spaCy_analysis_cwe_capec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:01.692659Z",
     "iopub.status.busy": "2022-05-30T09:17:01.689270Z",
     "iopub.status.idle": "2022-05-30T09:17:02.743144Z",
     "shell.execute_reply": "2022-05-30T09:17:02.742563Z"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from scikit_posthocs import posthoc_wilcoxon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:02.751923Z",
     "iopub.status.busy": "2022-05-30T09:17:02.751240Z",
     "iopub.status.idle": "2022-05-30T09:17:02.769410Z",
     "shell.execute_reply": "2022-05-30T09:17:02.769975Z"
    }
   },
   "outputs": [],
   "source": [
    "f = open(\"results_100_with_spaCy_cwe_capec.json\")\n",
    "results = json.load(f)\n",
    "\n",
    "f = open(\"results_100_with_spaCy_cwe_capec_with_cve_2021.json\")\n",
    "results_with_cve = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:02.778867Z",
     "iopub.status.busy": "2022-05-30T09:17:02.777726Z",
     "iopub.status.idle": "2022-05-30T09:17:02.786566Z",
     "shell.execute_reply": "2022-05-30T09:17:02.787400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1600983683097673\n",
      "0.16505538182493149\n",
      "0.8332605531295488\n",
      "0.8241193595342068\n"
     ]
    }
   ],
   "source": [
    "results_fp = results[\"5\"][\"A1 + MI\"][\"fp\"]\n",
    "results_with_cve_fp = results_with_cve[\"5\"][\"A1 + MI\"][\"fp\"]\n",
    "\n",
    "results_acc = results[\"5\"][\"A1 + MI\"][\"acc\"]\n",
    "results_with_cve_acc = results_with_cve[\"5\"][\"A1 + MI\"][\"acc\"]\n",
    "\n",
    "print(np.mean(results_fp))\n",
    "print(np.mean(results_with_cve_fp))\n",
    "\n",
    "print(np.mean(results_acc))\n",
    "print(np.mean(results_with_cve_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:02.793485Z",
     "iopub.status.busy": "2022-05-30T09:17:02.792450Z",
     "iopub.status.idle": "2022-05-30T09:17:02.797495Z",
     "shell.execute_reply": "2022-05-30T09:17:02.796398Z"
    }
   },
   "outputs": [],
   "source": [
    "results = {\"4\": results[\"4\"], \"5\": results[\"5\"], \"6\": results[\"6\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:02.809104Z",
     "iopub.status.busy": "2022-05-30T09:17:02.808161Z",
     "iopub.status.idle": "2022-05-30T09:17:02.813560Z",
     "shell.execute_reply": "2022-05-30T09:17:02.814358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('5', 'A1 + MI')\n",
      "0.8332605531295488\n"
     ]
    }
   ],
   "source": [
    "highest_val = 0\n",
    "highest_encoding = None\n",
    "metric = \"acc\"\n",
    "for encoding in results:\n",
    "    for data_combo in results[encoding]:\n",
    "        mean_metric = np.mean(results[encoding][data_combo][metric])\n",
    "        if mean_metric > highest_val:\n",
    "            highest_val = mean_metric\n",
    "            highest_encoding = (encoding, data_combo)\n",
    "\n",
    "print(highest_encoding)\n",
    "print(highest_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:02.825456Z",
     "iopub.status.busy": "2022-05-30T09:17:02.824510Z",
     "iopub.status.idle": "2022-05-30T09:17:03.128074Z",
     "shell.execute_reply": "2022-05-30T09:17:03.129182Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding method:  5 , Data combo:  A0\n",
      "0.816943231441048\n",
      "Encoding method:  5 , Data combo:  A1\n",
      "0.8171906841339155\n",
      "Encoding method:  5 , Data combo:  A1 + MI\n",
      "0.8332605531295488\n",
      "Encoding method:  5 , Data combo:  A1 + D\n",
      "0.8042794759825328\n",
      "Encoding method:  5 , Data combo:  A1 + MI + D\n",
      "0.8192285298398836\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Data combo'), Text(0.5, 0, 'Metric')]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoding = \"5\"\n",
