06a_random_decay_mdl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join as oj\n",
    "import sys, time\n",
    "sys.path.insert(1, oj(sys.path[0], '..'))  # insert parent path\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pmlb\n",
    "from tqdm import tqdm\n",
    "from copy import deepcopy\n",
    "import pickle as pkl\n",
    "import pandas as pd\n",
    "import data\n",
    "import numpy.linalg as npl \n",
    "import numpy.random as npr\n",
    "from scipy.optimize import minimize\n",
    "# from process_results import process_results, aggregate_results, best_ridge\n",
    "from scipy.stats import ortho_group\n",
    "from sklearn.linear_model import LinearRegression, RidgeCV\n",
    "import scipy.linalg as scl\n",
    "import pickle\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "save_dir = 'results'\n",
    "os.makedirs(save_dir, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "label_size = 20\n",
    "mpl.rcParams['xtick.labelsize'] = label_size \n",
    "mpl.rcParams['ytick.labelsize'] = label_size \n",
    "mpl.rcParams['axes.labelsize'] = label_size\n",
    "mpl.rcParams['axes.titlesize'] = 14\n",
    "mpl.rcParams['figure.titlesize'] = 10\n",
    "# mpl.rcParams['lines.markersize'] = 8\n",
    "mpl.rcParams['grid.linewidth'] = 2.5\n",
    "mpl.rcParams['legend.fontsize'] = 20\n",
    "# pylab.rcParams['xtick.major.pad']=5\n",
    "# pylab.rcParams['ytick.major.pad']=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs=sns.color_palette()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MDL COMP FOR different eigenvalues decay of Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 100 0 1000 200\n",
      "2 100 1 1000 200\n",
      "2 400 0 1000 200\n",
      "2 400 1 1000 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "12it [00:21,  4.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 100 0 1200 200\n",
      "2 100 1 1200 200\n",
      "2 400 0 1200 200\n",
      "2 400 1 1200 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "13it [00:33,  6.69s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 100 0 1600 200\n",
      "2 100 1 1600 200\n",
      "2 400 0 1600 200\n",
      "2 400 1 1600 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 3/3 [05:18<00:00, 106.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 100 0 2000 200\n",
      "2 100 1 2000 200\n",
      "2 400 0 2000 200\n",
      "2 400 1 2000 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "save_data = True\n",
    "\n",
    "npr.seed(121123)\n",
    "\n",
    "\n",
    "for noise_std in [0.1, 1.]:\n",
    "    d0s = np.array([100, 400], dtype=int)\n",
    "    ds = np.array([5, 10, 20, 40, 60, 80, 100, 200, 400, 800, 1000, 1200, 1600, 2000])\n",
    "    ns = np.array([200])\n",
    "    epows = [0., 0.5]\n",
    "\n",
    "    num_norms = 2\n",
    "    num_trials = 3\n",
    "    rmt_mdl_comp = np.zeros((num_trials, num_norms, len(ds), len(ns), len(d0s), len(epows)))\n",
    "    rmt_mdl_comp_2 = np.zeros((num_trials, num_norms, len(ds), len(ns), len(d0s), len(epows)))\n",
    "    rmt_mdl_comp_3 = np.zeros((num_trials, num_norms, len(ds), len(ns), len(d0s), len(epows)))\n",
    "\n",
    "\n",
    "    new_rmt_mdl_comp = np.zeros((num_trials,num_norms, len(ds), len(ns), len(d0s), len(epows)))\n",
    "    new_rmt_mdl_comp_2 = np.zeros((num_trials,num_norms, len(ds), len(ns), len(d0s), len(epows)))\n",
    "    new_rmt_mdl_comp_3 = np.zeros((num_trials,num_norms, len(ds), len(ns), len(d0s), len(epows)))\n",
    "\n",
    "    dmax = max(ds)\n",
    "\n",
    "    beta_stars = np.zeros((len(d0s), dmax))\n",
    "    # initialize beta_Star fixed\n",
    "    for l, d0 in enumerate(d0s):\n",
    "        beta_fixed = npr.randn(d0)\n",
    "        beta_fixed /= npl.norm(beta_fixed)\n",
    "        beta_stars[l, :d0] = beta_fixed\n",
    "\n",
    "\n",
