229 lines
104 KiB
Plaintext
229 lines
104 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 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": "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": [
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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 Desktop is at: /Users/xuanyidong/Desktop\n",
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"> \u001b[0;32m<ipython-input-2-dec7d637caaa>\u001b[0m(89)\u001b[0;36mvisualize_syn\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;32m 87 \u001b[0;31m alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
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"\u001b[0m\u001b[0;32m 88 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\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",
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"\u001b[0m\u001b[0;32m---> 89 \u001b[0;31m draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
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"\u001b[0m\u001b[0;32m 90 \u001b[0;31m alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
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"\u001b[0m\u001b[0;32m 91 \u001b[0;31m draw_ax(cur_ax, test_times, test_preds, None, None,\n",
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"\u001b[0m\n",
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"ipdb> train_times\n",
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"[0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2.0, 2.1, 2.2, 2.3000000000000003, 2.4000000000000004, 2.5, 2.6, 2.7, 2.8000000000000003, 2.9000000000000004, 3.0, 3.1, 3.2, 3.3000000000000003, 3.4000000000000004, 3.5, 3.6, 3.7, 3.8000000000000003, 3.9000000000000004, 4.0, 4.1000000000000005, 4.2, 4.3, 4.4, 4.5, 4.6000000000000005, 4.7, 4.800000000000001, 4.9, 5.0, 5.1000000000000005, 5.2, 5.300000000000001, 5.4, 5.5, 5.6000000000000005, 5.7, 5.800000000000001, 5.9, 6.0, 6.1000000000000005, 6.2, 6.300000000000001, 6.4, 6.5, 6.6000000000000005, 6.7, 6.800000000000001, 6.9, 7.0, 7.1000000000000005, 7.2, 7.300000000000001, 7.4, 7.5, 7.6000000000000005, 7.7, 7.800000000000001, 7.9, 8.0, 8.1, 8.200000000000001, 8.3, 8.4, 8.5, 8.6, 8.700000000000001, 8.8, 8.9, 9.0, 9.1, 9.200000000000001, 9.3, 9.4, 9.5, 9.600000000000001, 9.700000000000001, 9.8, 9.9, 10.0, 10.100000000000001, 10.200000000000001, 10.3, 10.4, 10.5, 10.600000000000001, 10.700000000000001, 10.8, 10.9, 11.0, 11.100000000000001, 11.200000000000001, 11.3, 11.4, 11.5, 11.600000000000001, 11.700000000000001, 11.8, 11.9, 12.0, 12.100000000000001, 12.200000000000001, 12.3, 12.4, 12.5, 12.600000000000001, 12.700000000000001, 12.8, 12.9, 13.0, 13.100000000000001, 13.200000000000001, 13.3, 13.4, 13.5, 13.600000000000001, 13.700000000000001, 13.8, 13.9, 14.0, 14.100000000000001, 14.200000000000001, 14.3, 14.4, 14.5, 14.600000000000001, 14.700000000000001, 14.8, 14.9, 15.0, 15.100000000000001, 15.200000000000001, 15.3, 15.4, 15.5, 15.600000000000001, 15.700000000000001, 15.8, 15.9, 16.0, 16.1, 16.2, 16.3, 16.400000000000002, 16.5, 16.6, 16.7, 16.8, 16.900000000000002, 17.0, 17.1, 17.2, 17.3, 17.400000000000002, 17.5, 17.6, 17.7, 17.8, 17.900000000000002, 18.0, 18.1, 18.2, 18.3, 18.400000000000002, 18.5, 18.6, 18.7, 18.8, 18.900000000000002, 19.0, 19.1, 19.200000000000003, 19.3, 19.400000000000002, 19.5, 19.6, 19.700000000000003, 19.8, 19.900000000000002, 20.0, 20.1, 20.200000000000003, 20.3, 20.400000000000002, 20.5, 20.6, 20.700000000000003, 20.8, 20.900000000000002, 21.0, 21.1, 21.200000000000003, 21.3, 21.400000000000002, 21.5, 21.6, 21.700000000000003, 21.8, 21.900000000000002, 