    "\n",
    "data = {}\n",
    "for data_combo in results[encoding]:\n",
    "    print(\"Encoding method: \", encoding, \", Data combo: \", data_combo)\n",
    "    data[data_combo] = results[encoding][data_combo][\"acc\"]\n",
    "    print(np.mean(results[encoding][data_combo][\"acc\"]))\n",
    "\n",
    "data = pd.DataFrame(data)\n",
    "\n",
    "sns.boxplot(data=data, orient=\"h\").set(ylabel=\"Data combo\", xlabel=\"Metric\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:03.178016Z",
     "iopub.status.busy": "2022-05-30T09:17:03.176597Z",
     "iopub.status.idle": "2022-05-30T09:17:03.349740Z",
     "shell.execute_reply": "2022-05-30T09:17:03.348552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding method:  4 , Data combo:  A1\n",
      "Encoding method:  5 , Data combo:  A1\n",
      "Encoding method:  6 , Data combo:  A1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Encoding'), Text(0.5, 0, 'Metric')]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_combo = \"A1\"\n",
    "data = {}\n",
    "for encoding in results:\n",
    "    print(\"Encoding method: \", encoding, \", Data combo: \", data_combo)\n",
    "    data[encoding] = results[encoding][data_combo][\"acc\"]\n",
    "\n",
    "data = pd.DataFrame(data)\n",
    "\n",
    "sns.boxplot(data=data, orient=\"h\").set(ylabel=\"Encoding\", xlabel=\"Metric\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:03.361654Z",
     "iopub.status.busy": "2022-05-30T09:17:03.360220Z",
     "iopub.status.idle": "2022-05-30T09:17:03.398985Z",
     "shell.execute_reply": "2022-05-30T09:17:03.400094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1\n",
      "4\n",
      "A1\n",
      "5\n",
      "A1\n",
      "6\n"
     ]
    },
    {
     "data": {
      "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>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.156698e-17</td>\n",
       "      <td>1.158331e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.156698e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.509844e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.158331e-17</td>\n",
       "      <td>2.509844e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              4             5             6\n",
       "4  1.000000e+00  1.156698e-17  1.158331e-17\n",
       "5  1.156698e-17  1.000000e+00  2.509844e-01\n",
       "6  1.158331e-17  2.509844e-01  1.000000e+00"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_combo = \"A1\"\n",
    "accs = []\n",
    "encodings = []\n",
    "for encoding in results:\n",
    "    if data_combo != \"A0\" or encoding not in [\"2\", \"5\"]:\n",
    "        print(data_combo)\n",
    "        print(encoding)\n",
    "        encodings.append(encoding)\n",
    "        accs.append(results[encoding][data_combo][\"acc\"])\n",
    "\n",
    "rank_sums = posthoc_wilcoxon(accs, p_adjust=\"bonferroni\")\n",
    "rank_sums.columns = encodings\n",
    "rank_sums.index = encodings\n",
    "rank_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:03.411788Z",
     "iopub.status.busy": "2022-05-30T09:17:03.410624Z",
     "iopub.status.idle": "2022-05-30T09:17:03.453180Z",
     "shell.execute_reply": "2022-05-30T09:17:03.454409Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A0\n",
      "4\n",
      "A1\n",
      "4\n",
      "A1 + MI\n",
      "4\n",
      "A1 + D\n",
      "4\n",
      "A1 + MI + D\n",
      "4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>A0</th>\n",
       "      <th>A1</th>\n",
       "      <th>A1 + MI</th>\n",
       "      <th>A1 + D</th>\n",
       "      <th>A1 + MI + D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A0</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.879888e-17</td>\n",
