Example_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example using constrained GP model\n",
    "This is the code used to produce the 'corroded pipeline' example in the paper _'Gaussian processes with linear operator inequality constraints'_, https://arxiv.org/abs/1901.03134"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading constrained GP module from C:\\Data\\git repos\\gp_constr\n",
      "Loading R wrapper...\n",
      "Running R from rpy2: R version 3.4.3 (2017-11-30)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version 3.6.3 (v3.6.3:2c5fed8, Oct  3 2017, 18:11:49) [MSC v.1900 64 bit (AMD64)]\n"
     ]
    }
   ],
   "source": [
    "### Basic imports ###\n",
    "import sys, os\n",
    "\n",
    "# For plotting\n",
    "import plotly\n",
    "import plotly.plotly as pltly\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as pltlyoff\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "# This is for plotting as static images (to show on e.g. GitHub)\n",
    "import plotly.io as pio\n",
    "from IPython.display import Image\n",
    "\n",
    "# Numerics\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import itertools\n",
    "import pyDOE\n",
    "import pandas as pd\n",
    "\n",
    "### Custom files ###\n",
    "\n",
    "# Path to custom plotly module 'GPPlotly' for plotting \n",
    "# can be downloaded at https://github.com/cagrell/gp_plotly\n",
    "dir_GPPlotly = 'C:\\\\Data\\\\git repos\\\\gp_plotly\\\\'\n",
    "sys.path.append(dir_GPPlotly) \n",
    "\n",
    "# Path to the constrained GP moule \n",
    "# can be downloaded at https://github.com/cagrell/gp_constr\n",
    "dir_gp_constr = 'C:\\\\Data\\\\git repos\\\\gp_constr\\\\'\n",
    "sys.path.append(dir_gp_constr) \n",
    "\n",
    "# Import\n",
    "from GPPlotly.plottingfunctions import PlotGP2d, add_traces_to_fig, gp_diagnostics, pred_vs_error_perc\n",
    "from GPConstr.model import GPmodel, Constraint\n",
    "from GPConstr.kern import kernel_Matern52\n",
    "\n",
    "### Setup notebook ###\n",
    "pltlyoff.init_notebook_mode(connected=True)\n",
    "print('Python version', sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some helper functions for diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_diagnostics_df(x_test, y_test, mean, var):\n",
    "    \"\"\" Put test data in a dataframe\"\"\"\n",
    "    \n",
    "    df = pd.DataFrame(x_test)\n",
    "    df.columns = ['x' + str(i+1) for i in range(x_test.shape[1])]\n",
    "    df['y_true'] = y_test\n",
    "    df['y_mean'] = mean\n",
    "    df['y_var'] = var\n",
    "    \n",
    "    return df\n",
    "\n",
    "def fQ2(y_pred, y_test):\n",
    "    \"\"\"\n",
    "    Compute Q2 = determination coefficient (R2) computed from a test sample (y_pred, y_test)\n",
    "    \"\"\"\n",
    "\n",
    "    # Sum of squares of residuals\n",
    "    SS_res = ((y_pred - y_test)**2).sum()\n",
    "\n",
    "    # Total sum of squares\n",
    "    y_test_avg = y_test.mean()\n",
    "    SS_tot = ((y_test - y_test_avg)**2).sum()\n",
    "\n",
    "    #Q2\n",
    "    Q2 = 1 - SS_res/SS_tot\n",
    "\n",
    "    return Q2\n",
    "\n",
    "def fPVA(y_pred, y_pred_var, y_test):\n",
    "    \"\"\"\n",
    "    Return Predictive Variance Adequation (PVA)\n",
    "    \"\"\"\n",
    "    # Sum of sqares of scaled residuals\n",
    "    SS_res_var = (((y_pred - y_test)**2)/(y_pred_var**2)).sum()\n",
    "\n",
    "    PVA = np.abs(np.log((1/y_pred.shape[0])*SS_res_var))\n",
    "    \n",
    "    return PVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Define function for generating synthetic test/training data\n",
    "The simplified pipe burst capacity in RP-F101\n",
    "\n",
    "$P_{cap}(\\sigma_{u}, D, t, d, l) = 1.05\\frac{2t \\sigma_{u}}{D - t} \\frac{1 - d/t}{1 - \\frac{d/t}{Q}}$, $Q = \\sqrt{1 + 0.31\\frac{l^{2}}{Dt}}$\n",
    "\n",
