bayesian optimization finished

bayesian/bayesian_optimization/bayesian_optimization.ipynb

@@ -1,53 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting anastruct\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/1e/7ca168dffa4c38cae9e6269726bf1327075c93806c3ac8f3918816716503/anastruct-1.0.2-py3-none-any.whl (57kB)\n",
-      "\u001b[K     |████████████████████████████████| 61kB 6.3MB/s \n",
-      "\u001b[?25hRequirement already satisfied: numpy in /opt/miniconda3/lib/python3.7/site-packages (from anastruct) (1.16.4)\n",
-      "Requirement already satisfied: scipy in /opt/miniconda3/lib/python3.7/site-packages (from anastruct) (1.3.0)\n",
-      "Requirement already satisfied: matplotlib in /opt/miniconda3/lib/python3.7/site-packages (from anastruct) (3.0.3)\n",
-      "Requirement already satisfied: cycler>=0.10 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (0.10.0)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (1.1.0)\n",
-      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (2.4.1.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.1 in /opt/miniconda3/lib/python3.7/site-packages (from matplotlib->anastruct) (2.8.0)\n",
-      "Requirement already satisfied: six in /opt/miniconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->anastruct) (1.12.0)\n",
-      "Requirement already satisfied: setuptools in /opt/miniconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->anastruct) (41.0.1)\n",
-      "Installing collected packages: anastruct\n",
-      "Successfully installed anastruct-1.0.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install anastruct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Uninstalling anastruct-1.0.2:\n",
-      "  Successfully uninstalled anastruct-1.0.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip uninstall anastruct -y"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -56,16 +8,11 @@
    "source": [
     "from sklearn.datasets import fetch_openml\n",
     "import pandas as pd\n",
-    "from sklearn import preprocessing\n",
-    "from sklearn import metrics\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.model_selection import train_test_split\n",
     "import numpy as np\n",
     "import GPy\n",
     "from scipy import stats\n",
     "from scipy import optimize\n",
     "import matplotlib.pyplot as plt\n",
-    "from sklearn.neural_network import MLPClassifier\n",
     "from sklearn import metrics\n",
     "from tqdm import tqdm\n",
     "from functools import partial\n",
@@ -200,7 +147,7 @@
     {
      "data": {
       "text/plain": [
-       "{'foo': 1.9090909090909092, 'bar': 440}"
