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yamasakih/10_1-optimize_GTM_with_optuna.ipynb

Last active January 9, 2019 13:02
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10_1-optimize_GTM_with_optuna.ipynb
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10_2-optimize_GTM_with_gridsearch.ipynb
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10_3-optimize_GTM_with_optuna.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"boston 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",
"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": [
"boston = load_boston()\n",
"X = boston.data\n",
"color = boston.target\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": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[I 2019-01-09 21:01:28,954] Finished a trial resulted in value: inf. Current best value is inf with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 3.032859157879858, 'lambda_in_em_algorithm': 0.00887848732338742}.\n",
"[I 2019-01-09 21:01:28,963] Finished a trial resulted in value: inf. Current best value is inf with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 3.032859157879858, 'lambda_in_em_algorithm': 0.00887848732338742}.\n",
"[I 2019-01-09 21:01:28,967] Finished a trial resulted in value: inf. Current best value is inf with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 3.032859157879858, 'lambda_in_em_algorithm': 0.00887848732338742}.\n",
"[I 2019-01-09 21:01:51,651] Finished a trial resulted in value: 4.40417857449574. Current best value is 4.40417857449574 with parameters: {'shape_of_rbf_centers': 2, 'variance_of_rbfs': 0.03438509866118375, 'lambda_in_em_algorithm': 0.00024068908689111972}.\n",
"[I 2019-01-09 21:01:52,232] Finished a trial resulted in value: 1.4452641543499145. Current best value is 1.4452641543499145 with parameters: {'shape_of_rbf_centers': 3, 'variance_of_rbfs': 0.1681210262673916, 'lambda_in_em_algorithm': 0.1175007948452623}.\n",
"[I 2019-01-09 21:01:52,743] Finished a trial resulted in value: 0.9019786384928425. Current best value is 0.9019786384928425 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.08392668873069489, 'lambda_in_em_algorithm': 0.89835140506488}.\n",
"[I 2019-01-09 21:01:53,305] Finished a trial resulted in value: 0.812855587947027. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:01:53,919] Finished a trial resulted in value: 1.4252378381357662. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:01:53,941] Finished a trial resulted in value: 0.8156254515190078. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:01:54,404] Finished a trial resulted in value: 38.6907703172492. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:01:57,874] Finished a trial resulted in value: 1.2173151884188456. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:16,668] Finished a trial resulted in value: 1.3665601942244954. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:16,709] Finished a trial resulted in value: 1.27513455668162. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:16,830] Finished a trial resulted in value: inf. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:17,673] Finished a trial resulted in value: 0.9140973807203888. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:18,036] Finished a trial resulted in value: 1.0484298287166962. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:18,871] Finished a trial resulted in value: 1.3345871053070388. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:19,174] Finished a trial resulted in value: inf. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:19,261] Finished a trial resulted in value: inf. Current best value is 0.812855587947027 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.27177189539509533, 'lambda_in_em_algorithm': 0.3184816034688744}.\n",
"[I 2019-01-09 21:02:19,269] Finished a trial resulted in value: 0.7878016191630023. Current best value is 0.7878016191630023 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 1.3550353482589548, 'lambda_in_em_algorithm': 0.007577234505679381}.\n",
"[I 2019-01-09 21:02:21,936] Finished a trial resulted in value: 1.3372522686704578. Current best value is 0.7878016191630023 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 1.3550353482589548, 'lambda_in_em_algorithm': 0.007577234505679381}.\n",
"[I 2019-01-09 21:02:23,555] Finished a trial resulted in value: 0.9481868427115367. Current best value is 0.7878016191630023 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 1.3550353482589548, 'lambda_in_em_algorithm': 0.007577234505679381}.\n",
"[I 2019-01-09 21:02:42,184] Finished a trial resulted in value: 0.7562122601691431. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:02:42,474] Finished a trial resulted in value: 1.2926035351218164. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:02:43,460] Finished a trial resulted in value: 0.9650123239726606. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:02:43,465] Finished a trial resulted in value: 0.9663732021376836. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:02:43,470] Finished a trial resulted in value: 0.957631664456834. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:02:44,353] Finished a trial resulted in value: 0.8510370538763876. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:02:46,416] Finished a trial resulted in value: 0.8592443160323041. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:02:47,878] Finished a trial resulted in value: 0.9214123120290854. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:05,788] Finished a trial resulted in value: 0.9261353890395579. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:06,273] Finished a trial resulted in value: 0.9198273611508831. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:07,319] Finished a trial resulted in value: 0.9180914933262105. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:07,345] Finished a trial resulted in value: 0.9220687425372027. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:07,369] Finished a trial resulted in value: 0.9301054196182476. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:08,066] Finished a trial resulted in value: 0.918227995196838. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:10,989] Finished a trial resulted in value: 0.9181650605840821. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:12,722] Finished a trial resulted in value: 0.9664693008837884. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:31,987] Finished a trial resulted in value: 1.0091482749447955. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:34,170] Finished a trial resulted in value: 5689.686452183926. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:40,205] Finished a trial resulted in value: 1.252670877380584. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:40,253] Finished a trial resulted in value: 2.5544644706027677. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:40,261] Finished a trial resulted in value: 1.1039156706018687. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:40,388] Finished a trial resulted in value: 1.0218440849670403. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:40,635] Finished a trial resulted in value: inf. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:42,572] Finished a trial resulted in value: 1.2031532881105096. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:03:44,469] Finished a trial resulted in value: 24.130306517823207. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:04:04,092] Finished a trial resulted in value: 365.84571963716434. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:04:05,793] Finished a trial resulted in value: 20.94748297561393. Current best value is 0.7562122601691431 with parameters: {'shape_of_rbf_centers': 5, 'variance_of_rbfs': 0.38595437543432815, 'lambda_in_em_algorithm': 0.810007665982072}.\n",
"[I 2019-01-09 21:04:08,303] Finished a trial resulted in value: 0.7395242647264675. Current best value is 0.7395242647264675 with parameters: {'shape_of_rbf_centers': 7, 'variance_of_rbfs': 0.304788175170222, 'lambda_in_em_algorithm': 0.6929883032339137}.\n",
"[I 2019-01-09 21:04:09,458] Finished a trial resulted in value: 0.8817204279454034. Current best value is 0.7395242647264675 with parameters: {'shape_of_rbf_centers': 7, 'variance_of_rbfs': 0.304788175170222, 'lambda_in_em_algorithm': 0.6929883032339137}.\n",
"[I 2019-01-09 21:04:10,266] Finished a trial resulted in value: 0.6692664031908058. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:10,988] Finished a trial resulted in value: 0.9591774927398309. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:11,003] Finished a trial resulted in value: 0.9338729178823133. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:11,253] Finished a trial resulted in value: 0.94126921461793. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:30,494] Finished a trial resulted in value: 1.020852997994966. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:31,487] Finished a trial resulted in value: 0.8264224017800453. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:35,292] Finished a trial resulted in value: 1.0540780642378174. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:37,306] Finished a trial resulted in value: 0.9538785392838823. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:42,063] Finished a trial resulted in value: 2.0769250566882316. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:42,309] Finished a trial resulted in value: 1.1256179831838231. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:43,117] Finished a trial resulted in value: 259.0418845114817. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:04:43,468] Finished a trial resulted in value: 1.1803552504124621. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:02,166] Finished a trial resulted in value: 252.93407355546856. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:02,310] Finished a trial resulted in value: inf. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:03,561] Finished a trial resulted in value: 275.2080962200244. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:07,262] Finished a trial resulted in value: 229.8468395016399. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:09,212] Finished a trial resulted in value: 0.9292782741312786. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:12,621] Finished a trial resulted in value: 0.7488687023229913. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:12,939] Finished a trial resulted in value: 1.0576247284399933. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:13,262] Finished a trial resulted in value: 0.8308052594952862. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:13,745] Finished a trial resulted in value: 0.8904348934165283. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:28,974] Finished a trial resulted in value: 0.8553961641289483. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:29,415] Finished a trial resulted in value: 1.0547830759541286. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:33,172] Finished a trial resulted in value: 0.9664351895721672. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:34,253] Finished a trial resulted in value: 0.8796888863350141. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:42,900] Finished a trial resulted in value: 0.8826520885917468. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:42,950] Finished a trial resulted in value: 0.8612385057980362. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:43,131] Finished a trial resulted in value: 0.9494680801286945. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:43,746] Finished a trial resulted in value: 0.9944492123124032. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:05:59,860] Finished a trial resulted in value: 0.8326433512537967. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:00,703] Finished a trial resulted in value: 0.8671410312562224. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:03,797] Finished a trial resulted in value: 0.8750047866891204. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:05,880] Finished a trial resulted in value: 0.8053502996902505. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,076] Finished a trial resulted in value: 0.872773378143229. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,260] Finished a trial resulted in value: inf. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,299] Finished a trial resulted in value: 0.8986278081701519. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,329] Finished a trial resulted in value: 1.0118747039730676. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,567] Finished a trial resulted in value: inf. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,609] Finished a trial resulted in value: inf. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,694] Finished a trial resulted in value: 0.9258702255462252. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:14,728] Finished a trial resulted in value: inf. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:30,616] Finished a trial resulted in value: 1.0453519923631938. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:31,510] Finished a trial resulted in value: 0.9403634408316259. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:34,569] Finished a trial resulted in value: 0.8611244754110594. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:36,357] Finished a trial resulted in value: 0.8584251204052757. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:42,213] Finished a trial resulted in value: 0.7475439954609435. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:42,239] Finished a trial resulted in value: 0.8768358021062346. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:43,955] Finished a trial resulted in value: 0.8454643226688463. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:43,992] Finished a trial resulted in value: 0.7349349715900563. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:56,534] Finished a trial resulted in value: 0.7285997298347044. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:57,120] Finished a trial resulted in value: 0.8431324006691614. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:06:59,249] Finished a trial resulted in value: 0.7379522206466405. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:01,012] Finished a trial resulted in value: 0.7565070829390836. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:05,471] Finished a trial resulted in value: 0.9229421728580016. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:05,589] Finished a trial resulted in value: 0.9179514612071127. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:07,065] Finished a trial resulted in value: 0.9299168967279607. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:07,392] Finished a trial resulted in value: 0.9187958039085371. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:19,454] Finished a trial resulted in value: 0.9236569541142146. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:20,368] Finished a trial resulted in value: 0.9197070004498336. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:22,439] Finished a trial resulted in value: 0.9299645194169381. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:24,771] Finished a trial resulted in value: 0.9226460790192876. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:29,185] Finished a trial resulted in value: 0.9275492187313332. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:29,563] Finished a trial resulted in value: 0.9181030910904239. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:30,978] Finished a trial resulted in value: 0.917930711652515. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:31,120] Finished a trial resulted in value: 0.9225988903966611. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:44,478] Finished a trial resulted in value: 0.867176032758237. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:45,333] Finished a trial resulted in value: 0.9735822225414159. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:47,744] Finished a trial resulted in value: 1.0576253259224415. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:50,649] Finished a trial resulted in value: 0.8827370967795677. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:55,463] Finished a trial resulted in value: 0.8924760690743824. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:56,587] Finished a trial resulted in value: 0.7291627627751882. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:59,143] Finished a trial resulted in value: 0.8675906778491175. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:07:59,719] Finished a trial resulted in value: 0.7402643821787448. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:14,717] Finished a trial resulted in value: 0.848372717110669. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:15,582] Finished a trial resulted in value: 1.012790530215332. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:18,837] Finished a trial resulted in value: 1.011607351949412. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:22,294] Finished a trial resulted in value: 1.0199370992492873. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:26,585] Finished a trial resulted in value: 1.0580790364513912. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:27,130] Finished a trial resulted in value: 0.9762524866018667. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:28,806] Finished a trial resulted in value: 1.01233296190429. