multimodal_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1854 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.lines import Line2D  # For custom legend lines\n",
    "\n",
    "def custom_transform(y):\n",
    "    # 0-65 maps to 0-6\n",
    "    # 65-80 maps to 6-18\n",
    "    # 80-100 maps to 18-21\n",
    "    return np.where(y < 65, y * 6 / 65,\n",
    "                    np.where(y < 80, 6 + (y - 65) * 12 / (80 - 65), 18 + (y - 80) * 3 / (100 - 80)))\n",
    "\n",
    "def inverse_custom_transform(y):\n",
    "    # Inverse transformation, mapping 0-6 back to 0-65\n",
    "    # Mapping 6-18 back to 65-80\n",
    "    # Mapping 18-21 back to 80-100\n",
    "    return np.where(y < 6, y * 65 / 6,\n",
    "                    np.where(y < 18, 65 + (y - 6) * (80 - 65) / 12, 80 + (y - 18) * (100 - 80) / 3))\n",
    "\n",
    "# Your updated data and functions remain unchanged\n",
    "# Your data\n",
    "data1 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'UA': [0, 0, 0, 0, 0, 0, 0,  73.1 ,0, 0, 0, 0, 0, 0, 0]\n",
    "}\n",
    "df1 = pd.DataFrame(data1)\n",
    "\n",
    "data2 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'UA': [70.5,70.6,70.8,69.8,69.9,74.8,75.0,75.1,74.9,77.0,76.6,76.9,75.5,75.1,75.0]\n",
    "}\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "data3 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'UA': [0, 0, 0, 0, 0, 0, 0,  75.4 ,0, 0, 0, 0, 0, 0, 0]\n",
    "}\n",
    "df3 = pd.DataFrame(data3)\n",
    "\n",
    "colors = [\n",
    "    '#3C5A78',\n",
    "    '#5B84AD',\n",
    "    '#F1F0E7',\n",
    "]\n",
    "\n",
    "# Plotting with the new color scheme\n",
    "plt.figure(figsize=(10, 6.18), dpi=300)  # Adjust figure size\n",
    "\n",
    "# Re-using the width and space adjustments\n",
    "width = 0.5  # the width of the bars, reduced to make bars closer\n",
    "space = 0.05  # additional space between bars\n",
    "r1 = np.arange(len(df1['skewness']),  dtype=float)  # the label locations for the first dataset\n",
    "r1[:7] -= 0.5\n",
    "r1[8:] += 0.5\n",
    "r2 = [x + width + space for x in r1]  # the label locations for the second dataset\n",
    "r3 = [x + width for x in r2]  # the label locations for the third dataset\n",
    "\n",
    "\n",
    "bars1 = plt.bar(r1+0.05, custom_transform(df1['UA']), color=colors[0], width=width, label='Pairwise(D3)',edgecolor='black', linewidth=1)\n",
    "bars2 = plt.bar(r2, custom_transform(df2['UA']), color=colors[1], width=width, label='Skew-pair(D5)',edgecolor='black', linewidth=1)\n",
    "bars3 = plt.bar(r3, custom_transform(df3['UA']), color=colors[2], width=width, label='Late-fusion',edgecolor='black', linewidth=1)\n",
    "\n",
    "# Setting the labels and title\n",
    "# plt.title('Fusion Statergy Comparison', fontsize=22)\n",
    "plt.xlabel('Skewness (d)', fontsize=22)\n",
    "plt.ylabel('UA (%)', fontsize=22)\n",
    "adjusted_positions = [(r + width + (space / 2) - 0.5 if r < 7 else r + width + (space / 2) + 0.5 if r > 7 else r + width + (space / 2)) for r in range(len(df1['skewness']))]\n",
    "plt.xticks(adjusted_positions, df1['skewness'])\n",
    "plt.legend()\n",
    "\n",
    "# for bars in [bars1, bars2, bars3]:\n",
    "#     for bar in bars:\n",
    "#         height = bar.get_height()\n",
    "#         if height > 0:\n",
    "#             plt.text(bar.get_x() + bar.get_width()/2, height,\n",
    "#                      f'{inverse_custom_transform(height):.1f}',\n",
    "#                      ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.xlim(min(r1) - 0.35, max(r3) + 0.35)\n",
    "\n",
    "# Custom legend\n",
    "plt.legend([bars2, bars1, bars3], ['skew-pair fusion (η=5/12)', 'pairwise-fusion', 'late-fusion'],\n",
    "           frameon=False, loc='upper left', fontsize=20)\n",
    "\n",
    "# Formatting Y-axis\n",
    "plt.gca().yaxis.set_major_formatter(\n",
    "    FuncFormatter(lambda x, _: f\"{inverse_custom_transform(x):.0f}\"))\n",
    "actual_ticks = [0, 60, 70, 75, 100]\n",
    "transformed_ticks = [custom_transform(value) for value in actual_ticks]\n",
    "plt.yticks(transformed_ticks)\n",
    "\n",
    "plt.gca().tick_params(axis='both', which='both', direction='in', length=6, width=1, labelsize=20)\n",
    "plt.tick_params(axis='x', which='both', bottom=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/svg/Figure 5.svg', format='svg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/png/Figure 5.png', format='png')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/jpeg/Figure 5.jpeg', format='jpeg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/tif/Figure 5.tif', format='tif')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1854 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.lines import Line2D  # \n",
    "\n",
