experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "median-circle",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import random \n",
    "import pandas as pd\n",
    "from skimage.measure import block_reduce\n",
    "from scipy.special import entr\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "adaptive-latter",
   "metadata": {},
   "outputs": [],
   "source": [
    "PALETTE = ['r', 'b', 'g', 'k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "focal-louis",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digits(instances, images_per_row=10, size=28,**options):\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "\n",
    "\n",
    "def get_hash(Y):\n",
    "    \"\"\"\n",
    "    Get unique number for input\n",
    "    one hot vector\n",
    "    \"\"\"\n",
    "    hashes = []\n",
    "    \n",
    "    for i in range(len(Y)):\n",
    "        index = np.argmax(Y[i])\n",
    "        if Y[i][index] == 0:\n",
    "            index = len(PALETTE) - 1\n",
    "        hashes.append(index)\n",
    "    return hashes\n",
    "\n",
    "def get_classes(Y):\n",
    "    hashes = get_hash(Y)\n",
    "    return [PALETTE[index] for index in hashes]\n",
    "    \n",
    "\n",
    "def get_band(x_lim, y_lim, n=60):\n",
    "    X = []\n",
    "    for i in range(n):\n",
    "        x = random.uniform(x_lim[0], x_lim[1])\n",
    "        y = random.uniform(y_lim[0], y_lim[1])        \n",
    "        X.append((x, y))\n",
    "    return X\n",
    "\n",
    "def get_W(X, sigma):\n",
    "    X = np.array(X)\n",
    "    return  np.array([[np.exp(-(np.linalg.norm(a-b)/sigma)**2) for b in X] for a in X])\n",
    "\n",
    "def get_T(W):\n",
    "    T = [[W[i][j]/sum(W[:, j]) for j in range(len(W))] for i in range(len(W))]\n",
    "    return np.array(T)\n",
    "\n",
    "def label_bidding(Y_, size):\n",
    "    \"\"\"\n",
    "    size is the number of instances\n",
    "    of a particular type\n",
    "    \"\"\"\n",
    "    Y = Y_.copy()\n",
    "    colours = []\n",
    "    C = len(Y[0])\n",
    "    counts = [size] * C\n",
    "    \n",
    "    for i in range(len(Y)):\n",
    "        indices = np.argsort(Y[i])\n",
    "        for index in reversed(indices):\n",
    "            if counts[index] > 0:\n",
    "                colours.append(index)\n",
    "                counts[index] -= 1\n",
    "                break\n",
    "    assert counts == [0] * C, \"invalid proportion\"\n",
    "    return colours        \n",
    "\n",
    "def class_mass_normalization(Y_):\n",
    "    Y = np.array(Y_.copy())\n",
    "    C = len(Y[0])\n",
    "    alphas = [0] * C\n",
    "\n",
    "    Y_uc = [sum(Y[:, i]) for i in range(C)]\n",
    "    \n",
    "    for i in range(C-1, -1, -1):\n",
    "        if Y_uc[i] == 0:\n",
    "            alphas[i] = 0\n",
    "        else:\n",
    "            alphas[i] = 1/Y_uc[i]\n",
    "            last = i\n",
    "            break\n",
    "    \n",
    "    for i in range(last):\n",
    "        alphas[i] = alphas[last]*Y_uc[last]/Y_uc[i] # assuming uniform\n",
    "    \n",
    "    for i in range(C):\n",
    "        Y[:, i] *= alphas[i]\n",
    "        \n",
    "    return Y\n",
    "\n",
    "def get_H(Y):\n",
    "    return entr(Y).sum()\n",
    "\n",
    "def get_mnist_raw(size=1100):\n",
    "    df = pd.read_csv(\"train.csv\")\n",
    "    df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "    df = df[(df['label'] == 1) | (df['label'] == 2) | (df['label'] == 3)]\n",
    "    \n",
    "    mat_one = df[df['label'] == 1].to_numpy()[:size]\n",
    "    mat_two = df[df['label'] == 2].to_numpy()[:size]\n",
    "    mat_three = df[df['label'] == 3].to_numpy()[:size]\n",
    "    \n",
    "    mat = mat_one\n",
    "    for row in mat_two:\n",
    "        mat = np.append(mat, [row], axis=0)\n",
    "    for row in mat_three:\n",
    "        mat = np.append(mat, [row], axis=0)\n",
    "    np.random.shuffle(mat)\n",
    "    return mat\n",
    "\n",
    "def one_hot(y):\n",