    "We have the following constraints\n",
    "\n",
    "1.) $\\frac{\\partial P_{cap}}{\\partial \\sigma_{u}} > 0$\n",
    "\n",
    "2.) $\\frac{\\partial P_{cap}}{\\partial D} < 0$\n",
    "\n",
    "3.) $\\frac{\\partial P_{cap}}{\\partial t} > 0$\n",
    "\n",
    "4.) $\\frac{\\partial P_{cap}}{\\partial d} < 0$\n",
    "\n",
    "5.) $\\frac{\\partial P_{cap}}{\\partial l} < 0$\n",
    "\n",
    "for $\\sigma_{u} \\in [450, 550]$ (MPa), $t \\in [5, 30]$ (mm), $D \\in [10t, 50t]$ (mm), $d \\in [0, t]$ (mm) and $l \\in [0, 1000]$ (mm)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let $\\textbf{x}$ denote the transformed input vector $\\textbf{x} = [x_{1}, \\dots, x_{5}]$ where $x_{1} = (\\sigma_{u} - 450)/(550-450)$, $x_{2} = (D/t - 10)/(50 - 10)$, $x_{3} = (t - 5)/(30-5)$, $x_{4} = d/t$ and $x_{5} = l/1000$.  \n",
    "\n",
    "We will make use of the function\n",
    "\n",
    "$f(\\textbf{x}) = P_{cap}(\\textbf{x})$ with the corresponding constraints\n",
    "\n",
    "1.) $\\frac{\\partial f}{\\partial x_{1}} > 0$\n",
    "\n",
    "2.) $\\frac{\\partial f}{\\partial x_{2}} < 0$\n",
    "\n",
    "3.) $\\frac{\\partial f}{\\partial x_{3}} > 0$\n",
    "\n",
    "4.) $\\frac{\\partial f}{\\partial x_{4}} < 0$\n",
    "\n",
    "5.) $\\frac{\\partial f}{\\partial x_{5}} < 0$\n",
    "\n",
    "for $\\textbf{x} \\in [0, 1]^{5}$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to emulate/estimate\n",
    "def burst_cap(su, D, t, d, l):\n",
    "    \"\"\" \n",
    "    Simplified burst capacity equation in DNV GL RP-F101 \n",
    "    \n",
    "    Input:    \n",
    "    su =  Ultimate tensile strength [MPa]\n",
    "    D  =  Pipe diameter             [mm]\n",
    "    t  =  Pipe wall thickness       [mm]\n",
    "    d  =  Defect depth              [mm]\n",
    "    l  =  Defect length             [mm]\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    p0 = 1.05*2*t*su/(D-t)               # Un-corroded capacity\n",
    "    Q = np.sqrt(1 + 0.31*(l**2)/(D*t))   # Length factor\n",
    "    d_t = d/t                            # Relative depth\n",
    "    R = (1-d_t)/(1 - d_t/Q)              # Capacity reduction factor due to the defect\n",
    "    \n",
    "    p_cap = p0*R                         # Capacity of pipe with defect\n",
    "    \n",
    "    return p_cap\n",
    "\n",
    "# Ranges of input variables\n",
    "rng = {\n",
    "    'su':(450, 550),\n",
    "    't': (5, 30),\n",
    "    'D_t': (10, 50),\n",
    "    'd_t': (0, 1),\n",
    "    'l': (0, 1000),\n",
    "}\n",
    "\n",
    "def phys_to_x(su, D, t, d, l):\n",
    "    \"\"\" Transform from physical to standardized domain \"\"\"\n",
    "    \n",
    "    x = np.array([\n",
    "        (su - rng['su'][0])/(rng['su'][1] - rng['su'][0]),\n",
    "        (D/t - rng['D_t'][0])/(rng['D_t'][1] - rng['D_t'][0]),\n",
    "        (t - rng['t'][0])/(rng['t'][1] - rng['t'][0]),\n",
    "        (d/t - rng['d_t'][0])/(rng['d_t'][1] - rng['d_t'][0]),\n",
    "        (l - rng['l'][0])/(rng['l'][1] - rng['l'][0])\n",
    "    ])\n",
    "    \n",
    "    return x\n",
    "\n",
    "def x_to_phys(x):\n",
    "    \"\"\" Transform from standardized to physical domain \"\"\"\n",
    "    su = x[0]*(rng['su'][1] - rng['su'][0]) + rng['su'][0]\n",
    "    D_t = x[1]*(rng['D_t'][1] - rng['D_t'][0]) + rng['D_t'][0]\n",
    "    t = x[2]*(rng['t'][1] - rng['t'][0]) + rng['t'][0]\n",
    "    d_t = x[3]*(rng['d_t'][1] - rng['d_t'][0]) + rng['d_t'][0]\n",
    "    l = x[4]*(rng['l'][1] - rng['l'][0]) + rng['l'][0]\n",
    "    \n",
    "    return su, D_t*t, t, d_t*t, l\n",
    "\n",
    "def fun(x):\n",
    "    \"\"\" function used to generate data \"\"\"\n",
    "    x5 = np.array([0.5]*5) # 5-dim input\n",
    "    x5[:len(x)] = x # Update first n elements of x5\n",
    "    \n",
    "    p_cap = burst_cap(*x_to_phys(x5))\n",
    "    return p_cap\n",