+       "{'foo': 9.363636363636363, 'bar': 720}"
       ]
      },
      "execution_count": 3,
@@ -422,7 +369,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f71598cd908>]"
+       "[<matplotlib.lines.Line2D at 0x7f11b2cc9240>]"
       ]
      },
      "execution_count": 5,
@@ -431,7 +378,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7UAAAEXCAYAAAByEmP5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xd81eXh/v/XnU0SyCSEEAIJYW8TNogKKi5E614gKOCu1Vq11bZq1WprHaDiAAEnIDhwUGXKJuxNQlgBEhIgm8xz//4I9sfHr0iAhPc5yfV8PPIgJ7zPOddBA7nOvYy1FhERERERERFP5OV0ABEREREREZEzpVIrIiIiIiIiHkulVkRERERERDyWSq2IiIiIiIh4LJVaERERERER8VgqtSIiIiIiIuKxVGpFRERERETEY6nUioiI/ApjzG5jzGCnc1SHMaatMWatMabAGPPgOX7uzcaYC2rpsRsbY34wxhw1xrxfG88hIiKez8fpACIiIqfLGLMA6ApEW2tLHY7jDh4DFlhru9fmkxhjdgN3WWt//Plr1tqOtfiUTwCp1tqLa/E5RETEw2mkVkREPIoxpiUwALDAUEfDuI8WwGanQ9SCwcB0p0OIiIh7U6kVERFPcwewHPgAGP5bFxpjrDEm8YTbHxhjnjv++W5jzKPGmA3GmDxjzGfGmICTPE47Y8wuY8xNp7qvMaa9MWaBMSb3+NTcoSc8zp3GmK9PuJ1mjJl2wu19xphup5ltHnAhMM4YU2iMafNbr7sa+ZsbY2YaY7KNMYeNMeOOf30qEAd8ffx5HjvhsQZX47WfzmvyM8bkAZ2PP9/GX7tOREQEVGpFRMTz3AF8dPzjUmNMk7N4rBuAIUA80AUY8csLjDHnAf8FHrDWfvpb9zXG+AJfH78+CngA+MgY0/b4fRYCA4wxXsaYpoAv0O/48yQAwcCG6mYDsNZeBPwE3G+tDbbW7jjT126M8QZmA3uAlkAz4NPjz3M7sBe46vjzvPSLP6dTvfbTeU1lQB/g0PHn6lzN1yQiIvWQSq2IiHgMY0x/qqbaTrPWrgZ2ArecxUO+bq09YK09QlUh6/aL3x8AfAUMt9bOrsZ9e1NVTF+01pZZa+dRVRJvBrDWpgMFx68dCMwB9htj2h2//ZO11lXNbGfr1x6/JxAD/NFaW2StLbHWLq7m4/3ma/+N5zyZbsD603xNIiJSD6nUioiIJxkO/Ndam3P89secYgryKWSe8HkxVaXsRGOBpdba+dW8bwyw74RiClWjns1OuL0QuAA4//jnC6gqtAOP365utrP1a4/fHNhjra04g8erzms/ndekUisiItWiUisiIh7BGNOAqumrA40xmcaYTOBhoKsxputJ7lYMBJ5wO/o0n3YsEGeM+U81rz8ANDfGnPjvaxyw/4TbP5faAcc/X8ivl9qzcaavex9Vr/dkpyPY37hvdV776eiKSq2IiFSDSq2IiHiKYUAl0IGqUbxuQHuq1pPecZL7rANuMcZ4G2OGUFUcT0cBVWtAzzfGvFiN61cARcBjxhjf4+e3XsXxdanHLaRqY6cG1tqM4/mHABHA2tPMdzJn+rpXAgeBF40xQcaYAGNMvxN+PwtIOMl9q/PaT4dKrYiIVItKrYiIeIrhwCRr7V5rbebPH8A44NaTjC4+RFWxygVuBb443Se11uYCFwOXGWOePcW1ZVQdM3QZkAO8Cdxhrd12wjU7gEKqyizW2nwgHVhira083XwncUav+/jzXwUkUrUpVAZw4wmXvAD85fjuxo/+4r6nfO3VZYyJBsKA076viIjUP8ba35pJJCIiIiIiIuK+NFIrIiIiIiIiHkulVkRERERERDyWSq2IiIiIiIh4LJVaERERERER8VgnO4fO7UVGRtqWLVs6HUNERERERERqwerVq3OstY1PdZ3HltqWLVuSkpLidAwRERERERGpBcaYPdW5TtOPRURERERExGOp1IqIiIiIiIjHUqkVERERERERj6VSKyIiIiIiIh5LpVZEREREREQ8lkqtiIiIiIiIeCy3KLXGmObGmPnGmK3GmM3GmIecziQiIiIiIiLuzy1KLVABPGKtbQ/0Bu4zxnRwOJOIiIiIiEids2bvUSYs3Im11ukoNcItSq219qC1ds3xzwuArUAzZ1OJiIiIiIjULWmHChn5wSo+XrmXwtIKp+PUCLcotScyxrQEugMrfuX3RhtjUowxKdnZ2ec6moiIiIiIiMfKyi9h+MSV+HgZpozsScMAX6cj1Qi3KrXGmGDgc+D31tr8X/6+tfYda22ytTa5cePG5z6giIiIiIiIB8ovKWfEpFUcLS5j0oietIgIcjpSjXGbUmuM8aWq0H5krZ3pdB4REREREZG6oLSikjFTVpOaVcDbtyXROTbE6Ug1ysfpAADGGAO8D2y11r7idB4REREREZG6