MCMC_RnD_Cost_Dur_P_adv_with_failure_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R&D via Monte Carlo Markov Chain\n",
    "\n",
    "This notebook is adapted from the notebook: MC_networkx_numpy_v1.ipynb. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of states:  10\n",
      "10\n",
      "Nominal total cost of a project:  15.0\n",
      "Nominal total duration of a project:  8\n",
      "[[0.1  0.85 0.   0.   0.   0.   0.   0.   0.   0.05]\n",
      " [0.   0.1  0.85 0.   0.   0.   0.   0.   0.   0.05]\n",
      " [0.   0.   0.1  0.85 0.   0.   0.   0.   0.   0.05]\n",
      " [0.   0.   0.   0.1  0.85 0.   0.   0.   0.   0.05]\n",
      " [0.   0.   0.   0.   0.1  0.85 0.   0.   0.   0.05]\n",
      " [0.   0.   0.   0.   0.   0.1  0.85 0.   0.   0.05]\n",
      " [0.   0.   0.   0.   0.   0.   0.1  0.85 0.   0.05]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.1  0.85 0.05]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.   1.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.   0.   1.  ]]\n",
      "sum check:  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "#import Project_3_1_3 as pj\n",
    "#import Project_3_1_1_v3 as pj\n",
    "import Project_4_2_2_v1 as pj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.1  0.85 0.   0.   0.   0.   0.   0.   0.   0.05]\n",
      " [0.   0.1  0.85 0.   0.   0.   0.   0.   0.   0.05]\n",
      " [0.   0.   0.1  0.85 0.   0.   0.   0.   0.   0.05]\n",
      " [0.   0.   0.   0.1  0.85 0.   0.   0.   0.   0.05]\n",
      " [0.   0.   0.   0.   0.1  0.85 0.   0.   0.   0.05]\n",
      " [0.   0.   0.   0.   0.   0.1  0.85 0.   0.   0.05]\n",
      " [0.   0.   0.   0.   0.   0.   0.1  0.85 0.   0.05]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.1  0.85 0.05]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.   1.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   0.   0.   0.   1.  ]]\n",
      "sum check:  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "P_repeat = pj.P_repeat\n",
    "P_Ab = pj.P_Ab\n",
    "\n",
    "transition_matrix = pj.mk_transition_matrix(P_repeat, P_Ab)\n",
    "\n",
    "print(transition_matrix)\n",
    "\n",
    "print(\"sum check: \", pj.sum_check(transition_matrix))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_project(state_names, transition_matrix, first_termination_state):\n",
    "    # iterate until in \"Roll_out\" or \"Abandoned\"\n",
    "    num_states = len(state_names)\n",
    "    state_choices = [x for x in range(num_states)]\n",
    "    state_count =   [0 for x in range(num_states)]\n",
    "    k_state = 0\n",
    "    past_state_names = [state_names[k_state]]\n",
    "    state_count[k_state] += 1\n",
    "\n",
    "    while True:\n",
    "        k_state = np.random.choice(state_choices, p=transition_matrix[k_state])\n",
    "        past_state_names.append(state_names[k_state])\n",
    "        state_count[k_state] += 1\n",
    "        if k_state >= first_termination_state:\n",
    "            break\n",
    "\n",
    "    return (past_state_names, state_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individual case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.05  0.949 0.    0.    0.    0.    0.    0.    0.    0.001]\n",
      " [0.    0.05  0.949 0.    0.    0.    0.    0.    0.    0.001]\n",
      " [0.    0.    0.05  0.949 0.    0.    0.    0.    0.    0.001]\n",
      " [0.    0.    0.    0.05  0.949 0.    0.    0.    0.    0.001]\n",
      " [0.    0.    0.    0.    0.05  0.949 0.    0.    0.    0.001]\n",
      " [0.    0.    0.    0.    0.    0.05  0.949 0.    0.    0.001]\n",
      " [0.    0.    0.    0.    0.    0.    0.05  0.949 0.    0.001]\n",
      " [0.    0.    0.    0.    0.    0.    0.    0.05  0.949 0.001]\n",
      " [0.    0.    0.    0.    0.    0.    0.    0.    1.    0.   ]\n",
      " [0.    0.    0.    0.    0.    0.    0.    0.    0.    1.   ]]\n"
     ]
    }
   ],
   "source": [
    "P_repeat = 0.05\n",