    "\n",
    "def fun_mult(X):\n",
    "    \"\"\" For multiple x in array X \"\"\"\n",
    "    return np.array([fun(X[i,:]) for i in range(X.shape[0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Regression in $\\mathbb{R}^{n}$, $n \\leq 5$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Generate synthetic training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Design data \n",
    "n_samples = 25\n",
    "input_dim = 5\n",
    "noise_std = 2\n",
    "\n",
    "x_design = pyDOE.lhs(input_dim, samples = n_samples, criterion = 'maximin', iterations = 1000)\n",
    "y_design = fun_mult(x_design) + np.random.normal(0, noise_std, n_samples) # With noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Define GP model (without constraints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up model\n",
    "ker = kernel_Matern52(variance = 1, lengthscale = [1]*input_dim)\n",
    "model = GPmodel(kernel = ker, likelihood = 1, mean = 0) # With noise\n",
    "\n",
    "# Training data\n",
    "model.X_training = x_design\n",
    "model.Y_training = y_design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..Running optimization for unconstrained GP ... DONE - Total time: 0.172 seconds\n",
      "----- GP model ----- \n",
      " mean = 0 \n",
      " likelihood = 35.01428222949203 \n",
      " kernel: \n",
      "   type = Matern52 \n",
      "   input dim = 5 \n",
      "   lenghtscale = [331.09375299   1.07366951 277.62237502   1.14824753   3.18353138] \n",
      "   variance = 2837.7778031033695 \n",
      " constraint: \n",
      "   No constraints \n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "# Optimize\n",
    "model.optimize(include_constraint = False, fix_likelihood = False)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test the unconstrained model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..Running calculation of K_w ... SKIP - (cached)\n",
      "..Running calculation of Cholesky factor for K_w ... SKIP - (cached)\n",
      "..Calculating f* | Y ... DONE - Total time: 0.003 seconds\n"
     ]
    }
   ],
   "source": [
    "# Test data\n",
    "n_test = 100\n",
    "x_test = np.random.rand(n_test, input_dim) # Uniform samples\n",
    "y_test = fun_mult(x_test)\n",
    "\n",
    "# Run unconstrained model\n",
    "mean_unconstr, var_unconstr = model.calc_posterior_unconstrained(x_test, full_cov = False)\n",
    "mean_unconstr = np.array(mean_unconstr).flatten()\n",
    "var_unconstr = np.array(var_unconstr).flatten()\n",
    "\n",
    "# Collect data in df\n",
    "df_unconstr = get_diagnostics_df(x_test, y_test, mean_unconstr, var_unconstr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>y_true</th>\n",
       "      <th>y_mean</th>\n",
       "      <th>y_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.186102</td>\n",
       "      <td>0.002923</td>\n",
       "      <td>0.904187</td>\n",
       "      <td>0.554136</td>\n",
       "      <td>0.576343</td>\n",
       "      <td>56.370435</td>\n",
       "      <td>53.143953</td>\n",
       "      <td>29.081748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.756212</td>\n",
       "      <td>0.072626</td>\n",
       "      <td>0.453577</td>\n",
       "      <td>0.173091</td>\n",
       "      <td>0.080145</td>\n",
       "      <td>88.921760</td>\n",
       "      <td>79.577009</td>\n",
       "      <td>29.016009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.813692</td>\n",
       "      <td>0.030448</td>\n",
       "      <td>0.514664</td>\n",
       "      <td>0.116867</td>\n",
       "      <td>0.498805</td>\n",
       "      <td>98.878867</td>\n",
       "      <td>80.405988</td>\n",
       "      <td>20.266488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.811806</td>\n",
       "      <td>0.045174</td>\n",
       "      <td>0.876904</td>\n",
       "      <td>0.374896</td>\n",
       "      <td>0.161163</td>\n",
