Randomized Hill Climbing Grid Search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlrose_hiive as mlrh\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [7, 7]\n",
    "\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import PrecisionRecallDisplay\n",
    "\n",
    "random_state = 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cred, y_cred = fetch_openml(\"credit-g\", return_X_y=True)\n",
    "\n",
    "# y_cred[0], cred_mapping = pd.factorize(y_cred[0])\n",
    "le = LabelEncoder()\n",
    "y_cred = le.fit_transform(y_cred)\n",
    "\n",
    "# switch labels around\n",
    "# good risk: 0\n",
    "# bad risk: 1\n",
    "y_cred[y_cred==1] = 3  # Temp\n",
    "y_cred[y_cred==0] = 1\n",
    "y_cred[y_cred==3] = 0\n",
    "\n",
    "# Create holdout test set\n",
    "X_cred_train, X_cred_test, y_cred_train, y_cred_test = train_test_split(X_cred, y_cred, test_size=0.2,\n",
    "                                                                        random_state=random_state,\n",
    "                                                                        stratify=y_cred)\n",
    "def get_data(data):\n",
    "    return data[\"X\"], data[\"y\"], data[\"name\"]\n",
    "\n",
    "# Standardize\n",
    "scaler_cred = StandardScaler()\n",
    "scaler_cred.fit(X_cred_train)\n",
    "X_cred_train = scaler_cred.transform(X_cred_train)\n",
    "X_cred_test = scaler_cred.transform(X_cred_test)\n",
    "\n",
    "# Create data sets\n",
    "cred = {\"X\": X_cred_train, \"y\": y_cred_train, \"name\": \"Credit Train\"}\n",
    "cred_test = {\"X\": X_cred_test, \"y\": y_cred_test, \"name\": \"Credit Test\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Randomized Hill Climbing Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running nngs_rhc\n",
      "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=-1)]: Done 152 tasks      | elapsed: 192.8min\n",
      "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 749.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[2.4470]\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[0.01], fitness:[2.4309]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[10], done:[False], time:[0.06], fitness:[2.3172]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[14], iteration:[50], done:[False], time:[0.12], fitness:[1.5944]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -1.         -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[7], iteration:[500], done:[False], time:[0.66], fitness:[0.4744]\n",
      "\t[ 1.          1.          0.01949105 -0.45632857  0.17383746 -1.// -0.94704571  0.18663119  0.22431787 -1.         -0.71822551  0.51416056//  1.         -0.28867382  0.68218603  0.8336061  -0.06579872...\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[11], iteration:[1000], done:[False], time:[1.18], fitness:[0.4354]\n",
      "\t[ 1.          1.          0.51949105 -0.45632857  0.67383746 -1.// -1.          0.18663119  0.22431787 -1.         -0.71822551  0.51416056//  0.5         0.21132618  1.          0.8336061   0.43420128...\n",
      "\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[2017], done:[True], time:[2.18], fitness:[0.4094]\n",
      "\t[ 1.          1.          0.51949105 -0.45632857  1.         -0.5// -0.5         0.18663119  0.22431787 -1.         -0.71822551  0.51416056//  1.          0.21132618  1.          1.          0.4342012...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[2.29], fitness:[2.4470]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[2.30], fitness:[2.4363]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[10], done:[False], time:[2.42], fitness:[2.4283]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -1.// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[50], done:[False], time:[2.57], fitness:[2.0068]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -1.// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[100], done:[False], time:[2.75], fitness:[1.5436]\n",
      "\t[ 0.47255366  0.74678389  0.01949105  0.04367143 -0.32616254 -1.// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[250], done:[False], time:[3.05], fitness:[0.6228]\n",
      "\t[-2.74463423e-02  7.46783892e-01  1.94910499e-02  4.36714286e-02//  1.73837455e-01 -1.00000000e+00 -4.47045714e-01  1.86631186e-01// -2.75682129e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
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      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
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      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[1]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[6], iteration:[500], done:[False], time:[3.45], fitness:[0.5091]\n",
      "\t[-2.74463423e-02  2.46783892e-01  1.94910499e-02  5.43671429e-01//  1.00000000e+00 -1.00000000e+00 -4.47045714e-01  1.86631186e-01// -7.75682129e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
      " -0.61614257 -0.14868803  0.02684042 -0.46124483  0.19804004 -0.55965211\n",
      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
      "  0.86465559 -0.17353901 -0.67864315 -0.06239494 -0.46142269  0.88853714\n",
      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[1]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[47], iteration:[1000], done:[False], time:[4.11], fitness:[0.4448]\n",
      "\t[-5.27446342e-01  2.46783892e-01  5.19491050e-01  5.43671429e-01//  1.00000000e+00 -5.00000000e-01 -4.47045714e-01  1.86631186e-01// -7.75682129e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01// -...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
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      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[1]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[60], iteration:[2500], done:[False], time:[5.79], fitness:[0.4161]\n",
