{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.optim as optim\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.style.use('seaborn-colorblind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_edge_pair(x):\n",
    "    plt.plot([x[0], x[2]], [x[1], x[3]])\n",
    "    plt.plot([x[4], x[6]], [x[5], x[7]])\n",
    "    \n",
    "    \n",
    "def are_edge_pairs_crossed(p):\n",
    "    '''\n",
    "    p - positions of n pairs edges in a [n,8] pytorch tensor, \n",
    "        where the postions of 8 nodes come in [ax, ay, bx, by, \n",
    "        cx, cy, dy, dy] for the edge pair a-b and c-d.\n",
    "    \n",
    "    return - an 1D tensor of boolean values, \n",
    "             where True means two edges cross each other. \n",
    "    '''\n",
    "    p1, p2, p3, p4 = p[:,:2], p[:,2:4], p[:,4:6], p[:,6:]\n",
    "    a = p2 - p1\n",
    "    b = p3 - p4\n",
    "    c = p1 - p3\n",
    "    ax, ay = a[:,0], a[:,1]\n",
    "    bx, by = b[:,0], b[:,1]\n",
    "    cx, cy = c[:,0], c[:,1]\n",
    "    \n",
    "    denom = ay*bx - ax*by\n",
    "    numer_alpha = by*cx-bx*cy\n",
    "    numer_beta = ax*cy-ay*cx\n",
    "    alpha = numer_alpha / denom\n",
    "    beta = numer_beta / denom\n",
    "    return torch.logical_and(\n",
    "        torch.logical_and(0<alpha, alpha<1),\n",
    "        torch.logical_and(0<beta, beta<1),\n",
    "    )\n",
    "\n",
    "\n",
    "\n",
    "class EdgePairDataset():\n",
    "    def __init__(self, n=10000):\n",
    "        super().__init__()\n",
    "        self.n = n\n",
    "        self.data = torch.randn(n, 8)\n",
    "        self.label = are_edge_pairs_crossed(self.data)\n",
    "        \n",
    "    def __len__(self):\n",
    "        return self.n\n",
    "    \n",
    "    def __getitem__(self, i):\n",
    "        return self.data[i], self.label[i]\n",
    "    \n",
    "    \n",
    "    \n",
    "class CrossingDetector(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layer_dims = [8,96,256,96,1]\n",
    "        self.layers = []\n",
    "        for i, (in_dim, out_dim) in enumerate(zip(self.layer_dims[:-1], self.layer_dims[1:])):\n",
    "            self.layers.append(nn.Linear(in_dim, out_dim))\n",
    "            if i < len(self.layer_dims)-2:\n",
    "                self.layers.append(nn.LeakyReLU())\n",
    "                self.layers.append(nn.LayerNorm(out_dim))\n",
    "            else:\n",
    "                self.layers.append(nn.Sigmoid())\n",
    "        self.main = nn.Sequential(*self.layers)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.main(x)\n",
    "    \n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = EdgePairDataset(n=int(1e6))\n",
    "dataloader = DataLoader(dataset, batch_size=1024, shuffle=True)\n",
    "\n",
    "device = 'cuda'\n",
    "model = CrossingDetector().to(device)\n",
    "bce = nn.BCELoss()\n",
    "optmizer = optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "loss_curve = []\n",
    "for epoch in tqdm(range(10)):\n",
    "    for edge_pairs, targets in dataloader:\n",
    "        edge_pairs, targets = edge_pairs.to(device), targets.to(device)\n",
    "        pred = model(edge_pairs)\n",
    "        loss = bce(pred, targets.float().view(-1,1))\n",
    "\n",
    "        optmizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optmizer.step()\n",
    "        \n",
    "    loss_curve.append(loss.item())\n",
    "    if epoch % 10 == 9:\n",
    "        plt.plot(loss_curve)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acurracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda'\n",
    "# model = CrossingDetector().to(device)\n",
    "# model.load_state_dict(torch.load('neural-crossing-detector.pth', map_location=device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loader = DataLoader(EdgePairDataset(n=int(1e4)), batch_size=1024, shuffle=True)\n",
    "\n",
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for edge_pairs, targets in tqdm(test_loader):\n",
    "        edge_pairs, targets = edge_pairs.to(device), targets.to(device)\n",
    "        pred = model(edge_pairs)\n",
    "        correct += ((pred>0.5) == targets.view(-1,1)).sum().item()\n",
    "        total += len(targets)\n",
    "        \n",
    "#         ## vis\n",
    "#         draw_edge_pair(edge_pairs[0])\n",
    "#         plt.title(f'{pred[0].item()}/{targets[0].item()}')\n",
    "#         plt.xlim([0,1])\n",
    "#         plt.ylim([0,1])\n",
    "#         plt.show()\n",
    "print(f'{correct}/{total} {correct/total}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.save(model.state_dict(), 'neural-crossing-detector.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test: Optimziation on a single pair of crossed edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import vis\n",
    "import random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gd2 import GD2\n",
    "import networkx as nx\n",
    "import itertools\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import criteria as C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cpu'\n",
    "model = CrossingDetector().to(device)\n",