    "    for m in tqdm(range(num_trials)):\n",
    "\n",
    "        # change X_train\n",
    "        for p, epow in enumerate(epows):\n",
    "\n",
    "            eigs = np.diag(np.arange(1, dmax+1)**(-epow))\n",
    "            X_train = npr.randn(max(ns), dmax) @ eigs\n",
    "\n",
    "            y_train_stars = np.zeros((len(d0s), ns[0]))\n",
    "\n",
    "            for ltemp, d0 in enumerate(d0s):\n",
    "                y_train_stars[ltemp, :] = X_train[:, :d0] @ beta_stars[ltemp, :d0]\n",
    "\n",
    "            for j, d in tqdm(enumerate(ds)):\n",
    "                for k, n in enumerate(ns):\n",
    "                    X_train_d = X_train[:n, :d]\n",
    "                    eigenvals, eigenvecs = scl.eigh(X_train_d.T @ X_train_d)\n",
    "                    eigenvals = eigenvals[::-1]\n",
    "                    V, s, Uh = scl.svd(X_train_d, full_matrices=False)\n",
    "\n",
    "                    for l, d0 in enumerate(d0s):\n",
    "\n",
    "                        w = eigenvecs.T @ beta_stars[l, :d]\n",
    "                        w = w[::-1] # arrange in order\n",
    "\n",
    "                        v = V.T @ y_train_stars[l, :]\n",
    "\n",
    "\n",
    "                        for i in range(num_norms):\n",
    "                            print(m, d0, i, d, n)\n",
    "                            f = (i+1)**2\n",
    "\n",
    "                            rmt_mdl_comp[m, i, j, k, l, p] = np.sum(np.log(1+f**2 *eigenvals[:min(n, d)] * (w**2/noise_std**2)[:min(n, d)])) / n\n",
    "                            rmt_mdl_comp_2[m, i, j, k, l, p]  = rmt_mdl_comp[m, i, j, k, l, p] + np.sum(np.log((noise_std**2 / (f**2 * w**2))[:min(n, d)])) / n\n",
    "                            rmt_mdl_comp_3[m, i, j, k, l, p] = rmt_mdl_comp_2[m, i, j, k, l, p] - np.log(0.005) * min(n, d)/n\n",
    "\n",
    "\n",
    "                            new_rmt_mdl_comp[m, i, j, k, l, p] = np.sum(np.log(1+f**2 * v**2/noise_std**2)) / n\n",
    "                            new_rmt_mdl_comp_2[m, i, j, k, l, p] = new_rmt_mdl_comp[m, i, j, k, l, p] + np.sum(np.log( noise_std**2 / (f**2 * v**2))) / n\n",
    "                            new_rmt_mdl_comp_3[m, i, j, k, l, p] = new_rmt_mdl_comp_2[m, i, j, k, l, p] - np.log(0.005) *min(n, d)/n \n",
    "\n",
    "    if save_data:\n",
    "        results = {'ns': ns, 'd0s': d0s,  'ds' : ds,\n",
    "                   'epows': epows,\n",
    "                   'num_trials': num_trials, \n",
    "                   'num_norms': num_norms,\n",
    "                   'noise_var': noise_std**2,\n",
    "                    'rmt_mdl_comp': rmt_mdl_comp,\n",
    "                   'rmt_mdl_comp_2':rmt_mdl_comp_2,\n",
    "                   'rmt_mdl_comp_3' : rmt_mdl_comp_3,\n",
    "                   'new_rmt_mdl_comp': new_rmt_mdl_comp,\n",
    "                   'new_rmt_mdl_comp_2': new_rmt_mdl_comp_2,\n",
    "                   'new_rmt_mdl_comp_3': new_rmt_mdl_comp_3\n",
    "                  }\n",
    "        file = open('decay_mdl_n_%d_noise_%d.txt'%(ns[0], noise_std), 'wb')\n",
    "        pickle.dump(results, file)\n",
    "        file.close()\n",
    "\n",
    "    # results = pickle.load(open('decay_mdl_n_200.txt', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_fig = True\n",
    "axes = plt.subplots(1, 2, sharex=True, sharey=True, figsize=[14, 6])\n",
    "for ff, (file, noise_std) in enumerate(zip(['decay_mdl_n_200_noise_1.txt', 'decay_mdl_n_200_noise_0.txt'], [1, .1])):\n",
    "    results = pickle.load(open(file, 'rb'))\n",
    "    \n",
    "    num_norms = results[\"num_norms\"]\n",
    "    d0s = results[\"d0s\"]\n",
    "    ns = results[\"ns\"]\n",
    "    ds = results[\"ds\"]\n",
    "    rmt_mdl_comp = results[\"rmt_mdl_comp\"]\n",
    "    rmt_mdl_comp_2 = results[\"rmt_mdl_comp_2\"]\n",
    "    rmt_mdl_comp_3 = results[\"rmt_mdl_comp_3\"]\n",
