classification_which_is_better_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b73737bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.linalg as LA\n",
    "import time\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from scipy.linalg import sqrtm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "35cf927c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [],
   "source": [
    "ambient_dimension = 128\n",
    "avg_num = int(1e+0) # number of experiment average\n",
    "noise_std = 0.5\n",
    "sign_false_rate = 0.1\n",
    "snr = 4"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [],
   "source": [
    "sample_num = 70\n",
    "\n",
    "\n",
    "# Data generation model:\n",
    "# 1. 1-sparse vector\n",
    "true_theta = np.zeros([ambient_dimension, 1])\n",
    "signal_position = [0]\n",
    "for i in signal_position:\n",
    "    true_theta[i] = 1\n",
    "\n",
    "# 2. random gaussian\n",
    "# true_theta = np.random.multivariate_normal([0] * ambient_dimension, np.identity(ambient_dimension)).T\n",
    "# true_theta /= LA.norm(true_theta)\n",
    "# true_theta = true_theta.reshape(-1, 1)\n",
    "\n",
    "# 3. dense signal with spikes\n",
    "#true_theta = np.random.multivariate_normal([0] * ambient_dimension, np.identity(ambient_dimension)).T\n",
    "#for i in range(20):\n",
    "#    true_theta[i] = 0\n",
    "#spike_strength = 10000\n",
    "#true_theta[0] = np.sqrt(spike_strength) * LA.norm(true_theta)\n",
    "\n",
    "# 2. input data distribution\n",
    "Sigma = np.identity(ambient_dimension)\n",
    "smallest_eig = .6  # choose 1 if you want uniform spectrum\n",
    "decay = smallest_eig ** (1 / ambient_dimension)\n",
    "decay = .95\n",
    "off_diag_factor = 0\n",
    "for i in range(ambient_dimension):\n",
    "    #Sigma[i, i] = decay ** abs(i) if i in signal_position else 0\n",
    "    Sigma[i, i] = decay ** abs(i)\n",
    "for i in range(0, ambient_dimension, 2):\n",
    "    Sigma[i+1, i] = off_diag_factor * Sigma[i, i]\n",
    "    Sigma[i, i+1] = off_diag_factor * Sigma[i, i]\n",
    "\n",
    "# 3. bi-level spectrum\n",
    "# Sigma = np.identity(ambient_dimension)\n",
    "# for i in range(int(ambient_dimension / 2), ambient_dimension):\n",
    "#     Sigma[i, i] = smallest_eig\n",
    "\n",
    "# 4. random spectrum\n",
    "# Sigma = np.random.random((ambient_dimension, ambient_dimension))\n",
    "# Sigma = Sigma.dot(Sigma.T)\n",
    "\n",
    "alpha = snr * noise_std ** 2 / (true_theta.T.dot(Sigma).dot(true_theta)[0][0])\n",
    "Sigma *= alpha\n",
    "\n",
    "#for i in signal_position:\n",
    "#    true_theta[i] /= np.sqrt(Sigma[i, i])\n",
    "#for i in range(20):\n",
    "#    Sigma[i, i] = 1\n",
    "#for i in range(20, ambient_dimension):\n",
    "#    Sigma[i, i] = 0.2\n",
    "\n",
    "#for i in range(ambient_dimension):\n",
    "#    Sigma[i, i] = random.random()"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5af77eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimator Function\n",
    "def get_ridge_est(X, y, ridge):\n",
    "    d = X.shape[1]\n",
