From cda79c3567e357844e1bf02b1841ffb1fb05cc15 Mon Sep 17 00:00:00 2001
From: Antonio MORAIS <amorais@lentix14.idiap.ch>
Date: Mon, 20 Sep 2021 13:57:23 +0200
Subject: [PATCH] Added Goutte implementation port

---
 .../goutte_port-checkpoint.ipynb              |  216 ++
 bob/measure/goutte_port.ipynb                 |  216 ++
 coverage.xml                                  | 2346 +++++++++++++++++
 3 files changed, 2778 insertions(+)
 create mode 100644 bob/measure/.ipynb_checkpoints/goutte_port-checkpoint.ipynb
 create mode 100644 bob/measure/goutte_port.ipynb
 create mode 100644 coverage.xml

diff --git a/bob/measure/.ipynb_checkpoints/goutte_port-checkpoint.ipynb b/bob/measure/.ipynb_checkpoints/goutte_port-checkpoint.ipynb
new file mode 100644
index 0000000..dc49291
--- /dev/null
+++ b/bob/measure/.ipynb_checkpoints/goutte_port-checkpoint.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import beta\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy\n",
+    "import random\n",
+    "\n",
+    "#Precision\n",
+    "\n",
+    "#System 1\n",
+    "TP1 = 10\n",
+    "FP1 = 10\n",
+    "# System 2\n",
+    "TP2 = 3\n",
+    "FP2 = 2\n",
+    "\n",
+    "nbsamples = 10000 # Sample size, higher is better \n",
+    "lbd = 0.5 # lambda\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f65a3d3c828>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "\n",
+    "x = numpy.linspace(0.01, 0.99, nbsamples)\n",
+    "pdf1 = beta.pdf(x, TP1 + lbd, FP1 + lbd)\n",
+    "pdf2 = beta.pdf(x, TP2 + lbd, FP2 + lbd)\n",
+    "plt.plot(x, pdf1, label='TP = {}, FP = {}'.format(TP1, FP1))\n",
+    "plt.plot(x, pdf2, label='TP = {}, FP = {}'.format(TP2, FP2))\n",
+    "plt.title('Beta distributions')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empirical probability that system 2 is better than system 1 = 0.6548\n"
+     ]
+    }
+   ],
+   "source": [
+    "def randombeta(nbsamples, shape1, shape2):\n",
+    "    betageneration = numpy.empty(nbsamples)\n",
+    "    for i in range(nbsamples):\n",
+    "        betageneration[i] = random.betavariate(shape1, shape2)\n",
+    "    return betageneration\n",
+    "\n",
+    "p1 = randombeta(nbsamples, TP1 + lbd, FP1 + lbd)\n",
+    "p2 = randombeta(nbsamples, TP2 + lbd, FP2 + lbd)\n",
+    "print('Empirical probability that system 2 is better than system 1 = {}'.format(numpy.count_nonzero(p2 > p1) / nbsamples))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "plt.hist(p2, bins=25)\n",
+    "plt.title(\"Distribution (empirical) for System 2 Precision\")\n",
+    "plt.xlabel(\"Precision\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empirical probability that system 1 is better than system 2 in F1 score = 0.9245\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# F1-score \n",
+    "\n",
+    "def randomgamma(nbsamples, shape, scale):\n",
+    "    gammageneration = numpy.empty(nbsamples)\n",
+    "    for i in range(nbsamples):\n",
+    "        gammageneration[i] = random.gammavariate(shape, scale)\n",
+    "    return gammageneration\n",
+    "\n",
+    "FN1 = 0\n",
+    "FN2 = 7\n",
+    "# Sampling Gamma distributions to produce F-score samples\n",
+    "U1 = randomgamma(nbsamples, shape=TP1+lbd, scale=2)\n",
+    "V1 = randomgamma(nbsamples, FP1+FN1+2*lbd, scale=1)\n",
+    "F1scores1 = U1/(U1+V1)\n",
+    "U2 = randomgamma(nbsamples, TP2+lbd, scale=2)\n",
+    "V2 = randomgamma(nbsamples, FP2+FN2+2*lbd, scale=1)\n",
+    "F1scores2 = U2/(U2+V2)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2,figsize=(15,5))\n",
+    "fig.suptitle('Empirical distribution for F1 scores')\n",
+    "axs[0].hist(F1scores1, bins=25)\n",
+    "axs[0].set_title('System 1')\n",
+    "axs[0].set(xlabel='F1 score')\n",
+    "axs[1].hist(F1scores2, bins=25)\n",
+    "axs[1].set_title('System 2')\n",
+    "axs[1].set(xlabel='F1 score', ylabel='count')\n",
+    "\n",
+    "# plt.hist(F1scores1, bins=25)\n",
+    "# plt.title(\"Distribution (empirical) for System 2 Precision\")\n",
+    "# plt.xlabel(\"Precision\")\n",
+    "# plt.ylabel(\"Count\")\n",
+    "\n",
+    "# plt.hist(F1scores2, bins=25)\n",
+    "# plt.title(\"Distribution (empirical) for System 1 Precision\")\n",
+    "# plt.xlabel(\"Precision\")\n",