    "\n",
    "\n",
    "    lss = ['-', '--', '-.', ':']\n",
    "    mss = ['s', 'd', 'o', '^']\n",
    "    n = ns[0]\n",
    "    gammas = ds/n\n",
    "\n",
    "    for i in range(1):\n",
    "        ax = axes[1][ff]\n",
    "        ax.loglog(gammas, rmt_mdl_comp_2.mean(0)[i, :, 0, 0, 0],   color=cs[0], linestyle=lss[0], linewidth=4.,  marker=mss[0],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[0], epows[0]))\n",
    "        ax.loglog(gammas, rmt_mdl_comp_2.mean(0)[i, :, 0, 0, 1],   color=cs[1], linestyle=lss[1], linewidth=4.,  marker=mss[1],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[0], epows[1]))\n",
    "        ax.loglog(gammas, rmt_mdl_comp_2.mean(0)[i, :, 0, 1, 0],   color=cs[2], linestyle=lss[2], linewidth=4.,  marker=mss[2],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[1], epows[0]))\n",
    "        ax.loglog(gammas, rmt_mdl_comp_2.mean(0)[i, :, 0, 1, 1],   color=cs[3], linestyle=lss[3], linewidth=4.,  marker=mss[3],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[1], epows[1]))\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        if ff == 0:\n",
    "            ax.set_title('A', loc='left', fontsize=20, fontweight='bold')\n",
    "            ax.set_ylabel(\"MDL-COMP\")\n",
    "        else:\n",
    "            ax.set_title('B', loc='left', fontsize=20, fontweight='bold')\n",
    "        ax.grid(True, alpha=0.5, linewidth=1.)\n",
    "        ax.legend(frameon=False, fontsize=20) \n",
    "        ax.set_xlabel('d/n')\n",
    "plt.tight_layout()\n",
    "if save_fig:\n",
    "    plt.savefig('fig3/gauss_results_n_%d.pdf'%(n))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "axes = plt.subplots(1, 2, sharex=True, sharey=True, figsize=[14, 6])\n",
    "for ff, (file, noise_std) in enumerate(zip(['decay_mdl_n_200_noise_1.txt', 'decay_mdl_n_200_noise_0.txt'], [1, .1])):\n",
    "    results = pickle.load(open(file, 'rb'))\n",
    "    \n",
    "    num_norms = results[\"num_norms\"]\n",
    "    d0s = results[\"d0s\"]\n",
    "    ns = results[\"ns\"]\n",
    "    ds = results[\"ds\"]\n",
    "    rmt_mdl_comp = results[\"rmt_mdl_comp\"]\n",
    "    rmt_mdl_comp_2 = results[\"rmt_mdl_comp_2\"]\n",
    "    rmt_mdl_comp_3 = results[\"rmt_mdl_comp_3\"]\n",
    "\n",
    "\n",
    "    lss = ['-', '--', '-.', ':']\n",
    "    mss = ['s', 'd', 'o', '^']\n",
    "    n = ns[0]\n",
    "    gammas = ds/n\n",
    "\n",
    "    for i in range(1):\n",
    "        ax = axes[1][ff]\n",
    "        ax.loglog(gammas, rmt_mdl_comp_3.mean(0)[i, :, 0, 0, 0],   color=cs[0], linestyle=lss[0], linewidth=4.,  marker=mss[0],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[0], epows[0]))\n",
    "        ax.loglog(gammas, rmt_mdl_comp_3.mean(0)[i, :, 0, 0, 1],   color=cs[1], linestyle=lss[1], linewidth=4.,  marker=mss[1],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[0], epows[1]))\n",
    "        ax.loglog(gammas, rmt_mdl_comp_3.mean(0)[i, :, 0, 1, 0],   color=cs[2], linestyle=lss[2], linewidth=4.,  marker=mss[2],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[1], epows[0]))\n",
    "        ax.loglog(gammas, rmt_mdl_comp_3.mean(0)[i, :, 0, 1, 1],   color=cs[3], linestyle=lss[3], linewidth=4.,  marker=mss[3],  markersize=10, label=r'$d_\\star=%d, \\alpha=%.1f$'%(d0s[1], epows[1]))\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        if ff == 0:\n",
    "            ax.set_title('A', loc='left', fontsize=20, fontweight='bold')\n",
    "            ax.set_ylabel(\"MDL-COMP\")\n",
    "        else:\n",
    "            ax.set_title('B', loc='left', fontsize=20, fontweight='bold')\n",
    "        ax.grid(True, alpha=0.5, linewidth=1.)\n",
    "        ax.legend(frameon=False, fontsize=20) \n",
    "        ax.set_xlabel('d/n')\n",
    "plt.tight_layout()\n",
    "if save_fig:\n",