    "def custom_transform(y):\n",
    "\n",
    "    return np.where(y < 70, y * 6 / 70,\n",
    "                    np.where(y < 85, 6 + (y - 70) * 12 / 15,\n",
    "                             18 + (y - 85) * 3 / 15))\n",
    "\n",
    "def inverse_custom_transform(y):\n",
    "\n",
    "    return np.where(y < 6, y * 70 / 6,\n",
    "                    np.where(y < 18, 70 + (y - 6) * 15 / 12,\n",
    "                             85 + (y - 18) * 15 / 3))\n",
    "\n",
    "\n",
    "data1 = {\n",
    "    'skewness': [-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    'roc_auc': [0, 0, 0, 0, 0, 0, 0, 0, 0, 80.5, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "}\n",
    "df1 = pd.DataFrame(data1)\n",
    "\n",
    "data2 = {\n",
    "    'skewness': [-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    'roc_auc': [71.3, 75.3, 74.8, 76.8, 79.6, 79.1, 79.6, 79.8, 81.0, 78.8, 79.1, 79.8, 81.5, 80.5, 79.8, 81.3, 79.8, 81.8, 78.6]\n",
    "}\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "data3 = {\n",
    "    'skewness': [-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    'roc_auc': [0, 0, 0, 0, 0, 0, 0, 0, 0, 78.8, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "}\n",
    "df3 = pd.DataFrame(data3)\n",
    "\n",
    "colors = [\n",
    "    '#3C5A78',\n",
    "    '#5B84AD',\n",
    "    '#F1F0E7',\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(10, 6.18), dpi=300)  \n",
    "\n",
    "\n",
    "width = 0.5  \n",
    "space = 0.05  \n",
    "r1 = np.arange(len(df1['skewness']), dtype=float)\n",
    "r1[:9] -= 0.5\n",
    "r1[10:] += 0.5\n",
    "r2 = [x + width + space for x in r1]\n",
    "r3 = [x + width for x in r2]\n",
    "\n",
    "bars1 = plt.bar(r1 + 0.05, custom_transform(df1['roc_auc']), color=colors[0], width=width, label='Pairwise(D3)', edgecolor='black', linewidth=1)\n",
    "bars2 = plt.bar(r2, custom_transform(df2['roc_auc']), color=colors[1], width=width, label='Skew-pair(D5)', edgecolor='black', linewidth=1)\n",
    "bars3 = plt.bar(r3, custom_transform(df3['roc_auc']), color=colors[2], width=width, label='Late-fusion', edgecolor='black', linewidth=1)\n",
    "\n",
    "# 设置标签和标题\n",
    "plt.xlabel('Skewness (d)', fontsize=22)\n",
    "plt.ylabel('Accuracy (%)', fontsize=22)\n",
    "adjusted_positions = [\n",
    "    (r + width + (space / 2) - 0.5 if r < 9 else r + width + (space / 2) + 0.5 if r > 9 else r + width + (space / 2))\n",
    "    for r in range(len(df1['skewness']))\n",
    "]\n",
    "plt.xticks(adjusted_positions, df1['skewness'])\n",
    "plt.xlim(min(r1) - 0.35, max(r3) + 0.35)\n",
    "\n",
    "\n",
    "plt.legend([bars2, bars1, bars3], ['skew-pair fusion (η=1/12)', 'pairwise fusion', 'late-fusion'], frameon=False, loc='upper left', fontsize=20)\n",
    "\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{inverse_custom_transform(x):.0f}\"))\n",
    "actual_ticks = [0, 70, 75, 80, 100]\n",
    "transformed_ticks = [custom_transform(value) for value in actual_ticks]\n",
    "plt.yticks(transformed_ticks, actual_ticks)\n",
    "\n",
    "plt.gca().tick_params(axis='both', which='both', direction='in', length=6, width=1, labelsize=20)\n",
    "plt.tick_params(axis='x', which='both', bottom=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/svg/Figure 6.svg', format='svg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/png/Figure 6.png', format='png')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/jpeg/Figure 6.jpeg', format='jpeg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/tif/Figure 6.tif', format='tif')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1854 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.lines import Line2D  # For custom legend lines\n",
    "\n",
    "def custom_transform(y):\n",
    "    # 0-60 maps to 0-3\n",
    "    # 60-67 maps to 3-20\n",
    "    # 67-100 maps to 20-21\n",
    "    return np.where(y < 60, y * 3 / 60,\n",
    "                    np.where(y < 67, 3 + (y - 60) * 17 / (67 - 60), 20 + (y - 67) * 1 / (100 - 67)))\n",
    "\n",
    "def inverse_custom_transform(y):\n",
    "    # Inverse transformation, mapping 0-3 back to 0-60\n",
    "    # Mapping 3-20 back to 60-67\n",
    "    # Mapping 20-21 back to 67-100\n",
    "    return np.where(y < 3, y * 60 / 3,\n",
    "                    np.where(y < 20, 60 + (y - 3) * (67 - 60) / 17, 67 + (y - 20) * (100 - 67) / 1))\n",
    "\n",
    "# Your updated data and functions remain unchanged\n",
    "# Your data\n",
    "data1 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'roc_auc': [0, 0, 0, 0, 0, 0, 0,  63.9, 0, 0, 0, 0, 0, 0, 0]\n",
    "}\n",
    "df1 = pd.DataFrame(data1)\n",
    "\n",
    "data2 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'roc_auc': [60.0, 62.2, 62.3, 62.1, 62.4, 62.0, 64.9, 65.0, 65.4, 66.8, 65.9, 64.0, 63.5, 64.6, 64.6]\n",