    "    if y == 1:\n",
    "        return [1, 0, 0]\n",
    "    elif y == 2:\n",
    "        return [0, 1, 0]\n",
    "    else:\n",
    "        return [0, 0, 1]\n",
    "\n",
    "def error_rate(y_pred, y_test):\n",
    "    return np.sum(y_pred != y_test)/len(y_test)\n",
    "\n",
    "def format_pred(y_pred_):\n",
    "    y_pred = y_pred_.copy()\n",
    "    if isinstance(y_pred_[0], str):\n",
    "        y_pred = [PALETTE.index(a) for a in y_pred_]\n",
    "    y_pred = np.array(y_pred) + 1\n",
    "    return y_pred\n",
    "\n",
    "def check_match(Y, y_pred, y_test):\n",
    "    \n",
    "    for i in range(len(Y)):\n",
    "        if sum(Y[i]):\n",
    "            assert y_pred[i] == y_test[i], \"Labels wrong\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "interracial-consciousness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Dataset:\n",
    "    \"\"\"\n",
    "    Returns the required dataset\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, l, size=2):\n",
    "        self.l = l\n",
    "        self.size = size\n",
    "        \n",
    "    def three_bands(self, n=60):\n",
    "        X = [(2.5, 2.5), (0, 1.25), (3, 0)]\n",
    "        Y_L = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]\n",
    "        \n",
    "        y_lim = (2.5, 3)\n",
    "        x_lim = (0, 3.5)\n",
    "        X += get_band(x_lim, y_lim, n)\n",
    "\n",
    "        y_lim = (1.25, 1.75)\n",
    "        x_lim = (0, 3.5)\n",
    "        X += get_band(x_lim, y_lim, n)\n",
    "\n",
    "        y_lim = (0, 0.5)\n",
    "        x_lim = (0, 3.5)\n",
    "        X += get_band(x_lim, y_lim, n)\n",
    "        \n",
    "\n",
    "        l = len(Y_L)\n",
    "        u = len(X) - l\n",
    "        Y_U = [[0, 0, 0]]*u\n",
    "        \n",
    "        Y = Y_L + Y_U\n",
    "    \n",
    "        return X, Y, None\n",
    "\n",
    "    def bridge(self):\n",
    "        red_x = (-3, 2)\n",
    "        blue_x = (3, 0)\n",
    "\n",
    "        Y_L = [[1, 0], [0, 1]]\n",
    "        X = [red_x, blue_x]\n",
    "\n",
    "\n",
    "        for i in range(-3, 4):\n",
    "            for j in range(2, 5):\n",
    "                if (i, j) != red_x:\n",
    "                    X.append((i, j))\n",
    "\n",
    "        X.append((0, 1.5))\n",
    "        X.append((0, 0.5))\n",
    "\n",
    "        for i in range(-3, 4):\n",
    "            for j in range(-2, 1):\n",
    "                if (i, j) != blue_x:\n",
    "                    X.append((i, j))\n",
    "\n",
    "        l = len(Y_L)\n",
    "        u = len(X) - l\n",
    "        Y_U = [[0, 0]]*u\n",
    "        Y = Y_L + Y_U\n",
    "\n",
    "        return X, Y, None\n",
    "    \n",
    "    def mnist(self, compress=True):\n",
    "        l = self.l\n",
    "        size = self.size\n",
    "        orig = get_mnist_raw(size)\n",
    "#         print(orig[:, 0])\n",
    "        for val in [1, 2, 3]:\n",
    "            for i, row in enumerate(orig):\n",
    "                if row[0] == val:\n",
    "                    orig[[val-1, i]] = orig[[i, val-1]]\n",
    "                    break\n",
    "        \n",
    "        X_ = orig[:, 1:]\n",
    "        if not compress:\n",
    "            X = X_\n",
    "        else:\n",
    "            X = []\n",
    "\n",
    "            for x in X_:\n",
    "                x = np.reshape(x, (28, 28))\n",
    "                x = block_reduce(x, block_size=(2, 2), func=np.mean, cval=np.mean(x))\n",
    "                X.append(x.flatten())\n",
    "                \n",
    "            X = np.array(X)\n",
    "            X_ = X\n",
    "        y_test = orig[:, 0]\n",
    "        Y = [[0, 0, 0]] * len(orig)\n",
    "        for i, row in enumerate(orig):\n",
    "            if l == 0:\n",
    "                break\n",
    "            else:\n",
    "                Y[i] = one_hot(row[0])\n",
    "                l -= 1\n",
    "        \n",
    "#         print(y_test)\n",
    "#         print(Y)\n",