wOWy/GHaepalH+aVG7pyfpu6N+PVXUZq+wG3AxcZY9Yd/7jc6VAiIiIiIiKeylrLs99s4ZsNB3nisnZce16s05FqhVuM1FprFwPG6RwiIiIiIiJ1xTuL0pm0ZDcj+8Uz+vwEp+PUGncZqRUREREREZEaMnNNBi98t40ruzTlL1e0p2rFZ92kUisiIiIiIlKHLNyRzWMzNtAnIYJ/39AVL6+6W2hBpVZERERERKTO2JCRyz0frqZ1k4ZMuCMJfx9vpyPVOpVaERERERGROmB3ThF3TlpFWKAfk+/sQaMAX6cjnRMqtSIiIiIiIh4uu6CU4ZNW4rKWKaN6EtUowOlI54xb7H4sIiIiIiIiZ6aotIKRH6wiK7+ET+7uTavGwU5HOqdUakVERERERDxUWYWLsR+uZsvBfN65PYnucWFORzrnNP1YRERERETEA7lclj99voGfUnN44ZrODGrfxOlIjlCpFRERERER8UD/nLONWWv38+glbbihR3On4zhGpVZERERERMTDTFy8iwkL07m9dwvuuzDR6TiOUqkVERERERHxIF+vP8Cz32xhSMdo/ja0I8YYpyM5SqVWRERERETEQ6zfl8sj09bTo0U4r97UDW+v+l1oQaVWRERERETEI+QdK+f+T9bQuKE/79yRRICvt9OR3IKO9BEREREREXFz1lqemLmBA7klTBvTh9BAP6cjuQ2N1IqIiIiIiLi5j1bs5duNmfzx0rYktah/Z9H+FpVaERERERERN7blQD7PzN7CwDaNGT0gwek4bkelVkRERERExE0VlVZw/8drCG3gyys3dMVLG0P9P7SmVkRERERExE099eUmdh8u4qO7ehMR7O90HLekkVoRERERERE3NGN1BjPX7OfBQa3p0yrC6ThuS6VWRERERETEzaQdKuCpLzbROyGcBy5q7XQct6ZSKyIiIiIi4kZKyiu576O1NPDz5rWbuuOtdbS/SWtqRURERERE3Mgzs7ewPauAD+7sQZNGAU7HcXsaqRUREREREXETX68/wMcr9jJmYAIXtI1yOo5HUKkVERERERFxA3sOF/HEzI10jwvl0UvaOh3HY6jUioiIiIiIOKy0opL7P16Ll4E3bu6Or7eqWnVpTa2IiIiIiIjD/vnddjbuz2PC7UnEhgU6HcejqP6LiIiIiIg46IctWUxcsosRfVtyacdop+N4HJVaERERERERh+zPPcaj09fTqVkjnri8ndNxPJJKrYiIiIiIiAPKK108+MlaKipdvHHzefj7eDsdySO5Tak1xkw0xhwyxmxyOouIiIiIiEht+88PO1i95yjPX9uZ+Mggp+N4LLcptcAHwBCnQ4iIiIiIiNS2RTuyeWvhTm7q0ZyruzVzOo5Hc5tSa61dBBxxOoeIiIiIiEhtOpRfwh+mraN1VDB/vaqj03E8ntuU2uowxow2xqQYY1Kys7OdjiMiIiIiInJaKl2W33+2jsLSCsbfch4N/LSO9mx5VKm11r5jrU221iY3btzY6TgiIiIiIiKn5a0FaSzdeZhnhnaidZOGTsepEzyq1IqIiIiIiHiqDRm5vPpjKld1jeH65Fin49QZKrUiIiIiIiK1rKS8kj9MW09ksD/PXd0JY4zTkeoMtym1xphPgGVAW2NMhjFmlNOZREREREREasJL328n7VAhL1/fhZBAX6fj1Ck+Tgf4mbX2ZqcziIiIiIiI1LSlO3OYuGQXd/RpwYDW2huoprnNSK2IiIiIiEhdk19Szh+nbyA+MojHL2vndJw6yW1GakVEREREROqaZ77ewsG8Y3x+T18C/VS/aoNGakVERERERGrBnM2ZzFidwX0XJtI9LszpOHWWSq2IiIiIiEgNyyks5cmZG+kY04gHLmrtdJw6TePfIiIiIiIiNchayxMzN1JQWsEnN3bDz0djibVJf7oiIiIiIiI1aMbqDH7YksVjl7alTZOGTsep81RqRUREREREakjG0WL+/vUWesWHM7JfvNNx6gWVWhERERERkRrgclkenb4eay3/ur4rXl7G6Uj1gkqtiIiIiIhIDZi0dDfL04/w16s60jw80Ok49YZKrYiIiIiIyFlKzSrgn99vY3D7JlyfHOt0nHpFpVZEREREROQslFe6eHjaOoL9fXjh2s4Yo2nH55KO9BERERERETkLb8xLY9P+fN6+LYnGDf2djlPvaKRWRERERETkDK3bl8v4+Wlce14zhnSKdjpOvaRSKyIiIiIicgaOlVXyh2nraNLQn78N7eh0nHpL049FRERERETOwD+/30Z6dhEf39WLRgG+TseptzRSKyIiIiIicpoWp+bwwdLd3NmvJX0TI52OU6+p1IqIiIiIiJyGvGPl/HHGelo1DuJPQ9o5Hafe0/Rj+VWlFZXkFZfj5WWIDNYObiIiIiIiP/v7V5s5VFDKzHv6EuDr7XScek+lto4rq3CRe6yM3OJycovLOVpcRl5xObnHyjh6/Gt5x8o4WlRO7rFy8oqrvn6svPJ/j5EYFUy/VhH0S4ykd6sIrRcQERERkXrru40Line truncated
       "text/plain": [
        "<Figure size 1152x288 with 1 Axes>"
       ]
@@ -446,7 +393,7 @@
