examples/Example 7 - Make the best brownie.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "853ce82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install --user greattunes\n",
    "\n",
    "# import\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from greattunes import TuneSession\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import multivariate_normal\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c83ff710",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sym_update(arr, entry, val):\n",
    "    \"\"\"\n",
    "    updates array 'arr' at positions 'entry'=[i][j] and the mirror [j][i] to the new value 'val'\n",
    "    \"\"\"\n",
    "    xi = entry[0]\n",
    "    yi = entry[1]\n",
    "    arr[xi][yi] = val\n",
    "    arr[yi][xi] = val\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3038c6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# === taste multiplication factors ===\n",
    "# the rank order\n",
    "# 1 white chocolate + almonds (right amounts)\n",
    "# 2 dark chocolate + hazelnuts (right amounts)\n",
    "# 3 dark chocolate + almonds\n",
    "# 4 white chocolate + hazelnuts\n",
    "\n",
    "mf_wa = 1.5#1.2  # white chocolate <> almonds\n",
    "mf_dh = 1.3#1.18  # dark chocolate <> hazelnuts\n",
    "mf_da = 1.2#1.12  # dark chocolate <> almonds\n",
    "mf_wh = 0.9#0.95  # white chocolate <> hazelnuts\n",
    "\n",
    "\n",
    "# === variables ===\n",
    "# flour, egg, sugar, chocolate_type, chocolate_amount, butter, nut_type, nut_amount\n",
    "# order of numerical variables\n",
    "# [flour, egg, sugar, chocolate_amount, butter, nut_amount]\n",
    "\n",
    "# === setting covariate structure and defining models ===\n",
    "\n",
    "# DARK CHOCOLATE + HAZELNUTS\n",
    "\n",
    "# correlations\n",
    "rho = np.identity(6)  # initialize\n",
    "\n",
    "rho = sym_update(rho, (0,1), 0.5)  # flour-egg\n",
    "rho = sym_update(rho, (0,2), 0.3)  # flour-sugar\n",
    "rho = sym_update(rho, (0,3), 0.3)  # flour-chocolate_amount\n",
    "rho = sym_update(rho, (0,4), 0.1)  # flour-butter\n",
    "rho = sym_update(rho, (0,5), 0.1)  # flour-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (1,2), 0.5)  # egg-sugar\n",
    "rho = sym_update(rho, (1,3), 0.5)  # egg-chocolate_amount\n",
    "rho = sym_update(rho, (1,4), 0.1)  # egg-butter\n",
    "rho = sym_update(rho, (1,5), 0.1)  # egg-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (2,3), 0.0)  # sugar-chocolate_amount\n",
    "rho = sym_update(rho, (2,4), 0.5)  # sugar-butter\n",
    "rho = sym_update(rho, (2,5), 0.0)  # sugar-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (3,4), 0.5)  # chocolate_amount-butter\n",
    "rho = sym_update(rho, (3,5), 0.3)  # chocolate_amount-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (4,5), 0.0)  # butter-nut_amount\n",
    "\n",
    "# variances\n",
    "sigv = np.array([[50, 2, 50, 30, 30, 30]])  # standard deviations\n",
    "var_arr = np.dot(sigv.T, sigv)\n",
    "\n",
    "# covariances\n",
    "cv_arr = np.multiply(rho, var_arr)\n",
    "\n",
    "# mean\n",
    "# [flour, egg, sugar, chocolate_amount, butter, nut_amount]\n",
    "mean_arr = np.array([125, 3, 150, 300, 100, 100])\n",
    "\n",
    "# the model\n",
    "rv_dh = multivariate_normal(mean_arr,cv_arr)\n",
    "\n",
    "# model scale for normalization \n",
    "rv_dh_norm = rv_dh.pdf(mean_arr)\n",
    "\n",
    "\n",
    "# DARK CHOCOLATE + ALMONDS\n",
    "\n",
    "# correlations\n",
    "rho = np.identity(6)  # initialize\n",
    "\n",
    "rho = sym_update(rho, (0,1), 0.5)  # flour-egg\n",
    "rho = sym_update(rho, (0,2), 0.3)  # flour-sugar\n",
    "rho = sym_update(rho, (0,3), 0.3)  # flour-chocolate_amount\n",
    "rho = sym_update(rho, (0,4), 0.1)  # flour-butter\n",
    "rho = sym_update(rho, (0,5), 0.1)  # flour-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (1,2), 0.5)  # egg-sugar\n",
    "rho = sym_update(rho, (1,3), 0.3)  # egg-chocolate_amount\n",
    "rho = sym_update(rho, (1,4), 0.1)  # egg-butter\n",
    "rho = sym_update(rho, (1,5), 0.1)  # egg-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (2,3), 0.0)  # sugar-chocolate_amount\n",
    "rho = sym_update(rho, (2,4), 0.5)  # sugar-butter\n",
    "rho = sym_update(rho, (2,5), 0.0)  # sugar-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (3,4), 0.3)  # chocolate_amount-butter\n",
    "rho = sym_update(rho, (3,5), 0.0)  # chocolate_amount-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (4,5), 0.0)  # butter-nut_amount\n",
    "\n",
    "# variances\n",
    "sigv = np.array([[50, 2, 50, 80, 30, 30]])  # standard deviations\n",
    "var_arr = np.dot(sigv.T, sigv)\n",
    "\n",
    "# covariances\n",
    "cv_arr = np.multiply(rho, var_arr)\n",
    "\n",
    "# mean\n",
    "# [flour, egg, sugar, chocolate_amount, butter, nut_amount]\n",