22.0, 22.1, 22.200000000000003, 22.3, 22.400000000000002, 22.5, 22.6, 22.700000000000003, 22.8, 22.900000000000002, 23.0, 23.1, 23.200000000000003, 23.3, 23.400000000000002, 23.5, 23.6, 23.700000000000003, 23.8, 23.900000000000002, 24.0, 24.1, 24.200000000000003, 24.3, 24.400000000000002, 24.5, 24.6, 24.700000000000003, 24.8, 24.900000000000002, 25.0, 25.1, 25.200000000000003, 25.3, 25.400000000000002, 25.5, 25.6, 25.700000000000003, 25.8, 25.900000000000002, 26.0, 26.1, 26.200000000000003, 26.3, 26.400000000000002, 26.5, 26.6, 26.700000000000003, 26.8, 26.900000000000002, 27.0, 27.1, 27.200000000000003, 27.3, 27.400000000000002, 27.5, 27.6, 27.700000000000003, 27.8, 27.900000000000002, 28.0, 28.1, 28.200000000000003, 28.3, 28.400000000000002, 28.5, 28.6, 28.700000000000003, 28.8, 28.900000000000002, 29.0, 29.1, 29.200000000000003, 29.3, 29.400000000000002, 29.5, 29.6, 29.700000000000003, 29.8, 29.900000000000002, 30.0, 30.1, 30.200000000000003, 30.3, 30.400000000000002, 30.5, 30.6, 30.700000000000003, 30.8, 30.900000000000002, 31.0, 31.1, 31.200000000000003, 31.3, 31.400000000000002, 31.5, 31.6, 31.700000000000003, 31.8, 31.900000000000002, 32.0, 32.1, 32.2, 32.300000000000004, 32.4, 32.5, 32.6, 32.7, 32.800000000000004, 32.9, 33.0, 33.1, 33.2, 33.300000000000004, 33.4, 33.5, 33.6, 33.7, 33.800000000000004, 33.9, 34.0, 34.1, 34.2, 34.300000000000004, 34.4, 34.5, 34.6, 34.7, 34.800000000000004, 34.9, 35.0, 35.1, 35.2, 35.300000000000004, 35.4, 35.5, 35.6, 35.7, 35.800000000000004, 35.9, 36.0, 36.1, 36.2, 36.300000000000004, 36.4, 36.5, 36.6, 36.7, 36.800000000000004, 36.9, 37.0, 37.1, 37.2, 37.300000000000004, 37.4, 37.5, 37.6, 37.7, 37.800000000000004, 37.9, 38.0, 38.1, 38.2, 38.300000000000004, 38.400000000000006, 38.5, 38.6, 38.7, 38.800000000000004, 38.900000000000006, 39.0, 39.1, 39.2, 39.300000000000004, 39.400000000000006, 39.5, 39.6, 39.7, 39.800000000000004, 39.900000000000006, 40.0, 40.1, 40.2, 40.300000000000004, 40.400000000000006, 40.5, 40.6, 40.7, 40.800000000000004, 40.900000000000006, 41.0, 41.1, 41.2, 41.300000000000004, 41.400000000000006, 41.5, 41.6, 41.7, 41.800000000000004, 41.900000000000006, 42.0, 42.1, 42.2, 42.300000000000004, 42.400000000000006, 42.5, 42.6, 42.7, 42.800000000000004, 42.900000000000006, 43.0, 43.1, 43.2, 43.300000000000004, 43.400000000000006, 43.5, 43.6, 43.7, 43.800000000000004, 43.900000000000006, 44.0, 44.1, 44.2, 44.300000000000004, 44.400000000000006, 44.5, 44.6, 44.7, 44.800000000000004, 44.900000000000006, 45.0, 45.1, 45.2, 45.300000000000004, 45.400000000000006, 45.5, 45.6, 45.7, 45.800000000000004, 45.900000000000006, 46.0, 46.1, 46.2, 46.300000000000004, 46.400000000000006, 46.5, 46.6, 46.7, 46.800000000000004, 46.900000000000006, 47.0, 47.1, 47.2, 47.300000000000004, 47.400000000000006, 47.5, 47.6, 47.7, 47.800000000000004, 47.900000000000006, 48.0, 48.1, 48.2, 48.300000000000004, 48.400000000000006, 48.5, 48.6, 48.7, 48.800000000000004, 48.900000000000006, 49.0, 49.1, 49.2, 49.300000000000004, 49.400000000000006, 49.5, 49.6, 49.7, 49.800000000000004, 49.900000000000006, 50.0, 50.1, 50.2, 50.300000000000004, 50.400000000000006, 50.5, 50.6, 50.7, 50.800000000000004, 50.900000000000006, 51.0, 51.1, 51.2, 51.300000000000004, 51.400000000000006, 51.5, 51.6, 51.7, 51.800000000000004, 51.900000000000006, 52.0, 52.1, 52.2, 52.300000000000004, 52.400000000000006, 52.5, 52.6, 52.7, 52.800000000000004, 52.900000000000006, 53.0, 53.1, 53.2, 53.300000000000004, 53.400000000000006, 53.5, 53.6, 53.7, 53.800000000000004, 53.900000000000006, 54.0, 54.1, 54.2, 54.300000000000004, 54.400000000000006, 54.5, 54.6, 54.7, 54.800000000000004, 