Tacton  data generator demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will show how to use and modify the data generator pack for Tacton.\n",
    "Data objects that are relevant for this project are:\n",
    "C_data - Combination data, \n",
    "D_data - Discount data, \n",
    "p_data - probability data, \n",
    "deal_data - Quote outcome. \n",
    "For more information regarding the algorithms behind the data generator: \n",
    "https://docs.google.com/document/d/10QUrTwTu61wxq57S-zQgsrRc2BtC6QuKa8mXj4Q4f84/edit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pylab as plt\n",
    "from generate_cpq_data_class import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Generator_pack creates a C-gen (Combinations/dimensions-generator), D_gen (Discount-generator), a P (Probability function) and a corresponding P_gen (Probability-generator).\n",
    "\n",
    "We start this tutortial by initiate a Generator_pack G. discount_max, discount_min, discount_mean, discount_std are parameters for the distribution of the discount data (truncated normal distribution). mean_p and max_p are parameters for the probability distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=Generator_pack(discount_max = 0.4, discount_min=0, discount_mean = 0.15, discount_std = 0.2, mean_p = 0.2,\n",
    "                  max_p = 0.4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate C-data and D-data from the Generator_pack G we just call G.C_gen(nr=50000) and G.D_gen(nr=50000), where nr is the number of data points you want to sample. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_data = G.C_gen(nr=50000)\n",
    "D_data = G.D_gen(nr=50000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we have C_data and D_data we can use G.P_gen to produce p_data (probability data), deal_data (Quote outcome: win=1, loss=0), y_data (meta data describing the probability function of p given C, y[:,0] = k and y[:,1] = m).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data, p_data, deal_data =G.P_gen(C_data,D_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the distribution of the generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u, frac = np.unique(deal_data, return_counts=True)\n",
    "\n",
    "# Ploting the distribution of the generated data\n",
    "bins = 25\n",
    "f, ax = plt.subplots(2,3,figsize=(15,7))\n",
    "ax[0,0].hist(y_data[:,0],bins=bins)\n",
    "ax[0,0].set_title(\"f(C) distribution\")\n",
    "ax[0,1].hist(y_data[:,1],bins=bins)\n",
    "ax[0,1].set_title(\"g(C) distribution\")\n",
    "ax[1,0].hist(D_data,bins=bins)\n",
    "ax[1,0].set_title(\"discount D distribution\")\n",
    "ax[1,1].hist(y_data[:, 0]/y_data[:, 1],bins=bins)\n",
    "ax[1,1].set_title(\"f(C)/g(C) distribution\")\n",
    "ax[1,2].hist(p_data, bins=bins)\n",
    "ax[1,2].set_title(\"probability p distribution\")\n",
    "ax[0, 2].hist(deal_data, bins=bins)\n",
    "deal_ratio = np.round(frac[1]/(frac[0] + frac[1]),5)\n",
    "ax[0, 2].set_title(\"deal ratio \" + str(deal_ratio))\n",
    "plt.show()\n",
    "# plt.savefig('distributions_cpq_data')\n",
    "plt.close()\n",
    "\n",
    "#\n",
    "p_stat = frac[1]/(frac[0] + frac[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to modify the probability function for a specific set of C, e.g. C_1 = [2,1,0,3,6,3] we start to transform C_1 with one hot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 1.]\n",
      "C_data shape:  (50000, 30)\n"
     ]
    }
   ],
   "source": [
    "C_1 = [2,1,0,3,6,3]\n",
    "C_1_oh = one_hot(C_1)\n",
    "print(C_1_oh)\n",
    "print(\"C_data shape: \", C_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want the probability function for C_1 to have a specific shape p(C_1,D)=sigmoid(k_desired*D-m_desired), we can just rebuild the M and/or the b of G.P to fit the desired form. \n",
    "To call M and b we simple write G.P.M, G.P.b:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M:  [[ 0.39572906 -0.3796552 ]\n",
      " [ 0.19107105 -0.2411752 ]\n",
      " [ 0.28992879 -0.30799786]\n",
      " [ 0.26260076 -0.33429401]\n",
      " [ 0.28921356 -0.22034297]\n",
      " [ 0.0707652  -0.38153981]\n",
      " [ 0.33803885 -0.01257659]\n",
      " [ 0.1257382  -0.12969593]\n",
      " [ 0.41226161 -0.22024107]\n",
      " [ 0.36200023 -0.31286984]\n",
      " [ 0.39628314 -0.03586047]\n",
      " [ 0.2194697  -0.01418335]\n",
      " [ 0.18816419 -0.08608495]\n",
      " [ 0.39748928 -0.11540389]\n",
      " [ 0.00175803 -0.08224703]\n",
      " [ 0.09471664 -0.20767707]\n",
      " [ 0.2168473  -0.24255207]\n",
      " [ 0.3241945  -0.1032592 ]\n",
      " [ 0.12452015 -0.17616715]\n",
      " [ 0.28360486 -0.29130801]\n",
      " [ 0.33724008 -0.12199823]\n",
      " [ 0.08685429 -0.36667013]\n",
      " [ 0.04444509 -0.29063335]\n",
      " [ 0.00633728 -0.21587295]\n",
      " [ 0.18871105 -0.38086564]\n",
      " [ 0.02989321 -0.20476283]\n",
      " [ 0.07444962 -0.03197351]\n",
      " [ 0.0011515  -0.23389119]\n",