    "    n = X.shape[0]\n",
    "    Cov = X.T.dot(X)\n",
    "    diag = np.diag(np.diag(Cov))\n",
    "    reg = np.identity(d)\n",
    "    reg2 = 0.3 / (1 - 0.3) * diag\n",
    "    S = LA.inv(X.T.dot(X) + n * ridge * reg).dot(X.T) if ridge > 0 else LA.pinv(X.T.dot(X)).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_het_ridge_est(X, y, ridge_list):\n",
    "    d = X.shape[1]\n",
    "    n = X.shape[0]\n",
    "    reg = n * np.identity(d)\n",
    "    for i in range(d):\n",
    "        reg[i, i] *= ridge_list[i]\n",
    "    S = LA.pinv(X.T.dot(X) + reg).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_pepper_est(X, y, ridge, p):\n",
    "    d = X.shape[1]\n",
    "    n = X.shape[0]\n",
    "    reg = np.identity(d)\n",
    "    psi = p / (1 - p)\n",
    "    Cov = X.T.dot(X)\n",
    "    diag = np.diag(np.diag(Cov))\n",
    "    S = LA.inv(Cov + psi / (1 - p) * n * ridge * reg + psi * diag).dot(X.T) if (ridge > 0 or p > 0) else LA.pinv(X.T.dot(X)).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_lse_est(X, y):\n",
    "    Cov = X.T.dot(X)\n",
    "    # faster LSE approximation\n",
    "    #est = LA.inv(Cov+1e-10*np.identity(d)).dot(X.T.dot(y))\n",
    "    S = LA.pinv(Cov).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_mask_lse_est(X, y):\n",
    "    Cov = X.T.dot(X)\n",
    "    n = X.shape[0]\n",
    "    cov = np.diag(np.diag(Cov)) / n\n",
    "    cov = sqrtm(cov)\n",
    "    cov = LA.inv(cov)\n",
    "    # faster LSE approximation\n",
    "    #est = LA.inv(Cov+1e-10*np.identity(d)).dot(X.T.dot(y))\n",
    "    S = cov.dot(LA.pinv(cov.dot(Cov).dot(cov))).dot(cov).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_lse_est_2(X, y):\n",
    "    Cov = X.T.dot(X)\n",
    "    mu = X.mean(axis=0).reshape(-1, 1)\n",
    "    n = X.shape[0]\n",
    "    #Z1 = n * mu.dot(mu.T)\n",
    "    Z1 = Cov - 0.5 * np.diag(np.diag(Cov))\n",
    "    A1 = np.concatenate((Cov, Z1), axis=1)\n",
    "    A2 = np.concatenate((Z1.T, Cov), axis=1)\n",
    "    Z = np.concatenate((A1, A2), axis=0)\n",
    "    #B = np.concatenate((X.T, mu.dot(np.ones([1, n]))), axis=0).dot(y)\n",
    "    B = np.concatenate((X.T, X.T), axis=0).dot(y)\n",
    "    # faster LSE approximation\n",
    "    #est = LA.inv(Cov+1e-10*np.identity(d)).dot(X.T.dot(y))\n",
    "    return LA.pinv(Z).dot(B)\n",
    "\n",
    "def get_knockoff_est(X, y, D):\n",
    "    Cov = X.T.dot(X)\n",
    "    A = Cov.dot(np.identity(ambient_dimension) - LA.inv(Sigma).dot(D))\n",
    "    A1 = np.concatenate((Cov, A), axis=1)\n",
    "    B = 2 * D - D.dot(LA.inv(Sigma)).dot(D) + Cov - Cov.dot(LA.inv(Sigma)).dot(D) - D.dot(LA.inv(Sigma)).dot(Cov) + \\\n",
    "        D.dot(LA.inv(Sigma)).dot(Cov).dot(LA.inv(Sigma)).dot(D)\n",
    "    A2 = np.concatenate((A.T, B), axis=1)\n",
    "    Z = np.concatenate((A1, A2), axis=0)\n",
    "    C = np.concatenate((np.identity(ambient_dimension), np.identity(ambient_dimension) - D.dot(LA.inv(Sigma))), axis=0)\n",
    "    return LA.pinv(Z).dot(C).dot(X.T).dot(y)\n",
    "\n",
    "def get_crop_est(X, y, p):\n",
