demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:18:34.990642Z",
     "start_time": "2020-09-18T11:17:49.315200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The nb_black extension is already loaded. To reload it, use:\n",
      "  %reload_ext nb_black\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel_launcher.py:107: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, max=2000.0), HTML(value='')))"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 166;\n",
       "                var nbb_unformatted_code = \"%matplotlib inline\\n%load_ext nb_black\\nimport matplotlib.pyplot as plt\\n\\nimport pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport copy\\n\\nimport torch.nn as nn\\nimport torch\\n\\nfrom sklearn.model_selection import train_test_split\\n\\nfrom torch.utils.data import DataLoader, TensorDataset\\nfrom tqdm import tqdm_notebook\\n\\n\\nimport rpy2.robjects as robjects\\nimport rpy2.robjects.numpy2ri\\n\\nrobjects.numpy2ri.activate()\\nfrom rpy2.robjects.packages import importr\\n\\nbase = importr(\\\"base\\\")\\nrvinecop = importr(\\\"rvinecopulib\\\")\\neecop = importr(\\\"eecop\\\")\\nstats = importr(\\\"stats\\\")\\n\\nlower_tau = 0.025\\nupper_tau = 0.975\\n\\nfrom sklearn.datasets import make_moons\\n\\n\\ndef circle(n):\\n    t = torch.rand(n, 1) * 3.14159 * 2\\n    x = torch.cat(\\n        (torch.cat((t.cos(), t.sin()), -1) * 2, torch.cat((t.cos(), t.sin()), -1) * 5)\\n    )\\n    y = torch.cat((torch.zeros(500, 1), torch.ones(500, 1)))\\n    return x, y\\n\\n\\ndef moons(n):\\n    x, y = make_moons(1000, noise=0.1)\\n    return torch.Tensor(x), torch.Tensor(y).view(-1, 1)\\n\\n\\ndef simple_network(n_inputs=1, n_outputs=1, n_hiddens=100):\\n    return torch.nn.Sequential(\\n        torch.nn.Linear(n_inputs, n_hiddens),\\n        torch.nn.ReLU(),\\n        torch.nn.Linear(n_hiddens, n_hiddens),\\n        torch.nn.ReLU(),\\n        torch.nn.Linear(n_hiddens, n_hiddens),\\n        torch.nn.ReLU(),\\n        torch.nn.Linear(n_hiddens, 1),\\n    )\\n\\n\\ndef generate_data(n=1024):\\n    sep = 1\\n    x = torch.zeros(n // 2, 1).uniform_(0, 0.5)\\n    x = torch.cat((x, torch.zeros(n // 2, 1).uniform_(0.5 + sep, 1 + sep)), 0)\\n    # noise = torch.zeros(n, 1).normal_(0, 0.3)\\n    m = torch.distributions.Exponential(torch.tensor([3.0]))\\n    noise = m.rsample((n,))\\n    y = (2 * 3.1416 * x).sin() + noise  # ((2 - x.abs()) / 2).sqrt() * noise\\n    x_test = torch.linspace(-0.5, 2.5, 100).view(-1, 1)\\n    return x, y, x_test\\n\\n\\ndef generate_data2(n=1024):\\n    f_c = 5\\n    A_m = 1\\n    A_c = 1\\n    f_m = 1\\n    modulation_index = 1\\n\\n    x1 = torch.linspace(0, 0.3, n // 3).view(-1, 1)\\n    x2 = torch.linspace(0.7, 1, n // 3).view(-1, 1)\\n    x3 = torch.linspace(0.4, 0.5, n // 100).view(-1, 1)\\n    x = torch.cat((x1, x3, x2))\\n\\n    carrier = (2 * np.pi * f_c * x).cos() * A_c\\n    modulator = (2 * np.pi * f_m * x).cos() * A_m\\n    signal = (\\n        A_c\\n        * (1 + modulation_index * (2 * np.pi * f_m * x).cos())\\n        * (2 * np.pi * f_c * x).cos()\\n    )\\n    # noise = torch.zeros(x.shape[0], 1).normal_(0, 0.3)\\n    noise = (3 * x) * torch.zeros(x.shape[0], 1).normal_(0, 0.3)\\n    y = signal + noise\\n    x_test = torch.linspace(-0.25, 1.25, 100).view(-1, 1)\\n\\n    return x, y, x_test\\n\\n\\ndef train_network(x, y, epochs=2000):\\n    net = simple_network(x.size(1), y.size(1))\\n    loss = torch.nn.MSELoss()\\n    optimizer = torch.optim.Adam(net.parameters())\\n    loader = DataLoader(TensorDataset(x, y), shuffle=True, batch_size=128)\\n\\n    for _ in tqdm_notebook(range(epochs)):\\n        for xi, yi in loader:\\n            optimizer.zero_grad()\\n            loss(net(xi), yi).backward()\\n            optimizer.step()\\n\\n    return net\\n\\n\\ndef boot_feat_target(net, x, y, test_x, vine_f_y, epochs=500, loss=\\\"MSE\\\"):\\n\\n    np.random.seed()\\n\\n    f_dim = net[:-2][-1]._parameters[\\\"weight\\\"].shape[0]\\n    bootstrap_data_new = np.asarray(rvinecop.rvine(x.shape[0], vine_f_y))\\n\\n    np.random.seed(1)\\n    torch.manual_seed(1)\\n    net_boot = copy.deepcopy(net[-2:])\\n    if loss == \\\"MSE\\\":\\n        loss = torch.nn.MSELoss()\\n    else:\\n        loss = torch.nn.BCEWithLogitsLoss()\\n\\n    optimizer_boot = torch.optim.Adam(net_boot.parameters())\\n    loader = DataLoader(\\n        TensorDataset(\\n            torch.Tensor(bootstrap_data_new[:, :f_dim]),\\n            torch.Tensor(bootstrap_data_new[:, f_dim]).view(-1, 1),\\n        ),\\n        shuffle=True,\\n        batch_size=bootstrap_data_new[:, :f_dim].shape[0],\\n    )  # batch training\\n\\n    for _ in range(epochs):\\n        for xi, yi in loader:\\n            optimizer_boot.zero_grad()\\n            loss(net_boot(xi), yi).backward()\\n            optimizer_boot.step()\\n\\n    pred_boot = net_boot(net[:-2](test_x)).detach().numpy().ravel()\\n\\n    return pred_boot\\n\\n\\n# train main network ########################################################\\n\\ntorch.manual_seed(1)\\n\\nx, y, test_x = generate_data2(2000)\\n\\nx_tr, x_val, y_tr, y_val = train_test_split(x, y, test_size=0.5, random_state=42)\\n\\n\\ntest_x = (test_x - x_tr.mean(0)) / x_tr.std(0)\\nx_val = (x_val - x_tr.mean(0)) / x_tr.std(0)\\nx_tr = (x_tr - x_tr.mean(0)) / x_tr.std(0)\\n\\nnet = train_network(x_tr, y_tr)\\n\\npred_med = net(test_x).detach().numpy().ravel()\";\n",