    "P_Ab = 0.001\n",
    "transition_matrix = pj.mk_transition_matrix(P_repeat, P_Ab)\n",
    "print(transition_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RnD_1', 'RnD_2', 'RnD_2', 'RnD_3', 'RnD_4', 'Transfer', 'Clearance', 'Role_out_1', 'Role_out_2', 'Succeed']\n",
      "[1, 2, 1, 1, 1, 1, 1, 1, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "past_state_names, state_count = run_project(pj.state_names, transition_matrix,pj.first_termination_state)\n",
    "print( past_state_names)\n",
    "print( state_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 1, 1, 1, 1, 1, 1, 1, 0]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(past_state_names, 'o')\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ensemble_of_project_tries(state_names, transition_matrix, first_termination_state, n_tries = 100):\n",
    "    all_state_names = []\n",
    "    all_state_counts = np.empty((0,pj.num_states), dtype=float)\n",
    "\n",
    "    for k in range(n_tries):\n",
    "        past_state_names, state_count = run_project(state_names, transition_matrix,first_termination_state)\n",
    "        all_state_names.append(past_state_names)\n",
    "        all_state_counts = np.append(all_state_counts, [state_count], axis=0)\n",
    "    return (all_state_names, all_state_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_state_names, all_state_counts = ensemble_of_project_tries(\n",
    "    pj.state_names, transition_matrix, pj.first_termination_state, n_tries = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_state_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_state_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scaled_aggregates( all_state_counts, state_scale_factors):\n",
    "    all_scaled_states = all_state_counts * state_scale_factors\n",
    "    sum_scaled_states = np.sum(all_scaled_states, axis=0)\n",
    "    sum_scaled_projects = np.sum(all_scaled_states, axis=1)\n",
    "    num_projects = all_scaled_states.shape[0]\n",
    "    return sum_scaled_states, sum_scaled_projects, num_projects\n",
    "\n",
    "def masked_states(all_state_counts, idx_mask):\n",
    "    state_count_mask   = all_state_counts[:,idx_mask]   > 0\n",
    "    all_masked_state_counts = all_state_counts[state_count_mask,:]\n",
    "    num_masked_states = all_masked_state_counts.shape[0]\n",
    "    return all_masked_state_counts, num_masked_states\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration ratio:  1.2248625\n",
      "cost ratio:  1.2252266666666667\n"
     ]
    }
   ],
   "source": [
    "all_costs_of_states = all_state_counts * pj.costs_of_states\n",
    "mean_costs_of_states = np.mean(all_costs_of_states, axis=0)\n",
    "mean_state_count = np.mean(all_state_counts, axis=0)\n",
    "all_durations_of_states = all_state_counts * pj.durations_of_states\n",
    "durations_of_projects = np.sum(all_durations_of_states, axis=1)\n",
    "costs_of_projects = np.sum(all_costs_of_states, axis=1)\n",
    "# mean_cost_of_project = np.mean(costs_of_projects)\n",
    "mean_duration = np.mean(durations_of_projects)\n",
    "duration_ratio = mean_duration / pj.nominal_total_duration\n",
    "mean_cost = np.mean(costs_of_projects)\n",
    "cost_ratio = mean_cost / pj.nominal_total_cost\n",
    "print(\"duration ratio: \", duration_ratio)\n",
    "print(\"cost ratio: \", cost_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6028, 3972)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def scaled_aggregates( all_state_counts, state_scale_factors):\n",
    "    all_scaled_states = all_state_counts * state_scale_factors\n",
    "    sum_scaled_states = np.sum(all_scaled_states, axis=0)\n",
    "    sum_scaled_projects = np.sum(all_scaled_states, axis=1)\n",
    "    num_projects = all_scaled_states.shape[0]\n",
    "    return sum_scaled_states, sum_scaled_projects, num_projects\n",
    "\n",
    "sum_costs_states, sum_costs_projects, num_projects = scaled_aggregates( all_state_counts, pj.costs_of_states)\n",