       "      <td>88.277907</td>\n",
       "      <td>74.171803</td>\n",
       "      <td>21.914233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.660506</td>\n",
       "      <td>0.078577</td>\n",
       "      <td>0.054488</td>\n",
       "      <td>0.466739</td>\n",
       "      <td>0.755361</td>\n",
       "      <td>48.839055</td>\n",
       "      <td>51.023141</td>\n",
       "      <td>24.778294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        x2        x3        x4        x5     y_true     y_mean  \\\n",
       "0  0.186102  0.002923  0.904187  0.554136  0.576343  56.370435  53.143953   \n",
       "1  0.756212  0.072626  0.453577  0.173091  0.080145  88.921760  79.577009   \n",
       "2  0.813692  0.030448  0.514664  0.116867  0.498805  98.878867  80.405988   \n",
       "3  0.811806  0.045174  0.876904  0.374896  0.161163  88.277907  74.171803   \n",
       "4  0.660506  0.078577  0.054488  0.466739  0.755361  48.839055  51.023141   \n",
       "\n",
       "       y_var  \n",
       "0  29.081748  \n",
       "1  29.016009  \n",
       "2  20.266488  \n",
       "3  21.914233  \n",
       "4  24.778294  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first couple of records of dataframe with testing data\n",
    "df_unconstr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....Summary statistics from 100 predictions....\n",
      "Q2 = 0.9389499174551315, PVA = 2.4748193974859807, AWoCI = 19.723282913967008\n"
     ]
    }
   ],
   "source": [
    "# Print some summary statistics: Q^2, PVA and AWoCI\n",
    "Q2, PVA = fQ2(df_unconstr['y_mean'], df_unconstr['y_true']), fPVA(df_unconstr['y_mean'], df_unconstr['y_var'], df_unconstr['y_true'])\n",
    "AWoCI = 2*1.959963984540054*np.sqrt(df_unconstr['y_var']).mean()\n",
    "\n",
    "print('....Summary statistics from {} predictions....'.format(n_test))\n",
    "print('Q2 = {}, PVA = {}, AWoCI = {}'.format(Q2, PVA, AWoCI))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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34.27866648656826, 48.427760381745315, 15.860348383396015, 73.03434906669229, 7.923505880972264, 24.083104589006446, 25.754727363968424, 4.705441664573026, 18.37882104107973, 37.30193243987426, 15.19024983410734, 8.806514194121632, 4.167359252544969, 19.13377045712011, 19.564839342008536, 32.718206265125914, 78.29618470824688, 68.16666732860124, 10.183374020682052, 1.7785963372518647, 11.58299490115383, 55.21669258643624, 24.174974458719046, 20.57046439445509, 19.54073546982498, 12.541530129157698, 20.65069681030747, 22.540937806437825, 8.990123054769253, 72.76619558621705], \"y\": [53.1439534333088, 79.5770091417036, 80.40598776464637, 74.17180297422604, 51.02314091416684, 34.64571783478169, 28.535056695842993, 25.839606852063437, 15.2442758202641, 31.535718375946892, 21.244749133317292, 17.45254367566032, 27.408025763467077, 18.43763549960842, 17.873502674434498, 70.77207250288991, 41.58760206943685, 3.255619658284058, 16.788683342784775, 23.53607715309752, 15.141210752592276, 40.70442044497463, 6.646516202258334, 44.480363491400254, 21.829624126351163, 31.115799354344365, 11.661068735810648, 59.672113514417504, 41.405454979949646, 15.543441568758018, 5.5985959645073455, 14.91380711196999, 32.6682020005748, 21.026658939834032, 32.42782534666335, 40.70803985294095, 14.742985820055457, 25.12616103186957, 22.737891740835856, 21.235206344523377, 12.025224215895507, 56.2183198254315, 21.99678651221317, 22.247652621173657, 30.739129210104178, 15.266928437846815, 6.067977191415565, 32.703708403400945, 54.713071531432576, 55.62966074754936, 1.9276254548675837, 13.87281066930681, 18.023873105620623, 67.8121690797781, 20.135900574506103, 14.722547951716365, 16.079149110579788, 14.612118706254721, 59.081315385773784, 24.550595279894758, 12.314533206104006, 16.036201876987473, 12.606662301307052, 26.04364471953151, 