demo_lrtc_scad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import torch\n",
    "import logging\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from utils import logger_info, logger_close\n",
    "from utils import load_data, missing_pattern, compute_rmse, compute_mape\n",
    "\n",
    "from utils_lrtc import unfolding, folding, shrinkage, svd_\n",
    "\n",
    "dataroot = \"./datasets\"\n",
    "save_dir = \"./loggers\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def svt_scad(mat, tau, gamma=20, lamb=10):\n",
    "    u, s, v = svd_(mat)\n",
    "    ss = shrinkage(s, [tau, gamma, lamb], mode=\"scad\")\n",
    "    idx = torch.where(ss > 0)[0]\n",
    "    return u[:, idx] @ torch.diag(ss[idx]) @ v[idx, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recover_data(sparse_tensor, dense_tensor, pos_test, rho, gamma, lamb, logger=None, factor=1.05, tol=1e-4, max_iter=200, checkpoint=1000):\n",
    "    # initialization\n",
    "    alpha = torch.ones(3) / 3\n",
    "    dim = len(sparse_tensor.shape)\n",
    "    dim_k = {k: [sparse_tensor.shape[d] for d in range(dim) if d != k] for k in range(dim)}\n",
    "\n",
    "    M = sparse_tensor.clone()\n",
    "    pos_missing = torch.where(sparse_tensor == 0)\n",
    "    M[pos_missing] = torch.mean(sparse_tensor[sparse_tensor != 0])\n",
    "    \n",
    "    # initialize Z3, T3\n",
    "    Z3 = torch.cat([torch.zeros(1, *sparse_tensor.shape) for _ in range(dim)], dim=0) # shape: 3 * dim1 * dim2 * dim3\n",
    "    T3 = Z3.clone() # shape: same as Z3\n",
    "\n",
    "    max_value = max(sparse_tensor.max(), 1e4)\n",
    "    min_value = min(sparse_tensor.min(), 0)\n",
    "    \n",
    "    # result recorder\n",
    "    RMSE = torch.zeros(max_iter+1)\n",
    "    MAPE = torch.zeros(max_iter+1)\n",
    "    \n",
    "    used_time = 0\n",
    "    for it in range(max_iter):\n",
    "\n",
    "        # compute the MAPE, RMSE\n",
    "        mape = compute_mape(dense_tensor[pos_test], M[pos_test]) * 100\n",
    "        rmse = compute_rmse(dense_tensor[pos_test], M[pos_test])\n",
    "        MAPE[it] = mape\n",
    "        RMSE[it] = rmse\n",
    "\n",
    "        if it % checkpoint == 0:\n",
    "            logger.info(f\"Iter: {it}, MAPE: {mape:.6f}, RMSE: {rmse:.6f}\")\n",
    "\n",
    "        start_time = time.time()\n",
    "\n",
    "        # update rho\n",
    "        M_latest = M.clone()\n",
    "        rho = np.clip(rho * factor, 1e-10, 1e5)\n",
    "        \n",
    "        for k in range(dim):\n",
    "            # update Zk\n",
    "            M_k = unfolding(M - T3[k] / rho, k)\n",
    "            Z_k = svt_scad(M_k, alpha[k] / rho, gamma=gamma, lamb=lamb)\n",
    "            Z3[k] = folding(Z_k, k, dim_k[k])\n",
    "            del M_k, Z_k\n",
    "        \n",
    "        # update M\n",
    "        M[pos_missing] = torch.mean(Z3 + T3 / rho, dim=0)[pos_missing]\n",
    "        M.clamp_(min_value, max_value)\n",
    "        \n",
    "        # update dual variable T\n",
    "        T3 += rho * (Z3 - torch.cat([M.clone().unsqueeze(0) for _ in range(dim)], dim=0))\n",
    "\n",