    "    Cov = X.T.dot(X)\n",
    "    n = X.shape[0]\n",
    "    diag = np.diag(np.diag(Cov))\n",
    "    #reg = np.trace(Cov) * np.identity(ambient_dimension) - Cov\n",
    "    #Z = Cov + reg / ambient_dimension * 8 * p\n",
    "    Z = Cov + p / (1 - p) * diag # + 0.41 * n * np.identity(ambient_dimension)\n",
    "    S = LA.inv(Z).dot(X.T)\n",
    "    # biased\n",
    "    #return S.dot(y) / (1- p)\n",
    "    # unbiased\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_crop_est_bar(X, y, p):\n",
    "    Cov = X.T.dot(X)\n",
    "    n = X.shape[0]\n",
    "    Z = Cov + p / (1 - p) * n * Sigma\n",
    "    S = LA.inv(Z).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_rotation_est(X, y, angle):\n",
    "    angle = angle * np.pi /180\n",
    "    Cov = X.T.dot(X)\n",
    "    n = X.shape[0]\n",
    "    diag = np.diag(np.diag(Cov))\n",
    "    reg = np.trace(Cov) * np.identity(ambient_dimension) - Cov\n",
    "    Z = Cov + reg / ambient_dimension * 4 * (1- np.cos(angle))\n",
    "    #Z = Cov + p / (1 - p) * diag # + 0.41 * n * np.identity(ambient_dimension)\n",
    "    S = LA.inv(Z).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "# heterogeneous mask\n",
    "def get_het_crop_est(X, y, p, het_ratio):\n",
    "    Cov = X.T.dot(X)\n",
    "    p1 = p / het_ratio\n",
    "    diag = np.diag(np.diag(Cov))\n",
    "    reg = diag\n",
    "    for i in range(Cov.shape[0]):\n",
    "        reg[i, i] *= p / (1 - p) if i != signal_position else p1 / (1 - p1)\n",
    "    Z = Cov + reg\n",
    "    S = LA.inv(Z).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_het_crop_est_bar(X, y, p, het_ratio):\n",
    "    Cov = X.T.dot(X)\n",
    "    n = X.shape[0]\n",
    "    p1 = p / het_ratio\n",
    "    reg = n * Sigma\n",
    "    for i in range(Cov.shape[0]):\n",
    "        reg[i, i] *= p / (1 - p) if i != signal_position else p1 / (1 - p1)\n",
    "    Z = Cov + reg\n",
    "    S = LA.inv(Z).dot(X.T)\n",
    "    return S.dot(y)\n",
    "\n",
    "def get_risk(a, b):\n",
    "    diff = a - b\n",
    "    return  diff.T.dot(Sigma).dot(diff)[0, 0]\n",
    "\n",
    "def get_risk_cls(su, cn):\n",
    "    return  0.5 - np.arctan(su/cn) / np.pi\n",
    "\n",
    "def get_norm(theta):\n",
    "    return  theta.T.dot(Sigma).dot(theta)[0, 0]\n",
    "\n",
    "def get_su(theta):\n",
    "    pos = signal_position[0]\n",
    "    return theta[pos,0] * np.sqrt(Sigma[pos,pos])\n",
    "\n",
    "def get_cn(theta):\n",
    "    pos = signal_position[0]\n",
    "    return np.sqrt(theta.T.dot(Sigma).dot(theta)[0, 0] - (theta[pos,0] * np.sqrt(Sigma[pos,pos]))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e8cc5150",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose the type of augmentation:\n",
    "# set range of parameters of DA\n",
    "# \"crop_finite\": dropout with k fixed patterns, \"crop\": dropout with p dropout rate, \n",
    "# \"ridge\": add gaussian with power sigma s^2, \"proj\": random projection with k dimensional space,\n",
    "# \"mixup\": mix-up with beta distribution parameter b\n",