       "                var nbb_formatted_code = \"%matplotlib inline\\n%load_ext nb_black\\nimport matplotlib.pyplot as plt\\n\\nimport pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport copy\\n\\nimport torch.nn as nn\\nimport torch\\n\\nfrom sklearn.model_selection import train_test_split\\n\\nfrom torch.utils.data import DataLoader, TensorDataset\\nfrom tqdm import tqdm_notebook\\n\\n\\nimport rpy2.robjects as robjects\\nimport rpy2.robjects.numpy2ri\\n\\nrobjects.numpy2ri.activate()\\nfrom rpy2.robjects.packages import importr\\n\\nbase = importr(\\\"base\\\")\\nrvinecop = importr(\\\"rvinecopulib\\\")\\neecop = importr(\\\"eecop\\\")\\nstats = importr(\\\"stats\\\")\\n\\nlower_tau = 0.025\\nupper_tau = 0.975\\n\\nfrom sklearn.datasets import make_moons\\n\\n\\ndef circle(n):\\n    t = torch.rand(n, 1) * 3.14159 * 2\\n    x = torch.cat(\\n        (torch.cat((t.cos(), t.sin()), -1) * 2, torch.cat((t.cos(), t.sin()), -1) * 5)\\n    )\\n    y = torch.cat((torch.zeros(500, 1), torch.ones(500, 1)))\\n    return x, y\\n\\n\\ndef moons(n):\\n    x, y = make_moons(1000, noise=0.1)\\n    return torch.Tensor(x), torch.Tensor(y).view(-1, 1)\\n\\n\\ndef simple_network(n_inputs=1, n_outputs=1, n_hiddens=100):\\n    return torch.nn.Sequential(\\n        torch.nn.Linear(n_inputs, n_hiddens),\\n        torch.nn.ReLU(),\\n        torch.nn.Linear(n_hiddens, n_hiddens),\\n        torch.nn.ReLU(),\\n        torch.nn.Linear(n_hiddens, n_hiddens),\\n        torch.nn.ReLU(),\\n        torch.nn.Linear(n_hiddens, 1),\\n    )\\n\\n\\ndef generate_data(n=1024):\\n    sep = 1\\n    x = torch.zeros(n // 2, 1).uniform_(0, 0.5)\\n    x = torch.cat((x, torch.zeros(n // 2, 1).uniform_(0.5 + sep, 1 + sep)), 0)\\n    # noise = torch.zeros(n, 1).normal_(0, 0.3)\\n    m = torch.distributions.Exponential(torch.tensor([3.0]))\\n    noise = m.rsample((n,))\\n    y = (2 * 3.1416 * x).sin() + noise  # ((2 - x.abs()) / 2).sqrt() * noise\\n    x_test = torch.linspace(-0.5, 2.5, 100).view(-1, 1)\\n    return x, y, x_test\\n\\n\\ndef generate_data2(n=1024):\\n    f_c = 5\\n    A_m = 1\\n    A_c = 1\\n    f_m = 1\\n    modulation_index = 1\\n\\n    x1 = torch.linspace(0, 0.3, n // 3).view(-1, 1)\\n    x2 = torch.linspace(0.7, 1, n // 3).view(-1, 1)\\n    x3 = torch.linspace(0.4, 0.5, n // 100).view(-1, 1)\\n    x = torch.cat((x1, x3, x2))\\n\\n    carrier = (2 * np.pi * f_c * x).cos() * A_c\\n    modulator = (2 * np.pi * f_m * x).cos() * A_m\\n    signal = (\\n        A_c\\n        * (1 + modulation_index * (2 * np.pi * f_m * x).cos())\\n        * (2 * np.pi * f_c * x).cos()\\n    )\\n    # noise = torch.zeros(x.shape[0], 1).normal_(0, 0.3)\\n    noise = (3 * x) * torch.zeros(x.shape[0], 1).normal_(0, 0.3)\\n    y = signal + noise\\n    x_test = torch.linspace(-0.25, 1.25, 100).view(-1, 1)\\n\\n    return x, y, x_test\\n\\n\\ndef train_network(x, y, epochs=2000):\\n    net = simple_network(x.size(1), y.size(1))\\n    loss = torch.nn.MSELoss()\\n    optimizer = torch.optim.Adam(net.parameters())\\n    loader = DataLoader(TensorDataset(x, y), shuffle=True, batch_size=128)\\n\\n    for _ in tqdm_notebook(range(epochs)):\\n        for xi, yi in loader:\\n            optimizer.zero_grad()\\n            loss(net(xi), yi).backward()\\n            optimizer.step()\\n\\n    return net\\n\\n\\ndef boot_feat_target(net, x, y, test_x, vine_f_y, epochs=500, loss=\\\"MSE\\\"):\\n\\n    np.random.seed()\\n\\n    f_dim = net[:-2][-1]._parameters[\\\"weight\\\"].shape[0]\\n    bootstrap_data_new = np.asarray(rvinecop.rvine(x.shape[0], vine_f_y))\\n\\n    np.random.seed(1)\\n    torch.manual_seed(1)\\n    net_boot = copy.deepcopy(net[-2:])\\n    if loss == \\\"MSE\\\":\\n        loss = torch.nn.MSELoss()\\n    else:\\n        loss = torch.nn.BCEWithLogitsLoss()\\n\\n    optimizer_boot = torch.optim.Adam(net_boot.parameters())\\n    loader = DataLoader(\\n        TensorDataset(\\n            torch.Tensor(bootstrap_data_new[:, :f_dim]),\\n            torch.Tensor(bootstrap_data_new[:, f_dim]).view(-1, 1),\\n        ),\\n        shuffle=True,\\n        batch_size=bootstrap_data_new[:, :f_dim].shape[0],\\n    )  # batch training\\n\\n    for _ in range(epochs):\\n        for xi, yi in loader:\\n            optimizer_boot.zero_grad()\\n            loss(net_boot(xi), yi).backward()\\n            optimizer_boot.step()\\n\\n    pred_boot = net_boot(net[:-2](test_x)).detach().numpy().ravel()\\n\\n    return pred_boot\\n\\n\\n# train main network ########################################################\\n\\ntorch.manual_seed(1)\\n\\nx, y, test_x = generate_data2(2000)\\n\\nx_tr, x_val, y_tr, y_val = train_test_split(x, y, test_size=0.5, random_state=42)\\n\\n\\ntest_x = (test_x - x_tr.mean(0)) / x_tr.std(0)\\nx_val = (x_val - x_tr.mean(0)) / x_tr.std(0)\\nx_tr = (x_tr - x_tr.mean(0)) / x_tr.std(0)\\n\\nnet = train_network(x_tr, y_tr)\\n\\npred_med = net(test_x).detach().numpy().ravel()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
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       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import copy\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "\n",
    "import rpy2.robjects as robjects\n",
    "import rpy2.robjects.numpy2ri\n",
    "\n",
    "robjects.numpy2ri.activate()\n",
    "from rpy2.robjects.packages import importr\n",
    "\n",
    "base = importr(\"base\")\n",
    "rvinecop = importr(\"rvinecopulib\")\n",
    "eecop = importr(\"eecop\")\n",
    "stats = importr(\"stats\")\n",
    "\n",
    "lower_tau = 0.025\n",
    "upper_tau = 0.975\n",
    "\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "\n",
    "def moons(n):\n",
    "    x, y = make_moons(1000, noise=0.1)\n",
    "    return torch.Tensor(x), torch.Tensor(y).view(-1, 1)\n",
    "\n",
    "\n",
    "def simple_network(n_inputs=1, n_outputs=1, n_hiddens=100):\n",
    "    return torch.nn.Sequential(\n",
    "        torch.nn.Linear(n_inputs, n_hiddens),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Linear(n_hiddens, n_hiddens),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Linear(n_hiddens, n_hiddens),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Linear(n_hiddens, 1),\n",
    "    )\n",
    "\n",
    "def generate_data(n=1024):\n",
    "    f_c = 5\n",
    "    A_m = 1\n",
    "    A_c = 1\n",
    "    f_m = 1\n",
    "    modulation_index = 1\n",
    "\n",
    "    x1 = torch.linspace(0, 0.3, n // 3).view(-1, 1)\n",
    "    x2 = torch.linspace(0.7, 1, n // 3).view(-1, 1)\n",
    "    x3 = torch.linspace(0.4, 0.5, n // 100).view(-1, 1)\n",
    "    x = torch.cat((x1, x3, x2))\n",
    "\n",
    "    carrier = (2 * np.pi * f_c * x).cos() * A_c\n",
    "    modulator = (2 * np.pi * f_m * x).cos() * A_m\n",
    "    signal = (\n",
    "        A_c\n",
    "        * (1 + modulation_index * (2 * np.pi * f_m * x).cos())\n",
    "        * (2 * np.pi * f_c * x).cos()\n",
    "    )\n",
    "    # noise = torch.zeros(x.shape[0], 1).normal_(0, 0.3)\n",
    "    noise = (3 * x) * torch.zeros(x.shape[0], 1).normal_(0, 0.3)\n",
    "    y = signal + noise\n",
    "    x_test = torch.linspace(-0.25, 1.25, 100).view(-1, 1)\n",
    "\n",
    "    return x, y, x_test\n",
    "\n",
    "\n",
    "def train_network(x, y, epochs=2000):\n",
