{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "filled-multiple", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The root path: /Users/xuanyidong\n", "The library path: /Users/xuanyidong/lib\n" ] }, { "ename": "AssertionError", "evalue": "/Users/xuanyidong/lib does not exist", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/Desktop/AutoDL-Projects\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The root path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The library path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlib_dir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{:} does not exist\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAssertionError\u001b[0m: /Users/xuanyidong/lib does not exist" ] } ], "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 ConstantGenerator, SinGenerator, SyntheticDEnv\n", "from datasets import DynamicQuadraticFunc\n", "from datasets.synthetic_example import create_example_v1" ] }, { "cell_type": "code", "execution_count": null, "id": "detected-second", "metadata": {}, "outputs": [], "source": [ "def visualize_env():\n", " \n", " dpi, width, height = 10, 800, 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", " dynamic_env, function = create_example_v1(100, num_per_task=250)\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": null, "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": null, "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 }