    "    plt.savefig('fig3/gauss_results_delta_n_%d.pdf'%(n))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loc_calc_thetahat(l):\n",
    "    inv = scl.pinv(X_train_d.T @ X_train_d + l * np.eye(d))\n",
    "#     inv = eigenvecs @ npl.pinv(np.eye(d) + np.diag(eigenvals)) @ eigenvecs.T\n",
    "    return inv @ X_train_d.T @ y_train_n\n",
    "\n",
    "def loc_mdl1_loss(l):\n",
    "    thetahat = loc_calc_thetahat(l)\n",
    "    mse_norm = npl.norm(y_train_n - X_train_d @ thetahat)**2 / (2 * var)\n",
    "    theta_norm = npl.norm(thetahat)**2 / (2 * var)\n",
    "    eigensum = 0.5 * np.sum(np.log((eigenvals + l) / l))\n",
    "    return (mse_norm + theta_norm + eigensum) / n\n",
    "\n",
    "\n",
    "def loc_mdl1_loss_ret_all(l):\n",
    "    thetahat = loc_calc_thetahat(l)\n",
    "    mse_norm = npl.norm(y_train_n - X_train_d @ thetahat)**2 / (2 * var)\n",
    "    theta_norm = npl.norm(thetahat)**2 / (2 * var)\n",
    "    eigensum = 0.5 * np.sum(np.log((eigenvals + l) / l))\n",
    "    return mse_norm/n , theta_norm/n,  eigensum / n\n",
    "\n",
    "def g(a, b):\n",
    "    val = np.sqrt(a*(1+np.sqrt(b))**2+1)\n",
    "    val -= np.sqrt(a*(1-np.sqrt(b))**2+1)\n",
    "    val = val**2\n",
    "    return(val/4)\n",
    "\n",
    "def thm3(gammas, snr):\n",
    "    delta = g(snr, gammas)\n",
    "    bnd = gammas * np.log(1+snr-delta)\n",
    "    bnd += np.log(1+gammas * snr - delta)\n",
    "    bnd -= delta/snr\n",
    "    return(bnd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'eigsum'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-7d83202ea20c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0md0s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"d0s\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mrmt_mdl_comp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"rmt_mdl_comp\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0meigsum\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"eigsum\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mgammas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'eigsum'"
     ]
    }
   ],
   "source": [
    "results = pickle.load(open('prac_mdl_n_200_noise_0.txt', 'rb'))\n",
    "\n",
    "# results = pickle.load(open('prac_mdl_n_%d_noise_%d.txt'%(ns[0], noise_std), 'rb'))\n",
    "save_fig = False\n",
    "\n",
    "ds = results[\"ds\"]\n",
    "ns = results[\"ns\"]\n",
    "d0s = results[\"d0s\"]\n",
    "rmt_mdl_comp = results[\"rmt_mdl_comp\"]\n",
    "eigsum  = results[\"eigsum\"]\n",
    "n = ns[0]\n",
    "gammas = ds/n\n",
    "\n",
    "lss = ['-', '--', '-.', ':']\n",
    "mss = ['s', 'd', 'o', '^']\n",
    "\n",
    "for i in range(1):\n",
    "    for l, d0 in enumerate(d0s):\n",
    "        plt.figure(figsize=[8, 6])\n",
    "\n",
    "        plt.loglog(gammas, rmt_mdl_comp.mean(0)[i, :, 0, l, 0],   color=cs[0], linestyle=lss[0], linewidth=4.,  marker=mss[0],  markersize=10, label='MDL-COMP (Thm1)')\n",
    "        plt.loglog(gammas, thm3(gammas, np.minimum(d0,ds)/ds),  color=cs[1], linestyle=lss[1], linewidth=4.,  marker=mss[1], markersize=10, label='MDL-COMP (Thm2)')\n",
    "        plt.loglog(gammas, eigsum.mean(0)[i, :, 0, l, 0], color=cs[2], linestyle=lss[2], linewidth=4.,  marker=mss[2], markersize=10, label= 'Prac-MDL-COMP (Eigensum)')\n",
    "        plt.grid(True, alpha=0.5, linewidth=1.)\n",
    "#         plt.ylim([1e-3, 8e-1])\n",
    "        plt.legend(frameon=False, fontsize=20) \n",
    "        plt.title(r'Different MDL-COMP ($n=%d, d_\\star=%d$)'%(ns[0], d0), fontsize=20)\n",
    "        plt.xlabel('d/n')\n",
    "        plt.tight_layout()\n",
    "        if save_fig:\n",
    "            plt.savefig('fig3/prac_mdl_comp_norm_%d_n_%d_noise_%d_d0_%d.pdf'%((i+1)**2, n, noise_std, d0))\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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