      "\t[-5.27446342e-01  2.46783892e-01  5.19491050e-01  5.43671429e-01//  1.00000000e+00 -5.00000000e-01 -4.47045714e-01  1.86631186e-01// -7.75682129e-01 -6.86600660e-01 -1.00000000e+00  1.00000000e+00// -...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[1]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[2868], done:[True], time:[6.43], fitness:[0.4071]\n",
      "\t[-5.27446342e-01  2.46783892e-01  5.19491050e-01  5.43671429e-01//  1.00000000e+00 -5.00000000e-01 -4.47045714e-01  1.86631186e-01// -7.75682129e-01 -6.86600660e-01 -5.00000000e-01  1.00000000e+00// -...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[6.69], fitness:[2.4470]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[50], done:[False], time:[7.28], fitness:[1.1506]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[250], done:[False], time:[8.06], fitness:[0.5769]\n",
      "\t[ 0.97255366 -0.25321611  0.51949105 -0.45632857 -0.32616254 -1.// -0.44704571 -0.81336881  0.72431787 -0.68660066 -0.71822551  0.51416056// -0.02735016  0.21132618 -0.31781397  0.8336061  -0.56579872...\n",
      "\n"
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     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[1696], done:[True], time:[10.66], fitness:[0.4285]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[11.00], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[11.01], fitness:[2.4090]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[6], iteration:[10], done:[False], time:[11.36], fitness:[2.3821]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[50], done:[False], time:[11.76], fitness:[1.7534]\n",
      "\t[ 1.          0.24678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.21822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[3]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[100], done:[False], time:[12.18], fitness:[1.3319]\n",
      "\t[ 1.00000000e+00  2.46783892e-01  1.94910499e-02  4.36714286e-02// -3.26162545e-01 -5.66091470e-01 -4.47045714e-01 -3.13368814e-01//  7.24317871e-01 -6.86600660e-01 -2.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
      " -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056\n",
      "  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872  0.12285396\n",
      " -0.75164244 -0.36052703  0.90642775 -0.72528642  0.13882619  0.95133097\n",
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      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
      " -0.61614257 -0.14868803  0.02684042 -0.46124483  0.19804004 -0.55965211\n",
      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
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      "  0.22831624 -0.96666104 -0.40135551  0.33708176  0.90510487  0.63241402\n",
      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
      "  0.86465559 -0.17353901 -0.67864315 -0.06239494 -0.46142269  0.88853714\n",
      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[3]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[250], done:[False], time:[12.74], fitness:[0.6095]\n",
      "\t[ 1.00000000e+00  2.46783892e-01 -4.80508950e-01  5.43671429e-01//  1.73837455e-01 -5.66091470e-01 -9.47045714e-01 -3.13368814e-01//  7.24317871e-01 -6.86600660e-01 -2.18225510e-01  5.14160561e-01//  ...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
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      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[3]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[500], done:[False], time:[13.42], fitness:[0.4794]\n",
      "\t[ 1.00000000e+00  2.46783892e-01 -9.80508950e-01  1.00000000e+00//  6.73837455e-01 -5.66091470e-01 -9.47045714e-01 -1.00000000e+00//  7.24317871e-01 -6.86600660e-01 -2.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[9], iteration:[1000], done:[False], time:[14.42], fitness:[0.4338]\n",
      "\t[ 1.00000000e+00 -2.53216108e-01 -9.80508950e-01  1.00000000e+00//  1.00000000e+00 -5.66091470e-01 -9.47045714e-01 -1.00000000e+00//  7.24317871e-01 -1.86600660e-01 -2.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[1548], done:[True], time:[15.48], fitness:[0.4223]\n",
      "\t[ 1.         -0.25321611 -0.98050895  1.          1.         -0.56609147// -0.94704571 -1.          0.72431787 -0.18660066 -0.21822551  0.51416056//  1.         -1.         -1.          0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[15.90], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
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      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
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      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[4]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[15.91], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[4]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[10], done:[False], time:[16.34], fitness:[2.0180]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -1.         -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[100], done:[False], time:[17.32], fitness:[0.6340]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[250], done:[False], time:[17.96], fitness:[0.5306]\n",
      "\t[ 1.          1.          0.01949105  0.04367143  0.67383746 -1.// -0.44704571 -0.81336881  0.72431787 -1.         -0.71822551  0.01416056//  0.47264984 -0.28867382 -0.31781397  0.8336061   0.43420128...\n",
      "\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[500], done:[False], time:[18.74], fitness:[0.4770]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[25], iteration:[1000], done:[False], time:[19.85], fitness:[0.4365]\n",
      "\t[ 1.          1.          0.01949105  0.54367143  0.67383746 -1.// -0.44704571 -1.          1.         -1.         -0.71822551  0.01416056//  0.47264984  0.71132618 -1.          0.8336061   0.43420128...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[22.52], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[22.52], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[10], done:[False], time:[23.07], fitness:[2.3628]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[50], done:[False], time:[23.67], fitness:[1.5448]\n",
