analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import pathlib\n",
    "from functools import reduce\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.transforms as transforms\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "from fastdtw import fastdtw\n",
    "import statsmodels\n",
    "import statsmodels.stats.multitest\n",
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "import cliffdelta\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "FRAMEWORKS = [\n",
    "    \"pytorch\",\n",
    "    \"tensorflow\",\n",
    "]\n",
    "\n",
    "MODELS = [\n",
    "    \"lenet1\",\n",
    "    \"lenet5\",\n",
    "    \"vgg16\",\n",
    "    \"lstm\",\n",
    "    \"gru\",\n",
    "    \"grurb\",\n",
    "    \"bert\",\n",
    "]\n",
    "\n",
    "LANGUAGES = [\n",
    "    \"py\",\n",
    "    \"dotnet\",\n",
    "    \"rs\",\n",
    "    \"ts\",\n",
    "]\n",
    "\n",
    "LANG_SHOW_MAP = {\n",
    "    \"py\": \"Python\",\n",
    "    \"dotnet\": \"C#\",\n",
    "    \"rs\": \"Rust\",\n",
    "    \"ts\": \"JS\",\n",
    "}\n",
    "\n",
    "MODELS_SHOW_MAP = {\n",
    "    \"lenet1\": \"LeNet-1\",\n",
    "    \"lenet5\": \"LeNet-5\",\n",
    "    \"vgg16\": \"VGG-16\",\n",
    "    \"lstm\": \"LSTM\",\n",
    "    \"gru\": \"GRU\",\n",
    "    \"bert\": \"BERT\",\n",
    "}\n",
    "\n",
    "MODE_SHOW_MAP = {\n",
    "    \"states\": \"states\",\n",
    "    \"serialization\": \"serialization\",\n",
    "}\n",
    "\n",
    "DATA_PATH = pathlib.Path(\"./data\")\n",
    "def getPath(framework, model, language):\n",
    "    dir_path = DATA_PATH / framework / model / language\n",
    "    return dir_path if dir_path.exists() else None\n",
    "\n",
    "def loadJson(path):\n",
    "    with open(path, 'r') as f:\n",
    "        return json.load(f)\n",
    "\n",
    "OUT_PATH = pathlib.Path(\"./out\")\n",
    "OUT_PATH.mkdir(exist_ok=True)\n",
    "OUT_FIGS_PATH = OUT_PATH / \"figs\"\n",
    "OUT_FIGS_PATH.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RQ1. How do the studied bindings impact the training accuracy and test accuracy of the studied DL models?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Collecting information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "info_files = {\n",
    "    \"training_errors\": [\"training_errors_\", \"txt\"],\n",
    "    \"testing_errors\": [\"testing_errors_\", \"txt\"],\n",
    "}\n",
    "infos = {}\n",
    "\n",
    "for info_name, file_fmt in info_files.items():\n",
    "    infos.setdefault(info_name, {})\n",
    "    for fw in FRAMEWORKS:\n",
    "        infos[info_name].setdefault(fw, {})\n",
    "        for m in MODELS:\n",
    "            infos[info_name][fw].setdefault(m, {})\n",
    "            for l in LANGUAGES:\n",
    "                dir_p = getPath(fw, m, l)\n",
    "                if dir_p is None:\n",
    "                    continue\n",
    "                for i in range(5):\n",
    "                    file_p = dir_p / f\"{file_fmt[0]}{i}.{file_fmt[1]}\"\n",
    "                    if not file_p.exists():\n",
    "                        continue\n",
    "                    infos[info_name][fw][m].setdefault(l, [])\n",
    "                    data = np.loadtxt(file_p)\n",
    "                    infos[info_name][fw][m][l].append(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training accuracies in each binding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "idxes = {}\n",
    "for fw, fw_info in infos[\"testing_errors\"].items():\n",
    "    idxes.setdefault(fw, {})\n",
    "    for m, m_info in fw_info.items():\n",
    "        idxes[fw].setdefault(m, {})\n",
    "        for l, l_info in m_info.items():\n",
    "            idxes[fw][m].setdefault(l, {})\n",
    "            # figure, (ax1, ax2) = plt.subplots(1, 2)\n",
    "            # figure.set_size_inches((12, 4))\n",
    "            # for idx in range(5):\n",
    "            #     ax1.plot(infos[\"training_errors\"][fw][m][l][idx], label=f\"{fw}_{m}_{l}_{idx}\")\n",
    "            #     ax2.plot(l_info[idx], label=f\"{fw}_{m}_{l}_{idx}\")\n",
    "            # ax1.set_title(\"Training accuracy\")\n",
    "            # ax2.set_title(\"Testing accuracy\")\n",
    "            # ax1.legend()\n",
    "            # ax2.legend()\n",
    "            # figure.savefig(OUT_FIGS_PATH / f\"{fw}_{m}_{l}.eps\", bbox_inches='tight')\n",
    "            idxes[fw][m][l][\"max\"] = max(range(len(l_info)), key=lambda i: l_info[i][-1])\n",
    "            idxes[fw][m][l][\"mid\"] = sorted(range(len(l_info)), key=lambda i: l_info[i][-1])[len(l_info) // 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training accuracies group by library x model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1656x540 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "COLOR_MAP = {\n",
    "    \"py\": \"#0072B2\",\n",
    "    \"dotnet\": \"#E69F00\",\n",
    "    \"ts\": \"#CC79A7\",\n",
    "    \"rs\": \"#009E73\",\n",
    "}\n",
    "\n",
    "MARKER_MAP = {\n",
    "    \"py\": \"X\",\n",
    "    \"dotnet\": \"v\",\n",
    "    \"ts\": \"s\",\n",
    "    \"rs\": \"^\",\n",
    "}\n",
    "\n",
    "MARKEVERY_MAP = {\n",
    "    \"grurb\": 5,\n",
    "    \"gru\": 5,\n",
    "    \"lstm\": 5,\n",
    "    \"lenet1\": 25,\n",
    "    \"lenet5\": 25,\n",
    "    \"vgg16\": 25,\n",
    "}\n",
    "\n",
    "LEGEND_SIZE = 22\n",
    "TICK_SIZE = 26\n",
    "LABEL_SIZE = 30\n",
    "TITLE_SIZE = 32\n",
    "\n",
    "def drawErrors(ax, errorName: str, draw_idx: str, draw_fws, draw_ms, draw_ls):\n",
    "    for fw in draw_fws:\n",
    "        for m in draw_ms:\n",
    "            if fw == \"tensorflow\" and m == \"gru\":\n",
    "                m = \"grurb\"\n",
    "            for l in draw_ls:\n",
    "                if l not in idxes[fw][m]:\n",
    "                    continue\n",
    "                if draw_idx == \"max\":\n",
    "                    idx = idxes[fw][m][l][\"max\"]\n",
    "                elif draw_idx == \"mid\":\n",
    "                    idx = idxes[fw][m][l][\"mid\"]\n",
    "                else:\n",
    "                    raise ValueError(\"draw_idx must be one of ['max', 'mid']\")\n",
    "                ax.plot(\n",
    "                    infos[errorName][fw][m][l][idx], color=COLOR_MAP[l],\n",
    "                    label=f\"{LANG_SHOW_MAP[l]}\", marker=MARKER_MAP[l],\n",
    "                    markevery=MARKEVERY_MAP[m], markersize=10\n",
    "                )\n",
    "\n",
    "\n",
    "def drawByIdx(\n",
    "    ax1, ax2,\n",
    "    draw_idx: str, draw_fws, draw_ms, draw_ls,\n",
    "    ax1_ymin=None, ax1_ymax=None, ax2_ymin=None, ax2_ymax=None,\n",
    "    errorName=\"training_error\", xsticks=None,\n",
    "):\n",
    "    drawErrors(ax1, errorName, draw_idx, [draw_fws[0]], draw_ms, draw_ls)\n",
    "    drawErrors(ax2, errorName, draw_idx, [draw_fws[1]], draw_ms, draw_ls)\n",
    "    if ax1_ymax is not None:\n",
    "        ax1.set_ylim(ymax=ax1_ymax)\n",
    "    if ax1_ymin is not None:\n",
    "        ax1.set_ylim(ymin=ax1_ymin)\n",
    "    if ax2_ymax is not None:\n",
    "        ax2.set_ylim(ymax=ax2_ymax)\n",
    "    if ax2_ymin is not None:\n",
    "        ax2.set_ylim(ymin=ax2_ymin)\n",
    "    ax2.set_xlabel(\"Epochs\", fontsize=LABEL_SIZE)\n",
    "    if xsticks is not None:\n",
    "        ax1.set_xticks(xsticks)\n",
    "        ax2.set_xticks(xsticks)\n",
    "\n",
    "    ax1.yaxis.set_tick_params(labelsize=TICK_SIZE)\n",
    "    ax1.xaxis.set_tick_params(labelsize=TICK_SIZE)\n",
    "    ax2.yaxis.set_tick_params(labelsize=TICK_SIZE)\n",
    "    ax2.xaxis.set_tick_params(labelsize=TICK_SIZE)\n",
    "    ax1.legend(loc=\"lower right\", fontsize=LEGEND_SIZE, markerscale=1.5, frameon=False)\n",
    "    ax2.legend(loc=\"lower right\", fontsize=LEGEND_SIZE, markerscale=1.5, frameon=False)\n",
    "    ax1.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "    ax2.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "    ax1.set_title(MODELS_SHOW_MAP[draw_ms[0]], fontsize=TITLE_SIZE)\n",
    "\n",
    "LENET1_YMAX = 1.005\n",
    "LENET5_YMAX = 1.005\n",
    "VGG16_YMAX = 1.05\n",
    "LSTM_YMAX = 1.03\n",
    "GRU_YMAX = 1.03\n",
    "FIG_SIZE = (23, 7.5)\n",
    "\n",
    "train_fig, train_axs = plt.subplots(2, 5)\n",
    "train_fig.set_size_inches(FIG_SIZE)\n",
    "drawByIdx(\n",
    "    train_axs[0, 0], train_axs[1, 0],\n",
    "    \"max\", [\"tensorflow\", \"pytorch\"], [\"lenet1\"], [\"py\", \"dotnet\", \"rs\", \"ts\"],\n",
    "    ax1_ymax=LENET1_YMAX, ax1_ymin=0.9, ax2_ymax=LENET1_YMAX, ax2_ymin=0.9, errorName=\"training_errors\",\n",
    ")\n",
    "drawByIdx(\n",
    "    train_axs[0, 1], train_axs[1, 1],\n",
    "    \"max\", [\"tensorflow\", \"pytorch\"], [\"lenet5\"], [\"py\", \"dotnet\", \"rs\", \"ts\"],\n",
    "    ax1_ymax=LENET5_YMAX, ax1_ymin=0.9, ax2_ymax=LENET5_YMAX, ax2_ymin=0.9, errorName=\"training_errors\"\n",
    ")\n",
    "drawByIdx(\n",
    "    train_axs[0, 2], train_axs[1, 2],\n",
    "    \"max\", [\"tensorflow\", \"pytorch\"], [\"vgg16\"], [\"py\", \"dotnet\", \"rs\", \"ts\"],\n",
    "    ax1_ymax=VGG16_YMAX, ax1_ymin=0.33, ax2_ymax=VGG16_YMAX, ax2_ymin=0.33, errorName=\"training_errors\"\n",
    ")\n",
    "drawByIdx(\n",
    "    train_axs[0, 3], train_axs[1, 3],\n",
    "    \"max\", [\"tensorflow\", \"pytorch\"], [\"lstm\"], [\"py\", \"dotnet\", \"rs\", \"ts\"],\n",
    "    ax1_ymax=LSTM_YMAX, ax1_ymin=0.57, ax2_ymax=LSTM_YMAX, ax2_ymin=0.57,\n",
    "    errorName=\"training_errors\", xsticks=[0, 10, 20, 30]\n",
    ")\n",
    "drawByIdx(\n",
    "    train_axs[0, 4], train_axs[1, 4],\n",
    "    \"max\", [\"tensorflow\", \"pytorch\"], [\"gru\"], [\"py\", \"dotnet\", \"rs\", \"ts\"],\n",
    "    ax1_ymax=GRU_YMAX, ax1_ymin=0.57, ax2_ymax=GRU_YMAX, ax2_ymin=0.57,\n",
    "    errorName=\"training_errors\", xsticks=[0, 10, 20, 30]\n",
    ")\n",
    "plt.subplots_adjust(wspace=0)\n",
    "train_fig.text(-0.015, 0.45, 'Accuracy', fontsize=LABEL_SIZE, rotation='vertical')\n",
    "train_fig.tight_layout()\n",
    "plt.show()\n",
    "train_fig.savefig(OUT_FIGS_PATH / \"Fig3.eps\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dynamic time wrapping distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>45</th>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>pytorch</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>pytorch</td>\n",
       "      <td>gru</td>\n",
       "      <td>rs - dotnet</td>\n",
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      "text/plain": [
       "   framework model     bindings    dtw\n",