       "      <td>3.995154e-17</td>\n",
       "      <td>3.870922e-17</td>\n",
       "      <td>3.997182e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <td>3.879888e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.891843e-12</td>\n",
       "      <td>1.702811e-16</td>\n",
       "      <td>1.109589e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1 + MI</th>\n",
       "      <td>3.995154e-17</td>\n",
       "      <td>9.891843e-12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.856040e-03</td>\n",
       "      <td>4.024811e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1 + D</th>\n",
       "      <td>3.870922e-17</td>\n",
       "      <td>1.702811e-16</td>\n",
       "      <td>9.856040e-03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.161590e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1 + MI + D</th>\n",
       "      <td>3.997182e-17</td>\n",
       "      <td>1.109589e-13</td>\n",
       "      <td>4.024811e-01</td>\n",
       "      <td>1.161590e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       A0            A1       A1 + MI        A1 + D  \\\n",
       "A0           1.000000e+00  3.879888e-17  3.995154e-17  3.870922e-17   \n",
       "A1           3.879888e-17  1.000000e+00  9.891843e-12  1.702811e-16   \n",
       "A1 + MI      3.995154e-17  9.891843e-12  1.000000e+00  9.856040e-03   \n",
       "A1 + D       3.870922e-17  1.702811e-16  9.856040e-03  1.000000e+00   \n",
       "A1 + MI + D  3.997182e-17  1.109589e-13  4.024811e-01  1.161590e-01   \n",
       "\n",
       "              A1 + MI + D  \n",
       "A0           3.997182e-17  \n",
       "A1           1.109589e-13  \n",
       "A1 + MI      4.024811e-01  \n",
       "A1 + D       1.161590e-01  \n",
       "A1 + MI + D  1.000000e+00  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding = \"4\"\n",
    "accs = []\n",
    "data_combo_list = []\n",
    "for data_combo in results[encoding]:\n",
    "    print(data_combo)\n",
    "    print(encoding)\n",
    "    data_combo_list.append(data_combo)\n",
    "    accs.append(results[encoding][data_combo][\"acc\"])\n",
    "rank_sums = posthoc_wilcoxon(accs, p_adjust=\"bonferroni\")\n",
    "rank_sums.columns = data_combo_list\n",
    "rank_sums.index = data_combo_list\n",
    "rank_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:03.464768Z",
     "iopub.status.busy": "2022-05-30T09:17:03.463744Z",
     "iopub.status.idle": "2022-05-30T09:17:04.017039Z",
     "shell.execute_reply": "2022-05-30T09:17:04.018077Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding method:  4 , Data combo:  A0\n",
      "Encoding method:  4 , Data combo:  A1\n",
      "Encoding method:  4 , Data combo:  A1 + MI\n",
      "Encoding method:  4 , Data combo:  A1 + D\n",
      "Encoding method:  4 , Data combo:  A1 + MI + D\n",
      "Encoding method:  5 , Data combo:  A0\n",
      "Encoding method:  5 , Data combo:  A1\n",
      "Encoding method:  5 , Data combo:  A1 + MI\n",
      "Encoding method:  5 , Data combo:  A1 + D\n",
      "Encoding method:  5 , Data combo:  A1 + MI + D\n",
      "Encoding method:  6 , Data combo:  A0\n",
      "Encoding method:  6 , Data combo:  A1\n",
      "Encoding method:  6 , Data combo:  A1 + MI\n",
      "Encoding method:  6 , Data combo:  A1 + D\n",
      "Encoding method:  6 , Data combo:  A1 + MI + D\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, '(Encoding, data combination)'),\n",
       " Text(0.5, 0, 'Accuracy'),\n",
       " Text(0.5, 1.0, 'spaCy Experiments')]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {}\n",
    "for encoding in results:\n",