    "sum_durs_states, sum_durs_projects, num_projects = scaled_aggregates( all_state_counts, pj.durations_of_states)\n",
    "\n",
    "def masked_states(all_state_counts, idx_mask):\n",
    "    state_count_mask   = all_state_counts[:,idx_mask]   > 0\n",
    "    all_masked_state_counts = all_state_counts[state_count_mask,:]\n",
    "    num_masked_states = all_masked_state_counts.shape[0]\n",
    "    return all_masked_state_counts, num_masked_states\n",
    "\n",
    "\n",
    "all_success_state_counts, num_success_projects = masked_states(all_state_counts, pj.idx_succeed)\n",
    "all_abandoned_state_counts, num_abandoned_projects = masked_states(all_state_counts, pj.idx_abandoned)\n",
    "sum_costs_success_states, sum_costs_success_projects, num_success_projects2 = scaled_aggregates( all_success_state_counts, pj.costs_of_states)\n",
    "sum_costs_abandoned_states, sum_costs_abandoned_projects, num_abandoned_projects2 = scaled_aggregates( all_abandoned_state_counts, pj.costs_of_states)\n",
    "assert num_projects == num_success_projects + num_abandoned_projects\n",
    "assert num_success_projects == num_success_projects2\n",
    "assert num_abandoned_projects == num_abandoned_projects2\n",
    "mean_cost_success_projects = sum_costs_success_projects / num_success_projects\n",
    "mean_cost_abandoned_projects = sum_costs_abandoned_projects / num_abandoned_projects\n",
    "burdened_cost_success_projects = np.sum(sum_costs_projects) / num_success_projects\n",
    "\n",
    "rate_abandoned = num_abandoned_projects / num_projects\n",
    "\n",
    "num_success_projects, num_abandoned_projects\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# all_costs_of_states = all_state_counts * pj.costs_of_states\n",
    "# mean_costs_of_states = np.mean(all_costs_of_states, axis=0)\n",
    "# mean_state_count = np.mean(all_state_counts, axis=0)\n",
    "# all_durations_of_states = all_state_counts * pj.durations_of_states\n",
    "# durations_of_projects = np.sum(all_durations_of_states, axis=1)\n",
    "# costs_of_projects = np.sum(all_costs_of_states, axis=1)\n",
    "# # mean_cost_of_project = np.mean(costs_of_projects)\n",
    "# mean_duration = np.mean(durations_of_projects)\n",
    "# duration_ratio = mean_duration / pj.nominal_total_duration\n",
    "# mean_cost = np.mean(costs_of_projects)\n",
    "# cost_ratio = mean_cost / pj.nominal_total_cost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6028"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burdened_cost_success_projects\n",
    "sum_costs_success_projects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loop over P_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.9411125, 0.979925, 1.0272625, 1.084725, 1.1440375],\n",
       " [0.9429666666666667,\n",
       "  0.9818933333333334,\n",
       "  1.02982,\n",
       "  1.08698,\n",
       "  1.1470666666666667])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens_P_rep = [0.01, 0.05, 0.10, 0.15, .20]\n",
    "ens_cost_ratio = []\n",
    "ens_duration_ratio = []\n",
    "n_tries  = 10000\n",
    "for k in range(len(ens_P_rep)):\n",
    "    P_repeat = ens_P_rep[k]\n",
    "    P_Ab = 0.02\n",
    "    transition_matrix = pj.mk_transition_matrix(P_repeat, P_Ab)\n",
    "\n",
    "    all_state_names, all_state_counts = ensemble_of_project_tries(\n",
    "    pj.state_names, transition_matrix, pj.first_termination_state, n_tries = n_tries)\n",
    "\n",
    "    all_costs_of_states = all_state_counts * pj.costs_of_states\n",
    "    mean_costs_of_states = np.mean(all_costs_of_states, axis=0)\n",
    "    mean_state_count = np.mean(all_state_counts, axis=0)\n",
    "    all_durations_of_states = all_state_counts * pj.durations_of_states\n",
    "    durations_of_projects = np.sum(all_durations_of_states, axis=1)\n",
    "    costs_of_projects = np.sum(all_costs_of_states, axis=1)\n",
    "    # mean_cost_of_project = np.mean(costs_of_projects)\n",