       "45   pytorch   gru  dotnet - py  0.011\n",
       "46   pytorch   gru      rs - py  0.009\n",
       "48   pytorch   gru  rs - dotnet  0.010"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <th>5</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pytorch</td>\n",
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       "      <td>rs - py</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pytorch</td>\n",
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       "      <td>rs - dotnet</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
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       "  framework   model     bindings  dtw\n",
       "5   pytorch  lenet1  dotnet - py  0.0\n",
       "6   pytorch  lenet1      rs - py  0.0\n",
       "8   pytorch  lenet1  rs - dotnet  0.0"
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       "     framework model bindings    dtw\n",
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       "26  tensorflow  vgg16  ts - dotnet  0.019"
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   "source": [
    "def calDTW(a, b, maxMeasure=1.0):\n",
    "    dis = fastdtw(a, b)[0]\n",
    "    max_value = len(a) * maxMeasure\n",
    "    dis_scaled = 1 - (max_value - dis) / max_value\n",
    "    return dis_scaled\n",
    "\n",
    "\n",
    "LANGUAGES = [\"py\", \"dotnet\", \"rs\", \"ts\"]\n",
    "def getDTWDistance(framework, model, language, refLang=\"py\", error=\"testing_errors\", idxName=\"max\", maxMeasure=1.0):\n",
    "    if language not in idxes[framework][model]:\n",
    "        return []\n",
    "\n",
    "    ret = []\n",
    "    data = infos[error][framework][model][language]\n",
    "    data_ref = infos[error][framework][model][refLang]\n",
    "\n",
    "    for a, b in zip(data, data_ref):\n",
    "        dis = calDTW(a, b)\n",
    "        ret.append(dis)\n",
    "    return ret\n",
    "\n",
    "dtw_info = {\n",
    "    \"framework\": [],\n",
    "    \"model\": [],\n",
    "    \"bindings\": [],\n",
    "    \"dtw\": [],\n",
    "}\n",
    "for model in [\"lenet1\", \"lenet5\", \"vgg16\", \"lstm\", \"gru\", \"grurb\"]:\n",
    "    for fw in [\"tensorflow\", \"pytorch\"]:\n",
    "        for i, ref_lang in enumerate(LANGUAGES):\n",
    "            if ref_lang not in infos[\"training_errors\"][fw][model]:\n",
    "                continue\n",
    "            for j in range(i + 1, len(LANGUAGES)):\n",
    "                lang = LANGUAGES[j]\n",
    "                dtw_dis_train = getDTWDistance(fw, model, lang, ref_lang, error=\"training_errors\")\n",
    "                if dtw_dis_train:\n",
    "                    dtw = np.max(dtw_dis_train)\n",
    "                else:\n",
    "                    dtw = np.nan\n",
    "                dtw_info[\"framework\"].append(fw)\n",
    "                dtw_info[\"model\"].append(model)\n",
    "                dtw_info[\"bindings\"].append(f\"{lang} - {ref_lang}\")\n",
    "                dtw_info[\"dtw\"].append(dtw)\n",
    "                # print(f\"{model}, {fw}, {lang} - {ref_lang}: train - {np.mean(dtw_dis_train)}\")\n",
    "dtw_info = pd.DataFrame.from_dict(dtw_info).dropna()\n",
    "dtw_info[\"dtw\"] = np.round(dtw_info[\"dtw\"], 3)\n",
    "dtw_info.groupby([\"framework\", \"model\"]).apply(display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Test accuracy in the final trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "      <th>28</th>\n",
       "      <td>pytorch</td>\n",
       "      <td>gru</td>\n",
       "      <td>rs</td>\n",
       "      <td>0.871088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   framework model language  test_acc\n",
       "26   pytorch   gru       py  0.879296\n",
       "27   pytorch   gru   dotnet  0.853616\n",
       "28   pytorch   gru       rs  0.871088"
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       "16   pytorch  lenet1       rs   0.98746"
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       "    framework  model language  test_acc\n",
       "6  tensorflow  vgg16       py   0.84874\n",
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    "draw_fws = [\"tensorflow\", \"pytorch\"]\n",
    "draw_ms = [\"lenet1\", \"lenet5\", \"vgg16\", \"lstm\", \"gru\", \"grurb\"]\n",
    "test_accs = {\n",
    "    \"framework\": [],\n",
    "    \"model\": [],\n",
    "    \"language\": [],\n",
    "    \"test_acc\": [],\n",
    "}\n",
    "for fw in draw_fws:\n",
    "    for m in draw_ms:\n",
    "        if \"py\" not in infos[\"training_errors\"][fw][m]:\n",
    "            continue\n",
    "\n",
    "        lang_test_errs = {}\n",
    "        for lang, test_errs in infos[\"testing_errors\"][fw][m].items():\n",
    "            final_test_errs = [errs[-1] for errs in test_errs]\n",
    "            lang_test_errs[lang] = final_test_errs\n",
    "\n",
    "            test_accs[\"framework\"].append(fw)\n",
    "            test_accs[\"model\"].append(m)\n",
    "            test_accs[\"language\"].append(lang)\n",
    "            test_accs[\"test_acc\"].append(np.mean(final_test_errs))\n",
    "\n",
    "test_accs = pd.DataFrame.from_dict(test_accs)\n",
    "test_accs.groupby([\"framework\", \"model\"]).apply(display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RQ2. How do the studied bindings impact the crossbinding test accuracy of pre-trained models?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Collecting results of inference experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PosixPath('data/pytorch/lstm/rs/deploy_eval_Cpu_states_scripted.txt')]\n",
      "[PosixPath('data/pytorch/lstm/rs/deploy_eval_Cpu_serialization_scripted.txt')]\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>model</th>\n",
       "      <th>framework</th>\n",
       "      <th>language</th>\n",
       "      <th>mode</th>\n",
       "      <th>device</th>\n",
       "      <th>reproduced</th>\n",
       "      <th>time_cost</th>\n",
       "      <th>testset_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lenet1</td>\n",
       "      <td>tensorflow</td>\n",
       "      <td>py</td>\n",
       "      <td>states</td>\n",
       "      <td>cpu</td>\n",
       "      <td>True</td>\n",
       "      <td>0.846118</td>\n",
       "      <td>[0.8578321849927306, 0.8349432363174856, 0.848...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lenet1</td>\n",
       "      <td>tensorflow</td>\n",
       "      <td>py</td>\n",
       "      <td>states</td>\n",
       "      <td>gpu</td>\n",
       "      <td>True</td>\n",
       "      <td>0.255006</td>\n",
       "      <td>[0.2536352723836899, 0.25739799509756267, 0.25...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lenet1</td>\n",
       "      <td>tensorflow</td>\n",
       "      <td>py</td>\n",
       "      <td>serialization</td>\n",
       "      <td>cpu</td>\n",
       "      <td>True</td>\n",
       "      <td>0.861444</td>\n",
       "      <td>[0.8456464579794556, 0.84889355301857, 0.88150...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lenet1</td>\n",
       "      <td>tensorflow</td>\n",
       "      <td>py</td>\n",
       "      <td>serialization</td>\n",
       "      <td>gpu</td>\n",
       "      <td>True</td>\n",
       "      <td>0.253446</td>\n",
       "      <td>[0.25531957019120455, 0.2511805791873485, 0.25...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lenet1</td>\n",
       "      <td>tensorflow</td>\n",
       "      <td>dotnet</td>\n",
       "      <td>states</td>\n",
       "      <td>cpu</td>\n",
       "      <td>True</td>\n",
       "      <td>2.865344</td>\n",
       "      <td>[2.8518869799999997, 2.9421466, 2.84642026, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>bert</td>\n",
       "      <td>pytorch</td>\n",
       "      <td>rs</td>\n",
       "      <td>serialization</td>\n",
       "      <td>gpu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>bert</td>\n",
       "      <td>pytorch</td>\n",
       "      <td>ts</td>\n",
       "      <td>states</td>\n",
       "      <td>cpu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>bert</td>\n",
       "      <td>pytorch</td>\n",
       "      <td>ts</td>\n",
       "      <td>states</td>\n",
       "      <td>gpu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>bert</td>\n",
       "      <td>pytorch</td>\n",
       "      <td>ts</td>\n",
       "      <td>serialization</td>\n",
       "      <td>cpu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>bert</td>\n",
       "      <td>pytorch</td>\n",
       "      <td>ts</td>\n",
       "      <td>serialization</td>\n",
       "      <td>gpu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      model   framework language           mode device reproduced  time_cost  \\\n",
       "0    lenet1  tensorflow       py         states    cpu       True   0.846118   \n",
       "1    lenet1  tensorflow       py         states    gpu       True   0.255006   \n",
       "2    lenet1  tensorflow       py  serialization    cpu       True   0.861444   \n",
       "3    lenet1  tensorflow       py  serialization    gpu       True   0.253446   \n",
       "4    lenet1  tensorflow   dotnet         states    cpu       True   2.865344   \n",
       "..      ...         ...      ...            ...    ...        ...        ...   \n",
       "219    bert     pytorch       rs  serialization    gpu        NaN        NaN   \n",
       "220    bert     pytorch       ts         states    cpu        NaN        NaN   \n",
       "221    bert     pytorch       ts         states    gpu        NaN        NaN   \n",
       "222    bert     pytorch       ts  serialization    cpu        NaN        NaN   \n",
       "223    bert     pytorch       ts  serialization    gpu        NaN        NaN   \n",
       "\n",
       "                                      testset_duration  \n",
       "0    [0.8578321849927306, 0.8349432363174856, 0.848...  \n",
       "1    [0.2536352723836899, 0.25739799509756267, 0.25...  \n",
       "2    [0.8456464579794556, 0.84889355301857, 0.88150...  \n",
       "3    [0.25531957019120455, 0.2511805791873485, 0.25...  \n",
       "4    [2.8518869799999997, 2.9421466, 2.84642026, 2....  \n",
       "..                                                 ...  \n",
       "219                                                 []  \n",
       "220                                                 []  \n",
       "221                                                 []  \n",
       "222                                                 []  \n",
       "223                                                 []  \n",