     "x = np.linspace(0, 10)\n",
     "\n",
     "def func(x):\n",
-    "    return np.sin(2 * x) + (x / 3)**2 - x + 50\n",
+    "    return np.sin(2 * x) + (x / 3)**2 - x \n",
     "\n",
     "plt.figure(figsize=(16, 4))\n",
     "plt.title('A unknown function $f$')\n",
@@ -481,7 +428,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2/2 [00:00<00:00, 3763.40it/s]"
+      "100%|██████████| 2/2 [00:00<00:00, 4742.01it/s]"
      ]
     },
     {
@@ -506,7 +453,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -518,7 +465,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIMAAAEVCAYAAABzDCUOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8VNX9//HXyWSZyQohoECEgIKgBAJEUEHEpVqX6k/qxpcKaP1Sq36VWmulbhSlaovKV3H5Uq221gWtuKFWxUoRa0WobIIsatiXJGSbZPY5vz8mxLCFEJLcLO/n4xFn7r3nnvsZIuTmc8/5HGOtRURERERERERE2oc4pwMQEREREREREZHmo2SQiIiIiIiIiEg7omSQiIiIiIiIiEg7omSQiIiIiIiIiEg7omSQiIiIiIiIiEg7omSQiIiIiIiIiEg7omSQiIiItHnGGK8xprfTcYiIiIi0BEoGiYiISJMyxhQYY86ufj/RGLOoia+3wBhzbe191tpUa+23jXydJGPMM8aYjcaYCmPMl8aY8xrzGiIiIiJNQckgERERaTWMMfFOx1BLPLAZOB3IAO4CXjHG5DgYk4iIiMghKRkkIiIizcIY0x94CjiletpWafX+JGPMDGPMJmPMTmPMU8YYT/Wx0caYLcaYXxtjdgDPGmM6GmPmGWMKjTEl1e+zq9tPB04DZlVfY1b1fmuMOa76fYYx5i/V5280xtxpjImrPjbRGLOoOp4SY8x3BxvtY62ttNZOtdYWWGuj1tp5wHfA0Cb9gxQRERE5QkoGiYiISLOw1q4BrgM+q5621aH60INAXyAPOA7oDtxd69SjgUygJzCJ2P3Ls9XbPQAfMKv6GncAnwA3Vl/jxgOE8hixkTy9iY3qGQ9cXev4cGAtkAX8HnjGGGMO9fmMMUdVf46vDtVWRERExElKBomIiIhjqpMs/w38wlq721pbAfwOuLJWsyhwj7U2YK31WWuLrbWvWWurqttPJ5bUqc/1XMAVwBRrbYW1tgB4CLiqVrON1to/WmsjwJ+BrsBRh+g3AXgB+LO19uv6xCIiIiLilJY0715ERETan85AMrC01uAbA7hqtSm01vprDhqTDDwC/BDoWL07zRjjqk7g1CULSAQ21tq3kdhopD127Hljra2qjiv1YB1WTzF7HggCBxqJJCIiItKiaGSQiIiINCe7z3YRsWleJ1prO1R/ZVhrU+s455fA8cBwa206MKp6vzlI+32vFyI2xWyPHsDWw/gMNapHNj1DbOTQj621oYb0IyIiItKclAwSERGR5rQTyDbGJAJYa6PAH4FHjDFdAIwx3Y0x59bRRxqxBFKpMSYTuOcA1+h9oBOrRw69Akw3xqQZY3oCtwB/beDneRLoD/zIWutrYB8iIiIizUrJIBEREWlO/yBWYHmHMaaoet+vgQ3Av40x5cB8YiN/DmYm4CE2yuffwN/3Of6/wKXVq4E9eoDz/weoBL4FFgEvAn863A9SnUj6GbHC1zuqVy/zGmPGHW5fIiIiIs3JWFvXSGoRkaZhjOlIbDWf0cCb1toSZyMSERERERFpH1RAWkScMhQ4j1jdjgRi00RERERERESkiWmamIg0iDGmszHmw+ppGM8YY+43xkw+3G4O0vdiY8yJjRCmiIiIiIiI7EPTxESkQYwxDwNua+31xpjOwDLguPoWUK1rmpgx5nLgCmvtjxs/chEREZH2wRhzP7DTWjvzEO0WA1dba79qnshExGlKBolIgxhjVgA3W2s/Nsb8Cuhrrf3vRurbDWwjttT09sboU0RERKS1q36YtptYEfza7gD+Un0sx1q78XAe1ulBnEj7o2liInJYjDGJxpgyIBd42xizkljtn3/WavN7Y8zrtbb/YIz5yBiTUJ9rWGv9wFLgnMaNXkRERKRVywN2W2tT9/n63+pjJdbajdVtJwLv1nPU9lvAGcaYrk0Ttoi0NEoGichhsdYGgVOAXdU3H7nEEkNrazV7kNgNRZ4x5jrgh8AYa23oMC61BhjUWHGLiIiItAF5wOo6ji2rtV3vh3V6ECfS/mg1MRFpiDxgea3tDkDFng1rbbExZiax4coZwEhrbdlhXqMC0NMpERERke8N5uDJoMHsnQw60MO6b4wxecDJxB7Wjaz1sE4P4kTaEY0MEpGG2DcZVAKk7dPmS2I3IVOstZsbcI00oLRh4YmIiIi0SXnAVcaY0lpfD9c69mWttvs9rAP2PKybApy/z8O6iupzRKQdUDJIRBpiEHsng1YAffdsGGNygSeBPwPXNPAa/fe5hoiIiEi7ZYxJInZ/NMpa26HW1y21jtUeGXS4D+v0IE6kHVEySEQaYt9k0LvA6QDGmO7A28B1wPVArjFm9OF0Xn1DMxT4sDGCFREREWkDBgAWWHmQYxFiU732ONyHdXoQJ9KOKBkkIofFGHM00BH4utbuvwDnG2MyiCWGHrbWvmWtrQL+AEw/zMtcBCyw1m5rjJhFRERE2oDBwFfW2sBBjq2y1oZr7av3wzo9iBNpf4y11ukYRKQNMMb8jtgKYzMboa/PgZ9aa1cdeWQiIiIirZ8xZhaxZI6/1m4LZBN78Oa21l5bq30WsWljJwILgWestY9WH7sVuMRaO6J6+zJgrLV2THN8FhFxnpJBIiIiIiIibVB9H9bpQZxI+6NkkIiIiIiIiIhIO6KaQSIiIiIiIiIi7YiSQSIiIiIiIiIi7YiSQSIiIiIiIiIi7Ui8ExfNysqyOTk5TlxaREREmsHSpUuLrLWdnY5D9qZ7MBERkbatvvdgjiSDcnJyWLJkiROXFhERkWZgjNnodAyyP92DiYiItG31vQfTNDERERERERERkXZEySARERERERERkXZEySARERERERERkXbEkZpBBxIKhdiyZQt+v9/pUKQWt9tNdnY2CQkJTociIiIiTUD3YC2T7sFERKQptZhk0JYtW0hLSyMnJwdjjNPhCGCtpbi4mC1bttCrVy+nwxEREZEmoHuwlkf3YCIi0tRazDQxv99Pp06ddBPSghhj6NSpk54UioiItGG6B2t5dA8mIiJNrcUkgwDdhLRA+p6IiIi0ffp53/LoeyIiIk2pxUwTExERERERETmgaBTCPgj5IFgJoarY+0gIoqHq1/D+29Ew2GjsKxqpfm/BRr7fv2cf9gCvUP2fGFvr/Z79e+3b59jhOGA/0mL0OQeOOcnpKBqNkkG1GGP4yU9+wvPPPw9AOByma9euDB8+nHnz5jkcnYiINKpICEI+IiE/oaAP2PMUPvZq4qpficMYiE90Y5LSwKViriKNzRjDLbfcwkMPPQTAjBkz8Hq9TJ061dnAmklBQQEXXnghq1atcjoUkeYRrISyLVBZCJVFUFUElcWx7aqi6n3FEPBCqDKW9AlVOR11M9GowBYrJUvJoLYqJSWFVatW4fP58Hg8fPjhh3Tv3t3psERE5EhZC+EAhH0E/VUEAlUEQ1H8NoGQScS6kms3/v6cmqd6lviKCpLZRYrbgzs1HRLTwKUfoyKNISkpiblz5zJlyhSysrIciyMSieByuRy7vkibU1kMRWuhcC0Urfv+tWzzgdu7O8R+4U7OgszekJQOCR5ITIaEWl81257YQ5q4hFqv8bW246u/XGDiqr/2vDex/ZjvtzGHeN2j1vs9+zW1s1WZP38+AGeffbbDkTjniO9ijTFuYCGQVN3f36y19xxpv04577zzeOedd7j00kt56aWXGDt2LJ988gkAlZWV/M///A8rV64kHA4zdepULr74YgoKCrjqqquorKwEYNasWZx66qksWLCAqVOnkpWVxapVqxg6dCh//etfNQdcRKQ5RKMQrMBfWUbQX4U/6iJAIuG4RKwrC9yHN8InBJRZS3nIT0JhGSlxO/F4knGnZEBiavUNpYg0RHx8PJMmTeKRRx5h+vTpex0rLCzkuuuuY9OmTQDMnDmTESNGkJubyyeffEJGRgZZWVk88sgjjB8/nquuuooJEybsdYO/YMEC7r77bjp16sTatWsZNWoUTzzxBHFxcaSmpnLLLbfw/vvv89BDDxEIBLj11lsJh8OcdNJJPPnkkyQlJZGTk8MVV1zBxx9/DMCLL77Icccdx8aNG7nmmmsoLCykc+fOPPvss/To0YNXX32V3/72t7hcLjIyMli4cOFB7xlF2gRrYccKWPcBfLsACtfERvfsEe+BrD5wzHAYMh469oolflI6VyeAOmn0rTSb++67D1Ay6EgFgDOttV5jTAKwyBjznrX23w3t8Ldvf8XqbeWNENr3TuiWzj0/OvGQ7a688kqmTZvGhRdeyIoVK7jmmmtqkkHTp0/nzDPP5E9/+hOlpaUMGzaMs88+my5duvDhhx/idrtZv349Y8eOZcmSJQB8+eWXfPXVV3Tr1o0RI0bw6aefMnLkyEb9bCIiUkuwirCvjCpvGRWRRIKuFGxCh9hTvyNlDDbBQzDBQ9BaSv1VJFbtJsVsJzk5lcT0LhCfeOTXEXHQ6NGj99t3+eWXc/3111NVVcX555+/3/GJEycyceJEioqKuPTSS/c6tmDBgnpd94YbbmDgwIHcdttte+2/+eab+cUvfsHIkSPZtGkT5557LmvWrKm5r+rZsye9e/fmk08+Yfz48fz73//mySef3K//xYsXs3r1anr27MkPf/hD5s6dy6WXXkplZSUDBgxg2rRp+P1++vTpw0cffUTfvn0ZP348Tz75JJMnTwYgPT2dxYsX85e//IXJkyczb948brzxRsaPH8+ECRP405/+xE033cQbb7zBtGnTeP/99+nevTulpaUAdd4zirRKgYpY4mfd+7D+Q/DuiO3vmgf9LoCs46Hz8ZDVFzKOgbgWtX6RSLt2xMkga60FvNWbCdVfrbby1cCBAykoKOCll17a72bngw8+4K233mLGjBlAbCnWTZs20a1bN2688UaWLVuGy+Vi3bp1NecMGzaM7OxsAPLy8igLine truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -530,7 +477,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -542,7 +489,7 @@
