plot_confusion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "root_Name = \"Result/Plot_data\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "# ReWiS_ProtoNet_confusion = pd.read_csv(root_Name + '/few-shot/ReWiS/ProtoNet/7/500/confusion.csv', index_col=0)\n",
    "# ReWiS_ViT_confusion = pd.read_csv(root_Name + '/few-shot/ReWiS/ViT/7/500/confusion.csv', index_col=0)\n",
    "ReWiS_ViT_confusion = pd.read_csv(root_Name + '/few-shot/Home/ViT/train_s5_train_q5_test_s10_test_q5/confusion.csv', index_col=0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "# ReWiS_ProtoNet_confusion"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "     0    1    2    3    4\n0  1.0  0.0  0.0  0.0  0.0\n1  0.0  1.0  0.0  0.0  0.0\n2  0.0  0.0  1.0  0.0  0.0\n3  0.0  0.0  0.0  1.0  0.0\n4  0.0  0.0  0.0  0.0  1.0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ReWiS_ViT_confusion"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "# categories = ['empty', 'jump', 'stand', 'walk']\n",
    "# ax = sns.heatmap(ReWiS_ProtoNet_confusion,annot=True,cmap='Blues')\n",
    "# ax.xaxis.set_ticklabels(categories)\n",
    "# ax.yaxis.set_ticklabels(categories)\n",
    "# ax.set_xlabel(\"Predicted Label\")\n",
    "# ax.set_ylabel(\"True Label\")\n",
    "# ax.set(title = \"ProtoNet Confusion Matrix\")\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Empty', 'Lying', 'Sitting', 'Standing', 'Walking']\n",
    "ax = sns.heatmap(ReWiS_ViT_confusion,annot=True, fmt='.2f', cmap='Blues')\n",
    "ax.xaxis.set_ticklabels(categories)\n",
    "ax.yaxis.set_ticklabels(categories)\n",
    "ax.set_xlabel(\"Predicted Label\", fontsize=12, fontweight='bold')\n",
    "ax.set_ylabel(\"True Label\", fontsize=12, fontweight='bold')\n",
    "ax.set(title = \"Collection Dataset ViT Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# plt.ylabel('Accuracy (%)', fontsize=12, fontweight='bold')\n",
    "# plt.xlabel('Support set / Query set', fontsize=12, fontweight='bold')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}