      "\t[ 9.72553658e-01  7.46783892e-01  1.94910499e-02 -4.56328571e-01// -3.26162545e-01 -5.66091470e-01 -4.47045714e-01 -3.13368814e-01//  7.24317871e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[100], done:[False], time:[24.28], fitness:[0.9617]\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[250], done:[False], time:[25.06], fitness:[0.6152]\n",
      "\t[ 9.72553658e-01 -2.53216108e-01  1.94910499e-02 -4.56328571e-01// -3.26162545e-01 -1.00000000e+00 -4.47045714e-01 -3.13368814e-01//  7.24317871e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n"
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[12], iteration:[500], done:[False], time:[26.00], fitness:[0.5129]\n",
      "\t[ 1.00000000e+00 -2.53216108e-01  1.94910499e-02 -4.56328571e-01// -3.26162545e-01 -1.00000000e+00 -4.47045714e-01 -3.13368814e-01//  2.24317871e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1000], done:[False], time:[27.28], fitness:[0.4601]\n",
      "\t[ 1.00000000e+00 -7.53216108e-01  1.94910499e-02  4.36714286e-02//  1.73837455e-01 -1.00000000e+00 -4.47045714e-01 -3.13368814e-01//  2.24317871e-01 -6.86600660e-01 -2.18225510e-01  5.14160561e-01//  ...\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[2500], done:[False], time:[30.07], fitness:[0.4091]\n",
      "\t[ 1.         -0.75321611  0.01949105  0.54367143  0.17383746 -1.// -0.44704571 -0.31336881  0.22431787 -0.68660066 -0.21822551  0.51416056//  0.97264984 -0.78867382 -0.31781397  0.5        -0.5       ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[2685], done:[True], time:[31.01], fitness:[0.4087]\n",
      "\t[ 1.         -0.75321611  0.01949105  0.54367143  0.17383746 -1.// -0.44704571 -0.31336881  0.22431787 -0.68660066 -0.21822551  0.51416056//  1.         -0.78867382 -0.31781397  0.5        -0.5       ...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[31.68], fitness:[2.4470]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[10], done:[False], time:[32.37], fitness:[2.0834]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[50], done:[False], time:[33.10], fitness:[1.3950]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[100], done:[False], time:[33.86], fitness:[1.0167]\n",
      "\t[ 1.          0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  1.         -1.         -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397 -0.1663939  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[6], iteration:[500], done:[False], time:[35.90], fitness:[0.5358]\n",
      "\t[ 5.00000000e-01  7.46783892e-01 -9.80508950e-01  1.00000000e+00//  1.73837455e-01 -5.66091470e-01  5.29542863e-02 -1.00000000e+00//  1.00000000e+00 -1.00000000e+00 -1.00000000e+00  1.00000000e+00//  ...\n",
      "\n",
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      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[1000], done:[False], time:[37.42], fitness:[0.4729]\n",
      "\t[ 1.00000000e+00  1.00000000e+00 -1.00000000e+00  1.00000000e+00//  6.73837455e-01 -5.66091470e-01 -4.47045714e-01 -1.00000000e+00//  1.00000000e+00 -5.00000000e-01 -5.00000000e-01  1.00000000e+00//  ...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[9], iteration:[2500], done:[False], time:[40.57], fitness:[0.4021]\n",
      "\t[ 1.          1.         -0.5         1.          1.         -1.//  0.55295429 -1.          1.          0.5        -1.          1.//  1.          0.21132618 -1.          0.5         0.43420128 -1.// -...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[2690], done:[True], time:[41.69], fitness:[0.4003]\n",
      "\t[ 1.          1.         -0.5         1.          1.         -1.//  0.55295429 -1.          1.          0.5        -1.          1.//  1.          0.21132618 -1.          0.5         0.43420128 -1.// -...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[42.50], fitness:[2.4296]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[10], done:[False], time:[43.33], fitness:[2.0360]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[50], done:[False], time:[44.20], fitness:[1.4093]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.01416056//  0.47264984 -0.28867382  0.18218603  0.8336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[100], done:[False], time:[45.10], fitness:[1.0530]\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[500], done:[False], time:[47.43], fitness:[0.5012]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[10], iteration:[1000], done:[False], time:[49.13], fitness:[0.4602]\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[2500], done:[False], time:[52.63], fitness:[0.4061]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[3031], done:[True], time:[54.55], fitness:[0.4012]\n",
      "\t[ 0.5         0.24678389 -1.          1.          1.         -1.// -0.44704571  0.18663119  1.         -1.          0.5         0.51416056//  0.47264984 -0.28867382  0.18218603  1.         -0.06579872...\n",
      "\n"
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[55.55], fitness:[2.4470]\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[10], done:[False], time:[56.57], fitness:[2.3835]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[50], done:[False], time:[57.64], fitness:[1.8695]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[100], done:[False], time:[58.72], fitness:[1.7172]\n",
      "\t[ 9.72553658e-01  7.46783892e-01  1.94910499e-02 -4.56328571e-01// -3.26162545e-01 -5.66091470e-01  5.29542863e-02 -3.13368814e-01//  7.24317871e-01 -1.00000000e+00 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[250], done:[False], time:[60.05], fitness:[0.5920]\n",
