Timing Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b78d59d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "plt.rcParams['text.usetex'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0104eef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "h10 = {\n",
    "    \"koopman\": {\n",
    "        \"train\": [4.779674053192139, 2.7271478176116943, 2.9460301399230957, 3.31054425239563, 3.135464906692505, 3.233924627304077, 3.737001657485962, 3.924687623977661, 4.285128116607666, 4.896482706069946],\n",
    "        \"infer\": [0.3134334087371826, 0.3985917568206787, 0.34282493591308594, 0.2964498996734619, 0.3803989887237549, 0.37729883193969727, 0.301865816116333, 0.3622739315032959, 0.3727996349334717, 0.4537169933319092]\n",
    "    },\n",
    "    \"rnn\": {\n",
    "        \"train\": [2.603893756866455, 0.35364818572998047, 0.4164917469024658, 0.42455005645751953, 0.3880729675292969, 0.3670799732208252, 0.3927490711212158, 0.38985204696655273, 0.39105892181396484, 0.3713538646697998],\n",
    "        \"infer\": [0.010353565216064453, 0.009846687316894531, 0.009724140167236328, 0.009647369384765625, 0.00960993766784668, 0.009485960006713867, 0.010303020477294922, 0.01024317741394043, 0.00985407829284668, 0.010033369064331055]\n",
    "    },\n",
    "    \"reflex\": {\n",
    "        \"train\": [1.8656294345855713, 0.0753018856048584, 0.07225775718688965, 0.09401726722717285, 0.12689590454101562, 0.07386302947998047, 0.1200108528137207, 0.12194204330444336, 0.07940101623535156, 0.07378959655761719],\n",
    "        \"infer\": [0.002093076705932617, 0.0016565322875976562, 0.0021550655364990234, 0.0017533302307128906, 0.001720428466796875, 0.0016751289367675781, 0.0016300678253173828, 0.0016870498657226562, 0.0016505718231201172, 0.0017235279083251953]\n",
    "    },\n",
    "    \"cyin\": {\n",
    "        \"train\": [3.2110331058502197, 1.270247459411621, 1.3623099327087402, 1.1074566841125488, 1.317047119140625, 1.1884257793426514, 1.5185699462890625, 1.3319885730743408, 1.3440372943878174, 1.25803542137146],\n",
    "        \"infer\": [0.02246236801147461, 0.03410744667053223, 0.02092146873474121, 0.02495288848876953, 0.022350311279296875, 0.02095818519592285, 0.020421981811523438, 0.021271467208862305, 0.023552417755126953, 0.027216672897338867]\n",
    "    }\n",
    "}\n",
    "\n",
    "h20 = {\n",
    "    \"koopman\": {\n",
    "        \"train\": [10.266776323318481, 10.952387571334839, 10.733768939971924, 12.181357622146606, 12.398614406585693, 12.300835132598877, 12.421929597854614, 12.4301278591156, 10.945276737213135, 12.33874249458313],\n",
    "        \"infer\": [1.0488777160644531, 1.0318992137908936, 1.2650980949401855, 1.3452024459838867, 1.0969433784484863, 1.5786447525024414, 1.1997957229614258, 1.2674634456634521, 1.0159943103790283, 1.2965636253356934]\n",
    "    },\n",
    "    \"rnn\": {\n",
    "        \"train\": [5.017014980316162, 0.6437451839447021, 0.6544826030731201, 0.7134201526641846, 0.7259464263916016, 0.6117806434631348, 0.7086565494537354, 0.8157744407653809, 0.7103004455566406, 0.6668047904968262],\n",
    "        \"infer\": [0.024918556213378906, 0.029378652572631836, 0.03747272491455078, 0.028362274169921875, 0.027591705322265625, 0.022797107696533203, 0.025671005249023438, 0.028809309005737305, 0.02881789207458496, 0.028728723526000977]\n",
    "    },\n",
    "    \"reflex\": {\n",
    "        \"train\": [2.042452335357666, 0.09210085868835449, 0.08116579055786133, 0.07837557792663574, 0.09552621841430664, 0.06574058532714844, 0.08968424797058105, 0.07595443725585938, 0.09659457206726074, 0.09284472465515137],\n",
    "        \"infer\": [0.0020613670349121094, 0.0015025138854980469, 0.0014967918395996094, 0.0014424324035644531, 0.0013680458068847656, 0.0013437271118164062, 0.0013816356658935547, 0.0014567375183105469, 0.001360177993774414, 0.0013413429260253906]\n",
    "    },\n",
