272 lines
284 KiB
Plaintext
272 lines
284 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 DynamicQuadraticFunc"
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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 2 Axes>"
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|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
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},
|
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"output_type": "display_data"
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}
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],
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"source": [
|
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"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",
|
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" \n",
|
|
" function = DynamicQuadraticFunc()\n",
|
|
" function_param = dict()\n",
|
|
" function_param[0] = SinGenerator(num=timestamps, num_sin_phase=4, phase_shift=1.0, max_amplitude=1.0)\n",
|
|
" function_param[1] = ConstantGenerator(constant=0.9)\n",
|
|
" function_param[2] = SinGenerator(num=timestamps, num_sin_phase=5, phase_shift=0.4, max_amplitude=0.9)\n",
|
|
" function.set(function_param)\n",
|
|
" \n",
|
|
" timeaxis, xaxis, yaxis = [], [], []\n",
|
|
" for timestamp, dataset in dynamic_env:\n",
|
|
" num = dataset.shape[0]\n",
|
|
" timeaxis.append(torch.zeros(num) + timestamp)\n",
|
|
" xaxis.append(dataset[:,0])\n",
|
|
" # compute the ground truth\n",
|
|
" function.set_timestamp(timestamp)\n",
|
|
" yaxis.append(function(dataset[:,0]))\n",
|
|
" timeaxis = torch.cat(timeaxis).numpy()\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(timeaxis, xaxis, color=\"k\", linestyle=\"-\", alpha=0.9, 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",
|
|
" \n",
|
|
" cur_ax = fig.add_subplot(2, 1, 2)\n",
|
|
" cur_ax.scatter(timeaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.9, label=None)\n",
|
|
" cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
|
|
" cur_ax.set_ylabel(\"Y\", 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
|
|
}
|