    "    mean_duration = np.mean(durations_of_projects)\n",
    "    duration_ratio = mean_duration / pj.nominal_total_duration\n",
    "    mean_cost = np.mean(costs_of_projects)\n",
    "    cost_ratio = mean_cost / pj.nominal_total_cost\n",
    "\n",
    "    ens_duration_ratio.append(duration_ratio)\n",
    "    ens_cost_ratio.append(cost_ratio)\n",
    "\n",
    "ens_duration_ratio, ens_cost_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.5056, 2.483 , 2.4538, 2.448 , 2.43  , 1.2121, 2.394 , 2.3872,\n",
       "       0.    , 0.    ])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_state_names\n",
    "mean_costs_of_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13fa29a30>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale_1_to_1 = [1 + x for x in ens_P_rep]\n",
    "plt.plot(ens_P_rep, ens_duration_ratio, '-bs', ens_P_rep, ens_cost_ratio, '--go',ens_P_rep, scale_1_to_1, ':k' )\n",
    "plt.xlabel(\"Probability of repeating a stage [0-1]\")\n",
    "plt.ylabel(\"Ratio of duration or costs to ideal\")\n",
    "plt.title( \"Scaling of duration and cost with P_rep\")\n",
    "plt.legend([\"Duration\", \"Cost\", \"1 to 1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loop over P_rep and P_Ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[16.703598820058996,\n",
       "   17.44959224677934,\n",
       "   18.51699291461511,\n",
       "   19.645740450656707,\n",
       "   21.178282208588957],\n",
       "  [18.374704327257547,\n",
       "   19.143194772695676,\n",
       "   20.553009819268535,\n",
       "   22.05010195164579,\n",
       "   23.918404351767904],\n",
       "  [22.591208365608054,\n",
       "   24.189003436426116,\n",
       "   25.980990181742218,\n",
       "   28.708970099667773,\n",
       "   31.373491179201487]],\n",
       " [[0.1525, 0.1539, 0.1673, 0.1701, 0.185],\n",
       "  [0.2813, 0.2807, 0.2973, 0.3134, 0.3382],\n",
       "  [0.4836, 0.5053, 0.5213, 0.5485, 0.5692]],\n",
       " array([[1.11357325, 1.16330615, 1.23446619, 1.30971603, 1.41188548],\n",
       "        [1.22498029, 1.27621298, 1.37020065, 1.4700068 , 1.59456029],\n",
       "        [1.50608056, 1.61260023, 1.73206601, 1.91393134, 2.09156608]]))"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens_P_Ab = [0.02,0.04,0.08]\n",
    "ens_P_rep = [0.01, 0.05, 0.10, 0.15, .20]\n",
    "ens2_cost_ratio = []\n",
    "ens2_duration_ratio = []\n",
    "ens2_burdened_cost_success = []\n",
    "ens2_rate_abandoned = []\n",
    "for m in range(len(ens_P_Ab)):\n",
    "    P_Ab = ens_P_Ab[m]\n",
    "    ens_cost_ratio = []\n",
    "    ens_duration_ratio = []\n",
    "    ens_burdened_cost_success = []\n",
    "    ens_rate_abandoned = []\n",
    "    n_tries = 10000\n",
    "    for k in range(len(ens_P_rep)):\n",
    "        P_repeat = ens_P_rep[k]\n",
    "        transition_matrix = pj.mk_transition_matrix(P_repeat, P_Ab)\n",
    "\n",
    "        all_state_names, all_state_counts = ensemble_of_project_tries(\n",
    "        pj.state_names, transition_matrix, pj.first_termination_state, n_tries = n_tries)\n",
    "\n",
    "        # all_costs_of_states = all_state_counts * pj.costs_of_states\n",
    "        # mean_costs_of_states = np.mean(all_costs_of_states, axis=0)\n",
    "        # mean_state_count = np.mean(all_state_counts, axis=0)\n",
    "        # all_durations_of_states = all_state_counts * pj.durations_of_states\n",
    "        # durations_of_projects = np.sum(all_durations_of_states, axis=1)\n",
    "        # costs_of_projects = np.sum(all_costs_of_states, axis=1)\n",
    "        # # mean_cost_of_project = np.mean(costs_of_projects)\n",
    "        # mean_duration = np.mean(durations_of_projects)\n",
    "        # duration_ratio = mean_duration / pj.nominal_total_duration\n",
    "        # mean_cost = np.mean(costs_of_projects)\n",
    "        # cost_ratio = mean_cost / pj.nominal_total_cost\n",
    "\n",