    "        tole = torch.linalg.norm(M - M_latest) / torch.linalg.norm(M_latest)\n",
    "\n",
    "        used_time += time.time() - start_time\n",
    "        # compute the tolerance\n",
    "        if tole < tol:\n",
    "            break\n",
    "\n",
    "\n",
    "    # compute the MAPE, RMSE\n",
    "    mape = compute_mape(dense_tensor[pos_test], M[pos_test]) * 100\n",
    "    rmse = compute_rmse(dense_tensor[pos_test], M[pos_test])\n",
    "    MAPE[it] = mape\n",
    "    RMSE[it] = rmse\n",
    "    \n",
    "    logger.info(f\"Total iteration: {it + 1}, Running time: {used_time:.5f}, Tolerance: {tole * 1e5:.2f}e-5, \")\n",
    "    logger.info(f\"Imputation MAPE / RMSE: {mape:.2f} / {rmse:.2f}.\")\n",
    "\n",
    "    return M, RMSE[:it+1], MAPE[:it+1], used_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyper_parameters = {\n",
    "    \"Hangzhou\": {\n",
    "        \"random\": [1000., 3.], \n",
    "        \"non-random\": [1000., 3.],\n",
    "        \"blackout\": [10000., 1.],\n",
    "    }, \n",
    "    \"Seattle\": {\n",
    "        \"random\": [1000., 1.], \n",
    "        \"non-random\": [1000., 1.],\n",
    "        \"blackout\": [1000., 1.],\n",
    "    }, \n",
    "    \"Portland\": {\n",
    "        \"random\": [1000., 6.], \n",
    "        \"non-random\": [1000., 6.],\n",
    "        \"blackout\": [1000., 6.],\n",
    "    }, \n",
    "    \"PeMS\": {\n",
    "        \"random\": [1000., 1.], \n",
    "        \"non-random\": [1000., 2.],\n",
    "        \"blackout\": [1000., 1.],\n",
    "    },\n",
    "}\n",
    "\n",
    "missing_rates = {\n",
    "    \"random\": [0.3, 0.5, 0.7],\n",
    "    \"non-random\": [0.3, 0.5, 0.7],\n",
    "    \"blackout\": [0.3, 0.5, 0.7],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:08:02.428 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:08:02.429 : ms: 0.3 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 1.0.\n",
      "24-11-19 10:08:02.534 : Iter: 0, MAPE: 27.570042, RMSE: 13.497121\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers setup!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:08:13.017 : Total iteration: 121, Running time: 9.90234, Tolerance: 8.85e-5, \n",
      "24-11-19 10:08:13.020 : Imputation MAPE / RMSE: 2.69 / 1.97.\n",
      "24-11-19 10:08:13.237 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:08:13.238 : ms: 0.5 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 1.0.\n",
      "24-11-19 10:08:13.312 : Iter: 0, MAPE: 27.464575, RMSE: 13.478112\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers exist!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:08:25.053 : Total iteration: 127, Running time: 10.83723, Tolerance: 9.90e-5, \n",
      "24-11-19 10:08:25.056 : Imputation MAPE / RMSE: 3.23 / 2.36.\n",
      "24-11-19 10:08:25.360 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:08:25.361 : ms: 0.7 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 1.0.\n",
      "24-11-19 10:08:25.439 : Iter: 0, MAPE: 27.468857, RMSE: 13.480422\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers exist!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:08:38.163 : Total iteration: 139, Running time: 11.62961, Tolerance: 9.54e-5, \n",
