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

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2021-04-22 14:31:20 +02:00
{
"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"
]
},
{
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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')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}