    "}\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "data3 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'roc_auc': [0, 0, 0, 0, 0, 0, 0,  65 ,0, 0, 0, 0, 0, 0, 0]\n",
    "}\n",
    "df3 = pd.DataFrame(data3)\n",
    "\n",
    "colors = [\n",
    "    '#3C5A78',\n",
    "    '#5B84AD',\n",
    "    '#F1F0E7',\n",
    "]\n",
    "\n",
    "# Plotting with the new color scheme\n",
    "plt.figure(figsize=(10, 6.18), dpi=300)  # Adjust figure size\n",
    "\n",
    "# Re-using the width and space adjustments\n",
    "width = 0.5  # the width of the bars, reduced to make bars closer\n",
    "space = 0.05  # additional space between bars\n",
    "r1 = np.arange(len(df1['skewness']),  dtype=float)  # the label locations for the first dataset\n",
    "r1[:7] -= 0.5\n",
    "r1[8:] += 0.5\n",
    "r2 = [x + width + space for x in r1]  # the label locations for the second dataset\n",
    "r3 = [x + width for x in r2]  # the label locations for the third dataset\n",
    "\n",
    "bars1 = plt.bar(r1+0.05, custom_transform(df1['roc_auc']), color=colors[0], width=width, label='Pairwise(D3)',edgecolor='black', linewidth=1)\n",
    "bars2 = plt.bar(r2, custom_transform(df2['roc_auc']), color=colors[1], width=width, label='Skew-pair(D5)',edgecolor='black', linewidth=1)\n",
    "bars3 = plt.bar(r3, custom_transform(df3['roc_auc']), color=colors[2], width=width, label='Late-fusion',edgecolor='black', linewidth=1)\n",
    "\n",
    "# Setting the labels and title\n",
    "# plt.title('Fusion Statergy Comparison', fontsize=22)\n",
    "plt.xlabel('Skewness (d)', fontsize=22)\n",
    "plt.ylabel('ROC AUC (%)', fontsize=22)\n",
    "adjusted_positions = [(r + width + (space / 2) - 0.5 if r < 7 else r + width + (space / 2) + 0.5 if r > 7 else r + width + (space / 2)) for r in range(len(df1['skewness']))]\n",
    "plt.xticks(adjusted_positions, df1['skewness'])\n",
    "plt.legend()\n",
    "\n",
    "# for bars in [bars1, bars2, bars3]:\n",
    "#     for bar in bars:\n",
    "#         height = bar.get_height()\n",
    "#         if height > 0:\n",
    "#             plt.text(bar.get_x() + bar.get_width()/2, height,\n",
    "#                      f'{inverse_custom_transform(height):.1f}',\n",
    "#                      ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.xlim(min(r1) - 0.35, max(r3) + 0.35)\n",
    "\n",
    "# Custom legend\n",
    "plt.legend([bars2, bars1, bars3], ['skew-pair fusion (η=1/12)', 'pairwise fusion', 'late-fusion'],\n",
    "           frameon=False, loc='upper left', fontsize=20)\n",
    "\n",
    "# Formatting Y-axis\n",
    "plt.gca().yaxis.set_major_formatter(\n",
    "    FuncFormatter(lambda x, _: f\"{inverse_custom_transform(x):.0f}\"))\n",
    "actual_ticks = [0, 60, 65, 100]\n",
    "transformed_ticks = [custom_transform(value) for value in actual_ticks]\n",
    "plt.yticks(transformed_ticks)\n",
    "\n",
    "plt.gca().tick_params(axis='both', which='both', direction='in', length=6, width=1, labelsize=20)\n",
    "plt.tick_params(axis='x', which='both', bottom=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/svg/Figure 7.svg', format='svg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/png/Figure 7.png', format='png')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/jpeg/Figure 7.jpeg', format='jpeg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/tif/Figure 7.tif', format='tif')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1500x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.lines import Line2D  # For custom legend lines\n",
    "\n",
    "# def custom_transform(y):\n",
    "#     # 0-68 maps to 0-3\n",
    "#     # 68-80 maps to 3-20\n",
    "#     # 80-100 maps to 20-21\n",
    "#     return np.where(y < 68, y * 3 / 68,\n",
    "#                     np.where(y < 80, 3 + (y - 68) * 17 / (80 - 68), 20 + (y - 80) * 1 / (100 - 80)))\n",
    "\n",
    "# def inverse_custom_transform(y):\n",
    "#     # Inverse transformation, mapping 0-3 back to 0-68\n",
    "#     # Mapping 3-20 back to 68-80\n",
    "#     # Mapping 20-21 back to 80-100\n",
    "#     return np.where(y < 3, y * 68 / 3,\n",
    "#                     np.where(y < 20, 68 + (y - 3) * (80 - 68) / 17, 80 + (y - 20) * (100 - 80) / 1))\n",
    "\n",
    "def custom_transform(y):\n",
    "    # 0-65 maps to 0-6\n",
    "    # 65-80 maps to 6-18\n",
    "    # 80-100 maps to 18-21\n",
    "    return np.where(y < 65, y * 6 / 65,\n",
    "                    np.where(y < 80, 6 + (y - 65) * 12 / (80 - 65), 18 + (y - 80) * 3 / (100 - 80)))\n",
    "\n",
    "def inverse_custom_transform(y):\n",
    "    # Inverse transformation, mapping 0-6 back to 0-65\n",
    "    # Mapping 6-18 back to 65-80\n",
    "    # Mapping 18-21 back to 80-100\n",
    "    return np.where(y < 6, y * 65 / 6,\n",