      "24-11-19 10:08:38.165 : Imputation MAPE / RMSE: 4.20 / 3.04.\n",
      "24-11-19 10:08:38.390 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:08:38.391 : ms: 0.3 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 2.0.\n",
      "24-11-19 10:08:38.437 : Iter: 0, MAPE: 27.391628, RMSE: 13.477327\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers setup!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:08:46.839 : Total iteration: 100, Running time: 7.99954, Tolerance: 9.88e-5, \n",
      "24-11-19 10:08:46.842 : Imputation MAPE / RMSE: 7.13 / 5.04.\n",
      "24-11-19 10:08:47.020 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:08:47.022 : ms: 0.5 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 2.0.\n",
      "24-11-19 10:08:47.072 : Iter: 0, MAPE: 27.598280, RMSE: 13.494247\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers exist!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:08:56.143 : Total iteration: 111, Running time: 8.50543, Tolerance: 9.31e-5, \n",
      "24-11-19 10:08:56.146 : Imputation MAPE / RMSE: 7.98 / 5.50.\n",
      "24-11-19 10:08:56.321 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:08:56.322 : ms: 0.7 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 2.0.\n",
      "24-11-19 10:08:56.360 : Iter: 0, MAPE: 27.606110, RMSE: 13.510813\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers exist!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:09:08.753 : Total iteration: 142, Running time: 11.44191, Tolerance: 8.60e-5, \n",
      "24-11-19 10:09:08.756 : Imputation MAPE / RMSE: 9.10 / 6.17.\n",
      "24-11-19 10:09:08.928 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:09:08.929 : ms: 0.3 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 1.0.\n",
      "24-11-19 10:09:08.965 : Iter: 0, MAPE: 27.401161, RMSE: 13.448531\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers setup!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:09:19.292 : Total iteration: 120, Running time: 9.69373, Tolerance: 9.84e-5, \n",
      "24-11-19 10:09:19.296 : Imputation MAPE / RMSE: 7.47 / 5.27.\n",
      "24-11-19 10:09:19.493 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:09:19.494 : ms: 0.5 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 1.0.\n",
      "24-11-19 10:09:19.535 : Iter: 0, MAPE: 26.954666, RMSE: 13.351610\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers exist!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:09:29.327 : Total iteration: 130, Running time: 9.17575, Tolerance: 9.38e-5, \n",
      "24-11-19 10:09:29.330 : Imputation MAPE / RMSE: 8.33 / 5.77.\n",
      "24-11-19 10:09:29.506 : --------------------------------------------------------------------------------------------------------\n",
      "24-11-19 10:09:29.507 : ms: 0.7 \t initial rho: 1e-05 \t gamma: 1000.0 \t lamb: 1.0.\n",
      "24-11-19 10:09:29.549 : Iter: 0, MAPE: 27.274790, RMSE: 13.435456\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogHandlers exist!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "24-11-19 10:09:41.813 : Total iteration: 137, Running time: 11.17114, Tolerance: 9.51e-5, \n",