    "    net = simple_network(x.size(1), y.size(1))\n",
    "    loss = torch.nn.MSELoss()\n",
    "    optimizer = torch.optim.Adam(net.parameters())\n",
    "    loader = DataLoader(TensorDataset(x, y), shuffle=True, batch_size=128)\n",
    "\n",
    "    for _ in tqdm_notebook(range(epochs)):\n",
    "        for xi, yi in loader:\n",
    "            optimizer.zero_grad()\n",
    "            loss(net(xi), yi).backward()\n",
    "            optimizer.step()\n",
    "\n",
    "    return net\n",
    "\n",
    "\n",
    "def boot_feat_target(net, x, y, test_x, vine_f_y, epochs=500, loss=\"MSE\"):\n",
    "\n",
    "    np.random.seed()\n",
    "\n",
    "    f_dim = net[:-2][-1]._parameters[\"weight\"].shape[0]\n",
    "    bootstrap_data_new = np.asarray(rvinecop.rvine(x.shape[0], vine_f_y))\n",
    "\n",
    "    np.random.seed(1)\n",
    "    torch.manual_seed(1)\n",
    "    net_boot = copy.deepcopy(net[-2:])\n",
    "    if loss == \"MSE\":\n",
    "        loss = torch.nn.MSELoss()\n",
    "    else:\n",
    "        loss = torch.nn.BCEWithLogitsLoss()\n",
    "\n",
    "    optimizer_boot = torch.optim.Adam(net_boot.parameters())\n",
    "    loader = DataLoader(\n",
    "        TensorDataset(\n",
    "            torch.Tensor(bootstrap_data_new[:, :f_dim]),\n",
    "            torch.Tensor(bootstrap_data_new[:, f_dim]).view(-1, 1),\n",
    "        ),\n",
    "        shuffle=True,\n",
    "        batch_size=bootstrap_data_new[:, :f_dim].shape[0],\n",
    "    )  # batch training\n",
    "\n",
    "    for _ in range(epochs):\n",
    "        for xi, yi in loader:\n",
    "            optimizer_boot.zero_grad()\n",
    "            loss(net_boot(xi), yi).backward()\n",
    "            optimizer_boot.step()\n",
    "\n",
    "    pred_boot = net_boot(net[:-2](test_x)).detach().numpy().ravel()\n",
    "\n",
    "    return pred_boot\n",
    "\n",
    "\n",
    "# train main network ########################################################\n",
    "\n",
    "torch.manual_seed(1)\n",
    "\n",
    "x, y, test_x = generate_data2(2000)\n",
    "\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(x, y, test_size=0.5, random_state=42)\n",
    "\n",
    "\n",
    "test_x = (test_x - x_tr.mean(0)) / x_tr.std(0)\n",
    "x_val = (x_val - x_tr.mean(0)) / x_tr.std(0)\n",
    "x_tr = (x_tr - x_tr.mean(0)) / x_tr.std(0)\n",
    "\n",
    "net = train_network(x_tr, y_tr)\n",
    "\n",
    "pred_med = net(test_x).detach().numpy().ravel()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:20:12.922583Z",
     "start_time": "2020-09-18T11:20:12.812922Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 167;\n",
       "                var nbb_unformatted_code = \"plt.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15)\\n\\nplt.plot(test_x.view(-1).numpy(), pred_med, \\\".\\\", color=\\\"gray\\\", alpha=0.5, lw=2)\\nplt.legend(loc=3)\\nplt.tight_layout(0, 0, 0)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15)\\n\\nplt.plot(test_x.view(-1).numpy(), pred_med, \\\".\\\", color=\\\"gray\\\", alpha=0.5, lw=2)\\nplt.legend(loc=3)\\nplt.tight_layout(0, 0, 0)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15)\n",
    "\n",
    "plt.plot(test_x.view(-1).numpy(), pred_med, \".\", color=\"gray\", alpha=0.5, lw=2)\n",
    "plt.legend(loc=3)\n",
    "plt.tight_layout(0, 0, 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:22:52.243719Z",
     "start_time": "2020-09-18T11:20:32.665374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a41831470>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 192;\n",
       "                var nbb_unformatted_code = \"# fit a vine copula on validation set, set aside from the train\\nf_val = net[:-2](x_val).detach()\\nbootstrap_data_val = torch.cat((f_val, y_val.view(-1, 1)), dim=1).numpy()\\ncopula_controls = base.list(mult=1, trunc_lvl=3, family_set=\\\"tll\\\")\\nvine_obj_new = rvinecop.vine(bootstrap_data_val, copula_controls=copula_controls)\\n\\npred_med = net(test_x).detach().numpy().ravel()\\nplt.figure(figsize=(10, 10))\\nplt.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15)\\nplt.plot(test_x.view(-1).numpy(), pred_med, \\\".\\\", color=\\\"red\\\", alpha=0.5, lw=1)\\n\\nbootstraped_low = []\\nbootstraped_upper = []\\nbootstraped_median = []\\nbootstraped_preds = []\\n\\nfor boot_p in range(5):\\n    new_pred = np.asarray(\\n        boot_feat_target(net, x_val, y_val, test_x, vine_obj_new, epochs=500)\\n    ).flatten()\\n    plt.plot(test_x.numpy(), new_pred, color=\\\"green\\\", alpha=0.5, lw=1)\\n\\n    bootstraped_preds.append(torch.Tensor(new_pred - pred_med))\\n\\nbootstraped_preds = torch.squeeze(torch.stack(bootstraped_preds, 1))\\n\\n# standard error is sample std dev divided by sqrt(sample size))\\nse = bootstraped_preds.std(1)  # .div(np.sqrt(bootstraped_preds.shape[0]))\\nmasd = np.apply_along_axis(np.max, 0, se.abs())  # apply(absDev, 2L, max)\\ncrit = np.quantile(masd, 0.95)\\n\\npred_med = net(test_x).detach()\\nuprS = (pred_med.view(-1) + (crit * se),)\\nlwrS = (pred_med.view(-1) - (crit * se),)\\n\\nuprP = (pred_med.view(-1) + (2 * se),)\\nlwrP = (pred_med.view(-1) - (2 * se),)\\n\\n\\nuper_q = np.apply_along_axis(np.quantile, 1, bootstraped_preds, 0.975)\\nlower_q = np.apply_along_axis(np.quantile, 1, bootstraped_preds, 0.025)\\n\\n\\nplt.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\nplt.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprS[0].view(-1).numpy(),\\n    lwrS[0].view(-1).numpy(),\\n    color=\\\"red\\\",\\n    alpha=0.7,\\n    label=\\\"Simultaneous CI\\\",\\n)\\nplt.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprP[0].view(-1).numpy(),\\n    lwrP[0].view(-1).numpy(),\\n    color=\\\"pink\\\",\\n    alpha=0.5,\\n    label=\\\"point-wise CI\\\",\\n)\\n\\nplt.legend()\";\n",