    "#ridge_params = np.concatenate((np.zeros(1), np.power(2, np.linspace(-10, -6, num=0)), np.power(2, np.linspace(-5, 2, num=10)), np.power(2, np.linspace(2, 20, num=0))))\n",
    "ridge_params = np.concatenate((np.linspace(0.01, 2, 10), np.linspace(2.1, 10, 10), np.linspace(11, 100, 5) ))\n",
    "ridge_params = np.power(2, np.linspace(-10, 10, 20))\n",
    "for i in range(len(ridge_params)):\n",
    "    ridge_params[i] = ridge_params[i] ** 2\n",
    "#crop_params = np.concatenate((np.power(2, np.linspace(-10, -5, num=10)), np.linspace(0.01, 0.4, num=10), np.linspace(.5, .99, num=10)))\n",
    "crop_params = np.linspace(0.001, 0.99, 20)\n",
    "rot_params = np.linspace(5, 180, 20)\n",
    "\n",
    "#ridge_params = [1]\n",
    "#crop_params = [0.3]\n",
    "#pepper_params = {\"ridge\": [1], \"drop_prob\": [0.3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d4dbbe9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete in 1.14 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "risk_lse = np.zeros([avg_num])\n",
    "su_lse = np.zeros([avg_num])\n",
    "cn_lse = np.zeros([avg_num])\n",
    "\n",
    "risk_lse_mask = np.zeros([avg_num])\n",
    "su_lse_mask = np.zeros([avg_num])\n",
    "cn_lse_mask = np.zeros([avg_num])\n",
    "\n",
    "risk_ridge = np.zeros([avg_num, len(ridge_params)])\n",
    "su_ridge = np.zeros([avg_num, len(ridge_params)])\n",
    "cn_ridge = np.zeros([avg_num, len(ridge_params)])\n",
    "\n",
    "risk_crop = np.zeros([avg_num, len(crop_params)])\n",
    "su_crop = np.zeros([avg_num, len(crop_params)])\n",
    "cn_crop = np.zeros([avg_num, len(crop_params)])\n",
    "#err_crop = np.zeros([avg_num, len(crop_params)])\n",
    "\n",
    "risk_rot = np.zeros([avg_num, len(rot_params)])\n",
    "su_rot = np.zeros([avg_num, len(rot_params)])\n",
    "cn_rot = np.zeros([avg_num, len(rot_params)])\n",
    "\n",
    "# Simulate risk\n",
    "np.random.seed(1)\n",
    "start = time.time()\n",
    "for i in tqdm(range(avg_num)):\n",
    "\n",
    "    # generate training data\n",
    "    X_tr = np.zeros([sample_num, ambient_dimension])\n",
    "    y_tr = np.zeros([sample_num, 1])\n",
    "    for j in range(X_tr.shape[0]):\n",
    "        X_tr[j, :] = np.random.multivariate_normal([0] * ambient_dimension, Sigma).reshape(-1)\n",
    "        y_tr[j, :] = np.sign(np.inner(true_theta.reshape(-1), X_tr[j, :]))\n",
    "        if random.uniform(0, 1) < sign_false_rate:\n",
    "            y_tr[j, :] *= -1\n",
    "\n",
    "    theta_lse = get_lse_est(X_tr, y_tr)\n",
    "    su_lse[i] = get_su(theta_lse)\n",
    "    cn_lse[i] = get_cn(theta_lse)\n",
    "    risk_lse[i] = get_risk_cls(su_lse[i], cn_lse[i])\n",
    "\n",
    "    theta_lse_mask = get_mask_lse_est(X_tr, y_tr)\n",
    "    su_lse_mask[i] = get_su(theta_lse_mask)\n",
    "    cn_lse_mask[i] = get_cn(theta_lse_mask)\n",
    "    risk_lse_mask[i] = get_risk_cls(su_lse_mask[i], cn_lse_mask[i])\n",
    "\n",
    "\n",
    "    # get loss\n",
    "    for j in range(len(ridge_params)):\n",