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:29,602] Finished a trial resulted in value: 1.6433072040123229. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:41,769] Finished a trial resulted in value: 0.8630239936877422. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:42,536] Finished a trial resulted in value: 0.9492214915948427. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:45,215] Finished a trial resulted in value: 0.8765198397469192. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:48,738] Finished a trial resulted in value: 0.8844906151719536. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:51,723] Finished a trial resulted in value: 0.8969146387657061. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:51,845] Finished a trial resulted in value: 0.8724683789301153. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:54,139] Finished a trial resulted in value: 0.8457408963343782. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:08:55,111] Finished a trial resulted in value: 0.7201687424147102. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:07,555] Finished a trial resulted in value: 0.7093830617688012. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:08,006] Finished a trial resulted in value: 0.733355866985893. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:11,509] Finished a trial resulted in value: 0.9111269128245928. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:14,906] Finished a trial resulted in value: 0.8068695330625969. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:17,853] Finished a trial resulted in value: 0.880327489101169. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:18,500] Finished a trial resulted in value: 0.8353426465477313. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:19,619] Finished a trial resulted in value: 1.0562661879740263. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:20,520] Finished a trial resulted in value: 0.8876936920754963. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:31,953] Finished a trial resulted in value: 0.9476673548717459. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:32,240] Finished a trial resulted in value: 0.9578869406371184. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:36,115] Finished a trial resulted in value: 1.0218800230527758. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:40,181] Finished a trial resulted in value: 0.7187103422942115. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:42,742] Finished a trial resulted in value: 1.1263241183218275. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:44,172] Finished a trial resulted in value: 1.1273176565172318. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:45,101] Finished a trial resulted in value: 1.165825921256224. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:46,779] Finished a trial resulted in value: 0.9417892184462927. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:09:59,611] Finished a trial resulted in value: 1.1464764115922195. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:00,065] Finished a trial resulted in value: 0.7171508945787164. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:03,403] Finished a trial resulted in value: 0.7083034454086846. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:06,715] Finished a trial resulted in value: 1.4166950378465617. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:09,196] Finished a trial resulted in value: 1.8968261278133607. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:10,943] Finished a trial resulted in value: 1.128110318953933. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:11,684] Finished a trial resulted in value: 1.316740753197533. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:12,843] Finished a trial resulted in value: 2.151775895996436. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:24,724] Finished a trial resulted in value: 1.9513676026475426. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:25,193] Finished a trial resulted in value: 1.2518530758877384. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:28,866] Finished a trial resulted in value: 1.15331980639774. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:31,824] Finished a trial resulted in value: 1.207402017652848. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:33,998] Finished a trial resulted in value: 1.0524380743894288. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:35,818] Finished a trial resulted in value: 1.2849011913744413. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:36,523] Finished a trial resulted in value: 1.325868108376039. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:37,480] Finished a trial resulted in value: 1.1433120780842991. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:49,295] Finished a trial resulted in value: 1.1439137054514834. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:49,674] Finished a trial resulted in value: 1.03053257887211. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:53,831] Finished a trial resulted in value: 1.145098252822073. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:56,817] Finished a trial resulted in value: 1.1512349049644226. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:10:59,340] Finished a trial resulted in value: 1.0926870326289901. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:01,550] Finished a trial resulted in value: 1.1016916001458905. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:01,935] Finished a trial resulted in value: 0.7018547841486702. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:03,154] Finished a trial resulted in value: 0.7837408132355035. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:15,226] Finished a trial resulted in value: 0.7141133278854126. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:16,323] Finished a trial resulted in value: 0.753777283217102. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:20,713] Finished a trial resulted in value: 0.7369857049376235. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:24,894] Finished a trial resulted in value: 0.8786318619914469. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:27,154] Finished a trial resulted in value: 0.8790705440476346. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:30,903] Finished a trial resulted in value: 0.8768528216426418. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:31,493] Finished a trial resulted in value: 0.8869432754911477. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:32,756] Finished a trial resulted in value: 0.8770798087180932. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:45,979] Finished a trial resulted in value: 0.860791193578836. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:47,364] Finished a trial resulted in value: 0.8953127594709385. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:51,644] Finished a trial resulted in value: 0.9492879103615864. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:55,582] Finished a trial resulted in value: 1.021847164514455. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:11:57,463] Finished a trial resulted in value: 0.9829865712936939. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:00,900] Finished a trial resulted in value: 0.9796249830971464. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:01,303] Finished a trial resulted in value: 0.839690191752297. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:01,871] Finished a trial resulted in value: 0.9664142742196484. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:08,394] Finished a trial resulted in value: 0.9773593377972822. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:09,446] Finished a trial resulted in value: 0.9069514129053754. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:11,037] Finished a trial resulted in value: 0.8431862676183742. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:12,252] Finished a trial resulted in value: 1.058319460940162. Current best value is 0.6692664031908058 with parameters: {'shape_of_rbf_centers': 9, 'variance_of_rbfs': 0.2638744886685693, 'lambda_in_em_algorithm': 0.9775188428654177}.\n",
"[I 2019-01-09 21:12:12,621] Finished a trial resulted in value: 0.6561641044505351. Current best value is 0.6561641044505351 with parameters: {'shape_of_rbf_centers': 6, 'variance_of_rbfs': 0.3670919320972898, 'lambda_in_em_algorithm': 0.032369334529596346}.\n"
]
}
],
"source": [
"study = optuna.create_study()\n",
"study.optimize(objective, timeout=600, 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': 6,\n",
" 'variance_of_rbfs': 0.3670919320972898,\n",
" 'lambda_in_em_algorithm': 0.032369334529596346}"
]
},
"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": [
"0.6561641044313343"
]
},
"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": [
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"</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>4.404179</td>\n",
" <td>2019-01-09 21:01:28.618498</td>\n",
" <td>2019-01-09 21:01:51.650348</td>\n",
" <td>0.000241</td>\n",
" <td>2</td>\n",
" <td>0.034385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>TrialState.COMPLETE</td>\n",
" <td>38.690770</td>\n",
" <td>2019-01-09 21:01:28.619536</td>\n",
" <td>2019-01-09 21:01:54.402055</td>\n",
" <td>0.001193</td>\n",
" <td>7</td>\n",
" <td>0.098424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>TrialState.COMPLETE</td>\n",
" <td>0.812856</td>\n",
" <td>2019-01-09 21:01:28.619989</td>\n",
" <td>2019-01-09 21:01:53.303502</td>\n",
" <td>0.318482</td>\n",
" <td>6</td>\n",
" <td>0.271772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>TrialState.COMPLETE</td>\n",
" <td>0.901979</td>\n",
" <td>2019-01-09 21:01:28.620459</td>\n",
" <td>2019-01-09 21:01:52.741692</td>\n",
" <td>0.898351</td>\n",
" <td>5</td>\n",
" <td>0.083927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>TrialState.COMPLETE</td>\n",
" <td>inf</td>\n",
" <td>2019-01-09 21:01:28.621284</td>\n",
" <td>2019-01-09 21:01:28.953101</td>\n",
" <td>0.008878</td>\n",
" <td>6</td>\n",
" <td>3.032859</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" trial_id state value datetime_start \\\n",
" \n",
"0 0 TrialState.COMPLETE 4.404179 2019-01-09 21:01:28.618498 \n",
"1 1 TrialState.COMPLETE 38.690770 2019-01-09 21:01:28.619536 \n",
"2 2 TrialState.COMPLETE 0.812856 2019-01-09 21:01:28.619989 \n",
"3 3 TrialState.COMPLETE 0.901979 2019-01-09 21:01:28.620459 \n",
"4 4 TrialState.COMPLETE inf 2019-01-09 21:01:28.621284 \n",
"\n",
" datetime_complete params \\\n",
" lambda_in_em_algorithm shape_of_rbf_centers \n",
"0 2019-01-09 21:01:51.650348 0.000241 2 \n",
"1 2019-01-09 21:01:54.402055 0.001193 7 \n",
"2 2019-01-09 21:01:53.303502 0.318482 6 \n",
"3 2019-01-09 21:01:52.741692 0.898351 5 \n",
"4 2019-01-09 21:01:28.953101 0.008878 6 \n",
"\n",
" \n",
" variance_of_rbfs \n",
"0 0.034385 \n",
"1 0.098424 \n",
"2 0.271772 \n",
"3 0.083927 \n",
"4 3.032859 "
]
},
"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": {
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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_boston.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_boston.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": [
"Text(0, 0.5, 'z2 (mean)')"
]
},
"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",
"plt.scatter(means[:, 0], means[:, 1], c=color)\n",
"plt.ylim(-1.1, 1.1)\n",
"plt.xlim(-1.1, 1.1)\n",
"plt.title('GTM boston dataset')\n",
"plt.xlabel('z1 (mean)')\n",
"plt.ylabel('z2 (mean)')"
]
},
{
"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
}
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10_4-optimize_GTM_with_gridsearch.ipynb
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