       "                var nbb_formatted_code = \"# fit a vine copula on validation set, set aside from the train\\nf_val = net[:-2](x_val).detach()\\nbootstrap_data_val = torch.cat((f_val, y_val.view(-1, 1)), dim=1).numpy()\\ncopula_controls = base.list(mult=1, trunc_lvl=3, family_set=\\\"tll\\\")\\nvine_obj_new = rvinecop.vine(bootstrap_data_val, copula_controls=copula_controls)\\n\\npred_med = net(test_x).detach().numpy().ravel()\\nplt.figure(figsize=(10, 10))\\nplt.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15)\\nplt.plot(test_x.view(-1).numpy(), pred_med, \\\".\\\", color=\\\"red\\\", alpha=0.5, lw=1)\\n\\nbootstraped_low = []\\nbootstraped_upper = []\\nbootstraped_median = []\\nbootstraped_preds = []\\n\\nfor boot_p in range(5):\\n    new_pred = np.asarray(\\n        boot_feat_target(net, x_val, y_val, test_x, vine_obj_new, epochs=500)\\n    ).flatten()\\n    plt.plot(test_x.numpy(), new_pred, color=\\\"green\\\", alpha=0.5, lw=1)\\n\\n    bootstraped_preds.append(torch.Tensor(new_pred - pred_med))\\n\\nbootstraped_preds = torch.squeeze(torch.stack(bootstraped_preds, 1))\\n\\n# standard error is sample std dev divided by sqrt(sample size))\\nse = bootstraped_preds.std(1)  # .div(np.sqrt(bootstraped_preds.shape[0]))\\nmasd = np.apply_along_axis(np.max, 0, se.abs())  # apply(absDev, 2L, max)\\ncrit = np.quantile(masd, 0.95)\\n\\npred_med = net(test_x).detach()\\nuprS = (pred_med.view(-1) + (crit * se),)\\nlwrS = (pred_med.view(-1) - (crit * se),)\\n\\nuprP = (pred_med.view(-1) + (2 * se),)\\nlwrP = (pred_med.view(-1) - (2 * se),)\\n\\n\\nuper_q = np.apply_along_axis(np.quantile, 1, bootstraped_preds, 0.975)\\nlower_q = np.apply_along_axis(np.quantile, 1, bootstraped_preds, 0.025)\\n\\n\\nplt.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\nplt.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprS[0].view(-1).numpy(),\\n    lwrS[0].view(-1).numpy(),\\n    color=\\\"red\\\",\\n    alpha=0.7,\\n    label=\\\"Simultaneous CI\\\",\\n)\\nplt.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprP[0].view(-1).numpy(),\\n    lwrP[0].view(-1).numpy(),\\n    color=\\\"pink\\\",\\n    alpha=0.5,\\n    label=\\\"point-wise CI\\\",\\n)\\n\\nplt.legend()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit a vine copula on validation set, set aside from the train\n",
    "f_val = net[:-2](x_val).detach()\n",
    "bootstrap_data_val = torch.cat((f_val, y_val.view(-1, 1)), dim=1).numpy()\n",
    "copula_controls = base.list(mult=1, trunc_lvl=3, family_set=\"tll\")\n",
    "vine_obj_new = rvinecop.vine(bootstrap_data_val, copula_controls=copula_controls)\n",
    "\n",
    "pred_med = net(test_x).detach().numpy().ravel()\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15)\n",
    "plt.plot(test_x.view(-1).numpy(), pred_med, \".\", color=\"red\", alpha=0.5, lw=1)\n",
    "\n",
    "bootstraped_low = []\n",
    "bootstraped_upper = []\n",
    "bootstraped_median = []\n",
    "bootstraped_preds = []\n",
    "\n",
    "for boot_p in range(5):\n",
    "    new_pred = np.asarray(\n",
    "        boot_feat_target(net, x_val, y_val, test_x, vine_obj_new, epochs=500)\n",
    "    ).flatten()\n",
    "    plt.plot(test_x.numpy(), new_pred, color=\"green\", alpha=0.5, lw=1)\n",
    "\n",
    "    bootstraped_preds.append(torch.Tensor(new_pred - pred_med))\n",
    "\n",
    "bootstraped_preds = torch.squeeze(torch.stack(bootstraped_preds, 1))\n",
    "\n",
    "# standard error is sample std dev divided by sqrt(sample size))\n",
    "se = bootstraped_preds.std(1).div(np.sqrt(bootstraped_preds.shape[0]))\n",
    "masd = np.apply_along_axis(np.max, 0, se.abs())  # apply(absDev, 2L, max)\n",
    "crit = np.quantile(masd, 0.95)\n",
    "\n",
    "pred_med = net(test_x).detach()\n",
    "uprS = (pred_med.view(-1) + (crit * se),)\n",
    "lwrS = (pred_med.view(-1) - (crit * se),)\n",
    "\n",
    "uprP = (pred_med.view(-1) + (2 * se),)\n",
    "lwrP = (pred_med.view(-1) - (2 * se),)\n",
    "\n",
    "\n",
    "uper_q = np.apply_along_axis(np.quantile, 1, bootstraped_preds, 0.975)\n",
    "lower_q = np.apply_along_axis(np.quantile, 1, bootstraped_preds, 0.025)\n",
    "\n",
    "\n",
    "plt.plot(test_x.view(-1).numpy(), pred_med, color=\"black\", alpha=0.5, lw=1)\n",
    "plt.fill_between(\n",
    "    test_x.view(-1).numpy(),\n",
    "    uprS[0].view(-1).numpy(),\n",
    "    lwrS[0].view(-1).numpy(),\n",
    "    color=\"red\",\n",
    "    alpha=0.7,\n",
    "    label=\"Simultaneous CI\",\n",
    ")\n",
    "plt.fill_between(\n",
    "    test_x.view(-1).numpy(),\n",
    "    uprP[0].view(-1).numpy(),\n",
    "    lwrP[0].view(-1).numpy(),\n",
    "    color=\"pink\",\n",
    "    alpha=0.5,\n",
    "    label=\"point-wise CI\",\n",
    ")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:24:39.124971Z",
     "start_time": "2020-09-18T11:23:02.521287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 173;\n",
       "                var nbb_unformatted_code = \"f_val = net[:-2](x_val).detach()\\nres_eecop = eecop.eecop(y_val.view(-1, 1).numpy(),\\n                            f_val.numpy(),\\n                            copula_method = \\\"vine\\\",\\n                            margin_method = \\\"kde\\\",\\n                            mult=0.01, \\n                            trunc_lvl=1)\\nf_test = net[:-2](test_x).detach()\\npred_low_b = []\\npred_hig_b = []\\nstep_size = 20\\nfor i in range(0, f_test.shape[0], step_size):\\n    pl = stats.predict(res_eecop, f_test.numpy()[i:i+step_size,:], type=\\\"quantile\\\", t=lower_tau)\\n    ph = stats.predict(res_eecop, f_test.numpy()[i:i+step_size,:], type=\\\"quantile\\\", t=upper_tau)\\n    pred_low_b.append(list(pl))\\n    pred_hig_b.append(list(ph))\";\n",
       "                var nbb_formatted_code = \"f_val = net[:-2](x_val).detach()\\nres_eecop = eecop.eecop(\\n    y_val.view(-1, 1).numpy(),\\n    f_val.numpy(),\\n    copula_method=\\\"vine\\\",\\n    margin_method=\\\"kde\\\",\\n    mult=0.01,\\n    trunc_lvl=1,\\n)\\nf_test = net[:-2](test_x).detach()\\npred_low_b = []\\npred_hig_b = []\\nstep_size = 20\\nfor i in range(0, f_test.shape[0], step_size):\\n    pl = stats.predict(\\n        res_eecop, f_test.numpy()[i : i + step_size, :], type=\\\"quantile\\\", t=lower_tau\\n    )\\n    ph = stats.predict(\\n        res_eecop, f_test.numpy()[i : i + step_size, :], type=\\\"quantile\\\", t=upper_tau\\n    )\\n    pred_low_b.append(list(pl))\\n    pred_hig_b.append(list(ph))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_val = net[:-2](x_val).detach()\n",
    "res_eecop = eecop.eecop(y_val.view(-1, 1).numpy(),\n",
    "                            f_val.numpy(),\n",
    "                            copula_method = \"vine\",\n",
    "                            margin_method = \"kde\",\n",
    "                            mult=0.01, \n",
    "                            trunc_lvl=1)\n",