    "        return X, y_test, Y    \n",
    "        \n",
    "    def get_data(self, name):\n",
    "        if name == \"bridge\":\n",
    "            return self.bridge()\n",
    "        elif name == \"three_bands\":\n",
    "            return self.three_bands()\n",
    "        elif name == \"mnist\":\n",
    "            return self.mnist()\n",
    "        else:\n",
    "            raise NotImplementedError\n",
    "        \n",
    "    def view(self, name): # will get updated each time\n",
    "        if name == \"mnist\":\n",
    "            X, y_test, Y = self.mnist(False)\n",
    "            example_images = X[:100]\n",
    "            plot_digits(example_images, images_per_row=10)\n",
    "            return \n",
    "        \n",
    "        elif name == \"bridge\":\n",
    "            X, Y, _ = self.bridge()\n",
    "        elif name == \"three_bands\":\n",
    "            X, Y, _ = self.three_bands()\n",
    "        else:\n",
    "            raise NotImplementedError\n",
    "        \n",
    "        colours = get_classes(Y)\n",
    "        plt.scatter(*zip(*X), c=colours)      \n",
    "        \n",
    "data = Dataset(l=3, size=100)\n",
    "data.view(\"mnist\")\n",
    "del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "norwegian-survivor",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classifier:\n",
    "    \"\"\"\n",
    "    Outputs the classes\n",
    "    \"\"\"\n",
    "    def __init__(self, X, Y, l):\n",
    "        self.X = X\n",
    "        self.Y = Y.copy()\n",
    "        self.l = l\n",
    "        self.u = len(Y) - l\n",
    "        self.Y_init = Y.copy()\n",
    "        \n",
    "        \n",
    "    def kNN(self):\n",
    "        X = np.array(self.X)\n",
    "        Y = self.Y\n",
    "        l = self.l\n",
    "        u = self.u\n",
    "        for i in range(l, l+u):\n",
    "            index = -1\n",
    "            mn = 10**9\n",
    "            for j in range(l):\n",
    "                dist = np.linalg.norm(X[i] - X[j])\n",
    "                if dist < mn:\n",
    "                    index = j\n",
    "                    mn = dist\n",
    "            assert mn < 10**9, \"exceeds\"\n",
    "            Y[i] = Y[index].copy()\n",
    "        return Y\n",
    "    \n",
    "    def p1NN(self):\n",
    "        X = np.array(self.X)\n",
    "        Y = self.Y\n",
    "        l = self.l\n",
    "        u = len(Y) - l\n",
    "        \n",
    "        XL = set()\n",
    "        XU = set()\n",
    "        \n",
    "        for i in range(0, l):\n",
    "            XL.add(j)\n",
    "        \n",
    "        for i in range(l, l+u):\n",
    "            XU.add(i)\n",
    "\n",
    "        while len(XU):\n",
    "            lmin = -1\n",
    "            umin = -1\n",
    "            mn = 10**9\n",
    "            for xl in XL:\n",
    "                for xu in XU:\n",
    "                    dist = np.linalg.norm(X[xu] - X[xl])\n",
    "                    if dist < mn:\n",
    "                        lmin = xl\n",
    "                        umin = xu\n",
    "                        mn = dist\n",
    "#             print(lmin, umin)\n",
    "            Y[umin] = Y[lmin].copy()\n",
    "            XU.remove(umin)\n",
    "            XL.add(umin)      \n",
    "        return Y\n",
    "    \n",
    "    def label_propagation(self, size=10, sigma=10, e=0, max_iter=10000):\n",
    "        X = self.X\n",
    "        Y = self.Y\n",
    "        l = self.l\n",
    "        u = len(Y) - l\n",
    "        count = max_iter\n",
    "        W = get_W(X, sigma)\n",
    "        assert len(W) == len(Y) and len(W[0]) == len(Y), \"invalid\"\n",
    "        T = np.array(get_T(W))\n",
    "        U = np.full((len(T), len(T)), 1/(len(T)))\n",
    "        if e is not None:\n",
    "            T = e*U + (1 - e)*T\n",
    "        prev_Y = Y.copy()\n",
    "        Y_L = Y[:l].copy()\n",
    "        \n",
    "        while True:\n",
    "            Y = np.matmul(T, Y)\n",