      "\t[ 9.72553658e-01  7.46783892e-01  1.94910499e-02 -4.56328571e-01// -3.26162545e-01 -5.66091470e-01  5.29542863e-02 -3.13368814e-01//  7.24317871e-01 -1.00000000e+00 -2.18225510e-01  1.41605613e-02// -...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[500], done:[False], time:[61.59], fitness:[0.4924]\n",
      "\t[ 4.72553658e-01  1.00000000e+00  5.19491050e-01  5.43671429e-01// -3.26162545e-01 -5.66091470e-01  5.29542863e-02 -3.13368814e-01//  7.24317871e-01 -1.00000000e+00 -7.18225510e-01  1.41605613e-02// -...\n",
      "\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[1000], done:[False], time:[63.53], fitness:[0.4440]\n",
      "\t[ 0.47255366  1.          0.51949105  0.54367143  1.         -1.//  0.55295429 -0.31336881  0.22431787 -1.         -0.21822551  0.01416056//  0.47264984  0.71132618  0.18218603  1.         -0.06579872...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[1453], done:[True], time:[65.44], fitness:[0.4186]\n",
      "\t[ 0.47255366  1.          0.51949105  1.          1.         -1.//  0.55295429 -0.31336881  0.22431787 -1.         -0.21822551  0.01416056//  0.47264984  0.71132618  0.18218603  1.         -0.56579872...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[66.49], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[66.50], fitness:[2.4404]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[100], done:[False], time:[69.94], fitness:[0.9212]\n",
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      "\t[ 9.72553658e-01  2.46783892e-01 -4.80508950e-01 -4.56328571e-01// -3.26162545e-01 -5.66091470e-01  5.29542863e-02 -3.13368814e-01//  5.00000000e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01// -...\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[500], done:[False], time:[72.95], fitness:[0.5275]\n",
      "\t[ 9.72553658e-01  2.46783892e-01 -4.80508950e-01  4.36714286e-02//  1.73837455e-01 -1.00000000e+00  5.52954286e-01 -3.13368814e-01//  5.00000000e-01 -1.00000000e+00 -1.00000000e+00  1.41605613e-02// -...\n",
      "\n"
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[8], iteration:[1000], done:[False], time:[75.03], fitness:[0.4516]\n",
      "\t[-2.74463423e-02  1.00000000e+00 -4.80508950e-01  4.36714286e-02//  6.73837455e-01 -1.00000000e+00  1.00000000e+00 -3.13368814e-01//  5.00000000e-01 -1.00000000e+00 -1.00000000e+00 -4.85839439e-01// -...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[33], iteration:[2500], done:[False], time:[79.13], fitness:[0.4132]\n",
      "\t[-0.52744634  1.         -1.          0.04367143  1.         -1.//  1.          0.68663119  1.         -1.         -1.         -0.48583944// -0.02735016  1.         -0.81781397  0.3336061   1.        ...\n",
      "\n",
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      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[3747], done:[True], time:[82.90], fitness:[0.3979]\n",
      "\t[-1.          1.         -1.          0.04367143  1.         -1.//  1.          0.68663119  0.5        -1.         -1.         -0.48583944//  0.47264984  1.         -0.81781397  0.3336061   1.        ...\n",
      "\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[10]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[84.12], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
      " -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056\n",
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      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
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      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
      "  0.86465559 -0.17353901 -0.67864315 -0.06239494 -0.46142269  0.88853714\n",
      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[10]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[84.13], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[100], done:[False], time:[88.11], fitness:[0.9320]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.82616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.8336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[13], iteration:[250], done:[False], time:[89.69], fitness:[0.6306]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.82616254 -0.56609147// -0.44704571  0.18663119  0.72431787 -1.         -0.71822551  0.51416056// -0.52735016 -0.28867382  0.18218603  0.8336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[500], done:[False], time:[91.51], fitness:[0.5315]\n",
      "\t[ 1.00000000e+00  7.46783892e-01  1.94910499e-02  4.36714286e-02//  6.73837455e-01 -6.60914700e-02 -4.47045714e-01  6.86631186e-01//  7.24317871e-01 -1.00000000e+00 -1.00000000e+00  5.14160561e-01// -...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[1000], done:[False], time:[93.88], fitness:[0.4512]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[99.67], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[99.68], fitness:[2.4441]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[10], done:[False], time:[101.04], fitness:[1.6232]\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[50], done:[False], time:[102.51], fitness:[1.2148]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[100], done:[False], time:[103.97], fitness:[0.8646]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[250], done:[False], time:[105.80], fitness:[0.5956]\n",
      "\t[-0.52744634 -0.25321611  1.         -0.45632857 -0.32616254 -0.56609147//  0.05295429 -0.31336881  0.5        -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.8336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[500], done:[False], time:[107.79], fitness:[0.5359]\n",
      "\t[-1.         -0.25321611  1.         -0.45632857 -0.32616254 -0.56609147//  0.55295429 -0.31336881  0.5        -0.68660066 -0.71822551  1.//  0.47264984 -0.28867382 -0.31781397  1.         -0.56579872...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1000], done:[False], time:[110.58], fitness:[0.4453]\n",