    "f_test = net[:-2](test_x).detach()\n",
    "pred_low_b = []\n",
    "pred_hig_b = []\n",
    "step_size = 20\n",
    "for i in range(0, f_test.shape[0], step_size):\n",
    "    pl = stats.predict(res_eecop, f_test.numpy()[i:i+step_size,:], type=\"quantile\", t=lower_tau)\n",
    "    ph = stats.predict(res_eecop, f_test.numpy()[i:i+step_size,:], type=\"quantile\", t=upper_tau)\n",
    "    pred_low_b.append(list(pl))\n",
    "    pred_hig_b.append(list(ph))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:24:50.494211Z",
     "start_time": "2020-09-18T11:24:50.209945Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a3d8c51d0>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 175;\n",
       "                var nbb_unformatted_code = \"fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\\n\\nax1.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, label=\\\"data\\\", color=\\\"gray\\\")\\n\\nax1.plot(\\n    test_x.view(-1).numpy(),\\n    pred_med,\\n    color=\\\"black\\\",\\n    alpha=0.5,\\n    lw=1,\\n    label=\\\"DNN prediction\\\",\\n)\\nax1.legend()\\n\\n\\nax2.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\n\\nax2.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\nax2.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprS[0].view(-1).numpy(),\\n    lwrS[0].view(-1).numpy(),\\n    color=\\\"red\\\",\\n    alpha=0.7,\\n    label=\\\"Simultaneous CI\\\",\\n)\\nax2.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprP[0].view(-1).numpy(),\\n    lwrP[0].view(-1).numpy(),\\n    color=\\\"pink\\\",\\n    alpha=0.5,\\n    label=\\\"point-wise CI\\\",\\n)\\n\\nax2.legend()\\nax3.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\nax3.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\nax3.fill_between(\\n    test_x.view(-1).numpy(),\\n    np.nan_to_num(np.asarray(pred_low_b).ravel()),\\n    np.nan_to_num(np.asarray(pred_hig_b).ravel()),\\n    alpha=0.3,\\n    color=\\\"orange\\\",\\n    label=\\\"95% PI\\\",\\n)\\nplt.ylim(-4, 4)\\n\\nax3.legend()\";\n",
       "                var nbb_formatted_code = \"fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\\n\\nax1.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, label=\\\"data\\\", color=\\\"gray\\\")\\n\\nax1.plot(\\n    test_x.view(-1).numpy(),\\n    pred_med,\\n    color=\\\"black\\\",\\n    alpha=0.5,\\n    lw=1,\\n    label=\\\"DNN prediction\\\",\\n)\\nax1.legend()\\n\\n\\nax2.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\n\\nax2.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\nax2.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprS[0].view(-1).numpy(),\\n    lwrS[0].view(-1).numpy(),\\n    color=\\\"red\\\",\\n    alpha=0.7,\\n    label=\\\"Simultaneous CI\\\",\\n)\\nax2.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprP[0].view(-1).numpy(),\\n    lwrP[0].view(-1).numpy(),\\n    color=\\\"pink\\\",\\n    alpha=0.5,\\n    label=\\\"point-wise CI\\\",\\n)\\n\\nax2.legend()\\nax3.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\nax3.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\nax3.fill_between(\\n    test_x.view(-1).numpy(),\\n    np.nan_to_num(np.asarray(pred_low_b).ravel()),\\n    np.nan_to_num(np.asarray(pred_hig_b).ravel()),\\n    alpha=0.3,\\n    color=\\\"orange\\\",\\n    label=\\\"95% PI\\\",\\n)\\nplt.ylim(-4, 4)\\n\\nax3.legend()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\n",
    "\n",
    "ax1.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15, label=\"data\", color=\"gray\")\n",
    "\n",
    "ax1.plot(\n",
    "    test_x.view(-1).numpy(),\n",
    "    pred_med,\n",
    "    color=\"black\",\n",
    "    alpha=0.5,\n",
    "    lw=1,\n",
    "    label=\"DNN prediction\",\n",
    ")\n",
    "ax1.legend()\n",
    "\n",
    "\n",
    "ax2.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15, color=\"gray\")\n",
    "\n",
    "ax2.plot(test_x.view(-1).numpy(), pred_med, color=\"black\", alpha=0.5, lw=1)\n",
    "ax2.fill_between(\n",
    "    test_x.view(-1).numpy(),\n",
    "    uprS[0].view(-1).numpy(),\n",
    "    lwrS[0].view(-1).numpy(),\n",
    "    color=\"red\",\n",
    "    alpha=0.7,\n",
    "    label=\"Simultaneous CI\",\n",
    ")\n",
    "ax2.fill_between(\n",
    "    test_x.view(-1).numpy(),\n",
    "    uprP[0].view(-1).numpy(),\n",
    "    lwrP[0].view(-1).numpy(),\n",
    "    color=\"pink\",\n",
    "    alpha=0.5,\n",
    "    label=\"point-wise CI\",\n",
    ")\n",
    "\n",
    "ax2.legend()\n",
    "ax3.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15, color=\"gray\")\n",
    "ax3.plot(test_x.view(-1).numpy(), pred_med, color=\"black\", alpha=0.5, lw=1)\n",
    "ax3.fill_between(\n",
    "    test_x.view(-1).numpy(),\n",
    "    np.nan_to_num(np.asarray(pred_low_b).ravel()),\n",
    "    np.nan_to_num(np.asarray(pred_hig_b).ravel()),\n",
    "    alpha=0.3,\n",
    "    color=\"orange\",\n",
    "    label=\"95% PI\",\n",
    ")\n",
    "plt.ylim(-4, 4)\n",
    "\n",
    "ax3.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classsification task - moons dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:28:18.937736Z",
     "start_time": "2020-09-18T11:25:14.281707Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel_launcher.py:31: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9cead1f5eef5405d90d98969a47bd970",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=500.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Vine copula fitted\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 182;\n",