       "\n",
       "[224 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getFloats(content: str):\n",
    "    return [float(c) for c in content]\n",
    "\n",
    "def globList(path: pathlib.Path, globs: list):\n",
    "    for g in globs:\n",
    "        res = list(path.glob(g))\n",
    "        if res:\n",
    "            return res\n",
    "    return []\n",
    "\n",
    "def getinferenceInfoPath(framework, model, language, device, mode):\n",
    "    out_path = getPath(framework, model, language)\n",
    "    if out_path is None:\n",
    "        return []\n",
    "    if device == \"cpu\":\n",
    "        device_str = [\"cpu\", \"CPU\", \"Cpu\"]\n",
    "    else:\n",
    "        device_str = [\"gpu\", \"CUDA\", \"Cuda(\", \"GPU\"]\n",
    "    globs = [f\"deploy_eval_{ds}*\" for ds in device_str]\n",
    "    deploy_info_paths = globList(out_path, globs)\n",
    "    deploy_info_paths = list(filter(lambda x: x.__str__().find(mode) != -1, deploy_info_paths))\n",
    "    return deploy_info_paths\n",
    "\n",
    "def readinferenceInfoTxtDotnet(path):\n",
    "    ret = {}\n",
    "    def getBools(contents):\n",
    "        ret = []\n",
    "        bool_strs = [c.split(\", \")[0] for c in contents]\n",
    "        for s in bool_strs:\n",
    "            if s == \"True\":\n",
    "                ret.append(\"T\")\n",
    "            elif s == \"False\":\n",
    "                ret.append(\"F\")\n",
    "            else:\n",
    "                raise ValueError(f\"invalid content:\\n{contents}\")\n",
    "        return \"\".join(ret)\n",
    "    with open(path, \"r\") as f:\n",
    "        content = f.readlines()\n",
    "        ret[\"test_average\"] = float(content[1])\n",
    "        ret[\"testset_same_acc\"] = getBools(content[9:14])\n",
    "        ret[\"train_average\"] = float(content[21])\n",
    "        ret[\"trainset_same_acc\"] = getBools(content[29:34])\n",
    "        ret[\"testset_acc\"] = [float(c.split(\", \")[1]) for c in content[9:14]]\n",
    "        ret[\"trainset_acc\"] = [float(c.split(\", \")[1]) for c in content[29:34]]\n",
    "        ret[\"testset_duration\"] = getFloats(content[3:8])\n",
    "    return ret\n",
    "\n",
    "def readinferenceInfoTxtTS(path):\n",
    "    ret = {}\n",
    "    def getBools(contents):\n",
    "        ret = []\n",
    "        for s in contents:\n",
    "            if s.startswith(\"true\"):\n",
    "                ret.append(\"T\")\n",
    "            elif s.startswith(\"false\"):\n",
    "                ret.append(\"F\")\n",
    "            else:\n",
    "                raise ValueError(f\"invalid content:\\n{contents}\")\n",
    "        return \"\".join(ret)\n",
    "    with open(path, \"r\") as f:\n",
    "        content = f.readlines()\n",
    "        ret[\"test_average\"] = float(content[1])\n",
    "        ret[\"testset_same_acc\"] = getBools(content[17:22])\n",
    "        ret[\"train_average\"] = float(content[9])\n",
    "        ret[\"trainset_same_acc\"] = getBools(content[23:28])\n",
    "        ret[\"testset_duration\"] = getFloats(content[3:8])\n",
    "    return ret\n",
    "\n",
    "def readinferenceInfoTxtRS(path):\n",
    "    ret = {}\n",
    "    def getBools(contents):\n",
    "        ret = []\n",
    "        bool_strs = [c.split(\", \")[0] for c in contents]\n",
    "        for s in bool_strs:\n",
    "            if s == \"true\":\n",
    "                ret.append(\"T\")\n",
    "            elif s == \"false\":\n",
    "                ret.append(\"F\")\n",
    "            else:\n",
    "                raise ValueError(f\"invalid content:\\n{contents}\")\n",
    "        return \"\".join(ret)\n",
    "    with open(path, \"r\") as f:\n",
    "        content = f.readlines()\n",
    "        ret[\"test_average\"] = float(content[0].split(\": \")[1])\n",
    "        ret[\"testset_same_acc\"] = getBools(content[7:12])\n",
    "        ret[\"train_average\"] = float(content[12].split(\": \")[1])\n",
    "        ret[\"trainset_same_acc\"] = getBools(content[19:24])\n",
    "        ret[\"testset_duration\"] = getFloats(content[1:6])\n",
    "    return ret\n",
    "\n",
    "def readinferenceInfoJson(path):\n",
    "    data = loadJson(path)\n",
    "    ret = {}\n",
    "    ret[\"test_average\"] = data[\"test_average\"]\n",
    "    if \"testset_same_acc\" not in data:\n",
    "        ret[\"testset_same_acc\"] = \"TTTTT\"\n",
    "    else:\n",
    "        ret[\"testset_same_acc\"] = \"\".join([d[0].__str__()[0] for d in data[\"testset_same_acc\"]])\n",
    "    ret[\"train_average\"] = data[\"train_average\"]\n",
    "    if \"trainset_same_acc\" not in data:\n",
    "        ret[\"trainset_same_acc\"] = \"TTTTT\"\n",
    "    else:\n",
    "        ret[\"trainset_same_acc\"] = \"\".join([d[0].__str__()[0] for d in data[\"trainset_same_acc\"]])\n",
    "    ret[\"testset_acc\"] = [d[1] for d in data[\"testset_same_acc\"]]\n",
    "    ret[\"trainset_acc\"] = [d[1] for d in data[\"trainset_same_acc\"]]\n",
    "    ret[\"testset_duration\"] = data[\"test\"]\n",
    "    return ret\n",
    "\n",
    "def getinferenceInfo(framework, model, language, device, mode):\n",
    "    paths = getinferenceInfoPath(framework, model, language, device, mode)\n",
    "    if len(paths) == 0:\n",
    "        return {}\n",
    "    if len(paths) > 1:\n",
    "        # print(paths)\n",
    "        paths = list(filter(lambda p: (\"scripted\" in str(p)) and (not \"train\" in str(p)), paths))\n",
    "        print(paths)\n",
    "    assert len(paths) == 1\n",
    "    if language == \"py\":\n",
    "        info = readinferenceInfoJson(paths[0])\n",
    "    elif language == \"dotnet\":\n",
    "        info = readinferenceInfoTxtDotnet(paths[0])\n",
    "    elif language == \"ts\":\n",
    "        info = readinferenceInfoTxtTS(paths[0])\n",
    "    else:\n",
    "        info = readinferenceInfoTxtRS(paths[0])\n",
    "    return info\n",
    "\n",
    "inference_infos = {\n",
    "    \"model\": [],\n",
    "    \"framework\": [],\n",
    "    \"language\": [],\n",
    "    \"mode\": [],\n",
    "    \"device\": [],\n",
    "    \"reproduced\": [],\n",
    "    \"time_cost\": [],\n",
    "    \"testset_duration\": [],\n",
    "}\n",
    "for model in [\"lenet1\", \"lenet5\", \"vgg16\", \"lstm\", \"gru\", \"grurb\", \"bert\"]:\n",
    "    for fw in [\"tensorflow\", \"pytorch\"]:\n",
    "        for lang in  [\"py\", \"dotnet\", \"rs\", \"ts\"]:\n",
    "            for mode in [\"states\", \"serialization\"]:\n",
    "                for device in [\"cpu\", \"gpu\"]:\n",
    "                    info = getinferenceInfo(fw, model, lang, device, mode)\n",
    "                    inference_infos[\"model\"].append(model)\n",
    "                    inference_infos[\"framework\"].append(fw)\n",
    "                    inference_infos[\"language\"].append(lang)\n",
    "                    inference_infos[\"mode\"].append(mode)\n",
    "                    inference_infos[\"device\"].append(device)\n",
    "                    if info:\n",
    "                        inference_infos[\"reproduced\"].append(not \"F\" in info[\"testset_same_acc\"])\n",
    "                        inference_infos[\"time_cost\"].append(info[\"test_average\"])\n",
    "                        inference_infos[\"testset_duration\"].append(info[\"testset_duration\"])\n",
    "                    else:\n",
    "                        inference_infos[\"reproduced\"].append(np.nan)\n",
    "                        inference_infos[\"time_cost\"].append(np.nan)\n",
    "                        inference_infos[\"testset_duration\"].append([])\n",
    "\n",
    "inference_infos_df = pd.DataFrame(data=inference_infos)\n",
    "inference_infos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0, 0]), array([ 1.5,  3.5,  5.5,  7.5,  9.5, 11.5]), 12)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inference_infos_df[inference_infos_df[\"model\"] == \"bert\"]\n",
    "\n",
    "np.full(5, 0), np.arange(1, r, 2)+gap, r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Can bindings reproduce the accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_names = [\"py\", \"dotnet\", \"rs\", \"ts\"] * 2\n",
    "models = [\"lenet1\", \"lenet5\", \"vgg16\", \"lstm\", \"gru\", \"bert\"]\n",
    "row_names = [\"states\", \"serialization\"] * len(models)\n",
    "r = len(row_names)\n",
    "c = len(col_names)\n",
    "plot_data = np.zeros((r, c)).tolist()\n",
    "for row in range(len(plot_data)):\n",
    "    for col in range(len(plot_data[0])):\n",
    "        fw = \"tensorflow\" if col < 4 else \"pytorch\"\n",
    "        lang = col_names[col]\n",
    "        mode = row_names[row]\n",
    "        model = models[row // 2]\n",
    "        if fw == \"tensorflow\" and model == \"gru\" and lang == \"ts\":\n",
    "            model = \"grurb\"\n",
    "        mask = \\\n",
    "            (inference_infos_df[\"framework\"] == fw) \\\n",
    "            & (inference_infos_df[\"mode\"] == mode) \\\n",
    "            & (inference_infos_df[\"model\"] == model) \\\n",
    "            & (inference_infos_df[\"device\"] == \"gpu\") \\\n",
    "            & (inference_infos_df[\"language\"] == lang)\n",
    "        value = inference_infos_df[mask][\"reproduced\"].values[0]\n",
    "        if np.isnan(value):\n",
    "            plot_data[row][col] = (238, 238, 238)\n",
    "        elif value == False:\n",
    "            plot_data[row][col] = (255, 87, 37)\n",
    "        elif value == True:\n",
    "            plot_data[row][col] = (0, 173, 181)\n",
    "        else:\n",
    "            raise ValueError()\n",
    "\n",
    "# TODO: read bert data to plot\n",
    "plot_data[-1][0] = (0, 173, 181)\n",
    "plot_data[-1][2] = (0, 173, 181)\n",
    "plot_data[-1][3] = (0, 173, 181)\n",
    "plot_data[-1][4] = (0, 173, 181)\n",
    "plot_data[-1][6] = (0, 173, 181)\n",
    "plot_data[-1][7] = (0, 173, 181)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches((4 + r), (c + 2))\n",
    "\n",
    "im = ax.imshow(plot_data, cmap=\"Blues\")\n",
    "\n",
    "gap = 0.5\n",
    "plt.hlines(y=np.arange(0, r)+gap, xmin=np.full(r, 0)-gap, xmax=np.full(r, c)-gap, color=\"w\")\n",
    "plt.vlines(x=np.arange(0, c)+gap, ymin=np.full(c, 0)-gap, ymax=np.full(c, r)-gap, color=\"w\")\n",
    "plt.vlines(x=c/2-gap, ymin=-gap, ymax=r-gap, color=\"#303841\")\n",
    "plt.hlines(y=np.arange(1, r, 2)+gap, xmin=np.full(r // 2, 0)-gap, xmax=np.full(r//2, c)-gap, color=\"#303841\")\n",
    "\n",
    "ax.set_xticks(np.arange(len(col_names)))\n",