33.70396865272392, 5.995906568343847, 17.998409682123217, 2.8176397027815487, 16.040168781298558, 11.163022631021802, 37.447337895735636, 54.17155792307319, 20.461889567046402, 71.49415805502028, 10.117741005402737, 20.93779844065884, 17.767503304352577, 8.440360579942544, 23.366154321550727, 35.30960834787833, 19.660361193877744, 11.666994372947249, 3.1676979324430627, 25.65999866540468, 17.646899698861368, 29.947810405725782, 76.03620957310629, 67.39340636229323, 9.649231219016663, -0.776916517203198, 10.654561626862309, 52.13539688250532, 24.740493213798413, 18.11096600326097, 15.048871228172784, 8.651254748520032, 18.367640877388663, 18.850340680434034, 9.27427408981265, 69.43354857691482], \"type\": \"scatter\", \"uid\": \"fcfe885f-9bec-11e9-8782-bc8385ea9e42\"}, {\"line\": {\"color\": \"rgb(0, 0, 0)\", \"dash\": \"dash\"}, \"mode\": \"lines\", \"name\": \"x = y\", \"x\": [-0.776916517203198, 98.87886652751953], \"y\": [-0.776916517203198, 98.87886652751953], \"type\": \"scatter\", \"uid\": \"fcfe8860-9bec-11e9-9731-bc8385ea9e42\"}], {\"autosize\": false, \"height\": 600, \"showlegend\": false, \"title\": \"Prediction vs test with 95% intervals\", \"width\": 700, \"xaxis\": {\"title\": \"True value\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}, \"yaxis\": {\"title\": \"Predicted value\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot diagnostics\n",
    "figs = gp_diagnostics(df_unconstr, y_name = 'y', subplots = False)\n",
    "pltlyoff.iplot(figs[1], filename='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show plot as static image\n",
    "Image(pio.to_image(figs[1], width=700, height=500, scale=1, format='png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot a 1D slice of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..Running calculation of K_w ... SKIP - (cached)\n",
      "..Running calculation of Cholesky factor for K_w ... SKIP - (cached)\n",
      "..Calculating f* | Y ... DONE - Total time: 0.004 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "legendgroup": "conf",
         "line": {
          "color": "rgb(105, 144, 193)",
          "width": 1
         },
         "mode": "lines",
         "showlegend": false,
         "type": "scatter",
         "uid": "02865050-9bed-11e9-9370-bc8385ea9e42",
         "x": [
          0,
          0.010101010101010102,
          0.020202020202020204,
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   "source": [
    "plot_x_dim = 1 # Dimension to plot\n",
    "x_base_val = 0.5 # All other x has this value\n",
    "\n",
    "# Test input\n",
    "px_test = np.linspace(0, 1, 100)\n",
    "px_test_arr = np.ones((len(px_test), input_dim))*x_base_val\n",
    "px_test_arr[:,plot_x_dim] = px_test\n",
    "\n",
    "# True function\n",
    "y_true = fun_mult(px_test_arr)\n",
    "\n",
    "# From GP\n",
    "mean_unconstr, cov_unconstr = model.calc_posterior_unconstrained(px_test_arr, full_cov = True)\n",
    "mean_unconstr = np.array(mean_unconstr).flatten()\n",
    "var_unconstr = np.diagonal(cov_unconstr)\n",
    "\n",
    "num_samples = 10\n",
    "show_samplepaths = True\n",
    "samplepaths_unconstr = []\n",
    "if show_samplepaths: samplepaths_unconstr = np.random.multivariate_normal(mean_unconstr, cov_unconstr, num_samples).T\n",
    "\n",
    "fig_unconstr_slice = PlotGP2d(x_mean = px_test, mean = mean_unconstr, var = var_unconstr,\n",
    "                        x_true = px_test, y_true = y_true,\n",
    "                        samplepaths = samplepaths_unconstr,\n",
    "                        title = 'f(x) as a function of x[{0}] where x[i] = {1} for i != {0}'.format(plot_x_dim, x_base_val), xrange = [0, 1], smoothing = False)\n",
    "\n",
    "pltlyoff.iplot(fig_unconstr_slice, filename='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show plot as static image\n",