    "    for data_combo in results[encoding]:\n",
    "        print(\"Encoding method: \", encoding, \", Data combo: \", data_combo)\n",
    "        data[(encoding, data_combo)] = results[encoding][data_combo][\"acc\"]\n",
    "\n",
    "data = pd.DataFrame(data)\n",
    "\n",
    "sns.boxplot(data=data, orient=\"h\").set(\n",
    "    ylabel=\"(Encoding, data combination)\", xlabel=\"Accuracy\", title=\"spaCy Experiments\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T09:17:04.028796Z",
     "iopub.status.busy": "2022-05-30T09:17:04.027599Z",
     "iopub.status.idle": "2022-05-30T09:17:04.126382Z",
     "shell.execute_reply": "2022-05-30T09:17:04.127268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        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>(4, A0)</th>\n",
       "      <th>(4, A1)</th>\n",
       "      <th>(4, A1 + MI)</th>\n",
       "      <th>(4, A1 + D)</th>\n",
       "      <th>(4, A1 + MI + D)</th>\n",
       "      <th>(5, A1)</th>\n",
       "      <th>(5, A1 + MI)</th>\n",
       "      <th>(5, A1 + D)</th>\n",
       "      <th>(5, A1 + MI + D)</th>\n",
       "      <th>(6, A0)</th>\n",
       "      <th>(6, A1)</th>\n",
       "      <th>(6, A1 + MI)</th>\n",
       "      <th>(6, A1 + D)</th>\n",
       "      <th>(6, A1 + MI + D)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(4, A0)</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.530698e-16</td>\n",
       "      <td>3.635590e-16</td>\n",
       "      <td>3.522539e-16</td>\n",
       "      <td>3.637436e-16</td>\n",
       "      <td>3.518367e-16</td>\n",
       "      <td>3.521148e-16</td>\n",
       "      <td>3.527512e-16</td>\n",
       "      <td>3.529105e-16</td>\n",
       "      <td>3.518168e-16</td>\n",
       "      <td>3.532491e-16</td>\n",
       "      <td>3.530499e-16</td>\n",
       "      <td>3.525920e-16</td>\n",
       "      <td>3.534086e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4, A1)</th>\n",
       "      <td>3.530698e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.001577e-11</td>\n",
       "      <td>1.549558e-15</td>\n",
       "      <td>1.009726e-12</td>\n",
       "      <td>3.508650e-16</td>\n",
       "      <td>3.513010e-16</td>\n",
       "      <td>1.020430e-15</td>\n",
       "      <td>3.721896e-16</td>\n",
       "      <td>5.307248e-16</td>\n",
       "      <td>3.513605e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.439012e-12</td>\n",
       "      <td>3.209500e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4, A1 + MI)</th>\n",
       "      <td>3.635590e-16</td>\n",
       "      <td>9.001577e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.968997e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.521148e-16</td>\n",
       "      <td>3.521347e-16</td>\n",
       "      <td>3.504494e-16</td>\n",
       "      <td>3.517573e-16</td>\n",
       "      <td>3.522340e-16</td>\n",
       "      <td>3.519956e-16</td>\n",
       "      <td>1.189461e-10</td>\n",
       "      <td>6.268484e-16</td>\n",
       "      <td>3.902477e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4, A1 + D)</th>\n",
       "      <td>3.522539e-16</td>\n",
       "      <td>1.549558e-15</td>\n",
       "      <td>8.968997e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.498564e-16</td>\n",
       "      <td>3.516779e-16</td>\n",
       "      <td>3.503307e-16</td>\n",
       "      <td>3.502516e-16</td>\n",
       "      <td>3.520552e-16</td>\n",
       "      <td>3.526119e-16</td>\n",
       "      <td>3.459473e-14</td>\n",
       "      <td>5.146407e-16</td>\n",
       "      <td>1.385379e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4, A1 + MI + D)</th>\n",
       "      <td>3.637436e-16</td>\n",