    "            for i in range(len(Y)):\n",
    "                Z = sum(Y[i])\n",
    "                if Z == 0:\n",
    "                    raise ValueError\n",
    "                for j in range(len(Y[i])):\n",
    "                    Y[i][j] /= Z\n",
    "            for i in range(l):\n",
    "                Y[i] = Y_L[i].copy()\n",
    "\n",
    "            count -= 1\n",
    "            if count == 0 or (prev_Y == Y).all():\n",
    "                break\n",
    "        return Y\n",
    "    \n",
    "    def get_pred(self, method, size=2, sigma=None, e=None, max_iter=10000):\n",
    "        self.Y = self.Y_init.copy()\n",
    "        \n",
    "        if method == \"kNN\":\n",
    "            return get_classes(self.kNN())\n",
    "            \n",
    "        \n",
    "        elif method == \"p1NN\":\n",
    "            return get_classes(self.p1NN())\n",
    "    \n",
    "        elif method[:2] == \"lp\":\n",
    "            Y = self.label_propagation(size, sigma, e, max_iter)\n",
    "            if method[2:] == \"ml\":\n",
    "                if sigma != None and len(X[1]) >= 196:\n",
    "                    if e != None:\n",
    "                        return Y\n",
    "                    else:\n",
    "                        return get_hash(Y)\n",
    "                else:\n",
    "                    return get_classes(Y)\n",
    "            elif method[2:] == \"lb\":\n",
    "                return label_bidding(Y, size)\n",
    "            elif method[2:] == \"cn\":\n",
    "                Y = class_mass_normalization(Y)\n",
    "                return get_hash(Y)\n",
    "            else:\n",
    "                raise NotImplementedError\n",
    "        else:\n",
    "            raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "described-battery",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3)\n",
    "fig.suptitle('Figure 1')\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "ds = Dataset(l=3)\n",
    "X, Y, _ = ds.get_data(\"three_bands\")\n",
    "\n",
    "classifier = Classifier(X, Y, 3)\n",
    "Y = get_classes(Y)\n",
    "ax1.scatter(*zip(*X), c=Y)\n",
    "\n",
    "#kNN\n",
    "y_knn = classifier.get_pred(\"kNN\")\n",
    "ax2.scatter(*zip(*X), c=y_knn)\n",
    "del y_knn, _\n",
    "\n",
    "\n",
    "# Label Propagation\n",
    "y_lp = classifier.get_pred(\"lpml\", sigma=0.22)\n",
    "ax3.scatter(*zip(*X), c=y_lp)\n",
    "del y_lp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "external-chapel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, axs = plt.subplots(3, 3)\n",
    "fig.tight_layout()\n",
    "fig.set_figheight(15)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "sigmas = [0.17, 0.4, 0.72, 1.3, 2]\n",
    "index = 0\n",
    "\n",
    "ds = Dataset(l=2)\n",
    "X, Y, _ = ds.get_data(\"bridge\")\n",
    "\n",
    "classifier = Classifier(X, Y, 2)\n",
    "Y = get_classes(Y)\n",
    "axs[0, 0].scatter(*zip(*X), c=Y)\n",
    "\n",
    "#p1NN\n",
    "y_p1nn = classifier.get_pred(\"p1NN\")\n",
    "axs[0, 1].scatter(*zip(*X), c=y_p1nn)\n",
    "\n",
    "#kNN\n",
    "y_knn = classifier.get_pred(\"kNN\")\n",
    "axs[0, 2].scatter(*zip(*X), c=y_knn)\n",
    "\n",
    "\n",
    "for i in range(1, 3):\n",
    "    for j in range(3):\n",
    "        if i == 2 and j == 2:\n",
    "            break\n",
    "        \n",
    "        # Label Propagation\n",
    "        y_lp = classifier.get_pred(\"lpml\", sigma=sigmas[index])\n",
    "        axs[i, j].scatter(*zip(*X), c=y_lp)\n",
    "        axs[i, j].set_title(str(sigmas[index]))\n",
    "            \n",
    "        \n",
    "        index += 1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "retained-calibration",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = Dataset(l=3, size=2)\n",
    "X, Y_test, Y = ds.get_data(\"mnist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "excessive-sheep",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = Classifier(X, Y, 3)\n",
    "\n",