    "mean_arr = np.array([125, 3, 150, 300, 100, 100])\n",
    "\n",
    "# the model\n",
    "rv_da = multivariate_normal(mean_arr,cv_arr)\n",
    "\n",
    "# model scale for normalization \n",
    "rv_da_norm = rv_da.pdf(mean_arr)\n",
    "\n",
    "\n",
    "# WHITE CHOCOLATE + ALMONDS\n",
    "\n",
    "# correlations\n",
    "rho = np.identity(6)  # initialize\n",
    "\n",
    "rho = sym_update(rho, (0,1), 0.5)  # flour-egg\n",
    "rho = sym_update(rho, (0,2), 0.3)  # flour-sugar\n",
    "rho = sym_update(rho, (0,3), 0.2)  # flour-chocolate_amount\n",
    "rho = sym_update(rho, (0,4), 0.1)  # flour-butter\n",
    "rho = sym_update(rho, (0,5), 0.1)  # flour-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (1,2), 0.5)  # egg-sugar\n",
    "rho = sym_update(rho, (1,3), 0.7)  # egg-chocolate_amount\n",
    "rho = sym_update(rho, (1,4), 0.3)  # egg-butter\n",
    "rho = sym_update(rho, (1,5), 0.1)  # egg-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (2,3), 0.4)  # sugar-chocolate_amount\n",
    "rho = sym_update(rho, (2,4), 0.5)  # sugar-butter\n",
    "rho = sym_update(rho, (2,5), 0.0)  # sugar-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (3,4), 0.3)  # chocolate_amount-butter\n",
    "rho = sym_update(rho, (3,5), 0.4)  # chocolate_amount-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (4,5), 0.0)  # butter-nut_amount\n",
    "\n",
    "# variances\n",
    "sigv = np.array([[25, 2, 40, 50, 30, 20]])  # standard deviations\n",
    "var_arr = np.dot(sigv.T, sigv)\n",
    "\n",
    "# covariances\n",
    "cv_arr = np.multiply(rho, var_arr)\n",
    "\n",
    "# mean\n",
    "# [flour, egg, sugar, chocolate_amount, butter, nut_amount]\n",
    "mean_arr = np.array([125, 3, 150, 300, 100, 100])\n",
    "\n",
    "# the model\n",
    "rv_wa = multivariate_normal(mean_arr,cv_arr)\n",
    "\n",
    "# model scale for normalization \n",
    "rv_wa_norm = rv_wa.pdf(mean_arr)\n",
    "\n",
    "\n",
    "# WHITE CHOCOLATE + HAZELNUTS\n",
    "\n",
    "# correlations\n",
    "rho = np.identity(6)  # initialize\n",
    "\n",
    "rho = sym_update(rho, (0,1), 0.5)  # flour-egg\n",
    "rho = sym_update(rho, (0,2), 0.3)  # flour-sugar\n",
    "rho = sym_update(rho, (0,3), 0.2)  # flour-chocolate_amount\n",
    "rho = sym_update(rho, (0,4), 0.1)  # flour-butter\n",
    "rho = sym_update(rho, (0,5), 0.1)  # flour-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (1,2), 0.5)  # egg-sugar\n",
    "rho = sym_update(rho, (1,3), 0.7)  # egg-chocolate_amount\n",
    "rho = sym_update(rho, (1,4), 0.3)  # egg-butter\n",
    "rho = sym_update(rho, (1,5), 0.1)  # egg-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (2,3), 0.4)  # sugar-chocolate_amount\n",
    "rho = sym_update(rho, (2,4), 0.5)  # sugar-butter\n",
    "rho = sym_update(rho, (2,5), 0.0)  # sugar-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (3,4), 0.3)  # chocolate_amount-butter\n",
    "rho = sym_update(rho, (3,5), 0.2)  # chocolate_amount-nut_amount\n",
    "\n",
    "rho = sym_update(rho, (4,5), 0.4)  # butter-nut_amount\n",
    "\n",
    "# variances\n",
    "sigv = np.array([[25, 2, 20, 80, 30, 50]])  # standard deviations\n",
    "var_arr = np.dot(sigv.T, sigv)\n",
    "\n",
    "# covariances\n",
    "cv_arr = np.multiply(rho, var_arr)\n",
    "\n",
    "# mean\n",
    "# [flour, egg, sugar, chocolate_amount, butter, nut_amount]\n",
    "mean_arr = np.array([125, 3, 100, 300, 100, 100])\n",
    "\n",
    "# the model\n",
    "rv_wh = multivariate_normal(mean_arr,cv_arr)\n",
    "\n",
    "# model scale for normalization \n",
    "rv_wh_norm = rv_wh.pdf(mean_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5150eb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "def brownie_score(xdf):\n",
    "    \"\"\"\n",
    "    score of the brownie (the objective function).\n",
    "    :param xdf (pandas df)\n",
    "    :return score (array or element)\n",
    "    \"\"\"\n",
    "    \n",
    "    # === get numerical content ===\n",
    "    nrows = xdf.shape[0]\n",
    "    \n",
    "    flour = np.reshape(xdf[\"flour\"].values, (nrows,1))\n",
    "    egg = np.reshape(xdf[\"egg\"].values, (nrows,1))\n",
    "    sugar = np.reshape(xdf[\"sugar\"].values, (nrows,1))\n",
    "    chocolate_amount = np.reshape(xdf[\"chocolate_amount\"].values, (nrows,1))\n",
    "    butter = np.reshape(xdf[\"butter\"].values, (nrows,1))\n",
    "    nut_amount = np.reshape(xdf[\"nut_amount\"].values, (nrows,1))\n",
    "    \n",
    "    entry = np.hstack((flour, egg, sugar, chocolate_amount, butter, nut_amount))\n",
    "    \n",
    "    # === get categorical entries ===\n",
    "    chocolate_type = xdf[\"chocolate_type\"].values\n",
    "    nut_type = xdf[\"nut_type\"].values\n",
    "    \n",
    "    # combines the categorical inputs\n",
    "    full_type = list(map('-'.join, zip(chocolate_type, nut_type)))\n",