       "      <td>1.009726e-12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.512018e-16</td>\n",
       "      <td>3.512613e-16</td>\n",
       "      <td>3.519558e-16</td>\n",
       "      <td>3.521545e-16</td>\n",
       "      <td>3.525124e-16</td>\n",
       "      <td>3.521347e-16</td>\n",
       "      <td>6.557884e-14</td>\n",
       "      <td>3.628423e-16</td>\n",
       "      <td>3.258894e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5, A1)</th>\n",
       "      <td>3.518367e-16</td>\n",
       "      <td>3.508650e-16</td>\n",
       "      <td>3.521148e-16</td>\n",
       "      <td>3.498564e-16</td>\n",
       "      <td>3.512018e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.524894e-15</td>\n",
       "      <td>1.770904e-12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.514596e-16</td>\n",
       "      <td>7.339210e-16</td>\n",
       "      <td>3.623924e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5, A1 + MI)</th>\n",
       "      <td>3.521148e-16</td>\n",
       "      <td>3.513010e-16</td>\n",
       "      <td>3.521347e-16</td>\n",
       "      <td>3.516779e-16</td>\n",
       "      <td>3.512613e-16</td>\n",
       "      <td>5.524894e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.365085e-16</td>\n",
       "      <td>1.292615e-13</td>\n",
       "      <td>7.104562e-14</td>\n",
       "      <td>1.191713e-12</td>\n",
       "      <td>3.521347e-16</td>\n",
       "      <td>3.513010e-16</td>\n",
       "      <td>3.519360e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5, A1 + D)</th>\n",
       "      <td>3.527512e-16</td>\n",
       "      <td>1.020430e-15</td>\n",
       "      <td>3.504494e-16</td>\n",
       "      <td>3.503307e-16</td>\n",
       "      <td>3.519558e-16</td>\n",
       "      <td>1.770904e-12</td>\n",
       "      <td>5.365085e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.728259e-14</td>\n",
       "      <td>8.553210e-11</td>\n",
       "      <td>8.040968e-14</td>\n",
       "      <td>5.297486e-16</td>\n",
       "      <td>3.250035e-14</td>\n",
       "      <td>3.592176e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5, A1 + MI + D)</th>\n",
       "      <td>3.529105e-16</td>\n",
       "      <td>3.721896e-16</td>\n",
       "      <td>3.517573e-16</td>\n",
       "      <td>3.502516e-16</td>\n",
       "      <td>3.521545e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.292615e-13</td>\n",
       "      <td>5.728259e-14</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.520949e-16</td>\n",
       "      <td>3.494419e-16</td>\n",
       "      <td>3.505681e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, A0)</th>\n",
       "      <td>3.518168e-16</td>\n",
       "      <td>5.307248e-16</td>\n",
       "      <td>3.522340e-16</td>\n",
       "      <td>3.520552e-16</td>\n",
       "      <td>3.525124e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.104562e-14</td>\n",
       "      <td>8.553210e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.519161e-16</td>\n",
       "      <td>9.594113e-16</td>\n",
       "      <td>3.616166e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, A1)</th>\n",
       "      <td>3.532491e-16</td>\n",
       "      <td>3.513605e-16</td>\n",
       "      <td>3.519956e-16</td>\n",
       "      <td>3.526119e-16</td>\n",
       "      <td>3.521347e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.191713e-12</td>\n",
       "      <td>8.040968e-14</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.524130e-16</td>\n",
       "      <td>7.941056e-16</td>\n",
       "      <td>3.627195e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, A1 + MI)</th>\n",