    "y_lp = classifier.get_pred(\"lpml\", sigma=340)\n",
    "error_rate(y_lp, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "direct-phrase",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = Classifier(X, Y, 3)\n",
    "\n",
    "y_lp = classifier.get_pred(\"lplb\", sigma=340)\n",
    "error_rate(y_lp, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "opening-instrument",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = Classifier(X, Y, 3)\n",
    "\n",
    "y_lp = classifier.get_pred(\"lpcn\", sigma=340)\n",
    "error_rate(y_lp, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "political-bottom",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-36c0386b7887>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0msigma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdelta\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelta\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m         \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_pred\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"lpml\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0mH_values\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_H\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0msigma_values\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-54-c2c39bf2da5a>\u001b[0m in \u001b[0;36mget_pred\u001b[1;34m(self, method, size, sigma, e, max_iter)\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"lp\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m             \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel_propagation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    107\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"ml\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0msigma\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m196\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-54-c2c39bf2da5a>\u001b[0m in \u001b[0;36mlabel_propagation\u001b[1;34m(self, size, sigma, e, max_iter)\u001b[0m\n\u001b[0;32m     82\u001b[0m                 \u001b[0mZ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mZ\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m                     \u001b[0mY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta = 20\n",
    "for i, e in enumerate([0.000005, 0.00005, 0.00005, 0.0005, 0.005, 0.05, 0]):\n",
    "    H_values = []\n",
    "    sigma_values = []\n",
    "    \n",
    "    for sigma in np.arange(delta + 0.1, delta + 2, 0.1):\n",
    "        Y = classifier.get_pred(\"lpml\", e=e, sigma=sigma)\n",
    "        H_values.append(get_H(Y))\n",
    "        sigma_values.append(sigma)\n",
    "    \n",
    "        \n",
    "    plt.scatter(sigma_values, H_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "returning-smoke",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n",
      "<ipython-input-123-3801add5c845>:124: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  return np.sum(y_pred != y_test)/len(y_test)\n"
     ]
    }
   ],
   "source": [
    "cols = ['method', 3, 6, 9, 12, 15, 20, 25, 30]\n",
    "table1 = pd.DataFrame(columns=cols)\n",
    "iterations = 5\n",
    "\n",
    "for method in [\"p1NN\", \"kNN\", \"lpml\", \"lpcn\", \"lplb\"]:\n",
    "    errors = []\n",
    "    for l in cols[1:]:\n",
    "        cur = 0\n",
    "        for _ in range(iterations):\n",
    "            ds = Dataset(l=l, size=60)\n",
    "            X, Y_test, Y = ds.get_data(\"mnist\")\n",
    "            classifier = Classifier(X, Y, l)\n",
    "            y = classifier.get_pred(method, sigma=340)\n",
    "            y = format_pred(y)\n",
    "    #         print(y_p1nn)\n",
    "    #         print(Y_test)\n",
    "    #         plot_digits(X, size=14)\n",
    "            if method not in [\"lpcn\", \"lplb\"]:\n",
    "                check_match(Y, y, Y_test)\n",
    "            cur += error_rate(y, Y_test)\n",
    "        errors.append(cur/iterations)   \n",
    "    table1 = pd.concat([pd.DataFrame([[method] + errors], columns=table1.columns), table1], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "floral-whale",