54.900000000000006, 55.0, 55.1, 55.2, 55.300000000000004, 55.400000000000006, 55.5, 55.6, 55.7, 55.800000000000004, 55.900000000000006, 56.0, 56.1, 56.2, 56.300000000000004, 56.400000000000006, 56.5, 56.6, 56.7, 56.800000000000004, 56.900000000000006, 57.0, 57.1, 57.2, 57.300000000000004, 57.400000000000006, 57.5, 57.6, 57.7, 57.800000000000004, 57.900000000000006, 58.0, 58.1, 58.2, 58.300000000000004, 58.400000000000006, 58.5, 58.6, 58.7, 58.800000000000004, 58.900000000000006, 59.0, 59.1, 59.2, 59.300000000000004, 59.400000000000006, 59.5, 59.6, 59.7, 59.800000000000004, 59.900000000000006, 60.0, 60.1, 60.2, 60.300000000000004, 60.400000000000006, 60.5, 60.6, 60.7, 60.800000000000004, 60.900000000000006, 61.0, 61.1, 61.2, 61.300000000000004, 61.400000000000006, 61.5, 61.6, 61.7, 61.800000000000004, 61.900000000000006, 62.0, 62.1, 62.2, 62.300000000000004, 62.400000000000006, 62.5, 62.6, 62.7, 62.800000000000004, 62.900000000000006, 63.0, 63.1, 63.2, 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172.10000000000002, 172.20000000000002, 172.3, 172.4, 172.5, 172.60000000000002, 172.70000000000002, 172.8, 172.9, 173.0, 173.10000000000002, 173.20000000000002, 173.3, 173.4, 173.5, 173.60000000000002, 173.70000000000002, 173.8, 173.9, 174.0, 174.10000000000002, 174.20000000000002, 174.3, 174.4, 174.5, 174.60000000000002, 174.70000000000002, 174.8, 174.9, 175.0, 175.10000000000002, 175.20000000000002, 175.3, 175.4, 175.5, 175.60000000000002, 175.70000000000002, 175.8, 175.9, 176.0, 176.10000000000002, 176.20000000000002, 176.3, 176.4, 176.5, 176.60000000000002, 176.70000000000002, 176.8, 176.9, 177.0, 177.10000000000002, 177.20000000000002, 177.3, 177.4, 177.5, 177.60000000000002, 177.70000000000002, 177.8, 177.9, 178.0, 178.10000000000002, 178.20000000000002, 178.3, 178.4, 178.5, 178.60000000000002, 178.70000000000002, 178.8, 178.9, 179.0, 179.10000000000002, 179.20000000000002, 179.3, 179.4, 179.5, 179.60000000000002, 179.70000000000002, 179.8, 179.9, 180.0, 180.10000000000002, 180.20000000000002, 180.3, 180.4, 180.5, 180.60000000000002, 180.70000000000002, 180.8, 180.9, 181.0, 181.10000000000002, 181.20000000000002, 181.3, 181.4, 181.5, 181.60000000000002, 181.70000000000002, 181.8, 181.9, 182.0, 182.10000000000002, 182.20000000000002, 182.3, 182.4, 182.5, 182.60000000000002, 182.70000000000002, 182.8, 182.9, 183.0, 183.10000000000002, 183.20000000000002, 183.3, 183.4, 183.5, 183.60000000000002, 183.70000000000002, 183.8, 183.9, 184.0, 184.10000000000002, 184.20000000000002, 184.3, 184.4, 184.5, 184.60000000000002, 184.70000000000002, 184.8, 184.9, 185.0, 185.10000000000002, 185.20000000000002, 185.3, 185.4, 185.5, 185.60000000000002, 185.70000000000002, 185.8, 185.9, 186.0, 186.10000000000002, 186.20000000000002, 186.3, 186.4, 186.5, 186.60000000000002, 186.70000000000002, 186.8, 186.9, 187.0, 187.10000000000002, 187.20000000000002, 187.3, 187.4, 187.5, 187.60000000000002, 187.70000000000002, 187.8, 187.9, 188.0, 188.10000000000002, 188.20000000000002, 188.3, 188.4]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"ipdb> train_preds\n",
|
|
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]
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},
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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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"ipdb> train_ys\n",
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|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"--KeyboardInterrupt--\n",
|
|
"\n",
|
|
"KeyboardInterrupt: Interrupted by user\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
|
|
}
|