       "      <td>3.530499e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.189461e-10</td>\n",
       "      <td>3.459473e-14</td>\n",
       "      <td>6.557884e-14</td>\n",
       "      <td>3.514596e-16</td>\n",
       "      <td>3.521347e-16</td>\n",
       "      <td>5.297486e-16</td>\n",
       "      <td>3.520949e-16</td>\n",
       "      <td>3.519161e-16</td>\n",
       "      <td>3.524130e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.761858e-15</td>\n",
       "      <td>1.170985e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, A1 + D)</th>\n",
       "      <td>3.525920e-16</td>\n",
       "      <td>6.439012e-12</td>\n",
       "      <td>6.268484e-16</td>\n",
       "      <td>5.146407e-16</td>\n",
       "      <td>3.628423e-16</td>\n",
       "      <td>7.339210e-16</td>\n",
       "      <td>3.513010e-16</td>\n",
       "      <td>3.250035e-14</td>\n",
       "      <td>3.494419e-16</td>\n",
       "      <td>9.594113e-16</td>\n",
       "      <td>7.941056e-16</td>\n",
       "      <td>4.761858e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.024940e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, A1 + MI + D)</th>\n",
       "      <td>3.534086e-16</td>\n",
       "      <td>3.209500e-06</td>\n",
       "      <td>3.902477e-03</td>\n",
       "      <td>1.385379e-10</td>\n",
       "      <td>3.258894e-06</td>\n",
       "      <td>3.623924e-16</td>\n",
       "      <td>3.519360e-16</td>\n",
       "      <td>3.592176e-16</td>\n",
       "      <td>3.505681e-16</td>\n",
       "      <td>3.616166e-16</td>\n",
       "      <td>3.627195e-16</td>\n",
       "      <td>1.170985e-05</td>\n",
       "      <td>4.024940e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       (4, A0)       (4, A1)  (4, A1 + MI)   (4, A1 + D)  \\\n",
       "(4, A0)           1.000000e+00  3.530698e-16  3.635590e-16  3.522539e-16   \n",
       "(4, A1)           3.530698e-16  1.000000e+00  9.001577e-11  1.549558e-15   \n",
       "(4, A1 + MI)      3.635590e-16  9.001577e-11  1.000000e+00  8.968997e-02   \n",
       "(4, A1 + D)       3.522539e-16  1.549558e-15  8.968997e-02  1.000000e+00   \n",
       "(4, A1 + MI + D)  3.637436e-16  1.009726e-12  1.000000e+00  1.000000e+00   \n",
       "(5, A1)           3.518367e-16  3.508650e-16  3.521148e-16  3.498564e-16   \n",
       "(5, A1 + MI)      3.521148e-16  3.513010e-16  3.521347e-16  3.516779e-16   \n",
       "(5, A1 + D)       3.527512e-16  1.020430e-15  3.504494e-16  3.503307e-16   \n",
       "(5, A1 + MI + D)  3.529105e-16  3.721896e-16  3.517573e-16  3.502516e-16   \n",
       "(6, A0)           3.518168e-16  5.307248e-16  3.522340e-16  3.520552e-16   \n",
       "(6, A1)           3.532491e-16  3.513605e-16  3.519956e-16  3.526119e-16   \n",
       "(6, A1 + MI)      3.530499e-16  1.000000e+00  1.189461e-10  3.459473e-14   \n",
       "(6, A1 + D)       3.525920e-16  6.439012e-12  6.268484e-16  5.146407e-16   \n",
       "(6, A1 + MI + D)  3.534086e-16  3.209500e-06  3.902477e-03  1.385379e-10   \n",
       "\n",
       "                  (4, A1 + MI + D)       (5, A1)  (5, A1 + MI)   (5, A1 + D)  \\\n",
       "(4, A0)               3.637436e-16  3.518367e-16  3.521148e-16  3.527512e-16   \n",
       "(4, A1)               1.009726e-12  3.508650e-16  3.513010e-16  1.020430e-15   \n",
       "(4, A1 + MI)          1.000000e+00  3.521148e-16  3.521347e-16  3.504494e-16   \n",
       "(4, A1 + D)           1.000000e+00  3.498564e-16  3.516779e-16  3.503307e-16   \n",
       "(4, A1 + MI + D)      1.000000e+00  3.512018e-16  3.512613e-16  3.519558e-16   \n",
       "(5, A1)               3.512018e-16  1.000000e+00  5.524894e-15  1.770904e-12   \n",