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -554,7 +501,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -566,7 +513,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -578,7 +525,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -590,7 +537,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1440x288 with 2 Axes>"
       ]
@@ -719,285 +666,103 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from water_accumulation import determine_max_water_cubic"
+    "x = np.linspace(0, 1000)\n",
+    "x = np.vstack([x, x, x, x]).T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "maximum_factor = 1.3\n",
-    "\n",
-    "param_ranges = {\n",
-    "    'mp_span_1': {\n",
-    "        'range': [70, int(70 * maximum_factor)],\n",
-    "        'type': 'float'\n",
-    "    },\n",
-    "    'mp_span_2': {\n",
-    "        'range': [240, int(240 * maximum_factor)],\n",
-    "        'type': 'float'\n",
-    "    },\n",
-    "    'mp_span_3': {\n",
-    "        'range': [25, int(25 * maximum_factor)],\n",
-    "        'type': 'float'\n",
-    "    },\n",
-    "    'k1': {\n",
-    "        'range': [2000, int(2000 * maximum_factor)],\n",
-    "        'type': 'float'\n",
-    "    },\n",
-    "    'k2': {\n",
-    "        'range': [3000, int(3000 * maximum_factor)],\n",
-    "        'type': 'float'\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bo = BayesOpt(param_ranges, \n",
-    "              determine_max_water_cubic, \n",
-    "              random_trials=5, \n",
-    "              optimization_trials=20, \n",
-    "              kernel=GPy.kern.Matern52(input_dim=3) + GPy.kern.Matern32(input_dim=3))\n",
-    "bo.fit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## With real data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# decision tree\n",
-    "param_ranges = {\n",
-    "    'min_samples_split': {\n",
-    "        'range': [2, 10],\n",
-    "        'type': 'int'\n",
-    "    },\n",
-    "    'min_samples_leaf': {\n",
-    "        'range': [1, 10],\n",
-    "        'type': 'int'\n",
-    "    },\n",
-    "    'max_depth': {\n",
-    "        'range': [3, 1000],\n",
-    "        'type': 'int'\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def evaluate_params(hyperparams):\n",
-    "    m = DecisionTreeClassifier(**hyperparams)\n",
-    "    m.fit(x_train, y_train)\n",