      "24-11-19 10:09:41.815 : Imputation MAPE / RMSE: 9.65 / 6.50.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fXl6O3r17Y82aNU1u85vf/AZ5eXmm29dff23VNnE+eCJSCpuOg09MTERiYuIdt9FoNNBqtW3UIuD+rgH4adGDcHexXmkyrpYh1NcNri6cDoGIrMfu++B3796NoKAgdO/eHU8//TQKCgqs+nxuaieE+LjBx906UxYczS7GgytT8PRHR6yyfyIiI7s+kzUxMREPP/wwIiMjkZmZiZdffhkPPvggjh49Co1G0+hj9Ho99Hq9abmkpAQAIMsyZFmGEAKybLvuly9SLwEAfkwvtGk7mmIPNWoPWKfmsUbmsWad7DrgH330UdPfMTExiIuLQ2RkJL766itMmDCh0ccsXboUS5YsaXD/1atXUVFRAZ1OByEEVKrGv7wUlVdj05F8OKskzHnA8lMVGKpqL+b96D1BVv828mvIstxsjYh1MgdrZB5jnaxRI7sO+PpCQkIQGRmJ9PT0JrdZtGgR5s+fb1ouKSlBREQEAgMD4enpCUmSEBgY2GQxy66WIen4CXi7OuNvv+9r8ddQXFV7BN811B9BQUEW339rybLcbI2IdTIHa2QeY52a6pVojXYV8EVFRbh48SJCQkKa3Eaj0TRaKJVKBZVKBUmSTH83xlVdWxJ9jWyVN+Wl67VH8PklemQUVqBrkKfFn6O1mqsR1WKdmscamcdYJ0uzadXLysqQmpqK1NRUAEBmZiZSU1ORk5ODsrIyLFiwAD/99BOysrKwe/dujBkzBgEBAfjd735ntTYZx8FXGWr7xSzt0vUbAIC1ezPxyaEci++fiMjIpkfwR44cwfDhw03Lxq6VqVOn4r333sPJkyfx0Ucf4fr16wgJCcHw4cOxefNmeHl5Wa1NxnHwQgDVBgG1s2SxfVdWG3C19NYPwOkFZRbbNxFRfTYN+GHDht3xKPm7775rw9bUMk5VANROV6B2ttyXnMpqA34bG4JvTuVBFsD5K6UW2zcRUX3sGKvn9oC39Nmsvu5qvPN4Xxx/OQEAcFlXidLKaos+BxGREQO+HkmSoHay7oySPu4uCPKq/SH4HI/iichKGPCN+GbeA/hx4XBTCLdG0qEcjHxzDy5cLUNFVQ1kubZL6u5wXwDA4aziVj8HEVFjGPCN6BLoiQh/dzg7tb48ZfoaZBWV442vzuAvn51Cz5e/xaYD2bivsz8A4GBGUaufg4ioMe1qHHx7NLBLB+hrZOw+dxVab1dUGWR4uzrj7q4BkCQJg7t2sHUTiUihGPCNWL8vE/m6Sjw2oCMiO3i0al+9Qn0wqEsH7L9QhEvXb8DfQ43BXQMQ4KnBU/dHWajFREQNMeAbsfnwRfySX4oHugW2KuCFEPhgTwb83NUAAF93F3z8xwEI8LT8KclERPUx4Bvh41Y7VXBRub6ZLe+sVF+DZd/8AgD415Q49AzxQrifu2l9cXkVktOuoFRfw6N5IrI4BnwjwvzcgEwgt/hGq/ZTXF4FAHBXO2FEdHCD9VlF5Vi49QT83F0wfXAnSJLlzpolIuIomkYYj7JbHfAVtScxGbto6usV6gONswrFFdXIKCxv1XMREdXHgG9EuK8bgFsTg/1axiN4P4/Grw6ldlah983x8EezOR6eiCyLAd+IcL/agM8trmjVfoorbgZ8E0fwANA30g8AcIwBT0QWxoBvRNjNgL9UfAPVBhnVhl83ZcG1m0fwvncI+H43A/4IA56ILIw/sjYizNcNX869HxF+7pj7n+PIKirHtlmD4K5uWbmu3+yD97/DBbyNAX++oAzXK6ru+GFARNQSDPhGODupEBPmg4KSSvyYfhVB3q4wyC2/+MfEeyMwoLM/Au8wp42/hxqRHdyRXVSBX/JLcV9nntlKRJbBgL+D3WevorzKAC9XZ3i5Nn0U3pRwP/c6496b8s8n4hDopYG/R+3R+yufn4LuRjXmjeiOTgGtO5OWiBwXA74Ju84WYOHWEwCAB3ta9+LYPbS3rlBVbZCxPfUydDeqsfd8ITZOvxe9Qn2s+vxEpEz8kbUJP124NcvjP/dkYF7S8Rbv47+HLyLpUA4Ky8w/I9ZJkrB2ahx6hXqjsKwKj//rIHKKWjeah4gcEwO+Cc6qW2eVVlQZcDSn5aNcViafxYvbTiLveuUdtyvT12Dp12cwbf0hSBIQ18kfnzxzH+4O98H1imr88aPDWLTtBDYdyG5xG4jIcTHgmzBtcCfEhHnjuQe7AqgdMtmSS/gJIW6dydrEiU5GGmcV1u3LxO6zV9HjL9/iWE4xvF1d8MET/RDgqca5K2X45NBFeLve6lH7Pu0KPk+9ZFp+P+UCZn98DAc4vzwR3cQ++CYEebniy7kPQAiBD3/MxI1qAy5dv4EoM3/0rKgymD4Q7nSiEwC4OKkQ7ueOzMJyVBlk05WkQnzcsHZqf7y7+zw6B3pizN2hpscs+/YX5F2/gdgwH3T0d8emn7Jx6foNq/9eQETtB4/gmyFJEiI71I6EySoyf74Y41msamcV3NVOzW6vrzaY/g67OVUCAPSO8MUHT8Thhd/0hOpmt9GZvBKcLyhDtUFg7/lCdH3pG9O0CgYh8M6u82a3k4iUiwFvBmPAZ7dgQjDjRGUBHmqzZolM6KUFAET4u91x++Xf/oLEt38EADzQLQBdAj1N6/w91Fi45QRW7jiLgtI79/sTkfIx4M1gvOhH9jXzR7MY+8L73DxTtTkvJvbEi4k9sfmZgXfcLl93K7h/e3cI7o3yNy0nxmjRt6MvZAFsOZprdluJSJkY8GboEugBHzcXs05aMjIG/OAuAWZt7+rihBlDuyD0tu6Zxoy8eaQPACOig+HipMKixJ7oEeyF2cO7YtK9HQEAH+3Pxr8PZEOIlp+BS0TKIAmFJ0BJSQl8fHyg0+ng6emJgoICBAUFQaUy/7OtoqoGKkmCq0vzfelGZfoaHM68hl5h3gjycv01TW+UQRb4v+/OokugBx6Oi2iwvrLagAFv/ADdjdoRPM