    "                    np.where(y < 18, 65 + (y - 6) * (80 - 65) / 12, 80 + (y - 18) * (100 - 80) / 3))\n",
    "\n",
    "\n",
    "colors = [\n",
    "    '#3C5A78',\n",
    "    '#5B84AD',\n",
    "    '#F1F0E7',\n",
    "]\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "# Your updated data and functions remain unchanged\n",
    "# Your data\n",
    "data1 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'roc_auc': [70.5,70.6,70.8,69.8,69.9,74.8,75.0,75.1,74.9,77.0,76.6,76.9,75.5,75.1,75.0]\n",
    "}\n",
    "df1 = pd.DataFrame(data1)\n",
    "\n",
    "data2 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'roc_auc': [69.1,68.7,68.8,68.8,70.5,74.9,75.3,75.4,75.4,76.0,76.0,75.6,75.5,74.9,73.8]\n",
    "}\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "# Updated color scheme from your image\n",
    "\n",
    "# Plotting with the new color scheme\n",
    "plt.figure(figsize=(10, 6.18), dpi=150)  # Adjust figure size\n",
    "\n",
    "width = 0.3  # the width of the bars, reduced to fit three sets\n",
    "r1 = np.arange(len(df1['skewness']))  # the label locations for the first dataset\n",
    "r2 = [x + width for x in r1]  # the label locations for the second dataset\n",
    "\n",
    "bars1 = plt.bar(r1, custom_transform(df1['roc_auc']), color=colors[0], width=width, label='skew-pair fusion (η=5/12)', edgecolor='black', linewidth=1)\n",
    "bars2 = plt.bar(r2, custom_transform(df2['roc_auc']), color=colors[1], width=width, label='skew-pair fusion (η=1/12)', edgecolor='black', linewidth=1)\n",
    "\n",
    "\n",
    "# Setting the labels and title\n",
    "# plt.title('η=3/12 vs. η=1/12 Comparison', fontsize=22)\n",
    "plt.xlabel('Skewness (d)', fontsize=22)\n",
    "plt.ylabel('UA (%)', fontsize=22)\n",
    "plt.xticks([r + width/2 for r in range(len(df1['skewness']))], df1['skewness'])\n",
    "plt.xlim(min(r1) - 0.5, max(r1) + 0.7)  # Adjusting x-limits to reduce the empty space on the sides\n",
    "# 自定义图例位置\n",
    "plt.legend(loc = 'upper left', frameon=False, fontsize=20)  # 图例将放在图的正中央\n",
    "\n",
    "# for bars in [bars1, bars2]:\n",
    "#     for bar in bars:\n",
    "#         height = bar.get_height()\n",
    "#         if height > 0:\n",
    "#             plt.text(bar.get_x() + bar.get_width()/2, height,\n",
    "#                      f'{inverse_custom_transform(height):.1f}',\n",
    "#                      ha='center', va='bottom', fontsize=4.5)\n",
    "\n",
    "# Formatting Y-axis using your custom transformation functions\n",
    "plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{inverse_custom_transform(np.array([x]))[0]:.0f}\"))\n",
    "# Adjusting y-ticks handling\n",
    "actual_ticks = [0,60, 70, 75, 100]  # Actual values you want to display\n",
    "transformed_ticks = [custom_transform(np.array([value]))[0] for value in actual_ticks]  # Apply transformation individually\n",
    "plt.yticks(transformed_ticks, actual_ticks)  # Set the custom ticks with actual values\n",
    "plt.yticks(transformed_ticks, actual_ticks)  # Set the custom ticks\n",
    "plt.gca().tick_params(axis='both', which='both', direction='in', length=6, width=1, labelsize=20)\n",
    "plt.tick_params(axis='x',          \n",
    "                which='both',      \n",
    "                bottom=False) \n",
    "plt.tight_layout()\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/svg/Figure 10.svg', format='svg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/png/Figure 10.png', format='png')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/jpeg/Figure 10.jpeg', format='jpeg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/tif/Figure 10.tif', format='tif')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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99ZfKlSvnlG0UddOmTdOoUaNMy5555plcr14pyYrD88QRe/bsUaNGjazt0NBQu77qm9U777yjMWPGWNv9+vXTV1995awSi6SS9jhwFl4rclfS30tK4mcOdyvJn0klzl8Kqjid3zoDGYitkpST2cPP0wUAztatWze7183t70W+vr767rvvNG7cOE2bNi3fr974+/vrP//5j5577jl9/vnnpt95+oNnYY/H6tWrTW8UlSpV0tKlS/O9I/Idd9yhDz74QEOHDrUuGzdunO677z6bOUbdzR1B1zfffGP6MFG5cmV17969QGO5WmEeI2XKlJGvr6/pOVK2bFk1a9bM7jE7dOhg+sCZ01203c0TYWjp0qU1d+5cXXvttUpLS5MknT59Wr/++qvNnHHuVtjj8dhjj1lPXKtUqaI33njDWaV5jDPeaxxVr149LV++XM2bN9exY8ckXf065JtvvqlXX33VKdsoKHc8Z7744gs99dRTpmWDBw/W66+/XqDxXM0TryPFWXBwsKmdlJQkwzBksVjsHiP714KzjwnvUNxeK9ytKL+XOENJ/MzhbkX5M6kzlNTzF1crTue3hVUSMxBnKEk5mV08eHU74BKy8+7F9j789+zZYwwdOjTHu2IHBQUZAwcONLZv325dPzw83LTOn3/+6aI9tU9hj0fv3r1N67z22mt2bzs9Pd1o0KCBqf/8+fOdtWsF5uzHSE7at29vGufpp5924h44V2GPR9WqVU3rNGvWzKHtL1y40NS/VatWztitQnHHYyQ3ffr0MY0/YsQIp2/DUYU5HgsWLDD9/uuvv853e8Xh69GefIzMnDnTNH61atWcvg1Hufp4LFmyxPDz8zON06dPHyMtLc3Je+I8rj4mxeF54oikpCSbrzKfPHnSoTGGDRtm6v/cc8+5qNqio6Q9DgqrOL5WeEpRfC8prJL6mcNTiuJnUmcpiecvrlaczm8LqyRmIM5W3HMye3DTTyAfDRs21IwZMxQbG6tjx45p69atWrdunaKjo3Xu3DnNmTNHTZs2lSSdOXNGZ8+etfYNCgrStdde66HKC88wDK1cudK0zJGvXPn4+KhHjx6mZWvXrnVKbUXZwYMHtW7dOtOyov51o8LI/hgPCQlxqH/29c+fP1/omoqzLl26mNr79u3zUCXOMW7cOOu/e/TooXvvvdeD1ZQMvXv3Nl11e/z4cR05csSDFbnWqlWrdM8991ivcpOuXuH/5ZdfytfX14OVwZnKlCmjWrVqmZZlvbGYPbKv37Bhw0LXheKD1wrHlMT3Ej5zOFdJ+0yaFecvjvGm81syEPt4Q07GlCwocQwXTstfo0YN1ahRI9ff//nnn6Z206ZNPf4BvTDH4/z587p48aJpWe3atR0aI/v6sbGxBa6nuJg7d67puDdv3lxNmjTxYEV5K+xzplGjRqYPFVeuXHGof9Y53iSViLkRC6NmzZqm9pkzZzxUiXNknYN42bJlDk2vkOnIkSM2/bZv3279EOYJrnyvyU+5cuUUFhamuLg467KTJ08qIiLCYzW5yqZNm3THHXeYXifatGmj7777TgEBAR6sDK7QsGFDU2C3e/dutWzZ0u7+e/bssRkP3oHXCseVxPeSkvqZw1NK2mfSrDh/cUxxO78tDDIQxxXHnMweXGEOONGyZctM7eI+p1dOHxz8/Bz7O5u/v7+pnd88V+5gGIbdPwUZe+7cuaZlJeUmH7lp3ry5qX3q1CmH+p8+fdrUrlChQqFrKixXPkbyk/05k5qa6vRtOMqTxwP2yf64cTdXPEZ27dql7t27KyEhwbqsWbNm+vHHHxUUFOSK3XAqnjeOyx5Ibdy40e6+J06cMN2oz9/f33QTUZRcxf21oijx9HsJipai+JnUWUri+YureNv5bUnNQDypuOZkBOaAk6Snp+ubb76xtn19fYv9G0lOb/zHjx93aIzsf03N70YZxd2aNWt06NAhazsgIED9+/f3YEWu16NHD/n4/Pt2cujQIZ07d87u/lnvli1J11xzjdNqK45Onjxpapf05wwcFx8fb/Mcq1y5soeqcY19+/apW7dupq84X3vttfrll18UGhrqwcrgSj179jS1V6xYYfcfFLLefE2SOnXqxE0/vQCvFQXnDe8lKJyS/JmU8xf7edv5LRmIcxXnnIwpWQAnmT59uvVO85J0++23q1q1ah6sqPACAgJUtWpVnThxwrps5cqVprs+5+e3334ztevWreu0+oqiOXPmmNq9evVSWFiYh6pxj0qVKqlt27amee0WLVqkhx9+ON++aWlp+u6770zLOnbs6OwSi5X169eb2tm/Dlvc/PDDDw5fkbRz506NHTvW2q5cubLmz59vWqdevXpOqa84WrZsmSlErFixoqpWrerBipzryJEj6tq1q+nqrdq1a2v58uVefcLhDdq0aaPw8HDrPJcHDx7U6tWr1alTp3z7fvbZZ6b2nXfe6ZIaUXTwWlE4JfG9hM8czlXSPpNmxfmL/bzt/JYMxLmKdU5WuHuGAjAMw4iOjjYqVKhgveOvv7+/8ffff3u6LKd44IEHTHczbty4sZGammpX39WrV9vcMXndunUurthzEhISjODgYNP+LlmyxNNlucX8+fNN+92gQQPj8uXL+fb78MMPTf1CQkKMCxcuuKHioun8+fNGuXLlTMfks88+83RZbrdq1SrTMYiIiPB0SUVGUlKS0aBBA9PxGTJkiKfLcprjx48bdevWNe1f9erVjYMHD3q6tCKnpD5Pxo4da9qvDh06GBkZGXn2WbFihalP2bJljTNnzripYs8qqY+D/PBaUTgl/b3EEd76HMqPN3wm5fwlf956fksG4hzFPSdjShYgB0ePHlVSUpJd6+7Zs0ddunQx3TBn3LhxJWbezAceeMDU/uuvvzRy5EhlZGTk2S86Otrmq1r169dX69atnV5jUfHtt9+a5s+sUqWKbrvtNg9W5D7333+/6cYv//zzj4YNG5bn42TTpk165plnTMtGjhzp1V+hHjt2rOmGVQEBAcVmjjc45plnntHmzZsd6nPu3Dndcccd+ueff6zLfH19NXr0aGeX5xHnzp1Tt27ddODAAeuyihUravny5Q7fbAnF17PPPmuaSmXNmjWaPHlyruvHxsbaXBE4atQohYeHu6xGeBavFf/ivQSu4g2fSTl/yZ+3nt+SgeTM63IyTyf2QFH03nvvGRUqVDBGjRplrF692khKSrJZZ9euXca4ceOMgIAA018PW7dubSQnJ3ugatfp1KmTzV9Jb775ZmPFihU2f2k9e/as8dZbbxmhoaE2fb755hsP7YF7ZD9OY8aM8XRJbrVixQrDYrGYjkHXrl2NLVu2mNa7cOGC8fbbb9tcrdCgQQPj0qVLHqreuSZNmmSz33lJTU01nn76aZvnzJNPPunCKosub7ja64YbbjAkGa1atTLefvttY/v27UZKSorNehkZGcaePXuMl19+2QgPD7d5jIwdO9YD1TvfpUuXjJYtW5r2rVy5csb27ds9XVqRVZKfJ6+99prNY33EiBFGbGysdZ309HTju+++M2rVqmVar1q1asb58+c9V7ybleTHQU54rTDjvaTwSvpziM+keeP8JW/efH5LBmLL23Iyi2HYeScdwIu8//77euKJJ6xtX19f1a5dW+XLl1dycrJOnDhh+ktZppYtW+qnn34qcXfJPnnypNq0aWO62Uem4OBg1a5dW2XKlFFcXJwOHjyY4w26xowZo7feessd5XrEkSNHVLt2bdO+//nnn2rcuLEHq3K/yZMna/z48TbLq1Spoho1aigxMVEHDhxQSkqK6fcVKlTQqlWrTFd5FGcdO3bUmjVr1KZNG917773q0qWLGjZsaHOH9YsXL+rHH3/UG2+8oR07dph+V7duXW3atKnEvZ7YI/ucxRERETp8+LDnCnKBpk2baufOnaZlAQEBql69usqVK6eAgADFx8fr2LFjio+Pz3GMQYMGaebMmaabVhVXnTp10urVq03LXn755QJdkRMVFaXy5cs7qTLP27Bhg5KTk22W2zPvbqZq1aoVqyt6MjIydOedd2rp0qWm5b6+voqIiFBoaKgOHTpkuvpRksqUKaPly5erbdu2bqzWPbzxcZATXivMeC8pvJL+mYPPpPnj/CVn3n5+SwZiy+tyMk+m9UBR9d5779n8ZTCvH4vFYjz88MNGQkKCp0t3maNHjxodO3Z06Ljo/89T9frrr+c7/2hx9/LLL5v2OyoqytMlecy0adMMf39/ux8j11xzjfHPP/94umyn6tChg81+lipVyqhbt67RvHlzo2XLlkadOnUMHx+fHI9JlSpVStwxcURJv9rLMP69KrAgPyEhIcaHH35Yol5XC3oscvpZtWqVp3fHqSIiIgp9TAYNGuTp3XBYcnKycd9999m9jxUqVChx//dZeevjIDteK8x4Lym8kv6Zg8+k9uH8xRbnt2Qg2XlbTuadf0YG8tGxY0cNGjRIVapUyXO9UqVKqW/fvvrf//6nTz/9VEFBQW6q0P1q1qyp3377TQsWLFDHjh3zvQolNDRUI0aM0J9//qlnn31WFovFTZV6Rva7hw8ePNgzhRQBTzzxhHbt2qV+/frJ398/1/Vq166td999V7t27VL9+vXdWKFnXLlyRQcOHNC2bdu0efNmHTx4MMd58G6//Xbt3LnTK46JN/vyyy81efJkde3aVSEhIfmub7FYdP311+vNN99UdHS0RowYUeJfV+HdSpcurS+//FILFy5U06ZNc10vKChII0eO1O7du9WxY0e31QcUBbyXoCD4TGqL8xdbnN+SgWTnbTkZU7IA+Th48KD++usvHT16VJcuXZIkhYWF6ZprrtGNN96owMBAD1foGfHx8dqyZYsOHjyoCxcu6PLlywoJCVGFChV0/fXXq1GjRl7z1c7169erXbt21nZAQIBOnDihsLAwD1ZVNFy6dEkbN27U/v37dfHiRQUHB6ty5cpq3ry5rrnmGk+X5zLLly/XokWLtG7dOu3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",
      "text/plain": [
       "<Figure size 1500x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.lines import Line2D  # For custom legend lines\n",
    "\n",
    "# def custom_transform(y):\n",
    "#     # 0-68 maps to 0-3\n",
    "#     # 68-80 maps to 3-20\n",
    "#     # 80-100 maps to 20-21\n",
    "#     return np.where(y < 68, y * 3 / 68,\n",
    "#                     np.where(y < 80, 3 + (y - 68) * 17 / (80 - 68), 20 + (y - 80) * 1 / (100 - 80)))\n",
    "\n",
    "# def inverse_custom_transform(y):\n",
    "#     # Inverse transformation, mapping 0-3 back to 0-68\n",
    "#     # Mapping 3-20 back to 68-80\n",
    "#     # Mapping 20-21 back to 80-100\n",
    "#     return np.where(y < 3, y * 68 / 3,\n",