    "# model.load_state_dict(torch.load('neural-crossing-detector.pth', map_location=device))\n",
    "\n",
    "\n",
    "\n",
    "# G = nx.balanced_tree(2,7)\n",
    "G = nx.grid_2d_graph(20,40)\n",
    "# G = nx.hypercube_graph(3)\n",
    "# G = nx.cycle_graph(10)\n",
    "gd = GD2(G)\n",
    "\n",
    "## filter out incident edge pairs\n",
    "edge_pairs = [\n",
    "    [gd.k2i[e1[0]], gd.k2i[e1[1]], gd.k2i[e2[0]], gd.k2i[e2[1]]] \n",
    "    for e1,e2 in itertools.product(G.edges, G.edges) \n",
    "    if e1<e2 and len(set(e1).intersection(set(e2)))==0\n",
    "]\n",
    "\n",
    "bce_pos = nn.BCELoss(reduction='sum')\n",
    "bce_nn = nn.BCELoss()\n",
    "\n",
    "optmizer_nn = optim.SGD(model.parameters(), lr=0.01, momentum=0.1)\n",
    "optmizer_pos = optim.SGD([gd.pos], lr=0.1, momentum=0.9)\n",
    "# optmizer_nn = optim.RMSprop(model.parameters(), lr=0.001)\n",
    "# optmizer_pos = optim.RMSprop([gd.pos], lr=0.1)\n",
    "\n",
    "dataloader = DataLoader(edge_pairs, batch_size=1024, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1180586"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(edge_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ef05b0b5b8244b7adaf62bb86c7c108",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-874923c7c8ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m         )\n\u001b[1;32m     26\u001b[0m         \u001b[0moptmizer_pos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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---> 27\u001b[0;31m         \u001b[0mloss_pos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[0m\u001b[1;32m     28\u001b[0m         \u001b[0moptmizer_pos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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     29\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/env3/lib/python3.6/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\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    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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~/.virtualenvs/env3/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    147\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    148\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "loss_nn_curve, loss_pos_curve = [],[]\n",
    "\n",
    "\n",
    "#     sample = sum(random.sample(edge_pairs, 16), [])\n",
    "#     sample = edge_pairs\n",
    "    \n",
    "for epoch in tqdm(range(100)):\n",
    "    for i,sample in tqdm(enumerate(dataloader)):\n",
    "        sample = torch.stack(sample, 1)\n",
    "        edge_pair_pos = gd.pos[sample].view(-1,8)\n",
    "        labels = are_edge_pairs_crossed(edge_pair_pos)\n",
    "\n",
    "        model.train()\n",
    "        preds = model(edge_pair_pos.detach().to(device)).view(-1)\n",
    "        loss_nn = bce_nn(preds, (labels.float()*0.8).to(device))\n",
    "        optmizer_nn.zero_grad()\n",
    "        loss_nn.backward()\n",
    "        optmizer_nn.step()\n",
    "\n",
    "        model.eval()\n",
    "        preds = model(edge_pair_pos.to(device)).view(-1)\n",
    "        loss_pos = (\n",
    "            bce_pos(preds, (labels.float()*0.1).to(device))\n",
    "            +0.01*C.vertex_resolution(gd.pos, sampleSize=64)[0]\n",
    "        )\n",
    "        optmizer_pos.zero_grad()\n",
    "        loss_pos.backward()\n",
    "        optmizer_pos.step()\n",
    "        if i<100:\n",
    "    #         loss_nn_curve.append(loss_nn.item())\n",
    "            loss_pos_curve.append(np.log(loss_pos.item()))\n",
    "\n",
    "\n",
    "    with torch.no_grad():\n",
    "        clear_output(wait=True)\n",
    "\n",
    "        plt.figure(figsize=[16,4])\n",
    "\n",
    "        plt.subplot(131)\n",
    "#             plt.plot(loss_nn_curve, label='loss NN')\n",
    "        plt.plot(loss_pos_curve, label='loss pos')\n",
    "        plt.legend()\n",
    "\n",
    "        plt.subplot(132)\n",
    "        plt.hist([\n",
    "            preds[labels].cpu().numpy(), \n",
    "            preds[~labels].cpu().numpy()], \n",
    "            bins=np.linspace(0,1,11), \n",
    "            histtype='barstacked'\n",
    "        )\n",
    "        plt.title(f'epoch: {epoch}')\n",
    "        plt.xlim([-0.1, 1.1])\n",
    "        \n",
    "        ax = plt.subplot(133)\n",
    "        pos = gd.pos.detach().numpy()\n",
    "        pos_G = {k:pos[gd.k2i[k]] for k in gd.G.nodes}\n",
    "        vis.draw_graph(gd.G, pos_G, ax)\n",
    "        plt.autoscale()\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C.vertex_resolution(gd.pos, sampleSize=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd.pos[sample[0:2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = are_edge_pairs_crossed(edge_pair_pos)\n",
    "preds = model(edge_pair_pos.cuda()).detach().cpu().view(-1)\n",
    "acc = (labels == (preds>0.5)).float().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# emin = edge_pair_pos.min(1, keepdim=True).values\n",
    "# emax = edge_pair_pos.max(1, keepdim=True).values\n"
   ]
  }
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