    "        theta = get_ridge_est(X_tr, y_tr, ridge_params[j])\n",
    "        su_ridge[i, j] = get_su(theta)\n",
    "        cn_ridge[i, j] = get_cn(theta)\n",
    "        risk_ridge[i, j] = get_risk_cls(su_ridge[i, j], cn_ridge[i, j])\n",
    "    for j in range(len(crop_params)):\n",
    "        theta = get_crop_est(X_tr, y_tr, crop_params[j])\n",
    "        su_crop[i, j] = get_su(theta)\n",
    "        cn_crop[i, j] = get_cn(theta)\n",
    "        risk_crop[i, j] = get_risk_cls(su_crop[i, j], cn_crop[i, j])\n",
    "\n",
    "    for j in range(len(rot_params)):\n",
    "        theta = get_rotation_est(X_tr, y_tr, rot_params[j])\n",
    "        su_rot[i, j] = get_su(theta)\n",
    "        cn_rot[i, j] = get_cn(theta)\n",
    "        risk_rot[i, j] = get_risk_cls(su_rot[i, j], cn_rot[i, j])\n",
    "\n",
    "risk_ridge = np.mean(risk_ridge, axis=0)\n",
    "su_ridge = np.mean(su_ridge, axis=0)\n",
    "cn_ridge = np.mean(cn_ridge, axis=0)\n",
    "risk_crop = np.mean(risk_crop, axis=0)\n",
    "su_crop = np.mean(su_crop, axis=0)\n",
    "cn_crop = np.mean(cn_crop, axis=0)\n",
    "risk_rot = np.mean(risk_rot, axis=0)\n",
    "su_rot = np.mean(su_rot, axis=0)\n",
    "cn_rot = np.mean(cn_rot, axis=0)\n",
    "\n",
    "risk_lse = np.mean(risk_lse, axis=0)\n",
    "su_lse = np.mean(su_lse, axis=0)\n",
    "cn_lse = np.mean(cn_lse, axis=0)\n",
    "risk_lse_mask = np.mean(risk_lse_mask, axis=0)\n",
    "su_lse_mask = np.mean(su_lse_mask, axis=0)\n",
    "cn_lse_mask = np.mean(cn_lse_mask, axis=0)\n",
    "\n",
    "\n",
    "\n",
    "#risk_ridge_ce /= avg_num\n",
    "end = time.time()\n",
    "print('Complete in {} sec'.format(round(end-start, 2)))"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Random Mask ($\\\\beta$)')"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2880x720 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "\n",
    "linewidth = 2.0\n",
    "tick_size = 20\n",
    "markersize = 8\n",
    "title_name = 'Uniform Spectrum'\n",
    "viridis = cm.get_cmap('viridis', 8)\n",
    "plasma = cm.get_cmap('plasma', 8)\n",
    "lse_color = viridis(-100)\n",
    "y_min, y_max = 0.2, 1\n",
    "markersize = 8\n",
    "size_factor = 100\n",
    "\n",
    "def mse(x, y):\n",
    "    return x + y\n",
    "def poe(x, y):\n",
    "    return 0.5 - np.arctan(x / y) / np.pi\n",
    "def minus(x, y):\n",
    "    return  y - x\n",
    "\n",
    "color1 = 'magma'\n",
    "color2 = 'viridis_r'\n",
    "color3 = 'plasma'\n",
    "linewidth = 2.0\n",
    "tick_size = 25\n",
    "markersize = 8\n",
    "title_name = 'Uniform Spectrum'\n",
    "viridis = cm.get_cmap(color1, 8)\n",
    "plasma = cm.get_cmap(color1, 8)\n",
    "lse_color = viridis(-100)\n",
    "legendsize = 25\n",
    "x_min, x_max = -4, 0\n",
    "y_min, y_max = -4, 1\n",
    "X, Y = np.meshgrid(np.linspace(-x_max, -x_min), np.linspace(y_min, y_max))\n",
    "Z = mse(X, Y)\n",
    "position = \"upper right\"\n",
    "# plt.contourf(X, Y, Z, 20, cmap='viridis')\n",
    "# plt.colorbar()\n",
    "# markersize = 8\n",
    "\n",
    "\n",
    "size_factor = 100\n",
