261 lines
313 KiB
Plaintext
261 lines
313 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "filled-multiple",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The root path: /Users/xuanyidong/Desktop/AutoDL-Projects\n",
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"The library path: /Users/xuanyidong/Desktop/AutoDL-Projects/lib\n"
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]
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}
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],
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"source": [
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"import os, sys\n",
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"import torch\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"from matplotlib import cm\n",
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"# matplotlib.use(\"agg\")\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.ticker as ticker\n",
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"\n",
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"\n",
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"__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
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"root_dir = (Path(__file__).parent / \"..\").resolve()\n",
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"lib_dir = (root_dir / \"lib\").resolve()\n",
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"print(\"The root path: {:}\".format(root_dir))\n",
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"print(\"The library path: {:}\".format(lib_dir))\n",
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"assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
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"if str(lib_dir) not in sys.path:\n",
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" sys.path.insert(0, str(lib_dir))\n",
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"\n",
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"from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
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"from datasets import QuadraticFunc"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "detected-second",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"SyntheticDEnv(20/20 elements, ndim=1, num_per_task=100)\n"
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]
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},
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{
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"data": {
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|
|
"text/plain": [
|
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"<Figure size 5760x2880 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
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|
},
|
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"output_type": "display_data"
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|
}
|
|
],
|
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"source": [
|
|
"def visualize_env():\n",
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" \n",
|
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" dpi, width, height = 10, 800, 400\n",
|
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" figsize = width / float(dpi), height / float(dpi)\n",
|
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" LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
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" \n",
|
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" fig = plt.figure(figsize=figsize)\n",
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" \n",
|
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" timestamps = 20\n",
|
|
" mean_generator = SinGenerator(num=timestamps)\n",
|
|
" std_generator = SinGenerator(num=timestamps, min_amplitude=0.5, max_amplitude=0.5)\n",
|
|
" std_generator.set_transform(lambda x: x + 1)\n",
|
|
" dynamic_env = SyntheticDEnv([mean_generator], [[std_generator]], num_per_task=100)\n",
|
|
" print(dynamic_env)\n",
|
|
" \n",
|
|
" xaxis, yaxis = [], []\n",
|
|
" for timestamp, dataset in dynamic_env:\n",
|
|
" num = dataset.shape[0]\n",
|
|
" xaxis.append(torch.zeros(num) + timestamp)\n",
|
|
" yaxis.append(dataset[:,0])\n",
|
|
" xaxis = torch.cat(xaxis).numpy()\n",
|
|
" yaxis = torch.cat(yaxis).numpy()\n",
|
|
"\n",
|
|
" cur_ax = fig.add_subplot(2, 1, 1)\n",
|
|
" cur_ax.scatter(xaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.6, label=None)\n",
|
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" cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
|
|
" cur_ax.set_ylabel(\"X\", 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, 2)\n",
|
|
" cur_ax.scatter(xaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.6, label=None)\n",
|
|
" cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
|
|
" cur_ax.set_ylabel(\"X\", 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",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"visualize_env()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"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": 4,
|
|
"id": "shared-envelope",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"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
|
|
}
|