5, data3, width=bar_width, label=data3_label, color=color3, alpha=0.6,
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edgecolor='black')
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bars4 = ax1.bar(x + bar_width * 1.5, data4, width=bar_width, label=data4_label, color=color4, alpha=0.6,
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edgecolor='black')
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# 设置左侧Y轴标签和格式
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ax1.set_xlabel(xlabel)
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ax1.set_ylabel(leftLabel)
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ax1.set_xticks(x)
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ax1.set_xticklabels(x_value)
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ax1.tick_params(axis='y')
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ax1.grid(axis='y') # 只显示y轴的网格线
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for spine in ax1.spines.values():
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spine.set_linewidth(2) # 设置边框线宽
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# 格式化y轴刻度为指数形式
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def to_exponential(x, _):
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return f'{x * times:.0f}'
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ax1.yaxis.set_major_formatter(FuncFormatter(to_exponential))
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# 创建右侧Y轴
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ax2 = ax1.twinx()
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ax2.plot(x, line_data1, color=color1, marker='o', linestyle='-', linewidth=2, markersize=8)
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ax2.plot(x, line_data2, color=color2, marker='o', linestyle='-', linewidth=2, markersize=8)
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ax2.plot(x, line_data3, color=color3, marker='o', linestyle='-', linewidth=2, markersize=8)
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ax2.plot(x, line_data4, color=color4, marker='o', linestyle='-', linewidth=2, markersize=8)
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ax2.set_ylabel(rightLabel)
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ax2.tick_params(axis='y')
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# 将柱状图的图例放在图的上方，并设置字体大小
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bars_legend = ax1.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2), ncol=4, frameon=False)
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# 添加图例到图中
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ax1.add_artist(bars_legend)
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# 调整布局以防止标签被裁剪
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plt.tight_layout(rect=[0, 0, 1, 0.95])
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# 保存为PDF格式（矢量图）
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plt.savefig(filename, format='pdf')
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plt.show()
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def draw_diff_pert_info(data1, data2, data5, data6, labels, marker=None, linewidth=2, filename='line_chart_result.pdf'):
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x = np.arange(len(labels))
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color1 = '#F27970'
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color2 = '#54B345'
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color3 = '#05B9E2'
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color4 = '#8983BF'
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# 创建图表
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plt.figure(figsize=(7, 5))
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plt.plot(x, data1, label='Car', color=color1, marker=marker, linewidth=linewidth, markersize=8)