    "        sum_costs_states, sum_costs_projects, num_projects = scaled_aggregates( all_state_counts, pj.costs_of_states)\n",
    "        sum_durs_states, sum_durs_projects, num_projects = scaled_aggregates( all_state_counts, pj.durations_of_states)\n",
    "\n",
    "        all_success_state_counts, num_success_projects = masked_states(all_state_counts, pj.idx_succeed)\n",
    "        all_abandoned_state_counts, num_abandoned_projects = masked_states(all_state_counts, pj.idx_abandoned)\n",
    "        sum_costs_success_states, sum_costs_success_projects, num_success_projects2 = scaled_aggregates( all_success_state_counts, pj.costs_of_states)\n",
    "        sum_costs_abandoned_states, sum_costs_abandoned_projects, num_abandoned_projects2 = scaled_aggregates( all_abandoned_state_counts, pj.costs_of_states)\n",
    "        mean_cost_success_projects = sum_costs_success_projects / num_success_projects\n",
    "        mean_cost_abandoned_projects = sum_costs_abandoned_projects / num_abandoned_projects\n",
    "        burdened_cost_success_projects = np.sum(sum_costs_projects) / num_success_projects\n",
    "\n",
    "        rate_abandoned = num_abandoned_projects / num_projects\n",
    "\n",
    "        # ens_duration_ratio.append(duration_ratio)\n",
    "        # ens_cost_ratio.append(cost_ratio)\n",
    "        ens_burdened_cost_success.append(burdened_cost_success_projects)\n",
    "        ens_rate_abandoned.append(rate_abandoned)\n",
    "    \n",
    "    # ens2_cost_ratio.append(ens_cost_ratio)\n",
    "    # ens2_duration_ratio.append(ens_duration_ratio)\n",
    "    ens2_burdened_cost_success.append(ens_burdened_cost_success)\n",
    "    ens2_rate_abandoned.append(ens_rate_abandoned)\n",
    "\n",
    "ens2_burdened_cost_ratio = np.array(ens2_burdened_cost_success) / pj.nominal_total_cost\n",
    "ens2_burdened_cost_success, ens2_rate_abandoned, ens2_burdened_cost_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 1., ..., 1., 1., 0.],\n",
       "       [2., 1., 1., ..., 2., 1., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 0.],\n",
       "       [1., 1., 1., ..., 1., 1., 0.],\n",
       "       [1., 1., 1., ..., 1., 1., 0.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_state_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans(M):\n",
    "    return [[M[j][i] for j in range(len(M))] for i in range(len(M[0]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[137], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m scale_1_to_1 \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m ens_P_rep]\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(ens_P_rep, \u001b[43mtrans\u001b[49m\u001b[43m(\u001b[49m\u001b[43mens2_duration_ratio\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-bs\u001b[39m\u001b[38;5;124m'\u001b[39m, ens_P_rep, trans(ens2_cost_ratio), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--go\u001b[39m\u001b[38;5;124m'\u001b[39m,ens_P_rep, scale_1_to_1, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:k\u001b[39m\u001b[38;5;124m'\u001b[39m )\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProbability of repeating a stage [0-1]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRatio of duration or costs to ideal\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[49], line 2\u001b[0m, in \u001b[0;36mtrans\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrans\u001b[39m(M):\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [[M[j][i] \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(M))] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[43mM\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m))]\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "scale_1_to_1 = [1 + x for x in ens_P_rep]\n",
    "plt.plot(ens_P_rep, trans(ens2_duration_ratio), '-bs', ens_P_rep, trans(ens2_cost_ratio), '--go',ens_P_rep, scale_1_to_1, ':k' )\n",
    "plt.xlabel(\"Probability of repeating a stage [0-1]\")\n",
    "plt.ylabel(\"Ratio of duration or costs to ideal\")\n",