    "\n",
    "plt.figure(figsize=(40, 10))\n",
    "ax = plt.subplot(1, 3, 1)\n",
    "plt.contourf(X, Y, Z, 20, cmap=color2)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=25)\n",
    "cb.ax.set_ylabel('Log(CN/SU)', fontsize=25)\n",
    "plt.scatter(-np.log(su_lse), np.log(cn_lse), s=2000, marker='*', facecolors='#1f77b4', edgecolors='k', linewidths=1)\n",
    "plt.text(-np.log(su_lse) + 0.05, np.log(cn_lse) + 0.05, s='LSE', fontsize=25, c='k')\n",
    "# plt.scatter(-np.log(su_lse_mask), np.log(cn_lse_mask), s=1000, marker='*', facecolors='none', edgecolors='black', linewidths=4)\n",
    "# plt.text(-np.log(su_lse_mask) + 0.1, np.log(cn_lse_mask) - 0.1, s='M-LSE', fontsize=25)\n",
    "size_vec = ridge_params\n",
    "for i in range(len(size_vec)):\n",
    "    c = ''\n",
    "    if round(np.log10(ridge_params[i]),2) >= 0:\n",
    "        c = '+'\n",
    "    lab = \"\"\n",
    "    if i % 3 == 0:\n",
    "        lab = r'$\\sigma^2=$'+'1e'+c+format(round(np.log10(ridge_params[i]),2), '.1f')\n",
    "    #plt.scatter(-np.log(su_ridge[i]), np.log(cn_ridge[i]), c=viridis(i/len(ridge_params)*1.2), s=3000 * np.sqrt(size_vec[i]))\n",
    "    plt.scatter(-np.log(su_ridge[i]), np.log(cn_ridge[i]), c=viridis(i/len(ridge_params)*1.2), s=400, edgecolors='k', label=lab)\n",
    "    # if 25 >= i >= 20:\n",
    "    #     plt.text(-np.log(su_ridge[i]), np.log(cn_ridge[i]), s=format(np.sign(ridge_params[i]) * round(np.sqrt(np.abs(ridge_params[i])),3), '.2f'), fontsize=22, rotation=40, color=\"red\")\n",
    "leg = plt.legend(loc=position, prop={'size': legendsize})\n",
    "leg.get_frame().set_linewidth(1.0)\n",
    "leg.get_frame().set_edgecolor('black')\n",
    "plt.plot([-np.log(su_lse) + np.log(cn_lse) - 1, 4 ], [1, (-np.log(su_lse) + np.log(cn_lse))-4], linestyle='-', linewidth=5, alpha=0.7, color='red')\n",
    "plt.xlabel('-log(Survival)', fontsize=30)\n",
    "plt.ylabel('Log(Contamination)', fontsize=30)\n",
    "plt.xticks(fontsize=tick_size)\n",
    "plt.yticks(fontsize=tick_size)\n",
    "plt.xlim(-x_max, -x_min)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.title('Adding Gaussian Noise '+r'($\\sigma$)', fontsize=30)\n",
    "\n",
    "ax = plt.subplot(1, 3, 3)\n",
    "plt.contourf(X, Y, Z, 20, cmap=color2)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=25)\n",
    "cb.ax.set_ylabel('Log(CN/SU)', fontsize=25)\n",
    "plt.scatter(-np.log(su_lse), np.log(cn_lse), s=2000, marker='*', facecolors='#1f77b4', edgecolors='k', linewidths=1)\n",
    "plt.text(-np.log(su_lse) + 0.05, np.log(cn_lse) + 0.05, s='LSE', fontsize=25, c='k')\n",
    "# plt.scatter(-np.log(su_lse_mask), np.log(cn_lse_mask), s=1000, marker='*', facecolors='none', edgecolors='black', linewidths=4)\n",
    "# plt.text(-np.log(su_lse_mask) + 0.1, np.log(cn_lse_mask) - 0.1, s='M-LSE', fontsize=25)\n",
    "size_vec = rot_params\n",
    "for i in range(len(rot_params)):\n",
    "    #plt.scatter(-np.log(su_rot[i]), np.log(cn_rot[i]), c=viridis(i/len(rot_params)*1.2), s=5*(size_vec[i]))\n",
    "    lab = \"\"\n",
    "    if i % 3 == 0:\n",