    "ax.set_yticks(np.arange(len(row_names)))\n",
    "ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\n",
    "ax.set_xticklabels(\n",
    "    [LANG_SHOW_MAP[c] for c in col_names], size=20, rotation=30,\n",
    "    ha='center'\n",
    ")\n",
    "ax.set_yticklabels([MODE_SHOW_MAP[r] for r in row_names], size=20)\n",
    "\n",
    "# ax.text(0, 0, 'x', va='center', ha='center')\n",
    "LARGER_FONT = 24\n",
    "offset = -0.2\n",
    "ax.text(8 + offset, 10.5, 'BERT', va='center', ha='left', fontsize=LARGER_FONT)\n",
    "ax.text(8 + offset, 8.5, 'GRU', va='center', ha='left', fontsize=LARGER_FONT)\n",
    "ax.text(8 + offset, 6.5, 'LSTM', va='center', ha='left', fontsize=LARGER_FONT)\n",
    "ax.text(8 + offset, 4.5, 'VGG-16', va='center', ha='left', fontsize=LARGER_FONT)\n",
    "ax.text(8 + offset, 2.5, 'LeNet-5', va='center', ha='left', fontsize=LARGER_FONT)\n",
    "ax.text(8 + offset, 0.5, 'LeNet-1', va='center', ha='left', fontsize=LARGER_FONT)\n",
    "\n",
    "ax.text(1.5, -2, 'TensorFlow', va='center', ha='center', fontsize=LARGER_FONT)\n",
    "ax.text(5.5, -2, 'PyTorch', va='center', ha='center', fontsize=LARGER_FONT)\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(OUT_FIGS_PATH / f\"Fig5.eps\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RQ3. How do the studied bindings impact the training time of the studied DL models?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Collecting time cost of training experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/opt/tljh/user/lib/python3.9/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "def readTimeCostTxt(paths):\n",
    "    time_costs = []\n",
    "    for p in paths:\n",
    "        data = np.loadtxt(p)[2]\n",
    "        time_costs.append(data)\n",
    "    return time_costs\n",
    "\n",
    "def readTimeCostTxtTS(paths):\n",
    "    time_costs = []\n",
    "    for p in paths:\n",
    "        with open(p, \"r\") as f:\n",
    "            data = float(f.readlines()[1])\n",
    "        time_costs.append(data)\n",
    "    return time_costs\n",
    "\n",
    "def readTimeCostJson(paths):\n",
    "    time_costs = []\n",
    "    for p in paths:\n",
    "        data = loadJson(p)[\"training_time\"]\n",
    "        time_costs.append(data)\n",
    "    return time_costs\n",
    "\n",
    "def getTrainingTimeCostPath(framework, model, language):\n",
    "    out_path = getPath(framework, model, language)\n",
    "    if out_path is None:\n",
    "        return []\n",
    "    time_cost_path = out_path.glob(\"time_cost_*\")\n",
    "    return list(time_cost_path)\n",
    "\n",
    "def getTrainingTimeCost(framework, model, language):\n",
    "    paths = getTrainingTimeCostPath(framework, model, language)\n",
    "    if paths is None:\n",
    "        return []\n",
    "    if language == \"py\":\n",
    "        time_costs = readTimeCostJson(paths)\n",
    "    elif language == \"ts\":\n",
    "        time_costs = readTimeCostTxtTS(paths)\n",
    "    else:\n",
    "        time_costs = readTimeCostTxt(paths)\n",
    "    return time_costs\n",
    "\n",
    "time_costs_dic = {\n",
    "    \"framework\": [],\n",
    "    \"model\": [],\n",
    "    \"language\": [],\n",
    "    \"time_cost\": [],\n",
    "}\n",
    "time_costs_bar_plot = {}\n",
    "\n",
    "def collectTimeCosts(FRAMEWORKS, models, languages):\n",
    "    for fw in FRAMEWORKS:\n",
    "        fw_name = fw[:-len(\"_lr_only\")] if fw.endswith(\"_lr_only\") else fw\n",
    "        time_costs_bar_plot.setdefault(fw_name, {})\n",
    "        for model in models:\n",
    "            time_costs_bar_plot[fw_name].setdefault(model, {})\n",
    "            for lang in languages:\n",
    "                time_costs = getTrainingTimeCost(fw, model, lang)\n",
    "                mid_time_cost = np.median(time_costs)\n",
    "                time_costs_bar_plot[fw_name][model][lang] = time_costs\n",
    "                time_costs_dic[\"framework\"].append(fw_name)\n",
    "                time_costs_dic[\"model\"].append(model)\n",
    "                time_costs_dic[\"language\"].append(lang)\n",
    "                time_costs_dic[\"time_cost\"].append(mid_time_cost)\n",
    "\n",
    "collectTimeCosts(\n",
    "    [\"tensorflow\", \"pytorch\"],\n",
    "    [\"lenet1\", \"lenet5\", \"vgg16\"],\n",
    "    [\"py\", \"dotnet\", \"rs\", \"ts\"]\n",
    ")\n",
    "collectTimeCosts(\n",
    "    [\"tensorflow\", \"pytorch\"],\n",
    "    [\"lstm\", \"gru\", \"grurb\"],\n",
    "    [\"py\", \"dotnet\", \"rs\", \"ts\"]\n",
    ")\n",
    "\n",
    "time_costs_df = pd.DataFrame(data=time_costs_dic)\n",
    "# time_costs_df.groupby([\"framework\", \"model\"]).apply(display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Plotting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1656x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    }
   ],
   "source": [
    "TITLE_SIZE = 28\n",
    "XLABEL_SIZE = 26\n",
    "STICK_SIZE = 22\n",
    "LEGEND_SIZE = 20\n",
    "labels = ['TF', 'PT']\n",
    "\n",
    "x = np.array([1.1, 1.8])\n",
    "width = 0.18  # the width of the bars\n",
    "\n",
    "rects = []\n",
    "\n",
    "def axConfig(ax):\n",
    "    ax.tick_params(axis='y', labelsize=STICK_SIZE)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(labels, fontsize=XLABEL_SIZE)\n",
    "    ax.set_xlim([0.8, 2.1])\n",
    "\n",
    "def box_plot(ax, positions, data, edge_color, fill_color):\n",
    "    bp = ax.boxplot(\n",
    "        data, patch_artist=True, positions=positions,\n",
    "        flierprops={\"markeredgecolor\": edge_color},\n",
    "    )\n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:\n",
    "        plt.setp(bp[element], color=edge_color)\n",
    "\n",
    "    for patch in bp['boxes']:\n",
    "        patch.set(facecolor=fill_color)\n",
    "    return bp\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 5)\n",
    "fig.set_size_inches((23, 5))\n",
    "\n",
    "yticks = [\n",
    "    [300, 500, 1000],\n",
    "    [500, 1000],\n",
    "    [2000, 3000, 5000, 10000, 20000, 30000],\n",
    "]\n",
    "ytick_labels = [\n",
    "    [\"300\", \"500\", \"1,000\"],\n",
    "    [\"500\", \"1,000\"],\n",
    "    [\"2,000\", \"3,000\", \"5,000\", \"10,000\", \"20,000\", \"30,000\"],\n",
    "]\n",
    "for model, ax, ticks, ticklabels in zip([\"lenet1\", \"lenet5\", \"vgg16\"], axs[:3], yticks, ytick_labels):\n",
    "    bp1 = box_plot(\n",
    "        ax, x - width,\n",
    "        [time_costs_bar_plot[\"tensorflow\"][model][\"py\"], time_costs_bar_plot[\"pytorch\"][model][\"py\"]],\n",
    "        COLOR_MAP[\"py\"], COLOR_MAP[\"py\"]\n",
    "    )\n",
    "    bp2 = box_plot(\n",
    "        ax, x,\n",
    "        [time_costs_bar_plot[\"tensorflow\"][model][\"dotnet\"], time_costs_bar_plot[\"pytorch\"][model][\"dotnet\"]],\n",
    "        COLOR_MAP[\"dotnet\"], COLOR_MAP[\"dotnet\"]\n",
    "    )\n",
    "    bp3 = box_plot(\n",
    "        ax, [x[0] + width],\n",
    "        [time_costs_bar_plot[\"tensorflow\"][model][\"ts\"]],\n",
    "        COLOR_MAP[\"ts\"], COLOR_MAP[\"ts\"]\n",
    "    )\n",
    "    bp4 = box_plot(\n",
    "        ax, [x[1] + width],\n",
    "        [time_costs_bar_plot[\"pytorch\"][model][\"rs\"]],\n",
    "        COLOR_MAP[\"rs\"], COLOR_MAP[\"rs\"]\n",
    "    )\n",
    "    ax.axvline(np.median(x), ls=\"--\", color=\"gray\", alpha=0.3)\n",
    "    ax.set_title(MODELS_SHOW_MAP[model], fontsize=TITLE_SIZE)\n",
    "    axConfig(ax)\n",
    "\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_yticks(ticks)\n",
    "    ax.set_yticklabels(ticklabels, fontsize=STICK_SIZE)\n",
    "    ax.set_yticklabels([], minor=True)\n",
    "\n",
    "ax = axs[3]\n",
    "bp1 = box_plot(\n",
    "    ax, x - np.array([width / 2, width]),\n",
    "    [time_costs_bar_plot[\"tensorflow\"][\"lstm\"][\"py\"], time_costs_bar_plot[\"pytorch\"][\"lstm\"][\"py\"]],\n",
    "    COLOR_MAP[\"py\"], COLOR_MAP[\"py\"]\n",
    ")\n",
    "bp2 = box_plot(\n",
    "    ax, [x[1]],\n",
    "    [time_costs_bar_plot[\"pytorch\"][\"lstm\"][\"dotnet\"]],\n",
    "    COLOR_MAP[\"dotnet\"], COLOR_MAP[\"dotnet\"]\n",
    ")\n",
    "bp3 = box_plot(\n",
    "    ax, [x[0] + width / 2],\n",
    "    [time_costs_bar_plot[\"tensorflow\"][\"lstm\"][\"ts\"],],\n",
    "    COLOR_MAP[\"ts\"], COLOR_MAP[\"ts\"]\n",
    ")\n",
    "bp4 = box_plot(\n",
    "    ax, [x[1] + width],\n",
    "    [time_costs_bar_plot[\"pytorch\"][\"lstm\"][\"rs\"]],\n",
    "    COLOR_MAP[\"rs\"], COLOR_MAP[\"rs\"]\n",
    ")\n",
    "axConfig(ax)\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_yticks([300, 500, 1000, 4000])\n",
    "ax.set_yticklabels([\"300\", \"500\", \"1,000\", \"4,000\"], fontsize=STICK_SIZE)\n",
    "ax.axvline(np.median(x), ls=\"--\", color=\"gray\", alpha=0.3)\n",
    "ax.set_title(MODELS_SHOW_MAP[\"lstm\"], fontsize=TITLE_SIZE)\n",
    "\n",
    "ax = axs[4]\n",
    "bp1 = box_plot(\n",
    "    ax, x - np.array([width / 2, width]),\n",
    "    [time_costs_bar_plot[\"tensorflow\"][\"gru\"][\"py\"], time_costs_bar_plot[\"pytorch\"][\"gru\"][\"py\"]],\n",
    "    COLOR_MAP[\"py\"], COLOR_MAP[\"py\"]\n",
    ")\n",
    "bp2 = box_plot(\n",
    "    ax, [x[1]],\n",
    "    [time_costs_bar_plot[\"pytorch\"][\"gru\"][\"dotnet\"]],\n",
    "    COLOR_MAP[\"dotnet\"], COLOR_MAP[\"dotnet\"]\n",
    ")\n",
    "bp3 = box_plot(\n",
    "    ax, [x[0] + width / 2],\n",
    "    [time_costs_bar_plot[\"tensorflow\"][\"grurb\"][\"ts\"],],\n",
    "    COLOR_MAP[\"ts\"], COLOR_MAP[\"ts\"]\n",
    ")\n",
    "bp4 = box_plot(\n",
    "    ax, [x[1] + width],\n",
    "    [time_costs_bar_plot[\"pytorch\"][\"gru\"][\"rs\"]],\n",
    "    COLOR_MAP[\"rs\"], COLOR_MAP[\"rs\"]\n",
    ")\n",
    "\n",
    "axConfig(ax)\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_yticks([200, 300, 500, 1000, 5000])\n",
    "ax.set_yticklabels([\"200\", \"300\", \"500\", \"1,000\", \"5,000\"], fontsize=STICK_SIZE)\n",
    "ax.axvline(np.median(x), ls=\"--\", color=\"gray\", alpha=0.3)\n",
    "ax.set_title(MODELS_SHOW_MAP[\"gru\"], fontsize=TITLE_SIZE)\n",
    "plt.legend(\n",
    "    [bp1[\"boxes\"][0], bp2[\"boxes\"][0], bp4[\"boxes\"][0], bp3[\"boxes\"][0]],\n",
    "    ['Python', 'C#', \"Rust\", \"JS\"],\n",
    "    bbox_to_anchor=(1.05, 0.43), borderaxespad=0, fontsize=LEGEND_SIZE,\n",
    "    frameon=False\n",
    ")\n",
    "axs[0].set_ylabel(\"Training Time (Seconds)\", fontsize=XLABEL_SIZE)\n",
