Example codes of https://medium.com/@yamasaKit/10-optuna%E3%81%A7gtm%E3%81%AE%E3%83%91%E3%83%A9%E3%83%A1%E3%83%BC%E3%82%BF%E3%83%81%E3%83%A5%E3%83%BC%E3%83%8B%E3%83%B3%E3%82%B0%E3%82%92%E8%A1%8C%E3%81%86-b24c2289778e

10_1-optimize_GTM_with_optuna.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iris データセットに対する GTM のパラメータチューニングを行う"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "import optuna\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_boston, load_iris\n",
    "\n",
    "from gtm import gtm\n",
    "from k3nerror import k3nerror\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "データセットの準備"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "color = iris.target\n",
    "X = iris.data\n",
    "X = (X-X.mean(axis=0)) / X.std(axis=0, ddof=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "パラメータチューニングに必要な関数の定義"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optuna_gtm(shape_of_map, shape_of_rbf_centers, variance_of_rbfs,\n",
    "               lambda_in_em_algorithm, number_of_iterations=300):\n",
    "    return gtm([shape_of_map, shape_of_map],\n",
    "               [shape_of_rbf_centers, shape_of_rbf_centers],\n",
    "               variance_of_rbfs, lambda_in_em_algorithm, number_of_iterations, 0)\n",
    "\n",
    "\n",
    "def objective(trial):    \n",
    "    rbf_centers = trial.suggest_int('shape_of_rbf_centers', 2, 10)\n",
    "    variance_of_rbfs = trial.suggest_loguniform('variance_of_rbfs', 2**-5, 2**2)\n",
    "    _lambda = trial.suggest_loguniform('lambda_in_em_algorithm', 2**-15, 1)\n",
    "\n",
    "    model = optuna_gtm(30, rbf_centers, variance_of_rbfs, _lambda)\n",
    "    model.fit(X)\n",
    "    \n",
    "    if model.success_flag:\n",
    "        responsibilities = model.responsibility(X)\n",
    "        means = responsibilities.dot(model.map_grids)\n",
    "        k = 10\n",
    "        return k3nerror(X, means, k)\n",
    "    else:\n",
    "        return np.Inf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optunaによるパラメータチューニング"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2019-01-09 20:46:58,635] Finished a trial resulted in value: inf. Current best value is inf with parameters: {'shape_of_rbf_centers': 7, 'variance_of_rbfs': 1.8633175873063956, 'lambda_in_em_algorithm': 0.0028845707845841337}.\n",
      "[I 2019-01-09 20:47:10,127] Finished a trial resulted in value: 0.606224247324507. Current best value is 0.606224247324507 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 1.8845960524132352, 'lambda_in_em_algorithm': 0.46188526740983116}.\n",
      "[I 2019-01-09 20:47:10,186] Finished a trial resulted in value: inf. Current best value is 0.606224247324507 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 1.8845960524132352, 'lambda_in_em_algorithm': 0.46188526740983116}.\n",
      "[I 2019-01-09 20:47:10,239] Finished a trial resulted in value: 0.6058302075750112. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:10,271] Finished a trial resulted in value: 0.8233896322816427. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:10,289] Finished a trial resulted in value: inf. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:10,325] Finished a trial resulted in value: 0.9474587408763965. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:10,814] Finished a trial resulted in value: 1.0844700632684403. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:10,861] Finished a trial resulted in value: inf. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:10,906] Finished a trial resulted in value: 1.220858758726814. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:11,698] Finished a trial resulted in value: 0.8364561908269998. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:20,402] Finished a trial resulted in value: 1.3394582747189772. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:20,767] Finished a trial resulted in value: 0.7825730648156883. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:21,181] Finished a trial resulted in value: 0.7765392175535071. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:21,674] Finished a trial resulted in value: 0.8156338143957838. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:26,537] Finished a trial resulted in value: 1.2627152417921037. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:27,144] Finished a trial resulted in value: 0.9270379693317796. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:27,609] Finished a trial resulted in value: 1.1126515407109112. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:31,193] Finished a trial resulted in value: 1.3613498862077176. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:31,882] Finished a trial resulted in value: 0.7625330031779286. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:32,652] Finished a trial resulted in value: 0.7165988085874957. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:33,172] Finished a trial resulted in value: 0.9214263057352483. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:33,462] Finished a trial resulted in value: 0.7439370454649151. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:38,762] Finished a trial resulted in value: 0.7527726793123463. Current best value is 0.6058302075750112 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.656715043900635, 'lambda_in_em_algorithm': 9.368055042076014e-05}.\n",