       "                var nbb_unformatted_code = \"torch.manual_seed(0)\\nn = 1000\\nmargin = 0.25\\n\\nx_moons, y_moons = moons(n)\\n\\nlim = 11 #max(abs(x_moons.min()), x_moons.max()) * 2 #!!! changeable\\n\\nx1, x2 = torch.meshgrid(torch.linspace(-lim, lim, 25),\\n                            torch.linspace(-lim, lim, 25))\\n\\nx_test_moons = torch.cat((x1.reshape(-1, 1),\\n                        x2.reshape(-1, 1)), -1)\\nx_tr_m, x_val_m, y_tr_m, y_val_m = train_test_split(x_moons, y_moons, test_size=0.5, random_state=42)\\n\\n\\nloss = torch.nn.BCEWithLogitsLoss()\\n\\nnet_moons = torch.nn.Sequential(\\n            torch.nn.Linear(x_moons.size(1), 100),\\n            torch.nn.ReLU(),\\n            #torch.nn.Linear(50, 50),\\n            #torch.nn.ReLU(),\\n            #torch.nn.Linear(256, 100),\\n            #torch.nn.ReLU(),\\n            torch.nn.Linear(100, 1))\\n\\nopt_moons = torch.optim.Adam(net_moons.parameters(), weight_decay=1e-3)\\nloader_moons = DataLoader(TensorDataset(x_tr_m, y_tr_m), shuffle=True, batch_size=128)\\nepochs = 500\\nfor _ in tqdm_notebook(range(epochs)):\\n    for xi, yi in loader_moons:\\n            opt_moons.zero_grad()\\n            loss(net_moons(xi), yi).backward()\\n            opt_moons.step()\\n\\npreds = net_moons(x_test_moons).view(25, 25).detach()\\n\\nf_moons = net_moons[:-2](x_val_m).detach()\\nbootstrap_data_moons = torch.cat((f_moons, y_val_m.view(-1, 1)), dim=1).numpy()\\ncopula_controls = base.list(mult=1, trunc_lvl=3, family_set = \\\"tll\\\")\\nvine_obj_moons = rvinecop.vine(bootstrap_data_moons, copula_controls = copula_controls)\\nprint('Vine copula fitted')\\n\\nbootstraped_preds_moons2 = []\\nfor boot_p in range(15):\\n    new_pred = np.asarray(boot_feat_target(net_moons, x_val_m, y_val_m, x_test_moons, vine_obj_moons, epochs=500, loss=\\\"BCE\\\")).flatten()\\n    bootstraped_preds_moons2.append(torch.Tensor(new_pred - preds.numpy().flatten()))\\n\\nbootstraped_preds_moons_st2 = torch.squeeze(torch.stack(bootstraped_preds_moons2, 1))\\n\\nres_eecop_moons = eecop.eecop(net_moons(x_val_m).detach().view(-1, 1).numpy(),\\n                            f_moons.numpy(),\\n                            copula_method = \\\"vine\\\",\\n                            margin_method = \\\"kde\\\",\\n                            mult=1, \\n                            trunc_lvl=3)\\nf_test_moons = net_moons[:-2](x_test_moons).detach()\\n\\npred_low_b_moons = []\\npred_hig_b_moons = []\\nstep_size = 25\\nfor i in range(0, f_test_moons.shape[0], step_size):\\n    pl = stats.predict(res_eecop_moons, f_test_moons.numpy()[i:i+step_size,:], type=\\\"quantile\\\", t=lower_tau)\\n    ph = stats.predict(res_eecop_moons, f_test_moons.numpy()[i:i+step_size,:], type=\\\"quantile\\\", t=upper_tau)\\n    pred_low_b_moons.append(list(pl))\\n    pred_hig_b_moons.append(list(ph))\\n\\n\\nuncertainties_ale = torch.Tensor(np.nan_to_num(np.asarray(pred_hig_b_moons)).ravel() - np.nan_to_num(np.asarray(pred_low_b_moons)).ravel())\";\n",
       "                var nbb_formatted_code = \"torch.manual_seed(0)\\nn = 1000\\nmargin = 0.25\\n\\nx_moons, y_moons = moons(n)\\n\\nlim = 11  # max(abs(x_moons.min()), x_moons.max()) * 2 #!!! changeable\\n\\nx1, x2 = torch.meshgrid(torch.linspace(-lim, lim, 25), torch.linspace(-lim, lim, 25))\\n\\nx_test_moons = torch.cat((x1.reshape(-1, 1), x2.reshape(-1, 1)), -1)\\nx_tr_m, x_val_m, y_tr_m, y_val_m = train_test_split(\\n    x_moons, y_moons, test_size=0.5, random_state=42\\n)\\n\\n\\nloss = torch.nn.BCEWithLogitsLoss()\\n\\nnet_moons = torch.nn.Sequential(\\n    torch.nn.Linear(x_moons.size(1), 100),\\n    torch.nn.ReLU(),\\n    # torch.nn.Linear(50, 50),\\n    # torch.nn.ReLU(),\\n    # torch.nn.Linear(256, 100),\\n    # torch.nn.ReLU(),\\n    torch.nn.Linear(100, 1),\\n)\\n\\nopt_moons = torch.optim.Adam(net_moons.parameters(), weight_decay=1e-3)\\nloader_moons = DataLoader(TensorDataset(x_tr_m, y_tr_m), shuffle=True, batch_size=128)\\nepochs = 500\\nfor _ in tqdm_notebook(range(epochs)):\\n    for xi, yi in loader_moons:\\n        opt_moons.zero_grad()\\n        loss(net_moons(xi), yi).backward()\\n        opt_moons.step()\\n\\npreds = net_moons(x_test_moons).view(25, 25).detach()\\n\\nf_moons = net_moons[:-2](x_val_m).detach()\\nbootstrap_data_moons = torch.cat((f_moons, y_val_m.view(-1, 1)), dim=1).numpy()\\ncopula_controls = base.list(mult=1, trunc_lvl=3, family_set=\\\"tll\\\")\\nvine_obj_moons = rvinecop.vine(bootstrap_data_moons, copula_controls=copula_controls)\\nprint(\\\"Vine copula fitted\\\")\\n\\nbootstraped_preds_moons2 = []\\nfor boot_p in range(15):\\n    new_pred = np.asarray(\\n        boot_feat_target(\\n            net_moons,\\n            x_val_m,\\n            y_val_m,\\n            x_test_moons,\\n            vine_obj_moons,\\n            epochs=500,\\n            loss=\\\"BCE\\\",\\n        )\\n    ).flatten()\\n    bootstraped_preds_moons2.append(torch.Tensor(new_pred - preds.numpy().flatten()))\\n\\nbootstraped_preds_moons_st2 = torch.squeeze(torch.stack(bootstraped_preds_moons2, 1))\\n\\nres_eecop_moons = eecop.eecop(\\n    net_moons(x_val_m).detach().view(-1, 1).numpy(),\\n    f_moons.numpy(),\\n    copula_method=\\\"vine\\\",\\n    margin_method=\\\"kde\\\",\\n    mult=1,\\n    trunc_lvl=3,\\n)\\nf_test_moons = net_moons[:-2](x_test_moons).detach()\\n\\npred_low_b_moons = []\\npred_hig_b_moons = []\\nstep_size = 25\\nfor i in range(0, f_test_moons.shape[0], step_size):\\n    pl = stats.predict(\\n        res_eecop_moons,\\n        f_test_moons.numpy()[i : i + step_size, :],\\n        type=\\\"quantile\\\",\\n        t=lower_tau,\\n    )\\n    ph = stats.predict(\\n        res_eecop_moons,\\n        f_test_moons.numpy()[i : i + step_size, :],\\n        type=\\\"quantile\\\",\\n        t=upper_tau,\\n    )\\n    pred_low_b_moons.append(list(pl))\\n    pred_hig_b_moons.append(list(ph))\\n\\n\\nuncertainties_ale = torch.Tensor(\\n    np.nan_to_num(np.asarray(pred_hig_b_moons)).ravel()\\n    - np.nan_to_num(np.asarray(pred_low_b_moons)).ravel()\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "n = 1000\n",
    "margin = 0.25\n",
    "\n",
    "x_moons, y_moons = moons(n)\n",
    "\n",
    "lim = 11\n",
    "\n",
    "x1, x2 = torch.meshgrid(torch.linspace(-lim, lim, 25),\n",
    "                            torch.linspace(-lim, lim, 25))\n",
    "\n",
    "x_test_moons = torch.cat((x1.reshape(-1, 1),\n",
    "                        x2.reshape(-1, 1)), -1)\n",
    "x_tr_m, x_val_m, y_tr_m, y_val_m = train_test_split(x_moons, y_moons, test_size=0.5, random_state=42)\n",
    "\n",
    "\n",
    "loss = torch.nn.BCEWithLogitsLoss()\n",
    "\n",
    "net_moons = torch.nn.Sequential(\n",
    "            torch.nn.Linear(x_moons.size(1), 100),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(100, 1))\n",
    "\n",
    "opt_moons = torch.optim.Adam(net_moons.parameters(), weight_decay=1e-3)\n",
    "loader_moons = DataLoader(TensorDataset(x_tr_m, y_tr_m), shuffle=True, batch_size=128)\n",
    "epochs = 500\n",
    "for _ in tqdm_notebook(range(epochs)):\n",
    "    for xi, yi in loader_moons:\n",