8M6Yz/N+ous/cvy/KvqpGjYZ2axxqZx1gnV1dX+Pn5QafTwdvb2yL75igaM7R0kjEA8NQ4Y7gVRrQ4qSS8mNizyfWuLk54uF84/rU3E64uKkzs3/BDgIgcQ4s+VkeNGgWdTmdafv3113H9+nXTclFREaKjoy3WOHsjhMBPF4pQUVVj66bc0bNDu2Bkr2AsnRCLfecL8crnp2zdJCKygRYF/HfffQe9/tZp98uXL8e1a9dMyzU1NTh79qzlWmdnJq89iEkfHsCx7Ot33G7ZN7/g9a/SkGWjy/AFemnwwRNxSIjWYvH/0rDxp2zk6Vp3dSoian9aFPD1u+sV3n3fQKBn7QlIR7Jvfag1djT/6ZGL+PDHTJRUVrdZ2xrjoXHGXSG1E5kdyeIFRYgcDX/5aAHjxTmM10/dejQX0X/9Dmt2ppu2KSrTo+jmlZy6Bnk23Ekb69exts3fnsp3uA9kIkfXol8PJUlqcBKOOSfxKIXx+qmpOddhkAVWJZ8DAPx9xzlcK69GRVUNBnapvWBHhL/br/px1tJGxYbgowPZ+OpkHio21H7bmDW8K/p38m/mkUTU3rUogYQQmDZtGjSa2q6KyspKzJgxAx4etScC3d4/r0Q9td7wUDuhVF+Dc1dK8dr4GDy54TAAYN2+TABA0uGLAIDuQV5N7qctDejcAa+Oi8Fftp/CrrNXEeLjCo1z7Re38wWliArwhJPKcT6kiRxJiwJ+6tSpdZYnT57cYJspU6a0rkV2zEkloU9HP+w9X4hPDuXgb+NicO61RCzadhIXrpYh9eJ107bdgu0j4AFg8n2R8HJ1xolcHRKig3F3uC8A4KOfshHgqcFzD3WzbQOJyCpaFPDr16+3VjvajbhOtQFfc/MarWpnFVY+0hvVBhlL/nca/z6QAwDoHmz7/vfbjbsnDOPuCTMtn8krwfWKavzv58t4+oHOcDNjQjQial9a3EmcnZ2NHTt2oLq6GsOGDVP0uPfGTBnYCVdKKvGHfuF17ndxUuG18bHYm16IrKIKdLejI/jGdA/2wvGLxSiuqMY7u84jrpMf7uvcAWondtcQKUWLAn7Pnj0YNWoUKipqJ91ydnbGxo0bMWnSJKs0zh75e6ixdMLdTa7ftWAYrpTo0cHzznPA25qTSsL0wVFY8r80rLk5vXBsmA/efawPWn55cSKyRy0aJvnyyy9j+PDhyM3NRVFREaZPn46FCxdaq23tkiRJ0Pq4wsXJ/kegPhwXgY7+7nBzcYKXxhknL+kwee0h1Bg4nJJICVp0BH/y5Ens2bMHoaG1VxZauXIlPvzwQxQXF8PPz7xpccl+eGqcsXvBMMhCIL+kEn9KSsWfE7rD2al2OKUsC9NFRoio/WnRYeb169cRFHRrAi0PDw+4u7vXmY+G2heVSoLzzUsGbpkx0DS/vCwLPJd0HKt/SOcJUkTtVIt/ZE1LS0N+fr5pWQiBM2fOoLS01HTf3Xc33UdN9kuSJFOYp6RfxZcn8vAl8lCmr8GLiT0d6qQ2IiVoccA/9NBDDY7oRo8ebQoHSZJgMBiaeDS1F8N7BGHJ2F545YvT+GBPBpLTrkDr44q3Hr0HQd6Wm9+eiKynRQGfmZlprXaQHZo6qBNkIbDkf2nIKCxHRmE5nks6jn8/NQDO7eBHZCJH16KAj4yMbHab1NRUs7aj9uHJwVEY2j0Q6QVlmL85FQcyriGjsNzux/kTkYVmk9TpdHj33XfRt29f9OvXzxK7JDvSOdATI3tpsez3d+P+rgEMd6J2olUBv3PnTkyePBkhISFYvXo1Ro0ahSNHjliqbWRnxvQOxYdT4kzLsiwgyxxhQ2SvWvwja25uLjZs2IB169ahvLwcjzzyCKqrq7F161aHm7bAERnnrDmZq8NfPj8FP3cXPP1AZwzuGmDjlhFRfS2+Jmt0dDTS0tKwevVqXL58GatXr7ZW28iOHb9YjJ8vXsfus1fx+L8O4rUv01BjkG3dLCK6TYuO4Hfs2IHnnnsOM2fORLdunGLWkU0Z2AlRAR7438+X8d8jufjX3kwkn7mCuQ92w+/7hnHMPJEdaNER/I8//ojS0lLExcVhwIABWLNmDa5evWqttpGde6BbIFb8oTfefbwv/NxdkF1UgQWf/oyXPz9lOldCX8NzIohspUUBP3DgQHz44YfIy8vDs88+i6SkJISFhUGWZSQnJ9c5m5Ucx6jYEOx78UHMj+8OSQJCfd0gBPCvHzPw0MoULP36DH44c8XWzSRyOL9qFI27uzumT5+OvXv34uTJk3j++eexbNkyBAUFYezYsWbvZ8+ePRgzZgxCQ0MhSRK2b99eZ70QAosXL0ZoaCjc3NwwbNgwnD59+tc0mazMXe2M5x7qhv/NuR8zh3ZBlUFG0uGLyC2+gQ/2ZOCpjUcwf3MqZv77KP655wLntyFqA60eB9+jRw+sWLECubm5SEpKalHfa3l5OXr37o01a9Y0un7FihVYtWoV1qxZg8OHD0Or1SI+Pp7fFOxYTJgPJEmCq4sT/jUlDk/dH4UJfWqvJLXt+CV8cyofb3z9Cz47fsnGLSVSvhb9yDp9+vRmt+nQoYPZ+0tMTERiYmKj64QQeOutt/DSSy9hwoQJAICNGzciODgY//nPf/Dss8+a/TxkG50CPPDy6Nqhswm9tNh2LBf+HmoUlunx27tDTNvt/OUKHugW2C7m0CdqT1oU8Bs2bEBkZCT69OnT5FdsS42eyMzMRH5+PhISEkz3aTQaDB06FPv3728y4PV6PfR6vWm5pKQEACDLMmRZhhACsszhfE2xVo0SooOQEB108zlq55mXZRln8krw1MYjiA3zwaNx4QjydsXgLh3g6mLf14jle6l5rJF5rFmnFgX8jBkzkJSUhIyMDEyfPh2TJ0+Gv7+/xRsFwDQlcXBwcJ37g4ODkZ2d3eTjli5diiVLljS4/+rVq6ioqIBOp4MQAioVjxYbI8tym9bowqUSeKqdcCJXhxO5OgCAu4sKzw0Jx/jYQKs//6/V1nVqj1gj8xjrZI0atSjg3333Xbz55pvYtm0b1q1bh0WLFuG3v/0tnnrqKSQkJFhl7HP9fRqnJG7KokWLMH/+fNNySUkJIiIiEBgYCE9PT0iShMDAQL7hmiDLcpvWaHRQEPp1C8O7uzOQr6vEmfwSXL5