      "[I 2019-01-09 20:47:39,532] Finished a trial resulted in value: 0.23468784371970403. Current best value is 0.23468784371970403 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.130413586509928, 'lambda_in_em_algorithm': 0.7647931407226711}.\n",
      "[I 2019-01-09 20:47:39,908] Finished a trial resulted in value: 0.23249469372731024. Current best value is 0.23249469372731024 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.374108593985639, 'lambda_in_em_algorithm': 0.6127081890403736}.\n",
      "[I 2019-01-09 20:47:43,562] Finished a trial resulted in value: 0.2398016644088873. Current best value is 0.23249469372731024 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.374108593985639, 'lambda_in_em_algorithm': 0.6127081890403736}.\n",
      "[I 2019-01-09 20:47:44,101] Finished a trial resulted in value: 0.37995294413510083. Current best value is 0.23249469372731024 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.374108593985639, 'lambda_in_em_algorithm': 0.6127081890403736}.\n",
      "[I 2019-01-09 20:47:44,937] Finished a trial resulted in value: 0.23699026252733976. Current best value is 0.23249469372731024 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.374108593985639, 'lambda_in_em_algorithm': 0.6127081890403736}.\n",
      "[I 2019-01-09 20:47:45,468] Finished a trial resulted in value: 0.2318338887691323. Current best value is 0.2318338887691323 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.4257310290016205, 'lambda_in_em_algorithm': 0.5029087649847571}.\n",
      "[I 2019-01-09 20:47:45,837] Finished a trial resulted in value: 0.23778400264971378. Current best value is 0.2318338887691323 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.4257310290016205, 'lambda_in_em_algorithm': 0.5029087649847571}.\n",
      "[I 2019-01-09 20:47:50,753] Finished a trial resulted in value: 0.2400382496854307. Current best value is 0.2318338887691323 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.4257310290016205, 'lambda_in_em_algorithm': 0.5029087649847571}.\n",
      "[I 2019-01-09 20:47:51,495] Finished a trial resulted in value: 0.23858401402924656. Current best value is 0.2318338887691323 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.4257310290016205, 'lambda_in_em_algorithm': 0.5029087649847571}.\n",
      "[I 2019-01-09 20:47:51,877] Finished a trial resulted in value: 0.23314583377094533. Current best value is 0.2318338887691323 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.4257310290016205, 'lambda_in_em_algorithm': 0.5029087649847571}.\n",
      "[I 2019-01-09 20:47:55,815] Finished a trial resulted in value: 0.23929770105301404. Current best value is 0.2318338887691323 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.4257310290016205, 'lambda_in_em_algorithm': 0.5029087649847571}.\n",
      "[I 2019-01-09 20:47:56,309] Finished a trial resulted in value: 0.2376475182926116. Current best value is 0.2318338887691323 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.4257310290016205, 'lambda_in_em_algorithm': 0.5029087649847571}.\n",
      "[I 2019-01-09 20:47:57,164] Finished a trial resulted in value: 0.23133539780845544. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:47:57,467] Finished a trial resulted in value: 0.23386601038677107. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:47:58,047] Finished a trial resulted in value: 0.2351921396848194. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:03,398] Finished a trial resulted in value: 0.23318131447844204. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:03,895] Finished a trial resulted in value: 0.23819606361153864. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:04,250] Finished a trial resulted in value: 0.23234195981464634. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:08,319] Finished a trial resulted in value: 0.2369219477172788. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:08,700] Finished a trial resulted in value: 0.23820698148574296. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:08,866] Finished a trial resulted in value: inf. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:09,572] Finished a trial resulted in value: 0.23477700286992254. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:09,769] Finished a trial resulted in value: inf. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:10,067] Finished a trial resulted in value: 0.23354096224862161. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:10,300] Finished a trial resulted in value: inf. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:10,950] Finished a trial resulted in value: 0.23757779282035876. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:11,141] Finished a trial resulted in value: inf. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:15,972] Finished a trial resulted in value: 0.3793345155730491. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:16,150] Finished a trial resulted in value: inf. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:16,545] Finished a trial resulted in value: 0.2334093397418619. Current best value is 0.23133539780845544 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.6528975829555064, 'lambda_in_em_algorithm': 0.5113185775684145}.\n",
