plotting/Correlation_Coefficients.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8377e85e-a974-44c5-9621-947bd3f63548",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import glob\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "sns.set_theme(rc={'figure.figsize':(7, 16)})\n",
    "sns.set_style({'font.family':'serif', 'font.serif':'Times New Roman'})\n",
    "sns.set_color_codes(\"colorblind\")\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.set_context(\"talk\")\n",
    "sns.color_palette()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a49a592-2f9f-4412-b8f0-abd6fd0b3e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ['baseline', 'reconstruction', 'recollection', 'recitation']\n",
    "subcategories = ['code', 'natural_language', 'all']\n",
    "types = ['pearson', 'spearman', 'xi']\n",
    "ids = ['2024-06-05_14-56-53']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af4e31a0-cf3a-4a84-ad20-a5ea081c6ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "NameChanges = {\n",
    "    'roc': 'ROC',\n",
    "    'pr': 'PR',\n",
    "    'ece': 'ECE',\n",
    "    '0_8_templates': 'Textual Matches' ,\n",
    "    '0_8_snowclones': 'Semantic Matches',\n",
    "    'max_frequency': 'Max Token Freq',\n",
    "    'min_frequency': 'Min Token Freq',\n",
    "    'avg_frequency': 'Mean Token Freq',\n",
    "    'median_frequency': 'Median Token Freq',\n",
    "    'p25_frequency': 'P25 Token Freq',\n",
    "    'p75_frequency': 'P75 Token Freq',\n",
    "    'generation_perplexity': 'Continuation PPL',\n",
    "    'prompt_perplexity': 'Prompt PPL',\n",
    "    'sequence_perplexity': 'Sequence PPL',\n",
    "    'sequence_duplicates': 'Duplicates',\n",
    "    'huffman_coding_length': 'Huffman Length'\n",
    "}\n",
    "\n",
    "def setname(name):\n",
    "    if not isinstance(name, str):\n",
    "        return name\n",
    "    if name in NameChanges:\n",
    "        return NameChanges[name]\n",
    "    else:\n",
    "        return ' '.join(word.capitalize() for word in name.split('_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd748f40-1e9c-4a31-ad3a-414a2e277220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-06-05_14-56-53\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for id in ids:\n",
    "    print(id)\n",
    "    coefficients = json.load(open(f'/mnt/ssd-1/sai/semantic-memorization/experiments/{id}/deduped/12b/correlation_coefficients.json'))\n",
    "    \n",
    "    for t in types:\n",
    "        for subcat in subcategories:\n",
    "            fig, axs = plt.subplots(1, 4, figsize=(20, 6), sharey=True)\n",
    "            for i, cat in enumerate(categories):\n",
    "                combined = [(l[0], l[1]) for l in coefficients[cat][subcat][t]]\n",
    "                combined = pd.DataFrame(combined, columns=['Feature', 'Value'])\n",
    "                for col in combined.columns:\n",
    "                    combined[col] = combined[col].apply(setname)\n",
    "                sns.barplot(data=combined, x='Value', y='Feature', ax=axs[i])\n",
    "                axs[i].set_title(cat.capitalize())\n",
    "                axs[i].set_ylabel(\"\")\n",
    "        \n",
    "                if t != 'xi':\n",
    "                    axs[i].set_xlim(-1.0, 1.0)\n",
    "                else:\n",
    "                    axs[i].set_xlim(0.0, 1.0)\n",
    "            if subcat == \"all\":\n",
    "                plt.suptitle(setname(t))\n",
    "            else:\n",
    "                plt.suptitle(setname(subcat) + \" \" + setname(t))\n",
    "            plt.tight_layout()\n",
    "            plt.savefig(subcat + \"_\" + t + \".png\", bbox_inches=\"tight\")\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7e99ccf-d273-4a0f-858f-26b1baa7d443",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c471bb02-69a7-4a42-867e-f5c31bcf955f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}