eiRU7c3BXx2A80M0+z5Bt6zq1R6yReYx10mg0Ft93i6cq0Gg0mDRpEiZNmoTs7Gxs2LABs2bNQnV1NdLS0uDp6WmRhmm1WgC1R/IhIbf6awsKChoc1ddvX2OFUqlUUKlUkCTJ9Dc1rq1rFObngdd/Fwug9gN84ZYT+PRoLt7ZdQFDugfa7UlTfC81jzUyj7FOltaqPUqSZLrQh6X7j6KioqDVapGcnGy6r6qqCikpKRg0aJBFn4vshyRJeHV8DGYP74J1T/a323Anag9aHPB6vR6ffPIJ4uPj0aNHD5w8eRJr1qxBTk5Oi4/ey8rKkJqaitTUVAC1P6ympqYiJycHkiRh3rx5eOONN/DZZ5/h1KlTmDZtGtzd3fHYY4+1tNnUjri6OOHPI3vCU9PiL5hEdJsW/R80a9YsJCUloWPHjnjyySeRlJTUomGR9R05cgTDhw83LRv7zqdOnYoNGzZg4cKFuHHjBmbNmoXi4mIMGDAAO3bsgJcX5yN3FEIIrNuXBX8PF/yuT7itm0PUrkiiBacUqlQqdOzYEX369LnjV+dt27ZZpHGWUFJSAh8fH+h0Onh6eqKgoABBQUHsE2yCLMt2VaPPUy/hT0mpUDup8NxDXfHs0C52MV7e3upkj1gj8xjr5OrqCj8/P+h0Onh7e1tk3y06gp8yZQr7RKlNjb47FN+czMe3p/Px9x3n8FNGEd6f3A9eri62bhqR3WvxiU5EbclJJeG9yX2xPfUSXvrsFPadL8Lo1Xvx2L0d8UhcBPw81LZuIpHd4vcmsnuSJOF3fcKR9Mx9CPDUILuoAku/+QWLtp00bcPLBxI1xGEK1G7cHe6LnQuG4qsTedj0Uzam3x9lWncgowhPrDuEIC8Nemq94OuuhouThG5BXlCpJAzs3AHRobX9mhevVeDkpdqzZg2ygCzEzf8CshAY3DUAYb5uAIALV8uw73xh7QfIzfVA7QdKaVkpxvZzRzett2nbb0/VnoEthIAQgABu/ldgxF3BiAnzAQBkXC3DtmOXINBwOwggPjoYcZ1qzxLPLirHhv1ZMP5aJoSos31CtBZDutee9ZtbXIE1O8/f2heMj6n970N3BeM3MbXnmBSUVGL5t2dr1xu3ve0zcmiPQIy7p3aiuMpqg91PH0ENMeCpXfF2dcGkeztiYv+IOvcfyS6GQRbI01UiT1fZ4HHrp/U3Bfze84V1jv7r+9eUOFPAp+Zcx18/b3qK6q6hAaaAP5dfiv/77myT24b4uJoCPvtaBdbsOt/ktlofV1PA5+sqsX5fVpPbhvq6mQK+uLwaSYcv3nG/xoAvqazB1mO5TW7r76E2BXwNvx21Swx4apfq/9g/c1gXPNo/ArnFFUi7XAJ9jYwyfQ3SC8qgkiT00N4aWuvn7oJ7b4ank0qCSgWoJOnmDfD3vNWvH+7nhlGx2tozDW+ul1B7oKuvrESYn9tt27rjkbhwSJAgSUBtE2/+DaBL4K3zRCL83DBtUKebrwW3HnNz2fhBANQG+MxhXUzrbt8WkoS4yFvzQQX7aLAgobupPreXSYKEfpF+puUOHmosSux5W01vbQfUbYPaDkYuUcu1aJhke8Rhki3DoW3mYZ2axxqZx5rDJFl1IiKFYsATESkUA56ISKEY8ERECsWAJyJSKAY8EZFCMeCJiBSKAU9EpFAMeCIihWLAExEpFAOeiEihGPBERArFgCciUigGPBGRQjHgiYgUigFPRKRQDHgiIoViwBMRKRQDnohIoRjwREQKxYAnIlIoBjwRkUIx4ImIFIoBT0SkUAx4IiKFYsATESkUA56ISKEY8ERECsWAJyJSKAY8EZFCMeCJiBSKAU9EpFAMeCIihWLAExEpFAOeiEihGPBERArFgCciUigGPBGRQjHgiYgUigFPRKRQDHgiIoViwBMRKRQDnohIoew64BcvXgxJkurctFqtrZtFRNQuONu6Ac3p1asXvv/+e9Oyk5OTDVtDRNR+2H3AOzs786idiOhXsOsuGgBIT09HaGgooqKiMHHiRGRkZNi6SURE7YJdH8EPGDAAH330Ebp3744rV67gtddew6BBg3D69Gl06NCh0cfo9Xro9XrTcklJCQBAlmXIsgwhBGRZbpP2t0eskXlYp+axRuaxZp3sOuATExNNf8fGxmLgwIHo0qULNm7ciPnz5zf6mKVLl2LJkiUN7r969SoqKiqg0+kghIBKZfdfXmxClmXWyAysU/NYI/MY62SNGtl1wNfn4eGB2NhYpKenN7nNokWL6oR/SUkJIiIiEBgYCE9PT0iShMDAQL7hmiDLMmtkBtapeayReYx10mg0Ft93uwp4vV6PM2fO4IEHHmhyG41G02ihVCoVVCoVJEky/U2NY43Mwzo1jzUyj7FOlmbXVV+wYAFSUlKQmZmJgwcP4g9/+ANKSkowdepUWzeNiMju2fURfG5uLiZNmoTCwkIEBgbivvvuw4EDBxAZGWnrphER2T27DvikpCRbN4GIqN2y6y4aIiL69RjwREQKxYAnIlIoBjwRkUIx4ImIFIoBT0SkUAx4IiKFYsATESkUA56ISKEY8ERECsWAJyJSKAY8EZFCMeCJiBSKAU9EpFAMeCIihWLAExEpFAOeiEihGPBERArFgCciUigGPBGRQjHgiYgUigFPRKRQDHgiIoViwBMRKRQDnohIoRjwREQKxYAnIlIoBjwRkUIx4ImIFIoBT0SkUM62boC1CSEAACUlJZBlGaWlpXB1dYVKxc+2xrBG5mGdmscamcdYp6qqKgC3MssSFB/wpaWlAICIiAgbt4SIqHmlpaXw8fGxyL4kYcmPCzskyzIuX74MLy8vlJaWIiIiAhcvXoS3t7etm2aXSkpKWCMzsE7NY43MY6xTTk4OJElCaGioxb7xKP4IXqVSITw8HAAgSRIAwNvbm2+4ZrBG5mGdmscamcfHx8fidWLHGBGRQjHgiYgUyqECXqPR4JVXXoFGo7F1U+wWa2Qe1ql5rJF5rFknxf/ISkTkqBzqCJ6IyJEw4ImIFIoBT0SkUA4T8O+++y6ioqLg6uqKfv364ccff7R1k2xq8eLFkCSpzk2r1ZrWCyGwePFihIaGws3NDcOGDcPp06dt2GLr27NnD8aMGYPQ0FBIkoTt27fXWW9OTfR6PebOnYuAgAB