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plt.plot(x, data5, label='ECG200', color=color2, marker=marker, linewidth=linewidth, markersize=8)
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plt.plot(x, data2, label='Italy', color=color3, marker=marker, linewidth=linewidth, markersize=8)
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plt.plot(x, data6, label='Ligning7', color=color4, marker=marker, linewidth=linewidth, markersize=8)
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# 添加标签和图例
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plt.xlabel('$\\beta$')
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plt.ylabel('Success Rate')
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plt.xticks(x, labels, rotation=0, ha='center')
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# plt.legend(loc='center right') # 图例位置和字体大小
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plt.grid(True) # 网格线
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# 调整子图布局以防止重叠
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plt.tight_layout(rect=[0, 0.03, 1, 0.95])
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# 获取当前坐标轴对象
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ax = plt.gca()
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for spine in ax.spines.values():
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spine.set_linewidth(2) # 设置边框线宽
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# 保存为PDF格式（矢量图）
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plt.savefig(filename, format='pdf')
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plt.show()
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# 画大表
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def draw_all_data_set_info(data1, data2, data3, data4, data5, labels):
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def replace_neg_ones_with_nan(data):
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"""
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将数据中的所有-1替换为np.nan。
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参数:
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data (array-like): 输入数据数组。
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返回:
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numpy.ndarray: 将-1替换为np.nan后的数据数组。
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"""
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return np.array([np.nan if x == -1 else x for x in data])
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def move_neg_ones_to_end(arrays):
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for i in range(len(arrays)):
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# 跳过所有非数字数组
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if not isinstance(arrays[i][0], (int, float)):
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continue
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# 记录每个数组中的元素和对应的索引
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elements = [(val, idx) for idx, val in enumerate(arrays[i])]
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# 将-1移动到末尾
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sorted_elements = sorted(elements, key=lambda x: (x[0] == -1, 0))
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# 重新排列每个数组
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for array in arrays:
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sorted_array = [array[elem[1]] for elem in sorted_elements]
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array[:] = sorted_array
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# 修正数组
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move_neg_ones_to_end([labels, data1, data2, data3, data4, data5])
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# 将数据集中的-1替换为NaN