    "plt.subplots_adjust(wspace=-1)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig(OUT_FIGS_PATH / f\"Fig6.eps\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Mann-Whitney U test and Cliff's delta d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============= tensorflow x lenet1 ===============\n",
      "1065.114140700005 (dotnet) / 222.09142881911248 (py) = 4.795836320038771 tims slower\n",
      "537.631 (ts) / 222.09142881911248 (py) = 2.420764289998269 tims slower\n",
      "============= tensorflow x lenet5 ===============\n",
      "1334.8858165000001 (dotnet) / 267.81001583486795 (py) = 4.98445068358867 tims slower\n",
      "670.9459999999999 (ts) / 267.81001583486795 (py) = 2.505305852390921 tims slower\n",
      "============= tensorflow x vgg16 ===============\n",
      "17236.038959599995 (dotnet) / 2356.849294008687 (py) = 7.313169748874263 tims slower\n",
      "29488.418 (ts) / 2356.849294008687 (py) = 12.511796182709725 tims slower\n",
      "============= tensorflow x lstm ===============\n",
      "4421.887000000002 (ts) / 385.47948511876166 (py) = 11.471134446072199 tims slower\n",
      "============= tensorflow x gru ===============\n",
      "============= tensorflow x grurb ===============\n",
      "4655.218000000004 (ts) / 1354.2509506968781 (py) = 3.437485495287632 tims slower\n",
      "============= pytorch x lenet1 ===============\n",
      "1030.7277560820803 (py) / 222.4093405 (dotnet) = 4.634372611172237 times faster\n",
      "1030.7277560820803 (py) / 334.547384284 (rs) = 3.080961933951596 times faster\n",
      "============= pytorch x lenet5 ===============\n",
      "1052.1096372380853 (py) / 352.5207845 (dotnet) = 2.9845322134136048 times faster\n",
      "1052.1096372380853 (py) / 361.654029024 (rs) = 2.9091605589945337 times faster\n",
      "============= pytorch x vgg16 ===============\n",
      "8635.603379333392 (py) / 7727.527503200002 (dotnet) = 1.1175118271345332 times faster\n",
      "8635.603379333392 (py) / 7752.688414330995 (rs) = 1.113885005796223 times faster\n",
      "============= pytorch x lstm ===============\n",
      "273.63425507675856 (py) / 253.72968110000005 (dotnet) = 1.0784479525235904 times faster\n",
      "273.63425507675856 (py) / 248.10942064099999 (rs) = 1.102877328760086 times faster\n",
      "============= pytorch x gru ===============\n",
      "213.52856339793652 (py) / 207.48869850000005 (dotnet) = 1.0291093680841439 times faster\n",
      "213.52856339793652 (py) / 204.47226316599998 (rs) = 1.0442910940178924 times faster\n",
      "============= pytorch x grurb ===============\n"
     ]
    },
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       "  framework   model         lang  mann_u eff_size   p_value  cliff_d\n",
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       "9   pytorch  lenet1      py - rs    25.0    large  0.007937      1.0"
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       "   framework   model         lang  mann_u eff_size   p_value  cliff_d\n",
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       "11   pytorch  lenet5      py - rs    25.0    large  0.007937      1.0"
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       "      <th>15</th>\n",
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       "   framework model         lang  mann_u eff_size   p_value  cliff_d\n",
       "14   pytorch  lstm  py - dotnet    25.0    large  0.007937      1.0\n",
       "15   pytorch  lstm      py - rs    25.0    large  0.007937      1.0"
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       "13   pytorch  vgg16      py - rs    25.0    large  0.007937      1.0"
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       "    framework   model         lang  mann_u eff_size   p_value  cliff_d\n",
       "0  tensorflow  lenet1  py - dotnet     0.0    large  0.007937     -1.0\n",
       "1  tensorflow  lenet1      py - ts     0.0    large  0.007937     -1.0"
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       "      <td>large</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>lenet5</td>\n",
       "      <td>py - ts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    framework   model         lang  mann_u eff_size   p_value  cliff_d\n",
       "2  tensorflow  lenet5  py - dotnet     0.0    large  0.007937     -1.0\n",
       "3  tensorflow  lenet5      py - ts     0.0    large  0.007937     -1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>framework</th>\n",
       "      <th>model</th>\n",
       "      <th>lang</th>\n",
       "      <th>mann_u</th>\n",
       "      <th>eff_size</th>\n",
       "      <th>p_value</th>\n",
       "      <th>cliff_d</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>lstm</td>\n",
       "      <td>py - ts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    framework model     lang  mann_u eff_size   p_value  cliff_d\n",
       "6  tensorflow  lstm  py - ts     0.0    large  0.007937     -1.0"
      ]
     },
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       "      <th>framework</th>\n",
       "      <th>model</th>\n",
       "      <th>lang</th>\n",
       "      <th>mann_u</th>\n",
       "      <th>eff_size</th>\n",
       "      <th>p_value</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>vgg16</td>\n",
       "      <td>py - dotnet</td>\n",
       "      <td>0.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>vgg16</td>\n",
       "      <td>py - ts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    framework  model         lang  mann_u eff_size   p_value  cliff_d\n",
       "4  tensorflow  vgg16  py - dotnet     0.0    large  0.007937     -1.0\n",
       "5  tensorflow  vgg16      py - ts     0.0    large  0.007937     -1.0"
      ]
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    {
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       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def printScale(value, lang1, total, lang2):\n",
    "    if value > total:\n",
    "        print(f\"{value} ({lang1}) / {total} ({lang2}) = {value / total} times faster\")\n",
    "    else:\n",
    "        print(f\"{total} ({lang2}) / {value} ({lang1}) = {total / value} tims slower\")\n",
    "\n",
    "def mannUandCliffdelta(dist1, dist2, printRes=True):\n",
    "    d, size = cliffdelta.cliffsDelta(dist1, dist2)\n",
    "    u, p = mannwhitneyu(dist1, dist2, alternative=\"two-sided\")\n",
    "    if printRes:\n",
    "        print(f\"{np.median(dist1)} vs. {np.median(dist2)}\")\n",
    "        print(f\"Cliff's delta: {size}, d={d}\")\n",
    "        print(f\"Mann-Whitney-U-test: u={u} p={p}\")\n",
    "    return u, p, d, size\n",
    "\n",
    "p_values_info = {\n",
    "    \"framework\": [],\n",
    "    \"model\": [],\n",
    "    \"lang\": [],\n",
    "    \"mann_u\": [],\n",
    "    \"eff_size\": [],\n",
    "    \"p_value\": [],\n",
    "    \"cliff_d\": [],\n",
    "}\n",
    "p_values = {}\n",
    "for fw in [\"tensorflow\", \"pytorch\"]:\n",
    "    p_values.setdefault(fw, {})\n",
    "    for model in [\"lenet1\", \"lenet5\", \"vgg16\", \"lstm\", \"gru\", \"grurb\"]:\n",
    "        p_values[fw].setdefault(model, [])\n",
    "        print(f\"============= {fw} x {model} ===============\")\n",
    "        languages = [\"py\", \"dotnet\", \"rs\", \"ts\"]\n",
    "        i = 0\n",
    "        ref_lang = languages[i]\n",
    "        for j in range(i + 1, len(languages)):\n",
    "            lang = languages[j]\n",
    "            if lang not in time_costs_bar_plot[fw][model]:\n",
    "                continue\n",
    "            if len(time_costs_bar_plot[fw][model][lang]) == 0:\n",
    "                continue\n",
    "            printScale(\n",
    "                np.median(time_costs_bar_plot[fw][model][ref_lang]), ref_lang,\n",
    "                np.median(time_costs_bar_plot[fw][model][lang]), lang\n",
    "            )\n",
    "            u, p, d, size = mannUandCliffdelta(\n",
    "                time_costs_bar_plot[fw][model][ref_lang],\n",
    "                time_costs_bar_plot[fw][model][lang], printRes=False\n",
    "            )\n",
    "            p_values[fw][model].append(p)\n",
    "            p_values_info[\"framework\"].append(fw)\n",
    "            p_values_info[\"model\"].append(model)\n",
    "            p_values_info[\"lang\"].append(f\"{ref_lang} - {lang}\")\n",
    "            p_values_info[\"mann_u\"].append(u)\n",
    "            p_values_info[\"eff_size\"].append(size)\n",
    "            p_values_info[\"p_value\"].append(p)\n",
    "            p_values_info[\"cliff_d\"].append(d)\n",
    "p_values_info = pd.DataFrame.from_dict(p_values_info)\n",
    "p_values_info.groupby([\"framework\", \"model\"]).apply(display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Bonferroni correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "framework   model \n",
       "pytorch     gru       [True, True]\n",
       "            lenet1    [True, True]\n",
       "            lenet5    [True, True]\n",
       "            lstm      [True, True]\n",
       "            vgg16     [True, True]\n",
       "tensorflow  grurb           [True]\n",
       "            lenet1    [True, True]\n",
       "            lenet5    [True, True]\n",
       "            lstm            [True]\n",
       "            vgg16     [True, True]\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_values_info.groupby([\"framework\", \"model\"]).apply(\n",
    "    lambda df: statsmodels.stats.multitest.multipletests(df[\"p_value\"], method=\"bonferroni\")[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RQ4. How do the studied bindings impact the inference time of pre-trained models?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Collecting inference time cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "deploy_times = {}\n",
    "testset_durations = {}\n",
    "for fw in [\"tensorflow\", \"pytorch\"]:\n",
    "    for model in MODELS:\n",
    "        for lang in LANGUAGES:\n",
    "            for device in [\"cpu\", \"gpu\"]:\n",
    "                mask = \\\n",
    "                    (inference_infos_df[\"framework\"] == fw) \\\n",
    "                    & (inference_infos_df[\"model\"] == model) \\\n",
    "                    & (inference_infos_df[\"language\"] == lang) \\\n",
    "                    & (inference_infos_df[\"device\"] == device)\n",
    "                time_cost_vals = inference_infos_df[mask].dropna()[\"time_cost\"].values\n",
    "                testset_duration = inference_infos_df[mask].dropna()[\"testset_duration\"].values\n",
    "                if len(time_cost_vals):\n",
    "                    deploy_times.setdefault(f\"{fw}_{model}_{lang}\", {})\n",