       "(5, A1 + MI)          3.512613e-16  5.524894e-15  1.000000e+00  5.365085e-16   \n",
       "(5, A1 + D)           3.519558e-16  1.770904e-12  5.365085e-16  1.000000e+00   \n",
       "(5, A1 + MI + D)      3.521545e-16  1.000000e+00  1.292615e-13  5.728259e-14   \n",
       "(6, A0)               3.525124e-16  1.000000e+00  7.104562e-14  8.553210e-11   \n",
       "(6, A1)               3.521347e-16  1.000000e+00  1.191713e-12  8.040968e-14   \n",
       "(6, A1 + MI)          6.557884e-14  3.514596e-16  3.521347e-16  5.297486e-16   \n",
       "(6, A1 + D)           3.628423e-16  7.339210e-16  3.513010e-16  3.250035e-14   \n",
       "(6, A1 + MI + D)      3.258894e-06  3.623924e-16  3.519360e-16  3.592176e-16   \n",
       "\n",
       "                  (5, A1 + MI + D)       (6, A0)       (6, A1)  (6, A1 + MI)  \\\n",
       "(4, A0)               3.529105e-16  3.518168e-16  3.532491e-16  3.530499e-16   \n",
       "(4, A1)               3.721896e-16  5.307248e-16  3.513605e-16  1.000000e+00   \n",
       "(4, A1 + MI)          3.517573e-16  3.522340e-16  3.519956e-16  1.189461e-10   \n",
       "(4, A1 + D)           3.502516e-16  3.520552e-16  3.526119e-16  3.459473e-14   \n",
       "(4, A1 + MI + D)      3.521545e-16  3.525124e-16  3.521347e-16  6.557884e-14   \n",
       "(5, A1)               1.000000e+00  1.000000e+00  1.000000e+00  3.514596e-16   \n",
       "(5, A1 + MI)          1.292615e-13  7.104562e-14  1.191713e-12  3.521347e-16   \n",
       "(5, A1 + D)           5.728259e-14  8.553210e-11  8.040968e-14  5.297486e-16   \n",
       "(5, A1 + MI + D)      1.000000e+00  1.000000e+00  1.000000e+00  3.520949e-16   \n",
       "(6, A0)               1.000000e+00  1.000000e+00  1.000000e+00  3.519161e-16   \n",
       "(6, A1)               1.000000e+00  1.000000e+00  1.000000e+00  3.524130e-16   \n",
       "(6, A1 + MI)          3.520949e-16  3.519161e-16  3.524130e-16  1.000000e+00   \n",
       "(6, A1 + D)           3.494419e-16  9.594113e-16  7.941056e-16  4.761858e-15   \n",
       "(6, A1 + MI + D)      3.505681e-16  3.616166e-16  3.627195e-16  1.170985e-05   \n",
       "\n",
       "                   (6, A1 + D)  (6, A1 + MI + D)  \n",
       "(4, A0)           3.525920e-16      3.534086e-16  \n",
       "(4, A1)           6.439012e-12      3.209500e-06  \n",
       "(4, A1 + MI)      6.268484e-16      3.902477e-03  \n",
       "(4, A1 + D)       5.146407e-16      1.385379e-10  \n",
       "(4, A1 + MI + D)  3.628423e-16      3.258894e-06  \n",
       "(5, A1)           7.339210e-16      3.623924e-16  \n",
       "(5, A1 + MI)      3.513010e-16      3.519360e-16  \n",
       "(5, A1 + D)       3.250035e-14      3.592176e-16  \n",
       "(5, A1 + MI + D)  3.494419e-16      3.505681e-16  \n",
       "(6, A0)           9.594113e-16      3.616166e-16  \n",
       "(6, A1)           7.941056e-16      3.627195e-16  \n",
       "(6, A1 + MI)      4.761858e-15      1.170985e-05  \n",
       "(6, A1 + D)       1.000000e+00      4.024940e-16  \n",
       "(6, A1 + MI + D)  4.024940e-16      1.000000e+00  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accs = []\n",
    "data_combo_list = []\n",
    "for encoding in results:\n",
    "    for data_combo in results[encoding]:\n",
    "        if data_combo != \"A0\" or encoding not in [\"5\"]:\n",
    "            data_combo_list.append((encoding, data_combo))\n",
    "            accs.append(results[encoding][data_combo][\"acc\"])\n",
    "rank_sums = posthoc_wilcoxon(accs, p_adjust=\"bonferroni\")\n",
    "rank_sums.columns = data_combo_list\n",
    "rank_sums.index = data_combo_list\n",
    "rank_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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