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data1 = replace_neg_ones_with_nan(data1)
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data2 = replace_neg_ones_with_nan(data2)
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data3 = replace_neg_ones_with_nan(data3)
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data4 = replace_neg_ones_with_nan(data4)
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data5 = replace_neg_ones_with_nan(data5)
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# 创建x轴的值
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x = np.arange(len(labels))
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# 创建折线图
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plt.figure(figsize=(10, 5))
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plt.plot(x, data1, label='GATN MSE', color='black')
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plt.plot(x, data2, label='advGAN MSE', color='royalblue')
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plt.plot(x, data3, label='TSadv MSE $\\beta=0.04$', color='tomato')
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plt.plot(x, data4, label='CPadv MSE $\\beta=0.04$', color='sandybrown')
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plt.plot(x, data5, label='CPadv MSE $\\beta=0.02$', color='firebrick')
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# 添加标签和图例
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plt.xlabel('')
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plt.ylabel('MSE')
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plt.xticks(x, labels, rotation=30, ha='right', fontsize=5, fontweight='bold')
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plt.legend(loc='upper right') # 标签位置
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plt.grid(False) # 网格线
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# 调整布局以防止重叠
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plt.tight_layout(rect=[0, 0.03, 1, 0.95])
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# 添加标题
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plt.figtext(0.5, 0.01, 'Fig. 9. MSE results of TSadv over different $\\beta$ and GATN on 42 UCR datasets',
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ha='center', fontsize=12)
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# 保存为PDF文件
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plt.savefig('line_chart_result.pdf', format='pdf')
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plt.show()
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def heat_maps_of_classes(ori_data1, ori_data2, title, labels=None, cbar_ticks1=None, cbar_ticks2=None,
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cmap_color='Blues', filename='heatmap.pdf'):
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"""
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生成两个数据集的并排热图，并将其保存为PDF文件。
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参数:
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ori_data1 (list of int/float): 第一个数据集，将其可视化为热图。
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ori_data2 (list of int/float): 第二个数据集，将其可视化为热图。
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title (str): 热图的标题。
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labels (list of tuple of str, optional): 两个热图的x轴和y轴标签列表。默认值为[('', ''), ('', '')]。
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cbar_ticks1 (list of float, optional): 自定义color bar1的刻度列表。默认值为None。
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cbar_ticks2 (list of float, optional): 自定义color bar2的刻度列表。默认值为None。
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cmap_color (str): 热力图颜色，默认Blues
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"""
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# 设置字体
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# 设置颜色条的字体