      " [ 0.12497743 -0.22365725]\n",
      " [ 0.27498811 -0.33864307]]\n",
      "b:  [0.19400507 2.97550158]\n"
     ]
    }
   ],
   "source": [
    "print(\"M: \", G.P.M)\n",
    "print(\"b: \", G.P.b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check k and m for C_1 by simply do a matrix multiplication C_1*M + b:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k(C_1):  1.9766781359032681\n",
      "m(C_1):  1.4896776423139453\n"
     ]
    }
   ],
   "source": [
    "k_1,m_1 =np.matmul(C_1_oh,G.P.M)+G.P.b\n",
    "print(\"k(C_1): \", k_1)\n",
    "print(\"m(C_1): \", m_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot p(C_1,D) for D in range 0:0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_ = np.linspace(0,0.4,101)\n",
    "y_1, p_1 , deals_1 = G.P_gen(np.array([list(C_1_oh)]*101),d_)\n",
    "plt.plot(d_,p_1,label='p(C_1,D)')\n",
    "plt.xlabel(\"D discount %\")\n",
    "plt.ylabel(\"probability p\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k(C_1), m(C_1) is the matrix-multiplication of C_1_oh*G.P.M+G.P.b, therefore we can modify G.P.M and/or G.P.b so k(C_1), m(C_1) have the desired values. Simplest way is to just modify an element in G.P.M where C_1_oh is not 0 e.g. index 2 (G.P.M[2,0], G.P.M[2,1])."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-23ac2ca5145c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mm_desired\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mk_1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm_1\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC_1_oh\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mk_diff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk_desired\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mk_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "k_desired = 20\n",
    "m_desired = 2.5\n",
    "\n",
    "k_1,m_1 =np.matmul(C_1_oh,G.P.M)+G.P.b\n",
    "\n",
    "k_diff = k_desired - k_1\n",
    "m_diff = m_desired - m_1\n",
    "G.P.M[2,0] = k_diff + G.P.M[2,0]\n",
    "G.P.M[2,1] = m_diff + G.P.M[2,1]\n",
    "\n",
    "k_1u,m_1u =np.matmul(C_1_oh,G.P.M)+G.P.b\n",
    "print(\"new k(C_1): \", k_1u)\n",
    "print(\"new m(C_1): \", m_1u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_ = np.linspace(0,0.4,101)\n",
    "y_1, p_1u , deals_1 = G.P_gen(np.array([list(C_1_oh)]*101),d_)\n",
    "plt.plot(d_,p_1,label='old p(C_1,D)')\n",
    "plt.plot(d_,p_1u,label='new p(C_1,D)')\n",
    "\n",
    "plt.xlabel(\"D discount %\")\n",
    "plt.ylabel(\"probability p\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can control the number of outcomes for a specific combination of C.  Combination of interest is C_1 = [2,1,0,3,6,3] and say we want m=20 copies of it and 5 copys of the rest. We call the C_gen again, but set complete_set=True, this means that it generates \"copys\" # of every C combination. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len:  30720\n"
     ]
    }
   ],
   "source": [
    "C_data = G.C_gen(nr=50000, complete_set=True, copys=5)\n",
    "\n",
    "nr = len(C_data)\n",
    "print(\"len: \", nr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we remove every element where C is C_1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind1 = np.where(np.array([list(C_) == list(C_1_oh) for C_ in C_data]))\n",
    "C_data = np.delete(C_data,ind1[0],0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just add m=20 elements C_1 to the end of C_data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=20\n",
    "C_data = np.concatenate((C_data,np.array([list(C_1_oh)]*m)),0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that you need the same number of C_data and D_data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nr = len(C_data)\n",
    "D_data = G.D_gen(nr=nr)\n",
    "y_data, p_data, deal_data =G.P_gen(C_data,D_data)\n",
    "y_data, p_data, deal_data =G.P_gen(C_data,D_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u, frac = np.unique(deal_data, return_counts=True)\n",
    "\n",
    "# Ploting the distribution of the generated data\n",
    "bins = 25\n",
    "f, ax = plt.subplots(2,3,figsize=(15,7))\n",
    "ax[0,0].hist(y_data[:,0],bins=bins)\n",
    "ax[0,0].set_title(\"f(C) distribution\")\n",
    "ax[0,1].hist(y_data[:,1],bins=bins)\n",
    "ax[0,1].set_title(\"g(C) distribution\")\n",
    "ax[1,0].hist(D_data,bins=bins)\n",
    "ax[1,0].set_title(\"discount D distribution\")\n",
    "ax[1,1].hist(y_data[:, 0]/y_data[:, 1],bins=bins)\n",
    "ax[1,1].set_title(\"f(C)/g(C) distribution\")\n",
    "ax[1,2].hist(p_data, bins=bins)\n",
    "ax[1,2].set_title(\"probability p distribution\")\n",
    "ax[0, 2].hist(deal_data, bins=bins)\n",
    "deal_ratio = np.round(frac[1]/(frac[0] + frac[1]),5)\n",
    "ax[0, 2].set_title(\"deal ratio \" + str(deal_ratio))\n",
    "plt.show()\n",
    "# plt.savefig('distributions_cpq_data')\n",
    "plt.close()\n",
    "\n",
    "#\n",
    "p_stat = frac[1]/(frac[0] + frac[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}