    "#                     np.where(y < 20, 68 + (y - 3) * (80 - 68) / 17, 80 + (y - 20) * (100 - 80) / 1))\n",
    "\n",
    "def custom_transform(y):\n",
    "\n",
    "    return np.where(y < 70, y * 6 / 70,\n",
    "                    np.where(y < 85, 6 + (y - 70) * 12 / 15,\n",
    "                             18 + (y - 85) * 3 / 15))\n",
    "\n",
    "def inverse_custom_transform(y):\n",
    "\n",
    "    return np.where(y < 6, y * 70 / 6,\n",
    "                    np.where(y < 18, 70 + (y - 6) * 15 / 12,\n",
    "                             85 + (y - 18) * 15 / 3))\n",
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "# Your updated data and functions remain unchanged\n",
    "# Your data\n",
    "data1 = {\n",
    "    'skewness': [-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    'roc_auc': [67.6, 71.1, 73.3, 75.1, 76.8, 78.1, 78.3, 76.6, 80.3, 80.0, 77.8, 81.5, 79.8, 81.0, 80.0, 79.6, 76.8, 76.8, 73.3]\n",
    "}\n",
    "df1 = pd.DataFrame(data1)\n",
    "\n",
    "data2 = {\n",
    "    'skewness': [-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    'roc_auc': [71.3, 75.3, 74.8, 76.8, 79.6, 79.1, 79.6, 79.8, 81.0, 78.8, 79.1, 79.8, 81.5, 80.5, 79.8, 81.3, 79.8, 81.8, 78.6]\n",
    "}\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "colors = [\n",
    "    '#3C5A78',\n",
    "    '#5B84AD',\n",
    "    '#F1F0E7',\n",
    "]\n",
    "\n",
    "# Updated color scheme from your image\n",
    "\n",
    "# Plotting with the new color scheme\n",
    "plt.figure(figsize=(10, 6.18), dpi=150)  # Adjust figure size\n",
    "\n",
    "width = 0.3  # the width of the bars, reduced to fit three sets\n",
    "r1 = np.arange(len(df1['skewness']))  # the label locations for the first dataset\n",
    "r2 = [x + width for x in r1]  # the label locations for the second dataset\n",
    "\n",
    "bars1 = plt.bar(r1, custom_transform(df1['roc_auc']), color=colors[0], width=width, label='skew-pair fusion (η=3/12)', edgecolor='black', linewidth=1)\n",
    "bars2 = plt.bar(r2, custom_transform(df2['roc_auc']), color=colors[1], width=width, label='skew-pair fusion (η=1/12)', edgecolor='black', linewidth=1)\n",
    "\n",
    "\n",
    "# Setting the labels and title\n",
    "# plt.title('η=3/12 vs. η=1/12 Comparison', fontsize=22)\n",
    "plt.xlabel('Skewness (d)', fontsize=22)\n",
    "plt.ylabel('Accuracy (%)', fontsize=22)\n",
    "plt.xticks([r + width/2 for r in range(len(df1['skewness']))], df1['skewness'])\n",
    "plt.xlim(min(r1) - 0.5, max(r1) + 0.7)  # Adjusting x-limits to reduce the empty space on the sides\n",
    "\n",
    "plt.legend(loc = 'upper left', frameon=False, fontsize=20)  \n",
    "\n",
    "# for bars in [bars1, bars2]:\n",
    "#     for bar in bars:\n",
    "#         height = bar.get_height()\n",
    "#         if height > 0:\n",
    "#             plt.text(bar.get_x() + bar.get_width()/2, height,\n",
    "#                      f'{inverse_custom_transform(height):.1f}',\n",
    "#                      ha='center', va='bottom', fontsize=4.5)\n",
    "\n",
    "# Formatting Y-axis using your custom transformation functions\n",
    "plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{inverse_custom_transform(np.array([x]))[0]:.0f}\"))\n",
    "# Adjusting y-ticks handling\n",
    "actual_ticks = [0, 70, 75, 80, 100]\n",
    "transformed_ticks = [custom_transform(value) for value in actual_ticks]\n",
    "plt.yticks(transformed_ticks, actual_ticks)\n",
    "plt.yticks(transformed_ticks, actual_ticks)  # Set the custom ticks\n",
    "plt.gca().tick_params(axis='both', which='both', direction='in', length=6, width=1, labelsize=20)\n",
    "plt.tick_params(axis='x',          \n",
    "                which='both',      \n",
    "                bottom=False) \n",
    "plt.tight_layout()\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/svg/Figure 11.svg', format='svg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/png/Figure 11.png', format='png')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/jpeg/Figure 11.jpeg', format='jpeg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/tif/Figure 