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font = FontProperties()
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font.set_family('serif')
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font.set_name('Times New Roman')
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font.set_size(20) # 设置字体大小
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# 数据
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if labels is None:
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labels = [('', ''), ('', '')]
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data1 = np.array(ori_data1)
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data2 = np.array(ori_data2)
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fig, axs = plt.subplots(1, 2, figsize=(8, 3), gridspec_kw={'wspace': 0.4})
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# 第一个子图
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cax1 = axs[0].matshow(data1, cmap=cmap_color, alpha=0.8, vmin=min(cbar_ticks1), vmax=max(cbar_ticks1))
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# 添加网格
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axs[0].set_xticks(np.arange(data1.shape[1] + 1) - 0.5, minor=True)
|
||
axs[0].set_yticks(np.arange(data1.shape[0] + 1) - 0.5, minor=True)
|
||
axs[0].grid(which='minor', color='gray', linestyle='-', linewidth=0.5)
|
||
|
||
# 创建第一个子图的颜色条
|
||
divider1 = make_axes_locatable(axs[0])
|
||
cax1_cb = divider1.append_axes("right", size="5%", pad=0.1)
|
||
cb1 = fig.colorbar(cax1, cax=cax1_cb)
|
||
if cbar_ticks1:
|
||
cb1.set_ticks(cbar_ticks1)
|
||
# 应用字体设置到颜色条的标签
|
||
cb1.ax.yaxis.set_tick_params(labelsize=12)
|
||
for label in cb1.ax.get_yticklabels():
|
||
label.set_fontproperties(font)
|
||
|
||
# 第二个子图
|
||
cax2 = axs[1].matshow(data2, cmap=cmap_color, alpha=0.8, vmin=min(cbar_ticks2), vmax=max(cbar_ticks2))
|
||
|
||
# 添加网格
|
||
axs[1].set_xticks(np.arange(data2.shape[1] + 1) - 0.5, minor=True)
|
||
axs[1].set_yticks(np.arange(data2.shape[0] + 1) - 0.5, minor=True)
|
||
axs[1].grid(which='minor', color='gray', linestyle='-', linewidth=0.5)
|
||
|
||
# 创建第二个子图的颜色条
|
||
divider2 = make_axes_locatable(axs[1])
|
||
cax2_cb = divider2.append_axes("right", size="5%", pad=0.1)
|
||
cb2 = fig.colorbar(cax2, cax=cax2_cb)
|
||
if cbar_ticks2:
|
||
cb2.set_ticks(cbar_ticks2)
|
||
cb2.ax.yaxis.set_tick_params(labelsize=12)
|
||
for label in cb2.ax.get_yticklabels():
|
||
label.set_fontproperties(font)
|
||
|
||
for i in range(2):
|
||
axs[i].tick_params(axis='both', which='major', labelsize=12)
|
||
axs[i].xaxis.set_ticks_position('bottom')
|
||
axs[i].xaxis.set_label_position('bottom')
|
||
axs[i].invert_yaxis()
|
||
for label in (axs[i].get_xticklabels() + axs[i].get_yticklabels()):
|
||
label.set_fontproperties(font)
|
||
|
||
# 设置xy轴刻度间隔为1
|
||
axs[0].set_xticks(np.arange(data1.shape[1]))
|
||
axs[0].set_yticks(np.arange(data1.shape[0]))
|
||
axs[1].set_xticks(np.arange(data2.shape[1]))
|
||
axs[1].set_yticks(np.arange(data2.shape[0]))
|
||
|
||
# 添加描述文字
|
||
plt.figtext(0.5, 0.01, title, ha='center', va='center', fontsize=10, fontname='Times New Roman')
|
||
|
||
# 保存为矢量图（PDF格式）
|
||
plt.savefig(filename, format='pdf', bbox_inches='tight')
|
||
|
||
# 显示图像
|
||
plt.show()
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# 攻击样本对比
|
||