      "\t[-1.         -0.75321611  1.          0.54367143  0.17383746 -1.//  0.55295429 -0.31336881  0.5        -1.         -1.          1.//  0.47264984 -0.78867382  0.68218603  1.         -0.56579872  0.1228...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[2291], done:[True], time:[115.08], fitness:[0.4042]\n",
      "\t[-0.5        -0.75321611  1.          1.          0.67383746 -1.//  0.55295429 -0.81336881  0.5        -1.         -1.          1.//  0.47264984 -1.          1.          1.         -1.          0.1228...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[116.55], fitness:[2.4260]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[10], done:[False], time:[118.14], fitness:[1.9971]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[50], done:[False], time:[119.71], fitness:[1.0512]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056// -0.02735016 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[100], done:[False], time:[121.32], fitness:[0.8690]\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[10], iteration:[1000], done:[False], time:[128.12], fitness:[0.4692]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[2500], done:[False], time:[133.39], fitness:[0.3995]\n",
      "\t[ 1.00000000e+00  1.00000000e+00  5.19491050e-01  4.36714286e-02//  6.73837455e-01 -1.00000000e+00  5.29542863e-02 -3.13368814e-01//  5.00000000e-01 -1.00000000e+00  0.00000000e+00  5.14160561e-01// -...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[2740], done:[True], time:[135.58], fitness:[0.3991]\n",
      "\t[ 1.00000000e+00  1.00000000e+00  5.19491050e-01  4.36714286e-02//  6.73837455e-01 -1.00000000e+00  5.29542863e-02 -3.13368814e-01//  5.00000000e-01 -1.00000000e+00  0.00000000e+00  5.14160561e-01// -...\n",
      "\n"
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[10], done:[False], time:[138.83], fitness:[2.3932]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[50], done:[False], time:[140.56], fitness:[1.8468]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.82616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[100], done:[False], time:[142.30], fitness:[0.9840]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.82616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[250], done:[False], time:[144.35], fitness:[0.6412]\n",
      "\t[ 9.72553658e-01  2.46783892e-01  5.19491050e-01 -4.56328571e-01// -8.26162545e-01 -5.66091470e-01 -1.00000000e+00 -3.13368814e-01//  7.24317871e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01// -...\n",
      "\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[500], done:[False], time:[146.68], fitness:[0.5182]\n",
      "\t[ 1.00000000e+00  2.46783892e-01  5.19491050e-01 -4.56328571e-01//  1.73837455e-01 -1.00000000e+00 -1.00000000e+00 -1.00000000e+00//  7.24317871e-01 -6.86600660e-01 -1.00000000e+00  5.14160561e-01// -...\n",
      "\n",
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      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
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      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
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      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[22], iteration:[1000], done:[False], time:[149.65], fitness:[0.4311]\n",
      "\t[ 1.00000000e+00  2.46783892e-01  1.00000000e+00  4.36714286e-02//  6.73837455e-01 -1.00000000e+00 -1.00000000e+00 -1.00000000e+00//  7.24317871e-01 -1.86600660e-01 -1.00000000e+00  5.14160561e-01// -...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[1739], done:[True], time:[153.31], fitness:[0.4172]\n",
      "\t[ 1.00000000e+00  2.46783892e-01  1.00000000e+00  4.36714286e-02//  6.73837455e-01 -1.00000000e+00 -1.00000000e+00 -1.00000000e+00//  7.24317871e-01 -1.86600660e-01 -1.00000000e+00  1.41605613e-02// -...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[14]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[155.28], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[14]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[155.29], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[500], done:[False], time:[166.10], fitness:[0.5309]\n",
      "\t[-1.         -1.          1.          0.04367143 -0.32616254 -1.// -0.44704571 -0.31336881  0.22431787 -0.18660066 -1.          1.//  0.47264984 -0.78867382 -0.31781397  1.         -1.         -0.8771...\n",
      "\n"
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[180.74], fitness:[2.4470]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[180.76], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[100], done:[False], time:[186.61], fitness:[1.4047]\n",
      "\t[ 0.97255366  0.24678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571  0.18663119  0.72431787 -1.         -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[250], done:[False], time:[188.93], fitness:[0.7132]\n",
      "\t[ 0.97255366  0.24678389  0.01949105 -0.45632857 -0.82616254 -1.// -0.44704571  0.18663119  1.         -1.         -0.21822551  0.51416056//  0.47264984 -0.28867382 -1.          0.3336061  -0.56579872...\n",
      "\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[6], iteration:[500], done:[False], time:[191.53], fitness:[0.5267]\n",
      "\t[ 0.97255366  0.24678389  0.51949105  0.04367143 -1.         -1.// -0.44704571  0.18663119  1.         -1.          0.28177449  0.51416056//  1.         -0.28867382 -0.5         0.8336061  -1.        ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[203.13], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[203.14], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[10], done:[False], time:[205.12], fitness:[2.4237]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[100], done:[False], time:[209.38], fitness:[0.9722]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -1.// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056// -0.02735016 -0.28867382 -0.31781397  0.3336061  -0.56579872...\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[250], done:[False], time:[211.84], fitness:[0.5863]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -1.// -0.44704571 -0.81336881  0.72431787 -0.68660066 -0.71822551  0.51416056// -0.52735016 -0.28867382 -0.31781397  0.3336061  -0.56579872...\n",