    "    \"cyin\": {\n",
    "        \"train\": [1.6287741661071777, 1.3362650871276855, 1.3928403854370117, 1.168846607208252, 1.1990687847137451, 1.3433475494384766, 1.2659964561462402, 1.4219019412994385, 1.3531677722930908, 1.3473031520843506],\n",
    "        \"infer\": [0.032375335693359375, 0.030330181121826172, 0.030533790588378906, 0.030735015869140625, 0.03037095069885254, 0.030215978622436523, 0.030205965042114258, 0.030774593353271484, 0.0436859130859375, 0.03247666358947754]\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dcf29e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(data, ylim, horizon=\"10\"):\n",
    "    import matplotlib as mpl\n",
    "    mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "\n",
    "    method_names = [\"Training\", \"Inference\"]\n",
    "\n",
    "    results = []\n",
    "    for type_ in [\"train\", \"infer\"]:\n",
    "        results.append(data[\"koopman\"][type_])\n",
    "    print(results)\n",
    "    kcpo_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "\n",
    "    results = []\n",
    "    for type_ in [\"train\", \"infer\"]:\n",
    "        results.append(data[\"rnn\"][type_])\n",
    "    rnn_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "    \n",
    "    results = []\n",
    "    for type_ in [\"train\", \"infer\"]:\n",
    "        results.append(data[\"reflex\"][type_])\n",
    "    reflex_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "    \n",
    "    results = []\n",
    "    for type_ in [\"train\", \"infer\"]:\n",
    "        results.append(data[\"cyin\"][type_])\n",
    "    cyin_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "\n",
    "    # print(in_df)\n",
    "    # print(out_df)\n",
    "\n",
    "    pal = sns.color_palette(\"Set1\")\n",
    "\n",
    "    # Create the figure\n",
    "    fig, (first, second, third, fourth) = plt.subplots(1, 4, figsize=(10, 3))\n",
    "\n",
    "    first.set_title(\"KCPO\", size=15)\n",
    "    first.set_yscale(\"log\")\n",
    "    first.set_ylim(ylim)\n",
    "    first.set_ylabel(\"Seconds\", size=15)\n",
    "    first.axhline(y=1, color=\"black\", linewidth=4, linestyle=\"-\")\n",
    "    bp = sns.boxplot(data=kcpo_df, palette=pal, ax=first)\n",
    "\n",
    "    second.set_title(\"RNN\", size=15)\n",
    "    second.set_yscale(\"log\")\n",
    "    second.set_ylim(ylim)\n",
    "    second.axhline(y=1, color=\"black\", linewidth=4, linestyle=\"-\")\n",
    "    bp = sns.boxplot(data=rnn_df, palette=pal, ax=second)\n",
    "    \n",
    "    third.set_title(\"ReflexNet\", size=15)\n",
    "    third.set_yscale(\"log\")\n",
    "    third.set_ylim(ylim)\n",
    "    third.axhline(y=1, color=\"black\", linewidth=4, linestyle=\"-\")\n",
    "    bp = sns.boxplot(data=rnn_df, palette=pal, ax=third)\n",
    "    \n",
    "    fourth.set_title(\"RiccatiNet\", size=15)\n",
    "    fourth.set_yscale(\"log\")\n",
    "    fourth.set_ylim(ylim)\n",
    "    fourth.axhline(y=1, color=\"black\", linewidth=4, linestyle=\"-\")\n",
    "    bp = sns.boxplot(data=cyin_df, palette=pal, ax=fourth)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(f\"./timing_figures/timing-horizon-{horizon}.pdf\", transparent=True, bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1f95a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.779674053192139, 2.7271478176116943, 2.9460301399230957, 3.31054425239563, 3.135464906692505, 3.233924627304077, 3.737001657485962, 3.924687623977661, 4.285128116607666, 4.896482706069946], [0.3134334087371826, 0.3985917568206787, 0.34282493591308594, 0.2964498996734619, 0.3803989887237549, 0.37729883193969727, 0.301865816116333, 0.3622739315032959, 0.3727996349334717, 0.4537169933319092]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = h10\n",
    "horizon = \"10\"\n",
    "ylim = [2*10**-3,2*10**1]\n",
    "\n",