4eHhg7NixyM3NbcNXYV3N1WjatGkN3lf33XdfnW2UXqOlS5eif//+8PLyQlBQEMaPH4+zZ8/W2aat3ksOEfCbN2/GvHnz8NJLL+H48eN44IEHkJiYiJycHFs3zaZ69eqFvLw80+3kyZOmdStWrMCqVauwZs0aHD58GFqtFvHx8aapH5SovLwcvXv3xpo1axpdb05N5s2bh88++wxJSUnYu3cvysrKMHr0aBgMhrZ6GVbVXI0A4De/+U2d99XXX39dZ73Sa5SSkoLZs2fjwIEDSE5ORk1NDRISElBeXm7aps3eS8IB3HvvvWLGjBl17uvZs6d48cUXbdQi23vllVdE7969G10ny7LQarVi2bJlpvsqKyuFj4+PeP/999uohbYFQHz22WemZXNqcv36deHi4iKSkpJM21y6dEmoVCrx7bfftlnb20r9GgkhxNSpU8W4ceOafIyj1UgIIQoKCgQAkZKSIoRo2/eS4o/gq6qqcPToUSQkJNS5PyEhAfv377dRq+xDeno6QkNDERUVhYkTJyIjIwMAkJmZifz8/Do102g0GDp0qMPWzJyaHD16FNXV1XW2CQ0NRUxMjEPVbffu3QgKCkL37t3x9NNPo6CgwLTOEWuk0+kAAP7+/gDa9r2k+IAvLCyEwWBAcHBwnfuDg4ORn59vo1bZ3oABA/DRRx/hu+++w4cffoj8/HwMGjQIRUVFprqwZreYU5P8/Hyo1Wr4+fk1uY3SJSYm4uOPP8bOnTuxcuVKHD58GA8++CD0ej0Ax6uREALz58/H/fffj5iYGABt+15S/GRjRsaJxoyEEA3ucySJiYmmv2NjYzFw4EB06dIFGzduNP0oxpo19Gtq4kh1e/TRR01/x8TEIC4uDpGRkfjqq68wYcKEJh+n1BrNmTMHJ06cwN69exusa4v3kuKP4AMCAuDk5NTgU6+goKDBJ6gj8/DwQGxsLNLT002jaVizW8ypiVarRVVVFYqLi5vcxtGEhIQgMjIS6enpAByrRnPnzsUXX3yBXbt2mWa0Bdr2vaT4gFer1ejXrx+Sk5Pr3J+cnIxBgwbZqFX2R6/X48yZMwgJCUFUVBS0Wm2dmlVVVSElJcVha2ZOTfr16wcXF5c62+Tl5eHUqVMOW7eioiJcvHgRISEhAByjRkIIzJkzB9u2bcPOnTsRFRVVZ32bvpda9/tw+5CUlCRcXFzE2rVrRVpampg3b57w8PAQWVlZtm6azTz//PNi9+7dIiMjQxw4cECMHj1aeHl5mWqybNky4ePjI7Zt2yZOnjwpJk2aJEJCQkRJSYmNW249paWl4vjx4+L48eMCgFi1apU4fvy4yM7OFkKYV5MZM2aI8PBw8f3334tjx46JBx98UPTu3VvU1NTY6mVZ1J1qVFpaKp5//nmxf/9+kZmZKXbt2iUGDhwowsLCHKpGM2fOFD4+PmL37t0iLy/PdKuoqDBt01bvJYcIeCGEeOedd0RkZKRQq9Wib9++piFLjurRRx8VISEhwsXFRYSGhooJEyaI06dPm9bLsixeeeUVodVqhUajEUOGDBEnT560YYutb9euXQJAg9vUqVOFEObV5MaNG2LOnDnC399fuLm5idGjR4ucnBwbvBrruFONKioqREJCgggMDBQuLi6iY8eOYurUqQ1ev9Jr1Fh9AIj169ebtmmr9xJnkyQiUijF98ETETkqBjwRkUIx4ImIFIoBT0SkUAx4IiKFYsATESkUA56ISKEY8ERECsWAJ7KiTp064a233rJ1M8hBMeBJMaZNm4bx48cDAIYNG4Z58+a12XNv2LABvr6+De4/fPgwnnnmmTZrB9HtHGY+eKJfo6qqCmq1+lc/PjAw0IKtIWoZHsGT4kybNg0pKSl4++23IUkSJElCVlYWACAtLQ2jRo2Cp6cngoOD8cQTT6CwsND02GHDhmHOnDmYP38+AgICEB8fDwBYtWoVYmNj4eHhgYiICMyaNQtlZWUAai9R9+STT0Kn05meb/HixQAadtHk5ORg3Lhx8PT0hLe3Nx555BFcuXLFtH7x4sW45557sGnTJnTq1Ak+Pj6YOHGioi92TtbDgCfFefvttzFw4EA8/fTTyMvLQ15eHiIiIpCXl4ehQ4finnvuwZEjR/Dtt9/iypUreOSRR+o8fuPGjXB2dsa+ffvwwQcfAABUKhX+8Y9/4NSpU9i4cSN27tyJhQsXAgAGDRqEt956C97e3qbnW7BgQYN2CSEwfvx4XLt2DSkpKUhOTsaFCxfqXAUJAC5cuIDt27fjyy+/xJdffomUlBQsW7bMStUiJWMXDSmOj48P1Go13N3dTVfPAYD33nsPffv2xRtvvGG6b926dYiIiMC5c+fQvXt3AEDXrl2xYsWKOvu8vT8/KioKr776KmbOnIl3330XarUaPj4+kCSpzvPV9/333+PEiRPIzMxEREQEAGDTpk3o1asXDh8+jP79+wMAZFnGhg0b4OXlBQB44okn8MMPP+D1119vXWHI4fAInhzG0aNHsWvXLnh6eppuPXv2BFB71GwUFxfX4LG7du1CfHw8wsLC4OXlhSlTpqCoqAjl5eVmP/+ZM2cQERFhCncAiI6Ohq+vL86cOWO6r1OnTqZwB2ove1dQUNCi10oE8AieHIgsyxgzZgyWL1/eYJ3xknJA7fVpb5ednY1Ro0ZhxowZePXVV+Hv74+9e/fiqaeeQnV1tdnPL5q4YHL9+11cXOqslyQJsiyb/TxERgx4UiS1Wg2DwVDnvr59+2Lr1q3o1KkTnJ3Nf+sfOXIENTU1WLlyJVSq2i+9//3vf5t9vvqio6ORk5ODixcvmo7i09LSoNPpcNddd5ndHiJzsYuGFKlTp044ePAgsrKyUFhYCFmWMXv2bFy7dg2TJk3CoUOHkJGRgR07dmD69Ol3DOcuXbqgpqYGq1evRkZGBjZt2oT333+/wfOVlZXhhx9+QGFhISoqKhrsZ8SIEbj77rvx+OOP49ixYzh06BCmTJmCoUOHNtotRNRaDHhSpAULFsDJyQnR0dEIDAxETk4OQkNDsW/fPhgMBowcORIxMTH405/+BB8fH9OReWPuuecerFq1CsuXL0dMTAw+/vhjLF26tM42gwYNwowZM/Doo48iMDCwwY+0QG1Xy/bt2+Hn54chQ4ZgxIgR6Ny5MzZv3mzx108EALwmKxGRQvEInohIoRjwREQKxYAnIlIoBjwRkUIx4ImIFIoBT0SkUAx4IiKFYsATESkUA56ISKEY8ERECsWAJyJSKAY8EZFC/X/wXhNmABKaOgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets = [\"PeMS\"]\n",