      "\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[8], iteration:[500], done:[False], time:[214.64], fitness:[0.5014]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[8], iteration:[1000], done:[False], time:[218.23], fitness:[0.4119]\n",
      "\t[-0.52744634  1.         -0.48050895  0.04367143  0.67383746 -1.//  0.05295429  0.18663119  0.         -0.68660066 -1.          1.// -1.          0.21132618 -0.31781397  0.3336061   0.43420128 -0.3771...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[1925], done:[True], time:[223.19], fitness:[0.3855]\n",
      "\t[-0.52744634  1.         -0.48050895  0.04367143  1.         -0.5//  0.05295429  0.18663119  0.5        -0.68660066  0.          1.// -1.          0.21132618 -0.31781397  0.3336061  -0.06579872 -0.377...\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[225.25], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[10], done:[False], time:[227.35], fitness:[2.3863]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[50], done:[False], time:[229.57], fitness:[1.8517]\n",
      "\t[ 0.97255366  0.74678389  0.01949105  0.04367143 -0.32616254 -0.56609147// -0.44704571  0.18663119  0.72431787 -0.68660066 -0.21822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[250], done:[False], time:[234.39], fitness:[0.6575]\n",
      "\t[ 1.          0.24678389  0.01949105  0.04367143  0.67383746 -1.// -0.44704571 -0.31336881  0.22431787 -0.18660066 -0.21822551  1.//  0.47264984 -0.28867382  0.68218603  0.8336061  -0.06579872  0.1228...\n",
      "\n"
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    {
     "name": "stdout",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[500], done:[False], time:[237.33], fitness:[0.5135]\n",
      "\t[ 1.          0.24678389  0.01949105  0.04367143  1.         -1.// -0.44704571 -0.31336881 -0.77568213 -0.18660066 -0.21822551  1.// -0.02735016 -0.28867382  0.68218603  1.          0.43420128 -0.8771...\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[15], iteration:[1000], done:[False], time:[241.11], fitness:[0.4445]\n",
      "\t[ 1.          0.74678389  0.51949105  0.54367143  1.         -1.// -0.44704571 -0.31336881 -0.77568213 -0.18660066 -0.21822551  1.//  0.47264984 -0.28867382  0.68218603  1.          0.43420128 -1.//  ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[2274], done:[True], time:[247.50], fitness:[0.4173]\n",
      "\t[ 1.00000000e+00  2.46783892e-01  5.19491050e-01  1.00000000e+00//  1.00000000e+00 -1.00000000e+00 -4.47045714e-01 -8.13368814e-01// -1.00000000e+00 -1.86600660e-01  2.81774490e-01  1.00000000e+00//  ...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[249.69], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[5], iteration:[250], done:[False], time:[259.40], fitness:[0.5583]\n",
      "\t[ 1.         -0.25321611  0.01949105 -0.45632857 -0.82616254 -1.// -1.         -0.31336881  0.22431787 -0.68660066 -0.71822551  1.// -0.02735016  0.21132618 -0.31781397  0.3336061  -0.56579872 -0.3771...\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[33], iteration:[1000], done:[False], time:[266.47], fitness:[0.4297]\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[275.91], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[1], done:[False], time:[275.92], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[2], iteration:[10], done:[False], time:[278.27], fitness:[2.3368]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[50], done:[False], time:[280.73], fitness:[1.1945]\n",
      "\t[ 9.72553658e-01  7.46783892e-01  1.94910499e-02 -4.56328571e-01// -3.26162545e-01 -5.66091470e-01 -4.47045714e-01 -3.13368814e-01//  7.24317871e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[19]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[100], done:[False], time:[283.23], fitness:[0.9855]\n",
      "\t[ 9.72553658e-01  7.46783892e-01  1.94910499e-02 -4.56328571e-01// -3.26162545e-01 -5.66091470e-01 -4.47045714e-01 -3.13368814e-01//  7.24317871e-01 -6.86600660e-01 -7.18225510e-01  5.14160561e-01//  ...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
      " -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056\n",
      "  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872  0.12285396\n",
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      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
      " -0.61614257 -0.14868803  0.02684042 -0.46124483  0.19804004 -0.55965211\n",
      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
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      "  0.22831624 -0.96666104 -0.40135551  0.33708176  0.90510487  0.63241402\n",
      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
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      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[19]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[250], done:[False], time:[286.14], fitness:[0.6240]\n",
      "\t[ 9.72553658e-01  7.46783892e-01 -4.80508950e-01 -4.56328571e-01// -3.26162545e-01 -1.00000000e+00 -4.47045714e-01 -3.13368814e-01//  2.24317871e-01 -1.00000000e+00 -1.00000000e+00  5.14160561e-01// -...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[19]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[500], done:[False], time:[289.35], fitness:[0.5272]\n",
      "\t[ 9.72553658e-01  7.46783892e-01 -4.80508950e-01 -4.56328571e-01//  1.73837455e-01 -1.00000000e+00  5.29542863e-02 -1.00000000e+00//  2.24317871e-01 -1.00000000e+00 -1.00000000e+00  1.41605613e-02// -...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