      "[I 2019-01-09 20:48:16,926] Finished a trial resulted in value: 0.2310107292003231. Current best value is 0.2310107292003231 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.82387597683468, 'lambda_in_em_algorithm': 0.4857247100028766}.\n",
      "[I 2019-01-09 20:48:21,408] Finished a trial resulted in value: 0.40025974083953136. Current best value is 0.2310107292003231 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.82387597683468, 'lambda_in_em_algorithm': 0.4857247100028766}.\n",
      "[I 2019-01-09 20:48:22,668] Finished a trial resulted in value: 0.3929806131234902. Current best value is 0.2310107292003231 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 3.82387597683468, 'lambda_in_em_algorithm': 0.4857247100028766}.\n",
      "[I 2019-01-09 20:48:23,252] Finished a trial resulted in value: 0.22624007233341886. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:23,272] Finished a trial resulted in value: 0.23058108931984203. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:24,176] Finished a trial resulted in value: 0.7132744068446615. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:28,241] Finished a trial resulted in value: 0.5848517026302302. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:28,976] Finished a trial resulted in value: 1.0486777451767813. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:30,913] Finished a trial resulted in value: 0.7887978122080064. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:38,255] Finished a trial resulted in value: 1.19090709542072. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:39,215] Finished a trial resulted in value: 1.025754807820697. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:39,371] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:39,562] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:39,735] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:40,116] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:40,262] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:40,395] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:40,550] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:44,193] Finished a trial resulted in value: 1.3215782878034097. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:44,460] Finished a trial resulted in value: 1.4260910782362042. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:44,926] Finished a trial resulted in value: 1.2878064740767126. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:49,228] Finished a trial resulted in value: 1.0566098748963626. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:50,456] Finished a trial resulted in value: 1.3384053868059889. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:52,488] Finished a trial resulted in value: 1.3525501918273368. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:52,813] Finished a trial resulted in value: 0.3870749972514136. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:55,584] Finished a trial resulted in value: 0.6843301632170189. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:55,773] Finished a trial resulted in value: 0.6964290059202147. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:48:56,274] Finished a trial resulted in value: 0.7542700780592116. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:00,128] Finished a trial resulted in value: 1.2288119821769725. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:01,315] Finished a trial resulted in value: 0.7101386377018352. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:02,848] Finished a trial resulted in value: 0.3821485747828135. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:04,738] Finished a trial resulted in value: 0.3991615195059045. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:05,456] Finished a trial resulted in value: 0.6676393029471415. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:07,874] Finished a trial resulted in value: 0.39261326382718587. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:08,032] Finished a trial resulted in value: 0.238902127872117. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:08,578] Finished a trial resulted in value: 0.23856938249874143. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:12,819] Finished a trial resulted in value: 0.23208725384823384. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:13,593] Finished a trial resulted in value: 0.25271836435377. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:15,338] Finished a trial resulted in value: 0.23857536087665845. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:16,914] Finished a trial resulted in value: 0.23791786120181174. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:17,717] Finished a trial resulted in value: 0.2383958389385333. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:20,101] Finished a trial resulted in value: 0.23533404692386678. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:20,386] Finished a trial resulted in value: 0.2379740789668076. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:21,124] Finished a trial resulted in value: 0.23266393271214114. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:25,259] Finished a trial resulted in value: 0.23660068984544752. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:26,082] Finished a trial resulted in value: 0.23591089177727503. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:27,623] Finished a trial resulted in value: 0.7419135673136501. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:29,334] Finished a trial resulted in value: 0.6259959923128011. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:30,084] Finished a trial resulted in value: 0.7226829264946141. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:32,284] Finished a trial resulted in value: 0.7412302167888289. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:32,522] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:32,741] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:32,768] Finished a trial resulted in value: 0.7222525183514241. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:33,049] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:33,105] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:33,407] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:33,448] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:33,896] Finished a trial resulted in value: 0.6124694014407543. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:37,846] Finished a trial resulted in value: 0.6143942556268632. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:39,133] Finished a trial resulted in value: 0.8073628018885645. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:41,893] Finished a trial resulted in value: 0.8411177563354213. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:42,918] Finished a trial resulted in value: 0.3867368504331553. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:43,927] Finished a trial resulted in value: 0.22637732961504584. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:44,093] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:44,349] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:44,614] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:44,790] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:45,018] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:45,233] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:45,629] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:45,838] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:46,027] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:46,206] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:46,440] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:47,138] Finished a trial resulted in value: 0.3854854830520283. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:47,326] Finished a trial resulted in value: 0.2313873159806284. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:47,817] Finished a trial resulted in value: 0.5594548008857195. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:52,527] Finished a trial resulted in value: 0.6695045743674062. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:53,892] Finished a trial resulted in value: 0.8535135113151412. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:55,666] Finished a trial resulted in value: 0.6623033245404017. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:55,975] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:56,189] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:56,429] Finished a trial resulted in value: inf. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:49:57,515] Finished a trial resulted in value: 1.0272564166973637. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:00,604] Finished a trial resulted in value: 0.5522766217119599. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:01,342] Finished a trial resulted in value: 0.7851048463005393. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:02,036] Finished a trial resulted in value: 0.9609043791858204. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:02,225] Finished a trial resulted in value: 0.6994278573943107. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:06,669] Finished a trial resulted in value: 0.8261323394414966. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:07,773] Finished a trial resulted in value: 0.5399734285554789. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:09,751] Finished a trial resulted in value: 0.5735003826446865. Current best value is 0.22624007233341886 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.6344869853562325, 'lambda_in_em_algorithm': 0.8360111581839933}.\n",