    "    \n",
    "    # === determines score ===\n",
    "    output = np.zeros((nrows))\n",
    "    \n",
    "    for i in range(nrows):\n",
    "        # picks the right model to use\n",
    "        if full_type[i] == \"dark-almond\":\n",
    "            output[i] = mf_da*rv_da.pdf(entry[i,:]) / rv_da_norm  # normalizing\n",
    "        elif full_type[i] == \"dark-hazelnut\":\n",
    "            output[i] = mf_dh*rv_dh.pdf(entry[i,:]) / rv_dh_norm\n",
    "        elif full_type[i] == \"white-almond\":\n",
    "            output[i] = mf_wa*rv_wa.pdf(entry[i,:]) / rv_wa_norm\n",
    "        elif full_type[i] == \"white-hazelnut\":\n",
    "            output[i] = mf_wh*rv_wh.pdf(entry[i,:]) / rv_wh_norm\n",
    "            \n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afbfa8c5",
   "metadata": {},
   "source": [
    "Let's take a look at the output of the `brownie_score` function. It is a 1-entry array as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b46bacdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02663503])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try out the score function\n",
    "xtmp = pd.DataFrame({\"flour\": [130], \"egg\": [3], \"sugar\": [80], \"chocolate_type\": [\"dark\"], \"chocolate_amount\": [330], \"butter\": [120], \"nut_type\": [\"almond\"], \"nut_amount\": [75]})\n",
    "\n",
    "brownie_score(xtmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86efbd12",
   "metadata": {},
   "source": [
    "## Solving the problem\n",
    "\n",
    "The problem will be solved using the `auto`-method. We first define the problem covariate space before setting up the code to solve the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce34bf34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables: flour, egg, sugar, chocolate_type, chocolate_amount, butter, nut_type, nut_amount\n",
    "\n",
    "# define covariates\n",
    "covars_brownie = {\n",
    "    \"flour\": {\n",
    "        \"guess\": 100,\n",
    "        \"min\": 0,\n",
    "        \"max\": 200,\n",
    "        \"type\": float,\n",
    "    },\n",
    "    \"egg\": {\n",
    "        \"guess\": 2,\n",
    "        \"min\": 0,\n",
    "        \"max\": 5,\n",
    "        \"type\": int,\n",
    "    },\n",
    "    \"sugar\": {\n",
    "        \"guess\": 100,\n",
    "        \"min\": 50,\n",
    "        \"max\": 200,\n",
    "        \"type\": float,\n",
    "    },\n",
    "    \"chocolate_type\": {\n",
    "        \"guess\": \"dark\",\n",
    "        \"options\": {\"dark\", \"white\"}, \n",
    "        \"type\": str,\n",
    "    },\n",
    "    \"chocolate_amount\": {\n",
    "        \"guess\": 300,\n",
    "        \"min\": 150,\n",
    "        \"max\": 500,\n",
    "        \"type\": float,\n",
    "    },\n",
    "    \"butter\": {\n",
    "        \"guess\": 100,\n",
    "        \"min\": 0,\n",
    "        \"max\": 200,\n",
    "        \"type\": float,\n",
    "    },\n",
    "    \"nut_type\": {\n",
    "        \"guess\": \"almond\",\n",
    "        \"options\": {\"almond\", \"hazelnut\"}, \n",
    "        \"type\": str,\n",
    "    },\n",
    "    \"nut_amount\": {\n",
    "        \"guess\": 100,\n",
    "        \"min\": 0,\n",
    "        \"max\": 200,\n",
    "        \"type\": float,\n",
    "    },\n",
    "}\n",
    "\n",
    "# initialize class instance\n",
    "cc = TuneSession(covars=covars_brownie)\n",
    "\n",
    "# number of iterations\n",
    "max_iter = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72a1aa9",
   "metadata": {},
   "source": [
    "Solving the problem in closed-loop fashion using the `auto`-method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e8e6f10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 56:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 58:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 59:\n",
      "\tIdentify new covariate datapoint...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 60:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
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      "\tIdentify new covariate datapoint...\n",
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      "\tFinish iteration...\n",
      "ITERATION 64:\n",
      "\tIdentify new covariate datapoint...\n",
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      "ITERATION 117:\n",
      "\tIdentify new covariate datapoint...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 118:\n",
      "\tIdentify new covariate datapoint...\n",
