plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import argparse\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"result/lda-n.pkl\",'rb') as file:\n",
    "    object_file = pickle.load(file)\n",
    "    \n",
    "results_train = object_file['results_train']\n",
    "results_valid = object_file['results_valid']\n",
    "results_title = object_file['results_title']\n",
    "\n",
    "results_train = np.array(results_train).T\n",
    "results_train_metric = np.nanmedian(results_train, axis=0)\n",
    "results_valid = [valid[~np.isnan(valid)] for valid in results_valid]\n",
    "results_valid = np.array(results_valid).T\n",
    "\n",
    "plt.cla()\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# ax_r = plt.twinx()\n",
    "_ = ax.boxplot(results_valid)\n",
    "_ = ax.plot(np.arange(1, len(results_train_metric) + 1), results_train_metric, 'o-', label=f'trainset: ll')\n",
    "ax.set_xticks(range(1, len(results_train_metric) + 1))\n",
    "ax.set_xticklabels(results_title)\n",
    "ax.set_xlabel(f'N & K')\n",
    "ax.set_ylabel(f'll')\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt_path = 'result' + f'/lda-n-ll.jpg'\n",
    "plt.savefig(plt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"result/lda-nk.pkl\",'rb') as file:\n",
    "    object_file = pickle.load(file)\n",
    "    \n",
    "results_train = object_file['results_train']\n",
    "results_valid = object_file['results_valid']\n",
    "results_title = object_file['results_title']\n",
    "\n",
    "results_train = np.array(results_train).T\n",
    "results_train_metric = np.nanmedian(results_train, axis=0)\n",
    "results_valid = [valid[~np.isnan(valid)] for valid in results_valid]\n",
    "results_valid = np.array(results_valid).T\n",
    "\n",
    "plt.cla()\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# ax_r = plt.twinx()\n",
    "_ = ax.boxplot(results_valid)\n",
    "_ = ax.plot(np.arange(1, len(results_train_metric) + 1), results_train_metric, 'o-', label=f'trainset: ll')\n",
    "ax.set_xticks(range(1, len(results_train_metric) + 1))\n",
    "ax.set_xticklabels(results_title)\n",
    "ax.set_xlabel(f'N & K')\n",
    "ax.set_ylabel(f'll')\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt_path = 'result' + f'/lda-nk-ll.jpg'\n",
    "plt.savefig(plt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"result/lda-k.pkl\",'rb') as file:\n",
    "    object_file = pickle.load(file)\n",
    "    \n",
    "results_train = object_file['results_train']\n",
    "results_valid = object_file['results_valid']\n",
    "results_title = object_file['results_title']\n",
    "\n",
    "results_train = np.array(results_train).T\n",
    "results_train_metric = np.nanmedian(results_train, axis=0)\n",
    "results_valid = [valid[~np.isnan(valid)] for valid in results_valid]\n",
    "results_valid = np.array(results_valid).T\n",
    "\n",
    "plt.cla()\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# ax_r = plt.twinx()\n",
    "_ = ax.boxplot(results_valid)\n",
    "_ = ax.plot(np.arange(1, len(results_train_metric) + 1), results_train_metric, 'o-', label=f'trainset: ll')\n",
    "ax.set_xticks(range(1, len(results_train_metric) + 1))\n",
    "ax.set_xticklabels(results_title)\n",
    "ax.set_xlabel(f'N & K')\n",
    "ax.set_ylabel(f'll')\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt_path = 'result' + f'/lda-k-ll.jpg'\n",
    "plt.savefig(plt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"result/ctm-n.pkl\",'rb') as file:\n",
    "    object_file = pickle.load(file)\n",
    "    \n",
    "results_train = object_file['results_train']\n",
    "results_valid = object_file['results_valid']\n",
    "results_title = object_file['results_title']\n",
    "\n",
    "results_train = np.array(results_train).T\n",
    "results_train_metric = np.nanmedian(results_train, axis=0)\n",
    "results_valid = [valid[~np.isnan(valid)] for valid in results_valid]\n",
    "results_valid = np.array(results_valid).T\n",
    "\n",
    "plt.cla()\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# ax_r = plt.twinx()\n",
    "_ = ax.boxplot(results_valid)\n",
    "_ = ax.plot(np.arange(1, len(results_train_metric) + 1), results_train_metric, 'o-', label=f'trainset: ll')\n",
    "ax.set_xticks(range(1, len(results_train_metric) + 1))\n",
    "ax.set_xticklabels(results_title)\n",
    "ax.set_xlabel(f'N & K')\n",
    "ax.set_ylabel(f'll')\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt_path = 'result' + f'/ctm-k-ll-2.jpg'\n",
    "plt.savefig(plt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'results_train_metric': [-4.684813328364582],\n",
       " 'results_train': [array([-3.76517295, -3.19714127, -3.78149995, ..., -3.61582346,\n",
       "         -3.23212106, -3.61083272])],\n",
       " 'results_valid': [array([-3.4287679 , -3.78377695, -3.14588489, ..., -3.41694343,\n",
       "         -2.70806619, -3.32724846])],\n",
       " 'results_title': ['3000&20']}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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