    "plt.title( \"Scaling of duration and cost with P_rep\")\n",
    "plt.legend([\"Duration\", \"Cost\", \"1 to 1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, array([1.05906045, 1.10403656, 1.16878457, 1.23451939, 1.32196651]))"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ens_P_rep), np.squeeze(ens2_burdened_cost_ratio[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16b91a570>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale_1_to_1 = [1 + x for x in ens_P_rep]\n",
    "plt.figure()\n",
    "ax = plt.axes()\n",
    "ax1 = ax.plot(ens_P_rep, ens2_burdened_cost_ratio[2,:], '-bs',label=f\"P_Ab {ens_P_Ab[2]}\")\n",
    "ax2 = ax.plot(ens_P_rep, ens2_burdened_cost_ratio[1,:], '-bo',label=f\"P_Ab {ens_P_Ab[1]}\")\n",
    "ax3 = ax.plot(ens_P_rep, ens2_burdened_cost_ratio[0,:], '-bx',label=f\"P_Ab {ens_P_Ab[0]}\")\n",
    "plt.ylim(1.0,2.2)\n",
    "# ax4 = ax.plot(ens_P_rep, scale_1_to_1, ':k' )\n",
    "plt.xlabel(\"Probability of repeating a stage [0-1]\")\n",
    "plt.ylabel(\"Ratio of costs to ideal\")\n",
    "plt.title( \"Scaling of cost with P_rep by P_Ab\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0797, 0.0809, 0.084, 0.0838, 0.0962]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens2_rate_abandoned[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pct(lst_n):\n",
    "    return [100*x for x in lst_n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_costs_of_states = np.mean(all_costs_of_states, axis=0)\n",
    "# mean_costs_of_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_durations_of_states = all_state_counts * pj.durations_of_states\n",
    "# all_durations_of_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# durations_of_projects = np.sum(all_durations_of_states, axis=1)\n",
    "# durations_of_projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# costs_of_projects = np.sum(all_costs_of_states, axis=1)\n",
    "# costs_of_projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_cost_of_project = np.mean(costs_of_projects)\n",
    "# mean_cost_of_project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16a217980>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_cost = int(np.min(costs_of_projects))\n",
    "max_cost = int(np.max(costs_of_projects))\n",
    "min_dur  = int(np.min(durations_of_projects))\n",
    "max_dur  = int(np.max(durations_of_projects))\n",
    "bin_centers = [x for x in range(min_dur,(max_cost+1),2)]\n",
    "bins = np.array([x for x in range(min_dur,(max_cost+2))]) - 0.5\n",
    "\n",
    "plt.hist([durations_of_projects,costs_of_projects],bins=bins, rwidth=0.4, color=[\"blue\", \"green\"])\n",
    "plt.xticks(bin_centers)\n",
    "plt.xlabel(\"Duration (blue) or Cost (green) [rel]\")\n",
    "plt.ylabel(\"Number of projects [of 100]\")\n",
    "plt.title(f\"Histogram of project durations and costs for {n_tries} trials\")\n",
    "plt.legend([\"Duration\", \"Cost\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16a8a5a90>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_cost = int(min(np.min(sum_costs_success_projects),np.min(sum_costs_abandoned_projects)))\n",
    "max_cost = int(max(np.max(sum_costs_success_projects),np.max(sum_costs_abandoned_projects)))\n",
    "# min_dur  = int(np.min(durations_of_projects))\n",
    "# max_dur  = int(np.max(durations_of_projects))\n",
    "bin_centers = [x for x in range(min_cost,(max_cost+1),2)]\n",
    "bins = np.array([x for x in range(min_cost,(max_cost+2))]) - 0.5\n",
    "\n",
    "plt.hist([sum_costs_success_projects,sum_costs_abandoned_projects],bins=bins, rwidth=0.4, color=[\"green\", \"red\"])\n",
    "plt.xticks(bin_centers)\n",
    "plt.xlabel(\"Cost[rel]\")\n",
    "plt.ylabel(f\"Number of projects [of {n_tries}]\")\n",
    "plt.title(f\"Histogram of costs of projects: per success. P_Ab: {P_Ab}\")\n",
    "plt.legend([\"Success\", \"Abandoned\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_success = all_state_counts[:,pj.first_termination_state] > 0\n",