    "Image(pio.to_image(fig_unconstr_slice, width=700, height=500, scale=1, format='png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3. Include the constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def constant_function(val):\n",
    "    \"\"\" Return the constant function\"\"\"\n",
    "    def fun(x):\n",
    "        return np.array([val]*x.shape[0])\n",
    "    \n",
    "    return fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create derivative constraints\n",
    "num_constr = input_dim # Set number of constraints\n",
    "\n",
    "constr_bounded = Constraint(LB = constant_function(0), UB = constant_function(float('Inf')))\n",
    "\n",
    "constr_deriv = [\n",
    "    Constraint(LB = constant_function(0), UB = constant_function(float('Inf'))),\n",
    "    Constraint(LB = constant_function(float('-Inf')), UB = constant_function(0)),\n",
    "    Constraint(LB = constant_function(0), UB = constant_function(float('Inf'))),\n",
    "    Constraint(LB = constant_function(float('-Inf')), UB = constant_function(0)),\n",
    "    Constraint(LB = constant_function(float('-Inf')), UB = constant_function(0))\n",
    "]\n",
    "\n",
    "constr_deriv = constr_deriv[:num_constr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add constraints to model\n",
    "model.constr_deriv = constr_deriv\n",
    "model.constr_bounded = constr_bounded\n",
    "model.constr_likelihood = 1E-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- GP model ----- \n",
      " mean = 0 \n",
      " likelihood = 35.01428222949203 \n",
      " kernel: \n",
      "   type = Matern52 \n",
      "   input dim = 5 \n",
      "   lenghtscale = [331.09375299   1.07366951 277.62237502   1.14824753   3.18353138] \n",
      "   variance = 2837.7778031033695 \n",
      " constraint: \n",
      "   f [0], df/dx_1 [0], df/dx_2 [0], df/dx_3 [0], df/dx_4 [0], df/dx_5 [0] \n",
      "   constr_likelihood = 1e-06 \n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Searching for points XV s.t. P(a - nu < Lf < b + nu) > p_target = 0.99 for Lf = [f, df/dx_1, df/dx_2, df/dx_3, df/dx_4, df/dx_5] and nu = 2.326347874040841e-06 ...\n",
      "DONE - Found 1 points. Min. constraint prob = 0.16352598610536873. Total time spent = 2.590 seconds\n",
      "Searching for points XV s.t. P(a - nu < Lf < b + nu) > p_target = 0.7 for Lf = [f, df/dx_1, df/dx_2, df/dx_3, df/dx_4, df/dx_5] and nu = 5.244005127080406e-07 ...\n",
      "DONE - Found 20 points. Min. constraint prob = 0.7048351951679234. Total time spent = 40.850 seconds\n"
     ]
    }
   ],
   "source": [
    "# Search for a suitable set of virtual observation locations where the constraint is imposed -- finite search\n",
    "\n",
    "# First with a high p_target to ensure that we get at least one point\n",
    "Omega = np.random.uniform(size = (1000, input_dim)) # Candidate set\n",
    "df, num_pts, pc_min = model.find_XV_subop(p_target = 0.99, Omega = Omega, sampling_alg = 'minimax_tilting', num_samples = 1000,\n",
    "                         max_iterations = 1, print_intermediate = False)\n",
    "\n",
    "# Then we run multiple iterations with p_target = 0.7 \n",
    "Omega = np.random.uniform(size = (1000, input_dim)) # Candidate set\n",
    "df, num_pts, pc_min = model.find_XV_subop(p_target = 0.7, Omega = Omega, sampling_alg = 'minimax_tilting', num_samples = 1000,\n",
    "                         max_iterations = 200, print_intermediate = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test the constrained model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..Running calculation of K_w ... SKIP - (cached)\n",
      "..Running calculation of Cholesky factor for K_w ... SKIP - (cached)\n",
      "..Running preparation step 1 - dependence on (XS, X) ... DONE - time: 0.011 seconds\n",
      "..Running preparation step 2 - dependence on (XV, X) ... SKIP - (cached)\n",
      "..Running preparation step 3 - dependence on (XS, XV, X) ... DONE - time: 0.003 seconds\n",
