{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scanpy as sc\n",
    "from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score, normalized_mutual_info_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AnnData object with n_obs × n_vars = 4183 × 0\n",
      "    obs: 'SpatialGlue_level_1', 'SpatialGlue_level_2', 'SpatialGlue_level_3', 'SpatialCOC_level_1', 'SpatialCOC_level_2', 'SpatialCOC_level_3', 'MultiMAP_level_1', 'MultiMAP_level_2', 'MultiMAP_level_3'\n",
      "    uns: 'MultiMAP_level_1_colors', 'MultiMAP_level_2_colors', 'MultiMAP_level_3_colors', 'SpaKnit_level_1_colors', 'SpaKnit_level_2_colors', 'SpaKnit_level_3_colors', 'SpatialGlue_level_1_colors', 'SpatialGlue_level_2_colors', 'SpatialGlue_level_3_colors'\n",
      "    obsm: 'spatial'\n",
      "AnnData object with n_obs × n_vars = 4183 × 0\n",
      "    obs: 'SpatialCOC', 'SpatialGlue', 'STAGATE', 'Modality1', 'Modality2', 'SpaGCN', 'MultiMAP', 'MultiVI', 'Seurat', 'COSMOS'\n",
      "    obsm: 'COSMOS', 'Modality1', 'Modality2', 'MultiMAP', 'MultiVI', 'STAGATE', 'Seurat', 'SpatialCOC', 'SpatialGlue', 'spatial'\n"
     ]
    }
   ],
   "source": [
    "## results for three methods under different noise levels\n",
    "adata_results_noised = sc.read_h5ad(\"../../../Mouse_Thymus_Noised.h5ad\")\n",
    "print(adata_results_noised)\n",
    "\n",
    "## original results\n",
    "adata_results = sc.read_h5ad(\"../../../Mouse_Thymus_Replicate1.h5ad\")\n",
    "print(adata_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Method Noise Level       ARI       AMI       NMI\n",
      "0   SpatialCOC     level_1  0.319763  0.446140  0.447533\n",
      "1   SpatialCOC     level_2  0.180623  0.346918  0.348702\n",
      "2   SpatialCOC     level_3  0.250694  0.361333  0.362970\n",
      "3  SpatialGlue     level_1  0.227981  0.220949  0.223135\n",
      "4  SpatialGlue     level_2 -0.004561  0.010486  0.013236\n",
      "5  SpatialGlue     level_3 -0.000556  0.003481  0.006076\n",
      "6     MultiMAP     level_1  0.030888  0.047189  0.049527\n",
      "7     MultiMAP     level_2  0.013195  0.033981  0.036472\n",
      "8     MultiMAP     level_3  0.007821  0.038593  0.041075\n"
     ]
    }
   ],
   "source": [
    "methods = ['SpatialCOC', 'SpatialGlue', 'MultiMAP']\n",
    "levels = ['level_1', 'level_2', 'level_3']\n",
    "\n",
    "# 初始化结果存储\n",
    "results = []\n",
    "\n",
    "# 遍历每种方法和层次\n",
    "for method in methods:\n",
    "    for level in levels:\n",
    "        # 提取真实标签和预测标签\n",
    "        true_labels = adata_results.obs[method]  # 真实标签\n",
    "        pred_labels = adata_results_noised.obs[f\"{method}_{level}\"]  # 预测标签\n",
    "        \n",
    "        # 计算指标\n",
    "        ari = adjusted_rand_score(true_labels, pred_labels)\n",
    "        ami = adjusted_mutual_info_score(true_labels, pred_labels)\n",
    "        nmi = normalized_mutual_info_score(true_labels, pred_labels)\n",
    "        \n",
    "        # 存储结果\n",
    "        results.append({\n",
    "            'Method': method,\n",
    "            'Noise Level': level,\n",
    "            'ARI': ari,\n",
    "            'AMI': ami,\n",
    "            'NMI': nmi\n",
    "        })\n",
    "\n",
    "# 将结果转换为DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# 打印结果\n",
    "print(results_df)\n",
    "\n",
    "results_df.to_excel('Robustness.xlsx', index=False, engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Organize the data for plotting\n",
    "metrics_results = results_df.pivot(index='Method', columns='Level', values=['ARI', 'AMI', 'NMI'])\n",
    "metrics_results.columns = [f\"{metric}_{level}\" for metric, level in metrics_results.columns]\n",
    "\n",
    "# Extract the data\n",
    "box_data = []\n",
    "methods = ['SpaKnit', 'SpatialGlue', 'MultiMAP']  # Ensure the order of methods\n",
    "for method in methods:\n",
    "    for metric in ['ARI', 'AMI', 'NMI']:\n",
    "        box_data.append([metrics_results.loc[method, f\"{metric}_{level}\"] for level in levels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ploting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the font size and font family for matplotlib\n",
    "plt.rcParams.update({'font.size': 26, 'font.family': 'Arial'})\n",
    "box_width = 0.8\n",
    "\n",
    "# Create the boxplot\n",
    "fig, ax = plt.subplots(figsize=(12, 9))\n",
    "box = ax.boxplot(box_data, patch_artist=True, widths=box_width)\n",
    "\n",
    "# Customize the style of the boxplot elements\n",
    "for element in ['whiskers', 'caps', 'medians']:\n",
    "    for elem in box[element]:\n",
    "        elem.set_color('black')\n",
    "        elem.set_linewidth(1.5)\n",
    "\n",
    "# Set the colors for the boxes\n",
    "colors = ['#669bbc', '#f07167', '#e9c46a'] * len(methods)\n",
    "for patch, color in zip(box['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "# Set the x-axis tick labels\n",
    "num_methods = len(methods)\n",
    "num_metrics = 3\n",
    "tick_positions = np.arange(2, num_methods * num_metrics + 1, num_metrics)\n",
    "ax.set_xticks(tick_positions)\n",
    "ax.set_xticklabels(['SpatialCOC', 'SpatialGlue', 'MultiMAP'])\n",
    "\n",
    "plt.title('')  # Empty title\n",
    "plt.xlabel('Method')\n",
    "plt.ylabel('Metrics Value')\n",
    "\n",
    "# Customize the thickness of the plot's border\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_linewidth(2)\n",
    "\n",
    "plt.grid(True, linestyle='--', color='gray', linewidth=1)\n",
    "\n",
    "# Add the legend\n",
    "legend_elements = [Patch(facecolor='#669bbc', label='ARI'),\n",
    "                   Patch(facecolor='#f07167', label='AMI'),\n",
    "                   Patch(facecolor='#e9c46a', label='NMI')]\n",
    "ax.legend(handles=legend_elements, loc='upper right', frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot as PNG and EPS files\n",
    "plt.savefig('Metrics of Three Methods.png', dpi=500)\n",
    "plt.savefig('Metrics of Three Methods.eps')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Appeal",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}