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      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 160:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 161:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 162:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 163:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 164:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 165:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 166:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 167:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 168:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 169:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 170:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 171:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 172:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 173:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 174:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 175:\n",
      "\tIdentify new covariate datapoint...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 176:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 177:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 178:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 179:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 180:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 181:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 182:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 183:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 184:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 185:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 186:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 187:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 188:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 189:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 190:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 191:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 192:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 193:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 194:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 195:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 196:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 197:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 198:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 199:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n",
      "ITERATION 200:\n",
      "\tIdentify new covariate datapoint...\n",
      "\tGet response for new datapoint...\n",
      "\tSuccessfully trained GP model...\n",
      "\tFinish iteration...\n"
     ]
    }
   ],
   "source": [
    "# run the auto-method\n",
    "cc.auto(response_samp_func=brownie_score, max_iter=max_iter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fabf6e35",
   "metadata": {},
   "source": [
    "## Review results\n",
    "\n",
    "Let's take a look at the best result discovered by the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "282bdcbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum response value Y (iteration 200): max_Y = 4.92889e-01\n",
      "Corresponding covariate values resulting in max_Y:\n",
      "\t flour  egg  sugar chocolate_type  chocolate_amount  butter nut_type  nut_amount\n",
      "\t 100.0    2  100.0          white             300.0   100.0 hazelnut       100.0\n"
     ]
    }
   ],
   "source": [
    "# run current_best method\n",
    "cc.current_best()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c68d303",
   "metadata": {},
   "source": [
    "Let's see how the best result has improved through the iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "421164a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='Iteration $n$', ylabel='Best objective $y^{max}_n$ found up to iteration $n$'>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot best result vs iterations\n",
    "cc.plot_best_objective()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8a7177c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/svedel/python_examples_test/venv/lib/python3.8/site-packages/creative_project/_plot.py:202: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  ax.set_yscale(\"log\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='Iteration $n$', ylabel='Relative improvement between iterations, $(y_n-y_{n-1})/y_n$'>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot convergence\n",
    "cc.plot_convergence()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5d9d12",
   "metadata": {},
   "source": [
    "Let's also look at the sampled response values through the optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33ae3e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Brownie score obtained in iteration $n$')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fx, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "ax.plot(np.arange(0,cc.y_data.shape[0]), cc.y_data[\"Response\"], \"-b.\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel(\"Iteration $n$\")\n",
    "ax.set_ylabel(\"Brownie score obtained in iteration $n$\")"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}