expr_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import glob, os\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f83437c",
   "metadata": {},
   "source": [
    "# Notes about DF\n",
    "- `is_generated` : (bool) is the simulated tree, note when false, GED is just a single 0, not a dict\n",
    "- `is_original_bt_espresso` : (bool) run with kevin's algo\n",
    "- `num_unique_nodes` : (int) number of unique node types within the tree\n",
    "- `total_nodes` : (int) total number of nodes within the generated tree\n",
    "- `num_sub_trees` : (int) total number of sub trees within the generated tree\n",
    "- `unique_node_freq` : (dict<string, int>) dictionary of unique node to how many in the tree\n",
    "- `graph_edit_distance` : (Series[dict<string,Obj>]) series of scores for each sim tree\n",
    "- GED Example:\n",
    "```python\n",
    "\"graph_edit_distance\": [\n",
    "    {\n",
    "        \"sim_tree_num\": 0,\n",
    "        \"num_nodes_in_subtree\": 6,\n",
    "        \"num_uniq_nodes_in_subtree\": 4,\n",
    "        \"unique_node_dict\": {\n",
    "            \"Inverter\": 0,\n",
    "            \"Repeater\": 0,\n",
    "            \"LAT Sequence\": 0,\n",
    "            \"Sequence\": 1,\n",
    "            \"Parallel Selector\": 1,\n",
    "            \"Selector\": 0,\n",
    "            \"Parallel\": 0,\n",
    "            \"Action\": 3,\n",
    "            \"Condition\": 1\n",
    "        },\n",
    "        \"score\": 8.0\n",
    "    },\n",
    "    {\n",
    "        \"sim_tree_num\": 1,\n",
    "        \"num_nodes_in_subtree\": 3,\n",
    "        \"num_uniq_nodes_in_subtree\": 2,\n",
    "        \"unique_node_dict\": {\n",
    "            \"Inverter\": 0,\n",
    "            \"Repeater\": 0,\n",
    "            \"LAT Sequence\": 0,\n",
    "            \"Sequence\": 1,\n",
    "            \"Parallel Selector\": 0,\n",
    "            \"Selector\": 0,\n",
    "            \"Parallel\": 0,\n",
    "            \"Action\": 2,\n",
    "            \"Condition\": 0\n",
    "        },\n",
    "        \"score\": 1.0\n",
    "    },\n",
    "    {\n",
    "        \"sim_tree_num\": 2,\n",
    "        \"num_nodes_in_subtree\": 9,\n",
    "        \"num_uniq_nodes_in_subtree\": 5,\n",
    "        \"unique_node_dict\": {\n",
    "            \"Inverter\": 0,\n",
    "            \"Repeater\": 1,\n",
    "            \"LAT Sequence\": 0,\n",
    "            \"Sequence\": 0,\n",
    "            \"Parallel Selector\": 1,\n",
    "            \"Selector\": 1,\n",
    "            \"Parallel\": 0,\n",
    "            \"Action\": 3,\n",
    "            \"Condition\": 3\n",
    "        },\n",
    "        \"score\": 15.0\n",
    "    }\n",
    "]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "8da8ff43",
   "metadata": {},
   "outputs": [],
   "source": [
    "expr_num_key = \"expr_num\"\n",
    "unique_node_freq_key = \"unique_node_freq\"\n",
    "ged_key = \"graph_edit_distance\"\n",
    "is_gen_key = \"is_generated\"\n",
    "is_orig_key = \"is_original_bt_espresso\"\n",
    "score_key = \"score\"\n",
    "sim_tree_num_key = \"sim_tree_num\"\n",
    "score_diff_key = \"score_diff\"\n",
    "tot_nodes_key = \"total_nodes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bq/77cqsbrj0nnfyn947ttr9bxm0000gn/T/ipykernel_35881/4029955584.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  p[expr_num_key] = expr_num\n"
     ]
    }
   ],
   "source": [
    "def getNumFromExprFilename(filename):\n",
    "    numeric_filter = filter(str.isdigit, filename)\n",
    "    return \"\".join(numeric_filter)\n",
    "\n",
    "def expandDictCol(col_name):\n",
    "    return pd.concat([df.drop([col_name], axis=1), df[col_name].apply(pd.Series)], axis=1)\n",
    "\n",
    "results_path = \"results/\"\n",
    "results = []\n",
    "gen_trees = []\n",
    "df = None\n",
    "p = None\n",
    "for filename in glob.glob(results_path + \"*.json\"):\n",
    "    expr_num = getNumFromExprFilename(filename)\n",
    "    df = pd.read_json(filename).T\n",
    "    df = expandDictCol(unique_node_freq_key)\n",
    "    p =  df[df[is_gen_key] == False]\n",
    "    p[expr_num_key] = expr_num\n",
    "    gen_trees.append(p)\n",
    "    df = df.drop(p.index)\n",
    "    df = df.explode(ged_key)\n",
    "    df = expandDictCol(ged_key)\n",
    "    df[expr_num_key] = expr_num\n",
    "    results.append(df)\n",
    "    \n",
    "f_df = pd.concat(results, axis=0, ignore_index=True)\n",
    "s_df = pd.concat(gen_trees, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "920c47c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "e0346feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_score = f_df[score_key] >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bq/77cqsbrj0nnfyn947ttr9bxm0000gn/T/ipykernel_35881/3741353502.py:26: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5400x6000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def makehm(index, axes, scored_df, is_discrete, new_title, fig):\n",
    "    tmp_m = scored_df.unstack(level=1)\n",
    "    # True = BT-Espresso\n",
    "    # False = RE:BT-Espresso\n",
    "    min_r = tmp_m[True] - tmp_m[False]\n",
    "    min_r = min_r.reset_index()\n",
    "    min_r = min_r.rename(columns={0 : score_diff_key})\n",
    "    fminr = min_r[min_r[score_diff_key].round() != 0].round()\n",
    "    ax3 = sns.histplot(fminr, x=score_diff_key, discrete=is_discrete, ax=axes[index])\n",
    "    ax3.axvline(linewidth=2, color='#000000', linestyle='--')\n",
    "    ax3.set_xlabel(new_title + r\" $\\Delta(GED_{BTE}, GED_{RE:BTE})$\")\n",
    "    if index != 0:\n",
    "        ax3.set_ylabel(\"\")\n",
    "    #ax3.set_title(r\"Difference in \" + new_title +  r\" GED Score $\\forall$ Subtrees\")\n",
    "\n",
    "\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "figure(figsize=(18, 20), dpi=300)\n",
    "tmp_m = f_df[valid_score].groupby([expr_num_key, is_orig_key, sim_tree_num_key])[\"score\"]\n",
    "plt.clf()\n",
    "fig, axes = plt.subplots(1,2)\n",
    "makehm(0, axes, tmp_m.min(), True, \"Min\", fig)\n",
    "makehm(1, axes, tmp_m.median(), True, \"Median\", fig)\n",
    "fig.suptitle(r\"GED Score $\\Delta$s $\\forall$ Subtrees Where $\\Delta$ != 0\", fontsize=18)\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}