      "[I 2019-01-09 20:50:10,780] Finished a trial resulted in value: 0.22594388134521814. Current best value is 0.22594388134521814 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.65144121383011, 'lambda_in_em_algorithm': 0.8429719055428158}.\n",
      "[I 2019-01-09 20:50:13,628] Finished a trial resulted in value: 0.22552082497517617. Current best value is 0.22552082497517617 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.7894147839850643, 'lambda_in_em_algorithm': 0.7876126920383949}.\n",
      "[I 2019-01-09 20:50:14,386] Finished a trial resulted in value: 0.22860340769900223. Current best value is 0.22552082497517617 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.7894147839850643, 'lambda_in_em_algorithm': 0.7876126920383949}.\n",
      "[I 2019-01-09 20:50:14,628] Finished a trial resulted in value: 0.3885033167052366. Current best value is 0.22552082497517617 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.7894147839850643, 'lambda_in_em_algorithm': 0.7876126920383949}.\n",
      "[I 2019-01-09 20:50:14,805] Finished a trial resulted in value: 0.23118670737958552. Current best value is 0.22552082497517617 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 3.7894147839850643, 'lambda_in_em_algorithm': 0.7876126920383949}.\n",
      "[I 2019-01-09 20:50:16,719] Finished a trial resulted in value: 0.22472913812105122. Current best value is 0.22472913812105122 with parameters: {'shape_of_rbf_centers': 4, 'variance_of_rbfs': 3.8304481291506574, 'lambda_in_em_algorithm': 0.9810666986412782}.\n",
      "[I 2019-01-09 20:50:17,117] Finished a trial resulted in value: 0.677237634059279. Current best value is 0.22472913812105122 with parameters: {'shape_of_rbf_centers': 4, 'variance_of_rbfs': 3.8304481291506574, 'lambda_in_em_algorithm': 0.9810666986412782}.\n",
      "[I 2019-01-09 20:50:17,379] Finished a trial resulted in value: 0.2344891728998296. Current best value is 0.22472913812105122 with parameters: {'shape_of_rbf_centers': 4, 'variance_of_rbfs': 3.8304481291506574, 'lambda_in_em_algorithm': 0.9810666986412782}.\n",
      "[I 2019-01-09 20:50:17,528] Finished a trial resulted in value: 0.7419091811063516. Current best value is 0.22472913812105122 with parameters: {'shape_of_rbf_centers': 4, 'variance_of_rbfs': 3.8304481291506574, 'lambda_in_em_algorithm': 0.9810666986412782}.\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study()\n",
    "study.optimize(objective, timeout=180, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最も良かったパラメータを表示する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'shape_of_rbf_centers': 4,\n",
       " 'variance_of_rbfs': 3.8304481291506574,\n",
       " 'lambda_in_em_algorithm': 0.9810666986412782}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study.best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "チューニングしたパラメータで改めてGTMを行う"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape_of_map = [30, 30]\n",
    "shape_of_rbf_centers = [study.best_params['shape_of_rbf_centers'], study.best_params['shape_of_rbf_centers']]\n",
    "variance_of_rbfs = study.best_params['variance_of_rbfs']\n",
    "lambda_in_em_algorithm = study.best_params['lambda_in_em_algorithm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gtm(shape_of_map, shape_of_rbf_centers, variance_of_rbfs, lambda_in_em_algorithm, 300, 0)\n",
    "model.fit(X)\n",
    "\n",
    "responsibilities = model.responsibility(X)\n",
    "means = responsibilities.dot(model.map_grids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22472913812105122"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k3nerror(X, means, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "パラメータチューニングの流れを可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>trial_id</th>\n",
       "      <th>state</th>\n",
       "      <th>value</th>\n",
       "      <th>datetime_start</th>\n",
       "      <th>datetime_complete</th>\n",
       "      <th colspan=\"3\" halign=\"left\">params</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lambda_in_em_algorithm</th>\n",
       "      <th>shape_of_rbf_centers</th>\n",
       "      <th>variance_of_rbfs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>TrialState.COMPLETE</td>\n",
       "      <td>1.220859</td>\n",
       "      <td>2019-01-09 20:46:58.280351</td>\n",
       "      <td>2019-01-09 20:47:10.904458</td>\n",
       "      <td>0.012957</td>\n",
       "      <td>3</td>\n",
       "      <td>0.098349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>TrialState.COMPLETE</td>\n",
       "      <td>0.836456</td>\n",