# original_data = "0.95110175 0.03860069 0.001077176 0.001077176 0.001077176 -0.028107779 0.0052464556 -0.036446338 -0.040615617 -0.036446338 -0.028107779 -0.078139131 -0.08647769 -0.044784896 -0.078139131 -0.057292734 -0.057292734 -0.094816249 -0.08647769 -0.069800573 -0.069800573 -0.08647769 -0.1240012 -0.11566264 -0.078139131 -0.094816249 -0.094816249 -0.14067832 -0.14067832 -0.090646969 -0.14901688 -0.16986328 -0.098985528 -0.15735544 -0.1240012 -0.061462014 -0.0072613824 -0.0072613824 0.071954926 0.096970602 -0.032277059 -0.13233976 -0.19070967 -0.27826454 -0.21572535 -0.27409526 -0.26992598 -0.28660309 -0.25741814 -0.28243382 -0.24907958 -0.24907958 -0.24907958 -0.2240639 -0.098985528 1.0600742 1.7771901 -0.82027086 -3.8930297 -5.3647854 -4.0431238 -2.2294872 -1.0287348 -0.44920499 -0.23240247 -0.1240012 -0.08647769 -0.015599941 0.017754294 0.03860069 0.063616366 0.10530916 0.10530916 0.18869474 0.18035619 0.28458817 0.40549727 0.46803646 0.60979196 0.75988602 0.93499575 1.2018296 1.5312027 1.8522372 2.1565946 2.4943062 2.7402937 2.9445884 2.9154034 2.5443376 2.0148391 1.3811086 0.85161016 0.45135934 0.21371042 0.071954926 -0.0239385 -0.078139131 -0.10315481 -0.08647769 -0.13233976 -0.094816249 -0.090646969 -0.044784896 -0.053123455 -0.044784896 -0.057292734 -0.10315481 -0.08647769 -0.08647769 -0.11566264 -0.15735544 -0.165694 -0.20321751 -0.21572535 -0.28243382 -0.28243382 -0.26992598 -0.29077238 -0.3074495 -0.34914228 -0.34080373 -0.33246517 -0.34914228 -0.32829589 -0.34080373 -0.3658194 -0.3658194 -0.37832724 -0.38249653 -0.39083508 -0.39917364 -0.39500436 -0.39083508 -0.43252788 -0.40334292"
|
||
# perturbed_data = "0.9546 0.0387 0.0011 0.0011 0.0011 -0.0282 0.0053 -0.0366 -0.0408 -0.0366 -0.0282 -0.0784 -0.0868 -0.0450 -0.0784 -0.0575 -0.0575 -0.0952 -0.0868 -0.0701 -0.0701 -0.0868 -0.1245 -0.1161 -0.0784 -0.0952 -0.0952 -0.1412 -0.1412 -0.0910 -0.1496 -0.1705 -0.0994 -0.1579 -0.1245 -0.0617 -0.0073 -0.0073 0.0722 0.0973 -0.0324 -0.1328 -0.1914 -0.2793 -0.2165 -0.2751 -0.2709 -0.2877 -0.2584 -0.1592 -0.2146 0.0321 -0.0189 0.0241 0.1806 1.3527 1.6821 -1.0405 -3.7122 -5.2477 -4.2234 -2.4362 -1.3062 -0.6095 -0.2333 -0.1245 -0.0868 -0.0157 0.0178 0.0387 0.0639 0.1057 0.1057 0.1894 0.1810 0.4762 0.2598 0.1544 0.2773 0.8633 0.8632 1.0042 1.3689 1.9531 2.3896 2.7358 2.7239 2.8305 2.7696 2.5007 2.0223 1.3862 0.8548 0.4530 0.2145 0.0722 -0.0240 -0.0784 -0.1035 -0.0868 -0.1328 -0.0952 -0.0910 -0.0450 -0.0533 -0.0450 -0.0575 -0.1035 -0.0868 -0.0868 -0.1161 -0.1579 -0.1663 -0.2040 -0.2165 -0.2835 -0.2835 -0.2709 -0.2918 -0.3086 -0.3504 -0.3421 -0.3337 -0.3504 -0.3295 -0.3421 -0.3672 -0.3672 -0.3797 -0.3839 -0.3923 -0.4006 -0.3965 -0.3923 -0.4341 -0.4048"
|
||
# # # 样本4成功
|
||
# # -0.4898 -0.4707 -0.4611 -0.4323 -0.4898 -0.4515 -0.4036 -0.4228 -0.4898 -0.4707 -0.3461 -0.4132 -0.4898 -0.3173 -0.4707 -0.2982 -0.3653 -0.2886 -0.3461 -0.2694 -0.3844 -0.3077 -0.3365 -0.3269 -0.2119 -0.2598 -0.2694 -0.2311 -0.3269 -0.2215 -0.2694 -0.1640 -0.1256 0.0660 0.0564 -0.0202 -0.1927 -0.4803 -0.5569 -0.3077 -0.3557 -0.2119 -0.2502 -0.2694 -0.2311 -0.2790 -0.0969 -0.0011 -0.0969 0.1653 0.2051 0.2683 0.8980 -0.2241 -4.2003 -6.0178 -4.5483 -3.0059 -1.3216 -0.5447 -0.2506 0.1809 -0.1301 0.4711 0.2481 0.2673 0.2385 0.2865 0.2673 0.3631 0.3823 0.3056 0.3823 0.4877 0.5261 0.9649 0.6908 0.7372 0.6360 0.8578 1.0912 1.5034 1.6566 1.7832 2.1861 2.5592 2.3825 1.6257 1.4009 0.9878 0.4590 0.3248 0.2194 0.2290 0.1906 0.1906 0.1715 0.1906 0.1906 0.1906 0.3344 0.1715 0.2290 0.2098 0.2481 0.2098 0.2194 0.2385 0.1715 0.2673 0.2290 0.2098 0.2098 0.2769 0.1715 0.2002 0.1810 0.1715 0.1906 0.1810 0.2098 0.1715 0.1906 0.1140 0.2290 0.1906 0.1619 0.2290 0.2481 0.2290 0.2481 0.2290 0.1715 0.1906 0.2002 0.2481
|
||
# draw_ori_perturbed(original_data, perturbed_data,
|
||
# [(49, 64), (75, 90)],
|
||
# '') # 可以添加多个虚线框
|
||
# draw_ori_perturbed_with_inset(original_data, perturbed_data, 220, 230)
|
||
# draw_ori_perturbed(original_data, perturbed_data)
|
||
|
||
# 种群倍数
|
||
# car_data = [1, 1, 1, 1, 1]
|
||
# ecg200_data = [10.2, 9.2, 8.3, 10, 8.7]
|
||
# italy_data = [6.7, 5.8, 4.2, 6.2, 6.2]
|
||
# car_data = [0.0031, 0.003, 0.00315, 0.0032, 0.00325]
|
||
# ecg200_data = [0.0066, 0.00655, 0.00652, 0.0068, 0.0067]
|
||
# italy_data = [0.0024, 0.00245, 0.0024, 0.0026, 0.0027]
|
||
# draw_times_diff_bar(car_data, ecg200_data, italy_data, 'Car', 'ECG200', 'ItalyPowerDemand')
|
||
|
||
# 大表MSE
|
||
# labels = [
|
||