    "                    testset_durations.setdefault(f\"{fw}_{model}_{lang}\", {})\n",
    "                    # deploy_times[f\"{fw}_{model}_{lang}\"][device] = time_cost_vals\n",
    "                    testset_durations[f\"{fw}_{model}_{lang}\"][device] = reduce(lambda x, y: x + y, testset_duration)\n",
    "                    deploy_times[f\"{fw}_{model}_{lang}\"][device] = np.mean(time_cost_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Plotting inference time cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.8578321849927306, 0.8349432363174856, 0.8484589750878513, 0.8329676520079374, 0.8563888354692608, 0.8456464579794556, 0.84889355301857, 0.8815059142652899, 0.8691329618450254, 0.8620407367125154], [2.8518869799999997, 2.9421466, 2.84642026, 2.82668006, 2.8595876999999996], [0.5944625589999999, 0.5934407986, 0.6200759828, 0.5632670456, 0.5986516712000001], [1.6542000000000001, 1.6852, 1.6426000000000003, 1.6538, 1.6794], [0.9866661573760211, 1.1768949599936604, 0.9048424244392663, 0.8536215387284756, 0.9660377709660679, 0.8878365762066096, 0.8882840049918741, 0.872311650775373, 0.963793964125216, 0.936618132982403], [0.11734444000000002, 0.10852096, 0.11780786000000001, 0.12426725999999999, 0.1121022], [0.1050741754, 0.1037912354, 0.1047940668, 0.1099009324, 0.1040809734, 0.0998907292, 0.1136599056, 0.0967957282, 0.0981845106, 0.0993295654], [3.3728000000000002, 3.1567999999999996, 3.2252, 4.138199999999999, 3.9878]]\n",
      "[[0.6820610975846648, 0.6803916618227959, 0.69250662997365, 0.6791245019994676, 0.685960166156292, 0.6856175367720425, 0.6854151056613773, 0.6878588295076042, 0.6800780124031007, 0.6768844423349947], [2.7982858800000003, 2.8259624199999998, 2.79039956, 2.8544957600000003, 2.8404105800000004], [0.5739377158, 0.573087096, 0.5745132664, 0.5791055032, 0.5636563177999999], [1.9128, 1.9212, 2.1172, 2.0992, 2.1325999999999996], [0.9581971317529678, 0.8665457181632519, 0.9259389904327691, 0.938288982026279, 1.1001646283548325, 1.1926036172080785, 0.9341235226020217, 1.2279019576963037, 0.8250225968658924, 1.1658776823896915], [0.15239174000000003, 0.14256094000000002, 0.14621192, 0.1434443, 0.14317542], [0.1255559034, 0.1280919914, 0.1242705042, 0.1257100902, 0.1279315336, 0.1370174092, 0.1349998788, 0.1324874528, 0.1289575374, 0.1284664764], [4.5636, 4.4708000000000006, 4.33, 4.452, 4.3014]]\n",
      "[[9.966972066089511, 10.048796454910189, 10.07000445551239, 9.9559828331694, 10.013582708314061, 10.179876973386854, 9.959138898178935, 6.982475767377764, 7.695235155289993, 7.618982842890546], [9.67623436, 11.27301448, 12.0472173, 10.93995484, 11.9499456], [6.039216359799999, 5.7615718317999995, 6.5250505378, 6.0540562428, 6.0043438622], [11.343399999999999, 11.412400000000002, 11.338600000000001, 11.3666, 11.3164], [14.640493901679292, 14.691708943573758, 14.641905979951844, 14.978545395191759, 14.432907970855013, 16.28396111074835, 15.845540445297956, 15.070156789384782, 14.883445853134617, 15.62679511285387], [9.910082220000001, 11.94861568, 12.136486999999999, 8.67678124, 9.01613308], [8.2052433812, 8.528520577, 7.919479298000001, 7.938933193800001, 7.879667399000001, 10.671218759999999, 8.659470797800001, 10.477225225000002, 9.4778890676, 9.932821268800001], [24.2798, 24.698999999999998, 25.0772, 24.592399999999998, 24.5432]]\n",
      "[[62.48317881836556, 62.21732557378709, 61.9481120640412, 62.075222755549476, 62.16981232399121, 62.01468363474123, 61.86419977294281, 61.964285370195284, 62.165016126818955, 61.82494218880311], nan, [70.7067063594, 82.0397123068, 69.5367504144, 73.8718017532, 81.7352087322], [493.06180000000006, 492.91960000000006, 479.66459999999995, 648.0838, 455.59799999999996], [67.73829648946412, 68.17529142042622, 67.96647642087191, 67.32717037713155, 67.25635142694227, 66.58479544264264, 68.34335344284773, 66.86727236583829, 67.42900935118087, 66.47714232467115], [123.46785956000001, 240.12758284, 241.91684632000002, 165.34338024000002, 173.07024752], [68.14695599659998, 68.2338136768, 68.4216918824, 110.6807150696, 111.35906839660001, 71.1170555804, 72.5282029306, 77.5402817652, 82.6116726086, 71.3851956624], [132.3958, 131.7964, 129.9408, 130.2862, 131.03040000000001]]\n",
      "[[56.617443589959294, 56.51602565823123, 56.42449570540339, 56.39966913382523, 56.39405945385806, 56.487408491782844, 74.11361774220131, 75.25763251376338, 74.69874089933, 74.07897349679843], nan, [95.791231157, 95.6695723036, 94.6089768146, 94.4229600642, 95.0694551182], [690.5870000000001, 687.7306000000001, 686.5606, 686.5068, 708.2719999999999], [51.293865357059985, 51.65772910299711, 50.94444187544286, 50.614262951072305, 51.06638852972537, 49.58098274865188, 51.38640163745731, 51.505891176871955, 50.97373779583722, 50.959127398906276], [52.267295239999996, 52.70605112, 52.1859748, 52.62528472, 52.403485339999996], [132.7354479644, 132.6769347178, 132.4924131434, 127.542659424, 126.45108190820001, 133.1225694062, 136.4533543476, 137.7880434866, 138.4850775356, 138.365046175], [108.835, 108.83279999999999, 143.51639999999998, 142.87619999999998, 142.01299999999998]]\n"
     ]
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1656x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    }
   ],
   "source": [
    "YTICKS_SIZE = 22\n",
    "XTICKS_SIZE = 22\n",
    "YLABEL_SIZE = 26\n",
    "LEGEND_SIZE = 22\n",
    "XLABEL_SIZE = 28\n",
    "TITLE_SIZE = 30\n",
    "\n",
    "cpu_color = \"#CC79A7\"\n",
    "cpu_color_darker = \"#990054\"\n",
    "gpu_color = \"#1e96dc\"\n",
    "gpu_color_darker = \"#006ba8\"\n",
    "fig, axs = plt.subplots(1, 5)\n",
    "fig.set_size_inches((23, 7))\n",
    "x_ticks = [1.1, 2, 2.9, 3.8, 5.0, 5.9, 6.8, 7.7]\n",
    "\n",
    "data_cpus = {}\n",
    "data_gpus = {}\n",
    "for model, ax in zip(MODELS[:-1], axs):\n",
    "    data_cpu = []\n",
    "    data_gpu = []\n",
    "    for fw in [\"tensorflow\", \"pytorch\"]:\n",
    "        for lang in LANGUAGES:\n",
    "            if lang == \"ts\" and model == \"gru\" and fw == \"tensorflow\":\n",
    "                key_name = f\"{fw}_grurb_{lang}\"\n",
    "            else:\n",
    "                key_name = f\"{fw}_{model}_{lang}\"\n",
    "            if key_name not in testset_durations:\n",
    "                data_cpu.append(np.nan)\n",
    "                data_gpu.append(np.nan)\n",
    "            else:\n",
    "                data_cpu.append(testset_durations[key_name][\"cpu\"])\n",
    "                data_gpu.append(testset_durations[key_name][\"gpu\"])\n",
    "    print(data_cpu)\n",
    "    box_cpu = ax.boxplot(\n",
    "        data_cpu,\n",
    "        positions=x_ticks,\n",
    "        patch_artist=True, widths=0.7,\n",
    "        flierprops={\"markeredgecolor\": cpu_color, \"markersize\": 4},\n",
    "    )\n",
    "    box_gpu = ax.boxplot(\n",
    "        data_gpu,\n",
    "        positions=x_ticks,\n",
    "        patch_artist=True, widths=0.7,\n",
    "        flierprops={\"markeredgecolor\": gpu_color, \"markersize\": 4}\n",
    "    )\n",
    "\n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'caps']:\n",
    "        plt.setp(box_cpu[element], color=cpu_color)\n",
    "    plt.setp(box_cpu['medians'], color=cpu_color_darker)\n",
    "    for patch in box_cpu['boxes']:\n",
    "        patch.set(facecolor=cpu_color)\n",
    "\n",
    "    for element in ['boxes', 'whiskers', 'fliers', 'caps']:\n",
    "        plt.setp(box_gpu[element], color=gpu_color)\n",
    "    plt.setp(box_gpu['medians'], color=gpu_color_darker)\n",
    "    for patch in box_gpu['boxes']:\n",
    "        patch.set(facecolor=gpu_color)\n",
    "\n",
    "    ax.axvline(np.median(x_ticks), ls=\"--\", color=\"gray\", alpha=0.3)\n",
    "    ax.set_xticks(x_ticks)\n",
    "    ax.set_xticklabels(\n",
    "        [LANG_SHOW_MAP[c] for c in col_names], size=XTICKS_SIZE, rotation=55,\n",
    "        ha='right', rotation_mode='anchor'\n",
    "    )\n",
    "    ax.set_title(MODELS_SHOW_MAP[model], fontsize=TITLE_SIZE)\n",
    "\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "    trans = transforms.blended_transform_factory(\n",
    "        ax.transData, ax.transAxes)\n",
    "\n",
    "    ax.text(2, -0.35, \"TF\", transform=trans, fontsize=XLABEL_SIZE)\n",
    "    ax.text(5.9, -0.35, \"PT\", transform=trans, fontsize=XLABEL_SIZE)\n",
    "    ax.tick_params(axis='y', labelsize=YTICKS_SIZE)\n",
    "    data_cpus[model] = data_cpu\n",
    "    data_gpus[model] = data_gpu\n",
    "\n",
    "\n",
    "plt.legend(\n",
    "    [box_cpu[\"boxes\"][0], box_gpu[\"boxes\"][0]],\n",
    "    ['CPU', 'GPU'], bbox_to_anchor=(1.03, 0.2), borderaxespad=0, fontsize=LEGEND_SIZE,\n",
    "    frameon=False\n",
    ")\n",
    "axs[0].set_ylabel(\"Inference Time (Seconds)\", fontsize=YLABEL_SIZE)\n",
    "plt.subplots_adjust(wspace=0)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig(OUT_FIGS_PATH / f\"Fig7.eps\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mann-Whitney U test and Cliff's delta d for Python vs. Non-Python bindings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*************** TensorFlow x lenet1 cpu ***************\n",
      "2.8518869799999997 (dotnet) / 0.8526411942439154 (py) = 3.3447679976674447 tims slower\n",
      "0.8526411942439154 (py) / 0.5944625589999999 (rs) = 1.434305964833549 times faster\n",
      "1.6542000000000001 (ts) / 0.8526411942439154 (py) = 1.9400892323374919 tims slower\n",
      "*************** TensorFlow x lenet1 gpu ***************\n",
      "3.64313712 (dotnet) / 0.25389744411222637 (py) = 14.348853068365983 tims slower\n",
      "0.25389744411222637 (py) / 0.1417206336 (rs) = 1.7915347798178793 times faster\n",
      "0.6616000000000001 (ts) / 0.25389744411222637 (py) = 2.6057765264764274 tims slower\n",
      "*************** PyTorch x lenet1 cpu ***************\n",
      "0.9207302787108347 (py) / 0.11734444000000002 (dotnet) = 7.846390325019528 times faster\n",
      "0.9207302787108347 (py) / 0.1039361044 (rs) = 8.858618321573678 times faster\n",
      "3.3728000000000002 (ts) / 0.9207302787108347 (py) = 3.663179193718321 tims slower\n",
      "*************** PyTorch x lenet1 gpu ***************\n",
      "0.5929149857256562 (py) / 0.03416326 (dotnet) = 17.35533979267951 times faster\n",
      "0.5929149857256562 (py) / 0.0292730131 (rs) = 20.25466198850764 times faster\n",
      "0.5929149857256562 (py) / 0.07740000000000001 (ts) = 7.660400332372817 times faster\n",
      "*************** TensorFlow x lenet5 cpu ***************\n",
      "2.8259624199999998 (dotnet) / 0.6837381016230211 (py) = 4.133106540782035 tims slower\n",