   "metadata": {},
   "outputs": [],
   "source": [
    "ML_error_rates = []\n",
    "for l in cols[1:]:\n",
    "    cur = 0\n",
    "    for _ in range(iterations):\n",
    "        mnist = get_mnist(100)\n",
    "        X, Y_test, Y = get_XY(mnist[:], l)\n",
    "        Y = get_lp_pred(X, Y, 340, l)\n",
    "        y_pred = get_colours(Y)\n",
    "        y_test = get_colours(Y_test)\n",
    "        y_pred = np.array(y_pred)\n",
    "        y_test = np.array(y_test)\n",
    "        cur += np.sum(y_pred != y_test)/len(y_test)\n",
    "    ML_error_rates.append(cur/iterations)\n",
    "                \n",
    "table1 = pd.concat([pd.DataFrame([['ML'] + ML_error_rates], columns=table1.columns), table1], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "blond-building",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>3</th>\n",
       "      <th>6</th>\n",
       "      <th>9</th>\n",
       "      <th>12</th>\n",
       "      <th>15</th>\n",
       "      <th>20</th>\n",
       "      <th>25</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p1NN</td>\n",
       "      <td>0.655556</td>\n",
       "      <td>0.646667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.618889</td>\n",
       "      <td>0.601111</td>\n",
       "      <td>0.593333</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.552222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kNN</td>\n",
       "      <td>0.285556</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.248889</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>0.192222</td>\n",
       "      <td>0.128889</td>\n",
       "      <td>0.131111</td>\n",
       "      <td>0.097778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lpml</td>\n",
       "      <td>0.514444</td>\n",
       "      <td>0.352222</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.231111</td>\n",
       "      <td>0.241111</td>\n",
       "      <td>0.218889</td>\n",
       "      <td>0.185556</td>\n",
       "      <td>0.092222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lpcn</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.063333</td>\n",
       "      <td>0.074444</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  method         3         6         9        12        15        20  \\\n",
       "4   p1NN  0.655556  0.646667  0.633333  0.618889  0.601111  0.593333   \n",
       "3    kNN  0.285556  0.290000  0.248889  0.244444  0.192222  0.128889   \n",
       "2   lpml  0.514444  0.352222  0.361111  0.231111  0.241111  0.218889   \n",
       "1   lpcn  0.216667  0.093333  0.063333  0.074444  0.044444  0.053333   \n",
       "\n",
       "         25        30  \n",
       "4  0.577778  0.552222  \n",
       "3  0.131111  0.097778  \n",
       "2  0.185556  0.092222  \n",
       "1  0.046667  0.040000  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1.iloc[::-1].head(4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14276580",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "r = 2\n",
    "\n",
    "X = []\n",
    "X_ = []\n",
    "for i in range(25):\n",
    "    angle = 2*math.pi*i/25\n",
    "    x = r*math.cos(angle)\n",
    "    y = r*math.sin(angle)\n",
    "    z = i\n",
    "    X.append((x, y, z))\n",
    "    X_.append((y, x, z))\n",
    "\n",
    "red_x = (2, 0, 0)\n",
    "blue_x = (0, 2, 25) \n",
    "\n",
    "\n",
    "X = [red_x, blue_x] + X + X_\n",
    "Y_L = [[1, 0], [0, 1]]\n",
    "l = len(Y_L)\n",
    "u = len(X) - l\n",
    "Y = Y_L + [[0, 0]]*u\n",
    "\n",
    "ax.scatter3D(*zip(*X))\n",
    "Y = get_lp_pred(X, Y, 0.25, 2)\n",
    "colours = get_colours(Y)\n",
    "h, v, d = zip(*X)\n",
    "ax.scatter3D(h, v, d, c=colours)\n",
    "\n",
    "\n",
    "arr = block_reduce(pixels, block_size=(2, 2), func=np.mean, cval=np.mean(pixels))\n",
    "plt.imshow(arr)\n",
    "\n",
    "arr.reshape((1, -1)).shape"
   ]
  }
 ],
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