    "\n",
    "for dataset_name in datasets:\n",
    "    for kind in [\"random\", \"non-random\", \"blackout\"]:\n",
    "        logger_dir = f\"scad_{kind}_{dataset_name}\"\n",
    "        logging_save_dir = os.path.join(save_dir, logger_dir)\n",
    "        # log the iteration results\n",
    "        if not os.path.exists(logging_save_dir):\n",
    "            os.makedirs(logging_save_dir)\n",
    "\n",
    "        initial_rho = 1e-5\n",
    "        gamma, lamb = hyper_parameters[dataset_name][kind]\n",
    "        for missing_rate in missing_rates[kind]:\n",
    "            logger_info(logger_dir, log_path=os.path.join(logging_save_dir, f\"completion_results.log\"))\n",
    "            result_logger = logging.getLogger(logger_dir)\n",
    "            \n",
    "            result_logger.info(\"--------------------------------------------------------------------------------------------------------\")\n",
    "            result_logger.info(f\"ms: {missing_rate} \\t initial rho: {initial_rho} \\t gamma: {gamma} \\t lamb: {lamb}.\")\n",
    "            dense_tensor = load_data(dataset_name, dataroot)\n",
    "\n",
    "            if kind == \"blackout\":\n",
    "                if dataset_name in [\"Seattle\", \"PeMS\"]:\n",
    "                    block_window = 12\n",
    "                elif dataset_name in [\"Hangzhou\", \"Portland\"]:\n",
    "                    block_window = 6\n",
    "                binary_tensor, binary_blocks = missing_pattern(dense_tensor, missing_rate, kind=kind, block_window=block_window)\n",
    "            else:\n",
    "                binary_tensor = missing_pattern(dense_tensor, missing_rate, kind=kind)\n",
    "            \n",
    "            sparse_tensor = dense_tensor * binary_tensor\n",
    "            pos_test = torch.where((dense_tensor != 0) & (sparse_tensor == 0))\n",
    "\n",
    "            M, RMSE, MAPE, used_time = recover_data(\n",
    "                sparse_tensor, dense_tensor, pos_test, # data and test mask\n",
    "                initial_rho, gamma, lamb, # algorithm parameters\n",
    "                logger=result_logger, # logger\n",
    "                )\n",
    "        \n",
    "            fig = plt.figure(figsize=(4, 3))\n",
    "            plt.grid(alpha=0.3)\n",
    "            plt.plot(MAPE, \"--\", label=\"LRTC-SCAD\")\n",
    "            plt.xlabel(\"Iteration\")\n",
    "            plt.ylabel(\"MAPE\")\n",
    "            plt.legend()\n",
    "            plt.axis([-2, 202, 2, 20])\n",
    "            plt.tight_layout()\n",
    "            plt.savefig(os.path.join(logging_save_dir, f\"missing_rate_{missing_rate}_MAPE.png\"), dpi=300, transparent=True)\n",
    "\n",
    "        logger_close(result_logger)     # close the logger file"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lrtc",
   "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": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}