    "plot(data, ylim, horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c620e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.8941176470588236, 0.10196078431372549, 0.10980392156862745), (0.21568627450980393, 0.49411764705882355, 0.7215686274509804), (0.30196078431372547, 0.6862745098039216, 0.2901960784313726), (0.596078431372549, 0.3058823529411765, 0.6392156862745098), (1.0, 0.4980392156862745, 0.0), (1.0, 1.0, 0.2), (0.6509803921568628, 0.33725490196078434, 0.1568627450980392), (0.9686274509803922, 0.5058823529411764, 0.7490196078431373), (0.6, 0.6, 0.6)]\n"
     ]
    }
   ],
   "source": [
    "sns.set_theme(style=\"white\")\n",
    "pal = sns.color_palette(\"Set1\")\n",
    "print(pal)\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "mpl.rcParams['figure.dpi'] = 300\n",
    "\n",
    "def plot2(data, ylim, horizon=\"10\", inference_only=True):\n",
    "#     method_names = [\"Training\", \"Inference\"]\n",
    "    method_names = [\"KCPO\", \"RNN\", \"ReflexNet\", \"RiccatiNet\"]\n",
    "\n",
    "    results = []\n",
    "    for type_ in [\"koopman\", \"rnn\", \"reflex\", \"cyin\"]:\n",
    "        results.append(data[type_][\"train\"])\n",
    "    print(results)\n",
    "    train_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "    \n",
    "    results = []\n",
    "    for type_ in [\"koopman\", \"rnn\", \"reflex\", \"cyin\"]:\n",
    "        results.append(data[type_][\"infer\"])\n",
    "    print(results)\n",
    "    infer_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "\n",
    "    # Create the figure\n",
    "    if inference_only:\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(2, 3))\n",
    "\n",
    "        ax.set_title(\"Inference Time\", size=8)\n",
    "        ax.set_ylabel(\"Seconds\", size=8)\n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.set_ylim(ylim)\n",
    "        ax.axhline(y=1, color=\"black\", linewidth=2, linestyle=\"--\")\n",
    "        plt.xticks(rotation=45, ha=\"right\")\n",
    "        bp = sns.barplot(data=infer_df, palette=pal, ax=ax)\n",
    "        plt.tick_params(axis='y', labelsize=6)\n",
    "        plt.tick_params(axis='x', labelsize=8)\n",
    "    else:\n",
    "        fig, (first, second) = plt.subplots(1, 2, figsize=(8, 7), dpi=300)\n",
    "\n",
    "        first.set_title(\"Train\", size=15)\n",
    "        first.set_yscale(\"log\")\n",
    "        first.set_ylim(ylim)\n",
    "        first.set_ylabel(\"Seconds\", size=15)\n",
    "        first.axhline(y=1, color=\"black\", linewidth=4, linestyle=\"-\")\n",
    "        bp = sns.barplot(data=train_df, palette=pal, ax=first)\n",
    "\n",
    "        second.set_title(\"Inference\", size=15)\n",
    "        second.set_yscale(\"log\")\n",
    "        second.set_ylim(ylim)\n",
    "        second.axhline(y=1, color=\"black\", linewidth=4, linestyle=\"-\")\n",
    "        bp = sns.barplot(data=infer_df, palette=pal, ax=second)\n",
    "        second.get_yaxis().set_visible(False)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(f\"./timing_figures/timing-horizon-{horizon}.pdf\", transparent=True, bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9c81949",
   "metadata": {},
   "outputs": [],
   "source": [
    "pal = sns.color_palette(\"Set1\")\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "mpl.rcParams['figure.dpi'] = 300\n",
    "\n",
    "def plot2(data, ylim, horizon=\"10\", inference_only=True):\n",
    "#     method_names = [\"Training\", \"Inference\"]\n",
    "    method_names = [\"KCPO\", \"RNN\", \"ReflexNet\", \"RiccatiNet\"]\n",
    "\n",
    "    results = []\n",
    "    for type_ in [\"koopman\", \"rnn\", \"reflex\", \"cyin\"]:\n",
    "        results.append(data[type_][\"train\"])\n",
    "    print(results)\n",
    "    train_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "    \n",
    "    results = []\n",
    "    for type_ in [\"koopman\", \"rnn\", \"reflex\", \"cyin\"]:\n",
    "        results.append(data[type_][\"infer\"])\n",