      "0.6837381016230211 (py) / 0.5739377158 (rs) = 1.1913106297082647 times faster\n",
      "2.0992 (ts) / 0.6837381016230211 (py) = 3.070181396966223 tims slower\n",
      "*************** TensorFlow x lenet5 gpu ***************\n",
      "3.7965717 (dotnet) / 0.2819468055386096 (py) = 13.465560259664299 tims slower\n",
      "0.2819468055386096 (py) / 0.1548937452 (rs) = 1.8202594635087281 times faster\n",
      "0.7757999999999999 (ts) / 0.2819468055386096 (py) = 2.751582868683229 tims slower\n",
      "*************** PyTorch x lenet5 cpu ***************\n",
      "0.9482430568896234 (py) / 0.1434443 (dotnet) = 6.610531452902788 times faster\n",
      "0.9482430568896234 (py) / 0.1282792339 (rs) = 7.392023073889151 times faster\n",
      "4.452 (ts) / 0.9482430568896234 (py) = 4.694998785019544 tims slower\n",
      "*************** PyTorch x lenet5 gpu ***************\n",
      "0.603781255427748 (py) / 0.04163469999999999 (dotnet) = 14.501875969509763 times faster\n",
      "0.603781255427748 (py) / 0.034953548099999995 (rs) = 17.27381877514598 times faster\n",
      "0.603781255427748 (py) / 0.0632 (ts) = 9.553500877021328 times faster\n",
      "*************** TensorFlow x vgg16 cpu ***************\n",
      "11.27301448 (dotnet) / 9.963055482134223 (py) = 1.1314816524122344 tims slower\n",
      "9.963055482134223 (py) / 6.039216359799999 (rs) = 1.649726535458016 times faster\n",
      "11.343399999999999 (ts) / 9.963055482134223 (py) = 1.1385463044284971 tims slower\n",
      "*************** TensorFlow x vgg16 gpu ***************\n",
      "22.9400575 (dotnet) / 0.8982128466013819 (py) = 25.539667559643107 tims slower\n",
      "0.8982128466013819 (py) / 0.6012922745999999 (rs) = 1.493804069242206 times faster\n",
      "2.081 (ts) / 0.8982128466013819 (py) = 2.3168227974850235 tims slower\n",
      "*************** PyTorch x vgg16 cpu ***************\n",
      "14.930995624163188 (py) / 9.910082220000001 (dotnet) = 1.5066469977444028 times faster\n",
      "14.930995624163188 (py) / 8.5939956874 (rs) = 1.7373752753976963 times faster\n",
      "24.592399999999998 (ts) / 14.930995624163188 (py) = 1.6470703373726483 tims slower\n",
      "*************** PyTorch x vgg16 gpu ***************\n",
      "8.2344245 (dotnet) / 1.5166169997537509 (py) = 5.429468680185572 tims slower\n",
      "6.4331292999 (rs) / 1.5166169997537509 (py) = 4.241762620981125 tims slower\n",
      "6.4738 (ts) / 1.5166169997537509 (py) = 4.268579345379311 tims slower\n",
      "*************** TensorFlow x lstm cpu ***************\n",
      "73.8718017532 (rs) / 62.044953195145354 (py) = 1.1906174144552346 tims slower\n",
      "492.91960000000006 (ts) / 62.044953195145354 (py) = 7.944555916573212 tims slower\n",
      "*************** TensorFlow x lstm gpu ***************\n",
      "9.323155321200002 (rs) / 3.87204623199068 (py) = 2.407810951267186 tims slower\n",
      "177.69459999999998 (ts) / 3.87204623199068 (py) = 45.891652463210484 tims slower\n",
      "*************** PyTorch x lstm cpu ***************\n",
      "173.07024752 (dotnet) / 67.37808986415621 (py) = 2.5686428313556258 tims slower\n",
      "71.9566992965 (rs) / 67.37808986415621 (py) = 1.0679539809094458 tims slower\n",
      "131.03040000000001 (ts) / 67.37808986415621 (py) = 1.9447033934054214 tims slower\n",
      "*************** PyTorch x lstm gpu ***************\n",
      "2.549342345329933 (py) / 2.48119188 (dotnet) = 1.027466825874802 times faster\n",
      "2.6078975114 (rs) / 2.549342345329933 (py) = 1.0229687339471423 tims slower\n",
      "2.549342345329933 (py) / 2.5303999999999993 (ts) = 1.0074859094727844 times faster\n",
      "*************** TensorFlow x gru cpu ***************\n",
      "95.0694551182 (rs) / 56.56673462409526 (py) = 1.6806601220658768 tims slower\n",
      "687.7306000000001 (ts) / 56.56673462409526 (py) = 12.157862824683065 tims slower\n",
      "*************** TensorFlow x gru gpu ***************\n",
      "18.409799159 (rs) / 3.571245809085667 (py) = 5.155007564072827 tims slower\n",
      "253.48960000000002 (ts) / 3.571245809085667 (py) = 70.98072032876954 tims slower\n",
      "*************** PyTorch x gru cpu ***************\n",
      "52.403485339999996 (dotnet) / 51.0200631627813 (py) = 1.0271152580271186 tims slower\n",
      "132.9290086853 (rs) / 51.0200631627813 (py) = 2.605426188148481 tims slower\n",
      "142.01299999999998 (ts) / 51.0200631627813 (py) = 2.783473621875036 tims slower\n",
      "*************** PyTorch x gru gpu ***************\n",
      "2.3031613101484254 (py) / 2.2324370399999998 (dotnet) = 1.0316802977558668 times faster\n",
      "2.3031613101484254 (py) / 2.2564344052 (rs) = 1.0207082930665932 times faster\n",
      "2.3031613101484254 (py) / 2.207 (ts) = 1.0435710512679772 times faster\n"
     ]
    },
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       "      <th>50</th>\n",
       "      <td>pytorch</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
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       "      <td>py-rs</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
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       "      <td>py-ts</td>\n",
       "      <td>0.0</td>\n",
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       "   framework model       lang  mann_u eff_size   p_value  cliff_d device\n",
       "50   pytorch   gru  py-dotnet     0.0    large  0.000666     -1.0    cpu\n",
       "51   pytorch   gru      py-rs     0.0    large  0.000183     -1.0    cpu\n",
       "52   pytorch   gru      py-ts     0.0    large  0.000666     -1.0    cpu"
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>pytorch</td>\n",
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       "      <td>py-dotnet</td>\n",
       "      <td>50.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
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       "      <td>gpu</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>pytorch</td>\n",
       "      <td>gru</td>\n",
       "      <td>py-rs</td>\n",
       "      <td>54.0</td>\n",
       "      <td>negligible</td>\n",
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       "      <td>0.08</td>\n",
       "      <td>gpu</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>pytorch</td>\n",
       "      <td>gru</td>\n",
       "      <td>py-ts</td>\n",
       "      <td>50.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>1.00</td>\n",
       "      <td>gpu</td>\n",
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       "   framework model       lang  mann_u    eff_size   p_value  cliff_d device\n",
       "53   pytorch   gru  py-dotnet    50.0       large  0.000666     1.00    gpu\n",
       "54   pytorch   gru      py-rs    54.0  negligible  0.791337     0.08    gpu\n",
       "55   pytorch   gru      py-ts    50.0       large  0.000666     1.00    gpu"
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pytorch</td>\n",
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       "      <td>py-rs</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pytorch</td>\n",
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       "      <td>py-ts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
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       "  framework   model       lang  mann_u eff_size   p_value  cliff_d device\n",
       "6   pytorch  lenet1  py-dotnet    50.0    large  0.000666      1.0    cpu\n",
       "7   pytorch  lenet1      py-rs   100.0    large  0.000183      1.0    cpu\n",
       "8   pytorch  lenet1      py-ts     0.0    large  0.000666     -1.0    cpu"
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pytorch</td>\n",
       "      <td>lenet1</td>\n",
       "      <td>py-dotnet</td>\n",
       "      <td>50.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>1.0</td>\n",
       "      <td>gpu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pytorch</td>\n",
       "      <td>lenet1</td>\n",
       "      <td>py-rs</td>\n",
       "      <td>100.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>gpu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pytorch</td>\n",
       "      <td>lenet1</td>\n",
       "      <td>py-ts</td>\n",
       "      <td>50.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>1.0</td>\n",
       "      <td>gpu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "   framework   model       lang  mann_u eff_size   p_value  cliff_d device\n",
       "9    pytorch  lenet1  py-dotnet    50.0    large  0.000666      1.0    gpu\n",
       "10   pytorch  lenet1      py-rs   100.0    large  0.000183      1.0    gpu\n",
       "11   pytorch  lenet1      py-ts    50.0    large  0.000666      1.0    gpu"
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       "      <th>18</th>\n",
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       "   framework   model       lang  mann_u eff_size   p_value  cliff_d device\n",
       "18   pytorch  lenet5  py-dotnet    50.0    large  0.000666      1.0    cpu\n",
       "19   pytorch  lenet5      py-rs   100.0    large  0.000183      1.0    cpu\n",
       "20   pytorch  lenet5      py-ts     0.0    large  0.000666     -1.0    cpu"
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       "      <th>21</th>\n",
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       "22   pytorch  lenet5      py-rs   100.0    large  0.000183      1.0    gpu\n",
       "23   pytorch  lenet5      py-ts    50.0    large  0.000666      1.0    gpu"
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       "      <th>40</th>\n",
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       "      <th>41</th>\n",
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       "   framework model       lang  mann_u eff_size   p_value  cliff_d device\n",
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       "41   pytorch  lstm      py-rs     3.0    large  0.000440    -0.94    cpu\n",
       "42   pytorch  lstm      py-ts     0.0    large  0.000666    -1.00    cpu"
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       "      <th>43</th>\n",
       "      <td>pytorch</td>\n",
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       "      <th>44</th>\n",
       "      <td>pytorch</td>\n",
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       "      <td>py-ts</td>\n",
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       "   framework model       lang  mann_u eff_size   p_value  cliff_d device\n",
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       "44   pytorch  lstm      py-rs    34.0    small  0.241322    -0.32    gpu\n",