       "      <td>2019-01-09 20:46:58.280708</td>\n",
       "      <td>2019-01-09 20:47:11.696561</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>4</td>\n",
       "      <td>1.966541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>TrialState.COMPLETE</td>\n",
       "      <td>0.606224</td>\n",
       "      <td>2019-01-09 20:46:58.280962</td>\n",
       "      <td>2019-01-09 20:47:10.126131</td>\n",
       "      <td>0.461885</td>\n",
       "      <td>3</td>\n",
       "      <td>1.884596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>TrialState.COMPLETE</td>\n",
       "      <td>0.947459</td>\n",
       "      <td>2019-01-09 20:46:58.281250</td>\n",
       "      <td>2019-01-09 20:47:10.324097</td>\n",
       "      <td>0.051149</td>\n",
       "      <td>3</td>\n",
       "      <td>0.125583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>TrialState.COMPLETE</td>\n",
       "      <td>0.605830</td>\n",
       "      <td>2019-01-09 20:46:58.282365</td>\n",
       "      <td>2019-01-09 20:47:10.237419</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>3</td>\n",
       "      <td>0.656715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trial_id                state     value             datetime_start  \\\n",
       "                                                                       \n",
       "0        0  TrialState.COMPLETE  1.220859 2019-01-09 20:46:58.280351   \n",
       "1        1  TrialState.COMPLETE  0.836456 2019-01-09 20:46:58.280708   \n",
       "2        2  TrialState.COMPLETE  0.606224 2019-01-09 20:46:58.280962   \n",
       "3        3  TrialState.COMPLETE  0.947459 2019-01-09 20:46:58.281250   \n",
       "4        4  TrialState.COMPLETE  0.605830 2019-01-09 20:46:58.282365   \n",
       "\n",
       "           datetime_complete                 params                       \\\n",
       "                             lambda_in_em_algorithm shape_of_rbf_centers   \n",
       "0 2019-01-09 20:47:10.904458               0.012957                    3   \n",
       "1 2019-01-09 20:47:11.696561               0.000107                    4   \n",
       "2 2019-01-09 20:47:10.126131               0.461885                    3   \n",
       "3 2019-01-09 20:47:10.324097               0.051149                    3   \n",
       "4 2019-01-09 20:47:10.237419               0.000094                    3   \n",
       "\n",
       "                    \n",
       "  variance_of_rbfs  \n",
       "0         0.098349  \n",
       "1         1.966541  \n",
       "2         1.884596  \n",
       "3         0.125583  \n",
       "4         0.656715  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = study.trials_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "_lambda = df.params.lambda_in_em_algorithm\n",
    "rbf_centers = df.params.shape_of_rbf_centers\n",
    "variance_of_rbfs = df.params.variance_of_rbfs\n",
    "scores = df.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "scatter = ax.scatter3D(_lambda, rbf_centers, variance_of_rbfs, c=np.arange(df.shape[0]), cmap='bwr')\n",
    "ax.set_title(\"Scatter Plot: Color by search order\")\n",
    "ax.set_xlabel('lambda_in_em_algorith')\n",
    "ax.set_ylabel('shape_of_rbf_centers')\n",
    "ax.set_zlabel('variane_of_rbfs')\n",
    "plt.colorbar(scatter)\n",
    "plt.savefig('figure_search_order_iris.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "scatter = ax.scatter3D(_lambda, rbf_centers, variance_of_rbfs, c=scores, cmap='bwr')\n",
    "ax.set_title(\"Scatter Plot: Color by k3nerror\")\n",
    "ax.set_xlabel('lambda_in_em_algorith')\n",
    "ax.set_ylabel('shape_of_rbf_centers')\n",
    "ax.set_zlabel('variane_of_rbfs')\n",
    "plt.colorbar(scatter)\n",
    "plt.savefig('figure_k3nerror_iris.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可視化の結果を表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a27798898>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "for i in np.arange(3):\n",
    "    x = np.ma.masked_where(color!=i, means[:, 0])\n",
    "    y = np.ma.masked_where(color!=i, means[:, 1])\n",
    "    plt.scatter(x, y, c=f'C{i}', label=iris.target_names[i])\n",
    "plt.ylim(-1.1, 1.1)\n",
    "plt.xlim(-1.1, 1.1)\n",
    "plt.title('GTM iris dataset')\n",
    "plt.xlabel('z1 (mean)')\n",
    "plt.ylabel('z2 (mean)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比較のためにpairplotを表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a1985d6a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = iris.target.astype('object')\n",
    "target[target==0] = 'setosa'\n",
    "target[target==1] = 'versicolor'\n",
    "target[target==2] = 'virginica'\n",
    "df = pd.DataFrame(X)\n",
    "df.insert(loc=df.shape[1], column='species', value=target)\n",
    "sns.pairplot(df, hue='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EOF"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

10_2-optimize_GTM_with_gridsearch.ipynb

10_3-optimize_GTM_with_optuna.ipynb

10_4-optimize_GTM_with_gridsearch.ipynb