-    "    return m.score(x_test, y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<module 'sklearn.datasets.california_housing' from '/opt/miniconda3/lib/python3.7/site-packages/sklearn/datasets/california_housing.py'>"
+       "[<matplotlib.lines.Line2D at 0x7f11ae7272e8>]"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "datasets.load_diabetes()"
+    "def n_dim_func(n):\n",
+    "    a = np.random.uniform(-0.1, 0.1, (3, n))\n",
+    "    \n",
+    "    def f(x):\n",
+    "        y = a @ x.T + 0.1\n",
+    "        return np.sin(2 * y[0, :]) + np.abs(y[1, :])**0.5 + np.cos(y[2, :]**3)\n",
+    "        \n",
+    "    return f\n",
+    "    \n",
+    "    \n",
+    "plt.plot(n_dim_func(x.shape[1])(x))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 2/10 [00:00<00:00, 18.64it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting 10 random trials...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 19.36it/s]\n",
-      "  0%|          | 0/20 [00:00<?, ?it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
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-      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting 20 optimization trials...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-      " /opt/miniconda3/lib/python3.7/site-packages/GPy/kern/src/stationary.py:551: RuntimeWarning:invalid value encountered in multiply\n",
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-      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
-      " 95%|█████████▌| 19/20 [00:06<00:00,  2.48it/s] /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:111: RuntimeWarning:divide by zero encountered in log\n",
-      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:108: RuntimeWarning:invalid value encountered in greater\n",
-      " /opt/miniconda3/lib/python3.7/site-packages/paramz/transformations.py:113: RuntimeWarning:invalid value encountered in greater\n",
-      "100%|██████████| 20/20 [00:07<00:00,  2.51it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "bo = BayesOpt(param_ranges, \n",
+    "for n in range(10, 30, 2):\n",
+    "    \n",
+    "    func = n_dim_func(n)\n",
+    "\n",
+    "    def evaluate_params(hyperparams):\n",
+    "        x = np.array(list(hyperparams.values()))[None, :]\n",
+    "        return func(x)\n",
+    "    \n",
+    "    param_ranges = {k: {\n",
+    "        'range': [0, 100],\n",
+    "        'type': 'float'\n",
+    "    } for k in range(n)}\n",
+    "    \n",
+    "    bo = BayesOpt(param_ranges, \n",
     "              evaluate_params, \n",
-    "              random_trials=10, \n",
-    "              optimization_trials=20, \n",