# "Car", "Chlorine", "CinCECGTorso", "Earthquakes", "ECG200",
|
||
# "ECG5000", "ECGFiveDays", "FordA", "FordB", "InsectWngSnd",
|
||
# "ItalyPowerDemand", "Lightning2", "Lightning7", "MoteStrain", "NonIFECGTho1",
|
||
# "NonIFECGTho2", "Phoneme", "Plane", "SonyAIBOSurf1", "SonyAIBOSurf2",
|
||
# "StarLightCurves", "Trace", "TwoLeadECG", "Wafer", "AllGestureWiimoteX",
|
||
# "AllGestureWiimoteY", "AllGestureWiimoteZ", "FreezerRegularTrain", "FreezerSmallTrain", "PickupGestureZ",
|
||
# "PigAirwayPressure", "PigArtPressure", "PigCVP", "ShakeGestureZ", "Fungi",
|
||
# "GesturePebbleZ1", "GesturePebbleZ2", "DodgerLoopDay", "DodgerLoopWeekend", "DodgerLoopGame",
|
||
# "EOGHorizontalSignal", "EOGVerticalSignal"
|
||
# ]
|
||
#
|
||
# GATN = [
|
||
# 0.1130, 0.2690, 0.0490, 0.1220, 0.1380,
|
||
# 0.1530, 0.0830, 0.1130, 0.1190, 0.1580,
|
||
# 0.0800, 0.1330, 0.1320, 0.1430, 0.0840,
|
||
# 0.0870, 0.0750, 0.2730, 0.1680, 0.1790,
|
||
# 0.0640, 0.0780, 0.1080, 0.1330, 0.1200,
|
||
# 0.1220, 0.1090, 0.1490, 0.1310, 0.1350,
|
||
# 0.0400, 0.0380, 0.0410, 0.1220, 0.2050,
|
||
# 0.1290, 0.1300, 0.0950, 0.1370, 0.0260,
|
||
# 0.0580, 0.0640
|
||
# ]
|
||
#
|
||
# advGAN = [
|
||
# 0.246, 0.1223, 0.138, -1, 0.1257,
|
||
# 0.1796, 0.0331, 0.2281, 0.2466, 0.1306,
|
||
# 0.2473, 0.2102, 0.1856, 0.2194, 0.1587,
|
||
# 0.2434, 0.1778, 0.1774, 0.1924, 0.2033,
|
||
# 0.1479, 0.0406, 0.1169, 0.0743, 0.2397,
|
||
# 0.1447, 0.1936, 0.0533, 0.0624, 0.1253,
|
||
# -1, -1, -1, 0.1421, 0.1388,
|
||
# 0.124, 0.1386, -1, -1, -1,
|
||
# -1, -1
|
||
# ]
|
||
#
|
||
# TS_adv4 = [
|
||
# 0.0013, 0.0111, 0.017, 0.0052, 0.0079,
|
||
# 0.0062, 0.0139, 0.0067, 0.0079, 0.0063,
|
||
# 0.0036, 0.0383, 0.0138, 0.0048, 0.0085,
|
||
# 0.0049, 0.0048, 0.0068, 0.0063, 0.0073,
|
||
# 0.0022, 0.0024, 0.0034, 0.0031, 0.0201,
|
||
# 0.0096, 0.0019, 0.0012, 0.001, 0.006,
|
||
# 0.0022, 0.0014, 0.0052, 0.0087, 0.0086,
|
||
# 0.0345, 0.0204, 0.003, 0.003, 0.0044,
|
||
# 0.0023, 0.0043
|
||
# ]
|
||
#
|
||
# CP_adv4 = [
|
||
# 0.0029, 0.0042, 0.0179, 0.0042, 0.0065,
|
||
# 0.0085, 0.0092, 0.0091, 0.0094, 0.0064,
|
||
# 0.0042, 0.0208, 0.0143, 0.004, 0.0112,
|
||
# 0.0097, 0.0186, 0.0055, 0.0051, 0.0069,
|
||
# 0.0032, 0.0013, 0.0039, 0.0032, 0.0203,
|
||
# 0.0126, 0.0057, 0.0011, 0.0017, 0.005,
|
||
# 0.0045, 0.0044, 0.0058, 0.0097, 0.0066,
|
||
# 0.0281, 0.0226, 0.0036, 0.0038, 0.0057,
|
||
# 0.0043, 0.0051
|
||
# ]
|
||
#
|
||
# CP_adv2 = [
|
||
# 0.0008, 0.0011, 0.0046, 0.0011, 0.0018,
|
||
# 0.0022, 0.0027, 0.0023, 0.0024, 0.0016,
|
||
# 0.0008, 0.006, 0.0038, 0.0011, 0.0029,
|
||
# 0.0026, 0.0046, 0.0006, 0.0014, 0.0016,
|
||
# 0.0008, -1, 0.0012, 0.0008, 0.0052,
|
||
# 0.0031, 0.0014, 0.0003, 0.0004, 0.0013,
|
||
# 0.0011, 0.0011, 0.0013, 0.0023, 0.0022,
|
||
# 0.0067, 0.0053, 0.0009, 0.001, 0.0014,
|
||
# 0.0011, 0.0013
|
||
# ]
|
||
#
|
||
# draw_all_data_set_info(GATN, advGAN, TS_adv4, CP_adv4, CP_adv2, labels)
|
||
|
||
# 画不同扰乱因子下，mse/ani对比图
|
||
|
||
# 扰动因子和成功率
|
||
# data1 = [52, 81, 81, 81, 81, 81] # Car
|
||
# # data2 = [1, 8, 8, 19, 18, 29] # ItalyPowerDemand
|
||
# data3 = [3, 12, 17, 42, 59, 77] # SonyAIBORobotSurface2
|
||
# data4 = [0, 4, 65, 78, 78, 78] # Plane
|
||
# data5 = [17, 22, 82, 87, 88, 88] # ECG200
|
||
# data6 = [23, 43, 81.5, 100, 100, 100] # Ligning7
|
||
# labels = ['0.01', '0.02', '0.04', '0.06', '0.08', '0.1'] # x轴扰乱因子标记
|
||
#
|
||
# draw_diff_pert_info(data1, data3, data4, data5, data6, labels,marker='o',linewidth=3)
|
||
|
||
# 热力图
|
||
# data1 = generate_heat_maps_array(count_classes_samples('result_0.08_6_f/Lightning7', select_colum='Success'),
|
||
# classes_size=7)
|
||
#
|
||
# data2 = generate_heat_maps_array(count_classes_samples('result_0.08_8_f/Plane', select_colum='Success'),
|
||
# classes_size=7)
|
||
#
|
||
# heat_maps_of_classes(data1, data2, '', cbar_ticks1=[2,4,6,8,10], cbar_ticks2=[3,6,9,12,15,18])
|
||
pass
|