    "    print(results)\n",
    "    infer_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "\n",
    "    # Create the figure\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 17))\n",
    "\n",
    "    ax.set_title(\"Inference Time\", size=50)\n",
    "    ax.set_ylabel(\"Seconds\", size=40)\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.axhline(y=1, color=\"black\", linewidth=2, linestyle=\"--\")\n",
    "    plt.xticks(rotation=45, ha=\"right\")\n",
    "    bp = sns.barplot(data=infer_df, palette=pal, ax=ax)\n",
    "    plt.tick_params(axis='y', labelsize=30)\n",
    "    plt.tick_params(axis='x', labelsize=40)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(f\"./timing_figures/timing-horizon-{horizon}.pdf\", transparent=True, bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99d8e6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.offline as pyo\n",
    "\n",
    "def plotly_plot2(data, ylim, horizon=\"10\", inference_only=True):\n",
    "    method_names = [\"KCPO\", \"RNN\", \"ReflexNet\", \"RiccatiNet\"]\n",
    "    colors = px.colors.qualitative.Set1  # Using Set1 color scheme from Seaborn\n",
    "\n",
    "    results = []\n",
    "    for type_ in [\"koopman\", \"rnn\", \"reflex\", \"cyin\"]:\n",
    "        results.append(data[type_][\"train\"])\n",
    "    print(results)\n",
    "    train_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "    \n",
    "    results = []\n",
    "    for type_ in [\"koopman\", \"rnn\", \"reflex\", \"cyin\"]:\n",
    "        results.append(data[type_][\"infer\"])\n",
    "    print(results)\n",
    "    infer_df = pd.DataFrame(np.array(results).T, columns=method_names)\n",
    "\n",
    "    # Create the figure\n",
    "    fig = go.Figure()\n",
    "\n",
    "    # Add bars to the figure\n",
    "    for i, method in enumerate(method_names):\n",
    "        fig.add_trace(go.Bar(x=[method], \n",
    "                             y=[infer_df[method].mean()] if inference_only else [train_df[method].mean()],\n",
    "                             name=method, \n",
    "                             marker_color=colors[i], showlegend=False))\n",
    "\n",
    "    # Add line to the figure\n",
    "    fig.add_shape(type=\"line\",\n",
    "                  x0=-0.5, y0=1, x1=3.5, y1=1,\n",
    "                  line=dict(color=\"Black\",width=2, dash=\"dash\")\n",
    "    )\n",
    "\n",
    "    # Update layout\n",
    "    fig.update_layout(\n",
    "        title_text=\"Inference Time\" if inference_,\n",
    "        title_x=0.5,\n",
    "        title_font=dict(size=50),\n",
    "        yaxis_type=\"log\",\n",
    "        yaxis_range=[np.log10(ylim[0]), np.log10(ylim[1])],\n",
    "        yaxis_title=\"Seconds\",\n",
    "        yaxis_title_font=dict(size=40),\n",
    "        yaxis_tickfont_size=30,\n",
    "        xaxis_tickfont_size=40,\n",
    "        autosize=False,\n",
    "        width=500,\n",
    "        height=1200,\n",
    "        bargap=0.1, # gap between bars of adjacent location coordinates\n",
    "        bargroupgap=0.1 # gap between bars of the same location coordinate\n",
    "    )\n",
    "\n",
    "    # Show figure\n",
    "    fig.show()\n",
    "    \n",
    "    div_str = pyo.plot(fig, output_type=\"div\", include_plotlyjs=False)\n",
    "    print(div_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66b2e565",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
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        "plotlyServerURL": "https://plot.ly"
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    }
   ],
   "source": [
    "data = h10\n",
    "horizon = \"10\"\n",
    "# ylim = [2*10**-4,2*10**1]\n",
    "ylim = [10**-4,10**1]\n",
    "\n",
    "# plot2(data, ylim, horizon)\n",
    "plotly_plot2(data, ylim, horizon, inference_only=True)"
   ]
  }
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