    "mask_abandoned = all_state_counts[:,pj.idx_abandoned] > 0\n",
    "all_success_state_counts = all_state_counts[mask_success,:]\n",
    "all_abandoned_state_counts = all_state_counts[mask_abandoned,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., ..., 0., 0., 1.],\n",
       "       [1., 2., 3., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 0., 0., 1.],\n",
       "       [1., 1., 0., ..., 0., 0., 1.],\n",
       "       [2., 0., 0., ..., 0., 0., 1.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_abandoned_state_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_costs_of_states = all_state_counts * pj.costs_of_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_costs_of_successes = all_success_state_counts * pj.costs_of_states\n",
    "all_costs_of_abandoned = all_abandoned_state_counts * pj.costs_of_states\n",
    "costs_of_successes = np.sum(all_costs_of_successes, axis=1)\n",
    "costs_of_abandoned = np.sum(all_costs_of_abandoned, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([19., 25., 19., ..., 15., 17., 15.]),\n",
       " array([ 8., 17.,  2., ...,  8.,  4.,  4.])]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_costs = [costs_of_successes, costs_of_abandoned]\n",
    "hist_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 9.530e+02, 1.915e+03,\n",
       "         2.113e+03, 5.320e+02, 2.600e+02, 1.100e+02, 1.500e+01, 5.000e+00],\n",
       "        [1.002e+03, 4.750e+02, 8.470e+02, 4.270e+02, 6.600e+02, 3.480e+02,\n",
       "         2.450e+02, 6.300e+01, 2.400e+01, 4.000e+00, 2.000e+00, 0.000e+00]]),\n",
       " array([ 2.  ,  4.75,  7.5 , 10.25, 13.  , 15.75, 18.5 , 21.25, 24.  ,\n",
       "        26.75, 29.5 , 32.25, 35.  ]),\n",
       " <a list of 2 BarContainer objects>)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['green', 'red']\n",
    "plt.hist(hist_costs, bins=12, color=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4097.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count how many projects were Abandoned\n",
    "num_abandoned = np.sum(all_state_counts[:,pj.idx_abandoned])\n",
    "num_abandoned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3140"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of undercost projects\n",
    "np.sum(costs_of_projects < pj.nominal_total_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4097"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rate of abandonment\n",
    "rate_of_abandonment = num_abandoned / n_tries\n",
    "rate_of_abandonment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_state_count = np.mean(all_state_counts, axis=0)\n",
    "# mean_state_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_duration = np.mean(durations_of_projects)\n",
    "# duration_ratio = mean_duration / np.min(durations_of_projects)\n",
    "# duration_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_cost = np.mean(costs_of_projects)\n",
    "# cost_ratio = mean_cost / np.min(costs_of_projects)\n",
    "# cost_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This should sum to one: all projects either roll out or are abandoned\n",
    "np.sum(mean_state_count[pj.first_termination_state:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.7"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cost of successes\n",
    "cost_of_success = mean_cost_of_project / (1 - rate_of_abandonment)\n",
    "cost_of_success"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ratio: Cost of success / Nominal project cost\n",
    "cost_of_success / pj.nominal_total_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'transition_matrix' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtransition_matrix\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'transition_matrix' is not defined"
     ]
    }
   ],
   "source": [
    "transition_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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