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      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[19]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[36], iteration:[1000], done:[False], time:[293.56], fitness:[0.4518]\n",
      "\t[-2.74463423e-02  1.00000000e+00 -1.00000000e+00 -4.56328571e-01//  1.00000000e+00 -1.00000000e+00  5.29542863e-02 -1.00000000e+00// -2.75682129e-01 -5.00000000e-01 -5.00000000e-01  5.14160561e-01// -...\n",
      "\n",
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      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[1908], done:[True], time:[299.39], fitness:[0.4101]\n",
      "\t[-1.          1.         -1.         -0.45632857  1.         -0.5//  0.55295429 -1.          0.22431787 -1.          0.          0.51416056// -0.02735016  0.21132618 -1.          0.8336061   1.       ...\n",
      "\n",
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      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[0], iteration:[0], done:[False], time:[301.79], fitness:[2.4470]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
      " -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056\n",
      "  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872  0.12285396\n",
      " -0.75164244 -0.36052703  0.90642775 -0.72528642  0.13882619  0.95133097\n",
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      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
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      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
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      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[1], done:[False], time:[301.80], fitness:[2.4016]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[1], iteration:[10], done:[False], time:[304.27], fitness:[1.8934]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
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      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[7], iteration:[50], done:[False], time:[306.82], fitness:[1.6264]\n",
      "\t[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066  0.28177449  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[3], iteration:[100], done:[False], time:[309.47], fitness:[1.0948]\n",
      "\t[ 0.47255366  0.74678389  0.01949105  0.04367143 -0.32616254 -0.56609147// -0.44704571 -0.31336881  0.72431787 -0.68660066  0.28177449  0.51416056//  0.47264984 -0.28867382 -0.31781397  0.3336061  -0....\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
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      "  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872  0.12285396\n",
      " -0.75164244 -0.36052703  0.90642775 -0.72528642  0.13882619  0.95133097\n",
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      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
      " -0.61614257 -0.14868803  0.02684042 -0.46124483  0.19804004 -0.55965211\n",
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      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
      "  0.86465559 -0.17353901 -0.67864315 -0.06239494 -0.46142269  0.88853714\n",
      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[250], done:[False], time:[312.93], fitness:[0.5740]\n",
      "\t[ 0.47255366  1.         -0.48050895  0.04367143  0.17383746 -1.// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.21822551  0.51416056//  0.47264984 -0.28867382  0.18218603  0.8336061  -1.        ...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
      " -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056\n",
      "  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872  0.12285396\n",
      " -0.75164244 -0.36052703  0.90642775 -0.72528642  0.13882619  0.95133097\n",
      "  0.00673412  0.33532841 -0.931617   -0.08776127 -0.68829727 -0.04790207\n",
      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
      " -0.61614257 -0.14868803  0.02684042 -0.46124483  0.19804004 -0.55965211\n",
      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
      "  0.78856621  0.66306417  0.47568692  0.7969946  -0.53853172 -0.64007898\n",
      "  0.22831624 -0.96666104 -0.40135551  0.33708176  0.90510487  0.63241402\n",
      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
      "  0.86465559 -0.17353901 -0.67864315 -0.06239494 -0.46142269  0.88853714\n",
      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[13], iteration:[500], done:[False], time:[316.35], fitness:[0.4788]\n",
      "\t[ 0.47255366  1.         -0.48050895  1.          1.         -1.// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.21822551  0.51416056//  0.47264984 -0.28867382  0.18218603  0.8336061  -0.5       ...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
      " -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056\n",
      "  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872  0.12285396\n",
      " -0.75164244 -0.36052703  0.90642775 -0.72528642  0.13882619  0.95133097\n",
      "  0.00673412  0.33532841 -0.931617   -0.08776127 -0.68829727 -0.04790207\n",
      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
      " -0.61614257 -0.14868803  0.02684042 -0.46124483  0.19804004 -0.55965211\n",
      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
      "  0.78856621  0.66306417  0.47568692  0.7969946  -0.53853172 -0.64007898\n",
      "  0.22831624 -0.96666104 -0.40135551  0.33708176  0.90510487  0.63241402\n",
      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
      "  0.86465559 -0.17353901 -0.67864315 -0.06239494 -0.46142269  0.88853714\n",
      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[4], iteration:[1000], done:[False], time:[320.69], fitness:[0.4365]\n",