    "            opt_moons.zero_grad()\n",
    "            loss(net_moons(xi), yi).backward()\n",
    "            opt_moons.step()\n",
    "\n",
    "preds = net_moons(x_test_moons).view(25, 25).detach()\n",
    "\n",
    "f_moons = net_moons[:-2](x_val_m).detach()\n",
    "bootstrap_data_moons = torch.cat((f_moons, y_val_m.view(-1, 1)), dim=1).numpy()\n",
    "copula_controls = base.list(mult=1, trunc_lvl=3, family_set = \"tll\")\n",
    "vine_obj_moons = rvinecop.vine(bootstrap_data_moons, copula_controls = copula_controls)\n",
    "print('Vine copula fitted')\n",
    "\n",
    "bootstraped_preds_moons2 = []\n",
    "for boot_p in range(15):\n",
    "    new_pred = np.asarray(boot_feat_target(net_moons, x_val_m, y_val_m, x_test_moons, vine_obj_moons, epochs=500, loss=\"BCE\")).flatten()\n",
    "    bootstraped_preds_moons2.append(torch.Tensor(new_pred - preds.numpy().flatten()))\n",
    "\n",
    "bootstraped_preds_moons_st2 = torch.squeeze(torch.stack(bootstraped_preds_moons2, 1))\n",
    "\n",
    "res_eecop_moons = eecop.eecop(net_moons(x_val_m).detach().view(-1, 1).numpy(),\n",
    "                            f_moons.numpy(),\n",
    "                            copula_method = \"vine\",\n",
    "                            margin_method = \"kde\",\n",
    "                            mult=1, \n",
    "                            trunc_lvl=3)\n",
    "f_test_moons = net_moons[:-2](x_test_moons).detach()\n",
    "\n",
    "pred_low_b_moons = []\n",
    "pred_hig_b_moons = []\n",
    "step_size = 25\n",
    "for i in range(0, f_test_moons.shape[0], step_size):\n",
    "    pl = stats.predict(res_eecop_moons, f_test_moons.numpy()[i:i+step_size,:], type=\"quantile\", t=lower_tau)\n",
    "    ph = stats.predict(res_eecop_moons, f_test_moons.numpy()[i:i+step_size,:], type=\"quantile\", t=upper_tau)\n",
    "    pred_low_b_moons.append(list(pl))\n",
    "    pred_hig_b_moons.append(list(ph))\n",
    "\n",
    "\n",
    "uncertainties_ale = torch.Tensor(np.nan_to_num(np.asarray(pred_hig_b_moons)).ravel() - np.nan_to_num(np.asarray(pred_low_b_moons)).ravel())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:42:05.062559Z",
     "start_time": "2020-09-18T11:42:05.057446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 187;\n",
       "                var nbb_unformatted_code = \"uncertainties_epi = bootstraped_preds_moons_st2.view(25, 25, -1).numpy().std(axis=-1)\";\n",
       "                var nbb_formatted_code = \"uncertainties_epi = bootstraped_preds_moons_st2.view(25, 25, -1).numpy().std(axis=-1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uncertainties_epi = bootstraped_preds_moons_st2.view(25, 25, -1).numpy().std(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:42:06.305172Z",
     "start_time": "2020-09-18T11:42:05.970274Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 188;\n",
       "                var nbb_unformatted_code = \"fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\\n\\nax1.scatter(x_moons[:, 0], x_moons[:, 1], color=\\\"gray\\\", alpha=0.25, s=10, label=\\\"data\\\")\\n\\nax1.imshow(\\n    preds,\\n    cmap=\\\"coolwarm\\\",\\n    origin=\\\"upper\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"DNN prediction\\\",\\n)\\nax1.legend()\\nax2.imshow(\\n    uncertainties_epi, cmap=\\\"Reds\\\", extent=(-lim, lim, -lim, lim), label=\\\"Epistemic\\\"\\n)\\nax2.legend()\\n\\nax3.imshow(\\n    uncertainties_ale.reshape(25, 25),\\n    cmap=\\\"Reds\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"Aleatoric\\\",\\n)\\nax3.legend()\\nplt.legend()\\nplt.tight_layout()\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\\n\\nax1.scatter(x_moons[:, 0], x_moons[:, 1], color=\\\"gray\\\", alpha=0.25, s=10, label=\\\"data\\\")\\n\\nax1.imshow(\\n    preds,\\n    cmap=\\\"coolwarm\\\",\\n    origin=\\\"upper\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"DNN prediction\\\",\\n)\\nax1.legend()\\nax2.imshow(\\n    uncertainties_epi, cmap=\\\"Reds\\\", extent=(-lim, lim, -lim, lim), label=\\\"Epistemic\\\"\\n)\\nax2.legend()\\n\\nax3.imshow(\\n    uncertainties_ale.reshape(25, 25),\\n    cmap=\\\"Reds\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"Aleatoric\\\",\\n)\\nax3.legend()\\nplt.legend()\\nplt.tight_layout()\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\n",
    "\n",
    "ax1.scatter(x_moons[:, 0], x_moons[:, 1], color=\"gray\", alpha=0.25, s=10, label=\"data\")\n",
    "\n",
    "ax1.imshow(\n",
    "    preds,\n",
    "    cmap=\"coolwarm\",\n",
    "    origin=\"upper\",\n",
    "    extent=(-lim, lim, -lim, lim),\n",
    "    label=\"DNN prediction\",\n",
    ")\n",
    "ax1.legend()\n",
    "ax2.imshow(\n",
    "    uncertainties_epi, cmap=\"Reds\", extent=(-lim, lim, -lim, lim), label=\"Epistemic\"\n",
    ")\n",
    "ax2.legend()\n",
    "\n",
    "ax3.imshow(\n",
    "    uncertainties_ale.reshape(25, 25),\n",
    "    cmap=\"Reds\",\n",
    "    extent=(-lim, lim, -lim, lim),\n",
    "    label=\"Aleatoric\",\n",
    ")\n",
    "ax3.legend()\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T11:43:37.988600Z",
     "start_time": "2020-09-18T11:43:36.870441Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 193;\n",
       "                var nbb_unformatted_code = \"fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, figsize=(15, 10))\\n\\nax1.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, label=\\\"data\\\", color=\\\"gray\\\")\\n\\nax1.plot(\\n    test_x.view(-1).numpy(),\\n    pred_med,\\n    color=\\\"black\\\",\\n    alpha=0.5,\\n    lw=1,\\n    label=\\\"DNN prediction\\\",\\n)\\nax1.legend()\\nax1.set_title(\\\"Deterministic NN\\\")\\n\\nax2.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\n\\nax2.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\n\\nax2.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprS[0].view(-1).numpy(),\\n    lwrS[0].view(-1).numpy(),\\n    color=\\\"red\\\",\\n    alpha=0.7,\\n    label=\\\"Simultaneous CI\\\",\\n)\\nax2.legend()\\nax2.set_title(\\\"VCNN Epistemic\\\")\\n\\n\\nax3.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\nax3.