11.tif', format='tif')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7e/bsDA+Fq1evninJnhkfHx/98ssvcnL63/9if/jhB5vHZ4Wrq6v++9//ZikxVJhk5+fe29s73QM/7TVt2jRT7eyGDRtq5syZGZbGqVq1qn777TdTn8WLF+v48eM2r+vs7KzffvtN1atXz7DfxIkTTaVgVq5cmWmtd3vFxcWZdsiXK1fO9DORFcWKFdOCBQtUunTpdPs4OztrypQppmtJBzgWZrt371aLFi3022+/2T02eTkWHx8ftW7d2pGh5ZrPP/9c27ZtM9pOTk765JNPsjRX8k8KSbL7RgcKhujoaHl7e+fqV3R0dF6/bAAAYAMS5gAKpZQHvWWURHWkCxcuqF27dlq1apVxzd/fX1u2bFGNGjUyHPuf//zHdLjnpEmT5O7ubtO6np6eRlkO6e6O6x07dqTZN/mO74iICB04cCDNfskT3x06dDAlzIODg9PcJXXmzBlTXfTc2F2e5L333ku14zQtnTt3NsUVExOjX375xaGx1KhRQ82bNzfamzdvduj8KfXu3Ttb5S4Ki7z6uU/OarXq+++/N137/PPP0zzcN6VWrVqpd+/eprmS30TLzOOPP25TnXAfHx9TaZ3o6GgdOXLE5nVsER4ebvod4Yia8YMHD06zVnlK9erVM90YPHr0aIFLUpUqVUoDBw7U3LlztW/fPl29elXx8fG6du2a9u7dq6lTp6YqexQTE6NnnnnG7prbyRPmXbt2tem9mt9s2LAh1aeF3njjDTVu3DhL86V8v/7zzz9Zjg0AAAAFT8H7GzEA2CDl7rC5c+eaan3nhKNHj6pLly6mnWidO3fW77//nukuccm8C7Js2bJ215xOWVt6w4YNae5o79Chg77++mujHRQUlCrZeurUKdPraN++vYoVK2a0L126pH379qVK2KTceZ4b9culu4nRp59+2ub+zz77rOmGQHBwsAYPHuzQmKpVq6atW7dKurv7Myf16dMnR+cvKNL6uX/99ddzNYYjR46YDnqtXLmyXT8HgwYN0vz58422PcnP5Mn2zDRq1EiLFy822v/884/q1q1r8/jMpPzUhi2/AzNjz+tr3LixUUYjMTFR4eHhGR7++PPPP6e64ZITbPkUyNy5c/Xkk0/Kzc0t1XMlSpRQiRIl1KBBAw0ZMkTffvuthg0bZpxbERcXp759++rEiROpDpRNS1hYmPbu3Wu0C2I5llOnTqlXr16mG86NGjXS+PHjszxnyvdrRp9CQuHQvNdQObvkzE3WOwnxClnwdeYdAQBAvkHCHECh1L59ezk7OxtlSaZMmaLbt2/r7bffzla5lfRs2bJFPXr00JUrV4xrAwYM0MyZM23a5Xrt2jXTTm9/f3+7yxekrK2d1oGdktSuXTvT92bNmjUaNmyYqU/yxHeFChVUq1YtSVKdOnWMedesWZNpwjy3dpg3aNDAdGhiZgICAkztkJAQm8ZdvHhR//3vf7V161bt27dPERERunnzZqYHwkVHRysmJkYeHh42x2iP5LvZ72UPP/ywxo0bZ7SHDx+uiIgIvfbaa7lWriZ5SQjpbjmjjEqxpNS2bVu5uLgYyb/du3crLi4uzeRpSrbsLk+SssyQoxOCKXd0Z/e97+rqatdBsikPb87s9T300ENZiisn9OvXz+a+L7/8skqXLq0nn3zSKKtz9uxZ/ec//9Hw4cMzHZ98d3nSuRgFyeXLl9W1a1ddvnzZuFamTBktWLDA5k9opcXT09PUjoqKyvJcKBicXVzl7JL571kAAHBvoCQLgEKpUqVKqQ7u/Oabb1S9enU1bdpUo0aN0t9//206GDSrFi9erA4dOpiS5aNHj9acOXNsLglx9OhRU/mCv//+2+7D5Ly8vExzpvfaSpQoYfqY+rp161LVO09ZjiWtx2nVMV+7dq3xuE6dOukmKVevXm3zly3sLUdSuXJl0475M2fOZHgQ1+XLlzVo0CBVqFBBr7/+uubNm6cDBw4oIiIi02R5kuvXr9sVo628vb1VqlSpHJm7oHnwwQdNBz4mJCTok08+UYUKFdSmTRuNGzdOQUFBunXrVo7FkHQAbZIGDRrYNd7d3d104GBsbKwuXrxo01h7au2n/H2R07urs3vQXcmSJeXs7Gxz/5Q7hAtaSRZ79OrVS/379zddS36GRkaSJ8zbtm2rEiVKODK0HHXr1i117drVdHZB8eLFtWLFimyfncHBjAAAAPc2dpgDKLS++uorXbhwwZQQkKSdO3dq586dmjhxoiwWixo2bKiuXbuqX79+prq3tkp5wNzEiRM1YsQIu+ZInmx3lIx2VLZv396ocX7jxg3t2LHDVL4leeI7eZK8ffv2mjp1qqS7pSISEhKMereHDx/W+fPnTX3TkzypmRlbEhe+vr42z5ekZMmSunnzpqS7JRtu3ryp4sWLp+p38uRJBQQEKDw83O41kksqmeBoyRP/kObNm6cePXoY5XCku3++Gzdu1MaNGyXd3UnbtGlTde/eXf369TMdmJtd165dM7WzcjMj5Zhr167ZdCCpLSU40uPoBGHKHbq23lhKT3Zem1T4E6DDhw/XnDlzjPa+fft08eLFVDvtk4uMjFRwcLDRLkjlWG7fvq1HH33UdFaHp6enli5datcnEdKT8gZSyhtMAAAAKNzYYQ6g0CpSpIgWLVqkefPmqVGjRmn2sVqt2rNnjyZMmKAHHnhA3bt314kTJ+xaJ+XHvn///fdUSbPM5MTu46SP56clZU3l5DvKDxw4YNrRmrxvQECAUSrm1q1b2r59u/FcXtUvl1In52yRMgESGRmZqk9cXJy6deuWKlleo0YNvfDCC5o0aZLmzZunRYsWaeXKlVq1apXxlbKmfE7Ji4Mt87NSpUpp/fr1mjZtmu6///40+yQkJGjr1q167733VL16dfXv39/mXdyZSfk+ykqiLeWYnNwRn1NS7lQuiK+hIKlfv77pEwZWq9W08zotK1asMN3Ie/TRR3MsPkeKj4/XU089ZUr2u7m5acGCBQ4rrZPy5zitm6kAAAAovNhhDqBQs1gs6tOnj/r06aNDhw5p1apVCg4O1saNG001T5MsXbpU69ev19KlS9WmTRub1pg+fbo++ugjhYaGSpK2b9+u9u3ba9WqVTbvLk2Z8A0MDNSYMWNsGpseHx+fdJ9r3bq13NzcFBcXJ+lusnv06NGSzMnzmjVrqkKFCqY5/f39jV19QUFBatWqVapxTk5OqeqE56SslFtIWZM2rUMJp0+fbko6lSlTRj/88INNdX6///57u2OCY7i6uuqVV17RK6+8oh07digoKEjBwcHavHmz8amCJImJiZo7d65Wr16t4OBgo15/VqV8H2Wl9nHKMUWLFs1WTHmhQoUKslgsxs7u5J8+yY82bdqUK4d+tm7dOtu75dNTsWJF04Gzly5dyrB/8k9f1a1bN0fO93C0xMREDRgwwBS7s7Oz5s2bp86dOztsnXPnzpnaKc8IAQAAQOFGwhzAPaNu3bqqW7euhg0bJqvVqiNHjmjlypX6/fffjVIN0t2dkE888YROnjyZZhI1papVq2r9+vVq3769jh8/Lknas2ePAgICFBQUlOFH4pOkTKwXKVJEHTt2tPMV2s7T01MtWrTQhg0bJN1NFsXGxsrd3d20UzytXeLJy7kEBQXpvffeU2Jiomm3X6NGjTJM2Du6PEJaNz8yk7zGu5OTU5qlTebPn29qL1y40LhBYM/8yJ7s1J9u2rSpmjZtqpEjRyoxMVF79+7V8uXL9euvv2rv3r1GvwsXLuiJJ57Q3r177T5wN7mU7/uslFtK+X7O6Gcpv3Jzc1P58uV19uxZSXcT5omJidn63uakfv36pao/nxNCQ0MdWgIouZSfNomPj0+3b2Jiov7++2+jXRDKsVitVr300kum38sWi0UzZ87U448/7tC1UibMc+rPDAAAAPlT/vxXCwDkMIvFojp16mjYsGHasGGD1q9fb0paR0RE2HxomnR3Z9+6detUp04d49rBgwcVEBCQ6h/eaUl5QJm9ZWGyInmN8du3b2vz5s26c+eO1q9fb1xPK2Ge/NqWLVsUExOj3bt3m8rQZFS/PCccOHDArv5nzpwx7TSuUqWKLBaLqU9iYqKp5EyjRo1sTpZLd//88T/Jd9Xau5M3s52ytnJyclLjxo01evRo7dmzR3/88Yc8PDyM5w8cOKAVK1Zka40qVaqY2smT8raIjY3V0aNHjba7u7tNN93yo+QHniYkJOjkyZN5GE3hd+HCBVO7dOnS6fbdunWr6eeqIJRjefPNN1N9cuerr77SwIEDHb7WkSNHjMcuLi6qW7euw9cAAABA/kXCHAAktWnTRp9++qnpWvJd57YoV66cgoODVb9+fePakSNH1K5dO/3zzz8Zjq1YsaKp3vLx48czHZNdKZPhQUFB2rVrl1FP3cnJSYGBganGJZVzke4m9zZt2mQqx5LW3Dlt//79du3oXrdunandvHnzVH2uXLmihIQEo21PqY5jx44ZO2txV/Id/PbUC09MTNSuXbtyIiT16tVLw4cPN12z9+c+pZYtW5rawcHBdn2iYsOGDaadwf7+/sbPW0HTpEkTU3v//v15FEnhFx4enmqHfEYHxS5evNh4XLp06VTv2/zm/fff15dffmm6Nn78eL322msOXys2Ntb4tJgk1atXL8fK6AAAACB/ImEOAP9fysPCslLmw8/PT2vXrpW/v79x7cSJE2rbtq1R4zw9KetiT5061e717dGyZUtT7fQ1a9aYyrE0atRIJUuWTDXO09PTlFxJOc7V1dXm+u+OEh8fn6p8SkbmzJljardr1y5Vn5RJzqR677aYNm2azX3vFcl3XoeFhdl8g2PZsmWp6o47kiN+7pOrVauWaUf4mTNntHbtWpvHz5o1y9RO671ZUKT8PbBz5848iiRzp0+fltVqzfGvnCrtkXLndaVKlVSjRo10+yevAd6tW7d8WypHkiZNmqSPP/7YdG306NHGuRuOtnfvXtPN0rZt2+bIOgAAAMi/8u/fjgEglzm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",
      "text/plain": [
       "<Figure size 1500x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.lines import Line2D  # For custom legend lines\n",
    "\n",
    "# def custom_transform(y):\n",
    "#     # 0-68 maps to 0-3\n",
    "#     # 68-80 maps to 3-20\n",
    "#     # 80-100 maps to 20-21\n",
    "#     return np.where(y < 68, y * 3 / 68,\n",