      "\t[ 0.47255366  1.         -0.48050895  1.          1.         -1.// -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.21822551  0.51416056//  1.          0.21132618  0.18218603  0.8336061   0.        ...\n",
      "\n",
      "max_iters:[5000], hidden_layers_sizes:[[6, 6]], is_classifier:[True], restarts:[20], init_state:[[ 0.97255366  0.74678389  0.01949105 -0.45632857 -0.32616254 -0.56609147\n",
      " -0.44704571 -0.31336881  0.72431787 -0.68660066 -0.71822551  0.51416056\n",
      "  0.47264984 -0.28867382 -0.31781397  0.3336061  -0.56579872  0.12285396\n",
      " -0.75164244 -0.36052703  0.90642775 -0.72528642  0.13882619  0.95133097\n",
      "  0.00673412  0.33532841 -0.931617   -0.08776127 -0.68829727 -0.04790207\n",
      " -0.66059512  0.79251667 -0.25321248 -0.24061414  0.71663318  0.29212211\n",
      "  0.1669234   0.33670005 -0.64441476  0.69849603 -0.11525484  0.66293542\n",
      "  0.52784143  0.83938195 -0.85885315 -0.68766917  0.27378846  0.11139137\n",
      " -0.61614257 -0.14868803  0.02684042 -0.46124483  0.19804004 -0.55965211\n",
      " -0.39827501 -0.90321103  0.12864012  0.87206441  0.60605528  0.3946103\n",
      " -0.07723145  0.32486922  0.49933128  0.06290978 -0.03437975 -0.94145153\n",
      "  0.78856621  0.66306417  0.47568692  0.7969946  -0.53853172 -0.64007898\n",
      "  0.22831624 -0.96666104 -0.40135551  0.33708176  0.90510487  0.63241402\n",
      " -0.84427553 -0.73077414  0.15309623 -0.64544275  0.9889174  -0.76525026\n",
      "  0.68168967 -0.97194654  0.74337497  0.80481349 -0.10159106  0.23636395\n",
      "  0.95795627 -0.20544303 -0.10553833 -0.53348004  0.86345876 -0.45439506\n",
      "  0.86465559 -0.17353901 -0.67864315 -0.06239494 -0.46142269  0.88853714\n",
      " -0.40911813 -0.6968803  -0.45491341  0.72996218  0.83372281  0.49956119\n",
      "  0.80044412 -0.95518737 -0.91809525  0.79710602  0.40158629  0.33869411\n",
      "  0.10569156  0.44548332  0.70786261  0.21803715 -0.09517119 -0.40668094\n",
      "  0.26123965  0.01406969 -0.2594879  -0.72357331  0.51836879  0.9179564\n",
      " -0.44194373  0.4616088  -0.60016602  0.28753864 -0.59425312 -0.89911699\n",
      " -0.25600911 -0.97181459 -0.80083642  0.99869821  0.85154599  0.62980611\n",
      " -0.55525561 -0.80514984  0.89108183  0.52427005 -0.3426447   0.85191538\n",
      "  0.41136619 -0.88641156 -0.01005098 -0.21038713  0.12286428  0.46220998\n",
      "  0.15762318 -0.92411926 -0.60921573  0.33226852  0.41428631 -0.36390649\n",
      " -0.50632733  0.67691049 -0.83193079  0.5125929   0.43258464 -0.10394314\n",
      " -0.90394836  0.34583687 -0.60739141  0.88790091  0.11828607  0.66266327]], algorithm:[rhc], activation:[relu], bias:[True], early_stopping:[True], clip_max:[1], hidden_layer_sizes:[[6, 6]], learning_rate_init:[0.5], max_attempts:[100], current_restart:[20]\n",
      "runner_name:[nngs_rhc], experiment_name:[full_grid_search], attempt:[101], iteration:[1892], done:[True], time:[326.63], fitness:[0.4077]\n",
      "\t[-0.02744634  1.         -0.48050895  1.          1.         -0.5// -0.44704571 -0.31336881  1.         -0.68660066 -0.21822551  0.51416056//  1.          0.21132618 -0.31781397  0.8336061   0.       ...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 45290.44298943604\n",
      "Saving: [nn_randomized_hill_climbing/full_grid_search/nngs_rhc__full_grid_search__run_stats_df__36C368E09CC927058D07B614AF0CD015.csv]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving: [nn_randomized_hill_climbing/full_grid_search/nngs_rhc__full_grid_search__curves_df__36C368E09CC927058D07B614AF0CD015.csv]\n",
      "Saving: [nn_randomized_hill_climbing/full_grid_search/nngs_rhc__full_grid_search__cv_results_df__36C368E09CC927058D07B614AF0CD015.csv]\n",
      "Saving: [nn_randomized_hill_climbing/full_grid_search/nngs_rhc__full_grid_search__grid_search_results__36C368E09CC927058D07B614AF0CD015.p]\n"
     ]
    }
   ],
   "source": [
    "grid_search = {\n",
    "    \"max_iters\": [1000, 2500, 5000, 10000, 25000],\n",
    "    \"learning_rate_init\": [0.001, 0.001, 0.1, 0.5, 1],\n",
    "    \"hidden_layers_sizes\": [[6, 6]],\n",
    "    \"activation\": [mlrh.neural.activation.relu],\n",
    "    \"is_classifier\": [True],\n",
    "    \"restarts\": [0, 5, 20]\n",
    "}\n",
    "X, y, _ = get_data(cred)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=random_state)\n",
    "\n",
    "runner = mlrh.NNGSRunner(x_train=X_train, y_train=y_train,\n",
    "                           x_test=X_test, y_test=y_test,\n",
    "                           experiment_name=\"full_grid_search\",\n",
    "                           output_directory=\"nn_randomized_hill_climbing/\",\n",
    "                           algorithm=mlrh.algorithms.random_hill_climb,\n",
    "                           grid_search_parameters=grid_search,\n",
    "                           iteration_list = [1, 10, 50, 100, 250, 500, 1000, 2500, 5000, 10000],\n",
    "                           hidden_layer_sizes=[[6,6]],\n",
    "                           bias=True,\n",
    "                           early_stopping=True,\n",
    "                           clip_max=1,\n",
    "                           max_attempts=100,\n",
    "                           generate_curves=True,\n",
    "                           seed=random_state,\n",
    "                           n_jobs=-1\n",
    "                          )\n",
    "run_stats, curves, cv_results, best_est = runner.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'activation': <function mlrose_hiive.neural.activation.relu.relu(x, deriv=False)>,\n",
       " 'hidden_layer_sizes': [6, 6],\n",
       " 'hidden_layers_sizes': [6, 6],\n",
       " 'is_classifier': True,\n",
       " 'learning_rate_init': 0.5,\n",
       " 'max_iters': 5000,\n",
       " 'restarts': 20}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_est.best_params_"
   ]
  }
 ],
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