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\n\\nax3.fill_between(\\n    test_x.view(-1).numpy(),\\n    np.asarray(pred_hig_b).flatten(),\\n    np.asarray(pred_low_b).flatten(),\\n    alpha=0.3,\\n    color=\\\"orange\\\",\\n    label=\\\"95% PI\\\",\\n)\\nax3.legend()\\nax3.set_title(\\\"VCNN Aleatoric\\\")\\n\\nimport matplotlib\\n\\npreds_l = net_moons(x_moons).detach().numpy().ravel()\\nbs = net_moons(x_moons).detach().numpy().ravel()\\npreds_l[bs < 0] = 1\\npreds_l[bs > 0] = 0\\n\\ncolors = [\\\"red\\\", \\\"blue\\\"]\\nax4.scatter(\\n    x_moons[:, 0],\\n    x_moons[:, 1],\\n    c=preds_l,\\n    cmap=matplotlib.colors.ListedColormap(colors),\\n    alpha=0.25,\\n    s=10,\\n    label=\\\"data\\\",\\n)\\n\\nim1 = ax4.imshow(\\n    net_moons(x_test_moons).detach().numpy().reshape(25, 25),\\n    cmap=\\\"Blues\\\",\\n    origin=\\\"upper\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"DNN prediction\\\",\\n)\\nfig.colorbar(im1, ax=ax4)\\n\\nim2 = ax5.imshow(\\n    uncertainties_epi, cmap=\\\"Reds\\\", extent=(-lim, lim, -lim, lim), label=\\\"Epistemic\\\"\\n)\\nfig.colorbar(im2, ax=ax5)\\n\\nim3 = ax6.imshow(\\n    uncertainties_ale.reshape(25, 25),\\n    cmap=\\\"Oranges\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"Aleatoric\\\",\\n)\\n\\nfig.colorbar(im3, ax=ax6)\\nplt.savefig(\\\"motivating_example_best.pdf\\\")\";\n",
       "                var nbb_formatted_code = \"fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, figsize=(15, 10))\\n\\nax1.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, label=\\\"data\\\", color=\\\"gray\\\")\\n\\nax1.plot(\\n    test_x.view(-1).numpy(),\\n    pred_med,\\n    color=\\\"black\\\",\\n    alpha=0.5,\\n    lw=1,\\n    label=\\\"DNN prediction\\\",\\n)\\nax1.legend()\\nax1.set_title(\\\"Deterministic NN\\\")\\n\\nax2.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\n\\nax2.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\n\\nax2.fill_between(\\n    test_x.view(-1).numpy(),\\n    uprS[0].view(-1).numpy(),\\n    lwrS[0].view(-1).numpy(),\\n    color=\\\"red\\\",\\n    alpha=0.7,\\n    label=\\\"Simultaneous CI\\\",\\n)\\nax2.legend()\\nax2.set_title(\\\"VCNN Epistemic\\\")\\n\\n\\nax3.plot(x_tr.numpy(), y_tr.numpy(), \\\".\\\", alpha=0.15, color=\\\"gray\\\")\\nax3.plot(test_x.view(-1).numpy(), pred_med, color=\\\"black\\\", alpha=0.5, lw=1)\\n\\nax3.fill_between(\\n    test_x.view(-1).numpy(),\\n    np.asarray(pred_hig_b).flatten(),\\n    np.asarray(pred_low_b).flatten(),\\n    alpha=0.3,\\n    color=\\\"orange\\\",\\n    label=\\\"95% PI\\\",\\n)\\nax3.legend()\\nax3.set_title(\\\"VCNN Aleatoric\\\")\\n\\nimport matplotlib\\n\\npreds_l = net_moons(x_moons).detach().numpy().ravel()\\nbs = net_moons(x_moons).detach().numpy().ravel()\\npreds_l[bs < 0] = 1\\npreds_l[bs > 0] = 0\\n\\ncolors = [\\\"red\\\", \\\"blue\\\"]\\nax4.scatter(\\n    x_moons[:, 0],\\n    x_moons[:, 1],\\n    c=preds_l,\\n    cmap=matplotlib.colors.ListedColormap(colors),\\n    alpha=0.25,\\n    s=10,\\n    label=\\\"data\\\",\\n)\\n\\nim1 = ax4.imshow(\\n    net_moons(x_test_moons).detach().numpy().reshape(25, 25),\\n    cmap=\\\"Blues\\\",\\n    origin=\\\"upper\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"DNN prediction\\\",\\n)\\nfig.colorbar(im1, ax=ax4)\\n\\nim2 = ax5.imshow(\\n    uncertainties_epi, cmap=\\\"Reds\\\", extent=(-lim, lim, -lim, lim), label=\\\"Epistemic\\\"\\n)\\nfig.colorbar(im2, ax=ax5)\\n\\nim3 = ax6.imshow(\\n    uncertainties_ale.reshape(25, 25),\\n    cmap=\\\"Oranges\\\",\\n    extent=(-lim, lim, -lim, lim),\\n    label=\\\"Aleatoric\\\",\\n)\\n\\nfig.colorbar(im3, ax=ax6)\\nplt.savefig(\\\"motivating_example_best.pdf\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, figsize=(15, 10))\n",
    "\n",
    "ax1.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15, label=\"data\", color=\"gray\")\n",
    "\n",
    "ax1.plot(\n",
    "    test_x.view(-1).numpy(),\n",
    "    pred_med,\n",
    "    color=\"black\",\n",
    "    alpha=0.5,\n",
    "    lw=1,\n",
    "    label=\"DNN prediction\",\n",
    ")\n",
    "ax1.legend()\n",
    "ax1.set_title(\"Deterministic NN\")\n",
    "\n",
    "ax2.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15, color=\"gray\")\n",
    "\n",
    "ax2.plot(test_x.view(-1).numpy(), pred_med, color=\"black\", alpha=0.5, lw=1)\n",
    "\n",
    "ax2.fill_between(\n",
    "    test_x.view(-1).numpy(),\n",
    "    uprS[0].view(-1).numpy(),\n",
    "    lwrS[0].view(-1).numpy(),\n",
    "    color=\"red\",\n",
    "    alpha=0.7,\n",
    "    label=\"Simultaneous CI\",\n",
    ")\n",
    "ax2.legend()\n",
    "ax2.set_title(\"VCNN Epistemic\")\n",
    "\n",
    "\n",
    "ax3.plot(x_tr.numpy(), y_tr.numpy(), \".\", alpha=0.15, color=\"gray\")\n",
    "ax3.plot(test_x.view(-1).numpy(), pred_med, color=\"black\", alpha=0.5, lw=1)\n",
    "\n",
    "ax3.fill_between(\n",
    "    test_x.view(-1).numpy(),\n",
    "    np.asarray(pred_hig_b).flatten(),\n",
    "    np.asarray(pred_low_b).flatten(),\n",
    "    alpha=0.3,\n",
    "    color=\"orange\",\n",
    "    label=\"95% PI\",\n",
    ")\n",
    "ax3.legend()\n",
    "ax3.set_title(\"VCNN Aleatoric\")\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "preds_l = net_moons(x_moons).detach().numpy().ravel()\n",
    "bs = net_moons(x_moons).detach().numpy().ravel()\n",
    "preds_l[bs < 0] = 1\n",
    "preds_l[bs > 0] = 0\n",
    "\n",
    "colors = [\"red\", \"blue\"]\n",
    "ax4.scatter(\n",
    "    x_moons[:, 0],\n",
    "    x_moons[:, 1],\n",
    "    c=preds_l,\n",
    "    cmap=matplotlib.colors.ListedColormap(colors),\n",
    "    alpha=0.25,\n",
    "    s=10,\n",
    "    label=\"data\",\n",
    ")\n",
    "\n",
    "im1 = ax4.imshow(\n",
    "    net_moons(x_test_moons).detach().numpy().reshape(25, 25),\n",
    "    cmap=\"Blues\",\n",
    "    origin=\"upper\",\n",
    "    extent=(-lim, lim, -lim, lim),\n",
    "    label=\"DNN prediction\",\n",
    ")\n",
    "fig.colorbar(im1, ax=ax4)\n",
    "\n",
    "im2 = ax5.imshow(\n",
    "    uncertainties_epi, cmap=\"Reds\", extent=(-lim, lim, -lim, lim), label=\"Epistemic\"\n",
    ")\n",
    "fig.colorbar(im2, ax=ax5)\n",
    "\n",
    "im3 = ax6.imshow(\n",
    "    uncertainties_ale.reshape(25, 25),\n",
    "    cmap=\"Oranges\",\n",
    "    extent=(-lim, lim, -lim, lim),\n",
    "    label=\"Aleatoric\",\n",
    ")\n",
    "\n",
    "fig.colorbar(im3, ax=ax6)\n",
    "plt.savefig(\"motivating_example_best.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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