    "#                     np.where(y < 80, 3 + (y - 68) * 17 / (80 - 68), 20 + (y - 80) * 1 / (100 - 80)))\n",
    "\n",
    "# def inverse_custom_transform(y):\n",
    "#     # Inverse transformation, mapping 0-3 back to 0-68\n",
    "#     # Mapping 3-20 back to 68-80\n",
    "#     # Mapping 20-21 back to 80-100\n",
    "#     return np.where(y < 3, y * 68 / 3,\n",
    "#                     np.where(y < 20, 68 + (y - 3) * (80 - 68) / 17, 80 + (y - 20) * (100 - 80) / 1))\n",
    "\n",
    "def custom_transform(y):\n",
    "    # 0-60 maps to 0-3\n",
    "    # 60-67 maps to 3-20\n",
    "    # 67-100 maps to 20-21\n",
    "    return np.where(y < 60, y * 3 / 60,\n",
    "                    np.where(y < 67, 3 + (y - 60) * 17 / (67 - 60), 20 + (y - 67) * 1 / (100 - 67)))\n",
    "\n",
    "def inverse_custom_transform(y):\n",
    "    # Inverse transformation, mapping 0-3 back to 0-60\n",
    "    # Mapping 3-20 back to 60-67\n",
    "    # Mapping 20-21 back to 67-100\n",
    "    return np.where(y < 3, y * 60 / 3,\n",
    "                    np.where(y < 20, 60 + (y - 3) * (67 - 60) / 17, 67 + (y - 20) * (100 - 67) / 1))\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "# Your updated data and functions remain unchanged\n",
    "# Your data\n",
    "data1 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'roc_auc': [60.0, 62.2, 62.3, 62.1, 62.4, 62.0, 64.9, 65.0, 65.4, 66.8, 65.9, 64.0, 63.5, 64.6, 64.6]\n",
    "}\n",
    "df1 = pd.DataFrame(data1)\n",
    "\n",
    "data2 = {\n",
    "    'skewness': [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],\n",
    "    'roc_auc': [61.3, 59.8, 59.2, 60.1, 61.3, 61.2, 62.5, 63.9, 64.9, 63.5, 64.3, 65.1, 64.3, 64.3, 65.0]\n",
    "}\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "# Updated color scheme from your image\n",
    "colors = [\n",
    "    '#3C5A78',\n",
    "    '#5B84AD',\n",
    "    '#F1F0E7',\n",
    "]\n",
    "\n",
    "# Plotting with the new color scheme\n",
    "plt.figure(figsize=(10, 6.18), dpi=150)  # Adjust figure size\n",
    "\n",
    "width = 0.3  # the width of the bars, reduced to fit three sets\n",
    "r1 = np.arange(len(df1['skewness']))  # the label locations for the first dataset\n",
    "r2 = [x + width for x in r1]  # the label locations for the second dataset\n",
    "\n",
    "bars1 = plt.bar(r1, custom_transform(df1['roc_auc']), color=colors[0], width=width, label='skew-pair fusion (η=5/12)', edgecolor='black', linewidth=1)\n",
    "bars2 = plt.bar(r2, custom_transform(df2['roc_auc']), color=colors[1], width=width, label='skew-pair fusion (η=1/12)', edgecolor='black', linewidth=1)\n",
    "\n",
    "\n",
    "# Setting the labels and title\n",
    "# plt.title('η=3/12 vs. η=1/12 Comparison', fontsize=22)\n",
    "plt.xlabel('Skewness (d)', fontsize=22)\n",
    "plt.ylabel('ROC AUC (%)', fontsize=22)\n",
    "plt.xticks([r + width/2 for r in range(len(df1['skewness']))], df1['skewness'])\n",
    "plt.xlim(min(r1) - 0.5, max(r1) + 0.7)  # Adjusting x-limits to reduce the empty space on the sides\n",
    "\n",
    "plt.legend(loc = 'upper left', frameon=False, fontsize=20)  \n",
    "\n",
    "# for bars in [bars1, bars2]:\n",
    "#     for bar in bars:\n",
    "#         height = bar.get_height()\n",
    "#         if height > 0:\n",
    "#             plt.text(bar.get_x() + bar.get_width()/2, height,\n",
    "#                      f'{inverse_custom_transform(height):.1f}',\n",
    "#                      ha='center', va='bottom', fontsize=4.5)\n",
    "\n",
    "# Formatting Y-axis using your custom transformation functions\n",
    "plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{inverse_custom_transform(np.array([x]))[0]:.0f}\"))\n",
    "# Adjusting y-ticks handling\n",
    "actual_ticks = [0,60, 65, 100]  # Actual values you want to display\n",
    "transformed_ticks = [custom_transform(np.array([value]))[0] for value in actual_ticks]  # Apply transformation individually\n",
    "plt.yticks(transformed_ticks, actual_ticks)  # Set the custom ticks with actual values\n",
    "plt.yticks(transformed_ticks, actual_ticks)  # Set the custom ticks\n",
    "plt.gca().tick_params(axis='both', which='both', direction='in', length=6, width=1, labelsize=20)\n",
    "plt.tick_params(axis='x',         \n",
    "                which='both',      \n",
    "                bottom=False) \n",
    "plt.tight_layout()\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/svg/Figure 12.svg', format='svg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/png/Figure 12.png', format='png')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/jpeg/Figure 12.jpeg', format='jpeg')\n",
    "plt.savefig('C:/Users/rzc/Desktop/fig/tif/Figure 12.tif', format='tif')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
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}