-    "              kernel=GPy.kern.Matern52(input_dim=3) + GPy.kern.Matern32(input_dim=3) + GPy.kern.a)\n",
-    "bo.fit()"
+    "              random_trials=5, \n",
+    "              optimization_trials=20,\n",
+    "              kernel=GPy.kern.Matern32(input_dim=n) + GPy.kern.Matern52(input_dim=n, variance=5) + GPy.kern.RBF(input_dim=n))\n",
+    "    bo.fit()\n",
+    "    \n",
+    "    bo_random = BayesOpt(param_ranges, \n",
+    "              evaluate_params, \n",
+    "              random_trials=25, \n",
+    "              optimization_trials=0)\n",
+    "    bo_random.fit()\n",
+    "    \n",
+    "    y_max = max(bo.y.max(), bo_random.y.max())\n",
+    "    y_min = min(bo.y.min(), bo_random.y.min())\n",
+    "    \n",
+    "    plt.figure(figsize=(16, 4))\n",
+    "    plt.xlabel('iteration')\n",
+    "    plt.ylabel('$f(x)$')\n",
+    "    plt.ylim(y_min * 0.98, y_max * 1.02)\n",
+    "    plt.plot(np.arange(1, len(bo.y) + 1), np.maximum.accumulate(bo_random.y), '-o', label='random')\n",
+    "    plt.plot(np.arange(1, len(bo.y) + 1), np.maximum.accumulate(bo.y), '-o', label='Bayesian optimization')\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    \n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8276849642004773"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bo.y.max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8276849642004773"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bo.y.max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

readme.md

@@ -1,5 +1,6 @@
 # Various machine learning algorithms. Mostly vanilla.
 
+* [bayesian optimization](https://www.ritchievink.com/blog/2019/08/25/algorithm-breakdown-bayesian-optimization/)
 * [gaussian processes](https://www.ritchievink.com/blog/2019/02/01/an-intuitive-introduction-to-gaussian-processes/)
 * [bayesian generalized additive models](https://www.ritchievink.com/blog/2018/10/09/build-facebooks-prophet-in-pymc3-bayesian-time-series-analyis-with-generalized-additive-models/)
 * [gradient boosting machines](https://www.ritchievink.com/blog/2018/10/09/algorithm-breakdown-why-do-we-call-it-gradient-boosting/)
@@ -9,4 +10,4 @@
 * [support vector machine](https://www.ritchievink.com/blog/2017/11/27/implementing-a-support-vector-machine-in-scala/)
 * [neural network](https://ritchievink.com/blog/2017/07/10/programming-a-neural-network-from-scratch/)
 * [arima models](https://www.ritchievink.com/blog/2018/09/26/algorithm-breakdown-ar-ma-and-arima-models./)
-* [expectation_maximization](/clustering/expectation_maximization.ipynb)
+* [expectation maximization](/clustering/expectation_maximization.ipynb)