264 lines
103 KiB
Plaintext
264 lines
103 KiB
Plaintext
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{
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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 SynAdaptiveEnv\n",
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"from xlayers.super_core import SuperSequential, SuperMLPv1"
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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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"SynAdaptiveEnv(100/100 elements\n",
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"amplitude=QuadraticFunction(y = 4.8680419921875 * x^2 + 3.565875291824341 * x + 0.9999021291732788),\n",
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")period_phase_shift=QuadraticFunction(y = 0.00021915265824645758 * x^2 + 0.9999573826789856 * x + -1.2333193808444776e-05)\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 2880x2880 with 1 Axes>"
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]
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},
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"metadata": {
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"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_q_func():\n",
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" # save_dir = (save_path / '..').resolve()\n",
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" # save_dir.mkdir(parents=True, exist_ok=True)\n",
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" \n",
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" dpi, width, height = 10, 400, 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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" dataset = SynAdaptiveEnv()\n",
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" print(dataset)\n",
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" xaxis, yaxis = [], []\n",
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" for idx, position, value in dataset:\n",
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" xaxis.append(position)\n",
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" yaxis.append(value)\n",
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" \n",
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" def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
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" alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
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" if legend is not None:\n",
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" cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
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" \n",
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" if not plot_only:\n",
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" cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(10)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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" cur_ax = fig.add_subplot(1, 1, 1)\n",
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" cur_ax.plot(xaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.6, label=None)\n",
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"\n",
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" # fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
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" # plt.close(\"all\")\n",
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" # plt.show()\n",
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"visualize_q_func()"
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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": "supreme-basis",
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"metadata": {},
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"outputs": [],
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"source": [
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"def optimize_fn(xs, ys, test_sets):\n",
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" xs = torch.FloatTensor(xs).view(-1, 1)\n",
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" ys = torch.FloatTensor(ys).view(-1, 1)\n",
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" \n",
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" model = SuperSequential(\n",
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" SuperMLPv1(1, 10, 20, torch.nn.ReLU),\n",
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" SuperMLPv1(20, 10, 1, torch.nn.ReLU)\n",
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" )\n",
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" optimizer = torch.optim.Adam(\n",
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" model.parameters(),\n",
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" lr=0.01, weight_decay=1e-4, amsgrad=True\n",
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" )\n",
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" for _iter in range(100):\n",
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" preds = model(ys)\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss = torch.nn.functional.mse_loss(preds, ys)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" with torch.no_grad():\n",
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" answers = []\n",
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" for test_set in test_sets:\n",
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" test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
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" preds = model(test_set).view(-1).numpy()\n",
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" answers.append(preds.tolist())\n",
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" return answers\n",
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"\n",
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"def f(x):\n",
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" return np.cos( 0.5 * x + x * x)\n",
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"\n",
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"def get_data(mode):\n",
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" dataset = SynAdaptiveEnv(mode=mode)\n",
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" times, xs, ys = [], [], []\n",
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" for i, (_, t, x) in enumerate(dataset):\n",
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" times.append(t)\n",
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" xs.append(x)\n",
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" dataset.set_transform(f)\n",
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" for i, (_, _, y) in enumerate(dataset):\n",
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" ys.append(y)\n",
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" return times, xs, ys\n",
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"\n",
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"def visualize_syn(save_path):\n",
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" save_dir = (save_path / '..').resolve()\n",
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" save_dir.mkdir(parents=True, exist_ok=True)\n",
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" \n",
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" dpi, width, height = 40, 2000, 900\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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" times, xs, ys = get_data(None)\n",
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" \n",
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" def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
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" alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
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" if legend is not None:\n",
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" cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
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" cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
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" if not plot_only:\n",
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" cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(10)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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" cur_ax = fig.add_subplot(2, 1, 1)\n",
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" draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
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"\n",
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" cur_ax = fig.add_subplot(2, 1, 2)\n",
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" draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
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" \n",
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" train_times, train_xs, train_ys = get_data(\"train\")\n",
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" draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
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" \n",
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" valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
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" draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
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" \n",
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" test_times, test_xs, test_ys = get_data(\"test\")\n",
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" draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
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" \n",
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" # optimize MLP models\n",
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"# [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
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"# draw_ax(cur_ax, train_times, train_preds, None, None,\n",
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"# alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
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"# import pdb; pdb.set_trace()\n",
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"# draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
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"# alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
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"# draw_ax(cur_ax, test_times, test_preds, None, None,\n",
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"# alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
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"\n",
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" plt.legend(loc=1, fontsize=LegendFontsize)\n",
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"\n",
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" fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
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" plt.close(\"all\")\n",
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" # plt.show()"
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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": 3,
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"id": "shared-envelope",
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"metadata": {},
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"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
|
||
|
|
}
|