       "45   pytorch  lstm      py-ts    33.0    small  0.370962     0.32    gpu"
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       "      <th>32</th>\n",
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       "      <td>py-ts</td>\n",
       "      <td>0.0</td>\n",
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       "31   pytorch  vgg16      py-rs   100.0    large  0.000183      1.0    cpu\n",
       "32   pytorch  vgg16      py-ts     0.0    large  0.000666     -1.0    cpu"
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       "      <th>35</th>\n",
       "      <td>pytorch</td>\n",
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       "      <td>py-ts</td>\n",
       "      <td>0.0</td>\n",
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       "   framework  model       lang  mann_u eff_size   p_value  cliff_d device\n",
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       "34   pytorch  vgg16      py-rs     0.0    large  0.000183     -1.0    gpu\n",
       "35   pytorch  vgg16      py-ts     0.0    large  0.000666     -1.0    gpu"
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       "      <th>46</th>\n",
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       "      <th>47</th>\n",
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       "     framework model   lang  mann_u eff_size   p_value  cliff_d device\n",
       "46  tensorflow   gru  py-rs     0.0    large  0.000666     -1.0    cpu\n",
       "47  tensorflow   gru  py-ts     0.0    large  0.000666     -1.0    cpu"
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       "    <tr>\n",
       "      <th>48</th>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
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       "      <td>-1.0</td>\n",
       "      <td>gpu</td>\n",
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       "     framework model   lang  mann_u eff_size   p_value  cliff_d device\n",
       "48  tensorflow   gru  py-rs     0.0    large  0.000666     -1.0    gpu\n",
       "49  tensorflow   gru  py-ts     0.0    large  0.000666     -1.0    gpu"
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       "    framework   model       lang  mann_u eff_size   p_value  cliff_d device\n",
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       "1  tensorflow  lenet1      py-rs    50.0    large  0.000666      1.0    cpu\n",
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       "4  tensorflow  lenet1      py-rs    50.0    large  0.000666      1.0    gpu\n",
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       "16  tensorflow  lenet5      py-rs    50.0    large  0.000666      1.0    gpu\n",
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       "37  tensorflow  lstm  py-ts     0.0    large  0.000666     -1.0    cpu"
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       "38  tensorflow  lstm  py-rs     0.0    large  0.000666     -1.0    gpu\n",
       "39  tensorflow  lstm  py-ts     0.0    large  0.000666     -1.0    gpu"
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       "     framework  model       lang  mann_u eff_size   p_value  cliff_d device\n",
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       "25  tensorflow  vgg16      py-rs    50.0    large  0.000666     1.00    cpu\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>vgg16</td>\n",
       "      <td>py-dotnet</td>\n",
       "      <td>0.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>gpu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>vgg16</td>\n",
       "      <td>py-rs</td>\n",
       "      <td>50.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>1.0</td>\n",
       "      <td>gpu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>vgg16</td>\n",
       "      <td>py-ts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>large</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>gpu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     framework  model       lang  mann_u eff_size   p_value  cliff_d device\n",
       "27  tensorflow  vgg16  py-dotnet     0.0    large  0.000666     -1.0    gpu\n",
       "28  tensorflow  vgg16      py-rs    50.0    large  0.000666      1.0    gpu\n",
       "29  tensorflow  vgg16      py-ts     0.0    large  0.000666     -1.0    gpu"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infer_cmp = {\n",
    "    \"framework\": [],\n",
    "    \"model\": [],\n",
    "    \"lang\": [],\n",
    "    \"mann_u\": [],\n",
    "    \"eff_size\": [],\n",
    "    \"p_value\": [],\n",
    "    \"cliff_d\": [],\n",
    "    \"device\": [],\n",
    "}\n",
    "def calData(fw, model, device, data):\n",
    "    ps = []\n",
    "    i = 0\n",
    "    ref = data[0]\n",
    "    for j in range(i + 1, len(data)):\n",
    "        d = data[j]\n",
    "        if isinstance(d, float):\n",
    "            continue\n",
    "        # print(f\"=========== {LANGUAGES[i]} vs {LANGUAGES[j]} ============\")\n",
    "        printScale(np.median(ref), LANGUAGES[i], np.median(d), LANGUAGES[j])\n",
    "        u, p, cliff_d, size = mannUandCliffdelta(ref, d, printRes=False)\n",
    "        infer_cmp[\"framework\"].append(fw)\n",
    "        infer_cmp[\"model\"].append(model)\n",
    "        infer_cmp[\"lang\"].append(f\"{LANGUAGES[i]}-{LANGUAGES[j]}\")\n",
    "        infer_cmp[\"mann_u\"].append(u)\n",
    "        infer_cmp[\"eff_size\"].append(size)\n",
    "        infer_cmp[\"p_value\"].append(p)\n",
    "        infer_cmp[\"cliff_d\"].append(cliff_d)\n",
    "        infer_cmp[\"device\"].append(device)\n",
    "\n",
    "for model in MODELS[:-1]:\n",
    "    data_tf_cpu = data_cpus[model][:4]\n",
    "    data_tf_gpu = data_gpus[model][:4]\n",
    "    data_tch_cpu = data_cpus[model][-4:]\n",
    "    data_tch_gpu = data_gpus[model][-4:]\n",
    "    print(f\"*************** TensorFlow x {model} cpu ***************\")\n",
    "    calData(\"tensorflow\", model, \"cpu\", data_tf_cpu)\n",
    "    print(f\"*************** TensorFlow x {model} gpu ***************\")\n",
    "    calData(\"tensorflow\", model, \"gpu\", data_tf_gpu)\n",
    "    print(f\"*************** PyTorch x {model} cpu ***************\")\n",
    "    calData(\"pytorch\", model, \"cpu\", data_tch_cpu)\n",
    "    print(f\"*************** PyTorch x {model} gpu ***************\")\n",
    "    calData(\"pytorch\", model, \"gpu\", data_tch_gpu)\n",
    "infer_cmp = pd.DataFrame.from_dict(infer_cmp)\n",
    "infer_cmp.groupby([\"framework\", \"model\", \"device\"]).apply(display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonferroni correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "framework   model   device\n",
       "pytorch     gru     cpu          [True, True, True]\n",
       "                    gpu         [True, False, True]\n",
       "            lenet1  cpu          [True, True, True]\n",
       "                    gpu          [True, True, True]\n",
       "            lenet5  cpu          [True, True, True]\n",
       "                    gpu          [True, True, True]\n",
       "            lstm    cpu          [True, True, True]\n",
       "                    gpu       [False, False, False]\n",
       "            vgg16   cpu          [True, True, True]\n",
       "                    gpu          [True, True, True]\n",
       "tensorflow  gru     cpu                [True, True]\n",
       "                    gpu                [True, True]\n",
       "            lenet1  cpu          [True, True, True]\n",
       "                    gpu          [True, True, True]\n",
       "            lenet5  cpu          [True, True, True]\n",
       "                    gpu          [True, True, True]\n",
       "            lstm    cpu                [True, True]\n",
       "                    gpu                [True, True]\n",
       "            vgg16   cpu         [False, True, True]\n",
       "                    gpu          [True, True, True]\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infer_cmp.groupby([\"framework\", \"model\", \"device\"]).apply(\n",
    "    lambda df: statsmodels.stats.multitest.multipletests(df[\"p_value\"], method=\"bonferroni\")[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CPU vs. GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "how many p > 0.05? 0\n",
      "how many effect size not 'large'? 0\n"
     ]
    }
   ],
   "source": [
    "device_cmp = {\n",
    "    \"framework\": [],\n",
    "    \"model\": [],\n",
    "    \"lang\": [],\n",
    "    \"mann_u\": [],\n",
    "    \"eff_size\": [],\n",
    "    \"p_value\": [],\n",
    "    \"cliff_d\": [],\n",
    "}\n",
    "\n",
    "for model in MODELS[:-1]:\n",
    "    data_tf_cpu = data_cpus[model][:4]\n",
    "    data_tf_gpu = data_gpus[model][:4]\n",
    "    for i, lang in enumerate(LANGUAGES):\n",
    "        if isinstance(data_tf_cpu[i], float) or isinstance(data_tf_gpu[i], float):\n",
    "            continue\n",
    "        u, p, cliff_d, size = mannUandCliffdelta(data_tf_cpu[i], data_tf_gpu[i], printRes=False)\n",
    "        device_cmp[\"framework\"].append(\"tensorflow\")\n",
    "        device_cmp[\"model\"].append(model)\n",
    "        device_cmp[\"lang\"].append(lang)\n",
    "        device_cmp[\"mann_u\"].append(u)\n",
    "        device_cmp[\"eff_size\"].append(size)\n",
    "        device_cmp[\"p_value\"].append(p)\n",
    "        device_cmp[\"cliff_d\"].append(cliff_d)\n",
    "\n",
    "for model in MODELS[:-1]:\n",
    "    data_tch_cpu = data_cpus[model][-4:]\n",
    "    data_tch_gpu = data_gpus[model][-4:]\n",
    "    for i, lang in enumerate(LANGUAGES):\n",
    "        if isinstance(data_tch_cpu[i], float) or isinstance(data_tch_gpu[i], float):\n",
    "            continue\n",
    "        u, p, cliff_d, size = mannUandCliffdelta(data_tch_cpu[i], data_tch_gpu[i], printRes=False)\n",
    "        device_cmp[\"framework\"].append(\"pytorch\")\n",
    "        device_cmp[\"model\"].append(model)\n",
    "        device_cmp[\"lang\"].append(lang)\n",
    "        device_cmp[\"mann_u\"].append(u)\n",
    "        device_cmp[\"eff_size\"].append(size)\n",
    "        device_cmp[\"p_value\"].append(p)\n",
    "        device_cmp[\"cliff_d\"].append(cliff_d)\n",
    "\n",
    "device_cmp = pd.DataFrame.from_dict(device_cmp)\n",
    "print(\"how many p > 0.05?\", sum(device_cmp[\"p_value\"] > 0.05))\n",
    "print(\"how many effect size not 'large'?\", sum(device_cmp[\"eff_size\"] != \"large\"))"
   ]
  }
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