      "..sampling 10000 times from truncated constraint distribution C~|C, Y DONE - time: 0.534 seconds\n",
      "..sampling 10000 times from constrained GP f*|C, Y DONE - time: 0.077 seconds\n",
      "..computing statistics from samples DONE - time: 0.039 seconds\n",
      " DONE - Total time: 0.667 seconds\n"
     ]
    }
   ],
   "source": [
    "percentiles = [0.025, 0.5, 0.975]\n",
    "mean, var, perc, mode, samples, sampling_time = model.calc_posterior_constrained(x_test, compute_mode = False, num_samples = 10000, save_samples = 30, algorithm = 'minimax_tilting', resample = False)\n",
    "mean = np.array(mean).flatten()\n",
    "lower = perc[0]\n",
    "upper = perc[2]\n",
    "var = np.array(var).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....Summary statistics from 100 predictions....\n",
      "Q2 = 0.9204956380032929, PVA = 1.932845416756367, AWoCI = 10.825084912354814\n"
     ]
    }
   ],
   "source": [
    "# Print some summary statistics: Q^2, PVA and AWoCI\n",
    "Q2, PVA = fQ2(mean, y_test), fPVA(mean, var, y_test)\n",
    "AWoCI = (upper - lower).mean()\n",
    "\n",
    "print('....Summary statistics from {} predictions....'.format(n_test))\n",
    "print('Q2 = {}, PVA = {}, AWoCI = {}'.format(Q2, PVA, AWoCI))"
   ]
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\"width\": 700, \"xaxis\": {\"title\": \"True value\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}, \"yaxis\": {\"title\": \"Predicted value\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pred_vs_error_perc(mean, lower, upper, y_test, 95)\n",
    "pltlyoff.iplot(fig, filename='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show plot as static image\n",
    "Image(pio.to_image(fig, width=700, height=500, scale=1, format='png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot a 1D slice of the constrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..Running calculation of K_w ... SKIP - (cached)\n",
      "..Running calculation of Cholesky factor for K_w ... SKIP - (cached)\n",
      "..Running preparation step 1 - dependence on (XS, X) ... DONE - time: 0.007 seconds\n",
      "..Running preparation step 2 - dependence on (XV, X) ... SKIP - (cached)\n",
      "..Running preparation step 3 - dependence on (XS, XV, X) ... DONE - time: 0.012 seconds\n",
      "..sampling 1000 times from truncated constraint distribution C~|C, Y DONE - time: 0.200 seconds\n",
      "..sampling 1000 times from constrained GP f*|C, Y DONE - time: 0.010 seconds\n",
      "..computing statistics from samples DONE - time: 0.004 seconds\n",
      " DONE - Total time: 0.236 seconds\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot constrained\n",
    "mean, var, perc, mode, samples, times = model.calc_posterior_constrained(px_test_arr, compute_mode = False, num_samples = 1000, algorithm = 'minimax_tilting', resample = False)\n",
    "\n",
    "p_lower = perc[0]\n",
    "p_upper = perc[2]\n",
    "p_label = '[p{}, p{}] conf.'.format(10, 90)\n",
    "\n",
    "samplepaths_Z = []\n",
    "if show_samplepaths: \n",
    "    samplepaths_Z = np.array(samples)\n",
    "\n",
    "fig_constrained_slice = PlotGP2d(x_mean = px_test, mean = np.array(mean).flatten(),\n",
    "                        samplepaths =  samplepaths_Z,\n",
    "                        x_true = px_test, y_true = y_true,\n",
    "                        p_lower = p_lower, p_upper = p_upper, p_label = p_label,\n",
    "                        title = 'f(x) as a function of x[{0}] where x[i] = {1} for i != {0}'.format(plot_x_dim, x_base_val))\n",
    "\n",
    "pltlyoff.iplot(fig_constrained_slice, filename='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show plot as static image\n",
    "Image(pio.to_image(fig_constrained_slice, width=700, height=500, scale=1, format='png'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}