    "        lab = r'$\\alpha=$' + format(round(rot_params[i],3), '.0f')+r'$^\\circ$'\n",
    "    plt.scatter(-np.log(su_rot[i]), np.log(cn_rot[i]), c=viridis(i/len(rot_params)*1.2), s=400, edgecolors='k', label=lab)\n",
    "    # if (i % 2 ==0 ):\n",
    "    #     plt.text(-np.log(su_rot[i])+0.02, np.log(cn_rot[i])+0.02, s=format(round(rot_params[i])), fontsize=22, rotation=40, color=\"red\")\n",
    "leg = plt.legend(loc=position, prop={'size': legendsize})\n",
    "leg.get_frame().set_linewidth(1.0)\n",
    "leg.get_frame().set_edgecolor('black')\n",
    "plt.plot([-np.log(su_lse) + np.log(cn_lse) - 1, 4 ], [1, (-np.log(su_lse) + np.log(cn_lse))-4], linestyle='-', linewidth=5, alpha=0.7, color='red')\n",
    "plt.xlabel('-log(Survival)', fontsize=30)\n",
    "plt.ylabel('Log(Contamination)', fontsize=30)\n",
    "plt.xticks(fontsize=tick_size)\n",
    "plt.yticks(fontsize=tick_size)\n",
    "plt.xlim(-x_max, -x_min)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.title('Random Rotation '+r'($\\alpha$)', fontsize=30)\n",
    "\n",
    "ax = plt.subplot(1, 3, 2)\n",
    "plt.contourf(X, Y, Z, 20, cmap=color2)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=25)\n",
    "cb.ax.set_ylabel('Log(CN/SU)', fontsize=25)\n",
    "plt.scatter(-np.log(su_lse), np.log(cn_lse), s=2000, marker='*', facecolors='#1f77b4', edgecolors='k', linewidths=1)\n",
    "plt.text(-np.log(su_lse) + 0.05, np.log(cn_lse) + 0.05, s='LSE', fontsize=25, c='k')\n",
    "# plt.scatter(-np.log(su_lse_mask), np.log(cn_lse_mask), s=1000, marker='*', facecolors='none', edgecolors='black', linewidths=4)\n",
    "# plt.text(-np.log(su_lse_mask) + 0.1, np.log(cn_lse_mask) + 0.2, s='M-LSE', fontsize=25)\n",
    "size_vec = crop_params\n",
    "for i in range(len(size_vec)):\n",
    "    lab = \"\"\n",
    "    if i % 3 == 0:\n",
    "        lab = r'$\\beta=$' + format(round(crop_params[i],3), '.1f')\n",
    "    #plt.scatter(-np.log(su_crop[i]), np.log(cn_crop[i]), c=viridis(i/len(crop_params)*1.2), s=500*(size_vec[i]))\n",
    "    plt.scatter(-np.log(su_crop[i]), np.log(cn_crop[i]), c=viridis(i/len(crop_params)*1.2), s=400, edgecolors='k', label=lab)\n",
    "    # if (i <= 6 and i % 3 == 0) or (i > 6 and i % 3 == 0):\n",
    "    #     plt.text(-np.log(su_crop[i]), np.log(cn_crop[i]), s=format(round(crop_params[i],3), '.2f'), fontsize=22, rotation=40, color=\"red\")\n",
    "leg = plt.legend(loc=position, prop={'size': legendsize})\n",
    "leg.get_frame().set_linewidth(1.0)\n",
    "leg.get_frame().set_edgecolor('black')\n",
    "plt.plot([-np.log(su_lse) + np.log(cn_lse) - 1, 4 ], [1, (-np.log(su_lse) + np.log(cn_lse))-4], linestyle='-', linewidth=5, alpha=0.7, color='red')\n",
    "plt.xlabel('-log(Survival)', fontsize=30)\n",
    "plt.ylabel('Log(Contamination)', fontsize=30)\n",
    "plt.xticks(fontsize=tick_size)\n",
    "plt.yticks(fontsize=tick_size)\n",
    "plt.xlim(-x_max, -x_min)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.title('Random Mask '+r'($\\beta$)', fontsize=30)"
   ],
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