autodl-projects/notebooks/TOT/synthetic-env.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "filled-multiple",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The root path: /Users/xuanyidong/Desktop/AutoDL-Projects\n",
      "The library path: /Users/xuanyidong/Desktop/AutoDL-Projects/lib\n"
     ]
    }
   ],
   "source": [
    "import os, sys\n",
    "import torch\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "# matplotlib.use(\"agg\")\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "\n",
    "__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
    "root_dir = (Path(__file__).parent / \"..\").resolve()\n",
    "lib_dir = (root_dir / \"lib\").resolve()\n",
    "print(\"The root path: {:}\".format(root_dir))\n",
    "print(\"The library path: {:}\".format(lib_dir))\n",
    "assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
    "if str(lib_dir) not in sys.path:\n",
    "    sys.path.insert(0, str(lib_dir))\n",
    "\n",
    "from datasets import SynAdaptiveEnv\n",
    "from xlayers.super_core import SuperSequential, SuperMLPv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "detected-second",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SynAdaptiveEnv(100/100 elements\n",
      "amplitude=QuadraticFunction(y = 4.8680419921875 * x^2 + 3.565875291824341 * x + 0.9999021291732788),\n",
      ")period_phase_shift=QuadraticFunction(y = 0.00021915265824645758 * x^2 + 0.9999573826789856 * x + -1.2333193808444776e-05)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_q_func():\n",
    "    # save_dir = (save_path / '..').resolve()\n",
    "    # save_dir.mkdir(parents=True, exist_ok=True)\n",
    "    \n",
    "    dpi, width, height = 10, 400, 400\n",
    "    figsize = width / float(dpi), height / float(dpi)\n",
    "    LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    \n",
    "    dataset = SynAdaptiveEnv()\n",
    "    print(dataset)\n",
    "    xaxis, yaxis = [], []\n",
    "    for idx, position, value in dataset:\n",
    "        xaxis.append(position)\n",
    "        yaxis.append(value)\n",
    "    \n",
    "    def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
    "                alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
    "        if legend is not None:\n",
    "            cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
    "        \n",
    "        if not plot_only:\n",
    "            cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
    "            cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
    "            for tick in cur_ax.xaxis.get_major_ticks():\n",
    "                tick.label.set_fontsize(LabelSize - font_gap)\n",
    "                tick.label.set_rotation(10)\n",
    "            for tick in cur_ax.yaxis.get_major_ticks():\n",
    "                tick.label.set_fontsize(LabelSize - font_gap)\n",
    "    \n",
    "    cur_ax = fig.add_subplot(1, 1, 1)\n",
    "    cur_ax.plot(xaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.6, label=None)\n",
    "\n",
    "    # fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
    "    # plt.close(\"all\")\n",
    "    # plt.show()\n",
    "visualize_q_func()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "supreme-basis",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_fn(xs, ys, test_sets):\n",
    "    xs = torch.FloatTensor(xs).view(-1, 1)\n",
    "    ys = torch.FloatTensor(ys).view(-1, 1)\n",
    "    \n",
    "    model = SuperSequential(\n",
    "        SuperMLPv1(1, 10, 20, torch.nn.ReLU),\n",
    "        SuperMLPv1(20, 10, 1, torch.nn.ReLU)\n",
    "    )\n",
    "    optimizer = torch.optim.Adam(\n",
    "        model.parameters(),\n",
    "        lr=0.01, weight_decay=1e-4, amsgrad=True\n",
    "    )\n",
    "    for _iter in range(100):\n",
    "        preds = model(ys)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = torch.nn.functional.mse_loss(preds, ys)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "    with torch.no_grad():\n",
    "        answers = []\n",
    "        for test_set in test_sets:\n",
    "            test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
    "            preds = model(test_set).view(-1).numpy()\n",
    "            answers.append(preds.tolist())\n",
    "    return answers\n",
    "\n",
    "def f(x):\n",
    "    return np.cos( 0.5 * x + x * x)\n",
    "\n",
    "def get_data(mode):\n",
    "    dataset = SynAdaptiveEnv(mode=mode)\n",
    "    times, xs, ys = [], [], []\n",
    "    for i, (_, t, x) in enumerate(dataset):\n",
    "        times.append(t)\n",
    "        xs.append(x)\n",
    "    dataset.set_transform(f)\n",
    "    for i, (_, _, y) in enumerate(dataset):\n",
    "        ys.append(y)\n",
    "    return times, xs, ys\n",
    "\n",
    "def visualize_syn(save_path):\n",
    "    save_dir = (save_path / '..').resolve()\n",
    "    save_dir.mkdir(parents=True, exist_ok=True)\n",
    "    \n",
    "    dpi, width, height = 40, 2000, 900\n",
    "    figsize = width / float(dpi), height / float(dpi)\n",
    "    LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    \n",
    "    times, xs, ys = get_data(None)\n",
    "    \n",
    "    def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
    "                alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
    "        if legend is not None:\n",
    "            cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
    "        cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
    "        if not plot_only:\n",
    "            cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
    "            cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
    "            for tick in cur_ax.xaxis.get_major_ticks():\n",
    "                tick.label.set_fontsize(LabelSize - font_gap)\n",
    "                tick.label.set_rotation(10)\n",
    "            for tick in cur_ax.yaxis.get_major_ticks():\n",
    "                tick.label.set_fontsize(LabelSize - font_gap)\n",
    "    \n",
    "    cur_ax = fig.add_subplot(2, 1, 1)\n",
    "    draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
    "\n",
    "    cur_ax = fig.add_subplot(2, 1, 2)\n",
    "    draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
    "    \n",
    "    train_times, train_xs, train_ys = get_data(\"train\")\n",
    "    draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
    "    \n",
    "    valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
    "    draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
    "    \n",
    "    test_times, test_xs, test_ys = get_data(\"test\")\n",
    "    draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
    "    \n",
    "    # optimize MLP models\n",
    "#     [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
    "#     draw_ax(cur_ax, train_times, train_preds, None, None,\n",
    "#             alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
    "#     import pdb; pdb.set_trace()\n",
    "#     draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
    "#             alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
    "#     draw_ax(cur_ax, test_times, test_preds, None, None,\n",
    "#             alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
    "\n",
    "    plt.legend(loc=1, fontsize=LegendFontsize)\n",
    "\n",
    "    fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
    "    plt.close(\"all\")\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "shared-envelope",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Desktop is at: /Users/xuanyidong/Desktop\n"
     ]
    }
   ],
   "source": [
    "# Visualization\n",
    "# home_dir = Path.home()\n",
    "# desktop_dir = home_dir / 'Desktop'\n",
    "# print('The Desktop is at: {:}'.format(desktop_dir))\n",
    "# visualize_syn(desktop_dir / 'tot-synthetic-v0.pdf')"
   ]
  }
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