+    "# plt.ylabel(\"Count\")\n",
+    "\n",
+    "print('Empirical probability that system 1 is better than system 2 in F1 score = {}'\n",
+    "      .format(numpy.count_nonzero(F1scores1 > F1scores2) / nbsamples))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/bob/measure/goutte_port.ipynb b/bob/measure/goutte_port.ipynb
new file mode 100644
index 0000000..dc49291
--- /dev/null
+++ b/bob/measure/goutte_port.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import beta\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy\n",
+    "import random\n",
+    "\n",
+    "#Precision\n",
+    "\n",
+    "#System 1\n",
+    "TP1 = 10\n",
+    "FP1 = 10\n",
+    "# System 2\n",
+    "TP2 = 3\n",
+    "FP2 = 2\n",
+    "\n",
+    "nbsamples = 10000 # Sample size, higher is better \n",
+    "lbd = 0.5 # lambda\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f65a3d3c828>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "\n",
+    "x = numpy.linspace(0.01, 0.99, nbsamples)\n",
+    "pdf1 = beta.pdf(x, TP1 + lbd, FP1 + lbd)\n",
+    "pdf2 = beta.pdf(x, TP2 + lbd, FP2 + lbd)\n",
+    "plt.plot(x, pdf1, label='TP = {}, FP = {}'.format(TP1, FP1))\n",
+    "plt.plot(x, pdf2, label='TP = {}, FP = {}'.format(TP2, FP2))\n",
+    "plt.title('Beta distributions')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empirical probability that system 2 is better than system 1 = 0.6548\n"
+     ]
+    }
+   ],
+   "source": [
+    "def randombeta(nbsamples, shape1, shape2):\n",
+    "    betageneration = numpy.empty(nbsamples)\n",
+    "    for i in range(nbsamples):\n",
+    "        betageneration[i] = random.betavariate(shape1, shape2)\n",
+    "    return betageneration\n",
+    "\n",
+    "p1 = randombeta(nbsamples, TP1 + lbd, FP1 + lbd)\n",
+    "p2 = randombeta(nbsamples, TP2 + lbd, FP2 + lbd)\n",
+    "print('Empirical probability that system 2 is better than system 1 = {}'.format(numpy.count_nonzero(p2 > p1) / nbsamples))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "plt.hist(p2, bins=25)\n",
+    "plt.title(\"Distribution (empirical) for System 2 Precision\")\n",
+    "plt.xlabel(\"Precision\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empirical probability that system 1 is better than system 2 in F1 score = 0.9245\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# F1-score \n",
+    "\n",
+    "def randomgamma(nbsamples, shape, scale):\n",
+    "    gammageneration = numpy.empty(nbsamples)\n",
+    "    for i in range(nbsamples):\n",
+    "        gammageneration[i] = random.gammavariate(shape, scale)\n",
+    "    return gammageneration\n",
+    "\n",
+    "FN1 = 0\n",
+    "FN2 = 7\n",
+    "# Sampling Gamma distributions to produce F-score samples\n",
+    "U1 = randomgamma(nbsamples, shape=TP1+lbd, scale=2)\n",
+    "V1 = randomgamma(nbsamples, FP1+FN1+2*lbd, scale=1)\n",
+    "F1scores1 = U1/(U1+V1)\n",
+    "U2 = randomgamma(nbsamples, TP2+lbd, scale=2)\n",
+    "V2 = randomgamma(nbsamples, FP2+FN2+2*lbd, scale=1)\n",
+    "F1scores2 = U2/(U2+V2)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2,figsize=(15,5))\n",
+    "fig.suptitle('Empirical distribution for F1 scores')\n",
+    "axs[0].hist(F1scores1, bins=25)\n",
+    "axs[0].set_title('System 1')\n",
+    "axs[0].set(xlabel='F1 score')\n",
+    "axs[1].hist(F1scores2, bins=25)\n",
+    "axs[1].set_title('System 2')\n",
+    "axs[1].set(xlabel='F1 score', ylabel='count')\n",
+    "\n",
+    "# plt.hist(F1scores1, bins=25)\n",
+    "# plt.title(\"Distribution (empirical) for System 2 Precision\")\n",
+    "# plt.xlabel(\"Precision\")\n",
+    "# plt.ylabel(\"Count\")\n",
+    "\n",
+    "# plt.hist(F1scores2, bins=25)\n",
+    "# plt.title(\"Distribution (empirical) for System 1 Precision\")\n",
+    "# plt.xlabel(\"Precision\")\n",
+    "# plt.ylabel(\"Count\")\n",
+    "\n",
+    "print('Empirical probability that system 1 is better than system 2 in F1 score = {}'\n",
+    "      .format(numpy.count_nonzero(F1scores1 > F1scores2) / nbsamples))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/coverage.xml b/coverage.xml
new file mode 100644
index 0000000..ac0fa57
--- /dev/null
+++ b/coverage.xml
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-- 
GitLab